THE STATE OF THE

JAMAICAN CLIMATE (VOLUME Ill):

INFORMATION FOR

RESILIENCE BUILDING

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THE STATE OF THE

JAMAICAN CLIMATE (VOLUME III):

INFORMATION FOR

RESILIENCE BUILDING

Prepared by

Climate Studies Group, Mona

The University of the West Indies

For

Planning Institute ofjarnaica

16 Oxford Road, Kingston

June 1, 2022

CLIMATE

. DATA

Building Resilience through Improved (\"mate lnforvmtlari

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Published in 2022 by Planning Institute oflamaica

16 Oxford Road, Kingston 5, Jamaica

@ Climate Studies Group, lvlona 2022

All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any

means, without the prior written permission of the publisher, except in the case of briefquotations embodied in critical

reviews and certain noncommercial uses permitted by copyright law. For permission requests, contact the Planning

Institute oflamaica.

First published in Kingston, Jamaica by the Planning Institute oflamaica, 2022.

Paperback ISBN 97897681 03994

eBook ISBN 9789768328007

Cataloguing-In-Publication Data

(A catalogue record of this book is available at the National Library ofjamaical

Names: Climate Studies Group, Mona, author.

Title: The state of thejamaican climate volume Ill: information for resilience building I prepared by Climate Studies Group

lvlona for Planning Institute oflamaica.

Description: Kingston,Jamaica: Planning institute oflamaica, 2022. l Includes bibliographical references.

Subjects: LCSH: Climatology. | Climatic changes — Research -Jamaica.

lC|imatic changes — Risk assessment —Jamaica. | Climate changes

v Risk management —Jamaica. | Jamaica v Climate — Research.

Classification: DDC 551.6 —— dc23.

Printed iniamaica

This publication is to be cited as follows:

Climate Studies Group, Mona (CSGM), 2022: State of the Jamaican Climate (Volume lll): Information for Resilience

BuildingProduced for the Planning Institute ofjarnaica (PlOl), Kingston, Jamaica.

REPORT AUTHORS

Chapters1,Z,3. 4,5,6, and 8:

Michae|ATay|or Roxann Sterinett-Brown Matthew Williams

Tannecia S Stephenson Christina A Douglas Felicia Whyte

Leonardo Clarke Deron Maitland Alrick A Brown

Jayaka D Campbell Jhordannejones Rochelle N Walters

Pietra Brown Candice 5 Charlton Alton Daley

Chapter 7:

Dale Rankine, Cicero Lallo, Steve Maxirriay,Jane Cohen and Dionne Myrie (Agriculture)

Thera Edwards (Coastal Resources and Human Settlements)

Georgiana Gordon-Strachan, Shelly McFarlane, Natalie Guthrie-Di><ori, Eden August and Simon Anderson (Health)

Arpita Mandal, Melissa Curtis, Amitabh Sharma and Soumyaraj Chatterjee (Water)

Seleni Matus, Martine Bakker, Elecia Myers, Taylor Ruoff (Tourism)

Nekeisha Spencer and Alrick Campbell (Economy)

This publication or parts of it may be reproduced for educational or non-profit purposes without special permission,

provided acknowledgement of the source is made (see citation above).

The views expressed in this publication are those of the authors and do not necessarily represent those of the PIOJ.

ACKNOWLEDGEMENTS

- The Meteorological Service ofjamaica

- The Pilot Programme for Climate Resilience

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TABLE OF CONTENTS

List of Tables viii

List of Figures xiii

List of Abbreviations xx

Summary of Climate Trends and Projections xxiii

1.1 Jamaica 1

1.2 Pilot Programme for Climate Resilience 2

13 About this Document 2

2.1 Approach 4

2.2 Data Sources 5

2.3 Obtaining Future Projections from Models 8

2.3.1 Emission Scenarios 8

2.3.2 GCMS and RCMs 10

2.3.3 SDSM 14

2.3.4 SimCLlM 14

2.3.5 Presenting the Data 15

2.4 Climate Change Indicators for Key Sectors 15

2.5 Limitations and Constraints 17

3.1 Introduction 18

3.2 Temperature 19

Iv | That fthejamalcan C||mate(Vo H -, .

, -. AA

3.3 Rainfall 22

3.4 Hurricanes 29

3.5 Sea Surface Temperatures 31

3.6 Other Variables 32

3.6.1 Wind 32

3.6.2 Significant Wave Height 34

3.6.3 Solar Radiation 36

3.6.4 Relative Humidity, Sunshine Hours, and Evaporation 37

4.1 Introduction 38

4.2 Temperatures 38

4.2.1 Temperature Extremes 40

4.3 Rainfall 43

4.3.1 Rainfall Extremes 47

4.4 Hurricanes 51

441 North Atlantic Hurricane Activity 51

4.4.2 Hurricanes and Jamaica 52

45 Sea Surface Temperature 56

4.6 Droughts and Floods 57

4.7 Sea Levels 59

4.7.1 Sea Level Rise Extremes 61

5.1 Introduction 63

5.2 Temperature 64

5.2.1 GCMs 65

5.2.2 RCMS 69

5.2.3 Statistical Downscaling 73

5.3 Rainfall 74

5.3.1 GCM5 75

5.3.2 RCMS 78

5.3.3 Rainfall Extremes 80

5.4 Sea Levels 80

5.4.1 Sea Level Extremes 83

5.5 Sea Surface Temperature 35

5.6 Hurricanes 85

6.1 Introduction 94

6.2 Impacts of Climate Change on Key Sectors and Important Thematic Areas 95

6.2.1 Social and Economic Development 95

6.2.2 impacts of Climate Change on Education 97

6.2.3 impacts of Climate Change on Gender 98

6.2.4 impacts of Climate Change on Security 100

6.2.5 impacts of Climate Change on Agriculture 102

6.2.6 impacts of Climate Change on Marine and Terrestrial Biodiversity 106

6.2.7 impacts of Climate Change on Poverty 107

6.2.8 Impacts of Climate Change on Tourism 105

6.2.9 Impacts of Climate Change on Health 111

62.10 Impacts of Climate Change on Society 112

6.2.11 Impacts of Climate Change on Freshwater Resources 113

62.12 Impacts ofclimate Change on Energy 114

6.2.13 Impacts of Climate Change on Coastal Settlements 115

62.14 Impacts ofclimate Change on Occupational Safety 116

7.1 Introduction 117

72 Agriculture 118

7.2.1 Introduction 118

7.2.2 Indicators of Climate Change on Agriculture 118

7.2.3 Geographic Location or Most Suitable Focal Points for Ongoing Monitoring 121

of Climate Change Impacts

7.2.4 Recommendations to mitigate impact of climate change on Agriculture 121

7.2.5 Indicator Summary Sheet for Agriculture 122

7.3 Coastal Resources and Human Settlements 122

7.3.1 Introduction 122

7.3.2 Indicators of Climate Change on Coastal Resources and Human Settlements 122

7.3.3 Geographic Location or Most Suitable Focal Points for Ongoing Monitoring of Climate 127

Change Impacts

7.3.4 Recommendations to Mitigate impact of Climate Change on the Coastal Resources and 127

Human Settlements

7.3.5 Indicator Summary Sheets for Coastal Resources and Human Settlements 128

7.4 Health 129

7.4.1 Introduction 129

7.4.2 Indicators of Climate Change on Health 129

7.4.3 Geographic Location or Most Suitable Focal Points for Ongoing Monitoring of Climate 131

Change Impacts

7.4.4 Recommendations to mitigate impact of climate change on the Health Sector 132

7.4.5 Proposed Indicator Summaiy Sheet for the Health Sector 133

7.5 Water Sector 134

7.5.1 Introduction 134

7.5.2 Indicators of Climate Change on the Water Sector 135

7.5.3 Geographic Location for Ongoing Monitoring of Climate Change Impacts 141

7.5.4 Recommendations to mitigate impact of climate change on the Water Sector 141

7.5.5 Indicator Summary Sheet for the Water Sector 142

7.6 Tourism 143

7.6.1 Introduction 143

7.6.2 Indicators of Climate Change on Tourism 143

7.6.3 Geographic Location for Ongoing Monitoring of Climate Change Impacts 148

7.6.4 Recommendations to Mitigate impact of Climate Change on the Tourism Sector 149

7.6.5 Indicator Summary Sheets 150

77 Economy 151

7.7.1 Introduction 151

7.7.2 Indicators of Climate Change on the Economy 151

7.7.3 Short—term Impacts of Weather Shocks 154

7.7.4 Future Climate Change Impacts on the Economy 155

7.7.5 Recommendations to Mitigate impact of Climate Change on the Economy 157

8.1 Introduction 158

8.2 Climate Analysis Resources 159

3.3 Decision-Making within the Climate Context 160

8.4 Sector-Specific Climate Tools, Software and Resources 161

8.4.1 Climate Products and Services 161

8.4.2 Climate Tools, Software, and Models for the Agriculture Sector 163

85 Climate Literature (since 2012) 165

8.5.1 Historical Variability and Extremes 165

8.5.2 Modelling and the Future Climate 165

8.5.3 Impacts of Climate Change 166

10.1 Chapter 1: Rationale and Background 172

102 Chapter 2: Data and Methodologies 173

10.3 Chapter 3: Climatology 174

10.4 Chapter 4: Observed Variability, Trends, and Extremes 174

10.5 Chapter 5: Climate Scenarios and Projections 175

10.6 Chapter 6: Climate Change and Sector Impacts 177

10.7 Chapter 7: 180

10.7.1 Agriculture 180

10.7.2 Coastal Resources and Human Settlements 180

1073 Health Sector 181

10.7.4 Water Sector 181

10.7.5 Tourism 182

10.7.6 Economy 183

10.8 Glossary 184

Table 1.1 An outline and description of each chapter ofthe SOJC Report (Volume 3). 3

Table 2.1 Data sources used in the compilation of historical climatologies and future projections. 2

Table 2.2 Summary of RCM characteristics and experimental setups using the PRECIS and 12

RegCM4.3.5 models.

Table 2.3 Reporting blocks and grid box coordinates categorized by region (PREClS RCM). See Figure 13

2.4 for grid boxes.

Table 2.4 Reporting blocks and grid box coordinates categorized by region (RegCM4.3.5). (See Figure 14

2.5 for grid boxes).

Table 2.5 Summary of indicators selected for each priority sector. 15

Table 3.1 Mean temperature climatologies for nine meteorological stations acrossjamaica. Data are 20

averaged for varying periods over 1978 and 2019 for each stationUnits are in °C.

Source: Meteorological Service ofjamaica.

Table 3.2 Minimum temperature climatologies for nine meteorological stations across Jamaica. Data 21

are averaged for varying periods over 1978 and 2019 for each station, Units are in °C.

Data source: Meteorological Service oflamaica.

Table 3.3 Maximum temperature climatologies for nine meteorological stations acrossjamaica. Data 22

are averaged for varying periods over 1978 and 2019 for each station. Units are in °C.

Source: Meteorological Service ofjamaica.

Table 3.4 Mean seasonal rainfall totals (mm) and seasonal percentage ofannual totals for the period 25

1881 —ZO1 9.

Table 3.5 Monthly mean rainfall received per parish (mm). Means are calculated for 1996-2020 by 26

averaging all stations in the parish. Source: Meteorological Servicejamaica.

Table 3.6 Average annual rainfallvaluesimm) overthe period 1981-2010forthe four rainfallzones 28

compared to the all-island average.

Table 3.7 Total rainfall (mm) for a year for each zone, along with station average total (mm) and 29

percentage (%) rainfall for three seasons of the year, DJFMA (December-January-February-

March-April), MJ (May-June), ASON (August-September-October»November).

Table 3.8 Comparison of the number of storms by category passing within 200—ki|orneters of 30

Jamaica for consecutive 2U—year periods between 19402019. Categories indicate a storm's

maximum intensity withinJamaica's vicinity. Source: NOAA \[https://coast.noaa.gov/

hurricanes)

Table 3.9: Extremes ofannual mean wind speed for each parish, taken at 30 metres. 33

Source: Amarakoon et al 2001,

Table 3.10 Statistical summary ofsignificant wave height for the North Eastern, North Western, South 35

Eastern and South Western quadrant of the study area for data collected from NOAA/WW3

Simulation throughout the period 2005-2017.

Table 3.11 Mean daily global radiation in MJ/ml/day at several radiation stations injamaica. See notes 36

(i) and (ii) below. To convert from MJ/m2/day to Kilowatt-hour (KWH), divide (MJ/m2/day) by

3.6. Source: Solar radiation map forjamaica (1994).

Table 3.12 Mean monthly and annual observed values for relative humidity, sunshine hours and 37

evaporation for the Norman Manley and Donald Sangster International Airports for the

period 1997—2016.

Table 4.1 Correlation between Jamaica's rainfall zones. Bold numbers are statistically significant at 47

the 95% level.

Table 4.2 Table showing mean trend values for rainfall extreme indices. 48

Table 4.3 Extreme Rainfall slope values for stations injamaica for the period between 1980 —2018. 49

Table 4.4 Extreme Rainfall slope values forjamaica for the period between 1980 -2018. 50

Table 4.5 Total number of hurricanes (by category) passing within (a) S0-km, (b) 100-km, (c) 150-km 54

and (cl) 200-kmjamaica from 1950 to 2015. Impact on grid boxes previously defined are

shown. Data Source: NOAA (http://coast.noaa.gov/hurricanes).

Table 4.6 The number of dry periods as determined by SPI3 and SP|12 in each rainfall zone. 58

Table 4.7 Rates and absolute changes in global mean sea level from 1901 to 2014. 59

Table 4.8 Acceleration in the rate of sea level rise. 59

Table 4.9 Mean rate of sea level rise averaged over the Caribbean basin. 60

Table 4.10 Tide gauge observed sea—|eve| trends for stations across the Caribbean region. Adapted 60

from Torres and Tsimplis (2013).

Table 5.1 (a) Range of mean temperature change for each ofJamaica's four rainfall zones from an 64

RCM ensemble (three members) across three RCPs (2.6, 4.5 and 8.5). See Figure 2.5 for grid

boxes and Table 2.4 for grid boxes in each zone. Source: RegCM4.3.5 ensemble; (b) Range

of mean temperature change across the grid boxes in each zone from an RCM ensemble

(six members) running a high emissions scenario. See Figure 2.4 for grid boxes and Table

2.3 for grid boxes in each zone. Source PRECIS ensemble.

Table 5.2 (a) Mean annual absolute temperature change (°C) foriamaica with respect to 1986-2005. 65

Change shown for four RCP scenarios. Source: AR5 CMlP5 subset, KNMI Climate Explorer:

(b) HadGEM2-ES RCP 2.6.4.5 and 8.5 scenario ensemble mean projected changes in mean

temperature by season and for annual average (°C), for the 20305, 20505 and EOC by grid

box with respect to the 1960-1989 baseline.

Source: HadGEM2-ES runs for RCP2.6, 4.5, 8.5

Table 5.3 (a) Mean annual minimum temperature change (°C) forjamaica with respect to 1986-2005. 66

Change shown for four RCP scenarios. Source: AR5 CMIP5 subset, KNMI Climate Explorer;

(b) HadGEM2-ES RCP 2.6, 4.5 and 8.5 scenario ensemble mean projected changes in

minimum temperature by season and for annual average (°C), for the 2020s, 2030s, 2050s

and EOC by grid box with respect to the 1960-1989 baseline. Source: HadGEM2-ES runs for

RCPZ.6, 4.5, 8.

Table 5.4 (a) Mean annual maximum temperature change (°C) forjamaica with respect to 1986-2005. 67

Change shown for four RCP scenarios. Source: AR5 CMlP5 subset, KNMI Climate Explorer

and (b) HadGEM2-ES RCP 2.6.4.5 and 8.5 scenario ensemble mean projected changes in

maximum temperature by season and for annual average (°C), for the 2020s, 2030s, 2050s

and EOC by grid box with respectto the 1960-1989 baseline. Source: HadGEM2-ES runs for

RCP2.6, 4.5, 8.5.

Table 5.5 Projected absolute changes in mean temperature by season and for annual average (°C) 69

for the 2030's, 2050's and EOC relative to the 1960-1989 baseline. Data presented for

RegCM4.3.5 forced by CNRM-RCP 4.5 and HadGEM2-ES RCP 2.6, 8.5 and averaged over grid

boxes in each zone. Source: RegCM4.3.5

Table 5.6 Projected absolute changes in mean temperature by season and for annual average (°C) 70

for the 20305, 20505 and 2080s relative to the 1961-1990 baseline. Data presented for the

mean value ofa six-member ensemb|eRange shown is over all the grid boxes in the zone

(see Table 2.3). Source: PRECIS RCM perturbed physics ensemble run for A1 B scenario.

Table 5.7 Projected absolute changes in maximum temperature by season and for annual average 70

(°C) for the 20305, 20505 and EOC relative to the 1960-1989 baseline. Data presented for

RegCM4.3.S forced by CNRM-RCP 4.5 and HadGEM2-ES-RCP 2.5, 8.5 averaged over grid

boxes in each zone. Source: RegCM4.3.S

Table 5.8 Projected absolute changes in maximum temperature by season and for annual average 71

(\"(2) for the 2030s, 2050s and EOC relative to the 1960-1989 baseline. Data presented

for the mean value ofa six-member ensemble. Range shown is over all the grid boxes in

the zone (see Table 2.3). Source: PRECIS RCM perturbed physics ensemble run for A1 B

scenario

Table 5.9 Projected absolute changes in minimum temperature by season and for annual average 71

(°C) for the 20305, 20505 and EOC relative to the 1960-1989 baseline. Data presented for

RegCM4.3.5 forced CNRM-RCP 4.5 and HadGEM2-ES -RCP 2.5, 8.5 averaged over grid boxes

in each zone. Source: RegCM4.3.5

Table 5.10 Projected absolute changes in minimum temperature by season and for annual average 72

(°C) for the 2030s, 2050s and 2080s relative to the 1961-1990 baseline. Data presented for

mean value ofa six-member ensemble. Range shown is over all the grid boxes in the zone

(see Table 2.3). Source: PRECIS RCM perturbed physics ensemble run for A1 B scenario.

Table 5.11 (a) Range of mean percentage annual rainfall change for each ofJamaica‘s four rainfall 74

zones from an RCM ensemble (three members) across three RCPs (2.6. 4.5 and 8.5). See

Figure 2.5 for grid boxes and Table 2.4 for grid boxes in each zone. Source RegCM4.3.5

ensemble; (b) Range of mean percentage rainfall change across the grid boxes in each

zone from an RCM ensemble (six members) running a high emissions scenario. See Figure

2.4 for grid boxes and Table 2.3 for grid boxes in each zone. Source PRECIS ensemble.

Table 5.12 Mean percentage change in annual rainfall forjamaica with respect to 1986-2005. Changes 75

are shown for the four RCP scenarios. Source: AR5 CMIPS subset, KNMI Climate Change

Atlas.

Table 5.13 Mean percentage change in late season (August-November) rainfall forjamaica with 75

respect to 1986-2005. Changes are shown for the four RCP scenarios. Source: AR5 CMIP5

subset, KNMI Climate Change Atlas.

Table 5.14 Mean percentage change in dry season (January-March) rainfall forjarnaica with respect 75

to the 1986-2005. Changes are shown for four RCP scenarios. Source: AR5 CMIPS subset,

KNMI Climate Change Atlas.

Table 5.15 HadGEM2—ES RCP 2.6, 4.5 and 8.5 scenario ensemble mean projected percentage changes 76

in mean precipitation by season and for annual average (°C), for the 20305, 20505 and

EOC. Results shown for the four GCM grid boxes with respect to the 1960-1989 baseline.

Source: HadGEM2—ES runs for RCP2.6, 4.5, 8.5.

Table 5.16 Projected percentage changes in rainfall by season and for annual average for the 20305, 78

2050s and EOC relative to the 1960-1989 baseline. Data presented for CNRM-RCP 4.5 and

HadGEM2-ES -RCP 2.5, 8.5 averaged over grid boxes in each zone. Source: RegCM4.3.5.

Table 5.17 Projected percentage changes in rainfall by season and for annual average for the 20305. 79

20505 and 2080s relative to the 1961-1990 baseline. Data presented for the mean value of

a six-member ensemble. Range shown is over all the grid boxes in the zone (see Table 2.3).

Source: PRECIS RCM perturbed physics ensemble run for A1 B scenario.

Table 518 Projected changes in temperature per grid box by 20905 from a regional climate 80

m0de||PCC (2007).

Table 5.19 Projected increases in global mean sea level rise (m). Projections are relative to 1986- 81

ZOUSIPCC (2013).

Table 5.20 Projected increases in mean sea level rise (m) for the north and south coasts ofjamaica. 82

Range is the lowest projection under low sensitivity conditions to the highest annual

projection under high sensitivity during the period. Projections relative to 1986-2005

and are generated using a 24-model ensemble in SimCLIM (see section 2.3.4 for further

information on SimCL|M).

Table 521 Projected north tropical Atlantic SST trends (°C per century) for two future scenarios. 85

Bracketed numbers indicate standard errors. Source: Antuna et al 2015.

Table 6.1 Impact ofciimate change on social and economic development. 95

Table 6.2: Impacts of climate change on education. | 97

Table 6.3 Impacts of climate change on gender. 98

Table 6.4 Impact of climate change on security. 100

Table 6.5 Impact ofciimate change on agriculture & food security. 102

Table 6.6 Impact of climate change on marine and terrestrial biodiversity. 106

Table 6.7 Impacts of climate change on poverty. 107

Table 6.8 Impacts of climate change on tourism. 109

Table 6.9 Impacts of Climate Change on Human Health. 111

Table 6.10 Impacts of climate change on society. 112

Table 6.11 Impacts of climate change on freshwater resources. 113

Table 6.12 Impact of climate change on energy supply and distribution. 114

Table 6.13 Sea level rise and storm surge impacts on coastal infrastructure and settlements. 115

Table 6.14 Impacts of Climate Change on Occu pational Safety. 116

Table 7.1 Observed THI for Winter and Summer injamaica for four livestock (2001—201 2). 119

Table 7.2 Core and Optional Indicators of Climate Change on the Agricultural Sector. 122

Table 7.3 Shoreline Recession at Carlisle Bay. 128

Table 7.4 Mangrove Species Distribution and Physiochemical Parameters at Port Royal. 128

Table 7.5 Indicator Summary Sheet-Coastal Settlementlnundation Vulnerability. 129

Table 7.6 Core Indicators of Dengue Fever. 133

Table 7.7 Core Indicators for Heat Stress. 134

Table 7.8 Core or Optional Indicators for Streamflow. 142

Table 7.9 Core or Optional Indicators for Flood Flow and Peak Flows. 142

Table 7.10 Recommended destination indicators forjamaica. 144

Table 7.2 Core Indicators for Holiday Climate Index: Beach. 150

Table 7.12 Core Indicators for the Quality of Natural Resources. 150

Table 8.1 Climate tools that can provide users with local, regional, and international climate 159

information and future climate outputs.

Table 82 Climate tools that allow decision makers and policy makers to make informed decisions on 160

climate-sensitive projects.

Table 8.3 Outline of climate products and services specific to the agriculture sector. 161

Table 8.4 Outline of climate tools, software, and models specific to the Agriculture and Water sector. 163

Figure 1.1 Map ofjamaica. Inset shows Jamaica's location in the Caribbean Sea. Source: Nations 1

Online Project, 2021 (www.nationson|ine.org).

Figure 2.1 Map of the Caribbean showing its six defined rainfall zones (Jamaica is in Zone 3). 8

Source: State of the Caribbean Climate (CSGM 2020).

Figure 2.2 Two families of scenarios commonly used for future climate projections: the Special Report 9

on Emission Scenarios (SRES, left) and the Representative Concentration Pathways (RCP,

right). The SRES scenarios are named by family (A1, A2, B1. and B2), where each family

is designed around a set of consistent assumptions: for example, a world that is more

integrated or more divided. The RCP scenarios are simply numbered according to the

change in radiative forcing (from +2.6 to +8.5 watts per square metre) that results by 2100.

This figure compares SRES and RCP annual carbon emissions (top), and carbon dioxide

equivalent levels in the atmosphere (bottom). Source: Melillo, Richmond, and Yohe (2014).

Figure 2.3 HadGEM2—ES representation over the island ofjamaica. 11

Figure 2.4 PRECIS 25-km grid box representation over the island ofjamaica. 13

Figure 2.5 RegCM4.3.5 20-km grid box representation over the island ofjamaica. 13

Figure 3.1 Air temperature climatology in °C forjamaica calculated using the CRU dataset. 19

Climatologies are shown for four 30—year periods: 1900—1 929, 19304 959, 19604 989 and

1990—2019.

Figure 3.2 Temperature climatologies of nine meteorological sites acrossjarnaica. Maximum 20

temperatures are shown in red, mean temperatures in black and minimum temperatures

in blue. Data are averaged over varying periods between 1978 and 2019 for each station

Source: Meteorological Service ofjamaica.

Figure 3.3 Rainfall climatology in mm forjamaica as determined from the All-Jamaica rainfall index 23

ofthe Meteorological Service ofjamaica and CRU dataset. Climatologies are shown for the

entire period (1881-2019) as well as four 30-year averaging periods: 1900-1 929, 19304 959,

1960-1989 and 1990-2019.

Figure 3.4 Temperature versus rainfall climatology. Rainfall climatology is determined from the All— 25

Jamaica rainfall index of the Meteorological Sen/ice ofjamaica. Temperature climatology is

from the CRU dataset.

Figure 3.5 Parish monthly rainfall climatology calculated for the 25-year period, 1996 to 2020. 26

Figure 3.6 Distribution of mean annual rainfall forjamaica (in millimetres). Source: CSGM 27

Figure 3.7 Meteorological stations that cluster together with respect to rainfall variability and the 27

four rainfall zones they fall in. Bold lines show the rough delineation ofthe four zones

which are called the Interior zone or Zone 1 (dark blue), the East zone or Zone 2 (cyan), the

West zone or Zone 3 (yellow), and the Coastal zone or Zone 4 (red). Source: Meteorological

Sen/ice ofjamaica

Figure 3.8 Climatologies of the four rainfall zones for the years 1981-2010. Colours are as follows: 28

Interior (zone 1) — green, East (zone 2) — cyan, West (zone 3) — red, Coasts (zone 4) - navy

blue and the All-Island Index (purple)The All island index is averaged over the years 1891-

2009 (purple dotted line). Source: National Meteorological Service ofjamaica

Figure 3.9 Hurricane frequency for the Atlantic Ocean hurricane seasonSource: NOAA. 30

Figure 3.10 The percentage number of tropical storms, hurricanes (Categories 13) and major 30

hurricanes (Categories 4-5) passing within 200—ki|ometers ofjamaica from 1851-2019.

Source: NOAA (https://coast.noaa.gov/hurricanes/).

Figure 3.11 Climatology map of Sea Surface Temperature (SST) for the Caribbean region and Tropical 32

North Atlantic (TNA) over 1982 — 2016. Dataset: NOAA-0|.

Figure 3.12 Climatology series of Sea Surface Temperature (SST) for the Caribbean and the six defined 32

rainfall zones over 1982 to 2016Jamaica is in Zone 3Dataset: NOAA-OI.

Figure 3.13 Wind speed climatology ofjamaica based on data collected at a) the Norman Manley 33

International Airport and b) the Donald Sangster International Airport on an hourly basis.

Source: Meteorological Service ofjamaica.

Figure 3.14 Variation of wind speeds acrossjamaica. Source: Mona Geoiriformatics Institute. 34

Figure 3.15 Climatology ofJamaica's significant wave height. wind speed, period and wave direction 34

for two weather stations - 42058 (red) and 42057 (blue) for the period 2005-2017. Source:

NOAA NDBC.

Figure 3.16 Significant wave height information for the North Eastern, North Western, South Eastern 35

and South Western regions ofjamaica. Source: Simulation by WW3/NCEP/NOAA for period

2005-2017.

Figure 3.17 Solar Radiation Map: Global Horizontal Irradiation Map ofjamaica, 1999-2018. 36

Source: Solargis.

Figure 4.1 Annual maximum, minimum and mean temperatures forjamaica, 1900-2019. The linear 39

trend lines are inserted. Source: CRU TS3.24.

Figure 4.2 (a) Averagejuly-October temperature anomalies over the Caribbean from the late 1800s 40

with trend line inserted. Box Inset: Percentage of variance explained by trend line, decadal

variations > 10 years, interannual (year-to-year) variations; (b) Percentage variance inJuly-

October temperature anomalies (from late 1800s) accounted for by the ‘global warming’

trend line for grid boxes over the Caribbean. Source: Climate Research Unit (CRU)

Acknowledgements: IRI Map Room.

Figure 4.3 Temperature extreme trends for specific grid boxes acrossjamaica for the period 1980 — 41

2019. The map ofjamaica (0.5° x 0.5°) shows the grid box locations. Source ERAS.

Figure 4.4 Trends in selected historical temperature extremes for stations located at Donald Sangster 42

International Airport, Discovery Bay, Worthy Park, Bodles, Tulloch, Norman Manley

International Airport and Duckenfield. Figure shows (a) daily temperature range (DTR);

(b) growing season length (GSL); (c) nights warmer than 20 (TR20); (d) coolest minimum

temperatures (TNn); (e) warmest maximum temperatures (TXx). Direction of the arrow

indicates positive (upward) or negative (downward) trend. The size of arrow indicates the

magnitude relative to the largest trend in each panel.

Figure 4.5 Annual and seasonal rainfall trends ofjamaica for the period 1881-2019. Data source: The 43

Meteorological Service, Jamaica.

Figure 4.6 (a) Averagejuly-October rainfall anomalies over the Caribbean from the late 18005 with 44

trend line inserted,‘ (b) Trend and decadal components of the averagejuly-October rainfall

anomalies over the Caribbean from the late 1800sSource: CRU data. Acknowledgement: IRI

Map Room.

Figure 4.7 Meteorological season rainfall trends ofjarnaica for the period 1881-2019. Data source: 45

The Meteorological Service,Jamaica.

Figure 4.8 Smoothed anomalies oftheJamaica’s rainfall zones’ precipitationAll smoothing was done 47

through a running mean of 24-month anomalies are determined relative to the base

period 1970-2010.

Figure 4.9 Trends in selected historical rainfall extremes acrossjamaica for (A) Annual Total 48

Precipitation — PRCPTOT, (B) Simple Daily intensity Index — SDII, (C) Consecutive Dry Days —

CDD and (D) Consecutive Wet Days — CWD.

Figure 4.10 Trends in selected historical rainfall extremes acrossjamaica for (A) Very Wet Days — R95P, 49

(B) Extreme Wet Days — R99P, (C) Annual count of days when rainfall above 10 mm — R10

and (D) Annual count of days when rainfall above 20 mm — R20.

Figure 4.11 Variations in North Atlantic storm activity from 1950-2018 (Bell et a|2019). Seasonal Atlantic 52

hurricane activity during 1950-2018 based on HURDAT2 (Landsea and Franklin 2013)

(a) Number of named storms (green), hurricanes (red), and major hurricanes (blue), with

1981-2010 seasonal means shown by solid coloured lines; (b) Accumulated cyclone energy

(ACE) index expressed as a percent of the 1981-2010 median value. Red, yellow, and blue

shadings correspond to NDAA's classifications for above-, near-, below-normal seasons.

The thick red horizontal line at 165% ACE value denotes the threshold for an extremely

active seasonvertical brown lines separate high and low activity eras. ACE is a measure

of overall hurricane activity and is defined as the sum of squares ofa storm’s maximum

sustained wind speed at six-hourly intervals. ACE considers both the intensity, duration

and frequency of storms within a season.

Figure 4.12 (a) All hurricanes impacting the Caribbean basin between 1950 and 201 5; (b) Tropical 53

Depressions and Tropical Storms. Source: NOAA (http://coast.noaa.gov/hurricanes)

Figure 4.13 The 23 Grid locations used to determine hurricanes passingjamaica within a radius of 100 55

km.

Figure 4.14 Map ofjamaica showing the probability ofa hurricane passing within 50km ofa grid box 55

based on 66 years (1950 - 2015) of historical data.

Figure 4.15 Satellite-derived total accumulated rainfall (in mm and inches) associated with the passage 56

of (a) Major Hurricane Matthew in 2016 and; (b) Major Hurricane Irma in 2017. Light blue

circles indicate the position ofjamaica relative to storm trackwarmer colours indicate a

larger accumulated rainfall. Source: NASA; (c) Total daily rainfall (in mm) observed in the

years 2016 (light blue) and 2017 (dark blue) are compared to the daily rainfall climatology

(in gray) over the period 1981-2010Shaded regions indicate the passage and duration of

select hurricanes passing within 200 miles ofJamaica's coasts. Source: Global Historical

Climatology Network Daily (GHCN-D) Database.

Figure 4.16: Map showing sea surface temperature trends within the Caribbean and surrounding 57

regions over the period 1982 to 2016.

Figure 4.17: Severe flood climatology forjamaica for the period 1850 - 2010 for 198 events (top) 58

Occurrences of severe flood events per decade for the period 1850 - 2010 with decadal

meanjamaica Rainfall index (mm), Source: Burgess et al (2015).

Figure 4.18 Total sea level rise (in cm) relative to the mean sea level averaged over 1993-2014. 59

Source: NOAA/Laboratory for Satellite Altimetry

Figure 4.19 Bar graph of seasonal sea level variability in Kingston,Jamaica from Satellite altimetw 61

records 1993 to 2017. The seasons are defined as Winter (Dec.-Feb.), Spring (March- May),

Summer (June- Aug), Autumn (Sept.- Nov.). Source: Copernicus Marine Environment

Monitoring Service Database (CMEMS).

Figure 4.20 Annual maxima nontidal distribution through the year (black bars) and after the mean 62

seasonal cycle has been removed (light ochre bars). This shows the percent of annual

maxima occurrence in each month. The Port Royal Station,Jamaica is highlighted.

Figure 5.1 (a) Mean annual temperature change (°C); (b) Mean annual minimum temperature change 68

(‘’C); (c) Mean annual maximum temperature change (°C) forjamaica with respect to 1986-

2005 AR5 CM|P5 subsetOn the left, for each scenario one line per model is shown plus the

multi-model mean, on the right percentiles ofthe whole dataset: the box extends from

25% to 75%, the whiskers from 5% to 95% and the horizontal line denotes the median

(50%).

Figure 5.2: Summary map showing absolute change per grid box ofannual mean temperature (°C) for 72

the 2050s (top panel) and EOC (bottom panel). Mean change is shown in the centre of each

grid box while the ensemble minimum and maximum is also shown in each box. Source:

PRECIS RCM perturbed physics ensemble run forA1 B scenario relative to the 1961-1990

baseline.

Figure 5.3 Projections of mean and extreme daily maximum temperature for the Norman Manley 73

International Airport Weather Station for 2016-2035 and 2036-75. (A+D) Mean daily

maximum temperature; (B4-E) Maximum daily maximum temperature; (C+F) Warm day

frequency per decade.

Figure 5.4 Projections of mean and extreme daily minimum temperature for the Norman Manley 73

International Airport Weather Station for 2016-2035 and 2036-75. (A+D) Mean daily

minimum temperature; (B+E) Minimum daily minimum temperature; (C+F) Cool night

frequency per decade.

Figure 5.5 (a) Relative Annual Precipitation change W»); (b) Relative August-November Precipitation 77

change (%)7 (c) Relative January-March Precipitation change (%) forjamaica with respect to

1986-2005 AR5 CMIPS subset. On the left, for each scenario one line per model is shown

plus the multi—mode| mean, on the right percentiles of the whole dataset. The box extends

from 25% to 75%, the whiskers from 5% to 95% and the horizontal line denotes the

median (50%).

Figure 5.6 Summary map showing percentage change per grid box of annual rainfall for the 20505 79

(top panel) and EOC (bottom panel). Source: PRECIS RCM perturbed physics ensemble run

for A1 B scenario relative to the 1961-1990 baseline.

Figure 5.7 Projections of mean and extreme daily rainfall for the Norman Manley International 80

Airport Weather Station. (A+D) Mean daily rainfall; (B+E) maximum number of consecutive

dry days.

Figure 5.8 Sea level rise projections under RCP2.6, RCP4.S, RCP6.0 and RCP8.5 for a) a point 83

(-77.076“W, 18.8605°N) off the Northern coast ofjamaica; b) a point (-77.1 S7°W, 17.142°N)

off the southern coast ofjamaica.

Figure 5.9 Showing projections of extreme sea levels (ESL). The colours ofthe dots express the factor 84

by which the frequency of ESL events increase in the future for events which historically

have a return period of 100 years. Hence a value of 50 means that what is currently 1-in-

1OD year event will happen every 2 years due to a rise in mean sea level. Results are shown

for three RCP scenarios and two future time slices as median values. Results are shown for

tide gauges in the GESLA2 database. Source: Oppenheimer et al., 2019.

Figure 5.10 Summary of TC projections for a 2°C global anthropogenic warming. Shown for each basin 86

and the globe are median and percentile ranges for projected percentage changes in TC

frequency, category 45 TC frequency, TC intensity, and TC near—storm rain rateFor TC

frequency, the 5th—9Sth—percenti|e range across published estimates is shownFor category

4—5, TC frequency, TC intensity, and TC near—storm rain rates the 10th—90th—percenti|e

range is shownNote the different vertica|—axis scales for the combined TC frequency and

category #5 frequency plot vs the combined TC intensity and TC rain rate p|otSee the

supplemental material for further details on underlying studies used. Source: Knutson et al

2020.

Figure 5.11 Time series ofannual-mean latitude of tropical cyclone genesis calculated from the best- 87

track archive lBTrACS from 1980 to 2013 for: a) West Pacific basin, b) East Pacific basin, c)

South Pacific basin (Wellington) and d) North Atlantic basin. L/near trend lines are presented

with their 95% two-sided confidence intervals. Source: Daloz and Camargo 2018.

Figure 5.12 (a) Summary histogram of global mean projections of percentage changes in tropical 88

cyclone rainfall rates, (b) Projection distributions for individual basins with projections

for the North Atlantic highlighted by the purple rectangle and (c) Comparison of globally-

averaged rainfall rates under present versus global warming conditions for a simulated

hurricane. Source: Knutson et al (2020).

Figure 5.13 Summary histograms and distributions of projected changes in TC frequency (%) from 89

available studies, where the change in TC frequency for all Saffir—Simpson categories (05)

combined is considere (a) Distribution of projected changes in global TC frequency with a

global temperature change of 2\"C. The red distribution indicates projections sourced from

individual studies compared with the projections from Emanuel (2013), shaded gray; lb)

Box plot distributions of projected percentage change for each basin. The purple rectangle

highlights distributions for the North Atlantic basin. Source: Knutson et al 2020.

Figure 5.14 Observed trends in (a) global tropical cyclone (TC) and hurricane frequency from 1970- 89

2018, (b) global TC propagationl translation speed global frequency of tropical cyclones

from 1949-2016 (gray shading indicates the 95% confidence levels surrounding linear

trend) and (c) global TC landfalls from 1970-2017. Source: Knutson et al 2019.

Figure 5.15 Histogram of (a) maximum sustained wind speed and (la) cyclone damage potential for all 90

points along all tracks for hurricanes simulated in the current climate (blue) and a pseudo-

global warming (PGW) climate (orange) using the Weather Research and Forecasting (WRF)

model. Source: Gutmann et al (2018).

Figure 5.16 Same as Figure 5.13, but for projected changes in intense storm frequency (%) from 91

available studies, where the change in TC frequency for categories 3-5 are considered

(a) Distribution ofprojected changes in global TC frequency with a global temperature

change of 2°C. The red distribution indicates projections sourced from individual studies

compared with the projections from Emanuel (2013), shaded gray (b) Box plot distributions

of projected percentage change for each basin. The purple rectangle highlights

distributions for the North Atlantic basin (c) Distribution of projected changes in the global

proportion of TCs that reach very intense levels (e.g., category 4-5). Source: Knutson et al

2020.

Figure 7.1 Twenty-year (1 9932013) Plot of Rainfall vs Monthly SPI (SPI 6) for Bodles, St Catherine. 119

Source: Metrological Service ofjarnaica.

Figure 7.2 Observed mean monthly THI for broiler chickens, layer chickens, pigs and ruminants 120

(2001—2012); mean values from three locations injamaica (: standard error per livestock).

Source: Lallo et al (2018).

Figure 7.3 Future mean monthly THI for global warming targets of1.5, 2.0 and 2.5 \". Source: Lallo et 120

al. (2018) 140

Figure 7.4 Distance of Old House from shoreline and tree line on historical aerial photographs and 123

modern satellite imagery. Source: Royal Holloway, University of London Aerial Photograph

Collection.

Figure 7.5 Box Plots of Mean Red Mangrove Density with Standard Error for 20052016 (points below 124

zero appear that way for visual effect). Source: Thera Edwards and Kurt McLaren using

data provided by Mona Webber.

Figure 7.6 Pearson's Correlation Coefficient - Red Mangrove trees and physiochemical parameters. 125

Source: Thera Edwards & Kurt McLaren using data provided by Mona Webber.

Figure 7.7 Mitchell Town vulnerability to coastal inundation and flooding. Source: Thera Edwards. 126

World Imagery used as base imagery.

Figure 7.8 RCP 8.5 Number ofyears in the decade where a given month had temperatures 2 27'C. 130

Source: Data from the CSGM

Figure 79 Heat Map of Heat Stress for Heavy Labour WBGT 226. Work capacity reduced to 75%. 131

Figure 7.10 Map ofjarnaica showing the location ofthe Rio Cobre Basin. Source: Map created by 135

Authors with data from The Water Resources Authority ofjamaica.

Figure 7.11 The Rio Cobre Basin showing the different Watershed Management Areas. Source: Map 136

created by authors with data (shapefiles) from The Water Resources Authority ofjamaica.

Figure 7.12 Stream Gauges in the Upper and Lower Rio Cobre WMU. Source: Map created by authors 137

with data (shapefiles) from The Water Resources Authority ofjamalca.

Figure 7.13 Average yearly flows for a) Indiana River at Rio Magno, b) Rio Cobre at Bog Walk, c) Rio 138

Cobre near Spanish Town, d) Rio Doro at Williamsfield, e) Rio Pedro near Harkers Hall for

different RCP model scenarios and station data, Source: Modelled flow data from SWAT

(authors work).

Figure 7.14 Flow hydrographs for the three riverjunctions corresponding to a) Outlet A: Rio Cobre 139

near Sunnyside, la) Outlet B: Rio Cobre at Bog Walk, and c) Outlet C: Rio Cobre near

Thompson Pen. These are the areas which have shown repeated occurrences offlooding.

Source: Modelled flow hydrograph from HEC HMS. Modelled flow data simulated by

authors.

Figure 7.15 100 Yr Flow hyd rographs for the three rlverjunctions corresponding to a) Outlet A: Rio 140

Cobre near Sunnyslde. b) Outlet B: Rio Cobre at Bog Walk and c) Outlet C: Rio Cobre near

Thompson Pen. for the different RCP scenarios. Source: Modelled flow hydrograph from

HEC HMS. Modelled flow data simulated by authors.

Figure 7.16 Montego Bay HCI: Beach, 19802020. Source: Authors’ analysis. Data provided by the 145

CSGM.

Figure 7.17 HCI: Beach by month, 1980-2020. Source: Authors’ analysis, Data provided by the CSGM. 145

Figure 7.18 HCI: Beach estimates, 2019-2095. Source: Authors’ analysis. Data provided by the CSGM. 146

Figure 7.19 Coral Monitoring Sites by Coded Studies and Average Percent Coral Cover, Montego Bay. 147

Source:Jackson,Je.

Figure 7.20 Projected Ranges in the Onset of Annual Severe Bleaching Conditions. Dark grey, orange, 148

and blue correspond to a range <10, 10-15, >15 years, respectively. Source: Van Hooidonk

et al., 2015.

Figure 7.21 Carbon intensity of Energy Use, 1988-2017. Measured in kilograms of carbon emissions 152

per kilogram of oil equivalent energy use. Source: Authors’ analysis. Carbon intensity data

comes from the World Bank's World Development Indicators Database Archives

Figure 7.22 Energy Intensity, 1988»2017. Measured in 1000 British thermal units (Btu) per dollar 152

ofGross Domestic Product calculated in purchasing power parity (PPP) terms. Source:

Authors’ analysis. Energy intensity data comes from Meteorological Service ofjamaica.

Figure 723 Real GDP (2015\]MD) per unit of rainfall injamaica, 1971—2017. Source: Authors’ analysis 153

GDP per unit of rainfall is from the UNSTAT, U.S. Energy Information Administration.

Figure 7.24 Mean response of growth (pp) to hurricane shocks with 90% error bands. Source: Authors’ 154

analysis. Data are obtained from the Statistical Institute ofjamaica (STATIN) and from the

World Development Indicators ofthe World Bank.

Figure 7.25 Mean response of growth (pp) to temperature shocks with 90% error bands. Source: 155

Authors’ analysis. Data are obtained from the Statistical lnstitute ofjamaica (STATIN) and

from the World Development lndicators of the World Bank.

Figure 7.26 GDP per capita levels (RCP8.5, SSP1—SSPS) without climate change (Panel A) and with 156

climate change (Panel B). Source: Authors’ analysis. Data are obtained from the Statistical

Institute ofjamaica (STATIN) and from the World Development Indicators of the World

Bank.

LIST OF ABBREVIATIONS

AM\] April-June

AMO Atlantic Mu|ti—Decada| Oscillation

AR5 IPCC Fifth Assessment Report

AR5 CMIPS AR5 Coupled Model lntercomparison Project

ASO August—October

ASON August~November

CariSAM Caribbean Society for Agricultural Meteorology

CARIWIG Caribbean Weather Impacts Generator

CCCCC Caribbean Community Climate Change Centre

CCCMA Canadian Centre for Climate Modelling and Analysis

CCD Climate Change Division

CCID Caribbean Climate Impacts Database

CCORAL Caribbean Climate Online Risk and Adaptation Tool

CDD Consecutive Dry Days

CDEMA Caribbean Disaster Emergency Management Agency

CIF Climate Investment Funds

Climpag Climate Impacts on Agriculture

CMIP5 Coupled Models lntercomparison Project 5

C-ROADS World Climate Climate Simulation Model

CRU Climatic Research Unit

CSEC Caribbean Secondaiy Education Certificate

CSGM Climate Studies Group, Mona

CWD Consecutive Wet Days

DFID Department for International Development

DJFMA December-April

DSSAT Decision Support System for Agrotechnology Transfer

DTR Daily Temperature Range

ENSO El Nifio-Southern Oscillation

EOC End of Century

FAO Food and Agriculture Organization ofthe United Nations

FMA February-April

GCM General/Global Climate Model

GFDL Geophysical Fluid Dynamics Laboratory

GHG Greenhouse Gas

GIA Global isostatic Adjustment

GIS Geographic Information System

GMSL Global Mean Sea Levels

GO\] Government ofjamaica

GSL Growing Season Length

HAdGEM2-ES Hadley Centre Global Environmental Model version 2 Earth System configuration

HEC-GeoHMS The Geospatial Hydroiogic Modeling Extension

HEC-H MS The Hydroiogic Modeling System

HIV Human Immunodeficiency Virus

HURDAT Hurricane Database

ICDIMP Improving Climate Data and Information Management Project

ICTP international Centre for Theoretical Physics

IPCC Intergovernmental Panel on Climate Change

IRI international Research Institute

JMD Jamaican Dollar

KNMI i<on_ini<iijk Nederiands Meteorologisch Instituut (Royal Netherlands Meteorological

institute)

KSA Kingston and SoAndrew

KWH Kilowatt-hour

LAI Leaf Area Index

MICAF Ministry of Industry, Commerce, Agriculture and Fisheries

MJ May~June

MJJ May-July

MoAF Ministry of Agriculture and Fisheries

MOSAICC Modelling System forAgricu|turai impacts of Climate Change

MSD Mid-summer Drought

NAH North Atlantic High

NASA National Aeronautics and Space Administration

NDC Nationally Determined Contribution

NCEP National Centres for Environmental Prediction

ND\] November-January

NDVI Normalized Difference Vegetation Index

NEPA National Environment and Planning Agency

NMIA Norman Manley International Airport

NOAA National Oceanic and Atmospheric Administration

NOAA NDBC National Data Buoy Centre - NOAA

NRCA Natural Resources Conservation Authority

NWC National Water Commission

ODPEM Office of Disaster Preparedness and Emergency Management

OTEC Ocean Thermal Energy Conversions

PAR Photosyntheticaily Active Radiation

PIOJ Planning Institute ofjamaica

PPCR The Pilot Programme for Climate Resilience

PPE Perturbed Physics Experiment

PRCPTOT Annual Total Precipitation

PRECIS Providing Regional Climates for Impacts Studies

RCM Regional Climate Model

RCP Representative Concentration Pathways

ReCORD Regional Climate Observations Database

RegCM4 Regional Climate Model developed at the ICTP

SDII Simple Daily Intensity Index

SDSM Statistical Downscaling Model

SFCA Special Fishew Conservation Area

SIA Sangster International Airport

SIDS Small Island Developing States

SimCLlM ArcGlS-based Climate Simulation Model

SLR Sea Level Rise

SMASH Simple Model for the Advection Storms and hurricanes

SOJC State ofthejamaican Climate

SPCR Strategic Program for Climate Resilience

SRES Special Report on Emission Scenarios

SRS Spectral Reflectance Sensors

SST Sea Surface Temperature

TNn Coolest Minimum Temperatures

TOPEX NASA’s satellite

TR20 Nights warmer than 20°C

TXX Warmest Maximum Temperatures

UNEP United Nations Environment Programme

USD United States Dollar

WAMIS World AgroMeteoro|ogica| Information Service

WEAP Water Evaluation and Planning System

WMU Watershed Management Units

WRA Water Resources Authority

CLIMATE TRENDS AND PROJECTIONS FORJAMAICA AT A GLANCE

HISTORICAL TREND‘ PROJECTION‘

Temperatures

- Maximum, mean and minimum temperatures - Min, max and mean temperatures increase irrespective of scenario through

show upward (linear) trend. the end of the century,

- Average minimum temperatures increasing - The mean temperature increase (in °c) from the GcMs will be 0.a5°—o.s4“c by

faster than maximum temperatures (rate the 20305; 0,864.10 “C by the 2050s, 0.82°—3.09“C for 2081—Z10D with respect

increase of 0.01 1°C/year). Mean temperatures to a 19852005 baseline over all tour RCPS.

'\"°'“5'\"E 3‘ 3 me °f°i°°8\"C Yea“ - RCMS suggest higher magnitude increases for the downscaled grid boxes — up

- increases in temperature are consistent with to close to 4\"C by end of century.

g'°\"a' me‘ - Temperature Increases across all seasons ofthe year.

' De\"V‘e\"‘P9”\"“’e “\"35 \"35 \"e‘-\"9a5e“- - coastal regions show slightly smaller increases than interior regions.

- Mean daily maximum temperature each month at the Norman Manley

Internationai Airport station is expected to increase by 0.1 B—1 .3“C (1 —2—2,0\"C)

across all Rcl>s by early (mid) century.

- The annual ireouency of warm days in any given month at the Norman

Manley international Airport station may increase by 2—12(4—19) days across

all RCPS by early (mid) century.

- Significant year—to—year variability due to the - GCM projections suggest that the 20305 will be up to 4% drier, the 20505 up

influence of phenomenon like the El Nifio to 9% drier, while by the end or the centurythe country as a whole may be up

Southern Osciilation (ENSO). to 21% drier for ail 4 RCPS with respect to a 19862005 baseline.

- Insignificant upward trend. - The GCMs suggest that change in late rainfall season (ASON) is the primary

- strong decadal signal, with wet anomahes in driver ohf the drying trend. Dry season rainrall generally shows small increases

the ieeosi early 1980s, late i99os and mid to °' \"° C “get

late 2000s. Dry anomalies in the late 1970s, - RCM projections similarly reflect the onset or a drying trend from the mid—

mid and late ieaos and post 2010. 20305 which continues through to the end of the century. The amount or

_ Four rainfa“ Zones. drying differs depending on reference baseline, scenarios examined, and

region of the country being examined. In general RCM projections are larger

' '“te\"°' “ii Wes‘ (3) aed 5°35“ (4) ee\"'3'Y °\" than GCM projections in some regions being up to greater than 30% decrease

decadal time scale. East least well correlated. by the end DfCen\[ury'

- Intensity and occurrence of extreme reinfa\" - There is spatial variation (With the south and east tending to show greater

events increasing between 1940-2010. dweases man the \"mm and Wei

- The decreases are higher for the grid boxes in the RCM than for the GCM

projections for the entire country.

j

1 Historical trends are based an observations made aver196|e2DiD

2 GCM—genera!ed projections are relative to a wssezoos baseline, RCM—generaled projections are relative to a 19614990 baseline.

- An average regional rate of increase of1.8 : - For the caribbean, the combined range for projected SLR spans 0.254122 m

0.1 mm/year between 1950 and 2009. by 2100 relative to 19852005 levels. The range IS 0,170.38 for 2046 m 2065.

_ l_llelrer average rare ellrrrreaee lrl later years: other recent studies suggest an upper limit for the caribbean of up to 1.5 m

up to 2.5 mm/year between 1993 and 2010. “\"de’ “CF35-

_ carllalaearr Sea level erlarleee are rlearrlle - Forjamalca, projected mean SLR over all RCPS forethe north coast is 0.53

l la l — 0.87m by the end ofthe century. Maximum rise is i.o4 m. SLR rates are

g o a mean. r

similar for the south coast.

- SLR at Port Royailamaica ~1.66 mm/year.

- increase in category 4 and 5 hurricanes; - For global warming of PC:

\"“\"“\"r' i“‘e“5“Y' a5?°°““ed Peak WW - No change or slight decrease in frequency of hurricanes.

intensities, mean rainfall for same perlod.

r - shift toward stronger storms by the end of the century. intensity of storms

' 5°“”‘ ”‘°'e ‘“5°eP“\"'e ‘° ““’''”\"e is projected to increase by 2 to 11% with a shift in distribution toward higher

'””“e\"‘e- wlnd speeds and potential damages.

' MEJWY °““e 5‘°\"\"5 1\" ““\"‘<a”*‘-5 . +5% to +25% increase in hurricane rainfall rates.

impactinglamaica are categories 3 and 4.

- Median change in the proportion of very intense storms (calegorles 4 and 5)

Of +13%.

XXIV \\ TTIE State Df the Jamaican Climate (Vulume HI) il'WlJrH'latiDl'i Cir .Si . _

7i3\" 77930‘ 77° 7S‘30'

i I

—1s‘3n‘ . , H - = _- _ _ I8”3O‘

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\\ ' 11.: . Slzl U4, \\-\\c'n - ~ 0 sr\\1NB°°w\"§-\]\\:.,‘,'N,.\"L '\\-\\ ‘M C'“\"'”‘

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AMAICA W‘

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J \\J

—— Parish boundary

0 Nallonal capnal _

@ Fallsh capital

0 “’W\"vV\"'“9° (.'ARIIfHI'\[AN SIKA

._. Mainland

, , 4. Auporl iicccicccic Wiiiiiiiiic, _,,7 =30,

a 10 an an 40 sown

n In i an 30m‘

75' 77:31)‘ 7V7\" 75\"30’

Figure 1.1. Map ofjamaica. Inset shows\]amaica's location in the Caribbean Sea. Source: Nations Online Project,2U21(www.

nationsonlinesorg)

1 . INTRODUCTION

1 1 - five percent is I/O/CCIIIIC and cretoceous, ten percent alluvial

' Jarnalca and five percent yellow limestone. More than 120 rivers flow

th t ' t M t. Th rt 'h

Jamaica is the third largest island in the Caribbean Sea with \{gojgmafcgntxiphfilrgsgsgon 2:/.L;;Sme C:r;I.t:;eajfrOtl;e 1:25;:/ISTE:

9 mm, Ia\"dmaS§ of 10397, Square km?'\"etreS' Th? i5’a\"_d coastline is approximately 1,022 kilometres. The climate of

iS centred an latitude 18a15 N and longitude 77020 W. It is lama/fa is mainly tmpim, with the most important Elimat/6

approximately 145 kilometres south of the island of Cuba, . I b . th N rm tr d W, d d m ./ dr

Jamaica is elangated alang west-northwest ta east-northeast ZZ);:Z;:iCf::ti,eSE(m;.n0/E5758 Cernamil mggesoffnaunfa//2:2”;

alignment, roughly 230 kilometres long and 80 kilometres wide mm)

at its broadest paint. The islands exclusive economic zone is ’ _ V V _ V _ V

appmx,-mate/y 25 times the size of ,-,5 Iundmasi Jamaica has SauVrce:\]omaicaslmtIalNotionalcommunicotion to the United

several rugged mountain ranges, with the highestpaint, the Blue NW0” Fmmewark C°’7V5’7“°\" 0” Climate Change (5012000)

Ml7UI7t0i!| F80/<, S00’i\"é’ WUZ255 IHEUES (7r402f€‘€f)- NIGHT Jamaica’: economy, people and way of life have been

5iXU’P€’C9\"t0f1h€\"5/0\"d'5 1\"-‘d’0Cl< i5 White /imE5l‘0\"€r' WV€\"f,V significantly impacted by climate variability and change.

These impacts have been experienced at national, lamaica'sfuture nationalplanningforclimate resilience.thus

sectoral, community and individual levels, sometimes to enhancing the thrust towards long—term transformational

devastating degrees. It has become increasingly clear that change in the countiy Jamaica also features in the regional

characterizations of climate (historical and future) and its PPCR for the Caribbean, in which The University oftheWest

associated impacts will be critical to strengthening climate Indies acquired a high—performance computing system

resilience efforts in Jamaica. in keeping with this, The that allowed for the climate scenarios presented in this

Planning Institute ofjamaica (P|OJl, through Jamaica's Pilot document.

PI_'I'°glamme get Climate Resflllince lPP,CR)' c'I’,mml55l°\"ed The objectives of this third volume of the SOJC Report are

t e Zolz 3\" 20l5 State '3 ‘,9 lemalcan C \"hate (SOJC) to provide current climate information that may be used

ReP°”5' ahd more recently’ ms thlrd V°l”me ot the Sole to inform resilience building efforts at the COUTIEW and

Relmrt‘ sub—country level, and allow for improved sector—based

assessments for climate resilient planning and decision—

1_2 Pflot Programme for making. To this effect, this document provides:

climate Resilience a. a comprehensive review ofjamaicas climatology;

b. an analysis of the dominant drivers of the mean

The purpose of the PPCR is to help developing countries to Climate and Climate Vaflablllt)’ Pattell'i5F

lhteglate Cllmete l'e5lllefiCe ltlt0 deVel0Pme\"t Plahtllhg ahd c. an examination of variability on seasonal, interannuai

ettefaddltlehal tufitllllgto Si-lPP°l't PUhllCahd P_l'l‘/ate 5eCt0l and decadai scales as well as the historical long—term

investments for implementation. Donorcountries including din-‘ate Change 5r'gna|;

Australia’ Canada’ Denmark’ Germany’ Japan’ Spain’ and d futureclimatescenarios enerated from lobalclimate

the United Kingdom have pledged usoi.3 billioninfunding - d I (III. II I I5 I I I gt II I

for pilot programmes currently being implemented in me Egan lg Vresou loll regona Elma emo ES‘

18 developing and under-developed countries, including e. a comprehensive summary of known sector—specific

Jamaica, that demonstrate a high vulnerability to climate climate change impacts;

eha\"3e~l3malea features l” beth the Reg‘°”al 3\"d Ne‘l°”al f. a baseline ofindicators forassessing obsen/ed climate

‘reeks Ofthe PPeR- change impactson keysectors (Water, Health.Tourism,

Jamaica's PPCR was administered in two phases. The first Agriculture, Coastal Resources/Human Settlements

phase of the project resulted in the development of the and the Economy) and Pf°JeCtl0l'l5 0ttl'le5e lTlCllCat°f5

Strategic P|anforC|imateResilience(SPCR)in2011.TheSPCR at the end of the century based on future climate

was developed to helpjamaica with climate adaptation, It is change scenarios; and

aligned with Vision 2030Jamaica (PIOJ 2009) and addresses g_ Suggested clrrnare tools and resources that may help

and builds ongapsthathave been identified duringthe first in undersranding the effects or dirrrare Change and

?l:I\"5e5 a\"el makes the 'mPle”‘e”tat'?\" l\"'°deef55 eaietighe assist decision makers and policy makers in viewing

$‘;C\"I‘\{’l\"”g ‘\"\"e5‘me\"t Pteleets were °\"me ’°m ‘ e projects through the lens of climate change.

- Investment Project i: Improving Climate Data and -

Information Management Project 1'3 About thls Document

- Investment P|'0leCt_5 2 3<>33 AdaPtatl°h l’t°Et3m\"i_e ahd In accordance with Component 3 of the improving Climate

FlI:lahCltl8_ lVl_eCh3hl5m« lmPlemel'lteCl h)! the Climate Data and Information Management Project, this report

C ahge D\"\"5'°\" teem outlines in detail updates to observed variability and future

- Investment Project 4: Building Resilience in the Fisheries Climate 5CelTal'l°5 fol’ Jamaica 5li’iCe 2012- Thl5 Vel3°l't l5 a

Sector companion to several key reports produced within the

_ . . _ . . . last 10 years, including the 20‘i2 SOJC Report (CSGM

3'3???iii$W$i»‘3l’iéE5l§¥iE%Y“”'“W” 3:;::.:::.::i:..:.°:;:::::::;iti.;ze.::a::.:;

The investment Pwiecl it improving Climate Pete and National Communication on Climate Change (GOJ 2018).

lhtetmatleh Nlahagermeht l’l°JeCt Of J3m3lC35 PPCR, That is, this report does not replace the information in the

tmahCed by the Climate l\"VeStment Fund (CIFI and latter two SOJC reports but rather builds on them through

admlhlsteted thmllgh the Wofld Bank, l5 filmed at the incorporation of new knowledge generated since the

lmPf°Vlh8 the Cltlallty ahd Use Of Cllmatefelated data publication of both referenced reports and the includes

and information for effective planning and action at local new section;

and national levels. In keeping with Outcome 14 (Climate . . . . . I

Change Adaptation and Hazard Risk Reduction) of Vision We lmpertam new addlmns to We report mclude

2030 Jamaica - National Development Plan and with the i. the provision of sub-country level proiections that

Growth inducement Strategy (PIOJ 2012), the Project will utilize Representative Concentration Pathways(RCPs):

allow climate change considerations to be integrated into Ii_ the incorporation of oooared informaoon on the

climatology, trends and projections oftropical storms, climate change scenarios; and

hugrifianes and |5e_a IEVEIS “SW5 \"SW data5Et5' analyses v. an updated list of climate tools and resources that may

an E mate \"30 5' be useful fordecision making and/or forunderstanding

iii. an updated section summarizing likely sector impacts the influence of climate change.

draw\" from merature reviews; In addition to the above, every attempt has been made to

iv. the establishment of a baseline of climate change update any maps and tables previously offered in the 2015

impacts. where possible, for six priority sectors (Water, SOJC Report (CSGM 2017) which are provided again in this

Health. Tourism. Agriculture. Coastal Resourcesl document with additional data collected in the inten/ening

Human Settlements and the Economy). considering years or with data from new research published since then.

at lea?‘ MD mdicators per Seam and projections °f Table 1.1 below outlines the structure of the SOJC Report

these indicators at the end of century based on future

(Volume III).

Table 1.1. An outline and description of each chapter of the SOJC Report (Volume 3).

TITLE SUMMARV

Chapter 1 Introduction The rationale and the structure oftrie document.

Chapter 2 Data and Methodologies A description of data sources, methodologies, and resources used in generating

trend profiles for analysis.

chapter 3 Climatology An analysis of climate norms for a variety ofcllmate variables as compiled from a

variety of data sources.

Chapter4 Observed Variability, Trends A description of variability and trends in climate variables based on station and

& Extremes gridded data forjamaica.

Chapter 5 Climate Scenarios and Local and regional projections generated from global climate models (GCMs) and

Projections regional climate models (RCMS).

Chapter 5 General Sector Impacts A comprehensive list of climate change impacts on key sectors.

Chapter 7 Climate Change Indicators Analysis establishing a baseline for observed climate change impacts using at

for Key Sectors: Baseline and least 2 indicators for SlX priority sectors (Water, Health, Tourism, Agriculture,

Future Projections Coastal Resources/Human Settlements and the Economy) and proiectioris of

these indicators at end of century based on future climate change scenarios.

Chapter 8 Climate Resources and Tools A comprehensive list of useful climate tools and resources for select sectors.

Chapter 9 Glossary Definitions of key terms used throughout the report.

Chapter 10 References References used in the compiling of the document.

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2. DATA AND METHODOLOGIES

» Second and Third National Communications and Biennial

z“1 Apprflach Update Report submissions under the United Nations

_ __ _ Framework Convention on Climate Change (GOJ 20i1,

The ge\"e'|’a| approach taken m wmpflmg ms document 2016), thejamaicaz Future Climate Changes report (CSGM

was as fol 0W5‘ 2016) produced during the development ofJamaica’s Third

Literature Review (Climate-Based): A literature review National Communications, the Intergovernmental Panel

is conducted of authoritative works and recent studies on Climate Change's (|PCC's) Fifth Assessment Report

on climate change and climate variability forjamaica and (IPCC, 2013), the lPCC's Special Report on Global Warming

the Caribbean region. The authoritative works whose ofi.5 Degrees (IPCC 2018), and other reports and studies

results are utilized in this document include the Near- produced by the IPCC, Caribbean Community Climate

Term Climate Scenarios for Jamaica (CSGM 20i4), the Change Centre (CCCCC), and the Climate Studies Group

2015 State of the Jamaican Climate Report (CSGM 2017), Mona (CSGM), The literature review guided the preparation

the State of the Caribbean Climate (CSGM 2020), Jamaica's of, and provided context for, key sections of this report.

4 l The State ufthejamamari C|imate(Vu|ume|lI) lnlorrnation or Sl

Historical Data Analysis: Available historical observed data

are used to both characterise the climatology of relevant

variables describing the climate of Jamaica as at 2019

and examine variability and trends for the same climate

variables. Variability and trends in sea level rise and the

occurrence oftropical storms, hurricanes, and other climate

extremes were also examined. The literature review is also

used to complement the descriptions of the climatology

and the historical climate variability. Data from a variety A.

of sources are used including the Meteorological Service '

oflamaica. The data employed are listed in section 2.2. '

While even\] attempt was made to utilize updated datasets, \"'\\ K \\

this was not always possible owing to data availability '

and accessibility constraints. The analysis of the data is '

organised by variable analysed using tables, graphs, and I 41;\} \[

diagrams forlamaica as a whole, for specific stations and ,‘ / , ‘

climatic zones accounting for Eastern, Western, Central and ' / I /

Coastal regions ofjamaica. The latter regions approximate ' I /

the rainfall zones oflamaica (see section 3.3). ’.’/I Vi ',

Projections of Future Climate: Climate projections for *‘ '1. \" 1''

Jamaica are obtained from the outputs of a suite of global

climate models (GCMs), from two regional climate model

(RCM)ensemb|esandfromthe use ofstatisticaldownscaling ', ‘~_ ,

techniques. Future trends in climate and variability are . i

produced forjamaica over three future time slices: 2030s / ‘

(2030—2039), 2050s (20S0—2059), and end of the centuw \[

(2081-2100 or 2080—2097). For the GCM data, country

scale projections are generated using the representative

concentration pathway scenarios or RCPs (RCP2.6, RCP4.5.

RCP6.0, and RCP8.5) consistent with the |PCC's Fifth

Assessment Report. RCPs are explained in section 2.3.1.

Comparisons to past trends are made where appropriate.

For the RCM analyses, data are extracted from 2 RCM

ensembles. One with 6 members utilizing a 25 km grid

resolution covering Jamaica and using a perturbed physics

approach premised on a high emissions SRES scenario (see

section 2.3). The second RCM ensemble has three members

gggzéflitiriotim §‘ri'CdeSr:Sr:|gfgrtgjdng::%E:r:E'\\:%::::: tourism, health, human settlements, and coastal resources)

used to capture mean climate changes for Jamaica, which $::‘ir:pt:£|Se:g|‘e?'::eT: ‘;1:;ean;:a|m_:egftt°hghggigiétlsgi

is divided into four rainfall zones, and to illustrate sub— (CSGM 2017) butthey have been expanded on and updated

island Vanations on muted maps’ Pmjemons of extreme with new research in this version In addition to the impacts

‘mdices are also derived from rainfa” and temperature data tables this study also presents abaseline ofindicators that

for at least Mo stamens Wm‘ ‘ongterm time series’ Using can be tracked to determine climate change impacts on the

statistical downscaling techniques and GCM data. . .

5 PPCR sectors, and the Economy, as well as projections of

\"1 Pi°dUC'n8 the fut)-“'9 PF°J€Cfi0\"5. The data -'=i|'€ aiial)/59d these indicators at end of century based on future climate

to provide, as best as possible, a picture of the state of the change scenarios‘

climate forjamaica at the country and sub-country (~20»Z5

l<rn) levels for the near term to end of centuiy The literature

review was used to provide complementary pictures of the 2-2 Data sources

future with respect to other climatic variables, for example.

sea level rise and future tropical storms and hurricanes. A5 “med 3b0V€« multiple 5°U\"Ce5 3'9 U5ed i\" C0”iP”l\"E

Impact Tables, Climate Change Indicators & Maps: ‘Te narrative of this repfirt,‘1Tab|e 2|.'1 Shows the prime”,

or on

key sectors are summarized from an extensive literature trends The rgespemve US: madepof each Source is also

review and presented in tabular form. Sectors addressed Shown‘

under the PPCR initiative were targeted (water, agriculture, '

Table 2.1. Data sources used in the compilation of historical climatologies and future projections

Variable Analysis\] Data Datasets Analysed Data Source

Model Type Descriptor

HISTORICAL DATA

Temperature Climatology Station Monthly data for stations across the island with Meteorological Service oijamaica

and Historical Data less than 20% missing data and reporting between

Trends 1971-2019. Stations are listed in Chapter 3.

Trends Gridded CRU TS 3.24: fully interpolated dataset with University of East Anglia

Dataset high resolution (0.5°). Monthly gridded fields Climatic Research Unit (CRU)r'

based on monthly obsen/ational data, which Harris et al. (2014):

are calculated from daily or sub-daily data by Retrieved from KNN” Climate

National Meteorological Services and other Explorer on .

lmi1.LLc|ii:i:.exi2.

external agents. . E

Rainfall Climatology Station Monthly data for stations across the island Meteorological Service ofjamalca

and Historical Data with iess than 20% missing data and reporting

Trends between 197l and 2019. Stations listed in

pen IX .

Trends Grldded CRU TS 3.24: fully interpolated dataset with university of East Anglia

Dataset high resolution (o.5°i. Monthly gridded fields Climatic Research Unit,‘ Harris et

based on monthly observational data, Whl\{l’l al. (2014):

are calculated from daily or sulrdally data by Remeved from KNMI (“mate

National Meteorological Services and other Explerer en W .,,e,‘meX

/Rafi

“‘e’\"a' 339\"“ knrni.nl/plot atias forrnpy

Sea Levels Trends Gauge As reported in literature. various sources

Data

Sea Surface Climatology National Oceanic and

Temperature and Trends Atmospheric Administration

(Trends V _ V e (NOAA), Earth System Research

Gridded NOAA/OAR/ESRL PSD v2 High Resolution 0.25 lebmm .

analysed Dataset monthly dataset W’

by zones ' Optimum Interpolation (Ol) ssr

identified in 2. Rare Ed (mm hem 5. E

Figure 2.1) /

Hurricanes Historical Atlantic hurricane reanalysis project of Observed storm data available

Trends the National Oceanic and Atmospheric on .@M

Administration.

Wind Climatology Station Monthly data Meteorological Service of

and Trends Data (10-Meter level) for two stations (Norman Manley Jamaica

International Airport and Donald Sangster) for the

Pe\"°d l998'z°‘ 5' Amarakoon et al. (2001) and

Mona Geolniormatics Institute

Trends as reported in literature

Significant Climatology Weather Two weather buoys — 42058 (yellow) and 42057 NOAA

Wave Height and Trends Buoys (blue) - located southwest of Kingston and Negril

2005-Z017

Solar Mean daily global radiation in M\]/mi/day at 12 Chen (1994)

Radiation radiation stations in Jamaica

Average annual solar irradiation over the period Solargis

1999-Z018 forjamaica

variable Analysis\] Data Datasets Analysed Data Source

Model Type Descriptor

Relative

Humidity

Sunshine Station Data presented for two stations (Norman Manley Met§°'°l°El(3' 56\"/lie ‘If

Hours Data lnternatlonal Alrport and Donald Sangster) for the Jamaica

period 1997-2016.

Evaporation

Temperature GCM Data Gridded CMIPS (lPCC AR5 Atlas subset) This is the dataset Retrieved from KNMI Climate

& Rainfall Dataset used In the IPCC WGW AR5 Annex I \"Atlas\". Only Explorer on http://cllmexp.

a single realization from each of over 20 models knml.nl/plot atlas formpy

IS used. All models are weighed equally, where

model realizations differing only in model

parameter settings are treated as diirerent

models.

Data also presented for the four grld boxes over

Jamaica from the HadGEM2—ES model.

RCM Data Gridded PRECIS Perturbed Physics experiments performed Perlurbed physics data

Dataset for the Caribbean (slx member ensemble). available from the Caribbean

community Climate change

Centre on http://www.

Dynamital Downscallng of RCPS 2.5. 4.5 and 8.5 \{anhbean\{|ima\[e_bygene,a|/

using the RegcM4.3.s Model (three member \{gemnghou5e.5ea,ch..0a._htm.

ensemble).

Sea Levels GCM Data Gridded CMIPS model data accessed through SlMCLlMZO13 software,

Dataset SlMCLlM2013. available on

http://www.tllmsystems.tom/

slmcliml

Sea Surface As reported in literature. Various sources

Temperature

Hurricanes As reported in literature. Various sources

*Additional information on the model data is provided in Section 2.3.1.

Figure 2.1. Map of the Caribbean showing its six defined rainraii zones uarnaica is in Zone 3). Source: state of the Caribbean

Climate (CSGM 2o2o)3

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2.3 obtaining Future 2.1). However, one RCIM ensemble available for analysis

, , was run six times using the MB Special Report Emission

Projections from Models Scenario (Nakicenovic et al. 2000) and a perturbed physics

approach, while the other RCl\\/l ensemble was run three

times using two forcing GCMS covering RCPs 2.6, 4.5 and

2'3‘1 EMISSION SCENARIOS 8.5. As is explained later, more sub-island scale data are

lt is largely RCP based future data that are reported on in this currently available for a future Jamaica for the RCM run

document. The GCMs from which data were extracted for using the SRES scenario (6 possible futures), hence its use.

use in this study were run using the range of RCPs, namely The statistical downscaling also relied on the output of a

RCP2.6. RCP4.5, RCP6.0 and RCP85 (see lnformation Box GCM run using RCPs 2.6, 4.5 and 8.5.

3 The Caribbean region can be dIVldEd into Six rainraii zanes inurnherea 1 to 5) with sirniiar rainraii patterns These zones are adapted from a number arstuaies

which use a variety olstalislical techniques to group eciuntrieswith sirniiar rainfall tiirnatoiogieai patterns (see for example the Studies of ury et al 2007. McLean

etai ZUl5:S1eiineuE|Brown etai 2Ul7, and Maitinezetai ZOl9I.

I i me state nrtheianiaiean Climatewalumelll)‘ lnfcirmation or — si

Figure 2.2. Two families of scenarios commonly used for future climate projections: the Special Report on Emission Scenarios

(SRES, left) and the Representative Concentration Pathways (RCP, right). The SRES scenarios are named by family (A1, A2, B1,

and E2), where each family is designed around a set of consistent assumptions: for example, a world that is more Integrated

or more divided. The RC? scenarios are simply numbered according to the change In radiative forcing (from +2.6 to +8.5 watts

per square metre) that results by 2100. This figure compares SRES and RC? annual carbon emissions (top), and carbon dioxide

equivalent levels in the atmosphere (bottom). Source: Mellllo. Richmond, and Vohe (Z014).

SRESSoonanos RcPSoonorios

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with respect to comparability between

the two sets of scenarios used in this

document, in terms of carbon dioxide

concentrations and global temperature Data from in excess of

change, the SRES A1B IS comparable

to RCP6.0 by century's end and RCP8.5 20 GCM5 were ana|ysed,

through mid-century. Both RCP6.0 and _

the A1B scenarios are marked by an and projected annual Change

increase in carbon dioxide emissions .

through to (A1 B) or after (RCP6.0) extracted for the GCM grid

mid-century followed by a decrease -

approaching 2100 (see Figure 2.2). By boxes OVer.\]arna|Ca-

2100, carbon dioxide concentrations ‘ ‘

for both scenarios are very similar Is a slngle Country average

(over 600 ppm) as is the mean global is generated from

temperature anomaly (just under 3°C). _

In this document, then, the RCM data the Su|te of

reported on using the A1 B scenario are

representative of a high emissions (or

worst case) future scenario.

followed by a decrease. A1 B is often gas emissions (RCP85). The RCP

seen as a compromise between the scenarios are also considered

what is a A2 (high emissions) and B1 (lower plausible andillustrative, and do not

, , emissions) scenarios. have probabilities attached to them.

scenarl°' In the |PCC’s Fifth Assessment In comparing the SRES and RCP

_ _ _ _ Report (AR5) (IPCC 2013), however, scenarios it is noted that the SRES

'\" d'5t'ng”'5h'\"g betwee” SRES and outcomes of climate simulations scenarios resulted from specific

RCP 5ce\"ari°5' it is \"med that SRES use new scenarios referred to as socio-economic scenarios from

5ce\"a”°5 reP°'1ed °\" in (he wccls ‘Representative Concentration storylines about future demographic

Fmmh Assessment ReP°'1('PCC Pathways (RCPs) (van Vuuren et and economic development,

2007) mpresent plausible smryunes al 2011) These RCPs represent a regionalization energy production

°f h°W 3 fmure W°r|d Wm '°°k‘ The larger set of mitigation scenarios and use technology agriculture

SRES Scenams expme pamways °f and were selected to have different forestryland land use (IPCC 2006)

fu\"‘_\"'e gree\"h°”5e ga5_em'55'°\"5 targets in terms of radiative forcing however RCPs are new scenarios

dermd mum 5e'f’c°\"5'5te\"t Sets (cumulative measure of human that specify concentrations and

°f a55”\"'Pti°\"5 abwt energy use’ emissions of greenhouse gases from corresponding emissions but are

P°P”|a\[i°\" g'°w”\" ec°\"°miC all sources expressed in watts per not directly based on socio-economic

deVe'°P\"'_e_m' and “her fa“°\"5' square metre) of the atmosphere at storylines like the SRES scenarios.

They eXp\"C't|y emude any gmbal 2100. They are therefore defined by The RCPs can thus represent a range

p°“‘y t° reduce emissims t° avoid their total radiative forcing pathway of21st century climate policies, as

climate change‘ SRES 5‘e\"ari°5 and level by 2100 and hence are compared with the no-climate policy

3'9 3'°”P°d ”“° f“’\"\"‘°‘ (93-v denoted RCPZ.6. lzci=4.5 RCP6.0 and ofSRES. or the four RCPS many do

51' _B1'/513: °‘°’_a“°'d'T‘5‘° ‘he _ RCP8.5 (Figure 2.2)‘ The four RCPs not believe RCP2.6 or4.5 are feasible

5'm\"a\"t'e5 '\" the\" 5‘°ry\"ne5' '\" (“'5 include one mitigation scenario without considerable and concerted

d°c\"”\"e\"t data fmr\" °\"e RCM run leading to a very low forcing level global action cause or and that the

using the A1 B scenam are 'ep°'ted (RCP2 6) two stabilization scenarios world is currently on an emission

°n‘ The A1 B 5°e\"ari° is chamfierized (RCP4:5 and RCP5) and one pathway equivalent to RCP6.0 or

by an increase in carbon dimdde scenario with very high greenhouse higher (Meinshausen et al. 2015).

emissions through mid-century

2.3_2 GcM§ AND KCMS lélhlch dtp inte|:pret other sub-counrtry schale‘ projdectfio\}r1is

' RCM . P ‘ '

Data from bgth Glgbal Cfimam M°de|S.(GcMS,)’ 3'” known C:r:l\[\\:.leI3/ arl\"?emgteneeratedS, a:'i?dJe\\/catllttlgss tareogvgeragisrbveor th:

as General Circulation Models, and Regional Climate Models 20305 20505 and the and of cenmw Extraction was done

(RCM5) are used in this Study‘ See Information B0)‘ 21' for the four R075. The projections are presented in figures

GCM projections of rainfall and temperature characteristics and summaly tables.

for Jamaica were extracted from the subset of Coupled

Models lntercomparison Project 5 (CMIPS) models used to

develop the regional atof projections presented as a part of

the lPCC’s Fifth Assessment Report (AR5) (IPCC 2013). Data

from in excess of 20 GCMs were analysed, and projected Data frorn G|0ba|

annual change extracted for the GCM grid boxes over .

Jamaica. It is a single country average which is generated Cllfnate Models

from the suite of GCMs. Data are presented with respect .

to a near term baseline (1985-2005). Data is also extracted a nd Regional

for a single GCM (HadGEM2-ES) which has four grid boxes -

over Jamaica (Figure 2.3). This data is used to examine I rn ate M 0d els

differences between GCM projections for northern and ‘

southern Jamaica. It is also used because of the availability a re Used In

of baseline data from 1960-1989 which allows for the ’

changes determined to be compared to the RCM projection '5 Stu

data which has a similar baseline. HadGEM2-ES is used

because it has a history of use in the Caribbean (Lyra 20‘l7,

Vichot et al. 2020). The GCM results provide a context within

INFORMATION BOX 2.2 , V

r .,.t 1

u , .'\\Yl_1 . ,,__:

What s the ><c-3\\- :3 _.l.._it..___i

. _. ..~.,,_>

Difference ~. .rj,g.(,

‘in i .

Between GCMs i ; -f,.<

and RCMs? ‘ ~ \"cg

Global Climate Models (GCMs) are - ‘ J‘ '

useful tools for providing future ‘ _ ’ l» l

climate information. GCMs are ~.

mathematical representations ~ ll '~ .

of the physical and dynamical ‘ _ re-\" .1

processes in the atmosphere, ‘»__ »'_ __A'_ \",

ocean, cryosphere and land ' V ,, c \\ /

surfaces.Theirphysica| consistency _’,- - __ \" .\". . ’- s‘._..\"

and skillatrepresentingcurrentand ‘ . _ :_)_v_.- ~\"

past climates make them usefulfor \" r\"';'—‘ “ «

simulating future climates under ‘,t‘

differing scenarios of increasing

greenhouse gas concentrations.

(599 the P|'eVi°|J5 5€Cti0|'1 f0? ‘he techniquestoprovidemoredetailed some phenomena, for example,

diSC|J55i0|'1 On 5CE\"a|'i05)A information on a sub~countiy level. hurricanes. Dynamical downscaling

Gclvls have relatively coarse The additional information thatthe employs a regional climate model

resolutions relative to the scale of downscaling techniques provides (RCM) driven at its boundaries by

required information because of does’ not devalue the information theoutputsoftheGCMs.LikeGCl~/ls,

the computational requirements provided by the GCM: especially the RCMs ‘rely on mathematical

to model the entire globe‘ since:(1)ltoalarge extentjamaicas representations of the physical

Unfor-tunatelyl the size oflamaica climate is driven by large-scale processes, but are restricted tova

Versus the grid spacing ofthe Gclvls phenomenon, (2) the downscaling much smaller geographical domain

on which data are reported means techniques themselves are driven (the ‘Caribbean in this case). The

that lamaica is represented by at by the GCM outputs, and (3) at restriction enableslthe production

most a few grid boxes There is present, the GCMs are the best of data of much higher resolution

therefore a need for downscaling source of future information on (fYPICa|ly< 100 km).

Figure 2.3. HadGEM2-ES representation over the island ofjamaicai

4 3

,4’

283-125

18.75 233.125

— V

Available dynamically downscaled data for Jamaica were processes. They capture some major sources of modelling

obtained from two RCM ensembles. The firstis premised on uncertainty by running each member using identical

the Providing Regional Climates for Impact Studies (PRECIS) climate forcings. and the methodology is an alternative

model (Jones et al. 2004) run at a resolution of 25 km. The to using different driving GCMs developed at different

second is premised on the RegCM4.3.5 model (Giorgi et al. modelling centres around the world to create a multi—mode|

2074) run at a resolution of 20 km. Table 2.2 summarizes ensemble. The range of climate futures projected by the

key characteristics of each RCM and the experiments Hadley centre's PPE is considered equivalent to or greater

performed. than those based on the CMIP multi—model ensemble. Since

Although, the PRECIS model runs are premised on the (Te SRESdA1B mmgrs Rcdp :5 throughhthe f\"',St t_h\"eef\"me

SRES A1B scenario, its results are reported on because of ‘Aces an Rcpedfll 3/ e\" 3 Ce',\"tu¥' tde pmjemons mm

the availability of an ensemble of up to 6 members from ; e PREC'S‘m°, e rfiporte °n mt °:”',\"e”|t r_epre5e\"t

the Hadley Centre's Perturbed Physics Experiments(PPEs). “Sure E\"'_°J:ct‘°‘n5‘ mm an e,\"5em e O Sm” at'°\"5 run

PPEs are designed by varying parameters in the model's usmga '3 em'55'°”5 5cEnar'°'

representation of important physical and dynamical

Table 2.2. Summary of REM characteristics and experimental setups using the PRECIS and RegCM4.3.5 models.

Resolution O.22\"x0.22° or ~ 25 km 20 km

Grid Boxes overjamalca 25 51

Key Features Hydrostatic primitive equations grid point model. A hydrostatic, compressible, sigmap

19 levels In the Vem.Ca‘_ vertical coordinate model run on an

Arakawa B—grld

Dynamical flow, the atmospheric suiphurcycie, H _ h

clouds and precipitation, radiative processes, the G” ‘°\"Ve“'°\" 5‘ 9'“

land surface and the deep soil are all described in the

model.

Forcing GCM i-iadGEM2—ES CNRM and HadGEM2rES

Available Ensemble 6 members (through to end of century) using a i simulation using CNRM (RCP 4.5) and

perturbed physics approach. All ensemble members 2 with HadGEM2—Es (RCPS 2.5 and 8.5)

simulate SRES A18.

Validation for the Caribbean Campbell et al. I201 1) and Taylor et al. (2013) Martinez-Castro et al. IZCH6)

Reference Hadley Centre (UK) International Centre for Theoretical

,QLۤl.5Li.D.Ufl Physics

m&m

Figure 2.4 shows howjamaica is represented by the PRECIS presented in tables for Jamaica divided into four blocks

RCM. Future data for temperature (mean. maximum and roughly coinciding with the island's four rainfall zones (see

rriinimumiand rainfallforeach grid boxare extracted forthe section 3.3). The grid boxes comprising each block (rainfall

available ensemble of perturbations. The mean, minimum zone) are given in Table 2.3. if the reader is interested in

and maximum change on seasonal and annual time scales projections for a particular grid box, the reader is referred

for each variable and for each future time slice are then to the appendices of the report entitled Jamaica: Future

determined. However, though this data was extracted for Climate Projections (CSGM 2016) prepared during the

all 26 grid boxes shown in Figure 2.4. the volume of data development ofJamaica's Third National Communications

precluded the presentation of tables of projections for each as well as to the ReCORD tool discussed in Chapter 8 of this

grid box. As such. only maps showing the mean projected SOJC Report (Volume Ill).

changes across the perturbation ensemble are presented.

Additionally. mean, minimum and maximum changes are

Figure 2.4. PRECIS 25-km grid box representation over the island ofjamaica.

_._.i_._._.-5._._._.i._._._.i_._._._i_._._.i._._._.L._._._i_._._.i._._._.

! I ! ! ! ' ! ! !

I I I ! I I ! I I

I I I I I I I I

I 2 - ' _ . \" ' I 21 i I I

I i I I I

_._.I -I--_._.--p_._._..!._._._.

! I !

I I I

I : . ' I I 1 3 I

I I

. . . . _/ .

I I /\\I I / ‘

_._.i_._._._i._. ._\\Ia»(\\._. ._I...V._._ _._._.

I I I “I I I

I I 1 2 . I , I 3 '- ‘-

! I ! 5 /’ ! l ‘ *‘

I I I V, I J ! ' ' '

_._.I_._._._5._._._..I.._ __.1.._ «_ ._. ._g._._. _ ._._.

I I I ! . ‘s - i I I ‘ I

I I I I ‘ I ’“ g i i i i

I I I 4 I 3 I 1 I I I

I I I I I II I I I

! I ! ! ! ! ! ! I

Table 23. Reporting blocks and grid box coordinates categorized by region (PRECIS RCM). See Figure 2.4 for grid boxes

Grid Bax N0. Grid Box ND-

West ‘ll. 17, ‘I8. 19. 20, 25. 26 Coasts 112\] 21. 22123: 24

East 13,14 Interior 5,6.7,8.9,10, 15.16

Figure 2.5 shows how Jamaica is represented by the of results would be biased by the six PRECiS runs under the

RegCM4.3.5 modei with its finer resoiution. Again, the high emissions A1 B scenario. in presenting the results, two

voiume of data preciudes presentation of tabies of different approaches are taken.

projections for all grid boxes from this model. For this ‘ _ .

ensemble the data is similarly presented for the rainfall F'E“f‘9 25-REECM‘?-3~5 204\"“ 8\"“ 5°)‘ |’9PfE59|'|tiN°\" WE?

zones but are averaged for the grid boxes falling within a ‘he 5'5\"“ °fl3m3|=3-

zone to obtain a single value of the zone. The data is then §______I______;_____;______1______j__,_,§_,_,_,;______§_,____§_,_,__

presented across the three RCPS. 1 1 5 1 1 1

There isan ongoingeffortto expandthe ensembleofrunsat §_.3._.1..._ ;._3.9._£._._._;. .._,;....7! ._i._._._E..._._.E_._._.

this resolution toinclude additionaiforcing GCMS and other E 6 I 25 . A I I 22 I 21 l . E E

RCPs. Notwithstanding, the reader is encouraged to bearin E_._ .‘ ._ _.l _._.I.1.._‘ ' ' ' ' — I'_._._.

mind that the future data from the RegCM4.3.5 ensemble 3 : E 3 0

are presented for change across a range of low to high §______I__\}.8_.‘_Z“I__1___;__ ‘ _‘_‘ ‘ _

emissionswhiiethatforthe PRECIS ensemble are fora high Q 1 1 1 1 _‘ 1' 7

emissions scenario. Because the base and end of century 2 1 1 9 ; I . 1 _v- 1 \" 1 4 1 f 1

periods differ slightly for each RCM suite of experiments, E\"\"\"T\"\"T\"\"‘l\"\"\"l\"\"'3 17\"f\"'\"'l\"\"'_g\"\"'

the results are presented separately. Additionally, the E I 5 E I E E I E 5

mean that would result from an averaging of the two sets

Table 2.4. Reporting blocks and grid hox coordinates categorized by region (RegCM4.3.5). (See Figure 2.5 for grid boxes)

west 8, 9, 16, 17, 18, 25, 26, 31, Coasts 1, 4, 5, 6, 7, 19, 20, 21, 22,

32 23, 24, 27, 28, 29, 30

East 10, 11, 12 Interior 2,3,13,14, 15

2.3.3 SDSM 2005). For example, the analyses with Norman Manley/s

Statisticai downscaiing is a Second means Di Obtaining rainfall, maximumtemperatureand minimumtemperature

downscaled information. It is premised on the view that the mule only be eendueted for 1_993'e005' wlth the first hell

ciimate of a location is influenced by two types of factors _ of the data used for model calibration. Annual models are

the large scale climatic state and the regional/local features created for temperature end Seasonal models are created

(such as topography, land-sea distribution and land use). for remfe”' The eloeheetle eompenent of SDSM l_5 used

The approach first determines a statistical model which to generate 20 Slmuleuohe of Weather Senes “mg \[he

relates large-scale climate variables (called predictors, like methemetleal model estebllshed m the prevleus Step Wlth

relative humidity and Wind Velocity) to regional or imai observed predictors. over the second half of the data used

variables (called predictands, like rainfall and temperature). eS_ ‘newts’ These Senes are averaged and can be eompered

The large-scale output of a GCM simulation is then fed into wlth the Obsewetlen date set usmg a number of rrlemes

the model to estimate local or regional characteristics like \[0 Yal'‘la‘e the m°d_el' Oneethe models are ldentlfled a5

station rainfall and temperature. Statistical downscaling relleble represemetlons of hlsmrleel ellmete they are fed

can provide site-specific informationthatis criticalfor many wlth date them the cenES_M2 medel to generate future

ciimate Change impad SmdieS_ Wiiby ei ai (2004) provides weather series for analysis. The periods examined are

a guidance document on how climate scenarios may be 20154035 alld 2O36'2075'

developed from this approach. - - e - 2.14 SIMCLIM

In this document, results obtained using the statistical _ _ _

downscaling tool SDSM developed at Loughborough Sllhcl-ll\"l_ 20l3 '5 a Ve’5at'le 5°ttWa'e Pa_Cl<a.Ee_ “Sell t0

University in the United Kingdom are reported on. SDSM _5“PPlY Cllmate data ahd ‘\"t°”T‘atl°h t0 tacllltate lhtehhetl

is a freely available software tool that facilitates rapid 'mPaCt \"Sk ahall/5l5 ahel adaptatleh a55e55meht5 by the

development of multiple low cost, single-site scenarios ehd “5e’- T_he 5°ttWa’e alleW5 “5e’5 t°_Eehe'ate 5Pat'al

of daily weetnei. and Surface Venebies under present and represeritations of future climate scenarios at the global,

future Climate foreings (Dawson 2018)_ The model is a regional and local scales. Datasets are generated from the

combination ofa weathergeneratorapproach and a transfer lPC_C'5 Cl\"llP5 5‘“t_e et GCM5- l'_l°\"VeVe\"- ‘he‘”5e’ al5° has the

function model. A weather generator allows the generation °Pt'°\" 0t ”Pl°ad'h3 ahd lhahllmlatlhg the\" 0W” tlata5et5-

°f a “umber 0t Syhthetlc Fll'e5eht 0\" fUtUVe Weathe|' 5e|'le5 This report makes use of SimCL|M to proiect sea level rise

given Observed Or model Predictors The transferfuncticn (SLR) for Jamaica for time slices in the future. SimCL|M

3PPl'°3Ch e5lalbll5he5 3 mathematlcal \"elat'°h5hlP between Integrates global, regional and local factors in an internally

lecal Scale Pl’edlCtahd5 ahd laVEe'5C3le Pl'edlCt0l'5- l“ SDSM! consistent mannerthrough its sea-level scenario generator

the t\"a\"'5lel' fuhctleh l5 Uhtalhed U5lhS llheal \"eSl’e55l0h- contained within its larger integrated modelling system.

Predictors on daily time-steps and for a grid box closest to Pl°le_etl_°\"5 ale SlVeh _t°_’ three l_?ll5tlhCt leV_el5 °t Cllmate

the study area are obtained from two datasets: (1 ) the NCEP 5eh5ftfVftY ' l°W 5e\"5'tlV'tY_- '_T'ed'”r_h 5eh5lt'VltY_ ahd hlgh

Reeneiysis for 195-i_2005v and (2) the CenE5M2i a coupled sensitivity. In general equilibrium climate sensitivity refers

GEM developed by the Canadian Centie for Climate to the increase in surface _air temperature that would

Modelling andAna|ysis (CCCMA) of Environment Canada for °CC”\" lh ’_e5P°h5e t0 a 5“5t_a'”ed elouhllhg 0_t atm°5PhehC

3 nismricai period 195i_2005 and fo, a Continuous 2006 carbon dioxide concentrations. Low sensitivity indicates

2100 for RCP2.6, 4.5 and 8.5. Correlation analysis, partial a l°W teh_‘Pe_’at“\"e 'hC’ea5e lh \"e_5_P°h5e t0 a deabllhg 0t

correlation analysis and scatter plots are used to identify Catbeh d'°X'd_e- Whlle _h'Sh 5eh5't'VltY 5“SEe5t5 a_ hlghel’

a useful subset of predictors from the original suite of 26 lemFfe\"_at_“’e '_hCVea5e lh l'e5P°l\"5e- GCM5 haVe dlffeleht

predictors. A mathematical relationship is then created 5eh5't_'V'l'e5 (5'hCe thele are dltteleht \"eP\"e5e\"'tat_'0\"'5 ‘at

between ine nrediciand and predictor Subset in a process the climate system and its feedbacks) and hence different

known as model calibration. These first steps are executed GCM5 Pledllte d'tte\"ehl ':e5Lflt5 telthe Same GHG erhl_55l°h

using the first half of the available data. All the analyses with 5Ceha\"'_°5- The l'_“°del P\"°JeC_t'_°h5 lh Slmcl-llVl a\"e Paltltlehed

observed data are constrained by the availability ofdata and aeeeldlhg t° Cllmate 5eh5lt'V'tY- M°\"_e lht_°l'h\"atl°h may be

their overlap with the spa fthe predictor dataset (1961- aCCe55ed ‘

For the purposes of this document, SimCLlM 2013 is used Settlements. Health. Water. Tourism and the Economy) was

to generate sea level rise (SLR) projections for two points undertaken primarilythrough an extensive literature review

located off the northern and southern coast of Jamaica. and in some instances from available project data obtained

The SLR proiections are generated for time slices noted from recent studies. Some indicators were based on effects

previously using the mean value from the full ensemble of at select geographical locations. Each sector identified a

CMIPS GCMs for low, medium and high sensitivities. and minimum oftwo indicators based on the following criteria.

3?’ RCP2'6' RCP4'5'dRcP6‘0hand RCPS5‘ The flgureltrepd 1. Availabledata being in place to supportat minimum

lagrams presente are. owever. Presente on y or a historical analysis’ and

medium sensitivity.

2. A framework or mechanism could be found to

23.5 PRESENTING THE DATA ilptizogt continued monitoring by the respective

In presenting the future projection data. absolute change , , , ,

is presented for most variables. for example. temperature. The Selctor dfita a5.5°<‘:'a|Fed WM‘ eaéh mdécfator was aT‘a'¥59d

while percentage change is presented for rainfall. For :0 evaluatel, '5t°”ca C 'm_ate ‘(T 5 anh “_t\"”|E pr';JeC\"°|:5

temperatures and rainfall, the data are averaged for over °' Se ea C ‘mate 5ce'_1a\"°5' W“ emp 5'5 pace _°\" e

three—month seasons: Novembeflanuary (NDJ). February, RfCPhfuturde 8.5 scenario. The proces: tested the Sultfablllty

Apr” (FMAL May_Ju|y(Mm and Augustgoctober (A50) They D“ t e in icator as a metric to trac an measure uture

are roughly consistent with the Caribbean dry season C \"nate 'mpa“5'

(November — April) and wet season (May — October) (Taylor The process of analysis of the impact of climate scenarios

et al.. 2002). The mean annual change is also given. on the selected indicators supported the identification of

recommendations to mitigate climate change and variability

. 0 impacts on each sector.

2.4 Climate Change Indicators V V ,

Below is a summary of the indicators selected for each

f0l' Key seCt0rS sector, their units of measurement and the advantage

The evaluation of impacts of historical and future climate 3S50Ci3t9d With U5in8 the i|\"dlC3t0|' ‘D l'\"935U|'e Climate

on key sectors (Agriculture, Coastal Resources and Human Change iTT'P3Ct5-

Table 2.5. Summary of indicators selected for eacn priority sector

Advarnage 0' the lndicauw

Agriculture Standardized Precipitation lndex(SPl) it measures rainfall anomalies and supports drought

. f I . . . prediction and monitoring- at different time scales.

Raw 3 ‘ '\" \"\"H\"\"e\"eS W\") can be correlated with Agriculture Production index

(APl).

Temperature Humidity Index (TH!) Heat stress detection is possible in different livestock

Temperature m .C and relative hummy m % categories.-Very commonly used to characterize heat

stress and is easily understood

Coastal Shoreline Recession at Corlisle Buy, Clarendon It determines if the rate of shoreline recession is

Reéources Shoreline retreat m m stable or increasing.

an Human

Settlements , , A _ _ y _ _

Mangrove Species distribution and physioclieminil It monitors mangrove ecosystem health. species

parameters at Port Royal abundance and distribution.

Temperature - “C, Water depth — cm. DH. salinityi

Mangroves - species and distribution, number of

Pneumatophores

Eoostullniindution at Mitchell Town It determines long term changes in sea level rise.

inundation iieigiit in m

lndicator(s) /Measurement Units Advantage of the Indicator

Health Climate Sensitive Diseases: Dengue Fever The Increase In outbreaks of this disease and its

Dengue case count, ralrlfall (mm), Temperature, ;\"a'”\"‘e\"‘ “f \"V§“\"d|e'“\"“z ““‘.“: \"“ 2°19‘ . h

Sanitation and Health Expenditure rigoing researc re re ations Ip wit c Imate wit in

the Jamaican context and reliable historical data

avallablllty.

Heat Stress: Health efiects of temperature change an The Increase In temperature driven by accelerated

labour capacity climate change poses occupatlonal health hazards

Relative humidity (%), wet Bulb Globe Temperature Ewe.\" “k5 ‘we t° '\"\"eaSE heat ‘Ems ‘\" me ‘f\"°'k

(ac) environment for workers in construction, agriculture

and other areas.

Water Stream flow Source of surface water for irrlgatlon and potable

(MS) water. Flooding from increased flows.

Flood FIDW impact an flooding and drainage issues affecting

/50 and 100VR PEAK FLOWS (m3/s) '”\"a5\"“°“\"e5'

Tourism Holiday Climate Index: Beach Evaluates climate Informed by tourists’ stated climatic

Tc: Thermal Comfort: Hurnldex formula using daily P’9fe’°\"‘°‘ “V ‘°a5“\"\"”°“‘“ ‘°“\"\"\"-

maximum temperature (C) and mean relative daily

humidity (in)

A: Aesthetic: Percentage daily cloud cover

P: Physical: includes daily precipitation (mm) and

wind speed (km/h).

ualit of Natural Resources Evaluates the effects of climate chan e on the uall

Y E El

% we (“I Coverage of natural resources that are critical for coastal

tourism inlamaita.

% Coral bleaching

% Beach eruslon

Economy Carbon intensity These lndicatofs provlde Insight lnto how c|imate—

This measures the CO1 emissions per mm 0, energy induced conditions are related to the current state of

mnsumpmm the economy and how well a country is performing

relative to our comparators.

Kllograms of C02 per kilowatt—hour. (kg/kWh)

Energy intensity

Tnis is the amount of energy consumed per unit ofa

countrys GDP

Real GDP per unit of rainfall

15 I The State afthelamaican climate (Volume Ilil lrlli:lm'latlorl cir .si ,

2_5 Limitations and constraints rationalized. Instead, even! attempt is made to ensure it

is clearwhich baseline is being referred to for each set of

The reader should be mindful of several limitations pr°Je$‘f$nS‘fThe mphcanon ‘S that thefsactfprojectlogs

and constraints, which are largely linked to data and matyt ' (er mm E Vanfus |5°”rcE5' zret ore; W20;

information availability and accessibility when interfacing \"0 W 0 cqmpare exec Va “es across a age 5 (

with this third volume of the SOJC Report. Although some enSen.1b|e' Smgle G.CM' two Sets of RCM E.\"‘e”‘b'e\"'

important limitations have been presented throughout the f:P°“al'V (3.5 eajh '5 “‘.°””h‘° ragga that 5pa‘Ua|:_‘ca|e of

report, they are also being presented in this section for . e pmjec ',°\"' sers w' ave ' ere” Sc? Es ey are

completeness: interested in and should therefore be mindful of the

baseline being referenced.

- While the availability of historical climate data has . .

improved since the release of the 2012 SOJC, there are ' AS noted In the mtr°,dum°\" to Chapter 6' eV.ery a“°”.\"\"

still data gaps and issues that impacted the currency of was Page \[0 Provget uidateti data and ':f°m.‘|at|;n

what has been presented in some sections of Chapters SW0 'c °Jama'Ca' U W are eée were no ave‘ a. E’

3 and 4 of the report. For example‘ while every attempt relevant references (from the Caribbean, or otherwise)

was made to utilize updated datasets, this was notalways were med‘

possible owing to data availability and accessibility - The availability of data specific to each sector indicator

constraints. Additionally, In some instances, data of selected and analysed in Chapter7was limited primarily

sufficient length was not available to support analysis. on account of the fact that data collection in sectors

- Data was not available for all four RCPs (2.6, 4.5, 6.0 and '5‘. no: geared Fowamc r,\"°;\"t°r'\"g chmafe 'mpactb' Th:

8.5) for all RCP-based projections in Chapter 5. As such, Irma‘ e scegatnofana 3/as 2\" S°\"T1fi 5:,‘ orfs was fast:

a varying mix of RCP-based results is presented and argey 0\" a a mm 'era urei, E ‘me. rams 0 e

discussed to allow the reader to gain an understanding aSS'g\"\"i'1e\"t dalio ffuld got faclmahtef Satlslffictoy field

of the range of possible futures, despite the gaps in researc an O '5 en ' reéearc mm '9'? me’ m

avallable data. most instances, was used to inform the analysis. Some

of these constraints ultimately impacted the indicators

' l’|'0leCli°”5 \"1 Chapter 5 afe 8'Ve” eSei\"5‘ different and locations selected for review and recommended

baselines, depending on the data source used.The1986- for ongoing monito,-ing_ where iocations were aiready

2005 is the be5e'l\"e Pe\"i°d f°f ‘he 5U'te 0f /‘R5 CWP5 being studied and for which there was significant data

GCM5 3n3|Y5ed- The baseline Pefi°d Weileble f9\" the to substantiate useful analysis, the sector analysis was

HadGEM2-ES is 19604 989. Since the source institutions based on access to this readiiy avaiiabie data

providing the data are different, this could not be

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3. CLIMATOLOGY

added to produce a future climate scenario. Climatology iS

also indicative of the ‘mean’ against which year to year or

i —: \"t\" ' d.Th I ' ’ th’

values over the course of a year or the annual cycle, from the Metaomlogicav Service of Jamaica (M51) and

~o-

' ’ ’Th htth'ht, dtfth’ld

hurricanes and sea surface temperatures. Other variables pr;::§te%u hovlvseserapvvehrefevedrizeOfasamizentelggph aanr:

such as wind, significant wave height, solar radiation, quality ara available’ averages are provided for Specific

relative humidity, sunshine hours and evaporation are also Svamns '

examined, however the extent ofthe analysis is constrained '

by limitations in data availability. Climatology analysis is

important as it establishes the baseline to which future

change (for example, as deduced forrn GCMs and RCMs) is

3.2 Temperature

Air temperature in Jamaica is controlled largely by the Jamaica's topography

variation in solar insolation throughout a year. The earth d . f

orbits the sun with its axis tilted at a nearly fixed angle an 5'26 a 0W5 or some

of 23.5“ to the plane of its orbit, and this gives rise to |atitUdina| Variation in mean

variations in temperatures throughout the year. Figure 3.1

presents the average climatologies of mean, maximum and m 0 Nth tern peratu FGS as

minimum air temperature forjamaica. The climatology is ‘ ' ‘

presented for four 30~year periods (1900~1929, 1930-1959. mdlcated dlfferereces

1960-1989 and 1990201 9). It is to be noted thatthe average between the climatological

temperature climatology is unimodal in nature with the - ‘

highest temperatures occurring during the summer months Ots fo r the n I ne Stauons

fromjunetoseptemberand coolesttemperaturesoccurring

from December through March. The annual range of mean

monthly temperatures is small (~ 3-4 “G, ranging from

23.2 “C to 26.3 °C for 1 901-1 929 to 23.0 to 27.1 °C for 1990-

2019‘ The mean maximum (daytime) temperatures can as indicated by the differences between the climatological

reach as high as 31°Cduri\"g‘he warmest menths fer some plots forthe nine stations Ofthe stations analysed Norman

locations’ Whne mean minimum (night time) temperatures Manley International Airport (south coast) was in the mean

can be as low a518'4°Cduri\"g e°°'e5t months‘ found to be the warmest while Mason Riveriinterior regions)

Figure 3.2 presents the temperature climatologies (mean, was found to be the coolest (see Table 3.1). Occasional

maximum and minimum) calculated for nine stations surges of cooler air from continental North America from

across the island‘ (see Tables 3.1 to 3.3 for the tabulated October through early April during the passage of cold

values). Similar to Figure 3.1, a general unimodal pattern fronts contribute to minimum temperatures that can fall

is dominant. It is also noted that the interior of the island below 20 degrees, particularly for northern portions of the

experiences slightly lower temperature than the coastal island.

regions. That is, Jamaica's topography and size allows for

some latitudinal variation in mean monthly temperatures

Jamaica Air Temperature Climatology

:2

.. 2: I

g 26 -\" '

X 14 V '

1H

\[6

Jan ma Mar Apr May Inn Jul Aug Sep on Nov Dec

—1s'19M:x jisxswlean —19zsMm — — 19b9Mzx — — 19:v9Mean - - 19b9MIn

-—--lsssmx ----19ssMean ----1sasMm 1u1sMax 1u1sMean zu1sMm

Figure 3.1. Air temperature climatology in ‘C forjamaica calculated using the CRU dataset. climatologies are shown for four 30-

year periods: 1900-1929. 1930-1959, 1960-1989 and 1990-2019.

A There IS in general less availabletemperature data than rainfall forthe country.

Figure 3.2. Temperature climatologies of nine meteorological sites acrossjamaica. Maximum temperatures are shown in red.

mean temperatures in black and minimum temperatures in blue. Data are averaged over varying periods between 1978 and

2019 for each station. Source: Meteorological Service ofjamaica.

_ :......l'.‘?.'.'...’\"..'.'T.\".3.i'.»....\"\"\"-'»'oz.:au 3 \"\"'3'.'..c\"\"'.'..u-..°\"\"\"\"\"’ _ '€«?.3-'.r''F‘I'.-.‘’.'‘-'§'a'.‘§’.§3l’'

:' _//-~\\\\ :| ::

° \"\" “ ,._/r/\\/\\\\ -“ ”' W.»-/”\"\\\\.

§:_,/'‘**\\ 2: ,.//**\\ :2:

E: __/,»~4~._\\ ‘ gfi _r//—-\\ -E: ”’,»+_._..1

\" \"...r.....».-......»...s.o.«».m. \"‘.w.....».-.....u-...s.o«-.o..

,_ \"7'..l.'.'.\"..°l'L.\"'.§‘.\".°\" i ’v...«._.::_c“--;.a;«;_.‘y_

u --w ‘ 5......‘ » ‘LT.

== _,/——~-\\., — — . . »-—~ x «

,,.. » >5 S--» _ 1

:....-«»\"\"_\"‘~‘\\ we 5/ \"””I\" s ; —__ -.. 1h\"\"\"’ 5:.

T. N//«*1 ‘ W §; 1

~ In 3.45.... p . ,> ._ . ,,,_ :

.. um... s-at-—-\\ s----_— ,,_ 1

v- ' W... _ .;-_- ~\\,. ... 1

“w....-.-.».u~...s..m..n.. -‘ . mm. '

., ..................

r ,, nueimnlauznmanu

an mu-..=...-1 1.»-......... =-.....m.w

5: ‘ I-a-um-an _ min in“... ;

E: A ; .v’’_’/__,_\\‘‘N :3 _F’_’/,,.—...\\_\\.\\‘ :2 _’_‘/,»«_.4.._.\\_

2. , ,.. u

;« : \"st 5./\"\"*-

H W . .____,,/'—’‘‘*~x__ .. W ~_///-*\\.._.._\\

.. 2. §; :2: _/‘T E»

g; V‘/_\\,\\ , :: _~//——-—\\_ . 3,‘: _ :

'‘..:..u.».u....u-...s..ou-~.o« --- -- “-y......-.....-.=aa.........

“...-m......».u...».oa~.o. “..........-u-~..s.o.«»~.n.=

Yable 3.1. Mean temperature climatologies for nine meteorological stations across Jamaica. Data are averaged for varying

periods over 1978 and 2019 for each station. Units are in ‘C, Source: Meteorological Service ofjamaica.

Bodles St. Catherine 1987- 24.7 24.8 25.2 26.1 26.9 27.7 27.8 27.9 27.7 27.2 26.1 25.7

2019

Discovery Bay St. Ann 1992— 24.8 24.8 25 26 26.5 27.4 27.6 26.8 27.8 27.3 26.4 24.5

Marine Lab 2009

(DBML)

Duckenfleld St. Thomas 2000— 24.8 24.9 25 25.7 26.6 27.5 27.8 27.7 27.2 26.6 26.1 25.5

2019

Frome Westmoreland 1996- 25.1 25 25.6 26.3 27 27.7 27.8 28 27.8 27.4 26.8 25.9

2019

Norman Kingston 1992- 27.0 26,9 27.1 27.8 28.5 29.3 29,5 29.5 29.3 28.7 28.2 27,4

Manley 2015

International

Airport

(NMIA)

Mason River St. Ann 1978- 20.8 20.6 21.1 21.6 22.2 23 23.2 23.4 23.0 23.0 22.2 21.6

2019

Passley Portland 2000- 24.6 24.6 24.9 25.7 26.5 27.4 27.6 25.8 22.4 26.8 25.8 254

Garden 2019

Sangster MontegoBay 1992- 26.0 25.0 26.5 27.4 28.0 28.8 29.1 29.2 28.8 28.3 27.6 25.6

2015

Wor(hyPark Stcatherine 1973- 21.7 21.9 22.5 23.4 24.3 24.9 25 25.1 25.1 24.6 23.6 22.4

2015

Yable 3.2. Minimum temperature climatologies for nine meteorological stations acrossjamaica. Data are averaged for varying

periods over 1978 and 2019 for each station. Units are in “C. Data source: Meteorological Service ofjamaica

Bodies St.Catherine 1987-2019 19.1 19.4 19.9 21 22 23 22.7 22.8 22.7 22.3 20.9 20.7

DBML St.Ann 1992-2009 21.6 21.4 21.5 22.6 23.1 24 24.1 24.1 24.2 23.9 23.4 22.5

Duckenfield St.Thomas 2000-2019 21.2 21.1 21.1 22 22.9 24.3 24.3 24 23.2 22.8 22.6 22

Frame Westmoreland 1996-2019 20.1 19.8 20.3 21.3 22.2 23.1 22.9 23.1 23.1 22.8 22.3 20.9

NMIA Kingston 1992-2015 22.9 22.9 23.3 24.2 25.1 26.0 26.0 26.0 25.7 25.2 24.4 23.5

Masonkiver St.Ann 1978-2019 15.4 14.8 15.1 15.3 16.2 17.1 17.1 17.4 17 17.7 17.1 16.6

Passley Portland 2000-2019 21.3 21.1 21.2 22 22.9 24 24.2 19 18.8 23.1 22.5 22.2

Garden

Sangster MontegoBay 1992-2015 22.2 22.0 22.5 23.4 23.9 24.6 24.8 24.9 24.6 24.3 23.9 22.9

WorthyPark St.Catherine 1973-2015 15.9 15.7 16.2 17.3 18.6 19.3 19.1 19.3 19.3 19.1 18.3 16.8

Air temperature

in Jamaica is

controlled largely

*=by the variation

in solar insolation

roughout a year.

Table 3.3. Maximum temperature climatologies for nine meteorological stations acrossjamaica. Data are averaged for varying

periods over 1978 and 2019for each station. Units are in “E. Source: Meteorological Service ofjamaica

Bodies St. Catherine 1987-2019 30.3 30.3 30.5 31.1 31.7 32.3 33.0 33.0 32.6 31.9 31.3 30.7

DBML St. Ann 1992-2009 28.0 28.3 28.5 29.4 30.0 30.8 31.1 29.6 31.5 30.7 29.5 26.5

Duckenfield St. Thomas 2000-2019 28.2 28.5 28.8 29.4 30.1 30.7 31.1 31.4 31.2 30.4 29.6 28.9

Frome Westmorelarid 1995-2019 30.2 30.2 30.7 31.3 31.7 32.3 32.6 32.8 32.6 32.2 31.3 30.7

NMIA Kingston 1992-2015 30.9 30.8 30.9 31.5 31.9 32.6 33.1 33.0 32.8 32.3 31.9 31.4

Mason River St. Ann 1978-2019 25.8 25.9 26.6 27.5 27.8 28.3 28.8 28.9 28.3 27.8 26.9 26.1

Passley Portland 2000-2019 27.8 28.2 28.5 29.3 29.9 30.6 30.9 31.2 26.1 30.6 29.2 28.6

Garden

Sangster Montego Bay 1992-2015 29.7 30.0 30.4 31.4 32.0 33.1 33.3 33.4 33.0 32.3 31.2 30.2

Worthy Park St. Catherine 1973-2015 27.6 28.0 28.9 29.6 30.0 30.5 31.0 31.0 30.8 30.0 28.9 28.0

3 3 Rainfall seem to record similar amounts of precipitation. The shift

‘ in seasonal rainfall pattern is further supported in Figure

h I ‘ f . f H f . d h 3.4. The characteristic rainfall versus temperature pattern

T e.an.n|uadcyCfT 0 raga d°r|Jama'Ca' avelzfage gigeqhe shows a shift towards higher temperatures towards later

eimre '5 an ‘re e.fl5a. \"\"0 a pmermsee gum. ' I) e decades while early and late rainfall shifts to comparable

bimodal pattern is typical for most of the countries in the amounts

northwest Caribbean and is a result ofan interplay between '

the large scale climatic modulators of the lntra-Americas

region, including the North Atlantic Subtropical High (Azores

High), the trade winds, vertical wind shear in the Caribbean

basin, and the Atlantic Warm Pool (see Information Box

3.1). In tandem, the large-scale influences condition the

region to be conducive to rainfall during boreal summer Jamaica's topography

and dry during the cooler winter months. Forjamaica, this .

translates into a dry season spanning December-March and Size allows for some

and a rainy season spanning April-November, which can latitudinal Variation in mean

be divided into an early rainfall season (April-June) and a

late rainfall season (September-November). A mid-summer monthly temperatures as

minimum in July, termed the midsummer drought (MSD), < » -

separates the early and late wet seasons. Jamaica receives Indicated the differences

most of its rainfall during the late rainfall season (see Table between the c|imato|ogica|

3.4), with May (-12% of total annual rainfall) and October I f h . .

(-14% of total annual rainfall) being the rainiest months, p Ots Ort e nine Stations

while February and March are the driest months of the year.

Figure 3.3 also shows that although, in general, the October

peak is higher than the one occurring in May, in more

recent times (see the 1990-201 9 climatology) the two peaks

Figure 3.3. Rainfall climatology in mm forjamaica as determined from the All-Jamaica rainfall index of the Meteorological Service

ofjamaica and CRU dataset. Climatologies are shown for the entire period (1881-2019) as well as four 30-year averaging periods:

1900-1929, 1930-1959, 1960-1989 and 1990-2019.

Jamaica Rainfall Climatology

350

.\\

300 ,’

/ \\

E .

5 250 ’ A ‘.

' '\\ /1 \\

:3 I . .’ / \\ .

2 ' ’ A\\\\

_ 200 - , '

E \\ .

5 ‘ / \\

E ‘ .

150 ‘ V ,

’ \\\\V’

/ « ¥

1oo ‘ T

\\-'/

\\'r -/

50

fiflfl

—1881 1° 2019 EIEE

— 190° 1° 1919 E

— 193° 1° 1959 EEEEEEI

— 196° 1° 1999 E3

— 199° 1° 1°19 HIE

— - -CRU 1901 to 2019 111 92 82 127 250 175 141 187 241 315 240 150

‘_ \".

,i ‘ _, I3 ;;s'‘' e

_‘,~—»~- . :gi|._Km,..i7' I‘. Azores Hlgh

; t :“t,.- i._‘=v~_ _

1 -\\ it ii in -‘-‘’-r

r- -'1 ‘\\ » .

. \\. _ ‘

, I ; ‘ I /

. r. ,\\ fr / '~ \\—._~., A

v _ ‘ ' ' /5, / 3 :2 ..s_, \\>.;=..

c‘ 0 q‘ 4‘ ‘ s‘1 : ' 3

‘ i f“ i.«*‘r' 5' \\ -°° \\

' r ~ . §\\ .‘ 1 K

rrm N J.» .: Q.

source:NOAA

INFORMATION Box 3.1 vertical shearis diminished and the conditions that now exist. Around

Caribbean Sea becomes warmer July, a temporary retreat of the

is October due to the appearance ofthe north NAH equatorward is associated

tropical Atlantic warm pool. The with an unfavourable atmospheric

Usually Wet and result is that the southern flank environment, diminished rainfall

of the NAH becomes convergent and the occurrence of the Mid-

February usually and the region is conducive to summer drought (MSD). Enhanced

Dry? convective development. The precipitation follows the return

primary source of rainfall in boreal of the NAH to the north and the

The raii-ifa|| pattern for the summer comes from the passage passage of the Inter Tropical

caribheah is iargeiy conditioned of easterly tropical waves which Convergent Zone (ITCZ) northward.

by the North Atiahtic High (NAH) pegintraversing the Atlantic Ocean Th _ f I

pressure system which is ti iarge injune after leaving the west coast 9 C:5':3\"°” b0 9339;‘)! Wakl/95f

subtropicalsemi-permanent centre 0fAfV|C3~Th€ W3Ve5 are th9m5e'Ve5 ar:°“hrAH over er .3\" the (red of

of high atmospheric pressure a source of convection and can the 50% zgamdattf $19\" _O

that is typicaiiy found south ef deve|op_into depressions, storms ‘ 9)/Ear mg’ hf 9 9\" 0 t 9 '3'”);

the Azores in the Atientic ocean andhurricanesundertheconducive fifigsdorg 52250:‘ e r°'°'“e'89\"Ce 0

between 30°N and 35°N. During '

northern hemisphere winter, the

NAH is southernmost with strong

easterly trades on its equatorial

flank. Coupled with a strong

trade inversion, cold sea surface Around July, a temporary retreat Of

temperatures (SSTs) and reduced < »

atmospherichumidityvmmgionis the NAH equatorward IS associated

generally at its driest Precipitation with an unfavourable atmospheric

during boreal winter months is _ r , , _

therefore generally only due to the environment, dllTl|rl|Srled rainfall and

passage of mid-latitude cold fronts - _

which Passfarsoum the occurrence ofthe Mid summer

With the onset of boreal spring. drought‘

the NAH moves northward. the

trade wind intensity decreases.

Figure 3.4. Temperature versus rainfall climatology. Rainfall climatology is determined from the All-Jamaica rainfall index of

the Meteorological Service ofjamaica. Temperature climatology is from the CRU dataset.

Temperature vs Precipitation

300

Oct

A M3

250 ’ ' Y

I §-‘ L

__ 200 I A\\‘ 5%‘

' 1/ IL /. “\"9

E t

.§ 150 V, - - ~

.5 I V V

.9 /1

u

3 100 ?! _,, Ar Jul

A.

_ 1 Mar

Feb

50

0

23.0 23.5 24.0 24.5 25.0 25.5 26.0 26.5 27.0 27.5

Temperature (!?C)

-0- 1900 to 1929 -0- 1930 to 1959 -0- 1960 to 1989 -0- 1990 to 2019

Table 3.4. Mean seasonal rainfall totals (mm) and seasonal percentage of annual totals for the period 1881-2019

Seasons Mean Percentage Seasons Mean Percentage

DJFM 515 21% 3 ND\] M7 22%

u

E« AM\] 515 28% E FMA Z92 ‘l6%

._ a

g J 464 7% § MJ\] 5‘l7 28%

iv

ASON E21 44% E A50 631 34%

Annual 1856 100% Annual 1856 100%

The bimodal pattern depicted in Figure 3.3 and the relative dry season months of December and January. This latter

timing ofthe associated peaksarealso evidentinthe rainfall observation indicates that Jamaica is large enough that

climatologies for all parishes (averaging over available its topographical variations can modify the background

selected stations in a parish) (Table 3.5 and Figure 3.5). climatologicalpatterndetermined bythelarge-scaledrivers.

On an annual basis and for most months in the year, the That is, the location, height and orientation of Jamaica's

parish of Portland receives the highest amount of rainfall, topographical features (for example, the mountainous

accounting for approximately 15% of annual rainfall totals interior and coastal plains) with respect to the prevailing

over the period, while Clarendon receives the least amount trades causes differences in rainfall amounts and the timing

of rainfall, accounting for 5% of annual rainfall totals (Table of rainfall peaks over various parts of the island.

3.5 and Figure 3.3). However, unlike the other parishes,

Portland and St. Mary receive their maximum rainfall

in November and next highest rainfall totals during the

Table 3.5. Monthly mean rainfall received per parish (mm), Means are calculated for 1996-2020 by averaging all stations in the

parish‘ Source: Meteorological Servicejamaica.

Parishes Jan Feb Mar Apr May Jun Jul Aug Sep 0:: Nov Dec Mean

Clarendon 29 23 53 61 153 102 76 103 181 215 113 50 96

Hanover 67 60 74 125 251 189 189 235 271 230 111 76 157

KSA 52 44 59 84 152 88 87 164 24-4 239 146 76 120

Manchester 46 47 S3 154 230 114 101 170 237 248 122 65 135

Portland 361 220 238 235 331 180 199 234 228 401 493 424 295

St. Ann 124 76 93 97 184 99 93 121 184 217 177 143 134

St. Catherine 42 38 59 87 177 110 102 148 216 227 113 63 115

St. Elizabeth 50 53 93 158 213 104 120 180 224 230 132 53 135

Stljames 89 50 65 97 202 127 124 162 218 195 119 77 127

St. Mary 163 108 108 104 156 68 77 106 131 190 229 198 137

St. Thomas 77 54 58 73 201 142 109 174 208 281 193 121 141

Trelawny 93 57 75 101 180 100 102 128 199 193 142 90 122

Westmoreland 54 57 74 140 252 152 210 244 250 241 120 62 155

Jamaica 96 68 87 117 207 121 122 167 215 239 170 116 144

Figure 3.5. Parish monthly rainfall climatology calculated for the 25-year period, 1996 to 2020.

jclarendnn Jan Jamaica 25 Year Mean Rainfall

5CD

' ' H3\"°V9\" Dec Feb 1996 - 2020

4(1)

— KSA ’ ,- - - — \\

3CD

-— Manchester Nov ‘ ’ I ‘ \\ Mar

- - - Portland ‘I 320- ‘ \\ \\ \\

' -1.

: St. Ann I /t’:<*~> “

o I I \" A

jst. Catherine C ‘ \\ \\ W

‘ \\ <1 .— \\

T51. Elilabeth ‘ ’ ,._?_ _\\

\\-n-'

T St. James Sep May

- — — St. Mary

j St. Thomas Aug jun

Tfielawny Jul

- - Westmoreland

Figure 3.6. Distribution of mean annual rainfall forjamaica (in millimetres). Source: CSGM

u too R611 Inn zun saw ma non um

uso

mt

mm

1115 .

ma

4555 —u 2; —uno 47 73 4150 47:5 4100 4615 4550 as 15

Figure 3.6 gives the Spatial distribution of mean annual lines in Figure 3.7 show the best guess delineation of the

rainfall over theis|and.The mountainous interiorgenerally four rainfall zones premised on the co-varying stations

receives rainfall in excess of 1700 mm annually, while the and the mean rainfall map shown in Figure 3.6. The four

north and south coasts are significantly drier, with the zones identified are: the Interior (Zone 1) which largely

plains of the south coast being the driest region (1000 mm encompasses Jamaica's mountainous interior: the East

or less). Rainfall maxima occur on the west and east of the (Zone 2) which covers the rainfall maximum in Portland:

island, with highest rainfall amounts (up to 5000 mm or the West (Zone 3) covering westernjamaica and; the Coasts

more) occurring over far eastern Jamaica (Portland), likely (Zone 4) containing the dry north and south coasts. The

due to a convergence of mountain and sea breezes. rainfall zones are not limited to parish boundaries. As an

When cluster analysis is done using rainfail data from 129 a5'de' the figure a|5° h'gh|‘gh‘5 the_g|a”\"g gaps \"'_‘Jama'Ca'5

stations across the island with sufficient long-term data, mete°r°r|1°g‘_‘a| daga C°\"erage_W'th 5°me pa'_'5he|5_' e'g'

four rainfall zones emerge. Cluster analysis highlights St‘ avmg ma equate Stamns t° Capmre \"5 C 'mat“

sub-regions with similar patterns of variability. The bold Va\"a”°”5'

Figure 3.7. Meteorological stations that cluster together with respect to rainfall variability and the four rainfall zones they fall

in. Bold lines show the rough delineation of the four zones which are called the Interior zone or Zone 1 (dark blue), the East

zone or Zone 2 (cyan), the West zone or Zone 3 (yellow), and the Coastal zone or Zone 4 (red). Source: Meteorological Service of

Jamaica

I 2 3 4

Iain

ms

um

17 75 .

175n

—7I.5n 4125 —1Ioo 4775 —77.§u —n.zs —71.nu 45.75 46 so 46.15

Figure 3.8 shows the annual cycle associated with each from month to month and receives the most rainfall of

rainfall zone derived by averaging over the stations within all zones excepting the East (zone 2);

(me Z°ne‘b1\"he a|\",i5‘an‘; cfimemogy f(fi”\"p_|ef'i‘Te)|l5 a|f5° - The East (zone 2) deviates the most from the al|—island

5 °;’1V\"'Ta eile gwesf eT1eanT1°”t hy\"a”,‘af Ir: “e5 T; climatology with highest rainfall occurs in November

ea‘ forge‘ e 3'5 3 5° 5h°w5 dew‘ e ra_'”fe” °r eae (and comparable totals in December and Januan/i. and

Zanef 'ISI '5_\"' we amass I L: \"3 ‘none ram 3 5ea5°”5‘ a secondaw peak in April—May. Like the other zones, the

T e O Owmgt \"185 are note ' mid—summer minimum first appears in June. The East

- The characteristic bimodal pattern seen in the al|—island also receives the highest amounts of rainfall year—round

index is reflected in the rainfall climatology for three of — at all times exceeding the average rainfall ofany ofthe

the four rainfall zones; other three zones;

- The Interior (zone i) and Coasts (zone 4) follow the all— - Both the Interior and the Coasts receive the most rainfall

island climatology closely with an early season peak in during the late rainfall season (August—November). The

May, but with the late season peak and mid—summer West receives comparable amounts during the dry and

minimum occurring one month earlierin Septemberand late wet seasons, while the East receives most of its

June respectively. The Interior also has higher rainfall rainfall during the traditional dry season (see Table 3.7).

tfitaemfalr: the Ceasts which represent the driest Of 3\" In general, the climatological mean, annual, and monthly

t e ram 3 Zones’ rainfall across Jamaica is a complex function of fixed

- Rainfall in the Westizone 3) peaks in Mayand September— factors including the topography and shape of the country,

October and has the least pronounced mid—summer as well as the annual cycle of regional—scale oceanic and

rainfall minimum. The West also shows least variability atmospheric factors.

Figure 3.3. Climatologies onlie rour rainfall zones for clie years 1981-2010. Colours are as follows: Interior (zone 1) — green, East

(zone 2) - cyan, West (zone 3) - red, Coasts (zone 4) - navy blue and the All-Island lndex (purple). The All island index is averaged

over the years 1391-2009 (purple dotted line). source: National Meteorological service of Jamaica

500 T

-0-Zone 4 (Coast)

450 -0-Zone I (iruriol) _ _

—0—Zl:l1la 3 (wostnm)

—o—Znna 2 (Norltl-Ensmm

400 —o—Al|-lslmd Index

350 —

_ 300 -

3

>-

250 - -

200 -

150

100 -

50

0 2 4 6 B 10 12

Month

Table 3.6. Average annual rainfall values (mm) over the period 1581-2010 for the four rainfall zones compared to the all-island

average

Month Zone 1 Zone 2 Zone 3 Zone 4 Country

121.41 380.12 155.02 83.66 105.63

1.252 255.45 115.29 75.75 51.55

124.44 247.81 167.26 82.51 90.78

a 155.09 357.55 101.19 105.52 122.50

215.35 356.92 237.49 143.39 240.53

144.50 195.55 193.05 92.59 140.15

166.60 241.27 212.72 112.42 124.78

5 195.55 2.4.75 297.59 155.25

259.98 274.04 254.63 165.89 207.28

225.15 359.04 254.02 149.35 235.15

189.88 475.26 191.29 135.18 173.85

149.15 392.95 155.25 99.41 11255

Table 3.7. Total rainfall (mm) for a year for each zone, along with station average total (mm) and percentage ('11:) rainfall for

three seasons of the year. DJFMA (December-January-February-March-April), M\] (May-June), ASON (August-September-October-

November)

-rota\] Mean DJFMA M\] ASON

R“i\"'3\" Total (mm) Percentage Total (mm) Percentage Total (mm) Percentage

‘\"‘\"\" (V9) (Va) ('44)

lntenor (Zone 1) 1826.5 627.5 34.3 311.2 17.0 743.4 40.7

East (Zone 2) 3840.7 1635.5 42.6 565.3 14.8 1391.9 36.2

West (Zone 3) 2139.2 776.3 36.3 372.0 17.3 508.5 37.8

Coasts (Zone 4) 1218.7 419.3 34.4 207.5 17.1 492.3 40.4

- ofMexico) hurricane season runs fromjune 1 to November

3'4 Hurrlcanes 30. This coincides with the period when the Caribbean Sea

, , . is most conducive to convective activity (see Informatxon

Humcanes or tropmal Cyclones are rmanng Systems of Box 3.1) and w1thjamaica's rainfa1| season. This however

organized thunderstorms with maximum sustained surface does not preclude Storm or humcahe achvity in May or as

Winds Of 39 mph ‘NCAA’ 2020)‘ They tend to form °‘/ervew late as December The peak of the North Atlantic season

warrn tropica1 and subtropical ocean waters and in regions is September with most activity occurring mid_AugU5t to

with weak lowerrlevet winds (also known as trade winds). mid_Ocwber \{Figure 33)’ although a deadly hurricane may

The North At|ant1c(At|antIc Ocean, Caribbean Sea, and Gulf occur at any time during the Season

Figure 3.9. Hurricane frequency for the Atlantic ocean hurricane season. Source: NOAA

Atlantic Hurrica ne and Tropical Storm Activity

Based on Data from 1944 to 2020

tn 80

Z

< I Hurricanes and Tropical swims 9 G

'-‘-' 70 -

>- I Hurricanes

8

H 60

E

Q. 50

>-

<

D 40

E

:2. 30

E

Q: 20

9

V1 10

0 A A

May 1 Jun 1 Jul 1 Aug 1 Sep 1 Oct 1 Nov 1 Dec 1

Figure 3.‘l0 shows the percentage occurrence of storms by ofhurricanes with Category 1-3 strength remains high even

category passing within ZOO-kilometers ofjamaica for‘l851- in November compared to the occurrence of other storm

201 9. Tropical storms (with sustained winds between 39-73 categories.

mfilehs per hour) are more pr_eVa|ehm W Te fearlier pomon Viewedinconsecutive Z0-yearsegmentssincei940,Jamaica

0 I E h“m(_a”|e Season‘ Dunng ‘ re flea 0d the h“;”C:\"e has generally experienced more Category 4-5 hurricanes in

Season’ tromca Storms are more 'ke yrfo Z‘/elop “rt er the last fewdecades compared to earlier periods (see Table

‘HMO _Categ°'7_1 h“m(a”e:(75'129 mp )3?‘ (?EtT_iOryd4': 3.8). The number of storms passing by or directly affecting

hurrfcanes (“\"3 5greaEer\[_a”13gmP|h)'_T E |' e' 0° _° Jamaica in the 2000s has been at its highest since 1940-

umcanes an maim umcanes eve opmg nearjamama 1959. The Atlantic basin overall experienced low hurricane

increase during the peal<season.The percentage occurrence activity in (M19705 and1980S_

Figure 3.10. The percentage number of tropical storms, hurricanes (Categories 1-3) and major hurricanes (Categories 4-5)

passing within Z00-kilometers ofjamaica from 1851-2019. Source: NOAA (httpsillcoasnnoaagovlhurricanesl)

an mass 2:»: sun was smi sum me

May 5m

on

- sax

Jun. sou

on

Jan

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mt

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m

s‘'\"'\"''‘' ”‘a-aim ‘\"‘

T In

October was

x

November 1”‘ m

t

.i1ropI:ais:orms Cat. 1-: Hurricanes I Eat. I-5 Hurricanes

Table 3.8. Comparison of the number of storms by category passing within 200-kilometers ofjamaica for consecutive 20-year

periods between 1940-2019. Categories indicate a storm's maximum intensity within Jamaica's vicinity. Source: NDAA (httpszll

coast.noaa.govIhurricanesl)

Number of Storms

Period

33333

1940-1959 10 z - 4 - - 16

1960-1979 4 2 - 2 1 - 9

1980-1995 3 1 - - 2 1 7

20130-2019 11 4 1 - 5 1 22

th AWPd' b th t fth \"t th.|t

3'5 sea surface Temperatures is tehereforeStaop::ar|1:te)d \[h:(0iICaSl?m0iIiIateel'SWgl|l'\]e::eTt0hnaV7ihe

, , 27.5°C convection threshold exist over much of the north

Adapted from state of the Caflbbwn Chmate Rem\" (CSGM tropical Atlantic duringthe summerand late rainfall season

2020)’ This makes for extremely conducive conditions along the

Figure 3.11 presents mean SST maps for the wider tropical path traversed by tropical easterly waves and facilitates

Atlantic, including the Caribbean, for selected months. The their development into tropical storms and hurricanes.

appearance’ expansion and decline of the Atlantic warm Figure 3.12 depicts average SST values for the Caribbean

P°°' (AWP) and how it modmates Caribbean basin SST: region as a whole and for each of the six defined rainfall

are evident in the plots. At the start ofthe year and during Zones (Jamaica is in Zone 3 _ See again Figure 21) SST T5

the rprthem “eT\"‘5P*‘”e winuter Season’ the Caribbean is coolest in December/January (winter) for all six defined

relatively cool with SSTs of 27 C and below. SSTs gradually Zones ranging from 25°C to 26 89C and warmest in July,

increase, with warmer waters first appearing in the AuguS't(Summel,)ranging from 2'8 GJC to 29 6°C Except for

Western Caribbea\" (‘he GU” of Maxim) Md the” spreading Zone 2 all other zones and the Caribbean asla whole exhibit

eastward,>e\\/eritually reaching the tropical Atlantic coast 3 hTgh|'y Correlated SST pmem_ The difference Shown by

of the African continent during the summer months. SSTs Zone 2 can be attributed to its far north location which

exceed 29°C across the Gulf of Mexico and the Caribbean accounts for a Vwder extent Tn its temperature range

basin during summer (peaking in August), with SSTs greater '

than 27.S°C extending through to the east Coast of Africa.

The pattern reverses thereafter, as SSTs gradually cool and

Figure 3.1 1. Climatology map of Sea Surface Temperature (SST) for the Caribbean region and Tropical North Atlantic (TNA) over

1981 - Z016. Dataset: NOAA-DI.

_ ‘ Febmary _ 7' Apri

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7: Z .. :2 .

u. .. ‘.

u ‘ ‘=5 - ,— ~

.. «-\\ ..

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—

27 27.5 28 28.5 29

Figure 3.12. Climatology series of Sea Surface Temperature (SST) for the Caribbean and the six defined rainfall zones over 1582

to 2016.\]amaica is in Zone 3. Dataset: NDAA-OI.

Sea Surface Temperature Climatology 1982 - 2016

so

7° l J

l

9“ 27 - ‘

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Jan Feb Mar Apr May Jun Jul Aug Sea on Nov Dec

—o—CaIiblM:an —I—ZunL' 1 —-—zoa-c2 —-—ZuuL'3 —~—2u--ca a—zu-ms ——ZuI\\c5

32 i The State ufthejamawan C||mate(Vo|ume|iI) wmmauon or .5‘ ,

3.6 Other Variables

3.6.1 WIND

Winds injamaica are a combination of the prevailing winds, sea breezes and mountain and valley winds, which arise as a

result of heating and cooling in valleys. The strongest influence is the prevailing trade winds from the East or North East

associated with the North Atlantic High (NAH) (Information Box 3). In the mean, wind strengths vary inversely with rainfall.

Therefore. during the driest months (when the island is under the influence of the NAH. for e><ample,Januan/—Apri| and July)

wind speeds are largest while during the wettest months, wind speeds are lower. This is generally reflected in Figure 3.13

which shows the wind speed climatology for Norman Manley International Airport based on data collected over the period

201 1—201Z. This does not preclude veiy high wind speeds occurring when a tropical system is passing near or overjamaica.

Wind mapping campaigns (Amarakoon and Chen, 2001, 2002) show that winds are strongest in Portland, St. Thomas,

Manchesterand St. Elizabeth (seealso Figure 3.14). The extremes ofannual wind speed at 30 m foreach parish as determined

from one such wind mapping exercise are given in Table 3.9. The data also confirm the four above mentioned parishes as

those with strongest mean winds.

Figure 3.13. Wind speed climatology ofjamaica based on data collected at a) the Norman Manley International Airport and b)

the Donald Sangster International Airport on an hourly basis. Source: Meteorological Service ofjamaica.

Mean Monthly Wind speeds

11.0

12.0

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Table 3.9: Extremes of annual mean wind speed for each parish, taken at 30 metres. Source: Amarakoon et al. 2001.

n Win='Svee~*ws>

Portland 1.49 — 9.40

St. Thomas 1.48 — 8.59

Manchester 2.54 — 8.39

St. Elizabeth 1.61 —8.24

St. Catherine 2.18 — 7.18

Westmoreland 2.51 — 6.95

Snjames 2.09 — 5.57

St. Ann 2.39 — 5.40

Clarendon 2.57 — 6.01

St. Mary 2.57 — 6.26

Hanover 2.53 — 5.95

St. Andrew 2.30 — 5.52

Kingston 2.71 — 5.51

Trelawny 3.44 — 4.57

Figure 3.14. Variation of wind speeds acrossjamaica. Source: Mona Geoinfurmatics Institute

WIND VELOCITY

Mo N A@ @::— ;,.,.m.-n.

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3_6'2 s|GN|F|cAN'|' WAVE HE|GH'|' Zf the wives and shhould not gehconfusefd wri1th the ilertical

' t l t t t t t '

Very little information exists about marine variables a'S5t::C‘:aVeefi:iegnht :‘;rEi%c::t w:Ven::%gh(: V:rr‘:IS'S:et;oe\\::

relevant for Jamaica. Figure 3.15 shows the climatology of 0 7_2 3 m and gen'eraHy mirrors the wind Speed pattern

Jamaica's significant wave height, wind speed. Period and Highést waves occur when wind Speeds are Stmngesé

Wave direction deduced form 15 years of data from two which are in the drier months ofthe year The range for the

Weather buoys ‘ 42058 (yeuow) and 42057 (b|ue)’ ‘ocated dominant period is 5-7 s and again is generally strongest

southwest of Kingston and Negrii, respectiveiy. Significant when Surface Winds are Strongest

wave height is the average height of the highest one third

Figure 3.15. Climatology ofJaniaica's significant wave height, wind speed, period and wave direction for two weather stations —

42058 (red) and 42057 (blue) for the period 2005-2017. Source: NCAA NDBC

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Month Month

Figure 3.16 and Table 3.10 reveal the following:

- Significant wave height generally decreases from east

to west. Wind is one of the major drivers of waves in

Jamaica. The reduction in significant wave height from Significant Wave height is

east to west is due to the reduction in the strength ofthe _

wind from east to west; and i'T10i'E stable In the east bUt

- Significant wave height is more stable in the east. but f|uctuates in the west This

fluctuates in the west. This may be attributed to a more b d

reliable and constant driver of waves in the east. The may e attn Ute to a more

Northeast trade winds are reliable and constant winds reiiabie and Constant driver

which serve as the driver of waves in the eastern region _

ofjamaica. On the west, however, there is less influence Of waves In the east.

from these winds and therefore the waves are driven

by other factors. This leads to greater fluctuation in

significant wave height in the west of the island.

Figure 3.16. Significant wave height Information for the North Eastern, North Western, South Eastern and South Western

regions ofjamalca. Source: Simulation by WW3/NCEP/NOAA for period 2005-2017

7 ‘\"15\"

‘ \" ' T. WWl||b-Northwl Tf’ ‘ 1 T IIC03

‘J. _‘;’Txu T2: T mini It

T.::l‘:T”’:::':~T. ' c'V\"“\" . , . , <_ Jifl“. — -

f IS. 0.99 ' re 7 T; \" k

- ‘ 11°\" ~ -TTTiTlT’Tl:-TT

T, nu ‘ ' H8 . _‘TT .,.,

— ‘J 1 o.5 T , 1,3‘ l:\"——~

T;‘ 1;\}. I ‘zen T . “5

:‘ i‘.”';T‘ T 1‘ -

TTlu.‘- ’ _ _ mi _ _ _=-_-=2

\"L ,‘ \" \" T H 1.47 1.52

Ejfll 17iL1‘1il:‘“ T; ~.——:

-.-,5,‘ \"\"3 ., \" . “'5 cm

:l':'i \":1: ;_1'T Tr‘ ‘ Wwlllb-Southwest wwmb_s°umEm

:’_l‘l\"::‘iT ’TlT!’u b'\\

T '~ 59 fl o‘

vwwwmb (2 -13?‘ T1:\",;.~i‘; ) ** All values of wave _lig1117:lT1?i71V17Hilliiriilrhjl

Table 3.10. Statistical summary of slgnlflcant wave height for the North Eastern, North Western, South Eastern and South

Western quadrant of the study area for data collected from NOAAIWWB Simulation throughout the period 2005-1017.

Mean (III) 1.048 1.025 1.683 1.529

Std. DEV (m) 0.412 0.414 0.664 0.610

Minimum (m) 0.000 0.000 0.000 0.000

Maximum (NI) 5.660 4.798 6.168 5.027

3.53 so|_AR RA|)|A'|'|oN to be about 0.0|O5 Ii/legaw:tt—hour/mi. figure 3.317 maps the

' ' t' t ' 19992018

sg-gr _be«~;;n ijggjnd g :::i:%.::\"m..:::i::::..i:.:::: ‘:,::L..e.';e:'.°.

S a.|.‘°:|s 3 Wrfughoud Jtamalca (H en t) are kplow '9 Plains, while smallest amounts occur in eastern Jamaica

m .3 e .' ' e a a genera y Sugges a pe_a m 50 ar over high mountain regions.

radiation in June and July, and a minimum in January.

Average radiation for Jamaica from the data is calculated

Table 3.11. Mean daily global radiation In Mjlmzlday at several radiation stations injamalca. See notes (I) and (II) below. To

convert from Mjlm‘/day to Kilowatt-hour (KWH), divide (M1/mllday) by 3.6. Source: Solar radiation map forjamalca (1954).

STATION PARISH JAN FEB MAR APR MAV JUN JUL AUG SEP OCT NOV DEC

Alcan Manchester 14.6 15.1 18.0 18.9 -9.9 19.6 20.4 -9.9 -9.9 16.7 15.6 15.1

Allsides Trelawny 13.1 13.7 17.4 17.3 17.5 22.0 17.7 17.7 16.3 14.8 13.3 14.8

Black River St. Elizabeth 16.2 17.1 18.6 18.8 20.9 26.1 24.6 25.4 18.7 16.5 16.1 14.5

Bodies St. Catherine 15.2 16.8 19.5 21.5 21.2 19.7 20.8 20.4 19.0 18.0 16.2 15.5

Discovery Bay St. Ann 12.9 14.9 19.6 21.3 21.0 21.1 21.6 18.6 18.7 16.0 14.1 12.9

Duckensfield St. Thomas 16.5 15.8 21.1 22.9 21.9 22.4 22.3 21.1 21.4 17.6 18.4 16.4

Manley Kingston 15.9 18.0 20.3 20.7 20.0 19.5 19.9 21.4 19.0 17.3 15.8 15.4

Mona St. Andrew 14.4 17.0 19.5 19.5 20.0 20.5 19.5 18.7 17.8 15.4 15.7 15.2

Negril Westmoreland 15.8 17.5 18.4 19.7 18.4 19.9 18.7 17.8 18.6 16.1 15.2 147

Orange River St. Mary 159 13.0 13.0 18.7 18.0 19.0 17.9 19.5 17.8 15.9 16.1 15.5

Sangster Montego Bay 14.5 15.5 19.0 20.9 20.6 20.0 20.5 19.3 16.8 15.9 14.9 13.8

Smithfield Hanover 13.1 15.6 20.7 22.3 19.8 17.2 17.2 17.2 17.1 16.1 12.2 13.2

Note? (i) -9.9 denotes missing values

' (ii) High values for Black River inJune,Ju|y and August are questionable.

Figure 3.17. Solar Radiation Map: Global Horizontal Irradiation Map ofjamaica, 1999-2018. Source: Solargis 2019.

SOLAR RESOURCE MAP

WORLDBANKGRDUP

GLOBAL HORIZONTAL IRRADIATION

JAMAICA IESM/\\P

vrw 77-w

Saint Ann‘s Bay

Pm Marin

on Anmnio

il‘N iiru

.«-.:

, min lirwrvkl Kiirik

XWl\\¢ w..i mi km...

s.i. 1... Salim.)

Long term average of GHI. period 1999-2013 *9’ ‘U “‘“

Daily totals: 4.2 4.6 5.0 5.4 5.8

kWhlm’

Yearly totals: 1534 1680 1826 1972 2118

T’ .. V .. .i.,: :2 ...;:.».a».,. . mg, z;«.ii.: .. _:'...i4iL‘J3 3113:; :.'l‘ ,v. ,1i'JrZf’. . v... \\V thttp//globnlxolnrnli-x.infn

3.5_4 RE|_A‘|'|VE |-|UM|D|'|'Y' sU|\\|s|-||NE dawn, which is followed by a decrease through to early

HOURS, AND EVAPORATION afternoon when temperatures are highest.

Sunshine hours vary little throughout the year, ranging

As noted in the 2012 and 2015 State ofthelamaican Climate between Seven and nine hours per dey_ There are more

Reports (CSGM 2012: 2017), the lack of data hampers the sunlight hours in the dry season and less in the main rainy

extent to which analysis of other meteorological variables is season, with this being directly related to cloudiness. Spatial

possible, particularly with respect to their spatial variation. variations in sunshine hours are usually quite small, though

Values for Percentage Relative Humidity, Sunshine Hours, there are differences between coastal and inland stations.

and Evaporation for the Norman Manley and Sangster Mean sunshine in mountainous areas tends to be less than

International Airports are given in Table 3.12. 6 hours per day, caused mainly by the persistence of clouds,

Relative humidity does not vary Signifieamly throughout while in coastal areas it is near 8 hours per day (G01, 2000).

the year. Average humidity at the airport stations is higher Evaporation tends to be a function of both temperatures

during morning hours, ranging from 72-80%, and lower in and available moisture. For both stations, the values peak

the afternoon at 59-65%. Afternoon showers are the major during the monthsapproachingluly, therefore approaching

cause of most of the daily variation, therefore the highest the month ofhighest mean temperatures, but following the

values are recorded during the cooler morning hours near onset of the rainy season (May).

Table 3.12. Mean monthly and annual observed values for relative humidity, sunshine hours and evaporation for the Norman

Manley and Donald Sangster International Airports for the period 1957-2016.

‘

Evaporation (mm)

7am 1pm

75.23 50.23 5.33 10.45

75.92 51.00 5.97 10.39

74.34 51.50 5.73 10.79

73.23 52.54 7.05 11.12

73.33 54.71 7.15 10.47

@ 72.09 53.91 3.20 9.95

71.33 52.71 7.33 10.43

73.54 54.95 7.05 10.40

75.04 55.95 5.53 9.39

77.32 55.92 5.70 9.30

75.33 53.43 5.23 10.22

77.03 51.15 5.13 10.24

ANNUAL 74.79 63.27 6.51 10.39

32.92 73.04 4.55 7.77

31.33 71.17 5.20 3.40

30.17 70.33 5.21 3.55

R 73.92 71.13 5.73 9.03

73.29 73.33 5.47 3.32

fi 77.92 72.17 5.43 3.13

73.33 70.33 5.34 3.79

30.25 72.04 5.53 3.12

30.71 73.25 5.47 7.41

33.17 75.95 4.92 7.35

33.37 74.30 4.33 7.70

34.13 73.33 4.40 7.63

ANNUAL 80.88 72.66 5.73 8.10

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4. OBSERVED VARIABILITY, TRENDS AND

0 (HURDAT). In general, there is greater data availability for

4'1 lntroductlon temperatureandrainfallthanforseasurfacetemperatures,

Ob d . b.|. d d , d droughts and floods, and sea level rise. Where relevant,

. 59.“ Vana \"(.3/’ “en 5 a.n extremes are exanwe literature reviewis used to fill in gapsin the analysis.

in this chapter, with emphasis on temperature, rainfall,

hurricanes, sea surface temperatures, droughts and floods,

and sea level rise. The analyses, which are presented at the 4,2 Telnperatures

countw and sub»country scale, provide critical insight into

howthe climate ofiamaita has Changed Over the past few CRU gridded data forjamaica showsawarmingtrendforthe

decades. The data used is extracted from several datasets (Gunny for maximum, minimum and mean \[empera\[ure5

including (RU, NOAA, ERAS and the Hurricane Database (Figure 4_1). For \[he period ig()o.z019, minimum

, l

. av» _

The values forjamaica

.‘ = 1;, ‘_.l . are similar to the global

. 's ‘ 4 . .

3 x ._ g ’ ( ‘ and regional estimates of

7 V « \" ‘:' '.-\{r A .

‘I ‘\\ ’\\ ,x\\,\\‘ ’ temperature increase.

I .

_\\ l . . l

temperatures are, however, observed to be increasing at a lamaica, then, the results for the Caribbean also suggest a

faster rate (~0.27°C/decade) than maximum temperatures decrease in the mean annual daily temperature range over

(~0.06 “C/decade). This suggests that the daily temperature the period.

ramge is decreasing‘ Mean temperatures are mcreaslng at 5 Figure4 1 alsoshowsthatforthelamaicantemperature time

rate Of 0'16 ac/decade Qverme Same period‘ Using data for series it is the linear trend that dominates The dominance

thevairport stations, the trend in the mean temperature is of the global Walmmg Slgnel ll the historical temperature

esnmated at ‘(Mo “C/decade‘ data is also true for the entire Caribbean where the linear

The valuesforlamaica are similarto the globaland regional trend accounts for approximately half of the variability

estimates of temperature increase. Global mean surface seen (Figure 4.2). Figures 441 and 4.2 also show evidence

temperatureshaveincreased by0.85\"C:0.20°Cfrom l880— of decadal variability (groups of years which are warmer

20l2(lPCC20l3).Theannualmeanofdaytimetemperatures or colder) and significant interannual variability (swings

for the Caribbean region also shows a significant increase between one year and another)‘ These two timescales of

of 0,19 “C/decade over the period 1961—20lO (Stephenson variations, however, account for much less of the explained

et al. 20l4)i This is, however, smaller than the increase variancein the temperature time series.

in mean nighttime temperatures (0.28\"C/decade). Like

Figure 4.11 Annual maximum, minimum and mean temperatures forjamaica, 1900-2019. The linear trend lines are inserted‘

Source: CRU TS3.24.

Jamaica's Annual Temperature Trends

no

290 ——— i Th ——e — — T

G Gradienx=0.005

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moo mm win mm mm man 7070

Max Mean Min

Figure 4.2. (a) Average July-October temperature anomalies over the Caribbean from the late 1800s with trend line inserted.

Box Inset: Percentage of variance explained by trend line, decadal variations > 10 years, interannual (year-to-year) variations.

(I7) Percentage variance injuly-October temperature anomalies (from late 1800s) accounted for by the ‘global warming‘ trend

line for grid boxes over the Caribbean. Source: Climate Research Unit (CRU). Acknowledgements: IRI Map Room.

(3) - T raw

: trend

a 1

‘ E‘ ‘k Trend 44%

1 ' Decadal 13%

I

‘é ' ' ' ‘Md -'‘‘‘--|. l' ' ' |nter—annua| 40%

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1929 1940 1960 1960 2000

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longitude

OctTrend Time Scale UEA3p1

My WI. 275 M WI. M M M M W5 W1.

nriameuolimd

4.2.1 TEMPERATURE EXTREMES

Figure 4.3 shows trends in warm days/nights and cool The values forjamaica are

days/night for locations in Jamaica. Positive trends were . . .

observed forwarm days/nights and negative trends for cool Slmuar to the global and regmnal

days/nights. Generally, warm nights were increasing more estimates of temperature

rapidly than warm days. The greatest increase for warm .

nights was in Grid 7, while the greatest decrease in cool mCre35e~

days was in Grid 11.

Figure 4.3. Temperature extreme trends for specific grid boxes acrossjamaica for the period 1930 — 2019. The

map ofjamaica (0.5‘’ x 0.5‘) shows the grid box locations. Source ERAS

m —- _— —— —‘ -

-

9.

w

:1; r

- . —

.:‘:1 Elan.

Q 7

I”: L

n V s ‘ ._

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261 282 283 204

Ian

LOCATION Warm Days Cool Days Warm Nights Cool Nights

GRiD1 1.77 -1.11 1.35 -0.69

GRiD 2 1.42 -0.77 1.47 -0.83

GRiD 3 1.07 -0.42 1.84 -0.81

GRID 4 1.03 -0.18 1.84 -0.78

GRiD 5 1.16 -0.28 1.54 -0.87

GRID 6 1.42 -1.18 1.62 -0.96

GRID7 1.51 -1.12 2.05 -1.05

GRID B 1.00 -0.51 1.73 -0.92

GRID 9 1.03 -0.61 1.77 -0.95

GRID 10 1.50 -0.90 1.53 -0.82

GRID11 1.71 -1.31 1.51 -1.01

Country Average 1.33 41.76 1.66 41.88

Figure 4.4 shows five extreme temperature indices for - The opposite is true for growing season length (GSL)

the temperature stations— Donald Sangster International which has decreased at most sites except for Discovery

Airport, Discovery Bay, Worthy Park, Bodies, Tulloch, Bay and at the Norman Manley International Airport;

Norman Manley International Airport, and Duckenfieldr _ The number of nights warmer than 20¢ (1-R20) has also

using data for the period 1992 to 201 I . The following trends increased at an statbns except the far eastern one,

are noted. '

_ , - The warmest maximum temperatures (T><x) has risen

' The defly temperature range (DTR) has ‘nereesed at at four of the six stations and the coolest minimum

most sites analysed, with the exception of decreases in temperatures (TNn) has also risen at four of the six

Discovery Bay and at the Norman Manley International ssafions

Airport:

Figure 4.4‘ Trends in selected historical temperature extremes for stations located at Donald Sangster International Airport,

Discovery Bay. Worthy Park, Bodies, Tulloch, Norman Manley International Airport and Duckenfield. Figure shows (a) daily

temperature range (DTR) lb) growing season length (GSL) (cl nights warmer than 20 (TRZO) (d) coolest minimum temperatures

(TNn) (e) warmest maximum temperatures (Txx). Direction of the arrow indicates positive (upward) or negative (downward)

trend. The size of arrow indicates the magnitude relative to the largest trend in each panel.

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4_3 Rainfa\" particularly prevalent sinceflthe mid-19905, suggesting

increased short~term variability (swings between wet and

to

JamaicausingtheAllJamaica rainfallindex is plottedin Figure is true for most Of the Carlbgean (see Fl we 46) and is in

4.5. The record is dominated by year-to-year fluctuations large part due to the El Nlfiu/La Nlfia pllgenomgnon which

such that when a linear trend is fitted it is not statistically ‘ . . . , , , ,. .

significant, therefore, Over the entire period Jamaica is not :1: CS;E'l'g::aar: fi1rf':J’fr\"Tll:‘flO'Et§r0a)(r':‘ll‘la‘l)lfl|)'l';rl‘l:la:: :o\"l'_:b\['l|'ettyal'l:

getting wetter or drier with respect to mean annual rainfall. on this and HOW it is known ta ‘affect Caribbean rainfall

I’ h ' ' h ‘

most of the variability seen in the Jamaican rainfall record lr:r5\"”alt;n':ln:fifi_::g:_Zc;?::: flgfgtae 5a:“f'l_eo\"'l\"tllaet

(Figure 4'6)’ wavelet power spectrum analysis (not show\") time to resent there have been at least ei ht moderate or

confirms peaks in the Jamaican rainfall time series at 3 E‘: NI~ d L N“ 3 g

years. 9 years, 1344 years and 21 years. The first peak is Strong 'n°( )3\" a ‘\"a( )9‘/ens‘

Figure 4.5. Annual and seasonal rainfall trends ofjamaica for the period 1881-2019. Data source: The Meteorological Service,

Jamaica.

Jamaica's Anuual and Seasonal Rainfall Trends

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Figure 4.62 (a) Average July-October rainfall anomalies over the Caribbean from the late 1800s with trend line inserted. (bl

Irene! and decadal components of the averagejuly-October rainfall anomalies over the Caribbean from the late 1800s. Source:

CRU data. Acknowledgement: ll'(I Map Room.

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Figure 45 also gives the annual rainfall totals over the - There is significant inter-annual variability over the

period 1881 to 2019 for the dry period December-April period both in the annual totals and seasonal totals.

(DJFMA), early rainfall season April-June (AMJ). late rainfall

season August» November (ASON) and May-June which

represents the MSD period. Figure 4.7 presents similar data

but for the four defined meteorological seasons for the .

Caribbean: November-January(NDJ). February- April (FMA), The Values forjamalca are

May-Ju|y(MJJ) and August» October (ASO)- The following are similar to the global and regional

to be noted: .

, ESIIFTTEHIES Of temperature

- There IS a small annual and seasonal (except for DJFMA) .

downward trend, represented by the straight lines in Increase.

Figure 4.5. For Figure 4.7, a downward trend is observed

for NDJ, MJJ, ASO, but not for FMA.

’ ‘.\"‘~' ~ V*,. ‘:3. 5,\", ~

. .- Ea

. _ . .’ \\ ' , “ ' -. 1;:

l )\\:.‘‘-I: A ,

V ' it is likely that the El Nifio phewme on has V-9

7 resulted in the recent increas _ variabilifiy , . I

— observed in tlxaiéglamaicanvrainfall record

v the late 1990s7’as from that time to presentT;~-V ~_j._'

tbgze have been at |e_§,s_t§eight._rnoderate or.‘ ‘

strong Ell\\lifio (5)_a'r(1gl;La‘Nifia»(3) ev ~ .‘ ’ v

V./h ~ \"1 . \" ‘ \" \"-“'

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Figure 4.7. Meteorological season rainfall trends ofjamaica for the period 1881-2019‘ Data source: The Meteorological Service,

Jamaica.

Jamaica’: Meteorological Season Rainfall Trends

1500

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INFORMATION BOX 4.1 fl MM la Milo

What Does an El

Nifio Event Mean

for the Caribbean?

El Nifio conditions refer to periods

when the eastern Pacific Ocean off

the coast of Peru and Ecuador is

abnormally warm. La Nina refers to

the opposite conditions when the

eastern Pacific Ocean is abnormally

cold. When an El Nifio stans, it __,,__,__,,._______‘_,‘___,.

tends to last for about a year,

peaking in December and dying ' ’ ° ' '

out during the following spring. El source: NOAA

Nino events tend to occur every 3

to 5 years, though increases in the

f'eq”e\"CYi Severity arld d”\"\"‘_t‘°” frequency are also projected under in the southern Caribbean, but a

°f events have bee”_”°ted 5'”°_e climate change. transitioning to wetter conditions

me 19705‘ Fmther mcreases 'n e _~ over Jamaica and countries further

DUVWE 3” El Nlno eVell‘r ‘he north, In general, a La Nina event

Carmbea” tends toebe drlel 3”‘ produces the opposite conditions

h0g5’ tllarll “'5'-“('1 ‘U theh m°ia\"r in both the late wet season (wetter

* 3” Pamcu 3')’ Urmg \[ 9 ate conditions) and the dry season

D\"J~r-Ing an Wet 59350“ floml August through (producesa drier north Caribbean).

N|r'\]() evefltl N°Vembe’-Th9’e'5§'5°3t9\"d?UCY The low-level zonal component

. for reduced llurrleane aerlVltY- of the wind that flows over the

the Carlbbean R9Ce\"‘_ m5t9°V°L°E'CC3' _bdbr°USh_t5 Caribbean Sea is also associated

°CCU\"\"'l8 0V9’ t 5 3” 55\" 'n with rainfallconditionsin the north

terjds to be 2°10A~a\"d 201445 Coillcide With Caribbean. A westerly (easterly)

drler and El Nl\"° eV‘_9“‘5- H°WeVerv dulmg anomaly decreases the low-level

the early rainfall season (May-July) jet creating a wetter (drier) north

hOt'EEl’ than the );ear a§te\}r1a2E|l:\\lbifio (thedE| Caribbean The |0w_|eVe| jet rs

. ir\"io+ ear,t e an ean ten 5

Usual In the to be wxetter than usual. The El ::;n,§,):,tcu°,rem0||(e:5-Ey Segrzzfifti

mean Nine lmpacton the Caribbean do’ between the Pacific and Atlantic

period Uanuaiy-Marchilste lriljduee which are in turn modulated by El

opposite signa s overt e nort an NW0/La Nifia event;

south Caribbean, with strong drying

Since the 19505, Jamaica has experienced wet periods 19705, 1985-1988, and 1999-2001 being dry, and the 19905

(groups of years) in the 19605, early 19805, late 19905 and generally being wetter. Table 4.1 shows that variability

mid to late 20005. Dry anomalies are evident in the late across zones 1, 3 and 4 are indeed strongly correlated,

19705, mid and late 19805 and post 2010. Some of the suggesting that rainfall in these zones may be conditioned

decadal 5wings may be accounted for by swings in phase of by the same largerscale forcing5 such as the AMO. The East

the Atlantic Multidecadal Oscillation (AMO). (zone 2), however, was largely diy between 1985 and 1995,

Figure 4.8 shows the smoothed rainfall anomalies for but we‘ from 1995 through, 2005 and ‘l5 not §l$\"lfi‘a\"\"Y

Jamaicals four ramfa” Zonee A” Zones Show Strong decadal correlated with the other rainfall zones. The driving forces

variation. The Interior (zone 1), West (zone 3) and Coasts fer Zone Zara not funy understood‘

(zone 4) co-vaiy on a similar decadal time scale, with the

Figure 4.8. Smoothed anomalies of the\]amaiI:a's rainfall zones’ precipitation. All smoothing was done through a running mean

of 24-month anomalies are determined relative to the base period 1970-2010.

EEII

' Tznnei

v v—zm..2

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2:“ . . . . , . . ‘ . . ., TIN“

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Yable4.1.CorreIation hetweenJamaica's rainfal|zones.BoId over the seventy-year period 1940-2010. The trend is

numbers are statistically significant at the 95%IeveI. positive and increasing for all indices of extreme rainfall

when averaged over the nine stations. Therefore, there

is a positive trend with respect to annual total wet~day

2°“ 1 Z°\"e2 Z°\"e3 Z°\"e4 precipitation (PRCPTOT), annual total precipitation on the

wettest days on record (R95 and R99), monthly maximum

one and five»day precipitation (RX1 and RXS), and the

Zone 1 1.000 0.244 0.754 0.966 proportion of rainfall intensity to rainfall occurrence (SDH).

The averaged indices also indicate a decrease in consecutive

diy days.

0.244 1.000 -0.055 0363 Figures 4.9 and 410 show some sub~is|and distinctions.

The piotted stations suggest that the entire island (as

represented by the plotted stations) has experienced a

0.754 41055 “mo 0,675 positive (though small) rise in annual total precipitation

(PRCPTOT). The largest reiative trend is evident in the East

(zone 2) stations. There is a distinction between the north

and west versus the south and interior stations with respect

Z°\"94 \"355 \"353 \"575 1-00° to dry spells or consecutive dry days (CDD). The former

regions reflect a decrease in consecutive dry days while

the latter indicate relatively larger increases (see Figure

4.9c). The situation almost reverses when consecutive wet

4.3.1 RAINFALL EXTREMES days (CWD) or the length of wet spells are considered. The

Ten indices representing rainfali extremes were computed F’°°”\"e“‘e of heavy \"a‘\"fa'_' event; (R10 and R20)_a'5°

using daily station data for thirteen stations across the '\"CreaSe.d afioss mist “among MIM largest :‘agmtude

island (at least 2 per rainfall zone). Table 4.2 shows the trhems '|\" I e \"lo? Wes\" hCe\"\"a_ and 509‘ fefisf d(,\"°t

average trend calculated on the basis of using all nine : °w\")' ngegelawargest Cl arljges :j\"Pm°5|t '2'\": \",7 “:5

stations. Figures 4.9 and 4.10 show the station trends for ave Occur” '\" estmfor: a_\"| an °'1 an 't at '5\" e

eight of me indices. extreme west and east o t e is and.

In general, for the island as a whole, the intensity and

occurrence of extreme rainfail events have been increasing

Table 4.2. Table showing mean trend values for rainfall extreme indices

Index Definitiun Trend Units

cm) Annual maximum number ofconsecutive dry days —1.5 Days

cwn Annual maximum number or consecutive wet days 0.2 Days

PRCPTDT Anrluai Total Precipitation 71.3 mm

R10mm Annval munt of days when rainfall above 10 mm 1.2 Days

Rzomm Annual count oi days when rainfall above 20 mm 1.0 Days

1195? very wet days 13.9 mm

R95? Extreme wet days 5.2 mm

SDII Simple daily intensity index 0.4 mm/day

RX1 Maximum 1—day pretlpltallorl 4.4 mm

RX5 Maximum 5—day precipitation 10.5 mm

Figure 4.9. Trends in selected historical rainfall extremes acrossjamaica for (A) Annual Total Precipitation - PRCPTOT. (B)

Simple Daily Intensity Index - SDII, (C) Consecutive Dry Days - CDD and (D) Consecutive Wet Days - CWD.

1_.a..,..l.— 11-. 31 -‘.

W 0)

3‘—”‘f¢ M % T (D)

<. _ _

«er» i 5 1 . _«

V . I .. Iu.ox~n1!

v¢' \"Io.:.4.se

VA » . I > 0 1

Figure 4.10. Trends in selected historical rainfall extremes across Jamaica for (A) Very wet Days — R95P, (B) Extreme Wet Days -

R99P, (C) Annual count of days when rainfall above 10 mm — R10 and (D) Annual count of days when rainfall above 20 mm — R10,

a W E

i_‘ . \{ i .

4 .

I. .. A

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. . l. A ‘ __. .

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Table 4.3 shows extreme rainfall variables for stations extreme rainfallvariables R10, R20 and R95p for the period.

in Jamaica over the period 1980-2018. Table 4.4 presents R50 was observed to have an increasing trend, however it

similar analysis, but for the four rainfall zones. Slope values was negligible.

are shown for the four zones. CDD is shown to have a

decreasing trend for all zones and an increasing trend in the

Table 43. Extreme Rainfall slope values for stations in Jamaica for the period between 1980 -2018

Vears Missing CDD R10 R50 R95p SDII

years

Duckenfield 1980-2018 2014, 2015 -4.1408 0.6676 0.4451 0.2232 23.101 0.1667 1

Manchester Pasture 1980-2018 1988, 1990 -2.5817 0.8977 0.7294 0.3234 25.653 0.5329 1

Mason River 1980-2017 1993-1998, 2.5758 0.8459 0.4917 0.1258 10.705 -0.0732 1

2012

Warsop 1980-2018 2014 -1.3146 0.6946 0.6015 0.295 17.648 0.0479 1

Worthy Park 1980-Z017 none -0.4203 0.1148 0.0054 0.0211 1.0786 -0.0198 1

Stations Vears Missing CDD R10 R20 R50 R95p SDII Zones

years

Passley Gardens 1994-2018 1995.1997- -2.172 2.7335 1.2353 0.2439 15.87 -0.8368 2

1999. 2017

1989-2017 2014-2015 2.6645 0.7039 0.7087 0.1881 14.068 0.5288

1984-2018 1988, 2014- -1.775 0.5705 0.2904 0.0246 1.4036 0.016 3

2015

1980-2018 1998 -1.9058 0.7881 0.3729 0.078 5.139 -0.0341

1930-2015 -22304 0.4846 0.0654 1.9171 -OM14

1980-2017 2011, 2014- -0.5428 -0.0097 0.0809 0.1142 11.492 0.3626 4

2016

Discovery Bay 1980-2018 1981,1982, -5.7325 0.6097 0.3082 0.0819 6.6695 -0.1958 4

1986-1988.

1990. 1993,

2013

Georgia 1980-2017 2011, 2012, -1.551 0.1967 0.1213 0.0759 5.5783 0.2943 4

2014, 2016

Hampstead 1981-2018 none 2.0859 0.5913 0.2673 0.0698 0.3521 0.5407 4

Norman Manley 1980-2018 none -1.853 0.2362 0.1289 0.0836 4.5068 0.0798 4

Orange River 1980-2018 1993-1998, -2.7144 0.3814 0.229 0.0544 5.137 -0.0187 4

2015

Port Marla 1980-2018 1986, 1993- -3.2865 0.2637 0.1394 0.1014 7.812 0.3802 4

2000, 2011

Sangster Int'l 1981-2018 none -1.7948 0.3498 0.2068 0.0548 5.1651 0.0216 4

Table 4.4. Extreme Rainfall slope values forjamaica for the period between 1980 -2018

-1.17632 0.64412 0.45462 0.1977 15.63712 0.1309

-2.172 2.7335 1.2353 0.2439 15.87 -0.8368

-0.82418 0.636775 0.423 0.089775 5.631925 0.099825

-1.92364 0.327388 0.185225 0.0795 5.8391 0.183088

F

.x, ‘ . .

‘ , ~ I

¥ l ,

7' , ' '

, l

4_4 Hurricanes seasons with record-breaking hurricane activity. The more

notable seasons for the Caribbean region included the 2017

season which saw three major hurricanes make several

4.4.1 NORTH ATLANTIC HURRICANE landfalls across the Northern Caribbean, and the 2019

ACTIVITY season with extensive damage in The Bahamas caused by

Most measures of Atlantic hurricane activity show a Major H4urVriCane4DOrian' Both 2017 and 2019“/ere above‘

substantial increase since the early 1980s when high- normal In mtellslty and the number of named Storms‘

quality satellite data became available (Bell et al. 2012; Current abe‘/e*n0rmal actiVitY is c0nsistent With the

Bender et al. 2010; Emanuel 2007; Landsea and Franklin North Atlantic region e><PeriencinS a Prelenged era Oi

20l3). These include measures of intensity, frequency, and high hurricane actiVitY since i995 (see Figure 4-H). This

duration as well as the number of strongest (Categoiy 4 is attributed t0 tW0 key climate Patterns? a P°sitiVe’Phase

and 5) storms, There is little consensus, however, char rhe Atlantic Multidecadal Oscillation (AMO) and a sustained

increases in hurricane activity are attributable primarily to Weal<'t0’neutral Phase Pacific El Nine southern Oscillation

global warming, The El Nino»Southern Oscillation (ENSO) (ENSO). A P°sitiVe‘Phase /‘M0 indicates Warmer Ocean

phenomenon also plays a significant role in modulating temperatures in the North Atlantic reSi0ri~ This is a l<eY

hurricane activity in the North Atlantic from year to year. requirement ter hurricane tie‘/el°Pment~ An El Nine eVent

DuringanElNifioverticalshearisstrongacrosstheCaribbean indicates Warmer Ocean temperatures in the equatorial

basin resulting in fewerAt|antic hurricanes. The opposite is Pacitic Ocean resulting in strong Vertical Wind shear 0‘/er

true for La Nina. El Nino and La Nina also influence where the Atlantic that 5uPPresse5 hurricane deVel°Pmer‘t- N0

the Atlantic hurricanes form, For example, during El Nifio Substantially strong El Nifio event has developed since

events, fewer hurricanes and major hurricane; deveiop in late 2015. Therefore, the combination of these two climate

the deep Tropics from African easterly waves, while the Patterns: sustairied 0Ver the Past few Yearsr means that

converse occurs during La Nifia er/enr5_ environmental conditions have been and continue to be

Since the 2015 State ofthelamaican Climate Report (CSGM :V|eral.l Very‘ favourable for humcane awvlty m the North

2017), the North Atlantic region has seen consecutive talmc raglan‘

g

Figure 4.11. Variations in North Atlantic storm activity from 1950-2018 (Bell et al. 2019). Seasonal Atlantic hurricane activity

during 1950-1018 based on HURDAT2 (Landsea and Franklin 2013). (a) Number of named storms (green), hurricanes (red).

and major hurricanes (blue), with 1981-2010 seasonal means shown by solid coloured lines. (b) Accumulated cyclone energy

(ACE) index expressed as a percent of the 1981-2010 median value. Red, yellow, and blue shadings correspond to NOAA's

classifications for above-, near-, below-normal seasons. The thick red horizontal line at 165% ACE value denotes the threshold

for an extremely active season. Vertical brown lines separate high and low activity eras. ACE is a measure of overall hurricane

activity and is defined as the sum of squares ofa storm's maximum sustained wind speed at six-hourly intervals. ACE considers

both the intensity. duration and frequency of storms within a season.

(3)30

25

-0- Hurricanes

-0- Major Humcanes

E

O

1,1 . N Y _. .Al.|,I‘

8 V0 ‘(Vivi 5',’ aha ! ' \"1'

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l .1’ J11‘ ii W ‘V l\\ A. _A‘L ,1

0 hT.AhflflYfi11VI'flflfiVnfl

1950 1960 1970 1980 1990 2000 2010

(b)

300 , , .

HIQ!-Mllvltgy Era‘ Low-Acllvity Era lrllgh-Activity Era

A Extremely

% zoo -- I A°‘”e

E 165 I

.. 150 ll Above

° | I Normal

* 120 1 1 1 1 N

“’ . * 1 Z r ‘ J 1 ear

8 '1 l1 11 1 ‘ Normal

“ '|||lH ll III N II II I I l ||l|l|l| || |'l|

50 1 Below

| l | Normal

0 u I I

1950 1960 1970 1980 1990 2000 2010

4.4.2 HURRICANES AND JAMAICA hurricanes during that period. The temporal distribution of

Figure 4_12 Shows the historical paths of mjpical storms the1O hurricanes rndlcates 1 each-In the 19605 and 19705,

and depressions (panel a) and hurricanes (panel b) that igctrle 13525 a£d_198O5' ,ar:d itygcbe theezoofis Fri?‘ (3;

passed within 200 km ofjamaica (panel b) between 1950 1 ‘_a Tsto amja a'rC:h‘:aSS lmepfimee ey_a: qua nu er’)

and 201 5. The panels show thatjamaica was impacted by 10 mpma S r S We a ' p “O '

Fi ure 4.12. (a) All hurricanes im actin the Caribbean basin between 1950 and 2015. ib)Tro ical De ressions and Tro ical

8 P E P P P

Storms. source: NOAA i

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The panels also show that the preferred path of hurricanes (grid boxes 1—14) experience marginally fewer storm

that impact Jamaica is from the southeast to northwest, centres than those in the south (grid boxes 15 — 23). This is

with the majority approaching from south of the island. also reflected inthe probability map.South—easternJamaica

This makes the south coast of Jamaica more susceptible (grid boxes 20—22)and the southernmost tip ofjamaica (grid

to highest wind, rain and surge events associated with box 23) in comparison have the highest impact count and

hurricane passage. This susceptibility is captured in Table the highest probability of influence.

45 and Figure 4'14’ for which Jamaica was divided mm It is also noted that the majority ofthe storms or hurricanes

the 23 grid b°Xe5 show” i\" Figure 4'13‘ Tame 45 the” impacting the island ofjan-iaica are of categories 3 and 4

Shows ‘“e.\"““‘°ef °f times 3 grid b°X was \"\"P“F9“ strength (Table 4.5). This likely reflects the downstream

by a humcane ('nc|‘,‘d‘”g category) We’ the penod position ofjamaica relative to the main development region

1950,1014’ as determmed by 3 c°,u\"$ Of the \"umber °f of hurricanes in the tropical Atlantic east of the Lesser

2g(r)rt'<Ca\"ef5tf‘1’Vh°5etCen;\":‘P3555:w't2_'\" 50'J102' 150 atfld Antilles.Jamaica seems to be hardly impacted by a categoiy

\"lo, “en rec’ E3” _°X‘ '3”? 3 maps e 2 hurricane, whilst category 4 storms have the greatest

probability that a storm centre will pass within 50 lfm ofa impact on the island in terms ofgflrd boxes impacted.

grid box. The centre and northern regions of the islands

Table 4.5. Total number of hurrlcanes (by category) passlng wlthln (a)50-km, (b)10o-km, (c)150-km and (d)2|I0-km jamalca from

1950 to 2015. Impact on grid boxes previously defined are shown. Data Source: NDAA (httg:IIcoast.noaa.govIhurricanesfl

BE“

0 0 0 0 D 0 0 0 0 U 0 0 1 1 O 0 0 0 1 1 1 1 1

E - 1 0 0 o 0 0 1 0 0 0 0 o 0 0 o 0 0 o 0 0 0 1 0

E 1 2 2 0 0 0 1 2 Z Z 2 1 1 1 2 2 Z 1 I 1 1 1 1

5 0 0 0 1 1 1 0 0 O D 0 1 2 1 1 0 U 1 1 1 1 1 3

0 0 0 0 0 0 0 0 0 D 0 0 0 0 0 0 0 0 0 0 0 U 0

2 Z 2 1 1 1 2 2 2 2 2 Z 4 3 3 Z 2 2 3 3 3 4 5

NUMBER OF HURRICANES IMPACTING GRID BOX

IIEEIH

! 2 D 0 0 1 1 Z 2 1 0 1 1 1 1 3 3 4 3 3 2 Z 2 4

E o 0 0 o 0 1 0 0 o 0 o o 1 1 o 0 o o 0 1 1 1 o

E 2 Z 2 1 Z 1 2 Z 2 1 1 2 1 1 2 1 1 1 1 1 1 1 1

E 2 Z 1 Z Z 2 Z 3 3 4 4 4- Z 1 2 3 4 4 4 3 3 3 3

0 0 0 0 0 0 0 D 0 0 D 0 1 1 0 0 0 0 0 1 1 1 0

6 4 3 3 S 5 6 7 6 5 6 7 6 5 7 7 9 8 8 8 8 S 8

NUMBER OF HURRICANES IMPACTING GRID BOX

BEE

5 5 5 4 3 Z 5 5 4 4 4 4 3 3 5 4 4 4 4 4 5 5 4

E 0 U 0 D 1 1 0 0 0 U 1 1 1 1 0 U 0 1 1 1 1 1 0

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E 3 4 4 5 5 4 3 4 4 4 4 4 4 4 4 4 4 4 3 4 4 4 4

0 0 0 0 0 1 0 0 O 0 1 1 1 1 0 0 0 0 1 1 1 1 O

10 11 12 11 10 9 10 10 10 10 11 11 10 10 10 9 10 10 10 11 12 12 10

NUMBER OF HURRICANES IMPACIING GRID BOX

3

E 7 6 6 6 5 5 6 6 6 6 5 4 6 5 6 6 5 4 4 6 7 8 4

E 1 1 0 0 0 o 0 0 o 0 0 o 1 0 0 1 1 1 1 1 0 0 1

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E 7 6 6 5 5 5 6 7 7 5 5 5 4 4 5 6 5 5 4 4 4 4 5

0 0 0 1 1 1 0 U 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1

17 14 14 14 12 12 13 14 14 13 12 11 13 14 13 14 13 12 11 13 13 15 12

NUMBER OF HURRICANES IMPACTING GRID BOX

54 1 The State of the Jamaltan Cllmatewolumelll):1nformat1on or s

_ #4

Figure 4.13. The 23 Grid locations used to determine hurricanes passingjamaica within a radius of 1|)0km.

aims \"am him E

' Wakefield ‘ , N, Ms

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Figure 4.14. Map ofjaniaica showing the probability ofa hurricane passing within 50km ofa grid box based on 66 years (1950 —

2015) of historical data.

- 71%

Z 57%

43%

Z 29%

I - 14%

V F E

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While Jamaica has not experienced direct impacts from

landfalling hurricanes since 2012's Hurricane Sandy, the

The accumuiated ramfa” from potential to be impacted by an intense storm remains

. _ as obsen/ed with Hurricane Matthew in 2016. Hurricane

tropical storms tracking through Matthew passed within 100 miles oflamaica's shores as a

~ ~ Categoiy 4 storm. Furthermore, the accumulated rainfall

the Canbbean can also brmg from tropical storms tracking through the Caribbean can

substantial rain to surrounding also bring substantial rain to surrounding areas resulting

It. . . d. t. t in indirect impacts, particularly during the August-October

areas res” mg In m \"EC \"npac 5- period. Figure 4.15 shows the distribution of accumulated

rainfall throughout the Caribbean associated with the

passage ofHurricanes Matthew in 2016 and Irma in 2017.

Figure 4.15. Satellite-derived total accumulated rainfall (in mm and inches) associated with the passage of (a) Major Hurricane

Matthew in 2015 and (b) Major Hurricane lrma in 2017. Light blue circles indicate the position nfjamaica relative to storm

track. Warmer colours indicate a larger accumulated rainfall. Source: NASA. (c) Total daily rainfall (in mm) observed in the

years 2016 (light blue) and 2017 (dark blue) are compared to the daily rainfall climatology (in gray) over the period 1981-1010.

shaded regions indicate the passage and duration of select hurricanes passing within zoo miles of Jamaica's coasts. Source:

Global Historical Climatology Network Daily (GHCN-D) Database

3) b) '3

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4_5 sea surface Temperature and 0.04\"VC annually. Higher rates of warming can be

obsen/ed In the Gulf of Mexico and the eastern Caribbean

Figure 4.16 presents the trend in SST: for the Caribbean extending W0 the eastern tropical Atlantic‘ C°°“‘T‘-g ls only

region and surrounding area. It shows that over the period °,b.Se.':/edf \[:3 far northern Edge of the domalrh m the

19822016, SST: have warrned at a rate of between 0.01°C ‘/'c'\"' y o O\" 3'

Figure 4.16: Map showing sea surface temperature trends within the Caribbean and surrounding regions over the period 1982

to 2016.

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early 1980s) when the coast and interior experience drought

4_6 Droughts and Floods conditions which are not seen in the west. The east (Zone

2) is noted again as seemingly having a different underlying

A meteorological drought is a period of we||-be|ow- t°\"eih8meChehi5h\"W_hiCh 'e5U'ted ihe P'°'°“ged d\"YP_e\"l°d

norrnai nreeioitetion that spans a few months to a few from 1985-1995, while the other three zones experience

years. The Standardized Precipitation Index (SPI) allows Ye’)/“\"3 deE\"ee_5 Utwetehd d\"Y- A\" f°“\"Z°\"_e_5 d'5P'aY m°'e

for the oeterrninetion of the rarity of drought events (or interannual swings between extreme conditions, therefore

anomalously wet events) on a variety of time scales. For mere fl°°d ehd d\"°UEht5 5'hCe the 20005 <95 °PP°5ed t0 the

SPI analysis, positive values above +1 indicate wetter than decades betwe-

normal whilst those below -1 indicate drier than normal. -i-ahie 4_5 shows that the number of drought occurrences

V5'“e5 be'°W '2 are Cehsideied t0 be extremely dry: and for each zone for both SPI-3 (seasonal or 3-month drought)

above +2 to be extremely wet. SPI analyses were done for and gp|_12 (year ieng drought) over the period 1970.201;

the time 5e”e5 'eP\"e5e\"'ti”§ the \"ewe\" 2°\"'e5 “SW5 date The number ofseasonaldroughts is expectedlygreaterthan

UP t° 2012- the number ofyear-long drought periods. The middle ofthe

For SPI-12 (not shown) representing interannual variability, i5|ahd« Feiffesehted by the ihteiiei and C0«35tfl| Z0nES(Z0|'1e5

the |nterior(zone1),West(zone 3) and Coasts (zone 4) again 1 and 4), IS f-3|’ m0|'e Pmhe t0 5h°\"t'te\"m dmught the” the

etworyi with significant drying in the eeriy to rnid_1970r5t western or eastern ends. The coastal areas (zone 4) have

All zones experienced severe droughts centred on the year been ta’ mere _P’°he t0 Yee\"\"°hS dmught °CC“\"ehee than

2000, and again in 2010. Therefore, these were two recent the \"e5t °tthe '5'e\"‘d-

all-lsland droughts. There are other periods (for example,

Table 4.6. The number of dry periods as determined by SPI3 minor peak in Apri|—May—June (27% of occurrences) and a

and SPl12in each rainfall zone maximum in September—October—November (39%). Not

surprisingly, the mean monthly distribution of floods is

Number“ Dr Periods statistically correlated with the mean rainfall climatology

3’ suggesting that any changes in the mean rainfall regime

Interior Coasts will likely be accompanied by changes in the frequency of

5p. (mne 1) (lone 4) severe floods. Figure 4.17 also shows an increasing trend in

flood occurrences over the last century to present, with the

3 34 23 25 35 period 20002010 being the most intense decade on record

with 35 flood events.

12 6 6 5 11 Spectral analysis ofthe annualoccurrences ofsevere floods

reveals 30 and 9—year cycles as well as minor 3 and 6—year

cycles suggesting |arge—sca|e controls of floods, possibly

Burgess et al. (2015) and Taylor et al. (2014) offer a by the Atlantic Multi—decada| Oscillation (AMO) and ENSO

comprehensive analysis of floods in Jamaica over the events.Hurricanes,depressionsand waves accountfor46%

period 1850-2010. Flood occurrences in Jamaica have a of all the devastating flood events inlamaica while storms

bimodal pattern (see Figure 4.15) which mirrors that of the and troughs account for 21%. One and two—day rainfall

rainfall climatology previously shown in Chapter 3, with a events dominate (67%) ofthe occurrence of severe events.

Figure 4.17: Severe flood climatology forjamaica for the period 1850 - 2010 for 198 events (top). Occurrences of severe flood

events er decade for the eriod 1850-2010with decadal mean amaica Rainfall index (mm). Source: Bur ess et al2u15.

P P J E

1350 1370 1890 1910 19 30 1950 1970 1990 2010

40 Z 2200

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1 2 3 4 5 5 7 8 '3 10 11 12

Months

4.7 sea |_eVe|s doubling from 1.7 mrn/year to 3.1 mm/year through the

20\"‘ century (Table 47). Satellite altirnetry measurements

Sea levels have been rising since the end of the last glacial ‘mdmate a rise W GMSL at.a rate 083. i 0'4 \"\"“’V°.a’ (rpm

maximum‘. It is now virtually certain that this slow natural to 2016‘ Recs\"; ““\"|'e5‘|“F” a‘.\"|f‘a\" (201?) mgh;'g|_|;“

process has been sped up due to the impact of climate |'gb Current rates C’ \{see EV: gs: Sm/ne re%'°\"S:0t10e

change. Global mean sea levels (GMSL) continue to showa ggme seemg rates ° up to ' ' ' mm year mm '

sustained increase (Figure 418) with sea level rates almost '

Figure 4.18. Total sea level rise (in cm) relative to the mean sea level averaged over 1993-2014. Source: NOAA/Laboratory for

Satellite Altimetry

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-20 -16 -12 -8 -4 O 4 8 ‘l2 16 20

Sea level change (cm)

Table 4.7. Rates and absolute changes in global mean sea acceleration will lead to sea levels rising at an even quicker

level from 1901 to 2014 rate as we approach the end of the century, Sea levels are

also affected by other natural climate phenomena, In 2015,

Period Rate Total Sea Reference sea level anomalies achieved a record high ofapproximately

(mm/V93?) '-W5‘ Rise 80 mm above the i993 average because ofa very strong El

‘\"\" Nino (Merrifie|d etal. 2015).

1901 —1990 15:05 _ ii>cC(20l3)

Table 4.8. Acceleration in the rate of sea level rise

1901 -2010 1.7:0.2 0.19:0.02

1971 72010 2-0 i D-2 7 Ascgslleezagllwszf

2

1993 — 2012 3.2 : 0.4 — (mm/yr )

1807-2009 0.02 x 0.01 Jevrejeva et al. 2013

2005 — 2014 3.1 i 0.4 - Vl E‘ 5l- (2015)

1968-2015 0.06 x 0.01 Dagendorf et al.

. . . . 20l9

With sustained increases in global and regional

temperatures, there is an increasing risk ofaccelerated sea 1993407 5 Q03‘ * 0925 Nam” 9‘ ‘ll 2013

level rise (Vi et al. 2015). Dangendorf et al. 2019 points out

that there is an acceleration in the rate of global mean sea 19934018 0'1 WCRP G'°bE\" Sea

. . Level Budget Group

level rise that has been present since the 1 9605. He presents 2018

estimates of the current acceleration which is predicted

to continue into the 213‘ century (see Table 4.8). This

5 The Last Glacial Maximum lS defined as the period when the continental ice Sheets reached their maximum total mass during the last ice age It Should be noted

ma: mi; (Olfl(IdES with a minimum in global sea level (see Rahrnstorland Feulner ZDl3, lor more information).

Trends within the Caribbean closely follow the global of global warming to coastal recession rates, Robinson et

trend. Tables 4.9 and 4.10 give sea level trends in the al. (2012) maintain that sea level rise plays a significant role

Caribbean basin as measured from tide gauge data. In in the sustained increases in shoreline recession rates seen

comparison, current satellite altimetry measurements along Long Bay, Negril. lnterannual sea—level variability also

show that Caribbean trends are approximately 2.5 1 0.4 accounts for a significant part (approximately one—third)

mm/year, which is consistent with the trends deduced from of the total sea—level variability. Palanisamy et al. (2013)

measurements by Torres and Tsimplis (2013) (see Table obsen/ed that interannual sea—leve| variability in the north

4.10). In more recent years, the region has seen larger Caribbean is higher than for the southern Caribbean and

increases in sea levels due to the influence of warmer El strongly correlated with El Nifio.

Nifios (Blunden et al. 2016). During the 2015 El Nifio, sea _

level changes within the Caribbean reached a maximum of 73519 ‘-9’ M93!‘ '3“ ‘\"593 '°V°' \"59 W91‘-‘Bed °V9|‘ the

11.3 cm above the mean sea level. ca\"'b\"9\"\" hm\"

Sea levels measured at Port Royal, Jamaica. indicate an Rate (mm/year)

increase which has been estimated at 1.66 mm/year over a

17.8—year span (Table 4.10). Satellite altimetry data from the 1950 - Z009 1-3 1 0-1 P31501531\")! 913'-

TOPEX/Poseidon—2 experiments averaged over thejamaican (20121

coasts also confirm a substantial increase in sea surface 1993.19“; 1_7,1‘3 Torres and 15;.-“P1,;

heights since 1950. Sea surface height data. however. remain (2013)

l|'IhCOrl5|':tel'lt|:nd limited Eor the coadstal regions ofljamaica. 1993 _ 2010 25: 13 Torres and Tsimplis

T ere ave een a num er o stu ies on coasta erosion (2013,, after common

and storm surge mapping for areas such as Long Bay. Negril for Giobai isostatic

(for example, Robinson et al. 2012) and Kingston. Though Adjustment (GIAl

these studies do not focus on measuring the contribution

Table 4.10. Tide gauge observed sea-level trends for stations across the Caribbean region. Adapted from Torres and Tsimplis

(2013)

Station Country Span years % of data Trend Months Gauge

corrected

Puerto Limon Costa Rica 20.3 95.1 1.76108 216 2.1510.9

Cristobal Panama 101.7 86.9 13610.1 566 2.8610\]

Eartagena Colombia 44 90 53610.3 463 5.46103

Amuay Venezuela 33 93.4 0.Z610.5 370 016105

Eumana Venezuela 29 98.6 0.9510.5 331 0.7610.6

Lime Tree US Virgin Islands 322 81.9 1.86105 316 1.5610.5

Magueyes Fuerto Rico 55 96.2 1.3610.2 635 1.0610.2

P‘ Prince Halli 12.7 100 10.76115 144 12.26115

Guantanamo Cuba 34.6 89.9 1.7610.4 258 2.5610.6

Port Royal Jamaica 17.8 99.5 1.6611.6 212 13611.6

Eabo Cruz Cuba 10 90 21612.8 108 2.1612.8

South Sound Cayman 20.8 87.6 17611.5 219 12611.5

North Sound Cayman 27.7 89.2 2.7610.9 296 21610.9

C‘ San Antonio Cuba 38.3 76.7 0.86105 353 03610.5

Santa Tomas Mexico 20 85.4 2.0611.3 205 1.7611.3

Puerto Cortes Honduras 20.9 98 86610.6 224 8.86107

Puerto Castilla Honduras 13.3 100 31611.3 160 31611.3

Figure 4.19. Shows seasonal variability in sea level for Jamaica are significantly higher for the summer than any

Kingston. Jamaica. The figure reveals that the sea levels in other time in theyear.

Figure 4.19. Bargraph of seasonal sea level variability in Kingston. Jamaica from Satellite altimetry records 1993 to 2017. The

seasons are defined as Winter (Dec.-Feb.), Spring (March- May), Summer (|une- Aug.), Autumn (Sept.- Nov.). Source: Copernicus

Marine Environment Monitoring Service Database (EMEMS)

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WINTER SPRING SUMMER AUTUMN

4.7.1 SEA LEVEL RISE EXTREMES too short to calculate a return period of sea level extremes

On a regional scale. few studies have addressed the issue of WM‘ any cenamy‘

sea level extremes in the Caribbean Sea. Torres & Tsimplis

(2014) exammed Sea level extremes In “T9 Car'bbe,an and Figure 4.20. Annual maxima nontidal distribution through

found that the largest sea level extremes In the region are

the result of storm surges from tropical cyclone activity.

Storm surges from stationary cold fronts were also found

to bring extreme sea levels. The most extreme sea levels

are observed during the second half of the year. especially The most extreme Sea levels are

between _August and October. This is as a result of a Observed during the Second ha”

combination of sea level components which build on each _

other, compound and result in extremes. It is during this Of the year, especially between

time of year that the seasonal sea level cycle peaks (Torres - -

& Tsimplis 2014). the spring tides occur and tropical cyclone August a nd October’ Thls IS as

activity is prevalent. The combination these components a result Of a combination Of sea

give rise to the sea level extremes in the Caribbean. Using I I h. h b .|d

the limited tide gauge records from Port Royal, Torres and eve Components W ‘C L“ on

Tsimplis (2012) also concluded that extremes in Jamaica each Other’ compound and resuh;

occur during the month of August (see Figure 4.20). This can _

also be seen from the observed trend in the seasonal sea In extremes-

level variability (see Figure 4.18) which shows maximum sea

levels in the summer. However. the tide gauge record was

the year (black bars) and after the mean seasonal cycle has been removed (light ochre bars). This shows the percent of annual

maxima occurrence in each month. The Port Royal Station,Jamaica is highlighted. Source: Torres and Tsimplis (2014)

60

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5. CLIMATE SCENARIOS AND

a whole over the range of RCPs and for three time slices.

Data from a single GCM (HadGEMZ»ES) is also provided for

Climate projections for Jamaica are provided by climatic RCPs 2.6, 4.5 and 8.5 and for four grid boxes overjamaica

variable. For temperature and rainfall projections are from as opposed to a single average for the entire Jamaica (see

available model results which include a suite of GCMs, a Figure 2.3). The RCM ensemble provides additional sub-

single GCM with four grid boxes over Jamaica, a suite of island details. The RCM ensemble includeszli) six perturbed

RCMs and from statistical downscaling (see chapter 2). physics PRECIS 25 km simulations over a range of future

The use of each of these sources represents a refinement projections under a high emissions scenario; and (ii) three

of scale. The GCMs provide country scale projections, i.e. 20 km RegCM4.3.5 runs covering RCPs 2.6, 4.5 and 8.5.

they provide a mean climate change profile forlamaica as Because of the number of grid boxes available, the data

. E . aican C|imalElVci|umE|ll\] information for Resilience Euilding l 63

presentedaresummarised overJamaica’s fourrainfallzones temperature increases with respect to the 1960-1989

across all models, scenarios and futures. Finally, statistical baseline. For the southern parishes (boxes 1 and 2)

downscaling provides future projections of temperature projected annual changes are 1.44” —1.71°C by the

and rainfall extremes using data from the weather station 20305, 1.65°—2.31°C by the 20505 and 1.41“ —3.75\"C by

located at the Norman Manley lnternationalAirport. the end of century. For the northern parishes (boxes

Projections for other variables are drawn from a variety of 3 and 4) p\"°je:ted a\"n”a| charges are slight” higher

Other Sources. at 1.45°—1.72°C by the 20305, ‘l.7°—2.36°C by the 20505

and 1.45°—3.89“C by the end of century (see Table

The future projections ofthis chapter are presented as plots 5_2b). projected changes for minimum and maxjmum

and 1313155» A 5Umm3|'Y 01”‘? GCM1 RCM alld 5t3”5‘lCa\"Y temperatures generallyfall within the same range (see

downscaled results are provided in narrative form at the Tables 53b and 5_4b)_

beginning of the sections for temperature and rainfall _ j _ ‘

projections - The RCM simulations suggest a similar range of

temperature increase for the rainfall zones across the

RCPs (i.e. as for the single GCM). This is depicted in Table

5.2 Temperature 5.1a. When variation within the zone is considered for a

high emission scenario. increases ofup to approximately

Irrespective of the model used or scenario examined, 3.9°Care noted bythe end ofthe centun/.Thisisdepicted

Jamaica continues the warming trend seen in the historical in Table 5.1 b. These values are in general higher than the

data through to the end ofthe century. Major points to note values projected by the GCMs. This is not unexpected

about the future temperatures relative to a model baseline since the GCM represents average results across larger

are outlined as follows (refer to Section 3.2 for observed areas.

Cl1a“Se5 from h|5t0V|Cal ba5e|me)3 - There is evidence of spatial variation across the country

- The suite of GCMs suggest that the range of the mean and even within grid blocks. Northern and central

annual temperature increase (‘’C) over all four RCPs will sections of Jamaica will warm marginally more than

be 0.65-0.84 “C by the 20305, 0.86“-1.10°C by the 20505 other areas. There seems to be a warming maximum In

and 0.82-3.09 “C for 2081-2100 with respect to the 1986- central and northeastjamaica.

2005 m\"de' ba5e\"\"e1T\"b'e 5-213» - August—September—0ctober (A50) has slightly higher

- Increases will be of the same approximate magnitude values of temperature change than other times of the

for maximum and minimum temperatures. Projected year.

Cl‘a“ge5 f0\" minimum annual tempemtuie are 0-85°‘ - Mean daily maximum temperature each month at the

153°C by the 20505 3\"?‘ 0-32°'3-10°C bY ‘he 9”‘! 01 Norman Manley|nternationa|Airportstationis expected

°e”t“\"Y Tame 5-313» Pmlecged Chf”ge5 f°\" ma\"‘m”m to increase by 0.8—1.3°C by early centuryand 1.2—2.0°C by

3””l:a1temPe\"3t“\"9 3'9 0-85 '1-53 C b)! ‘he 20503 3”‘! mid—century across all RCPs. Warming of daily minimum

0-32 '3-12 C by the and 0f Century (566 Table 5-481 temperatures is anticipated to be greater: 1.2—1.7\"C by

- The single GCM (HadGEM2-ES) suggests higher mean early century and 1.7—3.6°C by mid—century.

Table 5.1. (a) Range of mean temperature change for each ofjamaica's four rainfall zones from an RCM ensemble (three

members) across three RCPs (2.6, 4.5 and 8.5). See Figure 2.5 for grid boxes and Table 2.4 for grid boxes in each zone. Source:

RegCM4.3.5 ensemble. (b) Range of mean temperature change across the grid boxes in each zone from an RCM ensemble (six

members) running a high emissions scenario. See Figure 2.4 for grid boxes and Table 2.3 for grid boxes in each zone. Source

PRECIS ensemble.

(a) Range of change in mean annual temperature for each zone across three RCPS

‘ Time slice West (Zone 3) Coasts (Zone 4) Interior (Zone 1) East (Zone 2)

T'\"“\" 20305 0.65 - 1.65 0.65 - 1.65 0.65 - 1.65 0.65 - 1.64

20505 1.15-2.32 1.19-2.32 1.19-2.33 1.21 -2.34

EOC 1.43 — 3.86 1.43— 3.57 1.44 — 3.89 1.43 — 3.91

(13) Range of change in mean annual temperature across grid boxes in the zone for MB

Tmeafl 20305 2.04 — 2.79 1.45 — 2.83 1.83 — 2.26 1.92 — 2.06

20505 2.77 — 2.96 2.11 — 2.98 2.51 — 3.12 2.65 — 2.85

EOC 3.40 — 3.69 2.76 — 3.62 3.12 — 3.90 3.22 — 3.48

- The maximum daily maximum temperature each decrease particularly forthe traditionally cooler months

month is expected to increase by 0.5-13°C across all ofJanuary—March by up to 6 days by early century and 7

RCPs by early centuiy and 1.1-Z.0°C by mid-century at days by mid-century.

the Norman Manley International Airport station. The

minimum daily minimum temperature each month is 5,2,1 GCMS

also expectfd to \"‘.°’ea‘e by:'M'8 C by early century Table 5.2a, 5.3a and 5.4a show the range of projected

and 1.4-2.4 C by mId—century. . . .

changes for minimum, mean and maximum annual

' The annual fiequency Of Winn d3y5i With Winn temperatures with respect to the 1986-2005 baseline

dayslnightsl in which the maximum(minimum) period from the suite of CMIPS GCMs. The projections are

t9TnP9\"3t|-\"9 l5 Ereafef than the 90“ Pefcenfilei in any illustrated as time series in Figure 5.1. Tables 5.2b. 5.3b,

8lV9n Vnnnth at the N°|'l'n3n Manley |nt€|'n3ti0n6l Ai|'P0|'l and S.4b show seasonal and annual projected changes in

Sfafon may in'5|'€35E by 242 days 33055 3” RCP5 by minimum, mean and maximum temperatures relative to

early century and 449 days by midrentun/. The annual a 1960-1989 baseline for the HadGEM2—ES GCM’s four grid

frequency of cool nights, with cool days(nights) in which boxes acrossjamaica,

the maximum(minimum) temperature is less than

the 10\"‘ percentile, in any given month is expected to

Table 5.2. (a) Mean annual absolute temperature change (“Cl forjamaica with respect to 1986-2005. Change shown for four

RCP scenarios. Source: AR5 CMIP5 subset, KNMI Climate Explorer; (b) HadGEM2-E5 REP 2.6, 4.5 and 8.5 scenario ensemble mean

projected changes in mean temperature by season and for annual average (°C), for the 20305, 20505 and EDC by grid box with

respect to the 1960-1989 baseline. Source: HadGEM2-ES runs for RCPZ.6, 4.5. 8.5

(al Mean Annual Absolute Temperature Change (Tmean) for Four REP Scenarios

Period Averaged 2030s 2050s End of Century

(2030-2039) (2050-2059) (2081-2100)

‘ MIN MEAN MAX MIN MEAN MAX MIN MEAN MAx

0.43 0.68 1.17 0.43 0.56 1.52 0.09 0.82 1.67

0.34 0.73 1.18 0.54 1.10 1.80 0.84 1.54 2.55

0.44 0.55 1.12 0.72 1.00 1.59 1.15 1.95 2.91

0.45 0.54 1.37 0.91 1.52 2.32 2.10 3.09 4.49

0.55 to 0.54 0.85 to 1.10 0.82 to 3.09

(b) Projected HadGEM2-ES changes in mean temperature by season and for annual average, by grid box under RCP Ensemble

Change in Mean Temperature (‘Cl (20305)

1 2 3 4

- MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

1.45 1.55 1.71 1.45 1.54 1.70 1.41 1.52 1.55 1.44 1.54 1.57

E 1.39 1.52 1.55 1.40 1.54 1.70 1.40 1.54 1.55 1.41 1.55 1.55

1.41 1.51 1.70 1.39 1.51 1.59 1.44 1.54 1.71 1.44 1.55 1.73

M 1.44 1.55 1.75 1.45 1.57 1.75 1.52 1.53 1.80 1.52 1.52 1.79

M 1.44 1.53 1.71 1.45 1.54 1.71 1.45 1.55 1.71 1.47 1.57 1.72

Change in Mean Temperature (‘’C) (20505)

1 2 3 4

— MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

M 1.57 1.95 2.35 1.55 1.95 2.37 1.72 1.99 2.40 1.70 1.95 2.35

M 1.54 1.89 2.30 1.53 1.89 2.30 1.57 1.93 2.29 1.55 1.92 2.25

1.50 1.83 2.25 1.50 1.84 2.29 1.54 1.89 2.35 1.54 1.91 2.35

E 1.59 1.89 2.27 1.70 1.90 2.30 1.79 2.01 2.41 1.77 1.99 2.33

M 1.55 1.39 2.30 1.55 1.90 2.31 1.70 1.96 2.36 1.59 1.95 2.35

5 The change in me lowest mlfllmum temperature identified torianiiaiy was up to 11 E“ and IS exeiiiaea mm the reported range aiie to ilS magnitude and

imaiieaiians.

Change in Mean Temperature (°C) (EOC)

1 2 3 4

‘ MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

1.41 2.54 3.83 1.43 2.55 3.87 1.44 2.50 3.90 1.43 2.59 3.89

E 1.52 2.55 3.78 1.53 2.58 3.80 1.53 2.58 3.82 1.55 2.59 3.82

E 1.40 2.41 3.52 1.41 2.43 3.55 1.40 2.55 3.91 1.42 2.54 3.88

E 1.31 2.38 3.51 1.34 2.41 3.57 1.44 2.58 3.93 1.43 2.55 3.90

w 1.41 2.47 3.71 1.43 2.50 3.75 1.45 2.55 3.89 1.45 2.57 3.87

Table 5.3. (a) Mean annual minimum temperature change (°C) forjamaica with respect to 1986-2005. Change shown for four

RCP scenarios. Source: AR5 CMIP5 subset, KNMI Climate Explorer; (b) HadGEM2-ES RC? 2.6, 4.5 and 8.5 scenario ensemble mean

projected changes in minimum temperature by season and for annual average (“C). for the 2020s,2D3Ds, 20505 and EOC by grid

box with respect to the 1960-1989 baseline. Source: HadGEMZ-ES runs for RCP2.6, 4.5, 8

2030s 2050s End of century

Averaged over

2030-2039 2050-2059 2081-2100

MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

rcp26 0.43 0.68 1.20 0.43 0.85 1.55 0.04 0.82 1.69

rcp45 0.33 0.72 1.19 0.53 1.10 1.79 0.79 1.54 2.53

rcp60 0.39 0.65 1.14 0.72 0.99 1.73 1.09 1.86 2.96

rcp85 0.44 0.84 1.44 0.89 1.53 2.39 2.07 3.10 4.55

Range of mean: 0.55 to 0.84 0.56 to 1.53 0.52 to 3.10

Change In Mlnlmum Temperature (“(2) (20305)

GRID BOX 1 Z 3 4

MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

ND\] 1.45 1.55 1.70 1.47 1.54 1.59 1.42 1.53 1.55 1.44 1.54 1.55

EMA 1.39 1.52 1.58 1.40 1.54 1.59 1.38 1.53 1.54 1.39 1.55 1.58

MJJ 1.40 1.51 1.70 1.38 1.50 1.59 1.42 1.51 1.59 1.43 1.54 1.71

A513 1.43 1.55 1.75 1.45 1.55 1.75 1.50 1.51 1.78 1.50 1.51 1.77

ANN 1.44 1.53 1.71 1.44 1.53 1.71 1.43 1.55 1.69 1.45 1.56 1.70

Change In Mlnlmum Temperature (\"C) (20505)

GRID aux 1 2 3 4

MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

ND\] 1.57 1.95 2.38 1.58 1.97 2.38 1.72 2.00 2.40 1.70 1.99 2.38

FMA 1.53 1.89 2.28 1.53 1.90 2.28 1.55 1.92 2.28 1.55 1.92 2.28

MJ\] 1.58 1.81 2.25 1.59 1.83 2.29 1.51 1.87 2.32 1.51 1.89 2.35

A50 1.57 1.87 2.25 1.59 1.89 2.29 1.77 2.00 2.41 1.75 1.97 2.35

ANN 1.65 1.88 2.29 1.65 1.90 2.31 1.69 1.95 2.35 1.68 1.94 2.34

Change in Minimum Temperature (°E) (E05)

1 2 3 4

- MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

1.43 2.56 3.85 1.44 2.53 3.89 1.46 2.63 3.92 1.46 2.62 3.92

W 1.52 2.56 3.73 1.54 2.53 3.80 1.53 2.58 3.83 1.55 2.60 3.84

M 1.39 2.39 3.61 1.40 2.42 3.66 1.39 2.53 3.88 1.40 2.52 3.86

W 1.31 2.36 3.59 1.33 2.40 3.65 1.42 2.56 3.91 1.41 2.53 3.87

W 1.41 2.47 3.71 1.43 2.50 3.75 1.45 2.58 3.89 1.45 2.51 3.87

Table 5.4. (a) Mean annual maximum temperature change (*0 forjamaica with respect to 1986-2005. Change shown for four

RCP scenarios. Source: AR5 CMIP5 subset, KNMI Climate Explorer and (b) HaI.1GEM2-ES RCP 2.6, 4.5 and 8.5 scenario ensemble

mean projected changes in maximum temperature by season and for annual average (°C). for the 20205, 20305, 20505 and 50C

by grid box with respect to the 1960-1989 baseline. Source: HadGEM2-ES runs for RCP2.6, 4.5, 8.5

2030's 2050's End of century

Averaged over

2030-2039 2050-2059 2081-2100

MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

RC? 2.5 0.40 0.63 1.14 0.44 0.86 1.50 0.13 0.82 1.64

RCP 4.5 0.35 0.73 1.19 0.55 1.12 1.84 0.88 1.56 2.58

RCP 5.0 0.48 0.65 1.08 0.73 1.00 1.63 1.19 1.85 2.84

RCP 8.5 0.47 0.85 1.27 0.93 1.53 2.31 2.13 3.12 4.37

Range of mean: 0.65 to 0.85 0.8610 1.53 0.8210 3.12

Ensemble.

Change in Maximum Temperature (‘’C) (20305)

1 2 3 4

‘ MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

1.44 1.54 1.72 1.44 1.53 1.70 1.41 1.53 1.67 1.43 1.54 1.68

E 1.40 1.52 1.69 1.42 1.54 1.70 1.43 1.55 1.69 1.45 1.57 1.69

E 1.42 1.52 1.70 1.40 1.52 1.70 1.45 1.55 1.75 1.45 1.59 1.75

E 1.45 1.57 1.78 1.47 1.58 1.77 1.55 1.65 1.83 1.55 1.65 1.82

M 1.45 1.54 1.72 1.45 1.54 1.72 1.47 1.58 1.73 1.49 1.59 1.73

Change in Maximum Temperature (‘’C) (20505)

GRID 130x 1 2 3 4

MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

ND\] 1.67 1.94 2.35 1.67 1.94 2.36 1.71 1.98 2.39 1.69 1.97 2.37

FMA 1.64 1.89 2.31 1.64 1.89 2.30 1.69 1.94 2.31 1.67 1.92 2.29

M11 1.62 1.84 2.28 1.61 1.85 2.31 1.66 1.92 2.38 1.65 1.94 2.41

A50 1.70 1.89 2.28 1.71 1.91 2.31 1.81 2.03 2.42 1.80 2.01 2.39

ANN 1.55 1.89 2.30 1.55 1.90 2.32 1 .72 1.95 2.37 1.70 1.95 2.37

Change in Maximum Temperature (°C) (EOC)

GRID Box 1 2 3 4

MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

ND\] 1.40 2.52 3.82 1.41 2.54 3.85 1.41 2.55 3.87 1.40 2.54 3.84

FMA 1.52 2.55 3.78 1.54 2.58 3.80 1.54 2.59 3.83 1.55 2.60 3.82

M\]_| 1.41 2.42 3.64 1.42 2.44 3.58 1.42 2.57 3.92 1.43 2.57 3.93

ASD 1.32 2.39 3.62 1.35 2.43 3.58 1.46 2.59 3.94 1.45 2.58 3.93

ANN 1.41 2.47 3.71 1.43 2.50 3.75 1.45 2.58 3.89 1.45 2.57 3.88

Figure 5.1. (a) Mean annual temperature change (°C); (b) Mean annual minimum temperature change

(°C); (c) Mean annual maximum temperature change (°C) for Jamaica with respect to 1986-2005 AR5 CMIPS

subset. On the left, for each scenario one line per model is shown plus the multi-model mean, on the right

percentiles of the whole dataset: the box extends from 25% to 75%, the whiskers from 5% to 95% and the

horizontal line denotes the median (50%).

Tmax change Jamaica Jan-Dec wrt 1986-2005 AF15 CMIP5 subset

6 6

3822.6 T

405 T

5 HCP6.0 T y.’ 5

4 _— 1 1 4

‘ ,1 ,-

3 , ':\"¢.’.‘r‘,“;¢~'1 3

ED 2 ‘ ,u;:~'l‘‘‘ ls 2

w , ,, “.1 mm .1

2 H.=’e“w\"‘

1 ‘ ‘~.u<.... «. 1

C ‘N’ 114 Mr

:. 1 0 ,,,,,,,,,

1 1 ‘ “ ‘ J “ ‘ 1

_1 H‘ ‘ ' _1

-2 -2

1900 1950 2000 2050 2100 20812100 mear

Tmax change Jamaica Jan-Dec wrl 1986-2005 AR5 CMIP5 subsel

6 6

ROP2.6 T

5 FlCP4.5 — 5

EEESD T .1:

B05 T '3

‘‘ hlslovlcal T ‘M5_l\"\" 4

3 A-\"‘ w.l£‘.»‘=~‘ 3

7 ‘‘‘,'-‘'v‘‘ “.j':£‘\\‘

§ 2 r\"'\}TK.'_wi\"/W: 2

. .- “'

—' 1 111.. -2'.\" 1

.. .,' .. ,...

-1 '“‘ ‘ ' l l ‘ . -1

-2 -2

%H%i

Tempevature change Jamaica Jan»Dec wrl 1986-2005 AFl5 CMIP5 subset

6 6

3832'? —

4. T

5 22:2-2 — ~ 5

4 historical — \"

3 .‘;~\":‘*v’.\"-\"\" 3

E 2 2

‘E “ , *.‘,'fi,,‘

I . 1- -w - 1

o v \" o ---—-—-—-

-I L \" V’ -1

-2 -2

1900 1950 2000 2050 2100 2081-2100 mear

5.21 RCMS projection forthe zone treated as a whole using this RCM.

Th t. ‘ th f . t d h _ These results are presented in Tables 5.5, 5.7 and 5.9. For

‘S Sec '0'.‘ presen S e range 0 pmlec e C anges m the PREC|S(high emissions) ensemble, the ensemble mean

mean’ m‘\"'m“m and maxmum temperatures’ armuany is calculated and the results presented across the range

and by Seasons for Jamamays fm\" rainfall Z°ne5' The of grid box values for a given zone This gives an idea of

resunssls p‘r?r\\;|'°uPSL/Erg)S\[egC\"';'/Ichapterglare fr.°m H1110 Rb“: spatial variation across a particular zone, bearing in mind

enSe.m es’ e . e.nSem e ‘ 5” pe Ur e that it is fora high emissions scenario. The PRECIS results

Phys“ r:nShf°rA1B 5Ce\"a\"°' WM :‘adGEfl2'ES as mmnfi are presented in Tables 5.6, 5.8 and 5.10 and supported by

GCM an t e RegCM4.3.5 ensem e - t ree ruris wit . . .

the ma sin Fi ure 5.2 for middle and end of cehtu .

CNRM irecp 4.5) and HadGEM2-ES (RCPs 2.6 and s.5) as P g W

forcing GcM5_ In general, it is to be noted that in the first time period

the PRECIS values are higher than the maximum for the

For the RegCM4.3.5 results, the mean value for the zone RegCM43 5 runs and may be Vlewed as the Worst case

is calculated for each ensemble member (each RCP), and projeCfio'n' The projections however become Comparable

the results presented across the range of R035 (i.e. the byme and ofthe century.

minimum, mean and maximum value of the average) for

the zone is given. This provides a best through worst case

Table 5.5. Projected absolute changes In mean temperature by season and for annual average (\"C) for the 2030's, 2050's and

EOC relative to the 1960-1989 haseline. Data presented for RegCM4.3.5 forced by CNRM-RCP 4.5 and HadGEM2»ES RC? 2.6, 8.5

and averaged over grid boxes in each zone. Source: RegCM4.3.5

Change In Mean Temperature (\"C) (305)

Rainfall Zone 1 Z 3 4

\{E

M

MERE

ME

Change in Mean Temperature (\"C) (505)

Rainfall Zone 1 Z 3 4

M

M

M

M

EZEZEZ

Change in Mean Temperature (\"C) (EOC)

Rainfall Zone 1 2 3 4

- MEAN Illlfl MEAN MEAN MEAN

M

M

M

M

% EEEEEߣ

Table 5.6. Projected absolute changes in mean temperature by season and for annual average (°c) for the 20305. 20505 and

20805 relative to the 1961-1990 baseline. Data presented for the mean value ofa six-member ensemble. Range shown is over all

the grid boxes in the zone (see Table 2.3). Source: PRECIS RCM perturbed physics ensemble run for A13 scenario

(a) we“ (Zone 3) (c) Interior(2one1)

2030': 2050': 2030':

NDJ 2_06_2_22 2_59_2_85 3_1O_3_58 ND\] 0.71—Z.25 Z.49—3.00 3.05—3.73

FMA 1_96_2_10 2_47_2_86 3_04_3_53 FMA 0.70—Z.09 Z.49—Z.88 3.19—3.74

M” 1_98_2_24 2_77_3_08 3_27_3_76 M\]_| 0.60—Z.25 Z.50—3.20 3.15—4.05

A50 2_14_2_4O 2_88_3_10 3_53_3_80 ASD 0.63—2.49 Z.55—3.39 3.11 —4.1Z

ANNUAL 2_04_2_79 2_77_2_96 3_40_3_59 ANNUAL 0.66—2.26 2.51 —3.1Z 3.1Z—3.90

(b) Coasts (Zone 4) 1“) 5”‘ 12°“ 2)

ND\] 1_20_2_18 1_53_2_89 2_21_3_54 ND\] 0.69—2.06 2.61 —2.77 3.13—3.34

FMA 1_35_2_02 1_93_2_83 2_52_3_49 FMA 0.71 —1.95 25772.70 3.20—3.38

M” 1_57_2_49 2_47_3_32 3_20_3_95 M\]\] 0.59—2.02 2.65—2.86 3.27—3.55

1.71 — 2.53 2.42 — 3.17 3.04 — 3.81 0-55 ’ 2-21 2-75 ’ 3-04 3-3° ’ 3-54

ANNUAL 1.43 — 2.83 2.11 — 2.93 2.75 — 3.52 ‘‘'‘\"‘‘U‘‘'- 0'55 ’ 2'05 2'55 ’ 2'35 3'22 ’ 3'43

Table 5.7. Projected absolute changes in maximum temperature by season and for annual average (°C) for the 10305, 20505 and

EOC relative to the 1960-1989 baseline. Data presented for RegCM4.3.5 forced by CNRM-RCP 4.5 and HadGEM2-ES-RCP 2.5. 8.5

averaged over grid boxes in each zone. Source: RegCM4.3.5

Change in Maximum Temperature (°C) (3o's)

MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

ND\] 0.78 1.23 1.57 0.85 1.27 1.62 0.80 1.27 1.52 0.84 1.27 1.53

FMA 0.75 1.20 1.51 0.75 1.20 1.59 0.64 1.21 1.51 0.76 1.24 1.51

M\]\] 0.42 1.23 1.82 0.44 1.26 1.84 0.61 1.25 1.70 0.48 1.19 1.69

A50 0.79 1.30 1.62 0.72 1.31 1.68 0.64 1.28 1.71 0.75 1.27 1.64

ANN 0.68 1.24 1.53 0.69 1.25 1.55 0.67 1.25 1.60 0.71 1.24 1.56

Change in Maximum Temperature (°C) (50's)

MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX 1 MIN 1 MEAN MAX

ND\] 1.18 1.69 2.36 1.19 1.76 2.46 1.12 1.70 2.37 1 1.20 1 1.73 2.37

FMA 0.95 1.62 2.24 1.08 1.70 2.31 1.13 1.68 2.25 1 1.03 1 1.65 2.26

M\]\] 1.28 1.82 2.46 1.23 1.84 2.59 1.13 1.72 2.34 1 1.24 1 1.75 2.33

ASO 1.29 1.76 2.43 1.27 1.79 2.54 1.23 1.75 2.37 1 1.29 1 1.73 2.33

1 1

ANN 1.18 1 1.72 2.37 1.19 1.77 2.47 1.15 1.71 1 2.33 1 1.19 1 1.71 2.32

Change in Maximum Temperature (“Cl (EOE)

MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX MIN MEAN MAX

ND\] 1.40 2.33 3.93 1.44 2.38 4.04 1.43 2.31 3.89 1.44 2.35 3.93

FMA 1.28 2.25 4.02 1.28 2.30 4.14 1.39 2.27 3.90 1.31 2.25 3.94

M\]\] 1.45 2.41 4.30 1.49 2.51 4.52 1.40 2.29 4.01 1.38 2.30 4.03

A50 1.34 2.46 4.41 1.38 2.54 4.65 1.37 2.31 4.05 1.33 2.33 4.08

ANN 1.41 2.36 4.16 1.46 2.43 4.34 1.43 2.29 3.96 1.41 2.31 3.99

Table 5.8. Projected absolute changes in maximum temperature by season and for annual average (°C) for the 2030:, 2050s and

EOC relative to the 1960-1989 baseline. Data presented for the mean value ofa six-member ensemble. Range shown is over all

the grid boxes in the zone (see Table 2.3). Source: PRECIS RCM perturbed physics ensemble run for MB scenario

(A) we“ (Zone 3) (c) Interior (Zone 1)

2030.5 2050.5 2080.5 2030': 2050': 2030':

NAN 1_73_3_24 2_23_3_m 3_33_3_61 ND\] 1.75-2.05 2.37-2.33 3.09-3.56

AMA 1_63_3_A7 2_12_3_33 2_e3_3_33 EMA 1.eI3- 1.92 2.25-2.55 3.03-3.37

MN 1% _ 338 365 _ A05 333 _ A63 Ml\] 1.93 - 2.43 2.71 - 3.58 3.55 - 4.59

A50 335 _ 335 387 _ 335 3_77 _ A54 A50 1.93 - 2.71 2.77 - 3.73 3.54 - 4.75

ANNUAL 1_89_3_33 2_A7_3_53 3_39_4_13 ANNuAI. 1.34-2.23 2.53-3.17 3.32-4.03

(1,, C055,; (Zone 4, (d) East (Zone 2)

2030.5 2050.5 2030.5 2030's 2050's 2080's

ND\] 134 _ 2767 363 _ 363 371 _ A713 NDJ 1.82 -1.93 2.43 - 2.64 3.13 - 3.33

AMA 1_82_3‘71 2_3A_A_51 2_93_5‘oA EMA 1.69-1.77 2.23-2.37 3.03-3.17

MN 2_7A_3_83 3_AA_5_56 3_8A_6_33 MJJ 2.04-2.21 2.89-3.16 3.75-4.07

ASO 2.53 - 3.64 3.37 - 5.04 4.03 - 5.63

ANNUAL A16 _ A51 A87 _ A63 364 _ 575 ANNUAL 1.93 - 2.03 2.67 - 2.83 3.42 - 3.67

Table 5.9. Projected absolute changes in minimum temperature by season and for annual average (°C) for the 2030:, 2050s

and EDC relative to the 1960-1989 baseline, Data presented for RegCM4.3.5 forced CNRM-RCP 4.5 and HadGEMZ-ES -RCP 2.5, 8.5

averaged over grid boxes in each zone. Source: RegCM4.3.5

Change in Minimum Temperature (°C) (305)

Rainfall Zone 1 Z 3 4

MIN MEAN 1 MAX MIN 1 MEAN 1 MAX MIN MEAN 1 MAX MIN MEAN 1 MAX

ND\] 0.88 1.47 1 1.95 0.35 1 1.44 1 1.92 0.33 1.35 1 1.69 0.86 1.38 1 1.76

EMA 0.77 1.42 1 1.92 0.30 1 1.43 1 1.93 0.65 1.29 1 1.70 0.77 1.37 1 1.78

MJ\] 0.92 1.27 1 1.58 0.38 1 1.27 1 1.59 0.75 1.25 1 1.63 0.91 1.30 1 1.63

Asu 0.93 1.35 1 1.73 0.90 1 1.35 1 1.74 0.36 1.33 1 1.73 0.89 1.35 1 1.75

1 1 1 1 1

ANN 0.87 1 1.38 1 1.80 0.36 1 1.37 1 1.79 0.77 1 1.31 1 1.69 0.86 1.35 1 1.73

Change in Minimum Temperature (°C) (50's)

Rainfall Zone 1 Z 3 4

MIN MEAN MAX MIN 1 MEAN 1 MAX MIN MEAN 1 MAX MIN MEAN 1 MAX

ND) 119 1.39 2.65 1.21 1 1.88 1 2.64 1.16 1.79 1 2.49 1.22 1.84 1 2.54

EMA 1.36 1.35 2.47 1.37 1 1.85 1 2.46 1.14 1.70 1 2.30 1.31 1.79 1 2.37

MJ\] 1.15 1.63 2.21 1.16 1 1.65 1 2.23 1.13 1.67 1 2.26 1.15 1.68 1 2.27

ASD 1.21 1.71 2.26 1.26 1 1.73 1 2.27 1.13 1.72 1 2.30 1.22 1.73 1 2.29

1 1 1 1

ANN 1 1.23 1 1.77 2.40 1.25 1 1.78 1 2.40 1.15 1 1.72 1 2.34 1.22 1.76 1 2.37

Change in Minimum Temperature (°C) (EOC)

Rainfall Zone 1 2 3 4

MIN MEAN MAX MIN 1 MEAN 1 MAX MIN MEAN 1 MAX MIN MEAN 1 MAX

ND\] 1.66 2.56 4.28 1.63 1 2.53 1 4.24 1.53 2.42 1 4.03 1.57 2.48 1 4.11

EMA 1.51 2.40 4.02 1.54 1 2.41 1 4.02 1.39 2.27 1 3.86 1.53 2.36 1 3.94

MJ\] 1.32 2.17 3.58 1.33 1 2.18 1 3.61 1.36 2.19 1 3.75 1.35 2.22 1 3.73

ASD 1.36 2.16 3.64 1.33 1 2.18 1 3.68 1.36 2.21 1 3.76 1.37 2.20 1 3.75

1 1 1 1

ANN 1 1.50 1 2.32 3.88 1.50 1 2.32 1 3.39 1.45 1 2.27 1 3.85 1.47 2.31 1 3.38

Table 510. Projected absolute changes in minimum temperature by season and for annual average (\"C) for the 20305, 20505 and

20805 relative to the 1961-1990 baseline, Data presented for mean value ofa six-member ensemble. Range shown is over all the

grid boxes in the zone (see Table 1.3). Source: PRECIS RCM perturbed physics ensemble run for A13 scenario

(a) Wes‘ (zone 3) (c) Interior (Zone 1)

2030.5 2050.5 2080.5 2030': 2050': 2080':

ND\] 1_74_2_32 2_31_2_84 3_11_3_71 ND\] 1.81—2.17 2.42—2.90 3.17—3.82

FMA 1_80_2_28 2_44_3_m 3_21_3_90 FMA 1.89—2.28 2.57—3.08 3.29—3.91

M” 2_03_2_42 2_73_3_21 3_49_4_08 Mjj 1.88—2.48 2.50—3.29 3.22—4.17

A50 2_03_2_45 2_72_ 322 3_52_4_06 ASD 1.93—2.54 2.59— 3.55 3.27—4.34

ANNUAL 2_13_2_88 2_55_3_06 3_35_3_92 ANNUAL 1.88—2.39 2.5Z—3.20 3.24—4.04

(b) Coast (Zone 4) W 5”‘ ‘Z‘\"‘° 2’

2030.5 2050.5 2080.5 2030 S 2050 S 2080 5

ND\] 0_16_2_19 0_2O_2_93 1_17_3_58 ND\] 1.85—1.95 Z.47—2.5Z 3.15—3.35

FMA 0_42_2_18 0_52_2_94 1_49_3_56 FMA 1.90—2.05 Z.50—2.79 3.2Z—3.49

M“ 0_72_2_23 0_92_2_95 1_95_3_54 Mjj 1.9Z—Z.16 Z.57—Z.88 3.21 —3.54

A50 Q53 _2_29 Q83 _3_07 1_79_3_74 A50 1.98—Z.24 Z.59—3.05 3.29—3.75

ANNUAL 0.45—2.22 0.52—2.97 1.5o—3.53 ANNUAL 1'92’2\"° 253’2'5“ 3'22’355

Figure 5.2. Summary map showing absolute change per grid box ofannual mean temperature (\"c) for the 2050': (top panel) and

EOC (bottom panel). Mean change is shown in the centre of each grid box while the ensemble minimum and maximum is also

shown in each box, Source: PRECIS RCM perturbed physics ensemble run for MB scenario relative to the 1961-1990 baseline.

1.17 an 1 u 2 . M. 2 n . n . u \" \"‘

20505

1... 2.5. 1... . 7 2 . . .. 1.51 LIA \"\"\"'

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12 1 The State of thejamaican Climate (Volume um mlmmamn or .51 . ,

5.2.3 STATISTICAL DOWNSCALING

Figure 5.3. Projections of mean and extreme daily maximum temperature for the Norman Manley International Airport

Weather Station for 2015-2035 and 2036-75. (A+D) Mean daily maximum temperature; (B+E) Maximum daily maximum

temperatu re: (C+F) warm day frequency per decade.

A 20i6—103S B 20252035 C 20162035

3“; if:

* ”x§ mm §:s’? mm gm mm

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Figure 5.4. Projections of mean and extreme daily minimum temperature for the Norman Manley International Airport

Weather Station for 2015-2035 and 2036-75. (A+D) Mean daily minimum temperature; (B+E) Minimum daily minimum

temperatu re: (C+F) Cool night frequency per decade.

A 2015-2035 5 20152035 C 20152035

01 xi _x

21 V: 5::

z 0

3: 53 as:

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D 2035 2075 E 2035 2075 F 2036 2075

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.4”... mm mm

5_3 Rainfall sgotvrvj decreases of 13-15% in the south and 2—3% in the

The following are noted about the future rainfall changes for - The single GCM generally consistently shows decreases

Jamaica from the GCMs, RCMs and statistical downscaling. in the late rainy period of August-October and in the div

- From the suite of GCMs, the scenario means suggest a Season from l:ebrl‘laW’Aprll for all time perlods except

diying trend. The 2030s will be up to 4% drier, the 2050s for grid box 4 over the northwest’

up to 9% drier, while by the end of the centuryjamaica - The RCM ensembles supportthe GCM suitein suggesting

as a whole may be up to 21% drier for the most severe the onset of a drying trend from the mid-2030s which

RCP scenario (RCP85). This is with respect to a 1986- continues into the 20505 and through the end of the

2005 baseline (see Table 5.12). centuw. The ensemble mean for the three-member RCM

. . . ' hows annual decreases across the three RCPs for

- The GCM suite also suggests that change in rainfall Sulte S . ,

during the late rainfall season is the primary driver of the four ralnlall Zones (23% l\" the 20305’ 7'9“/” for the

the drying trend noted. By the mid-2030s, late season 2030 33$ ~.l2% fir ‘T035: of °5\"“%'3”t'h*“ that .UPl::r

rainfall decreased by 13%, while by the end of the gang?’ lst 5135) \{O am , t gcreasz l\" f 6:15 ggfioe

centuw the mean decrease is 2—20% (see Table 5.13). d 5' “(P 335% dor E '\" em\" an” ea; \"lb :1

Dw season rainfall generally shows small increases or an up 0 acreage across a zqnes y e 5.\"

no change. Mean increases are consistently between of tlle, Cenmw (See Table .5’llal’ T“? SIX-member hlgh

1-4% across all time slices examined. Given the small 3:‘/:|5_;%'_1:rTe\";'|::glgglggii‘;/\";l:Jl3éEfgliggzgfslphilélgfiziglgg

amounts of rainfall received at this time, the increases . . . '

are not enough to offset the overall drying pattern (see Wlthln 5 Zone can be Eve\" hlgher (Table 5‘llb)‘

Table 5.14). - There is spatial variation (across the countw and even

- The single GCM (HadGEM2-ES) suggests a slightly writhlrl grid bfxegl wltll thihsoufr anifiastdgenegaflly

different picture with respect to the 1960-1989 baseline. 5 °rV1Vltr,lg glare ErTheCl:.a:e5 .al-.l e \"0 El WES Tr

For both northern and southern parishes (boxes 1 fit‘ blmtehs 2:50 at lg enmsslofns e”‘.§\"; E Suggejqs

through 4) mean projected annual changes across am‘ y ref 5 ' lstholg 3/he ew 5\" ‘axe: m the

the three RCPs are approximately +10% by the 20305, \"0 wbels O lamalca ,da Sd°V|v:,lnUE:S6es1Y|l_\" enh le

suggesting a slight increase in rainfall. By the 2050s. the Tsegll, e meanfls °d°T“' tire J‘ lg:l;E61 E W roe

southern parishes show smalldecreases(-3%)ora small '5 all d '5 genera y “er a\" E - age me

increase (+1%) while the northern boxes show a small perm ‘

increase (+6 to 9%). By the end of century, all grid boxes

Table 5.11. (a) Range of mean percentage annual rainfall change for each ofJamaica’s four rainfall zones from an RCM

ensemble (three members) across three RCPs (2.6, 4.5 and 8.5). See Figure 2.5 for grid boxes and Table 2.4 for grid boxes in each

zone. Source RegCM4.3.5 ensemble: (I7) Range of mean percentage rainfall change across the grid boxes in each zone from an

RCM ensemble (six members) running a high emissions scenario. See Figure 2.4 for grid boxes and Table 2.3 for grid boxes in

each 1one.5ource PRECIS ensemble

(a) Range of percentage change in mean annual rainfall for each zone across three RCPs,

Time slice West (Zone 3) Coasts (Zone 4) Interior (Zone 1) East (Zone 2)

zozos -5.35 - 1.22 -7.45 - 2.15 -9.92 - 5.70 -10.07 - 4.95

Rainfall 20505 -9.60 - -6.91 -10.19 - -3.89 -20.72 - 4.75 -13.29- -1.68

EOC -33.9i- -0.15 -32.33- -0.35 -37.25 - -0.52 -35.08 - -0.16

(b) Range of percentage change in mean annual rainfall across grid boxes in the tone for MB.

20305 -10.11 - 34.37 -29.85 - -5.00 -24.84 - -3.39 -13.91 - -8.82

zosos -5.70 - 9.95 -31.24 - -1.26 -25.25 - -2.16 -19.38- -14.73

Eoc -13.23 - 5.09 43.28 - -4.34 -37.03 - -9.70 -28.09- -22.91

- At the Norman Manley International Airport station, d°W\"5C3'l\"S EJ889515 that l”C'9a555 in maximum CW

statisticaldownscaling suggests strongincreasesin mean SD?\" length are W895‘ fol lVl3|'Ch UP \[0 7 (7) ClaY5 and

daily rainfallforjune of up to 57% (48%)and December 0C‘°b9|' UP \[0 10 (11) d3Y5 33055 3” RCP5 by Ball)’

of up to 108% (68%) by the early century (mid-century) CETTIUW (mid'CEntUW)-

across all RCPs.

- At the Norman Manley International Airport station 5'3'1 GCMS

statistical downscaling suggests increases in maximum Tables 5.12-5.14 show the range of projected changes for

5-day rainfall amounts forjune of up to 65% (77%) and annual. late rainfall season, and dry season rainfall with

December up to 186% (155%) by early century (mid- respect to a 1986-2005 baseline period from the suite of

centuw). Decreases for September-October of up 35% GCMs. The projections are illustrated as time series in

are also indicated for early century. Figure 5.5. Table 5.15 presents seasonal and annual rainfall

- At the Norman Manley International station statistical percentage changes averaged for HadGEM2'ES‘

Table 5.12. Mean percentage change in annual rainfall forjamaica with respect £01986-ZD05.Changes are shown for the four

RCP scenarios, Source: AR5 CMIPS subset, KNMI Climate Change Atlas

Annual Rainfall

Averaged over 2030-2039 2050-2059 2081-2100

min mean max min mean max min mean Max

rcp26 -11.39 -0.15 17.33 -13.57 -0.36 20.74 -38.16 -0.45 14.00

rcp45 -19.91 -3.76 12.75 -40.34 -6.10 23.56 -42.84 -7.47 25.20

rcp60 -19.36 -2.67 12.23 -18.98 -2.66 18.82 -46.27 -8.85 15.83

rcp85 -20.55 -3.84 17.58 -37.77 -8.52 30.57 -69.60 -21.02 2429

Range of mean: -3.84 to -0.15 -8.52 to -0.36 -21.02 to -0.45

Table 5.13. Mean percentage change in late season (August-November) rainfall for Jamaica with respect to 1986-2005. Changes

are shown for the four RCP scenarios. Source: AR5 CMIPS subset, KNMI Climate Change Atlas

Late Rainfall Season

Averaged over 2030-2039 2050-2059 2081-2100

mm mean max mm mean max mm mean max

rcp26 -16.87 -1.01 24.26 -19.74 -0.53 25.44 -39.40 -1.57 16.44

rcp45 -28.80 H -3.35 13.27 -45.99 -4.28 7 35.91 -41.47 -7.19 32.31

rcp60 -26.58 -2.81 15.25 -23.47 -3.19 24.90 -49.10 -8.96 20.55

rcp85 -14.92 -2.73 17.57 -48.21 -7.73 40.52 -75.08 -19.91 40.16

Range of mean: -3.86 to -1.01 -7.73 to -0.53 -19.91 to -1.57

Table 5.14. Mean percentage change in dry season Uanuary-March) rainfall forjamaica with respect to the 1986-2005. Changes

are shown for four RCP scenarios. Sour1:e:AR5 CMIPS subset. KNMI Climate Change Atlas.

Dry Season Rainfall

Averaged over 2030-2039 2050-2059 2081-1100

min mean max min mean max min mean max

rCp25 -17.02 3.38 27.89 -17.44 3.11 28.12 -26.06 2.94 23.37

rcp45 -34.74 0.79 51.99 -32.85 1.10 39.58 -37.59 1.05 41.00

rcp60 -18.97 4.26 28.91 -18.07 1.93 24.31 -30.93 -1.00 21.04

rcpss -26.20 -0.45 36.07 -28.35 0.12 30.98 -52.07 -9.15 36.41

Range 07 mean: -0.45 to 4.26 0.12 to 3.11 -9.15to 2.94

Yable 5.15. Hav.1GEM2-E5 RCP 2.6, 4.5 and 8.5 scenario ensemble mean projected percentage changes in mean precipitation by

season and for annual average (“(3). for the 20305, 20505 and EOC. Results shown for the four GEM grid boxes with respect to the

1960-1989 baseline. Source: HadGEM2-ES runs for RCP2.6, 4.5. 8.5

(b) Projected HadGEM2-ES percentage changes in mean precipitation by season and for annual average. by grid box under

RCP Ensemble

HADLEV Percentage change in Precipitation (99) (20305)

GRID

BOX 1 2 3 4

ND\] 10.33 27.68 42.57 —9.15 20.35 49.95 9.35 31.99 43.41 0.80 15.43 33.46

FMA —20.09 —10.59 5.30 —19.53 4.09 10.31 —22.24 —18.17 —10.36 —25.89 —11.37 12.79

Mjj 1.12 19.20 35.55 —0.24 15.77 40.46 4.65 11.64 23.14 —1.26 13.47 34.52

.00 00.10 0.70 7.05 03.10 0.70 0.70 0.00 0.0.

HADLEV Percentage change in Precipitation ('58) (20505)

GRID

BOX 1 2 3 4

E MEAN W W\" MEAN W EEEEE W

ND\] 0.88 10.32 15.57 16.04 19.95 26.81 6.18 19.15 33.39 1.55 17.16 30.75

FMA —Z2.66 —12.75 —3.71 —11.90 1.01 9.15 —17.24 —0.69 18.70

0,, 0.00 7.00 27.07 00.00 0.00 0.05 30.00

ASO —Z7.46 —Z1.47 —10.79 —Z3.Z2 —16.85 —10.1 5 —7.35 1.68 11.45 —8.1 5 5.90 16.00

HADLEV Percentage change in Precipitation ('16) (EDC)

GRID BOX 1 Z 3 4

W\" E W E MEAN EEEEEE MAX

ND\] —2.1 6 2.43 7.38 —3.87 7.23 13.21 13.04 15.84 18.88 5.96 11.70 16.34

FMA 42.87 —25.31 —7.11 —34.64 —13.78 1.50 —26.50 —11.12 0.95 —29.51 —11.50 3.93

MJJ —63.10 —18.97 27.61 —63.08 —25.93 14.07 46.27 —14.42 16.93 48.31 43.58 19.28

ASD —55.22 —21.97 10.61 42.49 —14.77 19.95 —17.53 3.96 33.97 —15.99 7.45 46.84

ANN -41.03 -15.33 9.17 -33.57 -12.58 12.42 -23.83 -3.28 18.47 -21.92 -2.09 22.97

Figure 5.5. (a) Relative Annual Precipitation change ('51:); (la) Relative August-November Precipitation change ('31:); (c) Relative

January-March Precipitation change ('51:) forjamaica with respect to 1986-2005 AR5 CMIP5 subset. on the left, for each scenario

one line per model is shown plus the multi-model mean. on the right percentiles of the whole dataset. The box extends from

25% to 75%, the whiskers from 5% to 95% and the horizontal line denotes the median (50%).

Relative Precipitation change Jamaica Jan-Dec wrt 1986-2005 AR5 CMIP5 subset

200 200

FlCP2.6 j

RCP4.5 T

150 HCP6.0 T I50

FiCP8.5 j ‘

historical — ‘

100 “ ‘i ll‘ 100

_. ‘ ‘ ‘V l 1‘ ‘lw‘w \" “ W ‘

53 5on‘v“l,J .t‘ Y‘ 50

-lm, nut‘... ‘ l “ -

0 0 f 1?? - -

-100 -100

1900 1950 2000 2050 2I00 2081-2I00 mear

Relative Precipitation change Jamaica Aug-Nov wrt 1586-2005 AR5 CM|P5 subset

300 300

RCP2.6 T

RCP4.5 j

250 RCP6.0 — 250

200 hfigfifij _ 200

150 ‘ ‘ I50

3 100 l\[ \\ ‘l , ‘ ““l I00

l l 1 .

1 - ll

5° it .t W l t‘ “ \" ' 5°

l V ‘ Vt l

0 0 -? - -

_5o 1 _50‘\}

‘ml,

-100 -100

1950 2000 2050 2100 2031-2100 mear

Relative Precipitation change Jamaica Jan-Mar wrt 1986-2005 AFl5 CMIPS subset

400 400

RCP2.6 j

350 RCP4.5 — 350

300 j l 300

250 historical —- 250

200 ‘ l ‘ ‘ fill 200

E 150 ‘ ‘ g ‘_ 150

1 t l l

100 i,i‘,1/\"|‘.‘llt\" ‘ ' ‘-A l 100

‘\"1 1 v’ ‘ ‘

50 “ltfl “v lw 50 ‘E.

0 a gag

l- ‘ ‘ls ‘ “ ’ l '

\"‘° '5”

-100 -100

1950 2000 2050 2100 2031-2100 mear

5.3_2 RCM; 4.5) and HadGEM2—ES (RCP5 2.6 and 8.5) as forcing GCM5.

Th. I. t . \[d h . .f” H The RegCM4.3.S resultsare providedinTab|eS.16whi|ethe

'5 Sec '0” presen Spr°J.ec,e C a.ngeSm mm a 'a_nn”a y PRECIS resultsare pre5entedinTab|e 5.l7.A5ummaIy map

and by Seasons forjamamas 4 mnfafl Z°neS' as Ewe\" by showing percentage change per grid box of annual rainfall

two climate models: PRECIS RCM — run for A1B scenano, (based on PRECIS RCM results) is given in Figure 5 6

with HadGEM2-ES as forcing GCM and, RegCM4.3.5 — ’ ’

averaged over grid boxes in each zone, with CNRM (RCP

Table 5.16. Projected percentage changes In ralnfall by season and for annual average for the 20305, 20505 and EOC relative to

the 1960-1989 baseline. Data presented for CNRM-RCP 4.5 and HadGEM2-ES -RCP 2.5. 8.5 averaged over grid boxes In each tone.

Source: RegCM-1.3.5

Percentage Change In Precipitation (%) (30‘s)

Rainfall

Zone 1 2 3 4

l MIN MEAN l MAX MIN MEAN MAX MIN I MEAN l MAX l MIN l MEAN MAX

NDJ -13.22 i 5.55 18.16 i -9.12 3.95 16.00 l -14.72 i 5.37 25.54 -11.92 6.16 l 15.24

EMA -23.51 i 1.96 20.11 I -20.75 2.02 20.79 l -22.40 i 1.76 20.87 -15.42 3.21 l 14.47

MJJ -25.50 l -13.73 5.36 I -22.65 -12.70 4.28 l -30.57 I -14.57 13.73 -25.24 -15.26 i 1.99

A50 -3.37 l 7.07 14.93 i -4.00 2.72 11.75 I -13.19 l 15.32 35.85 -6.36 5.59 l 22.15

ANN l -9.92 l -2.67 l 5.70 l -10.07 -3.82 4.95 l -5.35 l -2.42 1.22 -7.45 I -2.46 l 2.15

Percentage Change In Preclpltatlon (%) (50‘s)

Rainfall

Zone 1 2 3 4

I MIN MEAN\] MAX MIN MEAN MAX MIN MEAN l MAX l MIN ‘MEAN MAX

NDJ -37.48‘ -6.97 9.46 I -33.14 -10.73 1.63 I -35.43 I -7.74 23.05 -26.87 -4.59 l 13.44

EMA -14.97 i -2.05 18.71 l -12.92 -2.20 19.00 l -25.35 i 4.17 29.25 -12.11 1.80 l 23.11

MJJ -31.56 l -11.12 0.68 I -27.42 -10.05 -0.36 l -42.70 l -15.60 9.32 -33.02 -11.22 i 7.77

A50 -20.72\} -7.56 4.75 l -18.53 -9.33 -0.80 l -19.40 i -1.39 23.79 -18.23 -7.30 l 5.90

ANN l-13.37\} -5.52 l -0.25 l -13.29 -9.34 -1.68 l -9.60 l -5.52 l -6.91 l -10.19 i -7.35 l -3.89

Percentage Change in Precipitation ('15) (EOC)

Rainfall

Zone 1 2 3 4

l MIN MEAN‘ MAX MIN MEAN MAX MIN MEAN MAX MIN lMEAN MAX

NDJ -28.59 I -11.32 1.02 l -24.40 -8.96 3.32 l -37.09 l -11.52 12.37 -22.79 -10.49 i -0.96

EMA -23.66 i -5.52 14.48 l -22.74 -4.15 16.78 l -23.31 i -2.59 20.29 -17.59 -1.79 l 15.95

MJJ -47.66 l -16.72 0.37 l -44.21 -16.91 -2.07 l -46.50 l -16.25 7.57 -44.30 -17.41 i -0.66

A50 -35.74 l -11.52 6.03 l -38.53 -13.80 0.73 l -19.75 i -7.03 15.80 -31.75 -10.58 i 7.61

ANN l -37.26 l -12.57 i -0.52 l -35.08 -12.71 -0.16 l -33.91 l -12.20 i 5.06 l -32.33 l -12.24 i -0.36

Table 517. Projected percentage changes in rainfall by season and for annual average for the 2030's, 2050's and 2080's relative

to the 1961-1990 baseline. Data presented for the mean value ofa six-member ensemble. Range shown is over all the grid boxes

in the zone (see Table 2.3). source: PRECIS RCM perturbed physics ensemble run for A13 scenario

(3) west (zone 3) (6) Interior (Zone 1)

‘ 20305 zosos 20805 ‘ 20305 20505 20805

ND\] 115.2555 1‘63_29_71 7‘1o_35_10 ND\] -10.15-11.04 -16.04-12.14 -21.75-11.08

FMA .5_39 _ 25723 15.12 _ 3935 .1 _\[\}9 _ 35723 FMA -4.80 — 20.24 6.29 — 23.33 2.41 — 24.08

M11 .11_34 .1277 .854 .1759 .2945 _ 4'95 MJJ -22.96 — -2.58 -20.61 — 0.14 -48.77 — -23.15

Ago .25_13 _ .3,17 .2092 _ 4,13 .2532 _ .029 ASO -40.03 — -16.01 -42.70 — -15.73 -51.16 — -21.36

ANNUAL .1 ()_11 _ 34737 .5_7() _ 9_95 .1323 _ 5'09 ANNUAL -24.84 — -3.59 -25.25 — -2.16 -37.03 — -9.70

(b) Coasts (Zone 4) (I1) East (Z°“9 2)

‘ 2o3os zosos 20805 ‘ Z0305 Z0505 20805

ND\] -54.09 - -16.02 -63.50 - -33.28 -73.82 - -38.74 ND\] -9.43 - -5.28 -18-40 -14.16 -18.57 - 43-88

FMA -22.48 — -4.40 -26.71 — -0.67 -21.62 - 0,01 FMA 0-78 ' 5-91 L45 ' 524 1-34 ' 7-40

MJJ -48.18 - -11.94 -56.18 - -13.76 -65.23 - -19.47 MJJ -9.80 - -7.03 -11.86 - -11.61 -37.60 - -32.81

A59 .7337 _ .2472 .71 152 _ .2295 .7552 _ .2355 ASO -29.90 — -22.58 -36.56 — -29.79 -45.65 — -39.55

ANNUAL .2935 _ .5130 .3124 _ .126 .4323 _ .434 ANNUAL -13.9‘! — -8.82 -19.38 — -14.73 -28.09 — -22.91

Figure 5.6. Summary map shawlng percentage change per grld box of annual ralnfall for the 2050s (top panel) and EOC (bottom

panel). Source: PRECIS RCM perturbed physlcs ensemble run for A13 scenarlo relatlve to the 1961-1990 basellne.

i

mas 4... 5 ’ _ ' “G u. 1.55 at Jan” \" 7\" \" '

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century 11 ._,‘,~_~,

: .2 as .1; 0 .3 2; .1: 1) 4.4 11 .12 5; 4 -15 11.15

W _ K —-in u.

5.3.3 RAINFALL EXTREMES

Figure 5.7. Projections of mean and extreme daily rainfall for the Norman Manley International Airport Weather Station. (A+D))

Mean daily rainfall; (B+E) maximum number of consecutive dry days.

A mm was 3 20164035 C 2013-2035

Em lll l Elm l N E” H I

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5 .. IROJI xx» 2,.

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5.4 Sea Levels

It is projected that the world's largest 136 coastal cities will

see up to 0.9 m of seal level rise (SLR) by 2100 (jevrejeva

et al, 2016). With over two million square km of land and

over a trillion dollars in assets located under 1 m above ~

current sea level, sea level rise is one of the greatest socio- The future “Se \[Of Sea levels\]

economic hazards associated with climate change (Milne et in the Ca ribbean is not

al. 2009). The first three rows ofTab|e 5.18 provide a range . . ‘ .

of estimates for end of century sea level rise globally and in S|g|'1|f|Ca ntly d lffe re |\"|t from

the Caribbean Sea under a number of SRES scenarios. The . .

values are taken from the |PCC's Fourth Assessment Report the P|'0J€Cted gl0bal rlSe~

(IPCC 2007). The combined range over all scenarios spans

0.18 m to approximately 0.5 m by 21 00 relative to 1 980-1 999

levels. The future rise in the Caribbean is not significantly

different from the projected global rise.

Table 5.18. Projected changes in temperature per grid box by 10905 from a regional climate model. IPCC (1007)

Scenario Global Mean Sea Level Rise by 2100 Caribbean Mean Sea Level Rise by 2100

relative to 1980 — 1999 relative to 198D—1999(t 0.05m relative

to global mean)

IPCC B1 0.18 — 0.33 0.13 — 0.43

IPCC A15 021 — 0.43 0.16 — 0.53

IPCC A2 0.23 — 0.51 0.18 — 0.56

Rahmstorf, 2007 Up to 1.4 m Up to 1.45 m

Ferret at al., 2013 - Up to 1.50 m

Since the |PCC’s Fourth Assessment Report, however. a lPCC’s Fifth Assessment Report does not, however, provide

number of other studies (Rahrnstorf, 2007; Rignot and projections for the Caribbean separate from that for the

Kanargaratnam, 2006; Horton et al. 2008) including the Global mean. Nonetheless, the same assumption of SLR

lPCC’s Fifth Assessment Report (IPCC 2013) suggest that being similar for the region as for the globe may be taken.

the upper bound for the global estimates in Table S.l8 are Projections for the globe under the four RCPs from the

conservative and could be up to 0.98 m. with a rate during lPCC’s Fifth Assessment Report are shown in Table 5.19.

208l—2100 of 8 to l6 mm/ year. Diagrams from Perret et Through mid—centun/, the mean increase Is similar for all

al. (20l3) indicate the same kind of underestimation for RCPs. Distinctions in projected values arise toward the end

the Caribbean Sea and suggest a higher upper bound of ofthe century.

up to 1.5 m for the region by the end of the century. The

Table 5.19. Projected increases in global mean sea level rise (m). Projections are relative to1986-2005. IPCC (2013)

Scenario Mean Likely range Mean Likely range

RCPZ.6 0.24 0.17—0.32 0.40 0.26—O.55

RCP4.5 0.25 0.19 — 0.33 0.47 0.32 — 0.53

RCP5.0 0.25 0.18 — 0.32 0.48 0.33 — 0.53

RCP85 0.30 0.22 — 0.38 0.63 0.45 — 0.82

\\— g 'k E ‘ I: _ . ‘ I?“ _

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\\\\ \\\\\\V_,.-:1‘: ,\\‘__\\“,,/ I. . E that the worlds ‘e

'\\< \"1. V\" ;.f .. V ‘, A largest 136 coastal

'~ ‘g T; « '_ ,t- .. . '»

V , ‘.7 ’«.l ‘V ./ gr’, cltleswill see upto 5

w \\. rt ‘ g.‘ \" \" i’ ' 3‘

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V 1 -.’ “ , . ‘ ' , ’ ‘

, , . ‘ ' , . rise by 2100.

. ‘ - l - 2

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In the most recent IPCC Special Report on Global Warming

of 1.5 (IPCC 2018), it is suggested that sea level rise can be

constrained if the increase in global temperatures is limited , .

to l.5“C above preindustrial levels. The projections of sea Thls le‘/el of Sea Ie‘/el “Se would

level rise under these conditions range from 0.26 to 0.77m be on average 0.1 m iess than

by 2100. This level of sea level rise would be on average 1

01m less than estimates of SLR under 2°C Paris agreement estimates Of SLR under 2°C

conditions and coincide with a lower acceleration in the - « -

rate of sea level rise. This would allow for more time for Parls agreement Condmons and

adaptation to the new sea level conditions in Small Island coincide with a lower acceleration

Developing States (SIDS) like Jamaica. An increase of 15°C . h f I I .

instead of 2°C would mean 10.4 million fewer people m t e rate 0 Sea eve “Set

globally being exposed to the impacts of SLR at the end of

the centuiy (Hoegh-Guldberg et al. 2018).

Projections for SLR for the north coast versus the south

coast ofjamaica are extracted from the ensemble of models ‘ j ‘ ‘

available in SIMCLIM (see Chapter 2) and are summarised the Proiected largest rise In the mean is 0.39-9.40 rn across

in Table 5.20, and shown for medium sensitivity models b‘°th.C°35‘5- BYW3 e\"d Dfthe Ce\"tUW«l_he PT°J5Ct€d W895‘

in Figure 58. Generally. the difference between north and \"_5e '\" the 'T‘?a_” aC\"°55 bf’t'1 5°35“ _'5 0‘8?’0'90 m- The

south coast is approximately om m (1 cm) Bymid_Ce\"t,_,,y' highest sensitivity models indicate a rise of Just over 1 m

for RCP8.5.

Table 5.20. Projected increases in mean sea level rise (ml for the north and south coasts ofjamaica. Range is the lowest

projection under low sensitivity conditions to the highest annual projection under high sensitivity during the period.

Projections relative to 1986-2005 and are generated using a 24-model ensemble in SimCL|M (see section 2.3.4 for further

information on SimCLlM).

Sea Level Rise (m)

North Coast (-77.076W, 18.8605N)

Centred on 2035 2055 End of century

Averaged over 2030-2039 2050-2059 2080-2100

Mean Range Mean Range Mean Range

REP2.5 0.20 0.17 — 0.22 0.33 0.30 — 0.36 0.58 0.51 — 0.65

REP4.5 0.20 0.17 — 0.22 0.35 0.31 — 0.39 0.66 0.58 — 0.76

REP6.0 0.20 0.17 — 0.22 0.33 0.30 — 0.37 0.67 0.57 — 0.78

REP8.5 0.21 0.18 — 0.25 0.39 0.34 — 0.44 0.87 0.72 -1.04

Sea Level Rise (in)

South Coast (-77.1 57w, 17.142N)

Centred on 2035 2055 End of century

Averaged over 2030-2039 2050-2059 2080-2100

Mean Range Mean Range Mean Range

REP2.6 0.20 0.18 — 0.23 0.34 0.31 — 0.37 0.60 0.53 — 0.67

REP4.5 0.20 0.18 — 0.23 0.36 0.32 — 0.40 0.68 0.59 — 0.78

REP6.0 0.20 0.18 — 0.23 0.35 0.31 — 0.39 0.69 0.58 — 0.80

REP8.5 0.22 0.19 — 0.25 0.40 0.35 — 0.45 0.90 0.74 -1.08

Figure 5.8. Sea level rise projections under RCPZ.6. RCP4.5, RCP6.0 and RCP8.5 for a) a point (-77iI)76°W, 18.8605°N) off the

Northern coast ofjamaica: b) a point (-711 57°W, 17.142“N) offthe Southern coast ofjamaica.

Nonh

100

80

60

40

20

O

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.—i.—«N~~~~~~~~~~~~~~~~~~~~~~~~

iRCP2.6 jRCP4.5 jRCP6.0 jRCP8.5

a)

South

100

S0

60

40

20

O

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—RcP2.6 —RcP4.5 —ricP6.o :i1cPs.5

b)

lnterannual varlabllity of sea levels is not well captured by 5.4.1 SEA LEVEL EXTREMES

climate models. Tlde gauge recordsalso suggestthat models Adaptedfmm IPCC SRO“ (2019)

may underestimate changes in sea level associated with El _ _ _

NW0 events in some Cases (Meyssignac at 3'‘ 2017)_ Both As stated in Section 4.7.1, extreme sea levels (ESL) in the

interahnual variability and El Nifio havea significant impact Cmbbea” 3'5 35? ’§5”'t °f ‘°mb_'”ed 5e35°”5'_5_e3 'eVe'

onjamaican SLR, therefore the sea level rise projections for ‘We Peékrthe 5Pf'“E\['de53\"d”°l3'C3|§YC'V°_ne3C“V|Q/- EVE“

Jamaica may still be slightly understated. Further research 3 Sma\" '”C\"e355 \"\" U193” $95 le‘/9' Cal‘ Slgnlflcfinllyaugment

into Jamaican sea level changes is needed to understand Fhe f\"eq“enCY find |“tEn5|tY 0ffl00d|ng_-Th|5 '5 b€CfiU5€ 5|-R

how these processes affect the Jamaican sea ievei and increases the risk for storm surges, tides and waves, and

reduce the unsenainties in pm\]-estions_ because there is a log-linear relationshlp between a flood s

height and its occurrence interval.

The change in ESL events is commonly expressed in greatly decreased over recent decades and is also expected

terms of the amplification factor and the allowance. The to decrease greatly in the near future. The allowance

amplification factor denotes the amplification in the denotes the increased height ofthe water level with a given

average occurrence frequency of a certain extreme event, return period.Thisallowance equals the regional projection

often referenced to the water level with a 100—year return ofSLR with an additional height related to the uncertainty in

period during the historic period. The ESL return period has the projection (Hunter ml 2).

Figure 5.9. Showing projections of extreme sea levels (ESL). The colours of the dots express the factor by which the frequency

of ESL events increase in the future for events which historically have a return period of 100 years. Hence a value of 50 means

that what is currently 1-in-100 year event will happen every 2 years due to a rise in mean sea level‘ Results are shown for three

RCP scenarios and two future time slices as median values, Results are shown for tide gauges in the GESLA2 database. Source:

Oppenheimer et al., 2019

2046-2055 zu1—mo

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Although regional differences in projected mean SLR Global Warming of 1.5°C(|PCC 2018) and in Rasmussen

are small for the coming centuries, regional contrasts in et al. (2018). This indicates that, at these locations, water

amplification factors are considerable. In particular, many levels with return periods of 100 years during recent past

coastal areasinthe lowerlatitudes may expectampliflcation will become annual or more frequent events by mid—

factors of 100 or larger by mid—century, regardless of century. By end of century and in particular under RCP8.5,

the scenario as also shown in the IPCC Special Report on such amplification factors are widespread along the global

coastlines (Vousdoukas et al. 20l8a). This projected increase in Caribbean SSTs trends will likely

The probabilities of sea level extreme events induced by 5”gPI_'I°” the ge”e_\"atI'°\" Of r_\"°r_e mtense f“t”\"eI purfianes

a tropical cyclonic storm surge are very likely to increase an a‘I’e '\"c,\"ea5'ngIy\"egat'IVe \"ngacts °I_I\" Cora eat an

S-Ignificamly Over the 21st century. genera marine eco ogy (Tay or an Step enson 2017).

5.5 Sea Surface Temperature 5-5 H“\"\"‘a“e5

Adapted from State of the Caribbean Climate Report (CSGM me I'EFfrCe'I?n2eg1:gF£If_trrI‘e’§55:15e5r';:I'I'Ct) (5:5) :::CISPSe:I_?S| Rfggrt

X H I WI U l LI

) hurricanes under the SRES and RCP warming climate

Not many studies have examined projections of SSTs forthe Scenarios:

Caribbean. Antuna et al. (2015) determine future Caribbean h I rd _ _ I h _

SSTs for the period 2000-2099 for botha business-as-usual ' T e\"_e ‘I5 °‘I” °°” ' “Fe In pr_°Ject‘°n5I( ° dc arfges 'n

and a low CO1 emission scenario using a coupled ocean- \"°p'caf_Cy‘ °ne gene5'5' Manon’ \"ac 5' ”rat'°”' °r

atmosphere model. Their results are summarized in Table areas ° 'mpa“'

5.21.The results showa continuation ofthe recent warming - Based on the level of consistency among models, and

trend in SST (see Chapter 3). physical reasoning, it is likelythattropical cyclone related

rainfall rates will increase with greenhouse warming.

Table 5.21. Projected north tropical Atlantic SST trends (\"C _ I_I( I II II I b I I I I I

per century) for two future scenarios. Bracketed numbers . |t_'5 e yt an e 3 0 a requencyc? tmpma Cyc Ones

indicate standard errors‘ Source: Antuna at $2015 will either decrease or remain essentially unchanged.

- While it is likely that overall global frequency will either

decrease or rema'n essent'al| nchan ed, t s I kel

SST '\"‘''‘’'“° \"C Per °e\"\"\"Y) that the frequencylof the mdsgnrense stgormsl (clategory

4 or 5) will increase substantially in some ocean basins.

be released until 2022, the recent World Meteorological

Organization (WMO) Task Team published funher analyses

“dues 1'80 (om) 0'77 (038) in two parts, cited here as Knutson et al. (2019) and Knutson

et al. (2020). The report outlines more recent perspectives

widercaribhean 1_76(0_39) 0_g6(Q_43) on the confidence of projections under global warming.

Overall, the conclusions outlined above remain the same.

Below, we outline updated references, ways in which the

T'°Pi¢3'A“3\"\"¢ 1~72(0v42) 0~70(0~42) current scientific literature diverges or confirms each

conclusion, and their implications for Jamaica and the

Caribbean region. Figure 5.10 shows a summary oftropical

Nurse and Charlery (2014) also produced SST projections cyclone (TC) projections across the globe.

for two future SRES scenarios using data from a regional

climate model. They examined three future time slices _ _ _ _

and deduced that SSTs will increase across the region c°\"_°'“5\]°\" 1: The\"? '5 '?W C°\"_f'de\"C‘-' '\"

throughout the twenty-first century irrespective of scenario P\"°J9Cf'°\"5 °f Fha\"§e_5 l\" \"°P'Ca' CYC'°_\"°

examined. They note, however, that the mean decadal rate Ee\"e5'5r _Se\"e5'5 '°C3\"°\"v “'3Ck5- d\"\"\"‘\"°\"r W

of warming increases from 013°C for the 30-year period 37935 °f'mP3C‘-

2000-2029 to 0.31°C for 2030-2059, and eventually reaches

0.41°C for 2070-2099 (i.e., the warming intensifies). TIDPIEUI Cyclone \{TC\} 59919515 and 39919515 l|7“7f\"7n5-

Nurse and Charlery (2014) also suggest the following about Though few Smmes ha?/e analyéed Vanabmy m TC gflems’

. . , location and areas of impact since IPCC (2013), individual

me future warmmg of Canbbean SSTs‘ studies show little change in global tropical cyclone genesis

‘ BY mid'Ce”t“\"Y- the e><P3“d\"\"8 alid C°\"‘t|'aCting Of the and track variability under a warmed climate, particularly

Allafitic Warm P00| (AWP) i5 |'eP'aCed by 3 \"blanket\" 07 for the North Atlantic region. Projections also continue to

U”if°Vm'Y Wafm l9mP9\"3tU\"95 “T055 the Cafibbean 598 be heavily dependent on the basin of genesis, the ability

lhroughoutlhe emlfe yea’-The P\"°J'eC\[9d 55TSt\"|€|’ef0|’€ of models to simulate complex physical processes and

exceed 28°C across the entire Caribbean Sea year-round. jndjvidual storm case;

- The mean annual SST range of approximately 3.3°C

currently observed in the Caribbean Sea is projected to

contract to 29°C in the 2030s and to 2.3°C in the 2090s.

By the end ofthe century, years of coolest projected SSTs

fall within the range of the warmest years in the present.

Figure 510. Summary of TC projections for a 2°C global anthropogenic warming. Shown for each basin and the globe are

median and percentile ranges for projected percentage changes in TC frequency, category 4-5 TC frequency, TC intensity, and

TC near-storm rain rate. For TC frequency, the 5th-95th-percentile range across published estimates is shown. For category 4-5,

TC frequency, TC intensity, and TC near-storm rain rates the 10th-90th-percentile range is shown. Note the different vertical-

axis scales for the combined TC frequency and category 4-5 frequency plot vs the combined Tc intensity and TC rain rate plot.

See the supplemental material for further details on underlying studies used. Source: Knutson et al. 2020

Tropical cyclone Prolectlons (2°C Global warming)

a...-... _ .. - _

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Daloz and Camargo (2018) indicated that the mean latitude linear trend of -110 1 116 kilometres per decade from the

tropical cyciogenesis had shifted poleward over the last Equator was within natural variability and not statistically

30 years following a poleward shift in environmental significant. Therefore, due to the spread in scientific

conditions favourable for storm development. The North opinion and a lack ofexperiments underanthropogenic

Atlantic region was shown to have a weaker negative trend warming, confidence in the poleward migration of

compared to other basins. Figure 5.11 shows the variations storms remains low for the North Atlantic basin.

in the observed mean latitude ofTC genesis between 1980-

2013 forvarious TC basins. Forthe North Atlantic region, the

as 1 Yhe State ofthejamaican C||mate(Vo|ume|ill information or .51 ,

Figure 5.11: Time series of annual-mean latitude of tropical cyclone genesis calculated from the best-track archive IBTrACS

from 1980 to 2013 for: a) west Pacific basin, b) East Pacific basin, c) South Pacific basin (Wellington) and d) North Atlantic basin,

Linear trend lines are presented with their 95% two-sided confidence intervals. Source: Daloz and Camargo 2018.

a West Pacific b East Pacific

non moo

me

man

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ism

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c South Pacific d North Atlantic

.500 mo

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5 5

mm inon

asao ,

ioao was two iws me was 7010 ‘Rue was -990 ins‘ mo zoos nlo

Vui \"'

TC track, size and areas of occurrence. While there are Conclusion 2: Based on the level of consistency

studies that have indicated shifts in storm track in a warmer among models, and physical reasoning, it is

environment (e.g. Gutmann et al. 2018), the shifts in storm likely that tropical cyclone related rainfall rates

track and occurrence were mostly significant further north will increase with greenhouse warming.

of the tropics where the track is more likely to be impacted

by the rotation ofthe Earth. Some studies indicated that no wniie rainfa|| rates at the storm centre cannot as yet be

robust changes could be observed in storm track that could resoived by rnodeis with c|ear accuracy, approximate

ea5llY be amlbuted t° global Wamilng (DBIOZ e‘ al- 2075, simulations agree that rainfall near the centre is more

Knutson et ai. 2020). Other Studies indicate a Poleward than likely to increase. Studies continue to have medium-

migration ofintense storms, as previously described above. \[g.high and high gonfidenge in protected increases in

Am While the gerleral C°“5e“5U5 am°”El“dlVldUal Smdles TC-reiated rainfall rates, as shown in Figure 5.12. Most

is that TC size will increase due to a prevalence of warmer projections indicate an increase between 5 and 25% for

ocean waters. this increase is anticipated to be more the North Atiantic region. Under RCP4.5, some studies

significant in more intense storms (Yamada et ai. 2017). indicated increases as high as 20% while under RCP8.5, the

There is however little consensus among the studies on the highest percentage increase projected was 51%_ warmer

Significefice 0f the Pmjected i\"|Cie35e- ocean waters and increased water vapor in the atmosphere

In summary, TC track and areas of occurrence are more are eXP_e‘1ed \"3 C§'”5e m°_Ve l\"te”5e \"amt Pamculaily at

likely than not to remain unchanged. TC size is likely to ‘he \"ad'“5 °f mimmum W“\"d5 °’ eYe_\"Va” (F‘3“\"e 5-12‘)-

increase though there is low consensus in the degree and Th‘5 e'_:feCt_ \"35 3'50 bee” °b5e'_\"’ed WM‘ extreme ’a_'”f\"\"ll

manner ofincrease cvetthe North Atlantic region occurring in more recent hurricanes such as Hurricane

Han/ey in 2017 (Van Oldenborgh etal. 2017).

Figure 5.12. (a) Summary histogram of global mean projections of percentage changes In tropical cyclone rainfall rates, (b)

Projection distributions for Individual baslns with projections for the North Atlantic highlighted by the purple rectangle and

(c) Comparison of globally-averaged rainfall rates under present versus global warming conditions for a simulated hurricane.

Source: Knutson et al. (2020)

a) Tropical Cyclone Precipitation Rate Change Projections: Global 17) Tropica - . e Precipitation Change Projections: By Basin

Mini‘ E unrlil: rnngu: Ion./non. pemulilcs; full lung:

no 4,,

as ‘

8 30 .

K 6 25 5

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P\"°°\"'m\"\"‘° n- 16 ' 16 ‘ 13. '10- -I0- 10'

500

450 : $.‘i'Jl‘.‘.’.”c

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averaging radius (deg

Conclusion 3: Global frequency oftropical

cyclones is more likely than not to either

decrease or remain essentially unchanged. Whi|e rainfa|| rates at the

Most studies maintain a low-to-medium confidence in Storm Centre cannot as yet

projections of global TC frequency. Global TC frequency is be re5o|\\/ed by m0de|5 with

aboutaslikelyasnotto eitherdecreaseorremainunchanged .

across all RCP scenarios. The median in all projections is Clear accuracy: 3PP\"0X'm3te

around -15 to -20% for the North Atlantic region. However, Simulations agree that rainfa”

compared to otherTC basins, there is a very large spread in _

the projections (as shown in Figure 5.13b) and so a larger near the C€l1'(|’€ IS more than

uncertainty. The low confidence in projections is consistent < <

with observations of global TC frequency. Figure 5.14 shows Ilkely to mcrea Se’

that there is little trend in global TC frequencyand the global

TC Iandfalls between 1970-2018.

Fi ure 5.13. Fi ure 5.13. Summar histo rarns and distributions of ro‘ected chan es in TC fre uenc ($12) from available studies,

3 E Y 8 P J E 4 Y

where the change in TC frequency for all Saffir-Simpson categories (0-5) combined is considered‘ (a) Distribution of projected

changes in global TC frequency with a global temperature change of 2°C. The red distribution indicates projections sourced

from individual studies compared with the projections from Emanuel (Z013), shaded gray. (b) Box plot distributions of projected

percentage change for each basin. The purple rectangle highlights distributions for the North Atlantic basin‘ Source: Knutson

et al. 2020

8) Frequency “Tropical Cyclones (Cat. M): Global b) Tropical Cyclone frequency Change Projections: By Bnsm

Mulmn: inlcrqmirlilv mngc; sih/95m pcrrcnlilcx‘, run ningc

5\" 120

_ All amp: F-imnnucl (2013) I00 3 .

M, — Einanutl (2013) l i

xi) 3

2-» 2

3 2o E 20 t = : ,

5 E 0 ~ H ‘

7. 35 E

I0 I l -20 . ll

40 : i i ; ; ‘

0 — — —— 2 s 2

~35 -30 ~25 ~20 ~lS ~|0 5 0 5 I0 15 20 25 30 35 Global N All W Pnc. NE Pac. N Ind S, Ind SW Pat

Pmcfll Chimsv n : I40 141 MD 120 mi 115 I22

Figure 5.14: Observed trends in (a) global tropical cyclone (Tc) and hurricane frequency from 1970-2018, (b) global TC

propagationl translation speed global frequency of tropical cyclones from 1949-1016 (gray shading indicates the 95%

confidence levels surrounding linear trend) and (c) global TC landfalls from 1970-2017‘ Source: Knutson et aL 2019.

a) Global TC and Hurricane Frequency c) (;|oba| Tc propagation speed

120 l . l l l v v l l ‘

I00 :20

r» so E

E 5

2 -5 I9

4 °° §

. fl)

5 40 g 19

2° — Anrmpicalcyclanws E

— Humcnno-Fnloo Trolllod Cyclones >- ‘7

o 0 . l l l l . l l

1970 1980 I990 2000 2010 2020

V... 1950 I960 1970 1990 I990 2000 2010

Veal

b) Global TC Landfalls (1970-2017)

25

I (ategory 1,2 I Category 3,4,5

20

E

15

3

7’.

g ‘O l ‘

5 l

1970 1980 i990 2000 2010

Year

‘ ‘ l

globally will likely increase. ,3 l ; _

N \\

.I Q \\

Earlier assessments had projected significant increases _' i s

in the intensity of global tropical cyclones (Emanuel 2007, § 3 _

Bender et al. 2010, Knutson et al. 2010). Most models ‘$3

indicate +1% to +10% increase in global TC intensity with _ \" » C-. s 7

2°C global warming (GFDL 2020), TC intensity is quantified 3 \\\\’-

based on two key variables: the maximum sustained wind ; N '

speed and the minimum central pressure. The higher the ' _\\ 2 X:‘ s\\W 1

maximum wind speed and the lower the minimum central _ - , _ ‘

pressure, the more intense the storm. Ti‘ ’

Knutson et al. (2020) indicate that most studies show \" V ’ <_ b‘ .‘

medium-to-high and high confidence in projections ‘ 3,. ‘-3 3 _

of increased TC intensity. For the North Atlantic region, _ ' 1‘ ' 3‘ ‘

intensity is projected to increase between +2 to +11%. (fr, ' ‘ - i

Figure 5.15 below compares the maximum wind speed and \"v, _ _ . V, ‘V

potential damage of simulated North Atlantic hurricanes /‘L . ‘ \"

under present climateandglobalwarming. Both histograms ‘ i‘ ~_\\ ‘

show a shift in the distribution towards higherwind speeds ‘\\ - “Q ‘ _

and potential damage, g V,,_ . ,. ‘ ._ 3 ,

W W l 9.1;’ , ‘ ~.i

Figure 5.15. Histogram of (a) maximum sustained wind speed ‘ I

and (b) cyclone damage potential for an points along aii

tracks for hurricanes simulated in the current climate (blue)

and a pseudo-global warming (PGW) climate (orange) using _ . . e

the Weather Research and Forecasting (WRF) model. Source:

Gutmann et al. (2018) \"

3) 0 040 . ‘

0.035

3 0.030 I

§

:1,’ 0.025

‘f.; 0.020

E

g 0 015

5 001:: Conclusion 5: while it is likely that overall

global frequency will either decrease or remain

°-“\"5 essentially unchanged, it is more likely than not

0.000 that the frequency of the most intense storms

20 30 40 50 60 (category 4 or 5) will increase substantially in

Max. Wind Sneed lm/sl some ocean basil“

5) 0.25

Studies still indicate no consensus on projections of global

\[123 frequency for the most intense storms (Category 4 or 5)

g and maintain a low-to-medium confidence in projected

3' increases. Knutson etal, (2020) indicated thatthe projections

§ “5 in very intense TC frequency were veiy sensitive to model

'3', resolution. This sensitivity is likely the cause of such a

gum large spread in projections for the North Atlantic (Figure

3 S.16b). The median projected increase is only marginal

5 for the North Atlantic region. However, there is medium-

0-05 to-high confidence in an increase in the proportion of

intense storms, relative to total storm number, that reach

mm categonj 4-5 intensity. The median projected change for the

0 2 4 6 8 10 proportion of very intense storms is +13%.

Cyclone Damage Potential

& 4

Figure 5.16. Same as Figure 5.13, but for projected changes in intense storm frequency (V2) from available studies. where the

change in TC frequency for categories 3-5 are considered. (a) Distribution of projected changes in global TC frequency with a

global temperature change of 2°C. The red distribution indicates projections sourced from individual studies compared with

the projections from Emanuel (Z013). shaded gray. (b) Box plot distributions of projected percentage change for each basin.

The purple rectangle highlights distributions for the North Atlantic basin. (c) Distribution of projected changes in the global

proportion of Tcs that reach very intense levels (e.g.. category 4-5). Source: Knutson et al. 2020.

3) very ‘mm Tmpwal Gym“: FM’ chug: PmJ.Wmms: GUM h) Very Inle1ixeTmpiciaI Cyclone Freq. Change Pmjeclitms: Hy Basin

Multan. .n uunilc mugs: imwwm pcwrmxlu. full rang:

24 we .

33 — Emauu¢|A2|)l3l (shaded) '

2a — H|gImrRn.lvlxxlA~l\\|<‘= zxxm grill) nxI697 v T

H, — Lower Res,Mod:l<|5()-Mlkm gndl 5”\" '

I6 0 3 I

§u E“ mo ? = 1

Qt: 5 ; I ‘

= 2 . j

o - ' H

. ' n -v x ” ll 2 .

1 2 l—

-—-*-J_'=______-—-.

Jo is n is :0 45 so 75 no ins izo '\"\"' Global rum muznm Nlnd Slnd swrac

\"\"“'” c\"\"\"F‘ n = 48 7| SI 47 47 A7 48

c) Percent Change in Pmponion ofcat. 4-5 Tropical Cyclones: Global

u —

I — l3manue|(2DlSl (shaded)

,1 I _ yImlI:vl'(c\\.lv14x1A:I»l<= Zxlinflriill

I —l.ow<rl!.cx,Mod=lxl5(HlK)kmxndl

m I

§ I

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. I

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l1

45 o is so as so 75 911 ms on us

PeIccntCmsnjze

ADDITIONAL CONSIDERATIONS ON ’

TROPICAL CYCLONE PROJECTIONS ~

In summary, the North Atlantic and Caribbean regions will I... ' 4

more likely than not experience more intense rain rates ,¢\"‘ -\"H.

associated with tropical cyclones and storms with greater 7 ' 3 1°30 10

. . Th . I fd h . d I, _.\\,...i....,,._ 40

intensity. ere remains ow con: ence in t e projecte , . .,_

changes in TC frequency, track and size as these projections ’ - \"i

are very sensitive to model resolution and physics and may / I *

also be case-dependent. ’

Knutson et al. (2020) noted that the influence of global

warming is likely to augment TC impacts, particularly storm e

surge. With rising sea levels, coastal regions may experience \\ .

higher inundation levels and increased impact from storm ‘ V \\ \\

surge from a landfalling hurricane (Knutson et al. 2020). ~

Predicting hurricane development may also prove more ’ _\\

difficult in the future. As TC track and size become more ..

variable under global warming, predicting when and where

a storm will intensify and make landfall becomes more ‘ ‘N;

uncertain (Emanuel 2017). Q

r \\’

E, 4’ 2. E.

0 . V‘ ‘I. '_ 1: '» *\{, . , ‘_g'-_:

\\~ ~.' i'.,*'-.—'.. \\-I. _.‘,_.

2 . - ‘W; »' ‘ 9 ~v ‘

‘ _ “L3 :/ _ ' . > » _ ‘

4 . .L 7 4‘ ‘ I i

1‘ ‘ ' In-\"a\" ‘fl ‘ _

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3 E E . . V ‘

.5 ’€.: _ ' _

0 ~ g H 2

. ,. if > . _ — I —

. .- it _ Q .

A _ V I '

INFORMATION BOX 5.1 THE PAR|s AGREEMENT and transport; waste: industrial

. AND NAT|ONALLY processes and product use (IPPU);

Nauonafly lfand tus(e|:UlLaLrJIgF)usedchange|t and

. oresry ;an agricu ure.

Determln

_ The adoption of the PA by the SECTORAL EMISSIONS

Contrlbutlons UNFCCC represents a convergence

fth H I» d I I AND LONG-TERM LOW

(NDCs) ° 9 °° 9‘ .\"’e \"99 .° ”’3‘”“V CARBON STRATEGY

reduce the risks and Impacts of

. . _ . climate change. The goal of this Under Jamaica's |ong—term low

§°::(':::2gT‘?#;:°r:ir:\\d\\::/:T|ace agreement is to limit the increase carbon strategy, emissions

|:temat,‘0na|Cem3;e fog in average global temperature to of 25.4%/1.8 MtCO2eq6

E _ I dN I wellbelow 2°C above pre»industria| reduction from baseline by 2030

Sn_V\"°\"mLeJ\"t_a an, lfjchear levels and to pursue efforts to (unconditional) and 28.5%/2.0

vfenfiezi \"\"Y‘er5'1yK‘? t f 7 limit the temperature increase to lVltCO2eq reduction from baseline

Est n '5' ma’ mgs 0\" 15°C above pre-industrial levels by 2030 (conditional) have been

OVERVIEW (Article 2.1.a), Under the terms of projected. More specifically,

T f _ T the PA, each member state has emissions of 18.5%/1.5 MtCO2eq

W0 0 the W105‘ 5'8“ Kai“ S'°ba' committed to achieving a balance reduction from baseline by 2030

d“a_\"e”ge5_ °_f the A”th’°l’°‘e”e between reducing anthropogenic (unconditional) and 21.3%/1.7

3'3 ei3f1_'C_a\"“E hung?’ bl/2030 emissions by sources and removal lVltCO2eq reduction from baseline

and 5t'3b\"'Z'“E E‘°ba'C\"ma‘§'_\" ‘he by sinks of GHGs in the second by 2030 (conditional) have been

5eC°“d hélfofthe Ce\"W'Y-Ci\"'Ca\")/r half of the centuiy through NDCs projected forthe energy sector. This

‘W0 5°'”\"°\"5 l’a‘h\"\"aY5r the SD63 (\"unconditiona|\" and \"conditiona|\") is likely a function of an increase

arid the Pails Agleemenfi (PA in the context ofthe SDGs, Locally, in electricity generation from

\"e5PdeC\"Ve'YlhaVe bee” e5tab'l5hed five major energy producingl low—carbon sources, particularly

‘°a dress mesa Ch'3\"e”ge5- consuming sectors (each with its wind and solar photovoltaics

own emissions pathway) have been and technological changes in the

targeted for emissions reduction. transportinclustn/.Howevenfurther

These include energy (including gains towards decarbonisation

water abstraction and transport) of the energy sector can be made

6 co, equivalents based on Global Warming Potentials wttn a 1DDnyear time nonzun iawpml lrom the lPCC Fifth Assessment Report (ARE).

from the inclusion of bioenergy biophysical effects of forest cover the COVlD~19 induced restrictions

as a renewable energy source. may amplify or counterbalance the will resolve our climate issues, but

Climate change, population growth gains of carbon sequestration in almost certainly will exacerbate

and urbanization are key drivers forests. Further, planting trees only common social and economic

of energy demand and must be offset past emissions (not all) from vulnerabilities that were already

thoroughly considered in setting deforestation. A poorly executed evidentin climate matters.

sectoralemissionstargets. forest expansion programme

The agriculture and LULUCF sectors muld, impact water flows by CONCLUSION

are significant net contributors to reljuclng runoff; and .dEpl,ete GHG emissions are closely

GHG emissions and climate change. 5°” water re5°”rce5' mcludlng nnked to nanonai deveiopment

However, a well—managed LULUCF 3.’°””\"Wa‘e’ recharge .due to pathways. Therefore, national

Setter al5° PT°Vlde5 a |'aft Of hlgher We.’ honsumptlon from commitments to achieve the long—

°Ptl°n5 t0 Sttengthen the Fe5P0n5e evapotranspllatlon’ Therefore’ term temperature goal of the PA

t° Climate Change: making it One any attémpt at large-scale forest”, depend on the strength and timing

Of the i'n05t lmP°ftaht 5eCt0t5 in eXpa.\".sl°r.l must .c.°nSlder l°c.al of the sectoral envelopes applied,

the total planned reducfoh lh pI'eClpltatlOI'|‘ C0l'id|tl0l'1S to avoid which must be based on a sound

GHG emissions. For instance, the Water Scahclty 'SS.l‘l.Es and other GHG inventoiy Forestw expansion

F’|'°P°5ed e5tahll5hment 0f 3000 advetae 50” Commons that may programmes have the potential to

ha Of mixed f°fe5t l5 expetted \"°‘.e.”\"a\"V Te\"”‘e. ecosystem further reduce the emissions gap.

to contribute an additional 43 resilience against climate change although a wider plan for success

ktcozetl by 2022- Addltlonallyi through the hydmloglcal Cycle‘ should surround the development

the rehabilitation of 3500 ha of of a sustainabie fol-esny sector

mahS\"°Ve f°'e5t in 5°Utl\" Clatendeh (with a robust protection policy) that

l5 likely t0 C0nt|'llI>Ute 4-9 MtC02etl~ NATIONAL demands wood (wood products)

Slmllatlyi Ongelng Wetland COMMITMENTS IN THE and gives forests a value. Careful

restoration in the western Jamaica CONTEXT OF COVID-19 consideration should also be given

l5 e><PeCteel t° Vastly lmP|'°Ve land The Onset of the acute public to thetype offorest(e.g., plantation

5l|'|l<S (bll-Ile Ca|'l30n)- ln addition health Crisis triggered by vs agroforestry) being establishes,

tn P|'0V'Cl'n8 mefe Cn5t‘effeCt|Ve the Col/lD_l9 pandemic has how forest expansion ‘will ‘change

Climate Change mitigation 0Pt|0h5 undwhtabl l » the landscape,and theimplications

. y resu ted in strong

Telat'Ve t0 the enetgy and t|'an5P°|'t maCl,D_eCOnOmlc headwlhds with of these changes for other sectors.

S?§‘5?é'e‘“§‘Z?Z‘5”Jr“flifnilii ‘\"‘P\"‘a“°\" ‘°' \"W 5ZLZTSS!S§2\}?fliSli§$’§p'1E§E‘S.§E

(biodiversity, soil conservation, and ggsgfhrrfentonpofitfes Dfjhuerllnghatnhi sysvtsi-ns, improving nitrogen use

Water cyClmg)' pandemic have significantly altered efl'c'e\"cy' dletaw cl'a\"ge5' and

Altheugh t\"ee‘PlantlnS Pf0Vlde5 the demand for energy resulting eml55_l°n5 from ma””re‘There are

several social and environmental in daiiy giobai CO2 emissions al5° 'mP°\"taht °PP0|'tUn't|e5 and

benefits, it should not be viewed reduction of approx\];-na\[e|y 20%. le55°n5 f'°\"n the 00VlD Pandemlt

as 3 Simple Solution to achievlng Although the exact lmpact of the thatshould beconsidered alongside

national emissions reduction coviD,19 pandemic on Jai-naicars the def/elellamental80al5-lo‘/efallia

ta\"8et5- and tl'|e|‘ef0|'e 5l'|0l-Ild n0t development strategies (including redumon '\", emlsslcfns does

overshadow other sectoral actions emission i-educnoniai-gets).-emains net Only minimize the n5l<5 and

that may PT°Vlde greater heneflti to be quantified, what is certain is \"\"Pact5 °l Cllmat? change’ but

Climate Pellty ha5 largely f0tUSed that mitigation efforts must now be also, has mher Posmve Soclal and

On the ta|'b°n Setluestfatlnh addressed not only in the context ,e\"V\"'°\"me\"tal lmpacts Such as

potential of forests to mitigate of the SDGs, but also that of the \"\"P’°\"ed heath ?‘,a\"“a\"‘5 3\"“

global warming. However. the post COVID era. It is unlikely that e°°”°\"‘\"°PP°\"“\"'t'e5~

' ‘I . . uh

_ W ' I - A '.«:.,-

./ -.'~—- .» 1.\" .\" '- E

_ Ix, 4 V ,. I '3 a ,

., ,- ,4 , - '1. -. ~ ,

. icy :35.-—:.*,.' .

__ » . (- ,“ tat?!‘ ' = .,,

_ ‘ 3,‘; _ 7.. ,-’ 2:

~ _1.\" a .. _». ( - - V , ..

0*. ,.‘z s’, t V 7 — 1.‘

_’ - ‘I ‘ as‘ 03.\"; ‘ \{g ‘I’ .a_-:, ‘ _‘w‘ .,_,'r I ’-_'a -at — at

my’ ' ,t —_ ,5’; .4,‘ ‘j \\ ' ' /I’ \"_ -'.‘‘t.\\ ‘'4' » ' ’ 5.9\"

r I,‘ ’ ', ’ “E ' \"' \" 1- . ..

. aw...‘ , I . . F , _- at-~,;v.'

V 1.‘:-u .. _ t _» .-;.=_-

. g; 1*” _- j ‘-9.’ 1’ .

_‘,p' . xpt I ._ * -, t .- Q, ‘-. « ti-,_.

-V ., -— 3 _, 9.. to y:

4. .. ~,-, . , . i ' . ~ I ,' ; '

‘ .. i ,1 ~'.. - x «.. ‘‘‘,..;*~

-1“ 4.: ’ ' ' \\'

7,1\" w,\"3‘.':'~‘_«>: ,. _ , _ Z, . r \"’ “E.

I ’ ‘ - ’ ' :>. ,9 E .,

‘- tr , t t C‘ ... -~ rf.‘ .«'

‘ » \" 3\" ‘ -’*1',‘,i '. N‘ V I.

‘ ‘ .‘_v-., . ' ' .—\"i 7% ~ .;«. .c. ‘ as . _

7\": :3. t ';-‘i .--‘ T ‘ e?~’:€4“s-a . ..; 3 . V,

°*s— -.4 ’ * ~

4 ' *1. M2 ~53. ' and - ,» ‘ -. — 1;,‘ ’.=. -~. . -‘- ‘ ~

6. GENERAL SECTOR IMPACTS

contributing authors: Pietra Brown as lamaica that are very vulnerable to climate and weather

extremes including very warm temperatures, floods and

. droughts, intense hurricanes and rising sea levels. This

6'1 lntroductlon vulnerabilityisexpectedtoincreaseinthefutureduetothe

_ _ _ _ projected changes in both the magnitude and frequency

V'5‘°\" 2030 Jamama ‘ Na\"°\"a‘ De\"e'°Pme”‘ Fla” a‘m5 ‘O of recurrence of these c|imate—related threats. Jamaica's

l\"al<9 J<3m_al_Ca 3 W5‘ W0r'_d COUTWY b)’ 1'7? Yea’ 2030- ‘ts climate sensitivity is across all sectors and, ifnot accounted

”a”_°”a' \"'?'°\" 5ta‘e’\"e_’“ '5 ‘O Wake '\]a’\"a'Cai the Place Of for in planning, will continueto |imitJamaica’s development.

choice to live, work, raise families and do business\" (PlOj , _ _ _

2009). It has four national goals, with Goal 4 focusing Th‘? Chemer d_eta“5' m tebmar form’ the mam, Ways m

an Jamaica having a healthy natural environment’ and which climate impacts a number of key economic sectors

sustainable urban and rural development through the and lmportent meme“ areas 'd_e”t'f'ed by V'_5'°n 2030

sustainable use of natural resources and, hazard risk Jem_e'ea as relevam ‘O the Jamema” Way of Me and to

reduction and adaptation to climate change. There is, ”a\"°”_a| development‘ The tables are drawn from _an

thetefotev recognition that Climate Change Presents extensive review of the literature and are presented with

immense risks which can threaten and even derail national mu references‘ The 'nf°r_me\"°” 'n the (ewes '5 meenem

developmenttat Small lstand Developing States (EDS) Such be seen, not as a||—inclusive, but rather as a starting point

for further research for those who are interested. Whereas 6_2 Impacts of climate change

eveiy attempt was made to capture data and information

specific to Jamaica and current to the reporting year, this 0I'I Key Sectors and

was not always possible, and as such, other available -

related data and information (eg. for the Caribbean) are Important Thematlc Areas

reported. Furthermore, while the impacts of climate change

are overwhelmingly net negative as shown in this chapter, 6.23‘ SOCIAL AND ECONOMIC

there are indications that climate change can result in DEVELOFMENT

some positive impacts and opportunities for SIDS like _ . _ _

Jamaica. However, these impacts forjamaica and the wider C\"ma‘9_Ch§\"_Ee ‘5 3 deVe'°i3_me”t '55Ue 3\"d has We P019193‘

Caribbean are often not wel|—documented, and further ‘° de’3_\" V‘5'°“ 2030 J3f\"a'C3 §ff°\"‘5' The Pe’5'5‘e\"‘ 3”‘

research is needed to better understand and characterize i\"C|'§3SIng threat of climate impacts as suggested by

the nature and timeframe ofthese impacts. Where relevant, P\"°J9Ctl0\"'5 Of _'“CT9§5'\"E ISWIPSTSIUVSSI e><l\"€'“9 9V9\"t5

positive impacts of climate change have been reported in 3\"“ 593 '9\"e' \"'5e_\"_‘“\" 5eVef9')_’>\"\"Pa>Cl the I053‘ 9C0\"0m)/I

the following sections. workers‘ productivity and critical infrastructure located

along the coast. In particular, with respect to infrastructural

development, Vision 202-iojamaica in part premises national

development on an ‘expansion and improvement of

. . systems for land transport, including roads, rail and public

Cllmate Change ls 3 development transport, inland and overseas air transport and service’.

issue and has the potential to The continuous development of coastal infrastructure

_ y y g for housing settlements and road networks increases the

derail Vision 2030 Jamalca eff0rt5- countiys vulnerabilityto the impacts of climate change due

to the challenges posed by sea level rise and extreme storm

events. See Table 6.1 for further details.

Table 5.1. Impact of climate change on social and economic development

Climate Change Impacts of Climate Change on Social and Economic Development

Variable/Extreme

Events

Decline In population health and associated economic losses. Extreme temperatures are associated with

mcreasmg the emergence of vector borne diseases such as the Chikungunya virus. This virus transmitted by the same

Tempeyattme mosquito (Aedes aegypti) responsible for dengue fever and malaria severely impacted Jamaica's economy

in 2014. It was estimated that 60% of the local population was affected, with a loss of productive time for

recovery (5-1 0 days) and an economic loss ofJMD30 million (Gammon 2014).

Extreme temperatures and workers’ productivity. Increasing temperatures have the potential to threaten

social and economic development in the country. This is due to the correlation with body temperature, work

performance and alertness (GOJ 2011, 1\]. This has implications for outdoor workers such as sportspeopie,

farmers, manual laborers and indoor workers and students in classrooms without cooling aids. Higher

temperatures can lead to low productivity. This is due to the fact that heat exposure can affect physical and

mental capacity and lead to heat exhaustion or heat stroke in extreme cases.

Increased Incidents of sea level rise may displace coastal communities. Increased incidents of sea level

Storm Surge“ rise and storm surges would lead to displacement of the approximately 60% of the population which lives

’ within 5 km ofthe coastline (STATIN 201 1, 391). Areas like Portmore, which is a drained low-lying coastal area

555 LEW‘ RISE (population of170,000) would be at risk from flooding \[Richards 2008, 67).

inundation of coastal areas, settlements, loss of life and property are also features of continual coastal

development which exacerbate risks from these events (Richards 2008, 32, 2).

Coastal erosion could destroy economically critical Infrastructure (ports, tourism centres, airports, road

networks\] since 90% ofJamaica’s GDP is earned along the coastal zone (GOJ 201 1, 390). This could result in

massive economic losses for the country (CARIBSAVE 2009, 29).

Climate Change Impacts of Climate Change on Social and Economic Development

Variablelixtreme

Events

Frequency of extreme events has implications for freshwater availability. With a rise in the occurrence

Storm‘ Humcanes of extreme events, freshwater may be less available or it may be contaminated which will increase the

Dmuggts Tmpma, ' susceptibility, especially of some remote and rural communities, to infectious diseases that have minimal

Cyclones Floods public health care infrastructure (CAROBSAVE 2009. 35).

Improper land use/development in watershed/flood-prone areas increase vulnerabilitiesto landslides

and floods (ESL 2009, 67).

A deterioration in social and economic circumstances might arise from adverse impacts of climate

change on patterns ofemployment, population mobility, wealth distribution and limited resettlement

prospects (CARIBSAVE 2009, 35).

Creation of Jobs and Enterprises. Climate change adaptation and/or mitigation measures and their

associated supporting policies have led to the creation of new jobs and enterprises. For example, a policy

and implementation focus on renewable energy in Jamaica and other Caribbean countries has led to the

creation of greenlobs (ILO, 2014).

The unpredictable nature of climate change may affect insurance sector's ability to calculate risk.

Smrma Humcanes Weather and climate are ”core business\" for the insurance industry. Insurers underwrite weather-related

Tmp‘c\"a', Cycmneg ' catastrophes by calculating and pricing risks and then meeting claims when they arise. Therefore, an

“ unpredictable climate has the potential to reduce the sectors capacity to calculate and price this weather-

related risk (Coleman ZD03,1).

Theroleofinsuranceinunderwritingweather-related riskisanimportantcomponentofthenationaleconomy.

Any reduction in the industry's ability to underwrite weather-related risk will have serious ramifications for

vulnerable countries (likejamaical where climate and weather risk is greatest (Coleman Z003,l )4

The unpredictability of climate change is forcing insurers to develop adaptation strategies which

includes putting a price on current and future risks (CCRIF n.d.).

Banking sector would be affected by the adverse impacts of extreme climatic events. Banks will be

affected by climate change mostly indirectly to the extent that general economic activity is affected (Furor et

al. 2009. 11). It is estimated that up to 5% of market capitalization could be at risk from the consequences of

climate change (Furor et al. 2009, 11).

The effects of climate change on banking companies would be direct (for example, through extreme events

that put facilities at risk or indirect through imposed regulations or shifts Il'l social preferences) (Furor et al.

2009, 11).

Increased Opportunities for the Establishment and Uptake of New Finance and Insurance Products

and Programmes. Anecdotal evidence suggests that climate financing (from overseas and local sources) and

the number of climate-related insurance products have increased over the past decade.

Finance: Given their vulnerability to climate change, developing countries have been, and are expected

to remain, recipients of a majority of climate financing flows. While Jamaica has received climate-related

financial flows through a wide variety of channels (PIOJ 2020), the level of resources needed for climate

change adaptation in/by developing countries is much higherthan international funds committed or available

(Meirovich 2013).

Insurance: The many and varied impacts of climate change present increased opportunities for insurers

to develop risk mitigation portfolios which promote economic security by offering reliable protection. The

caveat, however, is that premiums would need to be (viewed as) affordable by target markets. Insurers

are called therefore to make climate risk a part of their management decisions, meaning they should use

their understanding of risk and climate science to mitigate the systemic effects of physical climate risk for

themselves and others (Grima|di et al. 2020).

as i The State ofthejamaicari C|>mate(Vo|ume|lIl lriforrriaticiri or ,Sl . ,

5.2_2 |MPAc'|'s 0|: c|_|MA'|'E CHANGE ON Additionally, more frequent extreme events, such as storms

EDucA‘|'|o|\\| and hurricanes, will further damage school infrastructure

. . . , _ and reduce classroom time, thereby negatively impacting

V\"1S.;Znb2023(g‘J\]am|a|':a Sefts E0 epsurgmat eyery Jamamag the time needed for guided assistance to promote student

:: ' hy h hwll ave ECSEC eag\"ntg .enV|'r§nm:nt1?nh success, particularly in Mathematics and Science subjects.

fiat: 'g ‘.5: 00d pafsslng ‘ 5“ lea? mi” fig ng '5 ' Furthermore, extreme events can contribute to reduced

3 etmtahms an a Or:'gt: anguageit mate C angf may academic learning time as many schools across the island

‘mpac e Success 0 ese. comm men 5 as ex r_e':ne are used as shelters. See Table 6.2 forfurther details.

temperatures affect concentration and student productivity.

Table 6.2: Impacts of climate change on education

Climate Change Impacts of Climate Change on Education

Variablel Extreme

Event

Increased water shortages can lead to reduced water quality, leading to an increase in water borne

Dmwms diseases (such as dengue fever and malaria). which can result in a decline in children's school

5 attendance and health. Children in female-headed households in rural areas are particularly affected (IGDS

2013, 20).

Children spend more time with labourltime intensive water carrying responsibilities at home, rather

than attending school (IGDS 2013, 8).

Longer drought periods may lead to water scarcity, threatening school terms across the island.

Drought like conditions in 2019 led to Governmental distribution of over lO0 tanks to schools across the

island to reduce the need for early closure or a shortened school week (The Gleaner 2019).

Infrastructural damage forces the closure of schools and shortens the school term, reducing the

Humcafles/storms numbers of days students attend classes. Hurricanes affect schools by damaging infrastructure, power

lines and forcing the closure of many schools, as exemplified in the aftermath of Hurricane lvan lnlamaica,

where 1000 schools were damaged, affecting 204,000 students. (Spencer et al. 2016, 6).

Hurricanes, occurring within the academic year, affect performance of students in standardized

examinations in the sciences across the Caribbean. This is because the number of hurricanes occurring

during the year increases the likelihood that school days and number of days of classroom time for guided

teaching, practicing problems and laboratony experiments are reduced (Spencer et al. 2016, 4).

Performance in standardized examinations for humanities subjects, however. are not affected by the

passage ofa hurricane (Spencer et al. 2016, 4).

Worsening post-disaster socioeconomic circumstances negatively affect school attendance. After the

passage ofdisasters some people become worse offeconomically, so they may remove children from schools

and put them to work to reduce economic burden and increase family income (Spencer etal. 2016, 8).

Reduced academic learning time as many schools across the island are used as shelters. Following the

passage of Hurricane lvan in September 2004, approximately i0 schools were still in use as shelters across

the country 1-2 months after the event (FlOl 2004).

Increasing temperature can disrupt the learning process. This is because of the correlation with body

Imeamg Tempemwre temperature, work performance and alertness, which has implications for students in classrooms without

cooling aids (Wright et al 2002,‘ Dapl et al. 2010). Higher temperatures can lead to lower productivity. This

is because heat exposure can affect physical and mental capacity and lead to exhaustion or heat strokes in

extreme cases. Increasing temperature lS a potential threat to the educational development of youth.

Sea Level Rise Schools are among infrastructure vulnerable to sea level rise. Schools and other educational institutions

are among the most vulnerable infrastructure that are likely to be adversely affected by sea-level rise and

floods, given the proximity ofthese institutions to the coast (Bureau of Statistics 2007).Jamaican schools such

as Tltchfield High School, Donald Quarrle High School, Ruseas High School and Marcus Garvey Technical High

School are examples of such institutions.

5.23 |MPAc'|'s 0|: c|_|MA'|'E CHANGE and the social roles that they are expected to fulfil as

0N GENDER women (caregiving and mothering). The review of climate

Gender is a social construct lnfluencin roles and change impacts on gender also addresses how gender

. .. . g determined roles of men and women increases their

responsmlmes of men an.d women‘ In Jamalm ‘_”°men vulnerability to danger at different stages of a hurricane.

are more vulnerable to climate changevthan their male See Table 63 (Dr further dew”;

counterparts due to their socioeconomic circumstances

Table 6.3. Impacts of climate change on gender

Climate Change Impacts of Climate Change on Gender

Variahlelixtreme

event

Huiilcanes,Floods. Women's socioeconomic circumstances are worsened during the times of disasters. Generally,

Tropical Storms there are more females than males in the population and female-headed households represent 14.4% of

the population living in poverty (PIOJ and STATIN 202i). This makes females more vulnerable to disasters

since they experience higher rates of poverty and unemployment than men (Dunn and Senior 2009, 14).

These vulnerabilities are expanded in times of disasters when their risks are increased. This is manifested

through higher levels of poverty, extensive responsibilities in caring forothers, domesticviolence and further

fulfilment of duties considered ”women’s work\" (Dunn and Senior 2009,10).

Older men and women's vulnerabilities are amplified in temporary hurricane shelters. Older males

may experience depression in shelters due to unfamiliar surroundings. They may also be forgotten in the

distribution offood. Older males and females may need adult diapers due to incontinencelllunn et al. 20183,

i9) They may also need assistance to go to the bathroom because ofreduced mobility (Dunn etal.2018a, 19).

Gender bias in education. In rural areas in Jamaica, there is a gender bias in the education of girls and boys,

as boys are more likely to be removed from school than girls to assist with recovery efforts alter disasters,

and to work on the farm (IGDS 20’l3, 8).

HIV rates increase among women in times of disasters. especially among those who engage in

transactional sex as a survival strategy (Dunn n.d.).

Human trafficking increases in the event of a disaster, disproportionately affecting females. The

majority of trafficking victims are women and girls, 35% or whom are at risk of being trafficked ior sexual

exploitation, while 25% of men and boys are at risk of being trafficked for forced labour. The human

displacement impact of hazards such as hurricanes, floods, droughts and earthquakes increase the risk of

human trafficking (Dunn 2013,1l).

Women's mortality increases when disasters occur. Many women sacrifice themselves during disasters

when their own traditional caregiving roles hamper their own rescue efforts (Dunn and Senior 2009, 3).

This reality also reflects women's social exclusion because they are less able than men to run, and have

behavioural restrictions that limit their mobility in the face of risk, especially since their voices are often not

as respected as the men's in their households. On the other hand, men suffer higher mortality rates because

they take more risks trying to save themselves and their families (Dunn 20l 5).

Women and girls are particularly vulnerable in post disaster situations. This is because they lack land

and other assets that could help them cope. Therefore, they are more likely to face food shortages, sexual

harassment, unwanted pregnancies and vulnerability to diseases and could be forced to drop out of school

or marry earlier (Dunn 2015).

Women are largely more vulnerable to disasters than men. impoverished women who are usually single

female heads of households are more vulnerable to a disaster due to limited resources because of lower

wages and larger numbers ofdependents including several adults and children (Dunn et ai. 20‘l so, 23).

Rural women's socioeconomic circumstances affect their abilities to respond and recover from

disasters. As a group, they have lower incomes because yoo opportunities are more limited in rural areas.

Many rural women experience various forms of inequality related to their gender roles in the household,

restricted access to credit to finance micro-businesses and more limited support services (Durlri 20’l 3, ’l0).

Climate Change Impacts of Climate Change on Gender

Variable/Extreme

event

Hurricanes, Floods. Greater loss of income for women in rural areas due to breakdown in road infrastructure, Women in

Tropical Storms rural areas also experience greater income losses than their male counterparts from the breakdown of road

(mnmj infrastructure after the passage of a disaster due to their role in market vending and their dependence on

road transportation, which would affect their food and livelihood security (IGDS 2013, 8).

Women will experience lower resilience after disasters given weaker socio-economic and lower asset

holdings; men are seen as being better able re-establish their income streams after disaster (IGDS

2013, 8). The gendered job profiles for both men (building and road construction, auto mechanics, tourism

transportation) and women dictate the recovery time for both sexes. Women engaged in stereotypically

gendered jobs (such as nursing, hospitality worker in tourism, teacher, domestic workers) are more likely to

be paid less for their work and lose lobs after a hurricane than their male counterparts. Men are more likely

to findlobs in post disaster reconstruction because of these realities (Dunn et al. 2018b, 25).

Women in drought affected areas have time consuming water carrying responsibilities which limits

Dmugms ability to earn and diversify her income. Women and children have the main responsibility for securing

water supplies daily from springs or other sources. A lot of commuting tlmelwork is spent performing such

duties. This has considerable Implications for how they use their time because of the distances they have to

travel to get water (lGDS 2013, 20).

(b) Gender determined roles of men and women at different stages ofa hurricane and associated risks

Both sexes assume that men will batten down windows Women as family caregivers are expected to stockpile

with ply board, clear drains to avoid flooding, prune trees, water for domestic use (consumption, hygiene, cooking),

ensure that their families are safe, and fix and repair sufficient food, medicines, flashlights, batteries. (Dunn et al.

leaking roofs (Dunn et al. 20'lSb, 3'll 2018b, 31)

Women, especially in rural areas, may have to go farther to

get household water. The distance ofthe water source from

5 their home and the environment may increase their risk of

5 being robbed or raped. (Dunn et al. 2018b, 36)

§

3 The needs of homeless persons, the majority ofwhom Women and girls who are displaced from home are more

E are men and may also include children living and working vulnerable to sexual violence and sexually transmitted

on the street, should be considered for temporary shelter diseases in shelters than their male counterparts. These

(Dunn et al. 2018a, ‘l 9). women face several risks including the outbreak of

diseases, especially when shelters are overcrowded, and

have inadequate and poor sanitation facilities (Dunn and

Poor and disabled men who are socially marginalized are gem, 20,3914)‘

at an increased risk of danger during a disaster (Dunn et al.

2018!), 33)

Men's roles as protectors can increase the rlsl-. of death or Women in their reproductive years will need extra water to

injury, during and after a disaster because they will stay manage their menstrual cycles (Dunn et al. 201 8b, 36).

U’ at home to protect property and family assets (Dunn et al.

3 ml Ea. l9).

§ The social perception and expectation of men being the

biologically ”stronger sex” and heroes, means that they are

more likely to take risks and action to rescue weaker men

or women, and to protect assets (Dunn et al. 20l8o, 32).

L

(b) Gender determined roles of men and women at different stages ofa hurricane and associated risks

Men Women

Practical needs fornien would Include access toaiob Practical needs for women may include the need for

to look after themselves and fulfil their role as family employment and access to essential resources to care for

g prtiividers. For male farmers, this may include access to their families (e.g., food. water, access to health services)

3 funding to repleht crops and repurchase llvestock (Dunn et (Dunn et al. 2018b, 30).

2 3' 20\"” 30* After a hurricane. a woman's workload may increase

5 significantly. Women caring for babies, elderly. sick and the

2 dlsabied may need more water for hygiene and sanitation

of the household, Including clearing debris inside the house

when it is flooded (Dunn et al. Z018b,36)

6.2.4 IMPACTS OF CLIMATE CHANGE ON SECURITY

Vision 203OJamalca aims to ensure that‘by theyear2030 everylamaican will live in a safe community and the security forces

will have modern and effective ways of maintaining law and order‘. Climate change is a local national security issue because

it exacerbates local vulnerabilities and developmental issues. Increasing temperatures, rising sea level and more frequent

storms can lead to increased incidence of violence or protests due to competition for scarce resources and little relief from

harsher environmental conditions. Increased civil unrest due to environmental stress may disrupt socioeconomic stability,

pose a threat to the safety of individuals and communities, and would likely place a heavy burden on the security forces.

See Table 6.4 for further information.

Table 6.4. Impact of climate change on security

Climate Change Impacts of Climate Change on Security

Variables! Extreme

events

Sea Level Rise Small Island Developing States. particularly the population and infrastructure existing along

the coastlines, are vulnerable on account of rising sea levels. beach erosion and under-resourced

emergency response agencies (Barrett 2014).

Relief efforts of military services in the Caribbean may be hampered by sea level rise. Caribbean

operational and training facilities of the military service that launch relief efforts are highly vulnerable to sea

level rise because many of them are located along the coast, 50 sea level rise and more intense storms can

lead to destructive inundation and erosion of coastal facilities. it can also Impact clean water supplies and

lead to an increase in maintenance costs of these coastal facilities (Barrett mi 5).

Regional counterdrug trafficking efforts may be hampered by sea level rise because of the location

of these facilities to coastal areas. This deterioration in facilities will impact on military readiness and their

capacity to continue in the American led counterdrug trafficking fight in the region since Caribbean security

facilities often serve as launching pads for maritime patrols and interdiction operations (Barrett 2015).

Caribbean economic pillars are climate sensitive. Caribbean islands are partitularlyvulnerable to sea level

rise because of the lnextrlcable link between its key economic activities (tourism and agriculture) and their

heavy reliance on extremely climate sensitive areas such as seas. beaches and ports. which are suoiected to

climate change impacts such as oeach erosion, sea level rise and port degradation (Barrett 201 5). lniamaica,

90% of gross domestic product is produced within the coastal zone(GDJ 2011. 39i).

Coastal penal facilities may likely relocate further inland to avoid possible inundation by rising

seas. in 2017, as a response to Hurricane Matthew, 145 inmates were relocated from the below sea level

correctional facility, Fort Augusta in st. Catherine, due to the prls0n's vulnerability to storm surge and rising

tides (TheJamalca Observer 2017).

mi: l The State artheiamaican climate (volurnellil information cir .si .

climate change Impacts of climate Change on Security

Variables! Extreme

events

Increasing storms and hurricanes can damage critical infrastructure. An increase in these extreme

Hurricanes, storms events can lead to the destruction of critical infrastructure such as ports and roadways. This is disastrous for

many island nations, and can severely disrupt their economic progress for many years (Barrett 2014).

More intense tropical storms increase search and rescue efforts of the Caribbean military. The

caribbean military forces in support of regional bodies like (caribbean Disaster Emergency Management

Agency) CDEMAcan expectan increase in requirements forsearch and rescue efforts and recoveryoperations

in the wake of more intense tropical storms (Barrett 2oi4).

Military resources will be stretched beyond capacity to respond to distressed communities. The

military will not only have to build their respective capacities (training and equipment) to assist distressed

communities but also need to work with other defence organizations like the lnter—Amerlcan Defense Board

to pool resources. share best practices and hone specialties (Barrett 2014). As storms become more frequent

and severe. the capabilities of Caribbean military and coast guard seNlce organizations to provide reiiefand

law and order to overwhelmed civilian response organizations will be undermined (Barrett 2015).

Storms can lead to increased migration. There is a possibility of increased migration of residents from

extremely fragile caribbean nations into neighbouring countries due to an extreme event (Barrett 2014).

More frequent extreme events increase threats to livelihood and security. Extreme events such as

hurricanes can lead to destruction of commercial infrastructure.particularly along the coast leading to Job

losses (Dunn 2013, ii). Additionally, agriculture—based livelihoods are particularly threatened by extreme

events. Eighty percent of Jamaica's rural population is involved in agriculture (small farming, fishing and

livestock rearing). Farmers are also among the nation's poor (75% of them rely on government aided

Programme forAdvancernent Through Health and Education) and are extremely sensitive to climate change

because of the related loss of income which leads to crop loss and lower yields (IGDS 2013, i7\].

Household workers in Jamaica are very vulnerable to climate change because extreme events such as

hurricanes can lead to destruction of their workplaces (homes) and job losses for their employers (Dunn

2013. 11).

Droughts Droughts can exacerbate civil unrest due to limitations in water resources. Longer dry periods especially

pose a distinct challenge for governments which will have to invest more heavily in water management

systems and infrastructural improvements to keep this most critical resource flowing. ifthey don't. civil and

even military public servants may firid themselves handing out emergency packages to the most affected

citizens (Barrett 2m 5).

Greater risk to public safety accompanies more frequent climate extremes. Longer periods of drought

that result in dry loose earth increase chances of flooding and landslides across vulnerable areas. This

can have consequential impacts on security. especially when national disaster and security responses are

unprepared \[Barrett 2015).

Increasing temperature could result in heightened aggression. There is a positive correlation between

increasing increasing temperature, body temperature changes and the production of adrenaline and testosterone

Tempemures hormones (flightand fright hormone). increasing bodytemperatures maytherefore lead to a rise in domestic

and physical altercations (silva, pers. com.. zoie).

5.25 |MPAc'|'s 0|: c|_|MA'|'E CHANGE ON food prices. The government, particularly the Ministry of

AGR|cuL'|'uRE Agriculture and Fisheries (MoAF) and its partners, through

. . . . _ several climate—resilient projects and programmes, has

Agnc.u|mre Bfne ofthe m,?eS§)'er;gj0°frTantec°n°;n'c Seec_t:rSt'n been working collaboratively with farmers to increase

J7agne:':a't:mp oymg OVERMHQO Z0???/|'iS'.atn \[ET 3 Utes the uptake of climate smart practices such as rainwater

' 0 e ecgnomy (J . . ' \"\"5 0 n “S W’ harvesting. construction and rehabilitation of ponds and

Commerce Agriculture & Fisheries 2018). It IS also one of drip irrigation

the most climate sensitive sectors. Vision 2030 Jamaica ' e .

‘aims to improve the agricultural sector by increasing the The ll‘/e5l°Cl< 'n¢“\{5\"'Y '5 3l5° Vulnefable Cl|m3te\"'5l5t9d

local farmers’ productivity by providing access to the best impacts. In the Caribbean, livestock lstraditlonally managed

technology and sustaining a healthy natural environment’. ll\". P35“-\"95 With?“ Wale’ arld Shade alld W b\"°\"5\"5- 0&9”

Projected increases in temperatures and an overall decline W'th°'—'l 3\")’ C_°°\"\"S 3'd_5 5- Exlieme l9mP9l'3tUiE5- d|'°U8ht5

in mean rainfall particularly in the traditional growing alid Om?\" Cllmate _V3f|3bl€5 may T9‘5|-'lY_ ll\" (3l'|j0\"8V Other

seasons as We” as longer drought periods and more things) heat stress in livestock, resulting in declines in egg.

frequent extreme events will result in crop losses, lower \"\"'”<_3nd meatqualltlty afld ClU3l|tY»55ET3blE6«5 70\" fU|'tl'|9|'

productivity and produce scarcity, which can drive up demis-

Tahle 6.5. Impact of climate change on agriculture at food security

Climate change impacts of climate Change on Agriculture (Crop Production, Fisheries, Livestock, Food Security) and

Variables/Extreme Food Security

Events

CROP PRODUCTION

Extreme temperatures can lead to growths in agricultural pest populations. increasing temperatures

mereaeing lead to an increase in the population ofthe Beet Army Pest Worm (an agricultural pest which thrives in harsh

Temperatures conditions wreaking havoc on escallion and onion crops).

Rising temperatures are expected to result in reduced yield and growth ofweeds. pests. bacteria and

diseases (UNECLAC 2011, 26).

Citrusand root crops will continue to be affected by changes intemperature and precipitation (CANARI

2009, 18). Changes in agro-climatic conditions (temperature and precipitation) lead to reduced yields in citrus

and root crops lGOJi 2021 l.

The years 2018 & 2019 had the highest temperatures on record which contributed to crop loss especially

during each years dry spell (Voung, pers. comm.. n.d.).

Drought threatens local agriculture, which demands 75% of local water supply (CARIBSAVE 2009, 29).

Drought Soil degradation and loss offertility due to droughts is likely (CARlBSAVE 200934).

More severe drought conditions will affect local food security. With projected decreases in precipitation

up to 40% and up to 2.8”C rise in temperature expected by 20805, many domestic crops will be under stress

and food security will be threatened (GOJ 20l’l. 262). The very severe drought in 2019 led to a decline in

crop yield which resulted in food shortages and an increase in prices for produce. It was most severe in the

parishes of Manchester and St. Elizabeth where most oflamalca's vegetables are sourced. Prices increased

to as much aslMD150 per pound for carrots and lMDl 20 per pound for cabbage (TelevisionJamaica 2ol 9).

Severe drought conditions may increase government support to local farmers. Ongoing drought

conditions from 2015 and into 2016 resulted ll'l bushfires in St. Andrew. The Ministry of Agriculture and

Fisheries allocated special funds to the Drought Mitigation Programme which included water deliven/. water

tank distribution and drip irrigation systems and increase in greenhouse capacity lVoung n.d.).

The hot, dry conditions in 2019 resulted in losses in the sector such that the Ministly of Industry, Cornrnercei

Agriculture and Fisheries spentJMDl 5 million to assist affected farmers as a part of the Drought Mitigation

Programme. Also, some JMD1935 million was allocated by Members of Parliament to assist in providing

inputs including drip irrigation systems to farmers islandwide (Voung n.d.; Trotman 2020).

E Commercial broilers use coomg aids/extractor rans; increased temperatures will increase energy demands which could potentially lead to increases in cost or

products.

Variables/Extreme Food Security

Events

Increasing Higher temperatures and dry conditions may increase wildfires which will continue to affect the

Temperature and agricultural sector. In June and July 2019 (the hottest months on record), firefighters battled bush fires

Declining Rainfall across several parishes as a result of high temperatures and dry conditions due to a lack of rainfall. During

these months, in the parish of St. Man/, firefighters responded to over 230 calls, most of which were for

bushfires UCN 2019). In addition, 99 farmers in the parishes of St. Andrew and St. Thomas sustained an

estimated loss of $20.9 million as a result of bushfires (PIOJ 2020). In August of the same year, crops of 47

farmers in Flagaman, St. Elizabeth were destroyed by a fire exacerbated by the drought conditions. A total of

200 acres were impacted, with losses estimated at $45.0 million (PIOJ 2020).

Improved suitability is expected for some crops. Although the overall impact of climate change on

agriculture production is expected to be negative, research anticipates that biophysical effects of climate

change on agricultural production will be positive in some agricultural systems and regions (UNECLAC 2011).

For example, Prager et al. (2020) indicate that sugarcane and yarn suitability (how well local precipitation and

temperature match the biophysical requirements ofa given crop) is projected to increase in certain areas of

Jamaica over the period 2020-2050.

Variable Rainfall Unreliablelunpredictable rainfall patterns would affect product distribution, distribution and quality

(CARIBSAVE 2009, 34). In 2019, annual rainfall declined by 22.6% resulting in normal to extreme drought

across eight parishes in Jamaica. As a result, 5,600 farmers were affected with estimated losses of 500

hectares of fruits, vegetables, condiments, fruits, vegetables, fruits, cereals, roots and tubers IPIOJ 2020).

Heavy rainfall events may affect crop quality, yield and could lead to crop loss. The extensive rainfall

period, from September to December 2017, disrupted the usual planting period for farmers which affected

their ability to start soil preparation. Planting was not possible due to the excessive wetness and high

proliferation of weeds. There was also a high incidence of fungal and bacterial diseases. Several hectares

of crops were washed away due to flooding and/or impacted on crop density and yields. Some of those

crops were those planted under the National Onion Development Programme. Crop production overall was

decreased by 4% due to excessive rainfall (Young n.d.).

Heavy rainfall may disrupt planting patterns. Prolonged rainfall from 2017 impacted crop planting efforts

in 2018, which in some cases is not a favourable period for crops as crop cycle would extend into summer

when dry conditions prevail.

Storms, Hurricanes Passage of extreme events incurs losses of agricultural assets, livestock, crops and agricultural

and Floods infrastructure (G01 2011, 254). This loss is especially severe for standing export crops like banana, sugar

cane, coffee (GOJ 201 1, 265).

Increased floodingwill lead to inundation ofproductionfields IUNECLAC 201 1, 27). Increased precipitation

and flooding also lead to more favourable conditions for crop disease (CARIBSAVE 2009, 34).

Increased food costs, increased costs of insurance and higher rates for capital cost loans ICARICOM

2010, 6).

Sea level intrusion in coastal agricultural areas and salinization of water supply IUNECLAC 201 ‘I, 27).

Sea Level Rise lnjamaica, some wells have been abandoned due to increased salinity and others produce water unsuitable

for agricultural use (ESL 2009, 74).

Climate Change Impacts of climate Change on Agriculture (Crop Production, Fisheries, Livestock, Food Security) and

Variables/Extreme Food Security

Events

FISHERIES

Increasing Warmer seas will negatively affect coral reefs and marine ecosystems. Warmer temperatures will

Temperatures make the seas more acidic, and this acidification which is due to carbon dioxide, will impact the functioning,

behaviour and dynamics of organisms. Reef building corals are highly susceptible to this acidification and

when coupled with larger scale changes including lower oxygen levels, impacts are amplified (IPCC 2014).

Extreme temperatures will increase coral bleaching and affect fish habitats and populations. Higher

temperatures will increase incidents of coral bleaching, which will lead to ultimate destruction of spawning

and feeding areas for many fish species. As a result, both fish populations and marine biodiversity will be

adversely impacted (CDKN 2014; Simpson 2010).

Warmer seas contribute to increases in sargussum, which negatively affects fisheries. The sargassum

seaweed may continue to clog propellers and prevent access to fish catch for local fishermen (Oxenford et

al. 2015).

Storms and More frequent and severe storms will increase damage to fish habitats and natural barriers. Increased

Hurricanes storm intensity which is proyected for the Caribbean could result in greater damageto coral reefs, mangroves

and seagrass beds. This could reduce habitats and in turn increase exposure of fisheries to harmful winds

(CMEP Z017).

LIVESTOCK

Increasing temperatures for poultry can affect reproduction rate, breast meat and egg quality.

Imreasin Chickens, especially inside broilers, are finding it increasingly difficult to cope with heat stress conditions.

3 . . .

Temperature The adverse effects of high temperature increase with age and may result in lower quality of breast meat

yield, carcass quality and high mortality (Lallo 1t al. 201 2, 171).

High temperatures also affect reproduction rate among chickens (Ronchi et al. 2010).

Hot environments impair poultry production which affects egg yield, weight and quality (Ronchi et al. 2010).

Livestock (chickens, goats and pigs) in the Caribbean are under considerable heat stress even in normal

conditions and especially during the summer period. Future temperature increases of 1.5\"C above pre-

industrial levels (since 1860), will result in heat stress eveiy month at dangerous or severe levels (Lallo et al.

Z018).

Z018 & 2019 had the highest temperatures on record which contributed to heat stress among livestock

especially during each yeafs diy spell (Voting, pers. comm., n.d.).

High temperatures affect growth rate and susceptibility of young goats and sheep to many diseases.

Heat stress has a direct impact on feed intake, growth rate and reproductive performance(Lallo and Rankine

2016, 45). It also suppresses the immune system and increases animal susceptibility to many diseases.

Heat stress in early and late gestation can cause a decrease in lamb birth weight (Avril et al. 201 1, 8).

Rising temperatures affect behavioural patterns in goats and sheep. Amidst rising temperatures, some

of the behavioural patterns observed among sheep include roaming pastures looking for shade, increased

water consumption and consuming less feed to maintain body temperature (Avril et al. 201 1, 2).

Increasing temperatures can affect milk quality. Heat stress directly and indirectly reduces the ability

of cows to lactate to their full potential. This affects the yield and composition of milk. Heat stress can also

adversely affect the rate of conception among herds, pregnancy and calf birth weight (Lallo et al. 1997,11).

1114 i The State ufthejamaicari C|irrlaite(Vu|ume|lIl li'iliJrri'iatic1i'i or .:i . .

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climate change impacts of climate Change on Agriculture (Crop Production, Fisheries, Livestock, Food Security) and

Variables/Extreme Food Security

Events

Drought conditions can affect birth weight and litter size among sheep. A study conducted at the

Drought Blenheim sheep station in Tobago over the period i999—2004 found that during the dry season, there is an

11% reduction in litter Slze, birth and weaning weight among hair sheep (Avil et al. 2011). This is due to the

decreased availability of forage in the dry season as compared to the rainy season. Therefore, lambs born

in dry season had heavier birth weight than those born in wet season because they were conceived during

the wet season when ewes had higher quality of pasture, with higher protein forages and concentrate

supplement. Animal performance is heavily dependent on the availability of forage and its nutritional value

(Avril et al. 201 i, 3).

Longer periods ofdrought may lead to a large-scale loss of cattle (UNECLAC 2m 1, 25).

More frequent extreme events can increase livestock mortality. increasing frequency of storms and its

Hurricanes storms associated effects bffloodvvater and high winds has the potential to increase the mortality among livestock

' (poultry, cattle, small ruminants) on a large scale (Clark 2005). The passage of Tropical storm Gustav over

lamaica led to livestock losses valued atlMDi 5 million (PIOJ 2008), while Hurricane sandy in 2012 resulted in

livestock (poultry, pigs, goats, cattle, rabbits, bee colonies, sheep) damage acrosslamaica ofilvll:-95 million,

covering 3,500 farmers (PIOJ 2m 3).

I. ldlng | us

6.2_6 will be achieved by efforts Including promoting the use oftechriologles

ON that will not harm the environment. Climate change in conjunction

3|°D|VER§|1-Y with poor environmental practices threatens biodiversity.

_ g _ increasing temperature, sea ievei rise and heavy rainfall events wlil

\"’5“”’ 103“ /‘’\"'\"’‘‘’ 5i’“’fi“ \"W by \"'9 Yea’ 1930' ””/\"’\"\"\"\"\"5 impart coral reefs, sea turtle nesting, birds and sea grasses. See

should III/e in a healthy and beautiful natural environment with clean Tame 66 for m \"he, mformauom

air, water, rich forests and an abundance ofpiarlts and anirnais. This

Table 6.6. Impact of climate change on marine and terrestrial blodlversity

Climate Change Impact of Climate Change on Marine and Terrestrial Biodiversity

Variab|eIExtreme

Events

Increasing Warmer seas contribute to northern migration of Caribbean coral reefs and commercially viable fish

Temperature stock. Rising sea surface temperatures are leading to the migration of Caribbean grazers iike parrotrish

and rabbitlish to more temperate seas, These rish species can now be found feeding on sea grasses and

kelp forests in areas such as the Mediterranean Seas, Japan and Austraiia, Caribbean corai reefs have also

foilowed these fish popuiations and are replacing the sea grasses and kelp forests (Struck 2014).

Sargassum seaweed affects marine life. Ocean acidification and increasing sea surface temperatures have

ied to the presence of the Atiantic Sargassum seaweed in the Caribbean region. This seaweed threatens

coastal ecosystems by smothering sea grass beds, coral reefs and mangroves. It aiso threatens endangered

species such as sea turtles by drowning and entangling them (oxenford et al. 2015).

Increasing temperatures will affect sea turtle populations. Rising temperatures are proiected to affect

reproduction of sea turtles since sex is determined by temperature (CANARI 2009, i5).

An increase in sea surface temperature of 1.0 degree Celsius will lead to coral reef bleaching. Bleaching

reduces the ability of corals to withstand impacts of extreme events and also leads to habitat loss for and

eventuai decline of reef fish (CARiBSAVE 2009, 36). For the fifth consecutive year, corai reef health across

Jamaica in 2oi9 was categorised as poor (PIOJ 2020).

Sea grass will decline. Sea grasses are also sensitive to thermai discharges and can only accept temperatures

up to Z I\] Babove summer temperatures (CARIESAVE 2009, 36).

Increasing Changes in temperature and precipitation will affect the frequency and extent of forest fires (CANARI

Temperatures 2009, 18).

find Pe.\"e.as'\"g Changes in temperature and precipitation contribute to reduced citrus and root crop yields (CANARI

'°\"\"'\"“'°\" 2009-, GO\] 2021).

Decreasing Rainfall Rainfall declines are affecting health and migratory patterns of birds. Decreases in rainfall during the

dry season in Jamaica has resulted in reduced avaiiability of food for migratory birds, and negativeiy affects

the physical conditions of these birds wintering on the island and their departure times (Taylor 2015, 25).

Sea Level Rise Sea level rise leads to a decrease in sea turtle nesting. Beach erosion as a resuit of 0.5-metre sea ievei

rise in the Caribbean is projected to cause a decrease in sea turtle nesting habitats by up to 35% (CARIBSAVE

2009, 35).

Storm surges and sea level rise could increase the salinity of estuaries and fresh water aquifers

(CARIBSAVE 2009, 36).

Degraded wetlands have a reduced capability to act as natural filters & buffering systems for

shorelines and coral reefs against severe events such as flooding (CARIES/-\\VE 2009, 36).

Mangrove vegetation will migrate landward in response to changing ecological conditions brought on

by inland movement of the sea and salt water intrusion into coastal waterways (UNECLAC 2011, .46).

Heavy Rainfall Sea grasses currently facethreatsfrom sedimentation, direct dredge and fill activities and wastewater

discharge. Increased storm events, flooding or high intensity rainiali, attributed to climate change, could

magnifythisthreat by increasingthe volume of polluted runofffrom upstream sources (CARiElSAVE 2009, 36).

ms i The State artheiarnairan C|imate(Vo|ume|ill inlarmatian nr .si . ,

5.2_1 |MPAc'|'s 0|: c|_|MA'|'E CHANGE ON living in poverty, an improvement over the i9.3% in 2017

POVERTY (PIOJ and STATIN 20i8). Harsher climate conditions (for

V. . 2030 . , _ I t t example, extreme temperatures) are likely to affect poorer

f'S'°Ph Jtammlca 3315 tad secure ST.C'ad 97° lecdmn communities the most and extreme events resulting in

‘fir F . m°.S V” nfrabe an . ma\";1g\":afi'1ZeG' mt U ‘ngt significant displacement of persons from their homes and

O5.e Nmg m l’°\"?'. y‘ y ensumg .‘? e ovemmen communities are likely to increase incidents of human

pmwdes these fammes Wm‘ °pp°rt”mt'e5 to make 3 30°C‘ trafficking. See Table 6.7 of the full document for further

living and ensuring that welfare and assistance reach the details

most needy’. In 2018, 12.6% oflamaica's population was '

Table 6.7. Impacts of climate change on poverty

Climate Change Impacts of climate Change on Poverty

Variab|eslExtreme

events

Increasing Severity of heat waves are likely to increase human mortality among the urban poor. It is anticipated

T9'“P9'3t“’° that an increased frequency or severity of heat waves in the Caribbean will cause an increase in human

mortality, especially among (urban) poor communities without access to cooling aids like air conditioners or

refrigerators (CARIBSAVE 2009, 35).

Droughts Droughts increase low income communities’ vulnerability to disease infection. Households, especially

in low income communities with no running water, are more at risk of dengue fever and infectious diseases

than those with piped water supply since water storage becomes necessary (Chen et al. 2006, 43).

During water shortages in some communities, diseases spread because of poor infrastructure, waste

disposal issues and lack of access to clean water sources lVassell 2009, 15).

Long drought periods and the associated disturbance in food production and distribution could result

in malnutrition (CARIESAVE 2009, 34).

The elderly poor experience increased vulnerability. The elderly poor in rural areas may face serious

health threats from the lack of water or adequate sanitation associated with climate change impacts.

These health issues will place a greater strain on care givers (who tend to be female) in the household and

community (Vasse|| 2009, 20\].

Variables/Extreme

events

Storms, Tropical Flooding and landslides lead to population displacement because of vulnerabilities of settlements in

Cyclones, Hurricanes floodplains (ESL 2009, 57).

Poor housing quality increases residents’ vulnerability to the ravages of extreme events (Dunn and

Mondesire 2009, 148).

Heavy rainfall affects the health and sanitation of some communities without proper toilet facilities.

Flooded pit latrines, during storms, release waste directly into the rivers which some residents use. This has

led to an increase in diseases associated with water sanitation and poor hygiene practices (Vassel| 2009, 15).

Passageofextremeeventsleadtoincreasedincidenceofhumantraffickinginlowincomecommunities.

Human displacement due to extreme events such as hurricanes, floods and droughts increases the risk of

human trafficking, especially in those communities facing chronic poverty and lack of security. Children are

particularly vulnerable to trafficking (Dunn 2013,11).

6.2.8 IMPACTS OF CLIMATE CHANGE ON and near shore water quality (for example, through

TOURISM seasonai sargassum events). Tourism infrastructure is also

Tourismisa ma\]-Orinmme eamerforthejamaican economy’ vulnerable to sea level rise and hurricanes, and increasing

generating USD3.64 billion in 2019 from 4.3 million visitors lempellllures Wlll lead m h?“ related lllllesges among

(Jamaica information Service 2020). At present, Jamaicals wollfels and 3_“e5‘5' and lllgllel_ opelallollal msls fol

tourism brand is predominantly premised on sun, sea and mollllg a_ld5' Cllmllte cll‘_allg_e also lmplllcts wmkels lll the

Sand_ wsion 2030 Jamaica enwsions that by the year 2030’ sector, with agendered division oflabourbetween maleand

jamaica will have a wider choice of taurist attractions in safe f_emale Wolkels lllal ollelexpcses female tollllsl wolkels to

and secure resort areas and that all hotels will be operated llsk dullng we llllll-lel_elll Stages ola lllllllclllla See Table 6'8

in harmony with the natural environment. Climate change for lllltllel llll°lmlltl°ll'

will directly impact the tourism sector through impact on

tourist arrivals, increased incidents of beach erosion and

through the degradation of both the natural environment

Table 6.8. Impacts of climate change on tourism

Climate Change Impacts of Climate Change on Tourism

Variables!

Extreme Events

Sea Level Rise Beaches respond to sea level rise by retreating inland at approximately 100 times the rate of sea

level rise KCANARI 2009, T3). Beaches in Jamaica accreted by 4.7% on average in 2019, compared with

2013 (PVOJ, 2020).

Increasing Warmer winters in North America and Europe may reduce the numbers ofvisitors to the Caribbean,

Temperature including islands such asjamaica, during the region's peak ’winter season’ (Dunn et al. 2018b, T2).

Sargassum blooms compromise the quality of the local tourism product. Increasing sea surface

temperature has contributed to the presence of the Sargassum seaweed covering white sandy beaches,

discolouring near shore waters in many coastal areas (including hotel faciiitiesl and emitting a pungent

odour across the Caribbean region, inciuding Jamaica. This compromises the scenic beauty of the local

tourism product (0xenford etal. 2015).

Persistent climate threats and environmental waste are likely to increase seasonal sargassum

blooms which may threaten the viability of the tourism product in the Caribbean including islands

likejamaica. Warmer seas, changing winds and oceanic patterns, Sahara dust, nutrients from rivers and

runofffrom nitrogen based fertilizers have all contributed to the presence of Sargassum in the Caribbean.

Minister Bartlett noted that for 2019, across the Caribbean region, the tourism sector suffered 35% losses

related to bookings. Many hotei facilities have incurred USD1Z0 million dollars in clean-up costs as a result

ofthis seaweed (Jamaica Observer 2019).

Temperature extremes can lead to increased incidence of heat stress and other heat related

illnesses. In extreme cases, heat can have fatal effects‘ Heat stress remains a concern with higher

temperatures affecting tourists and outdoor workers. Heat storage of built structures ieads to 'heat island

effect (Tayior, Chen. and Bailey 2009). This leads to additional operating costs for cooling aids (UNECLAC

201 1, 87).

A1\"C increase in sea surface temperature can trigger coral reef bleachinr (CANARI 2009, 14). These

reefs contribute toJamaica's tourism product through diving and fishing tours and are critical sources of

beach sand (Richards 2008, 6).

Heavy Rainfall Adverse rainfalllweather conditions could lead to cancellation of reservations or displacement of

visitors which would incur massive losses in revenue (CARIBSAVE 2009, 29).

More flooding and landslides due to heavy rainfall will continue to restrict travel of hotel staffers

to their workplaces (Dunn et al. 2018b 12).

Hurricanesl Extreme events such as storms and hurricanes result in increased infrastructural damage,

Storms additional emergency preparedness requirements and business interruptions, in the tourist

industry and other key sectors (UNECLAC 2011, 37).

Tropical storms and hurricanes appear to be the dominant causes of beach erosion (CAN/-\\Ri 200914).

9 Na base temperature was given for the i u degree rise.

(Ia) Impacts of Hurricanes on Gender in the Tourism Sector

In the tourism sector. there is a gendered division of labour between male and female workers that overexposes female tourist

workers to risk during the different stages of a hurricane.

MALES — Male tourist workers are involved in mostly male FEMALES — Female tourist wcirkers are involvecl mostly in

dominated yobs such as transportation. other male economic accommodation. They perform stereotypically gendered lcibs

activity inciucles tiuilcling and construction and hotel and raciiity such as liousekeepihg and liaregivlng services like nursing.

mallltellance (Dunn etal.Z0i8l:i,1Z). (Dunn et al. zclizlb, 25)

Pre-hurricane Issues for Men Pre—hurricane Issues for Women

Drivers (usually male) will need to check engines, tyres and Female workers who usually reside at their workplace will need

mechanical equipment and purchase petrol/gas (Dunn et al. to balance preparations at home and at work. Those without

2018b, 35). male partners and are heads of household will have to assume

additional responsibility for physical preparation of the home

(Dunn et al. zoi ab. 35).

Mid and Post Hurricane Issues for Men Mid and Post Hurricane Issues for Women

Male tourism workers who are mostly transport operators take women working as housekeepers race particular security risks

life threatening risk during a disaster to secure a job to meet especially early morning or late nights dueto darkness because

family commitments (Dunn et al. 2018b. 37). ofdlsruptlon in electricity and limited transportation because of

Male tour operators will lose their opportunities to earn after a “'3” damage “’ f‘°‘”\"\"5 ‘D“\"” 9‘ a'- 2°‘ 31* 37)‘

hurricane. due to damaged or washed away roads, bridges and lvlaiority of women are employed in low status and low skilled

vehicles (Dunn et al. 2018b, 12). work, and as such, are less likely to prepare forand recoverfmm

Alternative skills give men the opportunity to earn after the ““\"\"a\"e“D“\"” “El 20”” 14)‘

hurricane. Men, who for example work mostly in maintaining They are more likely to lose jobs arter a natural disaster If the

hotel grounds will thrive in post hurricane reconstruction. This buildings are damaged and the hotels are closed (Dunn et al.

is because they may have the skills needed for post disaster 2018b, 35). Also, unlike their male counterpartsthey do not have

reconstruction such as construction, road repairs, grounds the skills required to participate in reconstruction efforts (Dunn

maintenance and utility repairs (Dunn et al. 20i ab. 25). et al. mi 812. 37).

Female employers may have challenges accessing hnancial

resources to reopen their businesses and make repairs after a

hurricane (Dunn eta|.20i8b,16).

When tourism infrastructure is destroyed it will not recover

quickly due to the high concentration ofwomen thatwork at the

restaurants or bars in the sector (Dunn et al. 2018b, 15).

6.2.9 IMPACTS OF CLIMATE CHANGE ON

HEALTH have negative impacts on healthcare infrastructure and