WMO Statement on 

the State of the 

Global Climate in 2017

WMO-No. 1212

WEATHER CLIMATE WATER

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This publication was issued in collaboration with the African Center of Meteorological Applications for Development (ACMAD), Niger; Regional 

Climate Centre for the Southern South American Region (RCC-SSA); European Centre for Medium-Range Weather Forecasts (ECMWF), United 

Kingdom of Great Britain and Northern Ireland; Japan Meteorological Agency (JMA); Met Office Hadley Centre, United Kingdom; Climatic 

Research Unit (CRU), University of East Anglia, United Kingdom; Climate Prediction Center (CPC); the National Centers for Environmental 

Information (NCEI) and the National Hurricane Center (NHC) of the National Oceanic and Atmospheric Administration (NOAA), United States of 

America; National Aeronautics and Space Administration, Goddard Institute for Space Studies (NASA GISS), United States; Global Precipitation 

Climatology Centre (GPCC), Germany; National Snow and Ice Data Center (NSIDC), United States; Commonwealth Scientific and Industrial 

Research Organization (CSIRO) Marine and Atmospheric Research, Australia; Global Snow Lab, Rutgers University, United States; Regional Climate 

Centre for Regional Association VI, Climate Monitoring, Germany; Beijing Climate Centre, China; Tokyo Climate Centre, Japan; International 

Research Centre on El Niño (CIIFEN), Ecuador; Caribbean Institute for Meteorology and Hydrology, Bridgetown, Barbados; Royal Netherlands 

Meteorological Institute (KNMI), Netherlands; Institute on Global Climate and Ecology (IGCE), Russian Federation; All-Russia Research Institute 

for Hydrometeorological Information-World Data Center (ARIHMI-WDC), Russian Federation; Global Atmospheric Watch Station Information 

System (GAWSIS), MeteoSwiss, Switzerland; World Data Centre for Greenhouse Gases (WDCGG), Japan Meteorological Agency, Japan; World 

Glacier Monitoring Service (WGMS), Switzerland; World Ozone and UV Radiation Data Centre (WOUDC), Environment and Climate Change, 

Canada; Niger Basin Authority, Niger. Other contributors are the National Meteorological and Hydrological Services or equivalent of: Algeria, 

Argentina, Australia, Austria, Bangladesh, Belarus, Belgium, Bosnia and Herzegovina, Brazil, Bulgaria, Canada, Chile, China, Colombia, Costa 

Rica, Croatia, Cuba, Cyprus, Czechia, Denmark, Ecuador, Estonia, Fiji, Finland, France, Gambia, Georgia, Germany, Greece, Hungary, Iceland, 

India, Indonesia, Iran, Islamic Republic of, Ireland, Israel, Italy, Japan, Kenya, Latvia, Lithuania, Luxembourg, Malaysia, Mali, Malta, Mauritius, 

Mexico, Morocco, Netherlands, New Zealand, Nigeria, Norway, Oman, Pakistan, Paraguay, Peru, Philippines, Portugal, Republic of Korea, 

Republic of Moldova, Romania, Russian Federation, Serbia, Singapore, Slovakia, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, 

The former Yugoslav Republic of Macedonia, Tunisia, Turkey, Turkmenistan, Ukraine, United Arab Emirates, United Kingdom, United Republic 

of Tanzania, United States, Uruguay.

Various international organizations and national institutions contributed to this publication, including the Food and Agriculture Organization of 

the United Nations (FAO); Intergovernmental Oceanographic Commission of the United Nations Educational, Scientific and Cultural Organization 

(UNESCO); International Monetary Fund (IMF); International Organization for Migration (IOM); United Nations High Commissioner for Refugees 

(UNHCR); United Nations Office for Disaster Risk Reduction (UNISDR); United Nations Office for the Coordination of Humanitarian Affairs (OCHA), 

World Food Programme (WFP); World Health Organization (WHO); the Catholic University of Leuven, Belgium; the Centre for Research on the 

Epidemiology of Disasters (CRED); and Munich Re.

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extracts from WMO publications may be reproduced without authorization, provided that the complete source is 

clearly indicated. Editorial correspondence and requests to publish, reproduce or translate this publication in part 

or in whole should be addressed to:

Chairperson, Publications Board

World Meteorological Organization (WMO)

7 bis, avenue de la Paix Tel.: +41 (0) 22 730 84 03

P.O. Box 2300 Fax: +41 (0) 22 730 81 17

CH-1211 Geneva 2, Switzerland Email: publications@wmo.int

ISBN 978-92-63-11212-5

NOTE

The designations employed in WMO publications and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of WMO concerning the legal status of any country, territory, city or area, or of its authorities, or concerning the delimitation of its 

frontiers or boundaries.

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nature which are not mentioned or advertised.

The findings, interpretations and conclusions expressed in WMO publications with named authors are those of the authors alone and do not necessarily reflect those of WMO or its Members.

Cover illustration: Landi Bradshaw Photography 

WMO-No. 1212

© World Meteorological Organization, 2018

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Contents

Foreword 3

Executive summary 4

Key climate indicators 5

Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Greenhouse gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

The Global Carbon Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

The oceans in 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

The cryosphere in 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Major drivers of interannual climate variability in 2017 . . . . . . . . . . . . . . . . . . . 15

Precipitation in 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Extreme events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Climate risks and related impacts 29

Agriculture and food security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Population displacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Economic impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Vector-borne diseases: Zika in the Americas . . . . . . . . . . . . . . . . . . . . . . . 34

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Climate risks, 

extreme events 

and related 

impacts

2017

≈30%

OF THE WORLD’S 

POPULATION FACES 

EXTREME 

HEATWAVES

Food and Agriculture 

Organization of the United Nations

United Nations High Commissioner for Refugees

+892 000

DROUGHT 

RELATED 

INTERNAL 

DISPLACEMENTS

IN SOMALIA

DESTRUCTIVE 

WILDFIRES 

AROUND THE 

WORLD

EXCEPTIONALLY 

HEAVY RAINS 

TRIGGERED 

DEADLY 

LANDSLIDES

IN SIERRA LEONE 

AND COLOMBIA

MOST 

COSTLY 

HURRICANE 

SEASON 

ON RECORD

United Nations Office 

for the Coordination of 

Humanitarian Affairs

World Health 

Organization

+41 MILLION 

AFFECTED BY 

FLOODS 

IN SOUTH ASIA 2ND YEAR OF

MAJOR 

BLEACHING

IN THE GREAT 

BARRIER REEF

AGRICULTURE

ACCOUNTED FOR 

≈26%OF 

DAMAGES AND LOSSES

ASSOCIATED WITH 

CLIMATE-RELATED 

DISASTERS

State of the 

Global Climate 

2017 

2017:

WARMEST 

NON-EL NIÑO

YEAR

2013–2017:

WARMEST

5 YEARS

ON RECORD

SEA-LEVEL 

RISE 

CONTINUES 

GREENHOUSE 

GAS 

CONCENTRATIONS 

CONTINUE

RISING

ARCTIC AND 

ANTARCTIC 

SEA ICE

WELL BELOW 

AVERAGE 

OCEAN 

ACIDIFICATION

CONTINUES 

GLOBAL 

OCEAN HEAT

CONTENT AT

RECORD

LEVELS

2

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3

Foreword

For the past 25 years, the World Meteorological 

Organization (WMO) has published an annual 

Statement on the State of the Global Climate 

in order to provide authoritative scientific 

information about the global climate and 

significant weather and climate events 

occurring around the world. As we mark the 

25th anniversary, and following the entry into 

force of the Paris Agreement, the importance 

of the information contained in the WMO 

Statement is greater than ever. WMO will 

continue working to increase the relevance 

of the information it provides to the Parties to 

the United Nations Framework Convention on 

Climate Change through this Statement and the 

annual WMO Greenhouse Gas Bulletin. These 

publications complement the assessment 

reports that the Intergovernmental Panel on 

Climate Change (IPCC) produces every six to 

seven years. 

Since the inaugural Statement on the State 

of the Global Climate, in 1993, scientific 

understanding of our complex climate system 

has progressed rapidly. This is particularly 

true with respect to our understanding of 

mankind’s contribution to climate change, 

and the nature and degree of such change. 

This includes our ability to document the 

occurrence of extreme weather and climate 

events and the degree to which they can be 

attributed to human influences on the climate. 

In the past quarter of a century, atmospheric 

concentrations of carbon dioxide – whose 

rising emissions, along with those of other 

greenhouse gases, are driving anthropogenic 

climate change – have risen from 360 parts 

per million (ppm) to more than 400 ppm. They 

will remain above that level for generations 

to come, committing our planet to a warmer 

future, with more weather, climate and water 

extremes. Climate change is also increasingly 

manifested in sea level rise, ocean acidification 

and heat, melting sea ice and other climate 

indicators. 

The global mean temperature in 2017 

was approximately 1.1 °C above the preindustrial era, more than half way towards 

the maximum limit of temperature increase 

of 2 °C sought through the Paris Agreement, 

which further strives to limit the increase to 

1.5 °C above pre-industrial levels. The year 

2017 was the warmest on record without an 

El Niño event, and one of the three warmest 

years behind the record-setting 2016. The 

world’s nine warmest years have all occurred 

since 2005, and the five warmest since 2010.

Extreme weather claimed lives and destroyed 

livelihoods in many countries in 2017. Fuelled 

by warm sea-surface temperatures, the North 

Atlantic hurricane season was the costliest ever 

for the United States, and eradicated decades 

of development gains in small islands in the 

Caribbean such as Dominica. Floods uprooted 

millions of people on the Indian subcontinent, 

whilst drought is exacerbating poverty and 

increasing migration pressures in the Horn of 

Africa. It is no surprise that extreme weather 

events are identified as the most prominent 

risk facing humanity in the World Economic 

Forum’s Global Risks Report 2018.

Because the societal and economic impacts 

of climate change have become so severe, 

WMO has partnered with other United Nations 

organizations to include information in the 

Statement on how climate has affected 

migration patterns, food security, health and 

other sectors. Such impacts disproportionately 

affect vulnerable nations, as evidenced in a 

recent study by the International Monetary 

Fund, which warned that a 1 °C increase in 

temperature would cut significantly economic 

growth rates in many low-income countries.

I would like to take this opportunity to express 

my gratitude to the National Meteorological 

and Hydrological Services of WMO Members, 

international and regional data centres and 

agencies, and climate experts from around 

the world for their contributions, and to United 

Nations sister agencies for their valuable input 

on societal and economic impacts. They have 

greatly assisted in ensuring that this annual 

Statement achieves the highest scientific 

standards and societal relevance and informs 

action on the Paris Agreement, the Sendai 

Framework for Disaster Risk Reduction and 

the United Nations Sustainable Development 

Goals.

(P. Taalas)

Secretary-General 

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4

Executive summary

Global mean temperatures in 2017 were 

1.1 °C ± 0.1 °C above pre-industrial levels. 

Whilst 2017 was a cooler year than the recordsetting 2016, it was still one of the three 

warmest years on record, and the warmest not 

influenced by an El Niño event. The average 

global temperature for 2013–2017 is close 

to 1 °C above that for 1850–1900 and is also 

the highest five-year average on record. 

The world also continued to see rising sea 

levels, with some acceleration, and increasing 

concentrations of greenhouse gases. The 

cryosphere continued its contraction, with 

Arctic and Antarctic sea ice shrinking. 

The overall risk of heat-related illness or 

death has climbed steadily since 1980, with 

around 30% of the world’s population now 

living in climatic conditions that deliver deadly 

temperatures at least 20 days a year. 

There were many significant weather and 

climate events in 2017, including a very 

active North Atlantic hurricane season, major 

monsoon floods in the Indian subcontinent, 

and continuing severe drought in parts of east 

Africa. This contributed to 2017 being the year 

with the highest documented economic losses 

associated with severe weather and climate 

events. Extreme weather events continue to be 

rated by the World Economic Forum as amongst 

the most significant risks facing humanity, both 

in terms of likelihood and impact.1

Massive internal displacement in the context 

of drought and food insecurity continues 

1 World Economic Forum, 2018: The Global Risks Report 2018.

across Somalia. From November 2016 to 

December 2017, 892 000 drought-related 

displacements were recorded by the United 

Nations High Commissioner for Refugees 

(UNHCR). 

In August and September 2017, the three 

major and devastating hurricanes that made 

landfall in the southern United States and in 

several Caribbean islands in rapid succession 

broke modern records for such weather 

extremes and for loss and damage.

The information used in this report is 

sourced from a large number of National 

Meteorological and Hydrological Services 

(NMHSs) and associated institutions, as 

well as Regional Climate Centres, the World 

Climate Research Programme (WCRP), the 

Global Atmosphere Watch (GAW) and Global 

Cryosphere Watch (GCW). Information has 

also been supplied by a number of other 

international organizations, including the 

Food and Agriculture Organization of the 

United Nations (FAO), the World Food 

Programme (WFP), the World Health 

Organization (WHO), the United Nations 

High Commissioner for Refugees (UNHCR), 

the International Organization for Migration 

(IOM), the International Monetary Fund 

(IMF), the United Nations International 

Strategy for Disaster Reduction (UNISDR) 

and the Intergovernmental Oceanographic 

Commission of the United Nations 

Educational, Scientific and Cultural 

Organization (IOC-UNESCO).

Values of key climate indicators

Indicator Time period Value Ranking

Global mean surface-temperature 

anomaly (1981–2010 baseline)

2017, 

annual mean +0.46°C Second-highest on record

Global ocean heat content 

change, 0–700 metre layer

2017, 

annual mean 1.581 x 1023 J Highest on record

Global mean CO2

 surface mole 

fraction

2016, 

annual mean

403.3 parts 

per million

Highest on 

record

Global mean sea-level change 

since 1993 2017, December 8.0 cm Highest on record

Arctic sea-ice extent summer 

minimum 2017, September 4.64 million km2

Eighth-lowest 

on record

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5

TEMPERATURE

The year 2017 was one of the world’s three 

warmest years on record. A combination 

of five datasets, three of them using 

conventional surface observations and 

two of them reanalyses,2 shows that global 

mean temperatures were 0.46 °C ± 0.1 °C 

above the 1981–2010 average,3 and about 

1.1 °C ± 0.1 °C above pre-industrial levels.4 By 

this measure, 2017 and 2015 were effectively 

indistinguishable as the world’s second and 

third warmest years on record, ranking only 

behind 2016, which was 0.56 °C above the 

1981−2010 average. The years 2015, 2016 

and 2017 were clearly warmer than any year 

prior to 2015, with all pre-2015 years being at 

least 0.15 °C cooler than 2015, 2016 or 2017. 

The world’s nine warmest years have all 

occurred since 2005, and the five warmest 

since 2010, whilst even the coolest year of 

the 21st century – 2008, 0.09 °C above the 

1981−2010 average – would have ranked as 

the second-warmest year of the 20th century. 

The five-year mean temperature for 2013–

2017, 0.4 °C above the 1981–2010 average (and 

1.0 °C above pre-industrial values), is also the 

highest on record. A five-year average gives 

a longer-term perspective on recent global 

temperatures whilst being less influenced 

than annual temperatures by year-to-year 

fluctuations such as those associated with 

the El Niño/Southern Oscillation (ENSO). 

2 The conventional datasets used are those produced by the US 

National Oceanic and Atmospheric Administration (NOAA); 

the US National Aeronautics and Space Administration 

(NASA); and the Met Office, Hadley Centre/Climatic Research 

Unit (CRU), University of East Anglia (United Kingdom). The 

two reanalysis datasets used are the ERA-Interim dataset, 

produced by the European Centre for Medium-Range Weather 

Forecasts (ECMWF), and the JRA-55 dataset, produced by 

the Japan Meteorological Agency (JMA). 

3 For purposes other than comparison of temperatures with 

pre-industrial levels, this report uses 1981–2010 as a standard 

baseline period, as this is the period for which the widest 

range of datasets (especially satellite-based datasets) is 

available. 

4 For the purposes of this report, 1850–1900 is used as the 

baseline for pre-industrial temperatures. There is no appreciable difference between the temperature change derived 

from this baseline and that derived from other baselines 

used historically, such as 1880–1900. 

In the individual datasets, 2017 was secondwarmest in the two reanalysis datasets 

(ERA-Interim and JRA-55) and in the dataset 

from the US National Aeronautics and Space 

Administration (NASA), and third-warmest in 

the datasets from the US National Oceanic 

and Atmospheric Administration (NOAA) and 

the UK Met Office Hadley Centre/Climatic 

Research Unit (CRU). Differences between 

individual datasets primarily relate to different 

ways in which they analyse data-sparse areas, 

especially in the Arctic which has experienced 

some of the world’s strongest warming in 

recent years. 

Global temperatures were well above average 

throughout the year. The strongest anomalies 

were early in the year, with each of the months 

from January to March being at least 0.5 °C 

above the 1981–2010 average, and March 

0.64  °C above. For the remainder of the 

Key climate indicators

JRA-55

ERA-Interim

HadCRUT.4.6.0.0

GISTEMP

NOAAGlobalTemp

1.25

1.00

0.75

0.50

0.25

0.00

‒0.25

1860 1880 1900 1920 1940 1960 1980 2000 2020

Year

Global average temperature anomaly (°C)

Figure 1. Global mean 

temperature anomalies, 

with respect to the 

1850–1900 baseline, for 

the five global datasets

(Source: UK Met Office 

Hadley Centre)

The world’s warmest years on record

Year Anomaly in respect of the 

1981–2010 average (°C)

2016 +0.56

2017 +0.46

2015 +0.45

2014 +0.30

2010 +0.28

2005 +0.27

2013 +0.24

2006 +0.22

2009 +0.21

1998 +0.21

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6

year, monthly global temperature anomalies 

were between 0.3 °C and 0.5 °C, the smallest 

monthly anomaly being 0.34 °C in June. 

The year 2017 was clearly the warmest on 

record not influenced by an El Niño. Strong 

El Niño events, such as the one that occurred 

in 2015/2016, typically increase global mean 

temperatures by 0.1 °C to 0.2 °C in the year 

in which the event finishes, with a smaller 

increase in the event’s first year. In the case 

of the 2015/2016 event, global temperatures 

were strongly boosted from October 2015 to 

April 2016, having a substantial influence on 

both the 2015 and 2016 annual values. Neutral 

ENSO conditions prevailed for most of 2017, 

with a weak La Niña developing late in the year. 

Warmth in 2017 was notable for its spatial 

extent. The only land area of any size 

outside Antarctica that had annual mean 

temperatures in 2017 below the 1981–2010 

average in conventional surface analyses 

was a section of western Canada centred on 

the interior of British Columbia. Reanalysis 

data also indicated some areas of belowaverage temperatures in parts of Africa where 

conventional data are sparse, including Libya 

and parts of the interior of southern Africa. 

Temperatures were 1  °C or more above 

average over most of the higher latitudes 

of Asia, including the Asian part of Russia, 

Mongolia and northern China. Other regions 

where 2017 temperatures were at least 1 °C 

above average included north-west Canada 

and Alaska, the southern half of the United 

States and parts of northern Mexico, and parts 

of eastern Australia. The largest anomalies, 

above 2 °C, were found at high northern 

latitudes, particularly in eastern Russia and 

north-west North America. Some coastal 

locations experiencing feedback from reduced 

sea-ice presence (such as Svalbard) were as 

much as 4 °C above average. 

Despite the widespread high temperatures, 

only limited regions had their warmest year 

on record in 2017. Of 47 countries reporting 

mean temperatures at the national scale, 

only Argentina, Mauritius, Mexico, Spain and 

‒10 ‒5 ‒3 ‒2 ‒1 ‒0.5 0 0.5 1 2 3 5 10

Figure 2. Surface-air 

temperature anomaly for 

2017, with respect to the 

1981-2010 average 

(Source: ERA-Interim 

data, European Centre 

for Medium-range 

Weather Forecasts 

(ECMWF) Copernicus 

Climate Change Service)

Continental temperature anomalies

Region Anomaly in respect of 

the 1981–2010 average (°C) 2017 rank Existing record

North America +0.84 6 +1.32 (2016)

South America +0.54 2 +0.69 (2015)

Europe +0.73 5 +1.18 (2014)

Africa +0.54 4 +0.83 (2010)

Asia +0.88 3 +0.92 (2015)

Oceania +0.51 6 +0.73 (2013)

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7

0

 0.5

1

 1.5

2

 1985 1990 1995 2000 2005 2010 2015

Year

N O growth rate (ppb/yr)

2

 1600

 1650

 1700

 1750

 1800

 1850

 1900

 1985 1990 1995 2000 2005 2010 2015

Year

 CH4 mole fraction (ppb)

-5

0

5

 10

 15

 20

 1985 1990 1995 2000 2005 2010 2015

Year

 CH4 growth rate (ppb/yr)

Year

0

1

2

3

4

 1985 1990 1995 2000 2005 2010 2015

 CO2 growth rate (ppm/yr)

 300

 305

 310

 315

 320

 325

 330

 1985 1990 1995 2000 2005 2010 2015

Year

N O mole fraction (ppb)

2

 330

 340

 350

 360

 370

 380

 390

 400

 410

 1985 1990 1995 2000 2005 2010 2015

Year

 CO2 mole fraction (ppm)

Uruguay had their warmest year on record. 

The Asian part of Russia also had its warmest 

year on record (the Russian Federation as a 

whole ranked fourth), as did five states in 

the southern half of the United States, and 

the eastern Australian states of New South 

Wales and Queensland.

All continents had one of their six warmest 

years on record in 2017, with South America 

ranking second, Asia third, Africa fourth, 

Europe fifth, and North America and Oceania 

sixth5. Temperatures in Africa were at record 

levels through mid-year, with monthly records 

set in May, June, July and September, but it 

cooled considerably from October onwards. 

South America had its second-warmest 

summer and second-warmest winter on 

record, whilst Oceania had its warmest July. 

GREENHOUSE GASES

Increasing levels of greenhouse gases (GHGs) 

in the atmosphere are key drivers of climate 

change. Atmospheric concentrations are 

formed as a balance between emissions due 

to human activities and the net uptake from 

the biosphere and oceans. They are expressed 

in terms of dry mole fractions calculated 

5 Continental temperatures are as reported by NOAA, and 

are available at https://www.ncdc.noaa.gov/sotc/global

-regions/201801.

from a global in-situ observational network 

for carbon dioxide (CO2), methane (CH4) and 

nitrous oxide (N2O). 

Global average figures for 2017 will not be 

available until late 2018. Real-time data from 

a number of specific locations, including 

Mauna Loa (Hawaii) and Cape Grim (Tasmania) 

indicate that levels of CO2, CH4 and N2O 

continued to increase in 2017, but it is not 

yet clear how the rate of increase compares 

with that in 2016 or in previous years.

In 2016, GHG concentrations reached new 

highs with CO2 at 403.3±0.1 parts per million 

(ppm), CH4 at 1853±2 parts per billion (ppb) 

and N2O at 328.9±0.1 ppb. These values 

constitute, respectively, 145%, 257% and 

122% of pre-industrial (before 1750) levels. 

The increase in CO2

 from 2015 to 2016 was 

larger than the increase observed from 2014 to 

2015 and the average over the last decade, and 

it was the largest annual increase observed 

in the post-1984 period. The El Niño event 

contributed to the increased growth rate in 

2016, both through higher emissions from 

terrestrial sources (e.g. forest fires) and 

decreased uptake of CO2 by vegetation in 

drought-affected areas. The El Niño event in 

2015/2016 contributed to the increased growth 

rate through complex two-way interactions 

between climate change and the carbon cycle. 

Figure 3. Top row: 

Globally averaged mole 

fraction (measure of 

concentration), from 

1984 to 2016, of CO2

 in 

parts per million (left), 

CH4

 in parts per billion 

(middle) and N2

O in parts 

per billion (right). The 

red line is the monthly 

mean mole fraction with 

the seasonal variations 

removed; the blue 

dots and line depict 

the monthly averages. 

Bottom row: The growth 

rates representing 

increases in successive 

annual means of mole 

fractions for CO2

 in parts 

per million per year 

(left), CH4

 in parts per 

billion per year (middle) 

and N2

O in part per 

billion per year (right).

(Source: WMO Global 

Atmosphere Watch)

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The reconstruction of past climate provides 

an opportunity to learn how the Earth 

system responded to high concentrations 

of atmospheric carbon dioxide (CO2). To 

obtain information about the state of the 

atmosphere before instrumental records 

began, combinations of proxies are used 

in which physical characteristics of past 

environmental conditions are preserved. 

Tiny bubbles of ancient air captured in ice 

cores when new snow accumulating at the top 

solidified into ice, can be directly measured 

and give some insight into the composition 

of the atmosphere in the past. 

Direct measurements of atmospheric CO2 over 

the past 800 000 years (see figure) provide 

proof that over the past eight swings between 

ice ages (glacials) and warm periods similar to 

today (interglacials) atmospheric CO2

 varied 

between 180 and 280 parts per million (ppm), 

demonstrating that today’s CO2 concentration 

of 400 ppm exceeds the natural variability 

seen over hundreds of thousands of years. 

Over the past decade, new high-resolution ice 

core records have been used to investigate 

how fast atmospheric CO2

 changed in the past. 

After the last ice age, some 23 000 years ago, 

CO2 concentrations and temperature began to 

rise. During the period recorded in the West 

Antarctica ice core, fastest CO2 increases 

(16 000, 15 000 and 12 000 years ago) ranged 

between 10 and 15 ppm over 100–200 years. 

In comparison, CO2 has increased by 120 ppm 

in the last 150 years due to combustion of 

fossil fuel. 

Periods of the past with a CO2 concentration 

similar to the current one can provide estimates 

for the associated “equilibrium” climate. In 

the mid-Pliocene, 3–5  million years ago, 

the last time that the Earth’s atmosphere 

contained 400 ppm of CO2, global mean surface 

temperature was 2–3 °C warmer than today, 

the Greenland and West Antarctic ice sheets 

melted and even some of the East Antarctic 

ice was lost, leading to sea levels that were 

10–20 m higher than they are today. During 

the mid-Miocene (15–17 million years ago), 

atmospheric CO2

 reached 400–650 ppm and 

global mean surface temperature was 3–4 °C 

warmer than today. 

PALEO AND CURRENT CONCENTRATIONS OF CO2 

50 40 30 20 10

Million years ago Thousand years ago Year CE

1000

2000

900

800

700

600

500

400

300

200

Atmospheric CO2 (ppm)

800

600

400

200

0

5 4 3 2 1

1800

1900

2000

2100

2200

2300

Boron Alkenones Ice cores

isotopes

Leaf stomata

2017

Pre-industrial

RCP’s

Ice core data

+8 ̊C

+1 ̊C

CE – Common Era

8

Reconstructions of atmospheric CO2 over the past 55 million years are generated from proxy data that include boron isotopes 

(blue circles), alkenones (black triangles) and leaf stomata (green diamonds). Direct measurements from the past 800 000 years 

are acquired from Antarctic ice cores and modern instruments (pink). Future estimates include representative concentration 

pathways (RCPs) 8.5 (red), 6 (orange), 4.5 (light blue), and 2.6 (blue). References for all data shown in this plot are listed in the 

extended online version (http://www.wmo.int/pages/prog/arep/gaw/ghg/ghg-bulletin13).

P00000084593

Case 6:15-cv-01517-AA Document 341-376 Filed 08/24/18 Page 10 of 40
9

For CH4, the increase from 2015 to 2016 was 

slightly smaller than that observed from 2014 

to 2015 but larger than the average over the 

past decade. For N2O, the increase from 2015 

to 2016 was also slightly smaller than that 

observed from 2014 to 2015 and lower than 

the average growth rate over the past 10 years.

OZONE

The 2017 Antarctic ozone hole was relatively 

small by the standards of recent decades. This 

largely reflects local atmospheric conditions 

in 2017 and is not, in itself, indicative of a 

more sustained downward trend. Most ozone 

hole indicators show weak, non-significant 

downward trends over the last 20 years.

The daily ozone hole area reached a 

maximum for the season of 19.6 million km2

on 11 September. The first part of the season, 

up to the second week of September, saw the 

size of the Antarctic ozone hole at levels close 

to the 1979–2016 average. However, the polar 

vortex became unstable and elliptical in the 

third week of September, with temperatures 

at the polar cap (60–90°S) rising 5–7 °C above 

the long-term mean. This resulted in a rapid 

decrease in the size of the ozone hole before a 

small increase around the end of September. 

The average area of the ozone hole through 

the peak of the season (from 7 September to 

13 October) was 17.4 million km2. This is the 

smallest value since 2002 (12.0 million km2) 

and also smaller than in 2012, the lowest value 

in the 2003–2016 period (17.8 million km2). The 

average ozone hole area over the 30 worst 

consecutive days was 17.5 million km2. This 

is also the lowest value observed since 2002 

(15.5  million  km2) and again somewhat 

smaller than in 2012 (18.9 million km2).

 Jul Aug Sep Oct Nov Dec

0

5

10

15

20

25

30

Ozone hole area

1979‒2016

2013

2014

2015

2016

2017

Area [106 km2

]

 Month

Figure 4. Area (millions 

of km2

) where the total 

ozone column is less 

than 220 Dobson units. 

The year 2017 is shown 

in red. The most recent 

years are shown for 

comparison as indicated 

by the legend. The 

smooth, thick grey line is 

the 1979–2016 average. 

The dark green-blue 

shaded area represents 

the 30th to 70th

percentiles and the light 

green-blue shaded area 

represents the 10th and 

90th percentiles for the 

time period 1979–2016. 

The thin black lines 

show the maximum 

and minimum values 

for each day during the 

1979–2016 period. The 

plot is made at WMO 

on the basis of data 

downloaded from the 

Ozone Watch website 

at the US National 

Aeronautics and Space 

Administration (NASA). 

The NASA data are 

based on satellite 

observations from the 

Ozone Mapping and 

Profiler Suite (OMPS), 

Ozone Monitoring 

Instruments (OMI) and 

the Total Ozone Mapping 

Spectrometer (TOMS).

P00000084594

Case 6:15-cv-01517-AA Document 341-376 Filed 08/24/18 Page 11 of 40
The Global Carbon Budget

Josep G. Canadell, 1

1

 Corinne Le Quéré, 2

2

Glen Peters 3 ,

3

 Robbie Andrew, 4

3

 Pierre Fridlingstein,4

Robert B. Jackson5 ,

5

 Tatiana Ilyina6

6

Accurately assessing carbon dioxide (CO2) 

emissions and redistribution within the 

atmosphere, oceans, and land – the “global 

carbon budget” – helps us capture how humans 

are changing the Earth’s climate, supports the 

development of climate policies, and improves 

projections of future climate change.

Carbon dioxide emissions from fossil fuels and industry 

have been growing for decades with pauses only during 

global economic downturns. For the first time, emissions 

stalled from 2014 to 2016 while the global economy 

continued to expand. Nonetheless, CO2 accumulated in 

the atmosphere at unprecedented rates close to 3 parts 

per million (ppm) per year in 2015 and 2016, despite 

stable fossil fuel emissions (figure, top). This surprising 

dynamic was caused by strong El Niño warming in 2015 

and 2016, when the land CO2

 sink was less efficient in 

removing atmospheric CO2, and emissions from fires 

were above average (in 2015). Preliminary data for 2017 

show that emissions from fossil fuels and industry 

resumed growing at about 1.5% (0.7%–2.4%, leap year 

adjusted), from 36.2±2.0 billion tonnes of CO2 in 2016 

to a record high of 36.6±2.0 billion tonnes in 2017 – 65% 

higher than in 1990.

Carbon dioxide emissions from change in land use were 

4.8±2.6 billion tonnes in 2016, accounting for 12% of 

all anthropogenic CO2 emissions, and are expected to 

remain stable or slightly lower for 2017 on the basis 

of initial observations using satellite data. Together, 

land use change and fossil fuel emissions reached an 

estimated 41.5±4.4 billion tonnes of CO2 in 2017. 

Of all anthropogenic CO2 emissions, only about 45% 

remained in the atmosphere on an annual average over 

the past decade: 25% were removed by the oceans and 

30% were removed by the terrestrial biosphere (figure, 

bottom). However, due to the strong El Niño conditions, 

the increase from 2015 to 2016 in atmospheric CO2 

1 Global Carbon Project, Commonwealth Scientific and Industrial Research 

Organisation (CSIRO) Oceans and Atmosphere, Canberra, Australia

2 Tyndall Centre for Climate Change Research, University of East Anglia, 

Norwich, United Kingdom

3 Center for International Climate and Environmental Research (CICERO) 

– Oslo (CICERO), Oslo, Norway

4 College of Engineering, Mathematics and Physical Sciences, University 

of Exeter, United Kingdom

5 Department of Earth System Science, Woods Institute for the Environment 

and Precourt Institute for Energy, Stanford University, Stanford, United 

States

6 Max Planck Institute for Meteorology, Hamburg, Germany

concentration was 22.1±0.7 billion tonnes (54% of total 

emissions; 2.85 ppm) which is larger than the average for 

2007–2016. Ocean and terrestrial ecosystems removed 

9.5±1.8 billion tonnes of CO2 (23%) and 9.9±3.7 billion 

tonnes of CO2 (24%), respectively.

There are substantial uncertainties in the quantification 

of the land and ocean carbon sinks on sub-decadal 

and decadal time scales, and in the reconstruction of 

cumulative emissions across centuries of the industrial 

era, particularly historical emissions from changes in 

land use.

Trends in anthropogenic CO2 emissions and growth of atmospheric 

CO2

, 1980–2017. Total emissions minus fossil fuel emissions equals 

emissions from change in land use (top). The historical global carbon 

budget, 1900–2016 (bottom) (Source: Global Carbon Project, http://www.

globalcarbonproject.org/carbonbudget; Le Quéré, C. et al., 2018: The Global 

Carbon Budget 2017. Earth System Science Data, 10, 405–448); and March 

2018 updates). CO2 flux (Gt CO2/yr)

40

30

20

10

0

‒10

‒20

‒30

‒40

1900 1920 1940 1960 1980 2000 2016

Sources

Sinks

Budget imbalance

Atmosphere

Ocean

Land-use change

Fossil fuels and industry

Year

Land

1980 1990 2000 2010 2017

45

40

35

30

25

20

15

10

5

0

Carbon dioxide (Gt CO2/yr)

Total emissions

Fossil fuels 

and industry

Atmospheric increase

Year

10

P00000084595

Case 6:15-cv-01517-AA Document 341-376 Filed 08/24/18 Page 12 of 40
11

THE OCEANS IN 2017 

TEMPERATURE

Global sea-surface temperatures in 2017 

were somewhat below the levels of 2015 and 

2016, but still ranked as the third warmest 

on record. The most significant sea-surface 

temperature anomalies were in the western 

tropical Pacific and the western and central 

subtropical South Indian Ocean. In both 

regions, sea-surface temperatures were 

widely 0.5 °C to 1.0 °C above the 1981–2010 

average, locally exceeding 1.0  °C above 

average in the Indian Ocean, and were 

generally at record-high levels. In contrast, 

temperatures were slightly below average 

over most of the eastern Indian Ocean and 

over the central and eastern equatorial Pacific, 

the latter being consistent with weak La Niña 

conditions which developed late in the year. 

They were also slightly below average in 

parts of the far southern Atlantic. The area of 

cool waters in the north-east Atlantic south 

of Iceland was less prominent than in most 

recent years. 

For the second successive year, aboveaverage sea-surface temperatures off the 

east coast of Australia resulted in significant 

coral bleaching in the Great Barrier Reef, 

this time focused on central areas of the 

Reef rather than the northern areas affected 

in 2016.6 Significant bleaching was also 

reported in other parts of the western tropical 

Pacific,7 including Micronesia and Guam, 

although global bleaching was less extensive 

than it had been in 2016. Later in the year, 

exceptionally warm sea-surface temperatures 

(generally 2 °C or more above average, and 

0.5 °C or more above previous records for the 

time of year) affected the southern Tasman 

Sea, coinciding with record high monthly 

temperatures in New Zealand (especially the 

South Island) and Tasmania. Whilst marine 

impacts of this event are still becoming 

apparent, there has already been a shift in 

the distribution of fish species, with snapper 

being caught off Fiordland (far south-west 

New Zealand) for the first time.

6 Australian Research Council (ARC) Centre of Excellence, 

Coral Reef Studies, https://www.coralcoe.org.au/.

7 NOAA Coral Reef Watch, coralreefwatch.noaa.gov. 

Ocean heat content, a measure of the heat 

in the oceans through their upper layers, 

reached new record highs in 2017. Mean ocean 

heat content for 2017 for the 0–700 metre 

layer was 158.1 ZJ,8

 6.9 ZJ higher than the 

previous annual mean record set in 2015. The 

mean for the October–December 2017 quarter, 

163.4 ZJ, was also the highest quarterly 

value on record. The ocean heat content for 

the 0–2000 metre layer (233.5 ZJ) was also 

the highest on record, although records for 

this layer only extend back to 2005. Annual 

records for the 0–700 metre layer were also 

set for the northern hemisphere and for the 

Atlantic and Pacific Oceans, although the 

Indian Ocean had its lowest value since 2009. 

8 Data sourced from NOAA; 1 ZJ (zetajoule) = 1021 J, a standard 

unit of energy. 

Figure 5. 5 December 

2017 monthly seasurface temperature 

anomalies (°C), showing 

temperatures 2.5°C or 

more above average in 

the southern Tasman 

Sea. 

(Source: Australian 

Bureau of Meteorology)

2.5 2.0 1.5 1.0 0.5 0.0 ‒0.5 ‒1.0 ‒1.5 ‒2.0 ‒2.5

Figure 6. Global 

ocean heat content 

change (x 1022 J) for 

the 0–700 metre layer: 

three-monthly means 

(red), and annual (black) 

and 5-year (blue) 

running means, from the 

US National Oceanic 

and Atmospheric 

Administration (NOAA) 

dataset. 

(Source: prepared by 

WMO using data from 

NOAA National Centers 

for Environmental 

Information)

‒10

‒5

0

5

10

15

20

1950 1960 1970 1980 1990 2000 2010 2020

Yearly average to end of 2017

5-year average 2012–2017

3-month average October–December 2017

Year

Heat content (1022 Joules)

P00000084596

Case 6:15-cv-01517-AA Document 341-376 Filed 08/24/18 Page 13 of 40
12

Figure 7. Global mean 

sea-level time series 

(with seasonal cycle 

removed), January 

1993–January 2018, 

from satellite altimetry 

multi-missions. Data 

from AVISO 

(Source: CollecteLocalisation-Satellite 

(CLS) – Laboratoire 

d’Etudes en Géophysique 

et Océanographie 

Spatiales (LEGOS))

SEA LEVEL

The global mean sea level (GMSL) was 

relatively stable in 2016 and early 2017. This 

is because the temporary influence of the 

2015/2016 El Niño (during which the GMSL 

peaked in early 2016 at around 10 millimetres 

above the 2004–2015 trend) continued to 

diminish and the GMSL reverted to values 

closer to the long-term trend. However, most 

recent sea-level data indicate that the GMSL 

has been rising again since mid-2017.

The pie charts show the contributions of 

individual components of the sea-level budget 

(expressed in percentage of the observed 

global mean sea level) for two periods, 

1993–2004 and 2004–2015.9 It clearly shows 

that the magnitude of almost all components 

has increased in recent years, particularly 

melting of the polar ice sheets, mostly in 

Greenland and to a lesser extent in Antarctica. 

Accelerated ice-mass loss from the ice sheets 

is the main cause of acceleration of the global 

mean sea-level rise, as revealed by satellite 

altimetry. This is even clearer when year-toyear fluctuations due to El Niño and La Niña 

as well as temporary cooling from the 1991 

Mt Pinatubo eruption are removed.10

The bar chart (bottom) shows annual mean 

altimetry-based sea level (blue bars) and 

sum of thermal expansion and ocean mass 

component (red bars) for the years 2005 

to 2016. Black vertical bars are associated 

uncertainties. Thermal expansion is based 

on Argo data11 and ocean mass is derived 

from the Gravity Recovery and Climate 

Experiment (GRACE) (updates from Johnson 

and Chambers, 2013,12 Lutchke et al., 2013,13

9 Dieng, H. et al., 2017: New estimate of the current rate of 

sea level rise from a sea level budget approach. Geophysical 

Research Letters, 44, doi:10.1002/2017GL073308.

10 Nerem, R.S. et al., 2018: Climate-change-driven accelerated 

sea-level rise detected in the altimeter era. Proceedings of 

the National Academy of Sciences of the United States of 

America, published on line on 13 February 2018.

11 Ibid.

12 Johnson, G. C. and D. P. Chambers, 2013: Ocean bottom 

pressure seasonal cycles and decadal trends from GRACE 

Release-05: Ocean circulation implications. Journal of 

Geophysical Research, Oceans, Vol.118, 9:4228–4240, doi:

10.1002/jgrc.20307.

13 Luthcke, S.  B. et al., 2013: Antarctica, Greenland and 

Gulf of Alaska land-ice evolution from an iterated GRACE 

global mascon solution. Journal of Glaciology, 59:613–631, 

doi:10.3189/2013JoG12J147.

–10

100

1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Time (year)

20

30

40

0

10

50

60

70

80

Sea level (mm)

ESA Climate Change Initiative (SL_cci) data

AVISO+ near-real-time Jason-3 data

Total land ice: 47%

Glaciers

(0.78 mm/yr)

Greenland

(0.82 mm/yr)

Antarctica

(0.33 mm/yr)

Land waters

(0.23 mm/yr)

Water vapour

(–0.03 mm/yr)

Thermal

expansion

(1.14 mm/yr)

2004–2015 

Sea-level rise

3.5 mm/yr

Total land ice: 55%

Glaciers

(0.71 mm/yr)

Greenland

(0.3 mm/yr)

Antarctica

(0.29 mm/yr) Land waters Water vapour (0.25 mm/yr) (–0.03 mm/yr)

Thermal

expansion

(0.94 mm/yr)

1993–2004

Sea-level rise

2.7 mm/yr

Observed global mean sea level

Sum on thermal expansion and ocean mass

Sea level (mm) 50

45

40

35

30

0

25

20

15

10

5

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Time (year)

Figure 8. Percentage 

of individual 

contributions to global 

mean sea-level rise 

in 1993–2004 and 

2004–2015 (top); annual 

sea-level budget 

(2005–2016) (bottom)

(Source: Dieng, H. et al., 

2017: New estimate of 

the current rate of sea 

level rise from a sea 

level budget approach. 

Geophysical Research 

Letters, 44)

P00000084597

Case 6:15-cv-01517-AA Document 341-376 Filed 08/24/18 Page 14 of 40
13

Watkins et al., 201514). The sea-level budget is 

almost closed (i.e. the observed change can 

be almost fully accounted for by the known 

changes in the contributing components) 

within respective error bars, although since 

2012 the sum of contributions from thermal 

expansion and changes in ocean mass is 

generally slightly lower than the observed 

change in annual sea level. The plot also 

shows a clear increase of the mean sea level 

from one year to another.

OCEAN ACIDIFICATION

The ocean absorbs up to 30% of the annual 

emissions of anthropogenic CO2 into the 

atmosphere, helping to alleviate the impacts 

of climate change on the planet. However, 

this comes at a steep ecological cost, as 

the absorbed CO2 reacts in seawater and 

changes acidity levels in the ocean. More 

precisely, this involves a decrease in seawater 

pH together with closely linked shifts in the 

carbonate chemistry of the waters, including 

the saturation state of aragonite, which is the 

main form of calcium carbonate used by key 

species to form shells and skeletal material 

(e.g. reef-building corals and shelled molluscs). 

Observations of marine acidity in open ocean 

and coastal locations have revealed that 

present-day conditions are often outside 

pre-industrial bounds. In some regions, the 

changes are amplified by natural processes 

such as upwelling (where cold water that is 

rich in CO2 and nutrients rises from the deep 

toward the sea surface), resulting in conditions 

outside biologically relevant thresholds. 

Projections of future ocean conditions show 

that ocean acidification affects all areas of 

the ocean, while consequences for marine 

species, ecosystems and their functioning 

vary. Over the past 10 years, various studies 

have confirmed that ocean acidification is 

directly influencing the health of coral reefs; 

the success, quality and taste of aquacultureraised fish and seafood; and the survival and 

calcification of several key organisms. These 

alterations often affect species at lower trophic 

levels and have cascading effects within the 

14 Watkins, M. et al., 2015: Improved methods for observing 

Earth's time variable mass distribution with GRACE using 

spherical cap mascons. Journal of Geophysical Research, 

Solid Earth, 120:2648–2671, doi:10.1002/2014JB011547.

food web, which are expected to result in 

increasing impacts on coastal economies. 

Further, ocean acidification does not impact 

marine ecosystems in isolation. Multiple 

other environmental stressors can interact 

with ocean acidification, such as ocean 

warming and stratification, de-oxygenation 

and extreme events, as well as other 

anthropogenic perturbations such as 

overfishing and pollution.

There has been a consistent trend in ocean 

acidification over time. Since records at Aloha 

station (north of Hawaii) began in the late 

1980s, seawater pH has progressively fallen, 

from values above 8.10 in the early 1980s to 

between 8.04 and 8.09 in the last five years.

THE CRYOSPHERE IN 2017

Sea-ice extent was well below the 1981–2010 

average throughout 2017 in both the Arctic and 

Antarctic. The winter maximum of Arctic sea 

ice of 14.42 million square kilometres, reached 

on 7 March, was the lowest winter maximum 

in the satellite record, 0.10 million square 

kilometres below the previous record low set 

in 2015. However, melting during the spring 

and summer was slower than in some recent 

years. The summer minimum of 4.64 million 

square kilometres on 13 September was the 

eighth-lowest on record, 1.25 million square 

kilometres above the 2012 record low. A slow 

8.15

8.10

400 Carbon Dioxide

375

350

325

300

8.05

7.95

7.90

7.85

7.80

270

260

250

240

230

220

210

8.00

CO2‒(μmol/kg) 3

pH ppm

Year

1980 1985 1990 1995 2000 2005 2010 2015

Figure 9. Trends in 

surface (< 50 m) ocean 

carbonate chemistry 

calculated from 

observations obtained at 

the Hawaii Ocean Timeseries (HOT) Program in 

the North Pacific over 

1988–2015. The upper 

panel shows the linked 

increase in atmospheric 

(red points) and 

seawater (blue points) 

CO2

 concentrations. The 

bottom panel shows a 

decline in seawater pH 

(black points, primary 

y-axis) and carbonate 

ion concentration (green 

points, secondary 

y-axis). Ocean chemistry 

data were obtained from 

the Hawaii Ocean Timeseries Data Organization 

& Graphical System 

(HOT-DOGS). 

(Source: US 

National Oceanic 

and Atmospheric 

Administration (NOAA), 

Jewett and Romanou, 

2017)

P00000084598

Case 6:15-cv-01517-AA Document 341-376 Filed 08/24/18 Page 15 of 40
freeze-up during the autumn saw Arctic seaice extent once again at near record low levels 

for the time of year by the end of December. 

Antarctic sea-ice extent was at or near record 

low levels throughout the year. The summer 

minimum of 2.11 million square kilometres, 

recorded on 3 March, was 0.18 million square 

kilometres below the previous record set 

in 1997, whilst the winter maximum of 

18.03 million square kilometres, recorded 

on 12 October (the equal-latest maximum 

date on record), was second behind 1986. 

The mass balance change (the estimated 

change of the mass of ice from one year 

to the next) of the Greenland ice sheet in 

the year from September 2016 to August 

2017 was well above the 1981–2010 average, 

due mainly to unusually heavy precipitation 

during autumn 2016. The mass balance 

change from September to December 2017 

was close to average. Although the overall 

ice mass increased, this was only a small 

departure from the trend over the past two 

decades, with the Greenland ice sheet having 

lost approximately 3 600 billion tons of ice 

mass since 2002.

Mass balance change data for 2017 for 

glaciers outside major continental ice sheets 

are not yet available. For 2016, mass balance 

change, averaged across a set of 26 reference 

glaciers with data available at the time of 

writing, was approximately −900 mm water 

equivalent. This was a smaller decrease than 

in 2015, but close to the 2011–2016 mean. 

The glacial mass balance change has been 

negative in every year since 1988. 

The northern hemisphere snow cover extent 

was near or slightly above the 1981–2010 

average for most of the year, most significantly 

in May (9% above average, 12th highest on 

record). May snow cover extent was the 

highest since 1996, and the highest in Eurasia 

since 1985, with particularly strong anomalies 

in north-western Russia and northern 

Scandinavia, where May temperatures were 

well below average. Summer snow cover 

extent, which has been showing a strong 

downward trend, was close to the long-term 

average in 2017 for the first time in more than 

a decade, giving June, July and August the 

highest values since 2004, 2006 and 1998 

respectively. Similar to most recent years, 

autumn snow cover extent was above average, 

although not to the same extent as in 2016, 

with October and November both ranking 9th

highest. Snow cover extent returned to slightly 

below average in December. Contrasting 

precipitation anomalies during the 2016/2017 

winter saw alpine snow cover well below 

average in most of the European Alps, but at 

or near record high levels in Corsica. 

In the southern hemisphere, an extensive 

snow event in southern South America from 

14 to 21 June saw continental snow cover 

extent reach 750 000 square kilometres, the 

highest since satellite monitoring began in 

2005, whilst the alpine snowpack at high 

elevations in south-eastern Australia was 

the deepest since 2000.

14

2017 

–45

–35

–25

–15

–5

5

15

25

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014

Year

Percentage

–45

–35

–25

–15

–5

5

15

25

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017

Year

Percentage

Figure 10. (left) 

September sea-ice 

extent for the Arctic, 

and (right) September 

sea-ice extent for the 

Antarctic. Percentage 

of long-term average 

of the reference period 

1981–2010 

(Source: prepared by 

WMO using data from 

the US National Snow 

and Ice Data Center)

P00000084599

Case 6:15-cv-01517-AA Document 341-376 Filed 08/24/18 Page 16 of 40
MAJOR DRIVERS OF INTERANNUAL 

CLIMATE VARIABILITY IN 2017

There are several large-scale modes of 

variability in the world’s climate that influence 

conditions over large parts of the world on 

seasonal to interannual timescales. The El 

Niño/Southern Oscillation (ENSO) is probably 

the best-known of the major drivers of 

interannual climate variability. The equatorial 

Indian Ocean is also subject to fluctuations 

in sea-surface temperatures, although on a 

less regular basis than the Pacific. The Indian 

Ocean Dipole (IOD) describes a mode of 

variability that affects the western and eastern 

parts of the ocean. The Arctic Oscillation (AO) 

and North Atlantic Oscillation (NAO) are two 

closely related modes of variability in the 

atmospheric circulation at middle and higher 

latitudes of the northern hemisphere. In 

positive mode, the subtropical high-pressure 

ridge is stronger than normal, as are areas 

of low pressure at higher latitudes, such as 

the “Icelandic” and “Aleutian” lows, resulting 

in enhanced westerly circulation through 

mid-latitudes. In negative mode, the reverse 

is true, with a weakened subtropical ridge, 

weakened higher-latitude low pressure areas 

and an anomalous easterly flow through 

mid-latitudes. The Southern Annular Mode 

(SAM), also known as the Antarctic Oscillation 

(AAO), is the southern hemisphere analogue 

of the AO. 

In contrast with 2016, which saw the later 

part of one of the strongest El Niño events 

of the last 50 years, a neutral phase of ENSO 

prevailed for most of 2017. The year began 

with conditions slightly cooler than average 

in the central and eastern equatorial Pacific, 

consistent with the borderline cool neutral/

weak La Niña conditions which had existed 

in the last part of 2016. These cool anomalies 

had weakened by February, before becoming 

re-established later in 2017. By November, 

conditions had cooled to the point where a 

weak La Niña event had been declared by 

most agencies. 

Whilst there was no basin-wide El Niño in 

2017, there was a sharp warming near the 

South American coast early in the year, of a 

type more often seen during El Niño events. 

Temperatures near the coast of Ecuador and 

Peru were more than 2 °C above average 

in February and March, before declining in 

the following months. These warm coastal 

temperatures were associated with significant 

flooding, particularly in Peru (something 

which had been largely absent during the 

previous year’s El Niño), whilst there were 

also heavy rains and flooding in California 

to an extent which far exceeded that of the 

2015/2016 El Niño. 

The Indian Ocean Dipole was generally on 

the positive side of neutral for most of 2017, 

although the strength of the signal varied 

considerably between different datasets (the 

strongest cool signal in the eastern Indian 

Ocean was also south of the 10°S southern 

boundary of the area used to define IOD 

indices). The IOD state was associated with 

dry conditions in much of Australia between 

May and September, and with a return to 

average to above-average rains in the Horn 

of Africa late in the year after an extended 

period of drought. 

15 15

Figure 11. The Oceanic 

Niño Index (ONI) (top) 

and Indian Ocean Dipole 

(IOD) index (bottom). 

(Source: prepared by 

WMO using data from 

the US National Oceanic 

and Atmospheric 

Administration (NOAA) 

Climate Prediction 

Center (ONI) and the 

Australian Bureau of 

Meteorology (IOD))

Oceanic Niño Index (ONI)

–3

–2

–1

0

1

2

3

1950 1960 1970 1980 1990 2000 2010 2020

Year

Indian Ocean Dipole (IOD)

–1.5

-1

–0.5

0

0.5

1

1.5

1 Apr 2012 14 Aug 2013 27 Dec 2014 10 May 2016 22 Sept 2017 4 Feb 2019

Date

P00000084600

Case 6:15-cv-01517-AA Document 341-376 Filed 08/24/18 Page 17 of 40
16

The Arctic Oscillation and North Atlantic 

Oscillation were both generally positive in 

their season of peak influence, January to 

March, with index values of +0.88 and +0.74 

respectively, although in both cases these 

values were less strongly positive than in 

the equivalent period of 2016. These positive 

index values were associated with generally 

above-average temperatures in the 2016/2017 

winter in most of Europe (despite a cold 

January) and eastern North America, and with 

dry winter conditions in the Mediterranean. 

Arctic Oscillation index values at the start of 

the 2017/2018 winter were near zero. 

The Southern Annular Mode had its first period 

of sustained negative values for over two 

years in late 2016 and early 2017, with the 

three-month SAM index for November 2016 

to January 2017 reaching −1.07, the strongest 

negative value since late 2013. Positive values 

then resumed for most of the remainder of 

2017, although they were not as strong as 

those that prevailed for most of 2015 and 2016. 

PRECIPITATION IN 2017

There were fewer areas with large precipitation 

anomalies in 2017 than there had been in 2015 

or 2016, as the influence of the strong El Niño 

event of 2015/2016 ended. 

The most extensive area with annual rainfall 

above the 90th percentile in 2017 was in 

north-east Europe, extending from northern 

European Russia as far west as northern 

Germany and southern Norway. European 

Russia had its second-wettest year on record 

(as did Russia as a whole) and Norway its 

sixth-wettest. Autumn was especially wet in 

the Baltic region, with Estonia and Lithuania 

both having their wettest autumn on record 

and Latvia its second-wettest. 

Thailand had its wettest year on record, with 

national rainfall 27% above average. The 

south was especially wet with the east coast 

region 56% above average. However, the high 

rainfall was more evenly distributed through 

the year than it was in the previous record 

wet year of 2011. Even though that year’s 

extreme flooding was not repeated, there 

were significant local floods from time to time, 

particularly in the south of the country early 

in the year. Rainfall above the 90th percentile 

also occurred in the Philippines, parts of 

eastern Indonesia and the interior of Western 

Australia. 

Other areas with annual rainfall above the 90th

percentile included parts of inland southern 

Africa, scattered areas in the southern half 

of South America east of the Andes, and 

around the Great Lakes in North America. 

Michigan had its wettest year on record, with 

very wet conditions also in the Great Lakes 

and St Lawrence region of Canada. Rainfall 

significantly above average also affected many 

parts of Central America and the Caribbean 

islands, with the largest anomalies in those 

parts of the Eastern Caribbean that were most 

affected by hurricanes. 

Dry conditions with rainfall below the 10th 

percentile were most widespread around the 

Mediterranean, extending east as far as the 

Islamic Republic of Iran. They were especially 

16

90oN

60oN

30oN

EQ

30oS

60oS

90oS

180o 120oW 60oW 0 60oE 120oE 180o

0.1 0.2 0.3 0.4 0.6 0.7 0.8 0.9

Figure 12. Annual total 

precipitation expressed 

as a percentile of the 

1951–2010 reference 

period for areas that 

would have been in the 

driest 20% (brown) and 

wettest 20% (green) 

of years during the 

reference period, with 

darker shades of brown 

and green indicating the 

driest and wettest 10%, 

respectively

(Source: Global 

Precipitation Climatology 

Centre, Deutscher 

Wetterdienst, Germany)

Deviation, %

65.0

70.0

75.0

80.0

85.0

90.0

95.0

100.0

105.0

110.0

115.0

120.0

125.0

130.0

135.0

140.0

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2017

Year

Figure 13. Annual 

precipitation for Norway 

in percentage of normal 

(Source: Norwegian 

Meteorological Institute 

(Met.no))

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17

prominent in southern Europe, from Italy 

westwards to Portugal, in north-western 

Africa and in south-west Asia, from eastern 

Turkey and the western Islamic Republic of 

Iran south to Israel. A small but significant 

area with rainfall below the 10th percentile 

affected the far south-west of South Africa. 

Other major areas with rainfall below the 

10th percentile in 2017 included parts of 

central India and eastern Brazil, and the North 

American Prairies on both sides of the United 

States-Canada border. 

Monsoon season rainfall was generally fairly 

close to average in the Indian subcontinent 

(where all-India rainfall for June to September 

was 5% below average), although with local 

variations, including significantly aboveaverage totals in much of Bangladesh and 

parts of far eastern India. Monsoon season 

rainfall was also fairly close to average in the 

Sahel of west and central Africa, although 

flooding in late August from local heavy rains 

caused significant losses in Niger. Rainfall in 

2017 was also close to average over most of 

the more heavily populated parts of western 

and central Indonesia, in Singapore, in most 

of Japan (where an exceptionally wet October 

offset a dry first half of the year) and in northwestern South America. 

EXTREME EVENTS

Extreme events have many significant 

impacts in terms of casualties, other health 

effects, economic losses and population 

displacement.15 They are also a major driver 

of interannual variability in agricultural 

production. 

A DESTRUCTIVE NORTH ATLANTIC 

HURRICANE SEASON, BUT NEAR 

AVERAGE GLOBALLY

There were 84 tropical cyclones around 

the globe in 2017,16 very close to the long15 World Bank, 2017: A 360 degree look at Dominica post 

Hurricane Maria, 28 November, www.worldbank.org/en/

news/feature/2017/11/28/a-360-degree-look-at-dominica

-post-hurricane-maria

16 Consistent with standard practice, the 2017 value quoted 

here is the sum of the values from January to December 

2017 for northern hemisphere basins, and July 2016–June 

2017 for southern hemisphere basins. 

term average. A very active North Atlantic 

season was offset by near- or below-average 

seasons elsewhere. The North Atlantic had 

17 named storms, and the seventh-highest 

value of Accumulated Cyclone Energy (ACE) 

on record, including a record monthly value 

for September. The Northeast and Northwest 

Pacific basins both had a near-average number 

of cyclones but relatively few severe cyclones, 

leading to below-average ACE values in both 

basins. 

The 2016/2017 southern hemisphere season 

was below average on all measures, 

particularly in the first half of the season. 

Whilst the Australian region had a nearaverage number of cyclones, the south-west 

Indian Ocean and south-west Pacific (east 

of 160°E) were both well below average. 

The total hemispheric ACE was the lowest 

recorded since regular satellite coverage 

began in 1970. 

Three exceptionally destructive hurricanes 

occurred in rapid succession in the North 

Atlantic in late August and September. Harvey

made landfall in south Texas as a category 

4 system, then remained near-stationary in 

the Houston area for several days, producing 

exceptionally prolonged extreme rainfall and 

severe flooding. An exceptional 1 539 mm of 

rain fell from 25 August to 1 September at a 

gauge near Nederland, Texas — the largest 

amount of rain ever recorded in a tropical 

cyclone in the United States — whilst the 

storm total rainfall was in the 900–1 200 mm 

range in much of metropolitan Houston.17 One 

17 National Hurricane Center, 2018: National Hurricane Center 

Tropical Cyclone Report –Hurricane Harvey, https://www

.nhc.noaa.gov/data/tcr/AL092017_Harvey.pdf. 

1980 1985 1990 1995 2000 2005 2010 2015 2020

Year

80

40

60

120

100

Number of global tropical cyclones

Figure 14. Total 

number of tropical 

cyclones globally, by 

year 

(Source: WMO)

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18

study18 found that the maximum three-day 

rainfalls during Hurricane Harvey were made 

three times more likely by anthropogenic 

climate change. 

Harvey was followed by Hurricane Irma, in early 

September, and by Maria in mid-September. 

Both hurricanes peaked at category 5 intensity, 

with Irma maintaining that intensity for 

60 hours, which is longer than in any North 

Atlantic hurricane in the satellite era. Irma’s 

initial landfall, at near-peak intensity, led to 

extreme damage across numerous Caribbean 

islands, most significantly on Barbuda, which 

experienced near-total destruction, with only 

a few inhabitants having returned as of early 

2018. Other islands to experience major 

damage included Saint Martin/Sint Maarten, 

Anguilla, St Kitts and Nevis, the Turks and 

Caicos Islands, the Virgin Islands and the 

18 Van Oldenborgh, G.J. et al., 2017: Attribution of extreme 

rainfall from Hurricane Harvey, August 2017. Environmental 

Research Letters, 12, 124009. 

southern Bahamas. Irma went on to track 

along the northern coast of Cuba, leading 

to extensive damage there, before making 

landfall in south-west Florida at category 4 

intensity. 

Hurricane Maria made initial landfall on 

Dominica at near-peak intensity, making it 

the first category 5 hurricane to strike the 

island, and leading to major destruction there. 

The World Bank estimates Dominica’s total 

damages and losses from the hurricane at 

US$ 1.3 billion or 224% of its Gross Domestic 

Product (GDP). The storm weakened slightly 

but was still a category 4 hurricane when 

it reached Puerto Rico. Maria triggered 

widespread and severe damage on Puerto 

Rico from wind, flooding and landslides. 

Power was lost to the entire island, and 

had only been restored to just over half the 

population three months after the hurricane, 

whilst water supplies and communications 

were also severely affected. 

All three of these hurricanes were assessed 

by the National Centers for Environmental 

Information (NCEI) as ranking in the top five 

for hurricane-related economic losses in 

the United States (alongside Katrina in 2005 

and Sandy in 2012), with estimated costs of 

US$ 125 billion for Harvey, US$ 90 billion for 

Maria and US$ 50 billion for Irma.

19 Irma and 

Maria also led to substantial losses outside 

the United States. At least 251 deaths were 

attributed to the three hurricanes in the United 

States (including Puerto Rico and the US 

Virgin Islands) and 73 elsewhere.20

Other significant hurricanes during the 2017 

North Atlantic season, both in October, 

were Hurricane Nate, which was associated 

with significant flooding in Central America 

19 The total losses reported by NCEI for these three hurricanes 

(central estimate US$265 billion) are higher than the assessment by Munich Re (US$215 billion, including losses outside 

the United States), but this difference is within the margin 

of uncertainty. It may also reflect differences in accounting 

for indirect economic losses. 

20 Unless otherwise stated, casualty and economic loss data 

reported in this statement are sourced from the EM-DAT database, Centre for Research on the Epidemiology of Disasters, 

Université catholique de Louvain, Belgium, www.emdat.be. 

For the 2017 North Atlantic hurricane season, casualties and 

economic losses for the United States and its territories 

were as reported by NCEI. 

Hurricane Harvey, 25–31 August 2017

Annual Exceedance Probabilities (AEPs) for the Worst Case 4-day Rainfall

ÏÐ

^_

Waco

Tyler

Austin Conroe

Lufkin

Houston

Killeen

Victoria Victoria

Beaumont Beaumont

Longview

Galveston

Kingsville Kingsville

Huntsville Huntsville

Port Arthur Port Arthur

Nacogdoches Nacogdoches

Corpus 

Christi Christi

College College 

Station

Te x as

L o u i s iana

Hydrometeorological Design Studies Center

Office of Water Prediction, National Weather Service

National Oceanic and Atmospheric Administration

http://www.nws.noaa.gov/ohd/hdsc/

Created 16 November 2017

Rainfall frequency estimates are from preliminary NOAA Atlas 14, Volume 11, Version 1.

Rainfall values come from 6-hour Stage IV data.

0 12.5 25 50

Miles

> 1/10

1/50 - 1/10

1/100 - 1/50

1/200 - 1/100

1/500 - 1/200

1/1000 - 1/500

< 1/1000

HOUSTON

 ̄

Landfall: 

26 August 2017 around 0300 UTC

Track with Storm Type

Hurricane

Tropical Storm

* National Hurricane Center

Preliminary Best Track

Figure 15. Annual 

exceedance 

probabilities for the 

peak 4-day rainfall 

during Hurricane 

Harvey, showing that 

much of the area from 

east Houston to the 

Texas-Louisiana border 

had 4-day rainfalls with 

an annual exceedance 

probability less than 

1 in 1000. (Source: 

US National Oceanic 

and Atmospheric 

Administration (NOAA)

P00000084603

Case 6:15-cv-01517-AA Document 341-376 Filed 08/24/18 Page 20 of 40
19

(especially Costa Rica and Nicaragua), 

and Hurricane Ophelia, which became the 

easternmost hurricane on record to reach 

major (category 3) intensity, before crossing 

Ireland as a transitioning extratropical 

storm and leading to widespread damage. 

Ophelia’s broader wind field also contributed 

to destructive wildfires in Portugal. 

Whilst the number of severe cyclones in 

the Northwest Pacific in 2017 was low, a 

number of systems still brought widespread 

destruction and heavy casualties, mostly 

from flooding. The largest loss of life from a 

tropical cyclone in 2017 was in late December, 

when Typhoon Tembin (Vinta) crossed the 

island of Mindanao with a peak 10-minute 

wind speed of 36 m s−1 (70 kt), resulting in 

at least 129 deaths,21 mostly from flooding. 

21 Philippines Office of Civil Defense, Situation Report 25, 

7 February 2018. 

Two separate events in Vietnam, an unnamed 

tropical depression in October and Typhoon 

Damrey (Ramil) in early November, were 

both associated with over 100 deaths from 

flooding. The heaviest economic losses were 

from Typhoon Hato (Isang) in August, which 

hit Hong Kong, Macau and neighbouring areas 

of China on 23 August, with an estimated 

US$ 6 billion in losses and at least 32 deaths.22

It was the strongest impact in Macau for more 

than 50 years.

The two most significant cyclones of the 

year in the North Indian Ocean were Cyclone 

Mora in late May, and Cyclone Ockhi in early 

December, both of which caused substantial 

casualties. The major impact of both cyclones 

was severe flooding and landslides associated 

with their respective precursor lows. Sri Lanka 

22 Reports from the China Meteorological Administration and 

the government of the Macao SAR. 

When Hurricane Irma made landfall, it hit Barbuda 

with maximum sustained winds of 295 km/h, record 

rainfall and a storm surge of nearly three metres. 

Deaths were limited to one but an estimated 90% 

of properties were damaged. This prompted the 

Prime Minister to order the complete evacuation 

of all residents as Hurricane Jose approached. It 

was three weeks before residents were permitted 

to return, and three months later only an estimated 

20% of the population had returned. The long-term 

impact remains to be seen, with damage and loss 

estimated at US$ 155 million, and recovery and 

reconstruction needs estimated at US$ 222.2 million1

– together accounting for approximately 9% of the 

gross domestic product of Antigua and Barbuda.

Hurricane Maria proved still more devastating for 

Dominica. Total damages and losses were estimated 

at US$ 1.3 billion or 224% of GDP, with significant 

parts of the island’s rainforest damaged and 

destroyed. This has implications for the whole of 

1 Post-Disaster Needs Assessment (PDNA) carried out with the support 

of the European Union (EU), the United Nations Development Programme 

(UNDP), the World Bank and the Caribbean Disaster Emergency 

Management Agency (CDEMA).

society: the losses incurred by the tourist sector 

alone are estimated at 19%, and 38% of housing was 

damaged.2 Maria caused the longest blackout in the 

history of the United States in Puerto Rico, which 

affected 35% of the island’s population for at least 

three months – continued problems following the 

hurricane may see the privatization of the Puerto Rico 

Electric Power Authority (PREPA), the largest publicly 

owned corporation in the United States.3 The disaster 

prompted the Federal Emergency Management 

Agency to approve US$ 1.02 billion of assistance to 

the Individuals and Households Program and obligate 

US$ 555 million in Public Assistance grants.4

2 Government of the Commonwealth of Dominica, 2017: Post Disaster 

Needs Assessment – Hurricane Maria, September 18, 2017, https://

reliefweb.int/sites/reliefweb.int/files/resources/dominica-pdna-maria.

pdf 

3 Attributed to the Governor of Puerto Rico.

4 Government of the United States of America, Department of Homeland 

Security, Federal Emergency Management Agency.

NORTH ATLANTIC HURRICANE SEASON 2017: INDUCED LOSS AND DAMAGE

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20

was badly affected by both cyclones, whilst 

Ockhi also had major impacts in southern 

India, including a great number of fishermen 

going missing at sea. The largest impacts 

from Northeast Pacific systems in 2017 were 

from flooding, with Tropical Storm Lidia

leading to significant flooding in Mexico in 

August, and Tropical Storm Selma (the first 

recorded tropical cyclone to make landfall 

in El Salvador) doing likewise in El Salvador, 

Nicaragua and Honduras. 

Although the number of tropical cyclones 

in the south-west Indian Ocean was below 

average, there were two which had major 

impacts. Dineo, with maximum 10-minute 

winds of 39 m s−1 (75 kt), was the first to 

make landfall in Mozambique since 2008 

when it hit in early February. In addition to 

its effects in Mozambique, the subsequent 

overland low resulted in severe flooding in 

Zimbabwe and northern South Africa, and 

was the main contributor to the 246 floodrelated deaths reported in Zimbabwe during 

the 2016/2017 rainy season.23 Enawo, in early 

March, hit the east coast of Madagascar at 

near its peak intensity (10-minute winds of 

57 m s−1 (110 kt)). Enawo had major impacts 

23 United Nations Office for the Coordination of Humanitarian 

Affairs (OCHA), 2017: Zimbabwe Flood Snapshot, https://

reliefweb.int/sites/reliefweb.int/files/resources/zimbabwe

_flood_snapshot_3march2017.pdf.

on Madagascar,24 with at least 81 associated 

deaths reported and extensive damage to 

houses, infrastructure and crops. Agricultural 

losses were estimated by the World Bank at 

US$ 207 million, mostly from the destruction 

of vanilla plantations.

In the Southwest Pacific, Cyclone Debbie

hit the east coast of Australia in late March, 

making landfall in the Whitsunday region 

with maximum 10-minute winds of 43 m s−1

(80 kt) after earlier peaking at 49 m s−1 (95 kt), 

leading to extensive wind and flood damage. 

The system then tracked south and south-east 

as a tropical low, with widespread major 

flooding, especially on the east coast near the 

Queensland-New South Wales border. The 

remnant system then went on to be largely 

responsible for major flooding in much of the 

North Island of New Zealand in early April. 

Insured losses for Debbie in Australia were 

approximately US$ 1.3 billion,25 the secondhighest on record for an Australian tropical 

cyclone. Cyclone Donna was the strongest 

May cyclone on record in the Southwest 

Pacific region, with peak 10-minute winds 

24 World Bank, 2017: Estimation of Economic Losses from Tropical Cyclone Enawo, https://reliefweb.int/sites/reliefweb.int/

files/resources/MG-Report-on-the-Estimation-of-Economic

-Losses.pdf.

25 Insurance Council of Australia, media release 6 November 

2017. 

ST THOMAS, US VIRGIN ISLANDS 

Hurricane Irma destruction

Chris B. Pye

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21

reaching 57 m s−1 (111 kt) on 8 May, with some 

damage reported, especially in Vanuatu. 

HIGH WINDS AND SEVERE LOCAL 

STORMS

There were a number of destructive severe 

thunderstorms in 2017, with central and 

eastern Europe particularly affected during the 

spring and early summer. Winds, exceeding 

100 km/h during a thunderstorm resulted in 

widespread damage and at least 11 deaths 

in Moscow on 29 May. Other noteworthy 

storms included a severe hailstorm and 

tornado that affected the southern suburbs 

of Vienna on 10 July, a 165 km/h wind gust 

at Innsbruck on 30 July, a hailstorm with 

hailstones up to 9 cm in diameter in Istanbul 

on 27 July, and widespread thunderstorms 

that left 50 000 households without power 

in southern Finland on 12 August. Severe 

flash flooding affected parts of the Croatian 

coast on 11 September, with 283 mm of rain 

recorded in 12 hours at Zadar. 

For the first time since 2011, the United States 

had an above-average tornado season, with 

a preliminary annual total of 1 406 tornadoes, 

12% above the 1991–2010 average. However, 

the number of fatalities during the season 

(34) was below the long-term average. The 

most destructive storm of the season was 

a hailstorm that hit Denver on 8 May, with 

hailstones exceeding 5  cm in diameter. 

Insured losses from this event exceeded 

US$ 2.2 billion. 

A severe windstorm (known locally as Zeus) 

affected France on 6–7 March. Peak gusts 

reached 193 km/h at Camaret-sur-Mer in 

Brittany, and the storm was rated by MétéoFrance as the most significant windstorm in 

France since 2010. Later in the year, a storm in 

late October produced wind gusts exceeding 

170 km/h at high elevations and 140 km/h in 

the lowlands in Austria and Czechia, with 

11 deaths reported in total. 

FLOODING (NON-TROPICAL CYCLONE) 

AND ASSOCIATED PHENOMENA

One of the most significant weather-related 

disasters of 2017, in terms of casualties, 

was a landslide in Freetown, Sierra Leone, 

on 14 August, in which at least 500 deaths 

occurred.26 Exceptionally heavy rain was a 

major contributor to this disaster; Freetown 

received 1 459.2 mm in the period from 1 

to 14 August, about four times the average 

rainfall for this period. Another major landslide 

associated with heavy rainfall occurred in 

Mocoa, in southern Colombia, on 1 April, 

with at least 273 deaths reported.

Many parts of the Indian subcontinent were 

affected by flooding during the monsoon 

season between June and September, despite 

overall seasonal rainfall being near average 

over the region. The most serious flooding 

occurred in mid-August, after extremely 

heavy rainfall over a region centred on eastern 

Nepal, northern Bangladesh and adjacent 

areas of northern and north-eastern India. 

Mawsynram (India), near the Bangladesh 

border, received 1 479 mm in the four days 

from 9 to 12 August. Daily totals in excess 

of 400 mm also occurred near the IndiaNepal border, and the Rangpur region of 

northern Bangladesh received 360  mm, 

approximately the average monthly total, on 

11–12 August. Across the period as a whole, 

more than 1 200 deaths were reported in 

India, Bangladesh and Nepal,27 whilst more 

than 40 million people were affected. The 

World Health Organization (WHO) noted that 

in Bangladesh alone more than 13 000 cases 

of waterborne diseases and respiratory 

infections were reported over three weeks 

in August,28 whilst extensive damage was 

reported to public health facilities in Nepal.29

Earlier in the season, 292 deaths were reported 

in Sri Lanka in late May, principally in southern 

26 International Organization for Migration (IOM), 2017 : Sierra 

Leone Flood Response. Situation Report, 28 August 2017, 

https://reliefweb.int/sites/reliefweb.int/files/resources/

SL%20Floods%20Sitrep%201.pdf.

27 World Meteorological Organization, 2017: Rainfall extremes 

cause widespread socio-economic impacts, https://

public.wmo.int/en/media/news/rainfall-extremes-cause

-widespread-socio-economic-impacts.

28 International Federation of Red Cross and Red Crescent 

Societies (IFRC), 2017: South Asia flood crisis: Disease 

outbreaks, funding shortages compound suffering of flood 

survivors, https://media.ifrc.org/ifrc/press-release/south

-asia-flood-crisis-disease-outbreaks-funding-shortages

-compound-suffering-flood-survivors/.

29 World Health Organization (WHO), 2017: Nepal. Situation 

Report #5, https://reliefweb.int/sites/reliefweb.int/files/

resources/who_sitrep-06sept2017.pdf.

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22

and western parts of the country, due to 

heavy rains from the precursor low to Cyclone 

Mora. Ratnapura received 384 mm of rain in 

24 hours on 25/26 May. Some 650 000 people 

were affected in some way by the floods, 

but the rains did little to alleviate significant 

drought in northern and eastern parts of Sri 

Lanka.30 Cyclone Mora also had significant 

impacts in Bangladesh and Myanmar.31 Heavy 

rains flooded thousands of hectares of crop 

and damaged poultry sheds, fishing nets 

and boats, severely eroding the livelihoods 

of communities that depend on agriculture 

and fisheries in the affected rural districts.

Flooding affected many parts of Peru in 

March, after sustained heavy rains. At 

least 75 deaths32 were reported, and over 

625 000 people were affected, including more 

than 70 000 who lost their homes. The Food 

and Agriculture Organization of the United 

Nations reported that there were significant 

crop production losses,33 particularly maize, in 

the main producing regions of Lambayeque, 

Piura and Ica. Flooding of this type typically 

30 Food and Agriculture Organization of the United Nations (FAO) 

and World Food Programme (WFP), 2017: Special Report. 

FAO/WFP Crop and Food Security Assessment Mission to 

Sri Lanka, http://www.fao.org/3/a-i7450e.pdf.

31 International Federation of Red Cross and Red Crescent 

Societies (IFRC), 2017: Emergency appeal revision. Bangladesh: Cyclone Mora, http://reliefweb.int/sites/reliefweb

.int/files/resources/MDRBD019_RevEA.pdf.

32 From information supplied by the United Nations Office for 

Disaster Risk Reduction (UNISDR). 

33 Food and Agriculture Organization of the United Nations 

(FAO), 2017: GIEWS – Global Information and Early Warning 

System. Country Brief: Peru, http://www.fao.org/giews/

countrybrief/country.jsp?code=PER.

affects Peru during the late phase of El Niño 

events. Whilst there was no Pacific-wide El 

Niño during 2017, sea-surface temperatures 

near the Peruvian coast in March were 2 °C 

or more above average, values which would 

be more typical of an El Niño year than of 

a neutral year such as 2017. Major flooding 

occurred mid-year in parts of southern China, 

especially within the Yangtze River basin. The 

heaviest rain fell in the provinces of Hunan, 

Jiangxi, Guizhou and Guangxi. Peak totals 

during the period from 29 June to 2 July 

were in excess of 250 mm. Fifty-six deaths 

were reported and economic losses were 

estimated at more than US$ 5 billion.34

DROUGHT

The drought that affected significant parts of 

east Africa during 2016 continued into 2017. 

In the March to May rainy season, seasonal 

rainfall was at least 20% below average 

over most of Somalia, Kenya and southern 

Ethiopia, and more than 50% below average 

over most of the northern half of Kenya and 

parts of Somalia. There was some easing 

of conditions late in the year, with near- to 

above-average rainfall over most of the region 

in the October–December period. Reports for 

Somalia indicated that 6.7 million people were 

experiencing food insecurity as of October, 

this declined to 5.4 million by the end of 

December as crop and pasture conditions 

improved.35

Drought worsened significantly during 2017 in 

the Cape Province of South Africa. Following 

below-average rainfall in 2015 and 2016, Cape 

Town had its driest year on record in 2017 with 

a total of 285 mm (47% below the 1981–2010 

average). The three-year period 2015−2017 

was also the driest on record (36% below 

average). The dry conditions led to local water 

supplies becoming severely depleted, with no 

significant recovery as of early 2018. However, 

generally average to above-average rainfall 

further north in southern Africa during the 

2016/2017 rainy season led to an improvement 

34 From information supplied by the China Meteorological 

Administration. 

35 Food and Agriculture Organization of the United Nations 

(FAO), 2018: FSNAU-FEWS NET Technical Release, 29 

January 2018, http://www.fsnau.org/in-focus/fsnau-fews

-net-technical-release-january-29-2018. 

Figure 16. Annual 

rainfall for Cape Town, 

South Africa, 1951–2017. 

(Source: South African 

Weather Service)

900

800

700

600

500

400

300

200

100

0

1951195319551957195919611963196519671969197119731975197719791981198319851987198919911993199519971999200120032005200720092011201320152017

Year

Annual rainfall (mm)

y = –1.1842x + 564.74

R2

 = 0.0374

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23

in conditions there, with the total number of 

people experiencing food insecurity declining 

from 40 million at the peak of the 2014–2016 

drought to 26 million in late 2017.36

Many parts of the Mediterranean region 

experienced significant drought in 2017, as 

did parts of central Europe. In the first part 

of 2017, the most severe anomalies were in 

Italy, which had its driest January to August 

on record (and went on to have its driest 

year, with annual rainfall 26% below the 

1961–1990 average). Further north, Bratislava 

(Slovakia) had its driest December to August 

on record and southern Moravia (Czechia) its 

second-driest January to August. Later in 

the year the focus of the dry conditions was 

on south-west Europe. Spain had its driest 

autumn on record, the Provence-Haute AlpesCôte d’Azur region in south-east France had 

its driest May to November, whilst Portugal 

had its driest April to December and its thirddriest year (its four driest having all occurred 

since 2004). Autumn was also very dry in 

Morocco. The eastern Mediterranean was 

also badly affected by drought, including 

the eastern half of Turkey, Cyprus and most 

of Israel. The coastal plain of Israel had its 

driest year on record.

Drought also affected a region of central North 

America on both sides of the United StatesCanada border. Particularly affected were 

the states of North Dakota and Montana, and 

the Prairie provinces of Canada, with areas 

of severe drought identified on both sides 

of the border.37 After a period of prolonged 

drought, the 2016/2017 winter rainfall season 

brought heavy rains to much of California, 

and the Sierra Nevada snowpack was 66% 

above average, the heaviest since 1998. 

Large-scale evacuations were required in 

the state’s north in February because of the 

risk of failure of the Oroville Dam. However, 

dry conditions resumed in the second half 

of the year, contributing to numerous major 

wildfires. 

36 World Food Programme (WFP), 2018: Poor Rains and Crop 

Infestation Threaten Deeper Hunger Across Southern Africa. 

Media release, 9 February 2018. 

37 National Oceanic and Atmospheric Administration (NOAA), 

2017: North American Drought Monitor, December 2017. 

Although rainfall deficit was not particularly 

extreme during 2017, near-average to belowaverage rainfall resulted in a continuation of 

multi-year drought in many parts of Brazil 

north of 20°S, and in central Chile (where 

2017 was the wettest year since 2008, but 

still drier than the long-term average). In the 

Asia-Pacific region, abnormally dry conditions 

were reported in the Korean Peninsula in 

the first half of 2017, whilst New Caledonia 

experienced significant drought, especially 

later in the year. 

HEATWAVES, A REGULAR FEATURE 

OF 2017

There were numerous significant heatwaves 

around the world during 2017, in both the 

southern and northern hemisphere summers.

Southern South America experienced 

extreme heat on several occasions during 

the 2016/2017 summer. The heat peaked 

in late January, when numerous Chilean 

stations had their hottest days on record, 

including Santiago (37.4  °C) and Curico 

(37.3 °C) on the 25th, and Chillan (41.5 °C) 

and Concepcion (34.1 °C) on the 26th. The heat 

extended eastwards into Argentine Patagonia 

where Puerto Madryn reached 43.4 °C on 

27 January, the highest temperature ever 

recorded so far south. It was also a notably 

hot summer in much of eastern Australia, 

where Moree had 54 consecutive days of 

35 °C or above from 28 December 2016 to 

19 February 2017, the longest such sequence 

on record in New South Wales. Numerous 

locations, including Moree (47.3 °C), Dubbo 

(46.1 °C), Scone (46.5 °C), Bathurst (41.5 °C) 

and Williamtown (45.5 °C) had their highest 

recorded temperature on 11–12 February. 

Extreme heat affected south-west Asia at the 

end of May. The temperature at Turbat, in the 

far south-west of Pakistan, reached 54.0 °C 

on 28 May, a national record for Pakistan 

and (if confirmed)38 an equal record for Asia. 

During this event, sites in the Islamic Republic 

of Iran, Oman and the United Arab Emirates 

also exceeded 50 °C. 

38 This observation, and another of 54.0  °C at Mitribah 

(Kuwait), are currently being reviewed by a WMO evaluation 

committee. 

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There were numerous heatwaves during 

the European summer, particularly in the 

Mediterranean region. The most significant 

affected Turkey and Cyprus in late June 

and early July, the western Mediterranean 

(especially Spain and Morocco) in mid-July, 

and Italy and the Balkans in early August. 

All-time records were set in all three events, 

including Antalya, Turkey (45.4 °C on 1 July); 

Cordoba (46.9 °C on 13 July), Granada (45.7 °C 

on 12 July) and Badajoz (45.4 °C on 13 July) 

in Spain; and Pescara (41.0 °C on 4 August), 

Campobasso (38.4 °C on 5 August) and Trieste 

(38.0 °C on 5 August) in Italy. 

The south-western United States had a very 

hot summer. Death Valley had the highest 

monthly mean temperature (41.9  °C) on 

record for an American station in July. Later 

in the season, record-high temperatures 

occurred in coastal California in early 

September, including San Francisco (41.1 °C 

on 1 September). Eastern China was another 

area to experience extreme summer heat, with 

records set at Shanghai (40.9 °C on 21 July) 

and at the Hong Kong Observatory (36.6 °C 

on 22 August, associated with offshore flow 

during Typhoon Hato). 

SIGNIFICANT COLD PERIODS IN 2017

Whilst 2017 saw above-average annual mean 

temperatures over almost all inhabited land 

areas, there were still some noteworthy cold 

events during the year.

January was a cold month over much of 

central and south-east Europe. Several 

countries experienced their coldest January 

since 1987, with monthly mean temperatures 

more than 5 °C below average in places. The 

cold also extended to parts of northern Africa 

with snowfalls in some elevated parts of the 

Algerian Sahara. 

A major late-season storm then affected the 

region on 20–21 April. Moldova was the hardest 

hit, with heavy falls of snow and freezing rain 

– exceptional for the time of year – causing 

extensive forest and agricultural damage. 

Following the storm, severe late-season frosts 

contributed to significant agricultural losses, 

estimated by Munich Re at EUR 3.3 billion,39

across many countries, including Switzerland, 

Austria, Ukraine, Romania, and Slovenia. The 

losses were exacerbated in many areas by 

unusually early development of crops due 

to an unusually warm March. 

Extreme cold affected parts of Argentina in 

July. Bariloche fell to −25.4 °C on 16 July, 

4.3 °C below its previous record. Very low 

overnight temperatures also occurred in parts 

of south-eastern Australia in the first few 

days of July, with record lows set at locations 

including Sale, Deniliquin and West Wyalong. 

39 Munich Re, 2018: Spring frost losses and climate change – 

not a contradiction in terms, 29 January 2018, https://www

.munichre.com/topics-online/en/2018/01/spring-frost?ref=

social&ref=Facebook&tid=NatCat2017%20Review. 

SNOW IN THE ALGERIAN SAHARA (AIN SEFRA REGION) 

NASA

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25

At the end of the year, a significant cold spell 

affected the north-eastern United States and 

eastern Canada, with temperatures remaining 

significantly below average for two weeks or 

more. The cold spell was more notable for its 

persistence than its intensity, with a number 

of locations setting or approaching records for 

the longest continuous period below certain 

thresholds: one example was Boston, which 

had a record seven consecutive days with 

maximum temperatures of 20 °F (−6.7 °C) 

or below from 27 December to 2 January. 

GLOBAL ASSESSMENT OF TEMPERATURE 

EXTREMES

Whilst a fully global assessment of 

temperature extremes for 2017 is not yet 

possible, the GHCNDEX dataset40 of extreme 

temperature indices can be used to assess 

values over those parts of the world for which 

there is coverage. For minimum temperatures, 

the main areas of coverage are Europe, North 

America, Australia and parts of Russia; for 

maximum temperatures, coverage also 

extends to much of Asia (except the south 

and south-west) and South Africa. There 

is little or no coverage in South or Central 

America, or in Africa outside South Africa. 

Over the areas that do have coverage, 

maximum temperatures above the 90th 

percentile occurred on an average of 16.7% 

of days, the third-highest value on record 

after 2015 (18.5%) and 2016 (17.8%). They 

40 Donat, M.G. et al., 2013: Global Land-Based Datasets for 

Monitoring Climatic Extremes. Bulletin of the American 

Meteorological Society, 94:997–1006. This dataset uses 

a 1961–1990 baseline for the calculation of percentile 

thresholds. 

occurred locally on 25% or more of days in 

parts of southern Queensland (Australia) and 

the Iberian Peninsula, and on 20% or more 

of days in much of eastern Australia, central 

Asia and southern Europe. Only western 

Canada and parts of north-west Russia had 

values below 10%. Warm nights were not as 

prevalent as warm days, with the average 

global frequency (15.7%) below the average 

of the last 10 years. 

Cold days, with maximum temperatures 

below the 10th percentile, occurred on 6.2% of 

days, the third-lowest value on record, whilst 

cold nights, with minimum temperatures 

below the 10th percentile, had the fifth-lowest 

value (5.7%). Cold extremes, both by day and 

night, were particularly uncommon in 2017 

in north-western Europe, with values in a 

region stretching from the United Kingdom 

to Germany below 3% for cold days and 4% 

for cold nights. Values above 10% were found 

only in parts of Australia for cold nights, and 

in central Canada for cold days, although 

10 12 14 16 18 20 22 24 26 28 30 32

unit: % of days

GHCNDEX TX90p ANN Avg 2017-2017

0 °C

17

–17

–13

–3

–7

–10

3

13

10

7

Figure 18. Percentage 

of days in 2017 with 

daily maximum 

temperatures above the 

90th percentile, from the 

GHCNDEX dataset

(Source: University 

of New South Wales 

Climate Change 

Research Centre, 

Australia )

Figure 17. Temperature 

anomalies for the 

period 26 December 

2017–5 January 2018 

(relative to 1981–2010) 

showing the intense cold 

wave in eastern North 

America. 

(Source: European 

Centre for Mediumrange Weather 

Forecasts (ECMWF) 

Copernicus Climate 

Change Service)

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26

much of the northern United States and 

southern Canada had a frequency of cold 

days between 8% and 10%. 

HEAT AND DROUGHT CONTRIBUTE TO 

NUMEROUS DESTRUCTIVE WILDFIRES 

Extreme heat and drought contributed to 

many destructive wildfires in various parts of 

the world in 2017. Whilst a return to near- or 

above-average rainfall contributed to reduced 

fire activity (compared with recent years) 

in various tropical regions, numerous midlatitude regions had severe fire seasons.

Chile had the most significant forest fires in its 

history during the 2016/2017 summer, when 

exceptionally dry conditions during 2016 were 

followed by extreme heat in December and 

January. Eleven deaths were reported, and a 

total of 614 000 hectares of forest were burnt – 

the highest seasonal total on record and eight 

times the long-term average.41 There were 

also significant fires during the 2016/2017 

southern hemisphere summer in various parts 

of eastern Australia (especially eastern New 

South Wales) and in the Christchurch region 

of New Zealand, whilst the southern South 

African town of Knysna was badly affected 

by fire in June.

It was a very active fire season in the 

Mediterranean region. The worst single 

incident took place in central Portugal in 

June, where 64 people died in a fire near 

Pedrogao Grande. There were further major 

fire outbreaks in Portugal and north-western 

Spain in mid-October (exacerbated by strong 

winds associated with the circulation of 

Hurricane Ophelia), with a further 45 deaths 

reported. The area burned in Portugal in the 

period from January to October42 was more 

than five times the 2007–2016 median. Other 

significant fires were reported, including in 

Croatia, France and Italy. 

It was also an active fire season in western 

North America, both in the United States 

and Canada. A wet winter, which allowed 

41 From information supplied by the Chilean Directorate of 

Meteorology. 

42 Portuguese Institute for Nature Conservation and Forests, 

http://www.icnf.pt/portal/florestas/dfci/Resource/doc/rel/

2017/8-rel-prov-1jan-30set-2017.pdf.

the heavy growth of ground fuels, followed 

by a dry and hot summer, provided ideal 

conditions for high-intensity fires, the worst 

of which occurred north of San Francisco in 

early October. At least 44 people died, the 

worst loss of life in a wildfire in the United 

States since 1918. Insured losses from the fire 

were assessed as at least US$ 9.4 billion,43 the 

worst for a wildfire anywhere in the world, 

even surpassing the 2016 Fort McMurray 

fires in Canada. Total economic losses for the 

2017 California fire season were assessed at 

US$ 18 billion. A further fire north-west of 

Los Angeles in December became California’s 

largest fire in modern history, and indirectly 

resulted in 21 deaths in flash floods and debris 

flows when heavy rains fell on the burnt area 

in early January.44

The total area burned in the contiguous United 

States in 2017 was 53% above the 2007−2016 

average,45 just short of the record set in 

2015, whilst the area burned in the western 

provinces of Canada was also far above 

average, with over 1.2 million hectares burned 

in British Columbia, about eight times the 

2006–2015 seasonal average.46 Long-lived 

fires in British Columbia and the north-west 

United States also contributed to heavy 

smoke pollution across the region.

A significant tundra fire occurred in August 

in the Disko Bay area, on the central west 

coast of Greenland. 

THE INFLUENCE OF ANTHROPOGENIC 

CLIMATE CHANGE ON EXTREME EVENTS

Determining the extent, if any, to which 

anthropogenic climate change has influenced 

the occurrence of extreme events has been 

an active area of research in recent years. 

43 California Department of Insurance, media release of 

6 December 2017, http://www.insurance.ca.gov/0400-news/

0100-press-releases/2017/release135-17.cfm.

44 National Centers for Environmental Information (NCEI), 

National Climate Reports for December 2017 and January 

2018. 

45 National Interagency Coordination Center, Wildland Fire 

Summary and Statistics – Annual Report 2017 https://www

.predictiveservices.nifc.gov/intelligence/2017_statssumm/

intro_summary17.pdf.

46 British Columbia Wildfire Service, http://bcfireinfo.for.gov

.bc.ca/hprScripts/WildfireNews/Statistics.asp. 

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27

Such analyses are now routinely published 

in the peer-reviewed literature, many of them 

as part of an annual report prepared as a 

supplement to the Bulletin of the American 

Meteorological Society (BAMS). 

The most-recently published BAMS report 

included 27 analyses of extreme events that 

occurred in 2016 (some of them multiple 

analyses of the same event), and found 

that anthropogenic climate change was a 

significant driver of the frequency of the 

event concerned in 21 of the 27 cases. In 

particular, of 15 analyses that assessed 

extreme temperature events (either on 

land or in the ocean), 13 found that their 

probability had been significantly influenced 

by anthropogenic climate change in the 

“expected” direction (that is, that a warm 

event had become more likely or a cold 

event less likely). One counter-example of 

interest was a frost event in south-western 

Australia in September 2016, where it was 

found that anthropogenic climate change 

had significantly increased the chance of the 

circulation anomalies which were the primary 

driver for the event (notwithstanding the 

background warming signal). As in previous 

years, anthropogenic signals were found 

less consistently for extreme precipitation 

events, with such signals being found (in 

three different analyses) for extremely high 

rainfall in eastern China during the summer 

of 2016, but not for extreme precipitation 

events in other parts of the world. 

Given the timeframes involved, few studies 

of 2017 events have yet been published in the 

peer-reviewed literature. One exception is 

an assessment of the extremely high rainfall 

associated with Hurricane Harvey. The WMO 

Expert Team on Climate Impacts on Tropical 

Cyclones also found47 that, whilst there is 

no clear evidence that climate change is 

making the occurrence of slow-moving, landfalling hurricanes more or less likely, it is 

probable that anthropogenic climate change 

made rainfall rates more intense, and that 

ongoing sea-level rise exacerbated storm 

surge impacts. Assessments of recent events 

– most of which use methods that have been 

documented in the peer-reviewed literature, 

although the assessments themselves are 

not – are regularly published shortly after 

the event through a variety of channels, and 

it is likely that many of these events will be 

documented in the peer-reviewed literature 

in due course. 

47 WMO expert team statement on Hurricane Harvey, https://

public.wmo.int/en/media/news/wmo-expert-team-statement-hurricane-harvey.

CALIFORNIA, UNITED STATES

October wildfires

NASA

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28

A common question when an extreme climate event 

happens is “was this event caused by climate change?”. 

Scientists address this question in a different way: “Was 

the chance of this event happening affected by human 

influences on the climate, and if it was, by how much?”. 

Answering this question has become a very active 

area of research in the last few years. Whilst a range 

of approaches has been used, the most common is to 

use climate models. The approach consists in running 

these models with all known climate forcings, both 

anthropogenic and natural, and with natural forcings 

only. Comparing the probability of the event in question 

using the two sets of model runs allows the attribution 

of the event to anthropogenic versus natural factors. 

This is often expressed as the Fraction of Attributable 

Risk (FAR), which is the probability that the event was 

the result of anthropogenic influence on climate as 

opposed to natural variability. 

Many of these studies have found that the probability 

of the extreme event has been influenced by human 

activity, either directly, or indirectly through, for example, 

affecting the likelihood of occurrence of an unusual 

circulation anomaly which triggered the extreme event; 

sometimes in conjunction with other influences such 

as the El Niño/Southern Oscillation (ENSO). Of a set of 

131 studies published between 2011 and 2016 in the 

Bulletin of the American Meteorological Society, 65% 

found that the event’s probability was significantly 

affected by anthropogenic activities.

The strongest anthropogenic influence has been 

found on temperature extremes: the likelihood of 

warm extremes increases and that of cold extremes 

decreases. This is especially true for events considered 

over large areas and over a long period of time, such as 

a season or a year. As an example, it was found that the 

2016 record-breaking global mean temperature would 

have been almost impossible without human activity.1

Because there is a higher level of “natural” variability 

at individual locations and over shorter timescales, it is 

more difficult to find a significant human signal in the 

occurrence of short-term extremes at specific places, 

although studies of that type are also starting to emerge. 

It has been more difficult to identify anthropogenic 

influence in the attribution of precipitation extremes. 

1 Knutson, T.R. et al., 2017 : CMIP5 model-based assessment of anthropogenic 

influence on record global warmth during 2016. Bulletin of the American 

Meteorological Society, 99:S11-S15. 

Whilst some studies have found that the probability 

of some extreme precipitation events was increased, 

most often indirectly, by climate change, for many 

other studies the results have been inconclusive. This 

is because the underlying long-term climate signal 

in extreme precipitation is less clear than it is for 

temperature and, because extreme precipitation events 

typically occur on shorter spatial scales than extreme 

temperature events. 

At present, attribution studies are mostly carried out 

in research mode and the most common platform for 

publishing these studies is through the traditional 

peer-reviewed literature. This is mostly done through 

an annual supplement to the State of the Climate 

report published in the Bulletin of the American 

Meteorological Society. Such studies usually appear 

several months following the occurrence of the event 

under consideration.

For some types of extremes, especially extremes defined 

using standard indices, such as national mean monthly 

temperature, methods have been developed which 

allow an assessment of the FAR for the event in close to 

real time. At present, most such reports are published 

through other channels than the National Meteorological 

and Hydrological Services (NMHSs) such as blogs, 

university or NGO websites, or the media. Operational 

attribution services under the auspices of NMHSs or 

Regional Climate Centres are in their infancy, although 

many individual NMHS scientists have contributed to the 

studies that are currently being published. Nevertheless, 

it is expected that there will be substantial progress 

in this area in the next few years due to the increased 

demand from governments, the public and the media 

for these services on quasi-real time.

ATTRIBUTION OF EXTREME CLIMATE EVENTS

Probability distribution of annual mean Central England Temperature under 

natural (blue) and RCP 8.5 (brown) model simulations as of 2006, with the 

2006 value (the highest on record) shown as a black dashed line (Source: 

Andrew King, University of Melbourne, Australia)

0.1

0.08

0.06

0.04

0.02

0

‒3 ‒2 ‒1 0 1 2 3

Probability

Central England Temperature anomaly (°C)

FAR

Threshold

HistoricalNat

RCP8.5

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29

Climate-related extreme events and disasters 

take a heavy toll on human well-being and 

on various sectors of national economies. 

The year 2017 was particularly severe for 

disasters with high economic impacts. 

Munich Re assessed total disaster losses 

from weather and climate-related events in 

2017 at US$ 320 billion,48 the largest annual 

total on record (after adjustment for inflation).

AGRICULTURE AND FOOD 

SECURITY

Exposure and vulnerability to extreme 

events can destroy agricultural assets and 

infrastructure, causing serious damage to 

the livelihoods and food security of millions 

of people. A concern for the World Food 

Programme (WFP) is that more than 80% of the 

world’s food-insecure people live in countries 

with degraded environments prone to natural 

hazards. When climate-related events occur, 

the situation of already-vulnerable people can 

quickly deteriorate into food and nutrition 

crises. Problems of acute food insecurity 

and malnutrition tend to be magnified where 

natural hazards such as droughts and floods 

compound the consequences of conflicts.49

The disruption of agricultural production in 

rural areas of developing countries affects the 

already fragile livelihoods of the poorest and 

most vulnerable people in particular. A review 

carried out by FAO found that agriculture 

(crops, livestock, fisheries, aquaculture and 

forestry) accounted for 26% of all the damage 

and loss associated with medium to largescale climate-related disasters. 50

48 Munich Re, 2018: Hurricanes cause record losses in 2017 – 

The year in figures. Release of 4 January 2018. The losses 

quoted by Munich Re include both insured and non-insured 

losses, but may calculate indirect economic losses (e.g. 

business interruption) in a different way than some other 

sources.

49 Food and Agriculture Organization of the United Nations 

(FAO), International Fund for Agricultural Development 

(IFAD), United Nations Children's Fund (UNICEF), World Food 

Programme (WFP) and World Health Organization (WHO), 

2017: The State of Food Security and Nutrition in the World 

2017 – Building resilience for peace and food security. FAO, 

Rome, http://www.fao.org/3/a-I7695e.pdf.

50 Food and Agriculture Organization of the United Nations 

(FAO), 2017:The Impact of Disasters on Agriculture – Assessing the information gap, http://www.fao.org/3/a-i7279e.pdf.

In the Horn of Africa, rainfall deficits led to the 

failure of the 2016 rainy season, followed by 

a harsh January–February 2017 dry season, 

and a poor March–May rainy season. As a 

result, the number of food-insecure people 

rose significantly in Eastern Africa.51 The 

most affected areas include southern and 

south-eastern Ethiopia, northern and coastal 

Kenya, almost all of Somalia, south-eastern 

areas of South Sudan and north-eastern 

areas of Uganda. In Somalia, as of June 2017, 

more than half of the cropland was affected 

by drought, and herds had reduced by 40 to 

60% since December 2016 due to increased 

mortality and distress sales. 

In Ethiopia, prolonged drought jeopardized 

crop production and caused a reduction in the 

availability of pasture, severely constraining 

the purchasing power of pastoral households. 

In drought-affected areas of Kenya, according 

to the Vegetation Condition Index (VCI), as of 

May 2017 drought was associated with a sharp 

increase in staple crop prices coupled with 

declining livestock prices and consequent 

erosion of livelihoods and threat to food 

security.52

51 United Nations Office for the Coordination of Humanitarian 

Affairs (OCHA), 2017: Horn of Africa: Humanitarian Impacts 

of Drought, 9 (10 August 2017), https://reliefweb.int/report/

somalia/horn-africa-humanitarian-impacts-drought-issue

-9-10-aug-2017.

52 Food and Agriculture Organization of the United Nations 

(FAO), 2017: Global Information and Early Warning System 

on Food and Agriculture (GIEWS). Special Alert No. 339. 

Region: East Africa, http://www.fao.org/3/a-i7537e.pdf.

Climate risks and related impacts 

Figure 19. Number of 

severely food-insecure 

people in Kenya, 

Somalia and Ethiopia 

(Source: Horn of Africa: 

Humanitarian Impacts 

of Drought – Issue 9, 

20 August 2017 (OCHA))

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30

At the Seventy-first Session of the United 

Nations General Assembly1 and the Fortyeighth Session of the United Nations 

Statistical Commission,2 the data and 

indicators for the measurement of progress 

in achieving the global targets of the Sendai 

Framework for Disaster Risk Reduction 

2015–2030 and of the 2030 Agenda for 

Sustainable Development were adopted. 

This enabled integrated monitoring and 

reporting by countries of progress in 

managing disaster and climate risk and 

the corollary impacts, using multi-purpose 

datasets and common indicators.

Data are currently available in most countries 

to allow some degree of measurement 

of the impact of climate-related extreme 

events and disasters – including via the 

growing number of national disaster 

loss accounting systems – as detailed in 

the Sendai Framework Data Readiness 

Review 2017.3

 However, considerable work 

is required if countries are to be able to 

monitor the agreed indicators in the manner 

anticipated by the two intergovernmental 

working groups – the Open-ended 

Intergovernmental Expert Working 

Group on Indicators and Terminology 

Relating to Disaster Risk Reduction 

(OIEWG) and the Inter-agency and Expert 

Group on Sustainable Development Goal 

Indicators (IAEG-SDGs). Many countries are 

confronted with challenges related to data 

availability, accessibility and quality, which 

will need to be addressed if data are to be 

sufficiently consistent and comparable to 

allow meaningful measurement of progress 

and impact. 

1 Resolution (A/RES/71/276) approving the Report of the 

Open-ended Intergovernmental Expert Working Group 

on Indicators and Terminology relating to Disaster Risk 

Reduction (A/71/644).

2 Report of the Inter-agency and Expert Group on Sustainable Development Goal Indicators – Note by the 

Secretary-General (E/CN.3/2017/2).

3 United Nations Office for Disaster Risk Reduction 

(UNISDR): Sendai Framework data readiness review 

2017 – Global summary report, https://www.preventionweb.net/publications/view/53080.

Work is ongoing with the international 

statistical community to address some 

of these challenges. The outcomes of the 

Expert Group on Disaster-related Statistics 

will be presented at the Seventy-fourth 

Session of the Economic and Social 

Commission for Asia and the Pacific. They 

include the development of the disasterrelated statistical framework (DRSF) to 

monitor the achievement of the global 

targets of the Sendai Framework and the 

Sustainable Development Goals. 

Established by the Conference of European 

Statisticians of the United Nations Economic 

Commission for Europe, the Task Force on 

Measuring Extreme Events and Disasters 

is clarifying the role of official statistics 

in providing data related to extreme 

events and disasters, as well as possible 

support of national statistical offices to the 

implementation of the Sendai Framework 

and the 2030 Agenda.

Countries are supported in systematic 

data entry and reporting by the Sendai 

Framework Monitoring system, an online 

monitoring facility which became available 

on 1 March 2018, and which is supported 

by detailed guidance on metadata and 

computational methodologies. The 

integration of monitoring and reporting 

with The Paris Agreement will be discussed 

at the Thirteenth Meeting of the United 

Nations Framework Convention on Climate 

Change (UNFCCC) Adaptation Committee.

DATA FOR MONITORING IMPACTS OF CLIMATE-RELATED EXTREME 

EVENTS AND DISASTERS

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In the Philippines, over the last two 

decades, 15 times more infants have died 

in the 24 months after typhoons than in the 

typhoons themselves. Of those infants, 80% 

were girls.53 In Ethiopia, children born in an 

area affected by disasters are 35.5% more 

likely to be malnourished and 41% more likely 

to be stunted.54

Over the last three years, agricultural 

production and related livelihoods were 

heavily compromised by recurrent and intense 

floods in many countries. In Malawi, the 2015 

floods resulted in more than US$ 60 million 

in damage and losses to crops, livestock, 

fisheries and forestry assets, and production 

flows. More than 37% of the total economic 

impact of the 2015 floods in Myanmar occurred 

in the agricultural sector.55

In 2017, several flood events affected the 

agricultural sector, especially in Asian 

countries. Heavy rains in May 2017 triggered 

severe flooding and landslides in southwestern areas of Sri Lanka. The adverse 

impact of floods on crop production further 

aggravated the food security conditions in 

the country already stricken by drought.56 In 

July 2017, localized floods in south and central 

Myanmar contributed to losses in paddy crop, 

stored food and livestock, and affected at 

least 200 000 people in the Magway, Sagaing, 

Bago and Ayeyarwady regions and Mon State. 

It was the third consecutive year in which 

severe floods affected Myanmar during the 

monsoon season.

53 Anttila-Hughes, J. and S. Hsiang, 2013: Destruction, Disinvestment, and Death: Economic and Human Losses Following 

Environmental Disaster. Available at SSRN: http://ssrn.com/

abstract=2220501.

54 Intergovernmental Panel on Climate Change (IPCC), 2007: 

Fourth Assessment Report, https://www.ipcc.ch/report/

ar4/.

55 Government of Myanmar. (2015). Myanmar. Post-disaster 

needs assessment of floods and landslides, July–September 2015, http://documents.worldbank.org/curated/

en/646661467990966084/Myanmar-Post-disaster-needs

-assessment-of-floods-and-landslides-July-September

-2015.

56 Food and Agriculture Organization of the United nations (FAO) 

and World Food Programme (WFP), 2017: Special Report. 

FAO/WFP Crop and Food Security Assessment Mission to 

Sri Lanka, http://www.fao.org/3/a-i7450e.pdf.

Excess precipitation in late March and early 

April 2017 triggered floods in north-eastern 

agricultural areas of Bangladesh, affecting 

crop production in the Sylhet, Dhaka and 

Mymensingh divisions in particular.57 The 

monsoon season in South Asia brought 

the worst flooding in the region for years. 

Between June and August 2017, flooding 

in Nepal, Bangladesh and northern India 

affected millions of people and caused death 

and displacement across the three countries.

The end of the climate anomalies associated 

with the 2015/2016 El Niño, both on land and 

in the ocean, resulted in improved agricultural 

and fisheries production in some areas.58 More 

normal rainfall patterns have contributed 

to two successive record-breaking global 

cereal harvests since 2015. World wheat 

production hit an all-time high in 2016 and 

is expected to remain at near-record levels 

in 2017, mainly due to larger crops in India 

and the Russian Federation. More abundant 

rains since mid-2016 in India, Thailand and 

the Philippines have increased rice output 

to a level that marked recoveries in these 

countries. As a result, world rice production 

reached a fresh peak in 2016 and was expected 

to expand further in the 2017 season. As to 

annual oilcrops, global production recovered 

swiftly in 2016/2017, actually posting a new 

record, and is anticipated to grow modestly 

57 Food and Agriculture Organization of the United nations 

(FAO), 2017: GIEWS – Global Information and Early Warning 

System. Country Brief: Bangladesh, http://www.fao.org/

giews/countrybrief/country.jsp?code=BGD.

58 Food and Agriculture Organization of the United Nations 

(FAO), 2017: Food Outlook – Biannual Report on Global Food 

Markets, November 2017, http://www.fao.org/3/a-I8080e

.pdf.

Population affected (in millions) due to floods in 

Bangladesh, India and Nepal, 

as of 24 August 2017 

Country Total number of 

people affected

Bangladesh 6.9

India 32.1

Nepal 1.7

Source: United Nations Office for the Coordination of 

Humanitarian Affairs (OCHA)

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32

in 2017/2018. Conversely, the recovery in 

palm oil production was more gradual and 

is expected to revert to its usual growth rate 

only in 2018. There was also a strong recovery 

in the Anchoveta fishery off the Pacific coast of 

South America as sea-surface temperatures 

in the region reverted to near average.

HEALTH

The global health impacts of heatwaves 

depend not only on the overall warming trend, 

but also on how heatwaves are distributed 

across the area where people live. Recent 

research shows that the overall risk of heatrelated illness or death has climbed steadily 

since 1980, with around 30% of the world’s 

population now living in climatic conditions 

that deliver potentially deadly temperatures 

at least 20 days a year.59 Between 2000 and 

2016, the number of vulnerable people 

exposed to heatwave events has increased 

by approximately 125 million.60

In cholera-endemic countries, an estimated 

1.3 billion people are at risk, while in Africa 

alone about 40 million people live in cholera 

“hotspots”.61 These cholera “hotspots” 

have been identified across most endemic 

countries facing recurrent and predictable 

cholera outbreaks, often coinciding with the 

rainy season. The World Health Organization 

has recognized that large cholera outbreaks in 

59 Mora C. et al., 2017: Global risk of deadly heat. Nature Climate 

Change, 7. DOI:10.1038/nclimate3322.

60 Watts N. et al., 2017: The Lancet Countdown on Health 

and climate change: From 25 years of inaction to a global 

transformation for public health. Lancet, 30 October 2017.

61 World Health Organization (WHO), 2017: Weekly epidemiological record, No. 22, 2 June 2017, http://apps.who.int/

iris/bitstream/10665/255611/1/WER9222.pdf?ua=1.

eastern and central, and later southern Africa 

were likely aided by El Niño-driven weather 

conditions that accelerated transmission 

across the region starting in mid-2015, with 

control efforts still underway in several 

countries in 2017. Flood events are also often 

associated with outbreaks of water-borne 

diseases or those linked to poor sanitation, 

as was reported in Bangladesh during the 

August 2017 floods. 

POPULATION DISPLACEMENT 

One of the adverse consequences of 

climate variability and change is population 

displacement. Most such displacement 

is internal and linked to sudden onset of 

extreme weather events. However, slow 

onset phenomena, such as droughts, 

desertification, coastal erosion and sea-level 

rise, can also lead to internal and cross-border 

displacement. These slow-onset events can 

act as a threat multiplier by, for example, 

exacerbating conflict which, in turn, can 

contribute to population displacement. 

In 2016, weather-related disasters displaced 

23.5 million people.62 As in previous years, 

the majority of those internal displacements 

were associated with floods or storms and 

occurred in the Asia-Pacific region. The most 

striking example of displacement due to major 

climate events is from Somalia, where it was 

reported that 892 000 people were displaced 

internally, mostly in the first half of 2017.63 Of 

the displaced people who were surveyed, 

approximately 90% indicated that drought was 

the primary reason for displacement, while the 

remaining 10% gave reasons closely related 

to drought or cited drought as a contributing 

factor, such as food or livelihood insecurity.64

62 Internal Displacement Monitoring Centre (IDMC), 2017: 

Global Report on Internal Displacement 2017, http://www

.internal-displacement.org/global-report/grid2017/pdfs/

2017-GRID.pdf.

63 Office of the United Nations High Commissioner for Refugees 

(UNHCR), 2018: Somalia. UNHCR Emergency Response at 

31 December 2017, https://reliefweb.int/report/somalia/

somalia-unhcr-emergency-response-31-december-2017.

64 As of 23 June, 687 906 Somali IDPs interviewed by the 

UNHCR-led Protection & Return Monitoring Network 

(PRMN) indicated that drought was the primary reason for 

displacement, while 72 688 IDPs indicated that drought was 

a contributing factor, https://data2.unhcr.org/en/documents/

download/58290.

175

50

75

150

25

0

‒25

100

125

2000

Year

2002 2004 2006 2008 2010 2012 2014 2016

Change in exposure to heatwaves 

(millions per year)

Figure 20. The change 

in exposure (in people 

aged over 65 years) to 

heatwaves from 2000 

to 2016 relative to the 

heatwave exposure 

average from 1986 to 

2008 

(Source: World Health 

Organization (WHO))

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33

ECONOMIC IMPACTS

The International Monetary Fund (IMF) World 

Economic Outlook published in October 201765

indicates that increases in temperature have 

uneven macroeconomic effects. Adverse 

consequences are concentrated in regions 

with relatively hot climates, where a 

disproportionately large number of lowincome countries are located. In these 

countries, a rise in temperature lowers per 

capita output, in both the short and medium 

term, by reducing agricultural output, 

suppressing the productivity of workers 

exposed to heat, slowing investment and 

damaging health. 

The analysis confirms the existence of a 

statistically significant nonlinear effect of 

temperature on per capita economic growth. 

65 International Monetary Fund, 2017: World Economic Outlook, 

October 2017. Seeking Sustainable Growth: Short-Term Recovery, 

Long-Term Challenges, https://www.imf.org/en/Publications/

WEO/Issues/2017/09/19/world-economic-outlook-october

-2017.

In countries with high average temperatures, 

an increase in temperature dampens economic 

activity, whereas it has the opposite effect in 

much colder climates.

For the median emerging market economy, 

a 1  °C increase from an average annual 

temperature of 22 °C lowers growth in the 

same year by 0.9%. For a median low-income 

developing country, with an annual average 

temperature of 25 °C, the effect of a 1 °C 

increase in temperature is even larger: growth 

falls by 1.2%. Countries whose economies 

are projected to be significantly adversely 

affected by an increase in temperature 

produced only about 20% of global GDP in 

2016; however, they are currently home to 

nearly 60% of the global population and are 

projected to be home to more than 75% by 

the end of the century. 

4.23

3.58

2.93

2.28

1.63

0.98

0.33

-0.32

-0.97

-1.62

-2.27

Figure 21. The effect 

of a 1 °C increase in 

temperature on real per 

capita output at the grid 

level. 

(Sources: Natural 

Earth; ScapeToad; 

United Nations World 

Population Prospects: 

The 2015 Revision; World 

Bank Group Cartography 

Unit; and IMF staff 

calculations)

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Vector-borne diseases: 

Zika in the Americas

Ángel G. Muñoz,1 Rachel Lowe,2

Anna M Stewart-Ibarra,34 Joy ShumakeGuillemot,4

 Madeleine Thomson1

Vector-borne diseases are highly climate 

sensitive and favourable climate conditions 

can trigger and amplify disease transmission. 

Warm temperatures increase virus replication 

rates and drive the development of juvenile 

mosquitoes, adult feeding and egg laying 

behaviour. Rainfall excess and deficit have 

similar outcomes in terms of mosquito 

proliferation, as containers such as domestic 

pots, tires, drums and tanks tend to create 

suitable breeding sites in both cases. 

The emergence of the Zika virus (ZIKV) 

epidemic, mainly carried by the Aedes

mosquito in Latin America and the Caribbean in 

2014–2016, occurred during a period of severe 

drought and unusually high temperatures 

developed since at least 2013 (see top and 

middle panels of the figure). These conditions 

have been shown to be associated with a 

cross-timescale combination of signals 

including the 2015/2016 El Niño event, decadal 

variability and climate change (Muñoz et al., 

2016a,b). 

A common approach to assess the potential 

risk of transmission of Aedes -borne 

epidemics is via the estimation of the basic 

reproduction number, R0, which is in general 

a function of environmental variables, such 

as air temperature, and ento-epidemiological 

parameters (Mordecai et al, 2017). Using 

an R0 model which considers the two most 

common Aedes mosquitoes in Latin America 

and the Caribbean, a recent study (Muñoz 

et al., 2017) showed that high temperatures 

enhanced the risk of transmission during the 

2014–2016 ZIKV epidemic, and that neither El 

Niño nor climate change can be independently 

blamed for this event. While the potential 

1 International Research Institute for Climate and Society, 

Earth Institute, Columbia University, New York.

2 Centre for the Mathematical Modelling of Infectious Diseases 

and Department of Infectious Disease Epidemiology, London 

School of Hygiene & Tropical Medicine, London.

3 Department of Public Health and Preventive Medicine,Upstate Medical University, Syracuse, New York.

4 WHO-WMO Joint Office for Climate and Health, WMO, 

Geneva.

risk of transmission signal (black curve in 

the bottom panel of the figure) is consistent 

with long-term temperature increase due to 

global warming and with inter-annual climate 

variability modes (filled red/blue curve shown 

in bottom panel of the figure) such as El Niño, 

other non-climatic factors come into play to 

explain the 2014–2016 ZIKV epidemic. 

In fact, in addition to suitable climate conditions, 

the rapid transmission that occurred in the 

initial Brazilian outbreak appears to have been 

aided by a combination of factors including a 

massive susceptible population, alternative 

non-vector transmission, and a highly mobile 

population (Lowe et al., 2018). Furthermore, 

the occurrence of shocks such as natural 

disasters can also exacerbate population 

vulnerability. This was observed following 

the major earthquake in coastal Ecuador 

in April 2016, which seemingly enhanced 

the ZIKV transmission in that region where 

suitable hot and dry local climate conditions 

were already present (Sorensen et al., 2017).

Currently, local ZIKV transmission and the 

associated and resulting fetal malformations 

and neurological disorders continue to 

be monitored and recorded in the region 

and worldwide. Predictions of the timing 

and magnitude of outbreaks of multiple 

arboviruses, including ZIKV, can be improved 

using a combination of climate forecasts 

and sero-prevalence survey data (Lowe et 

al., 2017). For example, real-time seasonal 

climate forecasts have been used to produce 

dengue early warnings for Brazil (Lowe et 

al., 2014, 2016), and the use of the R0

 model 

mentioned above and state-of the-art climate 

forecasts provided by the North American 

Multi-Model Ensemble (NMME) project 

could have successfully predicted the recent 

epidemic at least 1–3 months in advance for 

several high-risk ZIKV zones, including its 

epicenter in Northeast Brazil (Muñoz et al., 

2017, Epstein et al., 2017). 

34

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Epstein, H. et al., 2017: A Menace Wrapped in a Protein: Zika 

and the Global Health Security Agenda. New York, Columbia 

University, doi: 10.13140/RG.2.2.25050.85443.

Lowe, R. et al., 2018: The Zika virus epidemic in Brazil: From 

discovery to future implications. International Journal of 

Environmental Research and Public Health, 15(1): 96

Lowe, R. et al., 2014: Dengue outlook for the World Cup in 

Brazil: An early warning model framework driven by realtime seasonal climate forecasts. The Lancet Infectious 

Diseases, 14 (7): 619–626.

Lowe, R. et al., 2016: Evaluating probabilistic dengue risk 

forecasts from a prototype early warning system for Brazil. 

eLIFE 5:e11285.

Lowe, R. et al., 2017: Climate services for health: Predicting the 

evolution of the 2016 dengue season in Machala, Ecuador. 

The Lancet Planetary Health 1 (4):e142–e151.

Mordecai, E. A. et al., 2017: Detecting the impact of temperature 

on transmission of Zika, dengue, and chikungunya using 

mechanistic models. PLOS Neglected Tropical Diseases, 

11(4):e0005568. doi: 10.1371/journal.pntd.0005568.

Muñoz, Á. G. et al., 2016a: The Latin American and Caribbean 

Climate Landscape for ZIKV Transmission. IRI Technical 

Report 2016-001. New York. doi: 10.7916/D8X34XHV.

Muñoz, Á. G. et al., 2016b: Analyzing climate variations at 

multiple timescales can guide Zika virus response measures. 

Gigascience, 5(1), 41.

Muñoz, Á. G. et al., 2017: Could the recent Zika epidemic have 

been predicted?’ Frontiers in Microbiology, 8: 1291, https://

www.frontiersin.org/articles/10.3389/fmicb.2017.01291/full.

Sorensen, C. J. et al., 2017: Climate variability, vulnerability, and 

natural disasters: A case study of Zika virus in Manabi, Ecuador following the 2016 earthquake. GeoHealth, 1(8):298–304, 

doi:10.1002/2017GH000104.

Annual rainfall 

anomalies (top panel) 

and temperature 

anomalies (middle 

panel) in 2013, 2014 and 

2015; anomalies are 

computed with respect 

to the climatological 

period 1981–2010. 

Standardized anomalies 

of R0

 (bottom panel; 

units in standard 

deviations). The 

total potential risk 

of transmission 

(black curve) shows 

an upward trend 

consistent with climate 

warming and cannot be 

explained only by the 

contribution of El Niño 

and other year-to-year 

climate modes (filled 

curve): a combination 

of climate signals has 

been driving the risk 

of transmission in the 

region. Black boxes 

indicate the sector of 

analysis (After Muñoz 

et al., 2016b, 2017).

300 200 100 0 –100 –200 –300 4 2 0 –2 –4 30oN

20oN

10oN

EQ

10oS

20oS

30oS

40oS

50oS

30oN

20oN

10oN

EQ

10oS

20oS

30oS

40oS

50oS

2013 2014 2015

2013 2014 2015

120oW 100oW 80oW 60oW 40oW 120oW 100oW 80oW 60oW 40oW 120oW 100oW 80oW 60oW 40oW

1950 1960 1970 1980 1990 2000 2010 2017

0.8

0.6

0.4

0.2

0

–0.2

–0.4

–0.6

R0 standardized anomalies (σ)

Rainfall anomaly

[mm]

Temperature anomaly

[oC] more rain

than

normal

less rain

than

normal

warmer

than

normal

colder

than

normal

35

References

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For more information, please contact:

World Meteorological Organization

7 bis, avenue de la Paix – P.O. Box 2300 – CH 1211 Geneva 2 – Switzerland

Communication and Public Affairs Office

Tel.: +41 (0) 22 730 83 14/15 – Fax: +41 (0) 22 730 80 27

Email: cpa@wmo.int 

public.wmo.int

JN 18331

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