It is now certain that human-induced climate change is
increasing the incidence of extreme temperature, precipitation and drought
events globally. A critical aspect of these extremes is their potential
concurrency that can result in substantial impacts on society and
environmental systems. Therefore, quantifying concurrent extremes in current
and projected climate is necessary to take measures and adapt to future
challenges associated with such conditions. Here we investigate changes in
individual and concurrent extremes in multi-model simulations of the sixth
phase of the Coupled Model Intercomparison Project (CMIP6) for different
global warming levels (GWLs). We focus on the individual and simultaneous
occurrence of the extreme events, encompassing heatwaves, droughts, maximum
1 d precipitation (Rx1day), and extreme wind (wind), as well as the compound
events heatwave–drought and Rx1day–wind in the pre-industrial period
(1850–1900; reference period), for approximately present conditions
(+1∘C of global warming), and at three higher global warming
levels (GWLs of +1.5, +2 and +3 ∘C). We focus our analysis on 139 countries and three climatic macro-regions:
northern mid- and high-latitude countries (MHC), subtropical countries
(STC), and tropical countries (TRC). We find that, on a global scale, most
individual extremes become more frequent and affect more land area for
higher GWLs. Changes in frequency of individual heatwaves, droughts, Rx1day and extreme wind with higher GWLs cause shifts in timing and disproportionate
increases in frequency of concurrent events across different months and
different regions. As a result, concurrent occurrences of the investigated
extremes become 2.0 to 9.6 times more frequent at +3 ∘C of
global warming compared to the pre-industrial period. At +3 ∘C
the most dramatic increase is identified for concurrent heatwave–drought
events, with a 9.6-times increase for MHC, an 8.4-times increase for STC
and a 6.8-times increase for TRC compared to the pre-industrial period. By
contrast, Rx1day–wind events increased the most in TRC (5.3 times), followed
by STC (2.3 times) and MHC (2.0 times) at +3 ∘C with respect to
the pre-industrial period. Based on the 2015 population, these frequency
changes imply an increase in the number of concurrent heatwave–drought
(Rx1day–wind) events per capita for 82 % (41 %) of countries. Our
results also suggest that there are almost no time periods (on average 0
or only 1 month per year) without heatwaves, droughts, Rx1day and extreme
wind for 21 countries at +1.5 ∘C of global warming, 37 countries
at +2 ∘C and 85 countries at +3 ∘C, compared to 2
countries at +1∘C of global warming. This shows that a large
number of countries will shift to near-permanent extreme conditions even at
global warming levels consistent with the limits of the Paris Agreement.
Given the projected disproportionate frequency increases and decreasing
non-event months across GWLs, our results strongly emphasize the risks of
uncurbed greenhouse gas emissions.
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungIZCOZ0_189941Introduction
The socioeconomic impacts of individual and concurrent extremes are
accelerating with increasing global warming (IPCC, 2021). The intervals between
extremes are becoming shorter, which puts vulnerable communities and
ecosystems at risk. In addition, while most countries are affected by
climate extremes, some economies in the global south are more vulnerable
than advanced economies in the Northern Hemisphere (Guo et
al., 2021). These emerging challenges motivate the need for a comprehensive
analysis of potential changes in exposure to individual and concurrent
extremes on the population and country level.
Human-induced climate change is exacerbating climate extremes in every
region across the globe (Seneviratne et al.,
2021). This increase in climate extremes cannot be explained without human
influence on the climate system and threatens both developed countries and
developing countries. It is noteworthy that low-income and high-population
countries have been the most affected by climate extremes in terms of
economic and environmental fatalities during the last 2 decades
(Eckstein et al., 2021). This indicates the inequity between
CO2 high-emitter and low-emitter countries when dealing with
climate-induced risks and impacts. Our motivation here is to provide a
comprehensive assessment of potential changes in population exposure to
climate extremes across countries with different climates.
Previous studies typically focus on current and/or projected changes of
single extremes (Tebaldi
et al., 2006; Orlowsky and Seneviratne, 2012; Alexander et al., 2006; Westra
et al., 2013; Mondal and Mujumdar, 2015; Bao et al., 2017; Alizadeh et al.,
2022), whereas recently there has been more attention on compound events –
multiple extremes occurring either simultaneously and/or consecutively – due
to the rising awareness about their potential amplified impacts
(Seneviratne
et al., 2010; Mazdiyasni and AghaKouchak, 2015; Forzieri et al., 2016;
Zscheischler and Seneviratne, 2017; Vogel et al., 2017; Batibeniz et al.,
2020a; Vogel et al., 2020; Saeed et al., 2021; Schwingshackl et al., 2021;
Kelebek et al., 2021). The impacts associated with compound events are
expected to be higher than impacts caused by individual extremes. For
example, a combination of extreme wind and extreme precipitation can
increase the destruction of infrastructure and economic losses. As climate
change alters the nature of weather and climate events (extreme or not),
compound events composed of these events are expected to be unprecedented in
terms of severity and intensity (Seneviratne et al.,
2021). This emerging understanding makes it necessary to quantify the
projected changes in the characteristics of both individual and compound
events.
A range of obstacles hinders a reliable estimation of the likelihood of
compound events. Extreme events are rare by definition and compound extreme
events even more so. Additionally, a robust understanding and detailed
spatiotemporal information on exposure to multivariate extremes require
high spatiotemporal coverage. This hinders the assessment of
observation-based compound events. Therefore, large model ensembles
(Champagne
et al., 2020; Poschlod et al., 2020; Vogel et al., 2020; Ridder et al.,
2021), process-based model simulations (Couasnon et al., 2020) and reanalysis
data (Martius et al., 2016) can complement
observational data. In particular, multi-GCM (global climate model) ensembles capture the
uncertainty in the large-scale climate and can be a useful tool to
investigate compound events in current and future climate.
In this study, we investigate the individual occurrences of heatwaves,
droughts, extreme precipitation, and extreme wind, as well as concurrent
heatwave–drought, and extreme precipitation and extreme wind events, all of
which can have severe impacts on different sectors. The first combination –
heatwave–drought – influences wildfire, crops, natural vegetation, power
plants and fisheries (Zscheischler et al., 2020). The
second combination – extreme wind and precipitation – can cause storm
surges, flooding, and result in the destruction of infrastructure and
damage to the economy. Several studies have found that heatwave–drought
occurrences have increased in the last 4 to 5 decades
(Schubert
et al., 2014; Mazdiyasni and AghaKouchak, 2015; Sharma and Mujumdar, 2017;
Zscheischler and Seneviratne, 2017; Kirono et al., 2017; Zhou and Liu, 2018;
Hao et al., 2018; Sarhadi et al., 2018; Manning et al., 2019; Alizadeh et
al., 2020; Feng et al., 2020; Kong et al., 2020; Li et al., 2020; Ridder et
al., 2020; Mukherjee and Mishra, 2021; Wu et al., 2021) and are projected to
increase in the future (Diffenbaugh
et al., 2015; Herrera-Estrada and Sheffield, 2017; Sedlmeier et al., 2018;
Li et al., 2019). This increase is mostly attributed to the increase in
heatwave occurrences (Bevacqua et al., 2022).
Indeed, even when droughts alone do not display an increasing tendency,
compound occurrences of heatwave and drought events are expected to increase
(Sarhadi et al., 2018; Yu and Zhai,
2020). Compound precipitation and wind extremes have also been investigated
in the observational period over many regions including the Mediterranean
Basin (Raveh-Rubin and Wernli, 2015), Europe (De
Luca et al., 2020; Zscheischler et al., 2021), Great Britain
(Tilloy et al., 2022) and
at the global scale (Martius et
al., 2016; Messmer and Simmonds, 2021). However, these studies differ in
methodology, time and spatial scale, and future changes of precipitation–wind
extremes have, to the best of our knowledge, not been covered in the
compound event context or not been evaluated together with heatwave–drought
events.
Here we analyse changes in frequency and timing of climate-induced
individual and concurrent extreme events, as well as the population exposure
to these events. It is important to note that for a risk assessment
vulnerability would also have to be considered, but this lies beyond the
scope of this study. Building on previous work on projected changes in
compound extreme events and human exposure
(Batibeniz
et al., 2020a; Lange et al., 2020; Chen et al., 2020; Mukherjee et al.,
2021; Liu et al., 2021; Alizadeh et al., 2022; Das et al., 2022; Shen et
al., 2022), we investigate for the first time the human exposure to
co-occurring extreme precipitation–wind events, in addition to co-occurring
heatwave–drought events and individual extremes. We do so in a manner
consistent with the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change (IPCC AR6) framework by analysing the projections for
different global warming levels (GWLs, +1, +1.5, +2 and +3 ∘C) relative to pre-industrial
conditions on country and regional scales.
Data and methodsClimate model data
We use CMIP6 simulations (Eyring et al., 2016) of 14
climate models to perform individual and concurrent event analysis in the
pre-industrial period (1850–1900) and at four GWLs (see below) for the
shared socioeconomic pathway (SSP) projection marking the high end of future
forcing pathways (SSP5-8.5) (Jones and O'Neill, 2016,
2020). The SSP5-8.5 experiment represents high-mitigation and low-adaptation
challenges resulting in radiative forcing of 8.5 W m-2 by the end of
2100. Because we present our results at GWLs we do not expect our results to
strongly depend on the choice of the scenario (Seneviratne
et al., 2016; Seneviratne and Hauser, 2020; Wartenburger et al., 2017). We
use the same ensemble member (r1i1p1) of each model. We retrieve daily
maximum temperature, precipitation, maximum wind, and soil moisture data from
each model and use conservative remapping (Jones,
1999) to regrid them onto a common 2.5∘× 2.5∘
longitude–latitude grid to enable comparison across different models. The
full list of models is provided in Table A1 in Appendix A.
Population counts
In the population exposure analysis, we use gridded population counts
retrieved from Gridded Population of the World version 4 (GPWv4) dataset
(CIESIN, 2018). The GPWv4 dataset provides population
distributions at various grid resolutions. For our analysis, we use the
1∘ resolution data which we transform into 2.5∘ grid
resolution to match the resolution of the climate data. GPWv4 data are
available for the period from 2000 to 2020 at 5-year intervals. However, we
only use 2015 population counts in this paper as they are representative of
the world population at +1 ∘C of global warming. To investigate
the effect of climate change, we keep the population fixed at 2015 levels
for approximately +1∘C of global warming while allowing the counts
of climate events to change at GWLs. This approach enables us to examine the
cause–effect relationship between increasing temperatures and projected
changes in extreme events. Furthermore, using climate change projections and
population distributions in combination allows us to investigate changes in
the exposure to climate extremes at the regional and country levels.
Climate regions
We focus our analysis on three climatic macro-regions: northern mid- and
high-latitude countries (MHC), subtropical countries (STC), and tropical
countries (TRC) (Fig. 1). These climate regions are created by aggregating
country polygons. The assessments are performed and presented on a
regional scale and country scale to emphasize the response of different climatic
regions and countries to individual and concurrent extremes. We show results for
climate regions in Figs. 3–5 and on the country level in Figs. 6–8.
World map is divided into three climatic macro-regions:
northern mid- and high-latitude countries (MHC), subtropical countries
(STC), and tropical countries (TRC).
Global temperature and warming level calculation
We perform our analysis considering +1, +1.5,
+2 and +3 ∘C global warming levels to be
consistent with the IPCC AR6 context (Seneviratne et al.,
2021). Warming levels are 20-year periods unique to each model due to
different climate sensitivity and internal variability. The warming levels
are defined as the first 20-year period where global mean temperature
anomalies exceed the given temperature (e.g. +2 ∘C). We first
calculate the annual average global temperature (Fig. 2a). Then, we
subtract the average global temperature of the pre-industrial period
(1850–1900; reference period) from every year between 1850–2100 and take the
20-year running mean (Fig. 2b). The first year a certain anomaly such as
+1, +1.5, +2
and +3 ∘C is exceeded is the central year of the warming
level period, and the warming level period is obtained by subtracting 10 and
adding 9 to the central year (Fig. 2b, c; horizontal bars). For
example, IPSL-CM6A-LR first exceeds +2 ∘C of warming in 2036,
so the period selected for this model is 2025–2044 (Fig. 2c, red bar). On
the other hand, MRI-ESM2-0 reaches +2 ∘C of warming in 2040,
and the period selected is 2029–2048 (Fig. 2c, orange bar).
Global warming level calculation steps. (a) Global
average temperature for four example models under SSP5-8.5 scenario.
Coloured lines refer to four models, and the shaded grey area refers to the
spread of temperature variability in all SSP5-8.5 CMIP6 models. (b) The 20-year
running average of temperature anomaly with respect to the pre-industrial
period. Horizontal bars represent warming level periods
(+1, +1.5, +2, +3 ∘C) for each model and are shifted vertically to
ease understanding. (c) Zoomed version of bars (warming level periods) in panel (b) to show corresponding years.
Definition of individual events
For our analysis, we calculate heatwaves, drought, heavy precipitation and
extreme wind events empirically. For each model, we define extreme events
based on their occurrences below (above) the 10th (90th) percentile during
the pre-industrial period with a bootstrap resampling procedure (Sect. 2.7
for details). All the calculations are performed on the 2.5∘× 2.5∘ grid for daily values. The daily events are then aggregated
to a monthly timescale, such that a month with one or more daily events is
marked an “event month” and otherwise as a “non-event month”.
Heatwave. We use daily maximum temperature to determine heatwave events. We
first calculate the 90th percentile for each calendar day using a 31 d
moving window over the pre-industrial period with a bootstrap resampling
procedure. We then identify a day as a heatwave event if the daily maximum
temperature exceeds the daily 90th percentile for at least 3 consecutive
days.
Drought. We compute drought using daily soil moisture data. We use soil
moisture to define drought events because it directly represents water
availability, in contrast to many other measures (e.g. the standardized
precipitation index, SPI) that are based on precipitation scarcity
(Seneviratne et al., 2010). We first normalize
soil moisture by subtracting the mean of each month and dividing it by its
standard deviation over the pre-industrial period. We then compute the 10th
percentile for each calendar day using a 31 d moving window over the
pre-industrial period as in heatwave calculation. The day is then defined as
a drought event if it falls below its 10th percentile.
Rx1day. We use daily precipitation to calculate monthly maximum 1 d
precipitation events. We find the maximum 1 d precipitation of each month
in the pre-industrial period and define the 90th percentile for each
calendar month. Heavy precipitation events are then defined as the days
where precipitation is above the monthly threshold.
Extreme wind. We use maximum daily wind speed to calculate extreme wind. For
the 90th-percentile calculation we use monthly maximum wind speeds in the
pre-industrial period. Extreme wind speed days are then defined as days
where daily wind speed is above the 90th percentile.
Definition of concurrent events
We define concurrent events as events that occur on the same day in a month
and affect the same location. We assess two types of concurrent events:
combined heatwave and drought events as well as Rx1day and extreme wind
events. Thus, if a specific month experiences two individual events on the
same day, it is marked as an “event month” for that grid cell and month. For
example, if there is a drought event occurring on the same days with a
heatwave event regardless of the number of concurrent events, we mark that
month as an “event month” and otherwise as a “non-event month”.
Bootstrap resampling procedure
Percentile-based indices for climate change detection may create artificial
jumps at the beginning and end of the reference period
(Zhang et al., 2005). These discontinuities can lead
to an artificial frequency increase outside the reference period. Therefore,
we used the bootstrap resampling procedure proposed by Zhang et al. (2005)
to overcome this problem. From the 51-year reference period we consecutively
excluded 1 year and included 1 random year from the remaining years in
which the thresholds are estimated. The threshold we found from every
iteration is used on the excluded year. Fifty-one thresholds obtained from
bootstrap resampling procedures are then averaged and used for the future
period. Applying this procedure improved our results in terms of
inhomogeneities occurring outside the reference period for heatwave, Rx1day
and extreme wind; however, it did not affect the drought frequencies.
Nevertheless, we used this approach to estimate the thresholds of all
extreme indices to be consistent methodologically. We refer readers to Zhang
et al. (2005) for detailed information about the bootstrap resampling
procedure.
ResultsFuture changes in individual and concurrent extremes over the climate regions
We illustrate the development of the investigated events with the help of
Venn diagrams, which allow us to analyse the frequencies of individual,
isolated and concurrent exceedances at the same time. We visualize the
individual events by circles and their concurrency by the intersection of
these circles. Given two event types A and B, the three numbers on the sets
represent the frequencies of an isolated first event (A - (A∩B)),
a concurrent event (A∩B) and an isolated second event (B - (A∩B)) in
percentage. The two numbers over the sets show the individual event shares
of the first (A) and second (B) event, respectively. The displayed results
represent the regional and multi-model mean. The reason we illustrate the
mean instead of a median is to avoid showing different shares from different
models for each set. We thereby focus on three continental climate regions
(MHC, STC and TRC) for pre-industrial, current (+1 ∘C) and future
climate (+1.5, +2 and +3 ∘C)
(Fig. 3a, b).
At the current warming level, the isolated heatwave frequency more than
doubled compared to pre-industrial levels in MHC (2.1 times more compared to
pre-industrial levels) and STC (2.6), and it quadrupled in TRC (4.4) (Table 1). The event fraction at +3 ∘C and the acceleration of the
increase across warming levels in isolated heatwave events are the most in
TRC (55.3 %, 7.5 times more compared to pre-industrial levels). Isolated
drought events, on the other hand, tend to decrease for higher GWLs in all
regions. This is mostly because drought events that occur together with
heatwave events increase with higher GWLs. Concurrent heatwave and drought
events are projected to increase in all climate regions with higher GWLs. At
the current GWL, the number of concurrent events is estimated to occur about
∼ 3 times more frequently for MHC and TRC and 4.6 times more
frequently for STC compared to the pre-industrial period. The strongest
increase across the warming levels occurs for MHC (9.6) and STC (8.4)
followed by TRC (6.8). The event fraction at +3 ∘C is similar
in MHC and STC (24.0 % and 23.6 %) and greater in TRC (33.2 %);
however, the proportional increase is the strongest in MHC compared to
pre-industrial levels.
Venn diagrams of (a) heatwave–drought and (b) Rx1day–wind
storm events at global warming levels. The values show the individual and
concurrent frequency of events in MHC, STC and TRC in the pre-industrial period
and at +1, +1.5, +2 and
+3 ∘C GWLs. Areas of the circles are proportional to the
frequencies ( %) and represent the multi-model mean. The numbers above the
Venn diagrams represent the total share of individual events including the
ones occurring during concurrent events.
Increase in isolated and concurrent events at global
warming levels relative to pre-industrial levels. Heatwave is denoted by ”hw”.
In Fig. 3b, we show Rx1day and wind events. The most dramatic increase in
isolated and individual Rx1day events is detected in MHC. The frequency of
isolated Rx1day events gradually increases across the warming levels by a
factor of 1.2, 1.4, 1.5 and 1.8 for +1, +1.5,
+2 and +3 ∘C with respect to pre-industrial
levels. The hotspot for isolated wind events is TRC. The increase reaches
2.0 times at the +1 ∘C GWL and continues to increase to 2.2, 2.3 and
2.5 for +1.5, +2 and +3 ∘C. For
MHC and STC, isolated and individual wind events show an increasing tendency
up to +1 ∘C and start to decrease at +1.5,
+2 and +3 ∘C. On the other hand, concurrent
Rx1day and wind events are already ∼ 1.5 times the
pre-industrial levels at +1∘C of warming and are projected to
increase further for the +3∘C GWL. Even though the percentage of
concurrent events is smaller compared to isolated and individual events, the
relative increase is larger across warming levels. Concurrent Rx1day–wind
event fractions are projected to increase 5.3 times for TRC, 2.3 times for
STC and 2.0 times for MHC at the +3 ∘C GWL.
Timing of individual and concurrent extremes
To gain further insight info future individual and concurrent extremes
across the climate regions, we now focus on their frequency and timing for
each calendar month under pre-industrial conditions and at GWLs (Figs. 4
and 5). Again, we first consider heatwave and drought events (Fig. 4). As
expected, heatwaves increase strongly with global warming (Fig. 4, top
row). At +1 ∘C of global warming, the associated changes are
already far beyond the conditions from pre-industrial levels and show
further gradual increase across the global warming levels. The increase in
heatwaves are heterogeneous across months. This unequal distribution leads
to much larger increases in some months than suggested by the annual average
(Fig. 3). The increase is especially inhomogeneous for MHC. At
+1 ∘C of global warming, heatwave events occur mostly in summer.
However, for higher GWLs there is a sharp increase for most months
especially July and August. In STC and TRC, the increase across warming
levels is more homogenous, with a slight shift towards June, July and
August in STC.
Due to its less variable structure in time, drought indicates a more
continuous increase across months for all regions (Fig. 4, middle row).
The most dramatic increase of drought is observed for summer months in MHC,
while STC and TRC show a relatively homogenous increase over the months.
Interestingly, STC sees a small decrease in individual drought events in
most months for +3∘C of warming.
The development of concurrent heatwave–drought events is not simply the
combination of the individual events (Fig. 4, bottom row). They also show
a general increase which, however, has some distinct features. The pattern
in MHC is especially interesting: the months from June to October indicate a
sharp increase, in contrast to the winter months. For STC, the frequency
increase is maximum in September for +3 ∘C of warming. With higher
GWLs there is a shift in timing of the highest values from summer to autumn
months. While it is at its highest in July for +1 ∘C of global
warming, it is at its highest in August for +3 ∘C of global
warming. In the case of TRC, there is also a shift in the timing of the
maximum frequency of heatwave–drought events. For +1 ∘C of
warming May shows the highest value, whereas for +3 ∘C of
warming June stands out. We observe highest frequency increases in summer
and autumn months with respect to pre-industrial levels.
The Rx1day, wind and Rx1day–wind events mostly indicate an increase across
all months and warming levels (Fig. 5). However, in some regions it is not
uniform across months. Individual occurrences of Rx1day events are on the
rise across the GWLs and regions. At +3 ∘C of global warming,
MHC indicates the highest increase in months between October and May. The
increase is more homogenous for STC and TRC. Nonetheless, events seem to
increase the most in August and September for STC and November for TRC for
the highest GWL. Wind extremes vary more compared to Rx1day events across
the regions. The most dramatic increase is identified in TRC from June to
November. The second-highest increase in frequency is observed for STC followed
by MHC. However, it is interesting to note that for STC while the months
between June and September indicate an increase, the rest of the months
indicate a decrease with higher GWLs. Additionally, the highest frequency in
extreme wind is observed in August. These increases in individual event
frequencies lead to a difference among the regions for concurrent
Rx1day–wind events. While there is an increase in winter and spring for MHC,
there is an increase in July for STC and all months but especially June to
October in TRC.
Timing and frequency of heatwave, drought and concurrent
heatwave–drought events in MHC, STC and TRC at the pre-industrial period and at
+1, +1.5, +2 and
+3 ∘C global warming levels in percent.
Same as Fig. 3 but for Rx1day, wind and concurrent
Rx1day–wind events.
Hotspots of changes in individual and concurrent extremes
This section presents the potential hotspots that are prone to an increase
in exposure to multiple hazards in a future climate (Fig. 6). We performed
this analysis for the four individual event types (Fig. 6, part [a]) and the two
concurrent event types (Fig. 6, part [b]) at GWLs. The first row shows how many
of the event types increased at least 20 % relative to the pre-industrial
period, and the second row shows how many of the event types increased at
least 100 % (i.e. a doubling of the event frequency).
Considering the individual extremes with the lower threshold (20 %; Fig. 6, part [a], top row), two out of four individual extremes show increase across
almost the entire globe – even at a GWL of +1∘C. There are three
countries that show an increase in all extremes at the +3∘C GWL,
namely Mali, Colombia and Peru. Many countries, including most of the South
American countries, European countries, the United States, Canada, China, and
some countries in central, west and south Africa, display a change in three
individual extremes at the +3∘C GWL (Fig. 6, part [a], and Fig. B1, part [a], in Appendix B). For the
higher threshold and +1 ∘C of global warming, two out of four
individual events already doubled pre-industrial levels for countries in
north and north-eastern South America and countries located in the south of
the Mediterranean Sea. This increase is projected to continue and affect
more land area for higher GWLs. The most prominent hotspots of change are
Ghana, the Republic of the Congo, Cameroon, Ecuador, Venezuela, Belize,
Nicaragua, Guyana and Colombia, where the common driver is the heatwave
events (Fig. 6, part [a], and Fig. B1, part [a]).
[a] Countries exposed to an increase in the event
frequency for one, two, three or four individual extremes (heatwave, drought, Rx1day,
wind) with relative increases over 20 % (top row) and 100 % (bottom row)
with respect to the pre-industrial period. [b] Countries exposed to 1 or 2
concurrent extremes (heatwave–drought, Rx1day–wind) with relative increases
over 20 % (top row) and 100 % (bottom row) with respect to the
pre-industrial period.
Both concurrent extreme pairs display a 20 % increase at all GWLs in most
countries except Mexico, India, some parts of Africa, Europe and western
Asia (Fig. 6, part [b], top row). This extends to almost the whole globe with
higher GWLs. Some countries in South America and western Africa already
double the pre-industrial levels of heatwave–drought and Rx1day–wind
frequency at +1∘C of warming (Fig. 6, part [b], bottom row). The regions
where only one event doubles in frequency are mostly driven by
heatwave–drought events (Fig. B1, part [b]). At +3∘C of global
warming both extreme pairs permanently double the pre-industrial levels for
the United States, most countries in South America and some countries
in Africa. At +3∘C of global warming, Rx1day–wind events occurring
in Mexico, western and central Europe, and some countries surrounding the
Mediterranean Sea do not contribute to 100 % change, whereas heatwave–drought events occurring in Kazakhstan and some countries in Africa do not contribute to 100 % change.
Population exposure
Projected changes in individual and concurrent occurrences of heatwave,
drought, Rx1day and wind events suggest a growing risk for population
exposure across the globe. In addition, the global population is expected to
continue its growth, further exacerbating the risk for human and natural
systems. For example, SSP5 projects the average world population to grow
from 7.29 billion in 2015 to its maximum in 2060 (8.6 billion) and decrease
thereafter to about 7.4 billion people by 2100 – the lowest population size
among SSPs (Jones and O'Neill, 2016, 2020). However,
to estimate the population exposure on a country-by-country basis we use
2015 levels (7.33 billion) provided in the GPWv4 data. Thus, in this study,
we do not consider increasing population from SSP5 but hold it constant at
2015 levels for several reasons. (i) Comparing GPWv4 with SSP5 projections
suggest that the population in 2015 is 39 million people higher (7.29 billion) in SSP5 than in GWPv4, with an even higher discrepancy for 2020.
(ii) Population projections are given for time periods while we report our
results for global warming levels. Because each GCM reaches a warming level
at a different period, it would be difficult to assign a population number to
the GWL. (iii) The projected population in SSP5 is strictly larger than in
2015, which suggests that our exposure based on 2015 population is
conservative and gives a lower estimate.
Number of individual and concurrent extremes per capita (a) at +1 ∘C and (b, c, d) change at +1.5,
+2 and +3 ∘C with respect to +1 ∘C.
Population counts for 2015 have been used for the analysis. Colours refer to
high model agreement, and hatched areas refer to lack of model agreement.
Figure 7 shows the number of events per capita for 139 countries. The
temporal span of this analysis is 20 years (20 years ⋅ 12 months = 240 time steps) for each GWL. We multiply hazards (binary) (Fig. B2) at each
grid cell with the gridded population (Fig. B3a). We then sum all the
values on the country level and divide it with the total population of that
country (Fig. B3b). The obtained value is the number of events (or
months) per capita in that specific country which cannot exceed 240. Using
this approach allows us to consider the hazard at grid cells where
population is not zero. Colours represent high model agreement (80 % and
above), and hatched areas represent low model agreement (less than 80 %)
in sign across models. In Fig. 7, the first column represents the current
(+1 ∘C) number of events per capita, and the second, third and
fourth columns show the projected changes in the number of events at
+1.5, +2 and +3 ∘C GWLs with
respect to +1 ∘C. Even when not taking the expected rise in the
human population into account, increases in extremes alone are projected to
increase the event number per capita in most countries. For +1 ∘C of global warming, heatwave events range between ∼ 34 and ∼ 181 events per capita. The number of events per capita increases by ∼ 10 to 51 events for
+1.5 ∘C, ∼ 18 to 85 events for +2 ∘C
and ∼ 45 to 146 events for the +3 ∘C GWL with high
model agreement. The increase in number of events per capita for 80 % of
the countries is above 25 events, 51 events and 86 events for
+1.5, +2 and +3 ∘C GWLs with
respect to +1 ∘C. In case of drought, half of the countries
indicate a continuous increase with higher GWLs up to ∼ 78
more events. The most vulnerable countries for drought are the Mediterranean
countries, China, some European countries, Mexico and north-western
countries of South America. Furthermore, the number of drought events per
capita seems to be the least recurring event for some countries in Africa. For +1 ∘C of global warming, concurrent heatwave–drought events range between ∼ 3 and
∼ 88 events per capita across the globe, and these numbers gradually increase for higher global warming levels for 82 % of the countries, with high model agreement. The number of
events per capita increases gradually across the globe except for some
countries in the African continent. The most dramatic increase is observed
for countries in the Mediterranean Basin. The number of events tends to
increase for all the countries in MHC, South America and Australia, with
more than 100 events per capita.
Individual Rx1day event numbers per capita are not very variable across the
globe for +1 ∘C of global warming (between ∼ 23
and 41). The number of Rx1day events per capita is on the rise for higher
global warming levels except Mediterranean countries, Australia, Mexico, and
north and south African countries, with some even showing a small decrease
(lack of model agreement). At +2 ∘C of global warming, Rx1day
events increase the most for tropical countries in the African continent.
This increase continues for +3 ∘C of global warming almost
everywhere across the globe. Wind extremes are increasing mostly for
tropical countries, including north-western countries of South America and
some countries in central Africa. Most of the MHC and STC countries
experience a decrease in the number of events per capita down to
∼ 7 events for higher global warming levels (lack of model
agreement). The number of events per capita for concurrent Rx1day and wind
events is increasing in 41 % of the countries. We observe the highest
increase over the tropical countries in Africa up to ∼ 10 more
events.
Non-event months
Figure 8 shows the percentage of “normal” – non-event – months that countries experience, i.e. the percentage of months without any individual
events studied in this paper (median of GCMs). We calculate the fraction of
non-event months for each year (e.g. 6 normal months out of 12 corresponds
to 0.5) over the 20-year period comprising the GWLs. We then take the mean
of fractions and multiply with 100 to calculate the percentage of non-event
months for each GWL. At the pre-industrial level, 60 % of the months are normal,
meaning that there are ∼ 7 normal months in every year across
the globe. At the first glance, we see that with higher global warming
levels, the percentage of normal months decreases gradually across the globe,
with some countries being more prone to the change. Independent of the
frequency of events, all countries become a hotspot for individual extremes
with increasing global warming. At current conditions, at ca.
+1 ∘C of global warming, 129 (out of 139) countries have 50 %
(6 months) or less normal months. A total of 23 of these countries, mostly with a
tropical climate, have less than 20 % (∼ 2–3 month) normal
months, and 2 countries have less than 10 % normal months (shown with grey
colour), meaning that there is either 1 or no single month without
individual events. At +1.5 ∘C of global warming, the percentage
of normal months is less than 20 % for 51 countries. A total of 21 of these countries
are projected to have less than 10 % normal months. These countries are
mostly located in tropical climates. At +2 ∘C of global
warming, 79 countries are projected to have less than 20 % of normal
months, whereas almost half of these countries (37) are projected to
experience extreme events every month. In a +3 ∘C world, 85
countries experience the above-mentioned four individual events almost every
month, whereas non-event months are between 10 %–20 % for 41 countries and
20 %–30 % for 11 countries. These results show that a large number of
countries will shift to near-permanent extreme conditions, even at global
warming levels consistent with the limits of the Paris Agreement.
Percentage of non-event months (months without individual
extremes) at warming levels (+1, +1.5,
+2 and +3 ∘C).
Discussion
Our results highlight the increasing frequency of heatwaves, droughts,
Rx1day and wind extremes with global mean warming. These findings, in
particular the respective spatial patterns and increasing signals, are in
accordance with the findings of the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change (IPCC AR6). In the IPCC AR6
report, projected changes in annual maximum daily precipitation (Rx1day) and
annual maximum temperature (TXx) indicate an increase over almost all land
areas, while soil moisture drought shows a heterogeneous pattern
(Seneviratne et al.,
2021). Additionally, mean wind is expected to increase gradually in the 21st
century in some tropical regions and decrease for the rest of the global
land areas (Ranasinghe et al.,
2021). In this work, individual heatwaves, droughts, Rx1day and wind
extremes present consistent results with the above-mentioned indices.
Increasing occurrence of these individual extremes can have important
implications for natural and human systems. Therefore, the compatibility
between the IPCC AR6 report and our results increases the confidence in our
estimates of concurrent extremes that are associated with even more severe
effects than the respective individual extremes.
With higher global warming levels, we have seen a sharp increase in
concurrent heatwave–drought events in three climate regions, with the most
dramatic increase in northern mid- and high-latitude countries (MHC)
followed by subtropical countries (STC) (Fig. 3a). As opposed to
heatwave–drought events, Rx1day–wind events increase the most in tropical
countries (TRC) (Fig. 3b). The frequency differences among regions can be
explained by varying climatic regimes. For instance, STC is more affected by
warm–dry conditions than TRC because arid climate zones have more climate
variability than equatorial climate zones. Another reason behind the
frequency differences across regions can be the underlying dynamical and
thermodynamic processes such as atmospheric circulation and teleconnection
patterns. For example, compound droughts in the Amazon are associated mainly
with El Niño–Southern Oscillation (ENSO) (Singh
et al., 2021), and wet and windy extremes in north-western Europe are
associated with the positive phase of the North Atlantic Oscillation (NAO)
(De Luca et al., 2020).
These findings correspond to regional findings in our analysis. Some studies
have found that polar amplification weakens the north–south temperature
gradient and warms up the cold extremes in mid- and high latitudes
(Holmes et al., 2016; Gross
et al., 2020), which is perhaps why MHC has prevailing heatwave–drought
conditions. Another important thermodynamic process that can amplify
temperature extremes is the lapse rate feedback mechanism. This mechanism
increases temperature extremes in mid- to high latitudes, while it decreases
temperature extremes in tropics (Seneviratne et al.,
2021). This direct influence on temperature extremes can be an indirect
influence on precipitation extremes by altering the circulation patterns
(dynamic processes) (Sillmann et al., 2017).
Another key mechanism responsible for frequency increase can be the
interaction between land and atmosphere
(Seneviratne et al., 2010). Lack of moisture
during droughts limits land evaporation, which leads to an increase in
sensible heat and in turn increases temperatures
(Chiang et al., 2018). Furthermore, the change in
moisture sources and sinks due to the future increases in greenhouse gas
forcing will likely alter the hydrologic cycle
(Batibeniz et al., 2020b), and such changes
will likely intensify the land–atmosphere feedback mechanism, causing
concurrent warm and dry conditions. These explain why we found an increase
in droughts occurring together with heatwaves in the projection
period. Additionally, the enhancement of the concurrent very hot–dry warm
seasons in many regions has also been linked with increasing dependence
between temperature and precipitation associated with global warming
(Zscheischler and Seneviratne, 2017). Moreover, it has been
found that future occurrences of compound hot–dry events over land are
connected with the variations in precipitation trends
(Bevacqua et al., 2022). Our analyses show similar
dipolar responses between heatwave–drought events and Rx1day–wind events in
some countries.
Our results highlight the positive trend both in individual and concurrent
events with higher global warming levels. The probability of occurrence of
compound extremes is much lower than individual extremes by definition. Our
results showcase this as individual events overall increase more than
concurrent events. However, the multivariate structure of heatwave–drought
and Rx1day–wind events changes in the future across all climate regions. The
interchangeable relationship between individual and concurrent events can be
a sign of distributional changes in mean climate. In any case, increasing
frequency decreases the number of non-event months without any individual
extreme (Fig. 8), which leaves less and less time for adaptation and
recovery. Additionally, timing analysis indicates either abrupt increases or
shifts in individual and concurrent extremes (Figs. 4, 5). The
inhomogeneous increases in frequency and changes in timing pose a risk for
different sectors such as agriculture, tourism and health. These changes may
serve as a red flag for countries with an economy depending on these
sectors.
Our analysis indicates that exposure to multivariate extremes is on the rise
across the globe. For some countries, there is a dipolar pattern between
exposures to heatwave–drought events and Rx1day–wind events. While the
Mediterranean countries, southern Africa and Mexico have an increase
(decrease) in heatwave–drought (Rx1day–wind) events, central Africa and the
Arabian Peninsula have a decrease (increase). Amazonia, southern Africa,
the Sahel, India, and Southeast Asia have been projected as a hotspot for
increasing temperatures and are the most vulnerable regions to extreme
events (Bathiany et al., 2018). We find similar
regional responses to increasing global warming levels. Low-income countries
have been found to be more economically vulnerable to weather and climate
extremes than rich countries (Jones and Olken,
2010; Dell et al., 2012, 2014). Therefore, these highly populated vulnerable
countries that are prone to the largest changes in multi-hazard exposures
could potentially be at larger risk.
The damage that extreme events cause is not only related to the frequency,
severity or magnitude of the events but also to socioeconomic factors (Botzen
et al., 2010; Jahn, 2015; Frame et al., 2020; IPCC, 2021) such as land use,
income, education, employment and community safety. Different economic and
social structures will alter the adaptive capacity to climate change. This
makes it difficult to disassociate climate-related hazards from
socioeconomic factors. Even so, assuming that projected future changes will
take place in a world with a society and economy similar to today would help
to understand the relative impacts of climate change on exposure. However,
the global population is currently growing at a rate of around 1.1 % per
year, with the majority of this growth occurring in developing countries
(Roser et al., 2013). The population living in the urban extent
of Europe in 2015 is projected to increase more than 5 % by 2050
(United Nations et al., 2019), and SSP population projections also
estimate an increase in population (Jones and O'Neill, 2016).
The distribution of population growth across different regions and
demographic groups can vary; therefore, using population projections to
investigate the human contribution to the change in exposure could help
understand future risks more (Batibeniz
et al., 2020a; Mukherjee et al., 2021). Our results provide evidence for an
already existing vulnerability that may further increase in regions where
extreme events will become more frequent due to climate change.
Conclusions
Investigating future changes in impactful individual and concurrent extremes
is important to prepare for future climate risks. In this study, we have
investigated the current state (∼+1∘C of global
warming) and projected change of individual and concurrent occurrences of
heatwave, drought, Rx1day and wind events at global warming levels (GWLs) of
+1.5, +2 and +3 ∘C relative to the
pre-industrial period on the level of countries and climatic macro-regions.
Projections as a function of GWLs provide useful information for
stakeholders in the context of the Paris Agreement, which has set a limit
for global warming stabilization “well below 2 ∘C” and an aim to
pursue efforts to limit global warming to +1.5∘C (UNFCCC,
2015). Analyses of simulations from 14 CMIP6 global climate models
allowed us to gain a robust understanding of extremes in current and future
climate.
Our results indicate that all climate regions are under the increasing
influence of concurrent hot–dry (heatwave–drought) events and Rx1day–wind
events with higher GWLs. Even though this change is more substantial for
heatwave–drought events, Rx1day–wind events are also on the rise. However,
the order of the increase of events across regions shows a clear contrast.
For heatwave–drought events, the increase is largest in northern mid- and
high-latitude countries (MHC), followed by subtropical countries (STC) and
tropical countries (TRC), whereas for Rx1day–wind events the order is the
opposite. While heatwave–drought events increased substantially, Rx1day–wind
events increased less in MHC and STC. However, in TRC the increasing rate of
heatwave–drought and Rx1day–wind events is similar, indicating the less
variable climate in TRC. Isolated events are on the rise for heatwave and
Rx1day events, whereas they are decreasing for drought and wind events, meaning
that towards a warmer world, drought (wind) events are projected to co-occur
with heatwave (Rx1day) events rather than occurring solely.
Our results also highlight the important timing shifts in the occurrence of
individual and concurrent extremes in the future climate. Individual extreme
events increase inhomogeneously across months, leading to unprecedented
frequency increases in some months in the future. Another important
highlight of our study is increasing human exposure to concurrent extremes
even without considering the expected rise in the human population. With
higher GWLs, the number of events per capita increases continuously in 53
countries for Rx1day–wind events, whereas this is valid for twice the number
of countries for heatwave–drought events. Our results also suggest non-event
months are gradually decreasing for countries and that 85 countries will
experience individual events nearly every month (i.e. less than 10 % of
non-event months) in a +3 ∘C warmer world. But this also
affects several countries at +1.5∘C (21 countries) or
+2∘C of global warming (37 countries). This shows that a
large number of countries will shift to near-permanent extreme conditions
(less than 10 % of non-event months) even at global warming levels
consistent with the limits of the Paris Agreement. Furthermore, our results
suggest that there is a prevailing increase in frequency, shifts in timing
of concurrent extremes from +1.5 to
+2 ∘C of global warming, thus exacerbating human
exposure to these extremes with increasing global warming.
Despite many robust findings of our study, which are consistent with past
assessments (Seneviratne et al.,
2021) but also provide some new insights on the projected changes in
extremes with increasing global warming, many sources of uncertainty need to
be emphasized. This study relies on climate model simulations for both past
and projected changes in climate extremes. For historical changes,
observational analyses could complement the provided results, but given the
difficulty of investigating extreme events statistically due to their rare
nature, climate models have been widely used for historical analyses in the
literature (Sillmann et al.,
2017; Miralles et al., 2019) using both regional and global climate models
(Zhu
and Yang, 2020; Zhu et al., 2020; Srivastava et al., 2020; Krishnan and
Bhaskaran, 2020). We focus here on global simulations of standard
resolution, which can be a limitation in regions of steep terrain. Indeed,
high-resolution regional models have been utilized especially for
replication of wind and precipitation extremes at regions with complex local
features (Coppola
et al., 2021; Outten and Sobolowski, 2021; Reale et al., 2021; Stocchi et
al., 2022), while global climate models are often used to investigate the
relationship between land surface conditions and extreme statistics (Seneviratne et al.,
2013; Hauser et al., 2016; Rasmijn et al., 2018). However, the robust,
large-scale investigation of extremes requires global model simulations with
standard resolution, which often have lower computational cost compared to
high-resolution global simulations and allow us to obtain global statistical
information compared to regional high-resolution simulations. Despite
remaining uncertainties related to model deficiencies in some physical
processes, natural variability (Wilcox
and Donner, 2007; Rossow et al., 2013; Pfahl et al., 2017) and feedback
mechanisms (Orlowsky and
Seneviratne, 2013; Mueller and Seneviratne, 2014), CMIP6 is widely regarded
as one of the most comprehensive and reliable sources for global information
on climate change and is used in many extreme studies. Additionally, these
models have a higher resolution, have mostly higher climate sensitivity and
produce better replication of physical, chemical and biological processes
compared to CMIP5 (Coupled Model Intercomparison Project 5) used in IPCC AR5
(IPCC, 2021).
In conclusion, this study highlights the increasing occurrence of several
single and compound extreme events with increasing global warming, with
major increases in affected countries and human exposure even at levels of
global warming consistent with the limits of the Paris Agreement. In
particular, a substantial fraction of countries would be near permanently
affected by extreme events already at +1.5∘C and even more so at
+2 and +3∘C of global warming. The identified
unprecedented changes in frequency and timing of extreme events would lead
to an elevated risk for the environment and society across the globe.
Therefore, our results suggest an urgent need for concrete actions to
mitigate the current greenhouse gas emissions.
The 20 % relative change (shown with 1) and 100 %
relative change (shown with 2) of each individual extreme [a] and concurrent
extreme [b].
Number of individual and concurrent extremes (a) at
+1 ∘C and (b, c, d) change at +1.5,
+2 and +3 ∘C with respect to +1 ∘C.
Colours refer to high model agreement, and hatched areas refer to lack of
model agreement.
Population from GWPv4 at (a) 2.5∘ grid
level and (b) country level.
Data availability
GPWv4 data used for population analysis are provided by the NASA Socioeconomic Data
and Applications Center (SEDAC) and are available at 10.7927/H4JW8BX5 (CIESIN, 2018).
Extreme indices have been generated using data archived on the ETH Zurich
CMIP6 repository (10.5281/ZENODO.3734128, Brunner et al., 2020). Access to CMIP6 model outputs is also possible through
different Earth System Grid Federation (ESGF) data nodes.
Author contributions
FB, MH and SIS planned the study; FB performed the analyses with support
and guidance from MH and SIS; FB wrote the manuscript draft; MH and SIS
reviewed and edited the manuscript. All authors were involved in discussions
of the results and streamlining the text.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This study was funded by the Swiss National Science Foundation (SNSF)
through the Compound Events in a Changing Climate (CECC) (grant agreement ID
IZCOZ0_189941) project contributing to the European COST
Action CA17109, “Understanding and modeling compound climate and weather
events” (DAMOCLES).
We acknowledge the World Climate Research Programme's Working Group on Coupled
Modelling, which is responsible for the Coupled Model Intercomparison
Project (CMIP), and we thank the climate modeling groups (listed in
Table A1 in Appendix A) for producing and making their model output available.
Furthermore, we are indebted to Urs Beyerle, Lukas Brunner and Ruth Lorenz
for downloading and curating the CMIP6 data.
Financial support
This research has been supported by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (grant no. IZCOZ0_189941).
Review statement
This paper was edited by Chandrika Thulaseedharan Dhanya and reviewed by Mojtaba Sadegh, Arpita Mondal, and one anonymous referee.
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