A typology of compound climate and weather events

Jakob Zscheischler*, Olivia Martius, Seth Westra, Emanuele Bevacqua, Colin Raymond, Radley M. Horton, Bart van den Hurk, Amir AghaKouchak, Aglaé Jézéquel, Miguel D. Mahecha, Douglas Maraun, Alexandre M. Ramos, Nina Ridder, Wim Thiery, Edoardo

Vignotto

*Climate and Environmental Physics and Oeschger Centre for Climate Change Research University of Bern, Bern Switzerland

Compound weather and climate events

Compound weather and climate events refer to“the combination of multiple drivers and/or hazards that contributes to societal or environmental risk.”

2Zscheischler et al. (2018) Nature Climate Change

Compound weather and climate events refer to“the combination of multiple drivers and/or hazards that contributes to societal or environmental risk.”

Compound weather and climate events refer to“the combination of multiple drivers and/or hazards that contributes to societal or environmental risk.”

à Links compound events with the IPCC risk concept. à Is being used in the current IPCC cycle.

Elements of a compound event

3

Compound weather and climate events refer to combination of multiple drivers and/or hazards that contributes to societal or environmental risk.

Hazard 1

ImpactDriver

Climate change

Hazard 1Hazard 1

HazardModulator

A typology of compound events

1. Preconditioned events

2. Multivariate events

3. Temporally compounding events

4. Spatially compounding events

4

1. Preconditioned events

5

Precondition

DriverHazard 1

Hazard 1

Driver

Impact

Early leave development,

budburst

Cold front Late spring frost

Warm winter and early spring

Leaf damage in maple trees,

decreased carbon uptake

“False” spring

(Sakai et al., 1997; Hollinger et al., 1999; Fitzjarrald &Acevedo, 2001; Levis & Bonan, 2004). Understandinghow phenology responds to climate variability, climatechange, and extreme events is therefore essential forimproving understanding of how coupled climate-ecosystem dynamics will evolve in the coming decades.Climate change is expected to increase the frequency

of extreme weather events (Solomon et al., 2007; Marinoet al., 2011). Specifically, and as a by-product of warmertemperatures, the occurrence of frost after leaf out isprojected to become more common in some parts of theworld (Meehl et al., 2000; Gu et al., 2008). This scenariohas a number of important ecological implications. Inparticular, newly developed leaves are sensitive to frostevents, as they lack the structural rigor necessary toprevent damage. Depending on the timing of springwarmth, early and accelerated leaf development hasthe potential to increase the frequency and magnitudeof leaf damage from freezing events (Norby et al., 2003;Inouye, 2008), and lasting effects may include loss ofstored carbon and nutrients, and reduced photosyn-thetic carbon gain (Gu et al., 2008; Martin et al., 2010).Global land surface temperatures in 2010 were

among the warmest of the last 131 years (Blunden et al.,2011). In the northeastern United States, extraordinarilywarm winter and spring temperatures were recorded,averaging 3 °C above the 1971–2000 climatologicalaverage for May and June (Blunden et al., 2011; Guir-guis et al., 2011). During this period, however, thenortheastern United States experienced a late springfreezing event from May 9 to 11 [day of year (DOY) 129–131]. The effects of this event were widespread, andranged from mild leaf damage to complete defoliationof the canopy (e.g. Fig. 1a and b). As stand level warm-ing and freezing experiments in forest ecosystems aredifficult to implement, the co-occurrence of an unusu-ally warm spring with a pronounced late spring frostpresents a rare opportunity to study the nature andmagnitude of ecosystem responses to climate changeand extreme events, and to assess species specificimpacts of extreme weather conditions on canopydevelopment.In this article, we examine the response of sugar

maple (Acer saccharum), American beech (Fagus grandi-folia), and yellow birch (Betula alleghaniensis) to theanomalous spring of 2010 in the northeastern UnitedStates. These three species are widespread in northernhardwood forests (Foster, 1992) and therefore providea good basis for understanding how northeastern for-ests respond to climate change and extreme events.We use the unique conditions provided by an unusu-ally warm early spring and late spring frost in 2010,in combination with several different data sourcesincluding field observations of vegetation phenology,

eddy covariance measurements, and near-surface andsatellite remote sensing imagery to address the follow-ing questions: (1) How did each species respondto differences in the intensity of the anomalous springwarmth and frost at different elevations? (2) How dodifferences in phenological growth strategy influencethis response? (3) How did the late spring frost affectecosystem productivity in northern hardwood forestswithin the study domain? (4) What are the potentiallong term impacts of more frequent spring frostevents on temperate hardwood forest communityecology?

Materials and methods

Study area and measurement sites

The study area encompasses the northeastern United Statescovering all of Pennsylvania, Vermont, New York, New

(a)

(b)

Fig. 1 (a) Frost damage to developing sugar maple leaves and

(b) landscape view of frost damage 15 days after the event

showing damaged sugar maple trees are interspersed with

healthy developing American Beech and yellow birch. Photos

supplied by Amey Bailey and Paul Schaberg.

© 2012 Blackwell Publishing Ltd, Global Change Biology, 18, 2365–2377

2366 K. HUFKENS et al.

Hufkens et al. (2012) Glob. Ch. Biol.

Snow covered surface

Heavy rainfall Flood

Snowfall

Damage to roads and buildings

Rain on snow events

Rössler et al. (2016) MWR

2. Concurrent events

8

Hazard 2

Hazard n

Hazard 1

DirectdriverDriver ImpactModulator

Flood

Heavy precipitation in

river catchment 1

Flood damage

Low pressure system

Heavy precipitation in

river catchment 2

Storm surge at the coast

Compound flooding

Bevacqua et al. (2017) HESS

Compound flooding

Modulators: storms, tropical cyclones, atmospheric rivers, etc.

10Figure credit: D. Maraun, E. Bevacqua

Heatwave

Drought

Stationary Rossbywaves

Agricultural losses, timber loss, wild fires

La Niña

Hoerling et al. (2013) J. Clim.

Concurrent drought and heat

Processes behind concurrent drought and heat

12

cloud cover/radiation (e.g., Gentine et al. 2013) andlarge-scale circulation (e.g., Haarsma et al. 2009) mayfurther amplify the effect of precipitation variability ontemperatures.The impact of soil moisture anomalies on subsequent

temperatures has been highlighted in a number of mech-anistic modeling studies that have isolated soil moisturevariability as a source of daily surface temperaturevariability in summer, especially in transitions betweenhumid and dry climates (Koster et al. 2006; Seneviratneet al. 2006; Koster et al. 2010). Observation-based esti-mates of soil moisture–temperature coupling are con-sistent with these patterns (Miralles et al. 2012). Soilmoisture–atmosphere interactions have been shown toplay an amplifying role in warm extremes, as noted forrecent European heat waves in observational (Vautardet al. 2007; Hirschi et al. 2011; Quesada et al. 2012) aswell as modeling (Fischer et al. 2007; Zampieri et al.2009) studies. Observations provide support for ante-cedent soil moisture deficits enhancing the probabilityof subsequent summer hot conditions across differentregions of the globe (Durre et al. 2000; Shinoda andYamaguchi 2003; Mueller and Seneviratne 2012).These lines of evidence point to coupled land–

atmosphere processes as the source for the regionallywidespread anticorrelations of summertime terrestrialtemperature and precipitation (Trenberth and Shea2005; Koster et al. 2009b). However, whether local landsurface processes are solely responsible for the large-scale, interannual covariability between summertime-averaged temperature and precipitation as depicted inFig. 1 (see also Trenberth and Shea 2005; Adler et al.2008; Wu et al. 2013) remains to be determined. Intheir analysis of the relationship between mean sum-mertime temperature and precipitation using a singleclimate model, Koster et al. (2009b) indicate that these

temperature–precipitation anticorrelations essentiallydisappear when simulated land–atmosphere interactionsare disabled by prescribing surface fluxes; they thusidentify land–atmosphere processes as the dominantdriver of these relationships. Krakauer et al. (2010) alsoreport reduced coupling of temperature and precipitationin another model when soil moisture–atmosphere cou-pling is suppressed through prescribing soil moisture, al-though they did not investigate this behavior in detail.The aim of the present study is to explore more ex-

tensively, across severalmodels, the correlations betweenmean temperature and precipitation in order to untanglethe contribution of the different processes illustrated inFig. 2. To do so, we make use of simulations from therecent phase 5 of the Coupled Model IntercomparisonProject (CMIP5) Global Land–Atmosphere CouplingExperiment (GLACE-CMIP5; Seneviratne et al. 2013),in which simulations spanning 1950–2100 were performedwith a suite of current-generation models following anexperimental setup disabling land–atmosphere inter-actions. The manuscript is organized as follows: we de-scribe themodels and fields analyzed in section 2. Section 3presents the temperature–precipitation correlations inthe GLACE-CMIP5 simulations. Land and atmosphericcontrols on these correlations are investigated in section 4,while section 5 describes the potential relevance of thesecorrelations for climate change projections. The principalresults and implications of our study are discussed insection 6.

2. Methods and datasets

In the context of the GLACE-CMIP5 experiment, fivemodeling centers performed a land–atmosphere-onlytransient climate change simulation (hereafter referredto as ‘‘expA’’) in which total soil moisture was overriddenin the respective models by the climatological values over1971–2000 from the corresponding historical, fully cou-pled CMIP5 simulation. The simulation expA extendsover 1950–2100, with transient sea surface temperatures(SSTs), sea ice, land use, and radiative forcing agentconcentrations prescribed from the correspondingCMIP5simulations [using the historical simulations over 1950–2005 and the representative concentration pathway 8.5(RCP8.5) scenario thereafter, characterized by high pop-ulation and energy consumption growth, no climate policyand unabated emissions]; however, soil moisture in eachmodel is overridden by the 1971–2000 climatologicalseasonal cycle of soil moisture, and thus maintains a cli-matological seasonal cycle throughout the transient sim-ulation. For each model, either the fully coupled CMIP5simulation, or, in cases where there were minor differ-ences in setup, a new reference simulation identical to

FIG. 2. Simplified representation of two pathways through whichcorrelations between seasonal mean temperature and precipitationcan occur in summer: red for atmospheric processes and blue forland–atmosphere interactions. Note that in the interest of clarity,not all physical relationships are depicted here (e.g., impacts oftemperature on soil moisture and feedbacks of surface fluxes tocloud cover are not represented).

1 FEBRUARY 2015 BERG ET AL . 1311

Berg et al. (2015) J. Clim.

3. Temporally compounding

13

Directdriver Hazard 2

Hazard n

time

DriverModulator

Hazard 1

Fixe

d ge

ogra

phica

l reg

ion

Impact

Lake flooding

Heavy precipitation

Upper-level breaking

Rossby wave

Lago Maggiore

Flood damage (fatalities, damaged

infrastructure)

Sep 18 Sep 25 Oct 02 Oct 09 Oct 16

193

195

197

020

4060

80

Wat

er le

vel (

m.a

.s.l)

Date

Prec

ipita

tion

(mm

)

Heavy precipitation

Heavy precipitation

Heavy precipitation

Barton et al. (2016) MWR

Precipitation clustering and lake flooding

Sequence of heatwaves

15

Earth’s Future 10.1029/2018EF000989

Figure 2. Schematic temperature time series to build intuition regardingthe heat wave definitions. Cartoon temperature (black) and a seasonallyvarying threshold (red) are plotted against time. At the top of the figure,threshold-exceeding hot days are marked with red Hs while belowthreshold cooler days are marked with black minus signs. According to theWarm Spell Duration Index no heat wave occurs, as the events are too short.According to another prior heat wave definition (i.e., Perkins & Alexander,2012) this would constitute two 3-day-long heat waves. In this paper wecount this event as having a total duration of 7 days, composed of an initial3-day-long heat wave with four additional hot days compounded onto it.

will already be enduring a constant heat wave state, with no or very fewdays a year below extreme hot day thresholds (Perkins-Kirkpatrick &Gibson, 2017).

Both changes to the mean and higher-order moments of temperaturedistributions can influence heat wave hazards. Trends in higher-ordermoments (such as variance) might result from interplay betweenthe radiative effects of increased CO2, circulation changes, andland-atmosphere interactions. In places with moderate levels of soilmoisture, projected summertime drying is expected to increase surfacetemperature response to circulation anomalies, and in turn likelihood ofheat events (e.g., Dirmeyer et al., 2012; Quesada et al., 2012; Seneviratneet al., 2010). Trends also may exist in the blocking events and other cir-culation anomalies associated with heat waves (Coumou et al., 2014,2015; Hoskins & Woollings, 2015; Petoukhov et al., 2013; Pfahl et al.,2015). However, these trends remain speculative, as the observed periodis short and climate model results are inconsistent (Horton et al., 2016).Overall, diverse phenomena might make temperature variability changealongside mean warming, but how and why is still highly uncertain.

1.2. Motivation and Goals of This StudyA necessary first step and complication in studying heat waves are defin-ing them. Common to most definitions is the choice of a threshold abovewhich a day's temperature, or a thermal stress metric, is considered hot. Ifa minimum number of hot days occur in a row, then a heat wave is said tohave occurred. Heat wave hazard then is the count of days meeting theserequirements that occur over a period of time. As a specific example, one

definition measuring heat wave duration is the Warm Spell Duration Index (WSDI), which uses a season-ally varying 90th percentile temperature threshold and requires at least six threshold-exceeding days in arow (see the supporting information for further definition details and alternatives; Sillmann et al., 2013b).For the rest of this paper we refer to days that exceed an assigned temperature threshold as “hot days,” anda set of hot days occurring close in time meeting certain duration requirements as a “heat wave.”

Temperature time series for major historical heat waves are compared to a corresponding local temperaturethreshold in Figure 1. We use the WSDI threshold as an instructive example, but other common hot daythresholds would produce similar results. According to our review of the existing literature, the heat wavesdepicted in Figure 1 are the four deadliest heat waves in Europe and the United States since 1980 (see thesupporting information for mortality estimates). Of the events, only Western Europe in 2003 and Russia in2010 clearly meet the six continuous hot days requirement of WSDI, and these were indeed associated withthe first and second highest mortality among the eight. Chicago in 1995 just misses the duration require-ment, with five threshold-exceeding hot days. The other deadly heat waves included in Figure 1 exhibit moreexotic temporal structures that do not appear to be well described by the continuous hot days requirement ofWSDI and other heat wave definitions, with temperature dipping below the threshold multiple times (Bel-gium in 1994 is a particularly striking example). This suggests that temperature extremes that occur close intime with short break periods of cooler days in between might compound together to create impacts similarto more consistent hot periods recognized by standard heat wave duration definitions. This variable temporalstructure resulting in high mortality also may point to heightened vulnerability to subsequent temperatureextremes after an initial heat wave.

Here we characterize heat waves with intermittent temporal structures as a type of compound extreme event(Figure 2 gives a cartoon example of this type of event to build intuition). Broadly, a compound extremeevent is a combination of climatic events that together constitute an extreme event in terms of the associatedclimatic anomaly or impacts. Even though many past climate-related natural disasters are best character-ized as compound extreme events (Leonard et al., 2014), such events and their future change are relativelyunderstudied (Field, 2012; Zscheischler et al., 2018). Recent work has made some advances in this area,including joint projections of temperature and humidity (Fischer & Knutti, 2013), storm surge associatedwith tropical cyclones combined with sea level rise to predict extremes of high water (Little et al., 2015),

BALDWIN ET AL. 413

Baldwin et al. (2019) GRL

Cluster of tropical cyclones

Katia, Irma, Jose in 2017

16https://en.wikipedia.org/wiki/2017_Atlantic_hurricane_season#/media/File:Katia,_Irma,_Jose_2017-09-08_1745Z–1935Z.jpg

4. Spatially compounding

17

Hazard 2

Hazard n

Modulator

Hazard 1

Mul

tiple

regi

ons

ImpactDriver 2

Driver n

Driver 1

18

El NiñoGlobally

synchronized maize failure

Drought and heat in North America

Drought in South

Africa

Drought in Northeast

Brazil

Anderson et al. (2019) Sci. Adv.

Concurrent impacts of El Niño on crop yields

Globally synchronized heatwaves

19Kornhuber et al. (2020) Nature Climate Change

LETTERS NATURE CLIMATE CHANGE

rates are found for (hs > 0:8;T > 1:5I

) and (hs > 0:7;T > 1I

), in line with the overall signal of highest coincidence rates occurring for wide-spread events of high magnitude (white boxes, Fig. 4b,d). To exem-plify coinciding heat events in multiple regions for wave 7, we again use hs > 0:4;T > 1

I events (white box, Fig. 4e–h), and find a threefold

increase in probabilities for simultaneous events in WCNA and WEU. Detected events include the summers of 2003, 1983 and 2015. The factor in WCNA and WAS is 2 (but is not significant) and for WEU and WAS, we find a 16-fold increase in probability. The likelihood of the simultaneous occurrence of such an event in all three regions WCNA, WEU and WAS is 22 times higher during a wave-7 event compared to the remaining summer weeks, although only three events are identified in total, of which two coincide with a wave-7 event (Supplementary Fig. 11). Those include weeks in the extreme summers of 1983 and 2003 (see Supplementary Information for additional robustness tests related to wave ampli-tudes, phase positions and coincident heat events in the identi-fied regions). We also find that waves 6 and 8 do have relevance for regional weather extremes, but as their phase-locking behav-iour is less pronounced (Fig. 1b,d), fewer statements can be made about the locations in which they occur and, by extension, their

physical linkages to simultaneous heat extremes in multiple regions (Supplementary Figs. 1, 2 and 16).

Some studies have reported an increase in waves 5 and 7 over recent summers7,22. Whether such trends are associated with anthropogenic climate change23 or multidecadal variability requires further research. The wave event time-series used in this study do not show significant trends in event frequency over the period 1979–2018 (Supplementary Fig. 8). Nevertheless, even without changes in high-amplitude wave events, the intensity of heat events associated with those waves would be amplified due to increasing mean temperatures. Thus, the impacts of such events will probably become more severe, for example for the agricultural sector24. The regions affected by waves 5 and 7 account for a large fraction of global food production (Fig. 5): for wheat, the United States, France and Russia produce 42% and for maize the United States and France alone produce 57%25,26. The two wave patterns show a large overlap in North America, which might suggest added vulnerability to agri-cultural impacts. Simultaneous heatwaves and associated produc-tion declines in the region highlighted here might even have the potential to trigger shocks in global food supply as affected coun-tries might impose export bans to ensure national food security8.

Longitude

Latit

ude

180° 90° W135° W 45° W 0° 45° E 90° E 135° E 180°30° N

40° N

50° N

60° N

70° N

a

1.1

1.2

1.4

1.4

1.5

1.5

1.8

1.9

2.4

1.4

1.5

1.7

1.6

1.9

2.4

2.6

2.3

1

1.6

2

2.4

2.3

2

1.7

2.3

2.4

5.4

2

2.3

1.9

2.2

1.4

2.7

5.4

Inf

1.8

1.1

2.2

1.8

1.8

11

1.4

1.1

2.2

0

1.4

2.7

0

03.0

0.2

0.4

0.6

0.8

b

Spa

tial c

over

age

≥ s

1.2

1.3

1.3

1.3

1.4

1.7

1.8

2.1

2.3

1.4

1.4

1.6

1.7

1.8

1.9

2.5

3.2

3.5

1.7

1.6

1.8

2.1

2.6

3.2

4.6

3.9

6.5

2.1

2.2

2.7

3.2

4.1

4.1

4.2

32

43

2.3

2.8

3

2.6

4.2

11

27

11

Inf

2.5

3.1

3.5

4.5

22

27

Inf

Inf

2.9

3.1

6

8.7

16

22

Inf

3

4.3

18

32

Inf

Inf

Inf

3.0

0.2

0.4

0.6

0.8

c

1.1

1.3

1.4

1.4

1.4

1.6

1.6

1.8

2

1.4

1.4

1.4

1.7

1.7

1.9

2

1.8

0.49

1.4

1.5

2

2.5

2.3

2.5

3.1

0.83

0

1.9

1.9

2

2

1.1

0.98

2.2

0

0

1.8

1.2

1.4

0.98

0

0

0

1.8

0.9

0

0

0

0

2.5

2.2

0

0

0

0

0

0 1.0 2.0 0 1.0 2.0 0 1.0 2.0 3.0

0.2

0.4

0.6

0.8

d

0 0.2 0.4 0.6 0.8 1.0

Coincidence rate

1.3

1.5

1.7

1.8

2.1

2.6

3.9

3.1

5

1.9

2.2

2.8

3

3.1

4.1

3.9

4.3

5.4

3.2

3.2

4.5

3.4

3.1

5.4

5.4

11

Inf

4.3

4.7

11

11

11

11

11

Inf

2.2

3.6

11

11

Inf

Inf

11

11

Inf

11

Inf

0 1.0 2.0 3.0

0.2

0.4

0.6

0.8

e

Spa

tial c

over

age

≥ s

Temperature anomaly ≥ T (σ)

1.3

1.5

1.9

1.8

1.9

2.2

2.2

1

0.98

2

2.1

2.3

2

2.2

2.8

1.5

2.2

5.4

2

2.6

3

2

1.1

1.5

5.4

Inf

2.3

0.77

1.2

1.8

5.4

Inf

Inf

2.4

1.8

5.4

0

7.2

0

Inf0 1.0 2.0 3.0

0.2

0.4

0.6

0.8

f

Temperature anomaly ≥ T (σ)

1.4

1.7

1.9

2.1

2.7

4.3

4.5

7

8.7

2.1

2.4

3

4.3

6

8.9

11

5.4

0

2.9

3.3

6.5

16

11

Inf

Inf

4.8

5.9

Inf

Inf

8.7

0

11 Inf

0.2

0.4

0.6

0.8

g

Temperature anomaly ≥ T (σ)

1.6

1.9

2.7

3

3.8

6.6

9.5

2.7

3.2

4.3

7

6.9

6.5

22

0

6.3

4.3

11

22

11

0

11

0

Inf Inf0 1.0 2.0 3.0 0 1.0 2.0 3.0

0.2

0.4

0.6

0.8

h

Temperature anomaly ≥ T (σ)

WCNA WASWEU

Affected regions wave 7

Fig. 4 | Coincidences of heatwaves in regions teleconnected by wave 7. a, Regions affected by anomalous heat during wave-7 events and an idealized depiction of the circulation when wave 7 is in its preferred phase with the width and position of the maximum (black dotted line) reflecting values shown in Fig. 1a. b–d, Regional coincidence rates of amplified wave-7 conditions with heat events of different intensity and spatial extent for WCNA (b), WEU (c) and WAS (d). The most severe heat events can be found in the upper right corner of each plot. The numbers refer to the factor by which wave events amplify the respective heat events. This factor is given as ‘Inf’ when all observed heat events coincide with wave events relative to non-event weeks. Statistical significance of coincidence rates at the 99% (95%) confidence level is marked with a white (black) dot. Examples discussed in the text are highlighted by a white box. e–g, As in b–d but for simultaneous heat events in two regions: WCNA and WEU (e); WCNA and WAS (f); WEU and WAS (g). h, Coincidence rates based on all three identified regions.

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange

Climate change effects on compound events

20

Change in the mean Change in variability Change in dependence

Conclusions

• The typology facilitates our thinking about the highly diverse set of compound events• Many events don’t fit completely into a single class, but often one

class covers the most important aspects of an events• The typology can be directly aligned with analysis and modelling tools

21

To appear in Nature Reviews Earth and Environment.

22

WG 1: Synthesis and

analysis framework

WG 3

Meta-database

of impact datasets

WG 2

Stakeholder

involve

ment

WG 4

New statis

tical

approachesWG 5

Realistic model

simulations

Classification of eventsCompendium of methods

Benefit of a scientific approachWay of interaction

Criteria for usefulnessEvent-impact relationships

Quality assessmentExperimental design

Criteria for usefulnessApplied to what kind of hypothesis?

COST Action DAMOCLES|Understanding and modeling compound climate and weather eventshttp://damocles.compoundevents.org

9/2018 – 9/2022

38 countriesChair: J.Z.