Abstract
Extreme weather and climate events have direct impacts on ecosystems and can further trigger ecosystem disturbances, often having impacts that last longer than the event’s duration. The projected increased frequency or intensity of extreme events could thus amplify ecological impacts and reduce the biosphere’s CO2 mitigation potential, but multiple feedbacks between ecosystems and climate extremes are often not considered in risk assessments. In this Perspective, we propose a systemic framework to analyse the causal relationships between climate extremes, disturbance regimes and ecosystems, building on two broadly used perspectives: climate risk assessment and disturbance ecology. Each has strengths and limitations, as each perspective places a different — and partly disjointed — focus on the physical and ecological processes that drive high-impact ecological events. We unify these approaches into a framework (compound ecoclimatic events) that decomposes events into climatic drivers, stressors, environmental factors, impacts and their sources of variability, and further incorporates feedbacks between ecosystem processes and stressors. This framework can be used to develop ecoclimatic storylines to better understand the role of each factor in influencing high-impact events; to incorporate uncertainties associated with internal climate and ecological variability, with scenario definitions, and with epistemic uncertainties; and to quantify the human fingerprint on high-impact ecoclimatic events.
Key points
-
Impacts of extreme weather and climate events on ecosystems are influenced by multiple processes and interactions with ecosystem disturbances.
-
A systemic perspective on the causal relationships between climate extremes, disturbance regimes and ecosystems is needed for improved process understanding and attribution of impacts.
-
Attribution of high-impact ecological events to human activities needs to go beyond climate change attribution and include the wide range of anthropogenic influence on environmental factors that influence ecosystem vulnerability and disturbances.
-
Natural climate variability is an irreducible source of uncertainty in attribution and projection of extreme events that needs to be propagated to the assessment of impacts.
-
Storylines are a useful approach to account for the multiple uncertainties influencing high-impact ecoclimatic events and to complement current approaches in impact attribution.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout





References
Intergovernmental Panel on Climate Change. IPCC glossary. IPCC. https://apps.ipcc.ch/glossary/ (2022).
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).
Bastos, A. et al. Impacts of extreme summers on European ecosystems: a comparative analysis of 2003, 2010 and 2018. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190507 (2020).
Abram, N. J. et al. Connections of climate change and variability to large and extreme forest fires in southeast Australia. Commun. Earth Environ. 2, 8 (2021).
Ciavarella, A. et al. Prolonged Siberian heat of 2020 almost impossible without human influence. Clim. Change 166, 9 (2021).
Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010).
Hammond, W. M. et al. Global field observations of tree die-off reveal hotter-drought fingerprint for Earth’s forests. Nat. Commun. 13, 1761 (2022).
Hartmann, H. et al. Climate change risks to global forest health: emergence of unexpected events of elevated tree mortality worldwide. Annu. Rev. Plant. Biol. 73, 673–702 (2022).
Boulton, C. A., Lenton, T. M. & Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Change 12, 271–278 (2022).
Seneviratne, S. I. et al. in Weather and Climate Extreme Events in a Changing Climate in Climate Change 2021: The Physical Science Basis. (eds Masson-Delmotte, V. et al.) Ch. 11 (Cambridge Univ. Press, 2021).
Turner, M. G. Disturbance and landscape dynamics in a changing world. Ecology 91, 2833–2849 (2010).
McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).
Seidl, R. & Turner, M. G. Post-disturbance reorganization of forest ecosystems in a changing world. Proc. Natl Acad. Sci. USA 119, e2202190119 (2022).
Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).
Zscheischler, J., Mahecha, M. D., Harmeling, S. & Reichstein, M. Detection and attribution of large spatiotemporal extreme events in Earth observation data. Ecol. Inform. 15, 66–73 (2013).
Bastos, A. et al. Impact of the 2015/2016 El Niño on the terrestrial carbon cycle constrained by bottom-up and top-down approaches. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170304 (2018).
Lewis, S. C. et al. Deconstructing factors contributing to the 2018 fire weather in Queensland, Australia. Bull. Am. Meteorol. Soc. 101, S115–S122 (2020).
Brown, T., Leach, S., Wachter, B. & Gardunio, B. The extreme 2018 Northern California fire season. Bull. Am. Meteorological Soc. 101, S1–S4 (2020).
Meddens, A. J., Hicke, J. A. & Ferguson, C. A. Spatiotemporal patterns of observed bark beetle-caused tree mortality in British Columbia and the western United States. Ecol. Appl. 22, 1876–1891 (2012).
Hlásny, T. et al. Devastating outbreak of bark beetles in the Czech Republic: drivers, impacts, and management implications. For. Ecol. Manag. 490, 119075 (2021).
Carnicer, J. et al. Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought. Proc. Natl Acad. Sci. USA 108, 1474–1478 (2011).
Oliva, J., Stenlid, J. & Martínez-Vilalta, J. The effect of fungal pathogens on the water and carbon economy of trees: implications for drought-induced mortality. New Phytol. 203, 1028–1035 (2014).
McDowell, N. G. et al. Mechanisms of woody-plant mortality under rising drought, CO2 and vapour pressure deficit. Nat. Rev. Earth Environ. 3, 294–308 (2022).
Kornhuber, K. et al. Amplified Rossby waves enhance risk of concurrent heatwaves in major breadbasket regions. Nat. Clim. Chang. 10, 48–53 (2020).
Ruehr, N. K., Grote, R., Mayr, S. & Arneth, A. Beyond the extreme: recovery of carbon and water relations in woody plants following heat and drought stress. Tree Physiol. 39, 1285–1299 (2019).
Wigneron, J. P. et al. Tropical forests did not recover from the strong 2015–2016 El Niño event. Sci. Adv. 6, eaay4603 (2020).
Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Change 7, 395–402 (2017).
Frank, D. et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob. Change Biol. 21, 2861–2880 (2015).
Anderegg, W. R. et al. Tree mortality from drought, insects, and their interactions in a changing climate. New Phytol. 208, 674–683 (2015). This work reviews the interactions between drought and insects in influencing tree mortality.
Bastos, A. et al. Vulnerability of European ecosystems to two compound dry and hot summers in 2018 and 2019. Earth Syst. Dyn. 12, 1015–1035 (2021).
Senf, C. & Seidl, R. Persistent impacts of the 2018 drought on forest disturbance regimes in Europe. Biogeosciences 18, 5223–5230 (2021).
Drouard, M., Kornhuber, K. & Woollings, T. Disentangling dynamic contributions to summer 2018 anomalous weather over Europe. Geophys. Res. Lett. 46, 12537–12546 (2019).
Mack, M. C. et al. Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science 372, 280–283 (2021).
Anderegg, W. R. L., Trugman, A. T., Badgley, G., Konings, A. G. & Shaw, J. Divergent forest sensitivity to repeated extreme droughts. Nat. Clim. Chang. 10, 1091–1095 (2020).
Jones, M. W. et al. Global and regional trends and drivers of fire under climate change. Rev. Geophys. 60, e2020RG000726 (2022).
Seneviratne, S. I. et al. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B., Barros, V., Stocker, T. F. & Dahe, Q.) 109–230 (Cambridge Univ. Press, 2012).
National Academies of Sciences, Engineering and Medicine. Attribution of Extreme Weather Events in the Context of Climate Change (National Academies Press, 2016). https://doi.org/10.17226/21852.
Reynaert, S. et al. Does previous exposure to extreme precipitation regimes result in acclimated grassland communities? Sci. Total Environ. 838, 156368 (2022).
Bloom, A. A. et al. Lagged effects regulate the inter-annual variability of the tropical carbon balance. Biogeosciences 17, 6393–6422 (2020).
Peters, D. P. C. et al. Cross-system comparisons elucidate disturbance complexities and generalities. Ecosphere 2, art81 (2011).
Smith, M. D. An ecological perspective on extreme climatic events: a synthetic definition and framework to guide future research. J. Ecol. 99, 656–663 (2011).
Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate‐change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).
Reisinger, A. et al. in Intergovernmental Panel on Climate Change 15 (CGIAR, 2020).
White, P. S. & Jentsch, A. The search for generality in studies of disturbance and ecosystem dynamics. Prog. Botany 62, 399–450 (2001).
Sillmann, J. et al. ISC-UNDRR-RISK KAN Briefing Note on Systemic Risk (NASA, 2022).
Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).
Zscheischler, J. et al. A few extreme events dominate global interannual variability in gross primary production. Environ. Res. Lett. 9, 035001 (2014).
Flach, M. et al. Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave. Biogeosciences 16, 6067–6085 (2018).
Zhang, F. et al. When does extreme drought elicit extreme ecological responses? J. Ecol. 107, 2553–2563 (2019).
Graham, E. B. et al. Toward a generalizable framework of disturbance ecology through crowdsourced science. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2021.588940 (2021).
Grime, J. P. et al. Long-term resistance to simulated climate change in an infertile grassland. Proc. Natl Acad. Sci. USA 105, 10028–10032 (2008).
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).
Mahecha, M. D. et al. Biodiversity loss and climate extremes—study the feedbacks. Nature 612, 30–32 (2022).
Jolly, W. M., Dobbertin, M., Zimmermann, N. E. & Reichstein, M. Divergent vegetation growth responses to the 2003 heat wave in the Swiss Alps. Geophys. Res. Lett. 32, L18409 (2005).
Bastos, A. et al. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv. 6, eaba2724 (2020).
Bevacqua, E. et al. Guidelines for studying diverse types of compound weather and climate events. Earths Future 9, e2021EF002340 (2021). This article provides methodological guidelines to study different types of compound weather and climate events including case studies.
Shukla, P. et al. Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and gReenhouse Gas Fluxes in Terrestrial Ecosystems (IPCC, 2019).
Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).
Rosan, T. M. et al. Fragmentation-driven divergent trends in burned area in Amazonia and Cerrado. Front. For. Glob. Change 5, 801408 (2022).
Gavinet, J., Ourcival, J.-M., Gauzere, J., García de Jalón, L. & Limousin, J.-M. Drought mitigation by thinning: benefits from the stem to the stand along 15 years of experimental rainfall exclusion in a holm oak coppice. For. Ecol. Manag. 473, 118266 (2020).
Belmonte, A., Ts. Sankey, T., Biederman, J., Bradford, J. B. & Kolb, T. Soil moisture response to seasonal drought conditions and post-thinning forest structure. Ecohydrology 15, e2406 (2022).
Chapin, F. S., Matson, P. A., Mooney, H. A. & Vitousek, P. M. Principles of Terrestrial Ecosystem Ecology (Springer, 2002).
Slette, I. J. et al. How ecologists define drought, and why we should do better. Glob. Change Biol. 25, 3193–3200 (2019).
Kröel-Dulay, G. et al. Field experiments underestimate aboveground biomass response to drought. Nat. Ecol. Evol. 6, 540–545 (2022).
Turner, M. G., Romme, W. H., Gardner, R. H., O’Neill, R. V. & Kratz, T. K. A revised concept of landscape equilibrium: disturbance and stability on scaled landscapes. Landsc. Ecol. 8, 213–227 (1993).
Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334, 230–232 (2011).
Bond, W. J., Woodward, F. I. & Midgley, G. F. The global distribution of ecosystems in a world without fire. New Phytol. 165, 525–538 (2005).
Naveh, Z. The evolutionary significance of fire in the Mediterranean region. Plant. Ecol. 29, 199–208 (1975).
Buma, B. Disturbance ecology and the problem of n = 1: a proposed framework for unifying disturbance ecology studies to address theory across multiple ecological systems. Methods Ecol. Evol. 12, 2276–2286 (2021).
Freedman, B. Environmental Ecology: The Impacts of Pollution and Other Stresses on Ecosystem Structure and Function (Elsevier, 2013).
Pausas, J. G. & Ribeiro, E. The global fire–productivity relationship. Glob. Ecol. Biogeogr. 22, 728–736 (2013).
Anderegg, W. R. L., Trugman, A. T., Bowling, D. R., Salvucci, G. & Tuttle, S. E. Plant functional traits and climate influence drought intensification and land–atmosphere feedbacks. Proc. Natl Acad. Sci. USA 116, 14071–14076 (2019).
El-Madany, T. S. et al. Drought and heatwave impacts on semi-arid ecosystems’ carbon fluxes along a precipitation gradient. Philos. Trans. R. Soc. B: Biol. Sci. 375, 20190519 (2020).
Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J. & Stephenson, N. L. Why is tree drought mortality so hard to predict? Trends Ecol. Evol. 36, 520–532 (2021).
Liu, L. et al. Bidirectional drought-related canopy dynamics across pantropical forests: a satellite-based statistical analysis. Remote. Sens. Ecol. Conserv. 8, 72–91 (2022).
Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).
Seddon, A. W., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).
Forzieri, G. et al. Increased control of vegetation on global terrestrial energy fluxes. Nat. Clim. Change 10, 356–362 (2020).
Duveiller, G. et al. Revealing the widespread potential of forests to increase low level cloud cover. Nat. Commun. 12, 4337 (2021).
Zscheischler, J. et al. A typology of compound weather and climate events. Nat. Rev. Earth Environ. 1, 333–347 (2020).
Swann, A. L. S., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).
Iglesias, V. et al. Fires that matter: reconceptualizing fire risk to include interactions between humans and the natural environment. Environ. Res. Lett. 17, 045014 (2022).
Piao, S. et al. Interannual variation of terrestrial carbon cycle: issues and perspectives. Glob. Change Biol. 26, 300–318 (2020).
Fernández-Martínez, M. et al. Global trends in carbon sinks and their relationships with CO2 and temperature. Nat. Clim. Change 9, 73–79 (2019).
Keeling, C. D. et al. in A History of Atmospheric CO2 and its Effects on Plants, Animals, and Ecosystems (eds Baldwin, I. T. et al.) 83–113 (Springer, 2005).
Le Page, Y. et al. Global fire activity patterns (1996–2006) and climatic influence: an analysis using the World Fire Atlas. Atmos. Chem. Phys. 8, 1911–1924 (2008).
Sippel, S. et al. Drought, heat, and the carbon cycle: a review. Curr. Clim. Change Rep. 4, 266–286 (2018).
Deser, C., Phillips, A., Bourdette, V. & Teng, H. Uncertainty in climate change projections: the role of internal variability. Clim. Dyn. 38, 527–546 (2012).
Huybers, P. & Curry, W. Links between annual, Milankovitch and continuum temperature variability. Nature 441, 329–332 (2006).
Eyring, V. et al. in Human Influence on the Climate System in Climate Change 2021: The Physical Science Basis. Ch. 3, 1–202 (IPCC, 2021).
Forzieri, G. et al. Emergent vulnerability to climate-driven disturbances in European forests. Nat. Commun. 12, 1081 (2021).
Zhu, W. et al. Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982–2006. Glob. Ecol. Biogeogr. 21, 260–271 (2012).
Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426 (2018).
Dial, R. J., Maher, C. T., Hewitt, R. E. & Sullivan, P. F. Sufficient conditions for rapid range expansion of a boreal conifer. Nature 608, 546–551 (2022).
Archibald, S., Lehmann, C. E. R., Gómez-Dans, J. L. & Bradstock, R. A. Defining pyromes and global syndromes of fire regimes. Proc. Natl Acad. Sci. USA 110, 6442–6447 (2013).
Harrison, S. P. et al. Understanding and modelling wildfire regimes: an ecological perspective. Environ. Res. Lett. 16, 125008 (2021).
Ghil, M. Natural climate variability. Ency. Global Environ. Change 1, 544–549 (2002).
Bastos, A. et al. Was the extreme northern hemisphere greening in 2015 predictable? Environ. Res. Lett. 12, 044016 (2017).
Zhu, Z. et al. The effects of teleconnections on carbon fluxes of global terrestrial ecosystems. Geophys. Res. Lett. 44, 3209–3218 (2017).
Vicente-Serrano, S. M. et al. A multiscalar global evaluation of the impact of ENSO on droughts. J. Geophys. Res. Atmos. 116, D20109 (2011).
Bastos, A., Running, S. W., Gouveia, C. & Trigo, R. M. The global NPP dependence on ENSO: La Niña and the extraordinary year of 2011. J. Geophys. Res. Biogeosci. 118, 1247–1255 (2013).
Sherriff, R. L., Berg, E. E. & Miller, A. E. Climate variability and spruce beetle (Dendroctonus rufipennis) outbreaks in south-central and southwest Alaska. Ecology 92, 1459–1470 (2011).
Seager, R. et al. Causes and Predictability of the 2011–14 California Drought: Assessment Report (Academia, 2014).
Marengo, J. A. et al. The drought of Amazonia in 2005. J. Clim. 21, 495–516 (2008).
Marengo, J. A., Tomasella, J., Alves, L. M., Soares, W. R. & Rodriguez, D. A. The drought of 2010 in the context of historical droughts in the Amazon regionshare. Geophys. Res. Lett. https://doi.org/10.1029/2011GL047436 (2011).
Carnicer, J. et al. Regime shifts of Mediterranean forest carbon uptake and reduced resilience driven by multidecadal ocean surface temperatures. Glob. Change Biol. 25, 2825–2840 (2019).
Li, N. et al. Interannual global carbon cycle variations linked to atmospheric circulation variability. Earth Syst. Dyn. 13, 1505–1533 (2022).
Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014).
Fereday, D., Chadwick, R., Knight, J. & Scaife, A. A. Atmospheric dynamics is the largest source of uncertainty in future winter European rainfall. J. Clim. 31, 963–977 (2018).
Huguenin, M. F. et al. Lack of change in the projected frequency and persistence of atmospheric circulation types over Central Europe. Geophys. Res. Lett. 47, e2019GL086132 (2020).
Coumou, D., Lehmann, J. & Beckmann, J. The weakening summer circulation in the northern hemisphere mid-latitudes. Science 348, 324 (2015).
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M. & García-Herrera, R. The hot summer of 2010: redrawing the temperature record map of Europe. Science 332, 220–224 (2011).
Di Capua, G. et al. Drivers behind the summer 2010 wave train leading to Russian heatwave and Pakistan flooding. npj Clim. Atmos. Sci. 4, 55 (2021).
Goulden, M. & Bales, R. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).
Senf, C., Buras, A., Zang, C. S., Rammig, A. & Seidl, R. Excess forest mortality is consistently linked to drought across Europe. Nat. Commun. 11, 6200 (2020).
Kornhuber, K. et al. Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern. Environ. Res. Lett. 14, 054002 (2019).
Sousa, P. M. et al. Distinct influences of large-scale circulation and regional feedbacks in two exceptional 2019 European heatwaves. Commun. Earth Environ. 1, 48 (2020).
Deser, C. Certain uncertainty: the role of internal climate variability in projections of regional climate change and risk management. Earth’s Future 8, e2020EF001854 (2020).
Bonan, G. B., Lombardozzi, D. L. & Wieder, W. R. The signature of internal variability in the terrestrial carbon cycle. Environ. Res. Lett. 16, 034022 (2021).
Loughran, T. F. et al. Past and future climate variability uncertainties in the global carbon budget using the MPI grand ensemble. Glob. Biogeochem. Cycles 35, e2021GB007019 (2021).
Lehner, F. et al. Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth Syst. Dyn. 11, 491–508 (2020).
Shepherd, T. G. et al. Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Climatic Change 151, 555–571 (2018).
Sippel, S. et al. Uncovering the forced climate response from a single ensemble member using statistical learning. J. Clim. 32, 5677–5699 (2019).
van Oldenborgh, G. J. et al. Attribution of the Australian bushfire risk to anthropogenic climate change. Nat. Hazards Earth Syst. Sci. 21, 941–960 (2021).
Philip, S. Y. et al. Rapid attribution analysis of the extraordinary heatwave on the Pacific Coast of the US and Canada June 2021. Earth Syst. Dyn. Discuss. 2021, 1–34 (2021).
Fischer, E. M., Sippel, S. & Knutti, R. Increasing probability of record-shattering climate extremes. Nat. Clim. Chang. 11, 689–695 (2021).
Bastos, A. et al. European land CO2 sink influenced by NAO and East-Atlantic Pattern coupling. Nat. Commun. 7, 10315 (2016).
Ahlström, A., Miller, P. A. & Smith, B. Too early to infer a global NPP decline since 2000. Geophys. Res. Lett. 39, L15403 (2012).
Anderegg, W. R. et al. Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proc. Natl Acad. Sci. USA 112, 15591–15596 (2015).
von Buttlar, J. et al. Impacts of droughts and extreme-temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones. Biogeosciences 15, 1293–1318 (2018).
Musavi, T. et al. Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity. Nat. Ecol. Evol. 1, 1–7 (2017).
Pfleiderer, P., Menke, I. & Schleussner, C.-F. Increasing risks of apple tree frost damage under climate change. Clim. Change 157, 515–525 (2019).
Vautard, R. et al. Human influence on growing-period frosts like the early April 2021 in central France. Nat. Hazards Earth Syst. Sci. Discuss. 2022, 1–25 (2022).
Kay, J. E. et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).
Maher, N. et al. The Max Planck Institute Grand Ensemble: enabling the exploration of climate system variability. J. Adv. Model. Earth Syst. 11, 2050–2069 (2019).
Temperli, C., Bugmann, H. & Elkin, C. Cross-scale interactions among bark beetles, climate change, and wind disturbances: a landscape modeling approach. Ecol. Monogr. 83, 383–402 (2013).
Temperli, C., Veblen, T. T., Hart, S. J., Kulakowski, D. & Tepley, A. J. Interactions among spruce beetle disturbance, climate change and forest dynamics captured by a forest landscape model. Ecosphere 6, art231 (2015).
Fu, Z. et al. Atmospheric dryness reduces photosynthesis along a large range of soil water deficits. Nat. Commun. 13, 989 (2022).
Shepherd, T. G. Storyline approach to the construction of regional climate change information. Proc. R. Soc. A: Math. Phys. Eng. Sci. 475, 20190013 (2019).
Trenberth, K. E., Fasullo, J. T. & Shepherd, T. G. Attribution of climate extreme events. Nat. Clim. Change 5, 725–730 (2015).
van Garderen, L., Feser, F. & Shepherd, T. G. A methodology for attributing the role of climate change in extreme events: a global spectrally nudged storyline. Nat. Hazards Earth Syst. Sci. 21, 171–186 (2021).
Goulart, H., Van Der Wiel, K., Folberth, C., Balkovic, J. & Van Den Hurk, B. Storylines of weather-induced crop failure events under climate change. Earth Syst. Dyn. 12, 1503–1527 (2021).
Danabasoglu, G. et al. The Community Earth System Model version 2 (CESM2). J. Adv. Model. Earth Syst. 12, e2019MS001916 (2020).
Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).
Rodgers, K. B. et al. Ubiquity of human-induced changes in climate variability. Earth Syst. Dyn. 12, 1393–1411 (2021).
Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytol. 229, 2413–2445 (2021).
Brondizio, E., Settele, J., Díaz, S. & Ngo, H. Global assessment report on biodiversity and ecosystem services (eds Brondizio, E. S. et al.) (IPBES, 2019); https://www.ipbes.net/global-assessment.
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).
Mahecha, M. D. et al. Detecting impacts of extreme events with ecological in situ monitoring networks. Biogeosciences 14, 4255–4277 (2017).
Poyatos, R. et al. SAPFLUXNET: towards a global database of sap flow measurements. Tree Physiol. 36, 1449–1455 (2016).
FAO. Global Forest Resources Assessment 2015 (FRA2015) (2015).
Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change https://doi.org/10.1038/s41558-020-00976-6 (2021).
Cooper, L. A., Ballantyne, A. P., Holden, Z. A. & Landguth, E. L. Disturbance impacts on land surface temperature and gross primary productivity in the western United States. J. Geophys. Res. Biogeosci. 122, 930–946 (2017).
Mildrexler, D. J., Zhao, M. & Running, S. W. Testing a MODIS global disturbance index across North America. Remote. Sens. Environ. 113, 2103–2117 (2009).
Zhang, W. et al. From woody cover to woody canopies: how Sentinel-1 and Sentinel-2 data advance the mapping of woody plants in savannas. Remote. Sens. Environ. 234, 111465 (2019).
Brandt, M. et al. An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587, 78–82 (2020).
Seidl, R., Schelhaas, M.-J., Rammer, W. & Verkerk, P. J. Increasing forest disturbances in Europe and their impact on carbon storage. Nat. Clim. Change 4, 806–810 (2014).
Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).
Yang, Y. et al. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Sci. Rep. 6, 23284 (2016).
Papastefanou, P. et al. A dynamic model for strategies and dynamics of plant water-potential regulation under drought conditions. Front. Plant. Sci. https://doi.org/10.3389/fpls.2020.00373 (2020).
Sabot, M. E. B. et al. One stomatal model to rule them all? Toward improved representation of carbon and water exchange in global models. J. Adv. Model. Earth Syst. 14, e2021MS002761 (2022).
Mu, M. et al. Exploring how groundwater buffers the influence of heatwaves on vegetation function during multi-year droughts. Earth Syst. Dyn. 12, 919–938 (2021).
Gentine, P., Guérin, M., Uriarte, M., McDowell, N. G. & Pockman, W. T. An allometry‐based model of the survival strategies of hydraulic failure and carbon starvation. Ecohydrology 9, 529–546 (2016).
Pugh, T. A. M. et al. Understanding the uncertainty in global forest carbon turnover. Biogeosciences 17, 3961–3989 (2020).
De Kauwe, M. G. et al. Towards species-level forecasts of drought-induced tree mortality risk. New Phytol. 235, 94–110 (2022).
Koven, C. D. et al. Benchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, Panama. Biogeosciences 17, 3017–3044 (2020).
Lawrence, D. M. et al. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci. Model. Dev. 9, 2973–2998 (2016).
Chen, Y.-Y. et al. Simulating damage for wind storms in the land surface model ORCHIDEE-CAN (revision 4262). Geosci. Model. Dev. 11, 771–791 (2018).
Kwon, M. J. et al. Siberian 2020 heatwave increased spring CO2 uptake but not annual CO2 uptake. Environ. Res. Lett. 16, 124030 (2021).
Novenko, E. Y. et al. Evidence that modern fires may be unprecedented during the last 3400 years in permafrost zone of Central Siberia, Russia. Environ. Res. Lett. 17, 025004 (2022).
Overland, J. E. & Wang, M. The 2020 Siberian heat wave. Int. J. Climatol. 41, E2341–E2346 (2021).
Yin, Y. et al. Variability of fire carbon emissions in equatorial Asia and its nonlinear sensitivity to El Niño. Geophys. Res. Lett. 43, 10,472–10,479 (2016).
Liu, J. et al. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. Science 358, eaam5690 (2017).
Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).
Werf, G. Rvander et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10, 16153–16230 (2010).
Varma, A. The economics of slash and burn: a case study of the 1997–1998 Indonesian forest fires. Ecol. Econ. 46, 159–171 (2003).
Fanin, T. & van der Werf, G. R. Precipitation–fire linkages in Indonesia (1997–2015). Biogeosciences 14, 3995–4008 (2017).
Masih, I., Maskey, S., Mussá, F. E. F. & Trambauer, P. A review of droughts on the African continent: a geospatial and long-term perspective. Hydrol. Earth Syst. Sci. 18, 3635–3649 (2014).
Manatsa, D., Chingombe, W., Matsikwa, H. & Matarira, C. H. The superior influence of Darwin Sea level pressure anomalies over ENSO as a simple drought predictor for Southern Africa. Theor. Appl. Climatol. 92, 1–14 (2008).
Mupepi, O. & Matsa, M. M. Spatio-temporal dynamics of drought in Zimbabwe between 1990 and 2020: a review. Spat. Inf. Res. 30, 117–130 (2022).
Wainwright, C. M., Finney, D. L., Kilavi, M., Black, E. & Marsham, J. H. Extreme rainfall in East Africa, October 2019–January 2020 and context under future climate change. Weather 76, 26–31 (2021).
Nicholson, S. E., Fink, A. H., Funk, C., Klotter, D. A. & Satheesh, A. R. Meteorological causes of the catastrophic rains of October/November 2019 in equatorial Africa. Glob. Planet. Change 208, 103687 (2022).
Chang’a, L. B. et al. Assessment of the evolution and socio-economic impacts of extreme rainfall events in October 2019 over the east Africa. Atmos. Clim. Sci. 10, 319–338 (2020).
Swemmer, A. Locally high, but regionally low: the impact of the 2014–2016 drought on the trees of semi-arid savannas, South Africa. Afr. J. Range Forage Sci. 37, 31–42 (2020).
Sousa, P. M., Blamey, R. C., Reason, C. J. C., Ramos, A. M. & Trigo, R. M. The ‘Day Zero’ Cape Town drought and the poleward migration of moisture corridors. Environ. Res. Lett. 13, 124025 (2018).
Rakovec, O. et al. The 2018–2020 multi-year drought sets a new benchmark in Europe. Earth’s Future 10, e2021EF002394 (2022).
Beillouin, D., Schauberger, B., Bastos, A., Ciais, P. & Makowski, D. Impact of extreme weather conditions on European crop production in 2018. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190510 (2020).
Schuldt, B. et al. A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl. Ecol. 45, 86–103 (2020).
Stott, P. A., Stone, D. A. & Allen, M. R. Human contribution to the European heatwave of 2003. Nature 432, 610–614 (2004).
Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).
Reichstein, M. et al. Determinants of terrestrial ecosystem carbon balance inferred from European eddy covariance flux sites. Geophys. Res. Lett. https://doi.org/10.1029/2006GL027880 (2007).
Rouault, G. et al. Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. Ann. For. Sci. 63, 613–624 (2006).
Rousi, E., Kornhuber, K., Beobide-Arsuaga, G., Luo, F. & Coumou, D. Accelerated western European heatwave trends linked to more-persistent double jets over Eurasia. Nat. Commun. 13, 3851 (2022).
King, A. D., Pitman, A. J., Henley, B. J., Ukkola, A. M. & Brown, J. R. The role of climate variability in Australian drought. Nat. Clim. Change 10, 177–179 (2020).
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 29, 600–603 (2014).
Cleverly, J. et al. The importance of interacting climate modes on Australia’s contribution to global carbon cycle extremes. Sci. Rep. 6, 23113 (2016).
Boening, C., Willis, J. K., Landerer, F. W., Nerem, R. S. & Fasullo, J. The 2011 La Niã: so strong, the oceans fell. Geophys. Res. Lett. 39, L19602 (2012).
van der Velde, I. R. et al. Vast CO2 release from Australian fires in 2019–2020 constrained by satellite. Nature 597, 366–369 (2021).
De Kauwe, M. G. et al. Identifying areas at risk of drought-induced tree mortality across South-Eastern Australia. Glob. Change Biol. 26, 5716–5733 (2020).
Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).
Schwalm, C. R. et al. Reduction in carbon uptake during turn of the century drought in western North America. Nat. Geosci. 5, 551–556 (2012).
Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318 (2020).
Lehner, F., Deser, C., Simpson, I. R. & Terray, L. Attributing the US Southwest’s recent shift into drier conditions. Geophys. Res. Lett. 45, 6251–6261 (2018).
Garreaud, R. D. et al. The Central Chile Mega drought (2010–2018): a climate dynamics perspective. Int. J. Climatol. 40, 421–439 (2020).
Garreaud, R. D. et al. The 2010–2015 megadrought in central Chile: impacts on regional hydroclimate and vegetation. Hydrol. Earth Syst. Sci. 21, 6307–6327 (2017).
Mo, R., Lin, H. & Vitart, F. An anomalous warm-season trans-Pacific atmospheric river linked to the 2021 western North America heatwave. Commun. Earth Env. 3, 1–12 (2022).
Environment and Climate Change Canada. Canada’s top 10 weather stories of 2021. Government of Canada https://www.canada.ca/en/environment-climate-change/services/top-ten-weather-stories/2021.html (2021).
Gloor, E. et al. Tropical land carbon cycle responses to 2015/16 El Niño as recorded by atmospheric greenhouse gas and remote sensing data. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170302 (2018).
van Schaik, E. et al. Changes in surface hydrology, soil moisture and gross primary production in the Amazon during the 2015/2016 El Niño. Philos. Trans. R. Soc. B Biol. Sci. 373, 20180084 (2018).
Lewis, S. L., Brando, P. M., Phillips, O. L., van der Heijden, G. M. F. & Nepstad, D. The 2010 Amazon drought. Science 331, 554 (2011).
Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).
Acknowledgements
This work was partly funded by the European Union’s Horizon 2020 research and innovation programme (project XAIDA, Grant No. 101003469 and European Research Council (ERC) Synergy Grant ‘Understanding and Modelling the Earth System with Machine Learning (USMILE)’ Grant agreement No. 855187). M.D.M. and M.R. acknowledge ESA for funding DeepExtremes. A.B. was funded by the European Union (ERC StG, ForExD, grant agreement No. 101039567). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. The authors thank U. Beyerle for setting up and management of forced and unforced but nudged Community Earth System Model Version 2.1.2 (CESM2) simulations. We thank A. Jézéquel for feedback on the framework proposed here.
Author information
Authors and Affiliations
Contributions
A.B. conceptualized the article and wrote the first draft. A.B., M.R., D.F. and S.S. prepared the figures. S.S. and A.B. analysed the Community Earth System Model Version 2.1.2 (CESM2) outputs. D.F., M.D.M., S.Z., S.S., M.R. and J.Z. contributed to the development of the article through extensive discussions and feedback on initial stages of the manuscript. All authors contributed to revisions of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare they have no competing interests.
Peer review
Peer review information
Nature Reviews Earth & Environment thanks Andrew Felton, Ashley Ballantyne and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Glossary
- Climate risk
-
According to the Intergovernmental Panel on Climate Change (IPCC), the potential for [climate change-related] adverse consequences for human or ecological systems, recognising the diversity of values and objectives associated with such systems. Risk is a function of hazard, vulnerability and exposure. Climate risk refers strictly to negative consequences of climate change, whereas positive consequences are referred to as opportunities or potential benefits; other fields treat risk as a value-neutral concept, and that the value of a given consequence might depend on the point of view.
- Compound weather and climate events
-
The combination of multiple drivers and/or hazards (such as droughts, heat waves, flooding and fires) that contribute to risk; compound events can be multivariate, preconditioned, temporally compounding or spatially compounding.
- Disturbance impact
-
The specific effects on ecosystem properties triggered by a given disturbance, such as the loss of organic matter by fires, removal or damage of organisms by hurricanes or logging, or mortality induced by droughts, floods or frost events.
- Disturbance severity
-
The magnitude of the impacts of a disturbance, which depends on ecosystem vulnerability.
- Ecosystem disturbance
-
Discrete events in time that disrupt the ecosystem, community or population structure and change resources, substrate availability or the physical environment, as defined by White and Picket; alternatively, can be defined as an event that results in biomass removal.
- Exposure
-
According to the Intergovernmental Panel on Climate Change (IPCC), the presence of people; livelihoods; species or ecosystems; environmental functions, services, resources; infrastructure; or economic, social, or cultural assets in places and settings that could be adversely affected.
- Extreme climatic event
-
An event in which a statistically rare or unusual climatic period alters ecosystem structure and⁄or function well outside the bounds of what is considered typical or normal variability.
- Hazards
-
According to the Intergovernmental Panel on Climate Change (IPCC), the potential occurrence of a natural or human-induced physical event or trend that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems and environmental resources. Hazards are based on the assessment of potential consequences of a given climate-related event or trend; climate extremes might not be hazardous if they are no negative consequences and non-extreme events might be hazardous if there are negative consequences.
- Internal climate variability
-
According to the Intergovernmental Panel on Climate Change (IPCC), the deviation of climate variables from a given mean state (including the occurrence of extremes and so on) at all spatial and temporal scales beyond that of individual weather events, Variability can be intrinsic, due to fluctuations of processes internal to the climate system.
- Post-disturbance recovery
-
The return of a disturbed system to a previous undisturbed or quasi-equilibrium state or to a new state; the time required to reach this state is the recovery time.
- Vulnerability
-
The propensity or predisposition to be adversely affected, encompassing various concepts that include sensitivity or susceptibility to harm and lack of capacity to cope and adapt.
- Weather and climate extremes
-
Unusual events at a given place and time of year, usually defined by the occurrence of a value of a weather or climate variable, or combination of variables, above (or below) a threshold value near the upper (or lower) ends of the observed distribution of the variable over a reference time frame.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Bastos, A., Sippel, S., Frank, D. et al. A joint framework for studying compound ecoclimatic events. Nat Rev Earth Environ 4, 333–350 (2023). https://doi.org/10.1038/s43017-023-00410-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43017-023-00410-3