Advancing research on compound weather and climate events via large ensemble model simulations

Abstract

Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events — such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events — require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks.

Origin, importance, and predictive limits of internal climate variability

“…Despite the focus on seasonal to annual temperature here, the lessons-learned apply equally to other variables and research questions, for example precipitation (Deser et al 2012b, Lehner et al 2018, Guo et al 2019, extreme event attribution (van Oldenborgh et al 2020, Philip et al 2022), compound event research (Bevacqua et al 2022, Zscheischler and, temperature response to volcanic eruptions (Lehner et al 2016, McGraw et al 2016, or ocean biogeochemical tracers (Rodgers et al 2015, Lovenduski et al 2016. Generally, the smaller the scales and the noisier the variables of interest, the more likely is the research to benefit from including climate model large ensembles (Milinski et al 2020, Bevacqua et al 2023. The implications of internal climate variability are therefore far-reaching, for both science and society.…”

Increasing Risks of Future Compound Climate Extremes With Warming Over Global Land Masses

“…Whereas a sample size with 50 years generally can yield a reasonable assessment for compound climate extremes (Wu, Su, & Singh, 2021), it is still insufficient for evaluating more extreme compound climate events (need more large sample size, see Figure B1 in Appendix B). Note that the length of hydrometeorological observations or reanalysis data routinely employed in climate studies is mostly less than 50 years long (Bevacqua et al., 2022, 2023). Fortunately, SMILEs contain numerous ensemble members from an individual climate model that are run many times with diverse initial perturbation conditions (Deser et al., 2020; Kay et al., 2015; Maher et al., 2019; Stevenson et al., 2022).…”

“…(2020) and Bevacqua et al. (2023) highlighted that SMILEs should become the standard testbeds for investigating and projecting compound events. In comparison with Coupled Model Intercomparison Project Phase 5 (CMIP5) products (no SMILEs counterparts with CMIP6 currently), SMILEs can better quantify the uncertainty in monitoring and attributing compound climate extremes (Maher, Power, & Marotzke, 2021).…”

“…Since SMILEs can describe model-to-model differences (reducible uncertainty) and internal variability (irreducible uncertainty from the inherently stochastic nature of the climate system) (Bevacqua et al, 2022;Deser et al, 2020;Maher, Power, & Marotzke, 2021), they have been extensively employed to project future changes of precipitation and temperature (Deser et al, 2020;Maher, Milinski, & Ludwig, 2021;Maher, Power, & Marotzke, 2021), and to assess risks of compound dry-hot events (Bevacqua et al, 2022). Deser et al (2020) and Bevacqua et al (2023) highlighted that SMILEs should become the standard testbeds for investigating and projecting compound events. In comparison with Coupled Model Intercomparison Project Phase 5 (CMIP5) products (no SMILEs counterparts with CMIP6 currently), SMILEs can better quantify the uncertainty in monitoring and attributing compound climate extremes (Maher, Power, & Marotzke, 2021).…”

The New Max Planck Institute Grand Ensemble With CMIP6 Forcing and High‐Frequency Model Output

“…SMILEs are also used to investigate ocean ecosystem drivers (Rodgers et al., 2015), and to identify systematic differences between simulated and observed patterns of sea‐surface temperature and sea‐level pressure change that are very unlikely to occur due to internal variability (Olonscheck et al., 2020; Wills et al., 2022). Furthermore, recent developments in compound event research highlight the importance of sufficiently sampling internal variability to robustly capture the risks associated with extreme values of multivariate extremes, which requires even larger ensemble sizes than conventional univariate extremes (Bevacqua et al., 2023; Burger et al., 2022). The availability of SMILEs from multiple models further allows us to better quantify and differentiate sources of uncertainty in climate projections, especially uncertainties arising from internal variability and those from model differences (Deser et al., 2020; Hawkins & Sutton, 2009, 2011; Lehner et al., 2020).…”