Do CMIP6 Climate Models Simulate Global or Regional Compound Events Skillfully?

Abstract

Global climate models are used to simulate climate and weather extremes, including extreme rainfall, high and low temperatures, droughts, and winds. Analyses of observations, historical simulations, and projections of extremes (Alexander, 2016; Bindoff et al., 2013; Sillman et al., 2013) have provided major advances in understanding how the statistics of extremes respond to natural variability and global warming. Many analyses of extremes focus on single hazards, such as how hot is the hottest day each year, or how much rain fell during the rainiest 5-day stretch of the year. An evaluation of models included in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) highlights that extremes are generally more difficult to represent realistically than the average (Flato et al., 2013; Sillman et al., 2013b). For instance, Flato et al. (2013) note that CMIP5 models generally capture observed trends in temperature extremes, but rainfall extremes are more challenging, although this might be partly due to higher observational uncertainty. Since this assessment, extensive literature has emerged demonstrating the improved skill of climate models in simulating temperature (e.g., Di Luca et al., 2020) and rainfall extremes (Bador et al., 2020), particularly in hot and cold extremes (Di Luca et al., 2020) and the intensity of heavy rainfall (Kim et al., 2020). The evaluation of wind extremes is more limited, but Kumar et al. (2015) noted that CMIP5 models simulated the multimodel mean (MMM) of spatial patterns of extreme winds with 25-100-year return periods (RPs) well. In the last decade, compound events (CEs) have emerged as a focus for understanding the link between changes in weather and climate and impacts on vulnerable systems (

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

“…• projecting the risk of compound extremes for different levels of future warming (Zscheischler et al, 2018;Wang et al, 2020); • evaluating the impacts of the compound extremes on natural and built environments (AghaKouchak et al, 2020;Zhang and Najafi, 2020); • developing adaptation measures to the changing risk of compound extremes (Weber et al, 2020;Clarke et al, 2021); • enhancing subseasonal-to-seasonal prediction of these extremes (Zamora et al, 2021;Zou, 2021); • improving the representation and evaluation of compound extremes in fully-coupled climate models (Ridder et al, 2021;Zscheischler et al, 2021) and developing multivariate bias correction for these models (Vezzoli et al, 2017;Zscheischler et al, 2019); • applying machine learning to understand these extremes Zou, 2021).…”

“…While regional climate models are extremely useful in resolving land conditions (Fischer et al, 2007;Stéfanon et al, 2014;Keune et al, 2016;Sillmann et al, 2017), global climate models are commonly used to assess changes in land conditions on extreme weather (e.g., drought and heat wave) (Hauser et al, 2016;Kala et al, 2016;Rasmijn et al, 2018). The models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibit some skill in simulating the co-occurrence of hot and dry compound events in North America and Europe (Ridder et al, 2021). Because the numerical models' outputs are limited by the climatological biases, univariate and multivariate bias correction methods have been used to correct the biases, and thus improving the performance and usability of the models (Maraun, 2016;Vezzoli et al, 2017;Vrac, 2018;Zscheischler et al, 2018;François et al, 2020).…”

“…Regional climate models also take initial and boundary conditions from the output of global climate models and can resolve small-scale processes, thereby perform well in simulating single events simulations (Fischer et al, 2007;Stéfanon et al, 2014;Keune et al, 2016;Sillmann et al, 2017). Therefore, regional climate models depend heavily on the simulation of global climate models, which are commonly used to simulate a longer simulation (e.g., years or decades) with a coarser spatial resolution (∼1-2 °) (Orlowsky and Seneviratne, 2013;Cook et al, 2020;Ridder et al, 2020Ridder et al, , 2021Ukkola et al, 2020;Vogel et al, 2020;Su et al, 2021).…”

Future Precipitation Patterns in the Lake Victoria Basin Using CMIP6 Projections

“…As noted by other CMIP6 model simulation evaluation studies (e.g., Akinsanola et al, 2021;Ngoma et al, 2021), CMIP6 model simulations generally show biases in representing regional precipitation patterns. For the current study domain, biases shown could be resulting from, among other factors, local climate and mesoscale convective systems (Ridder et al, 2021) and the general complexity of East Africa's large-scale controlled climate (Li et al, 2016;Nicholson, 2017).…”

“…For instance, the lake is notorious with intense lightning and convective storms (Albrecht et al, 2016) and its severe weather and water currents have been linked to boat accidents causing approximately 5000 deaths on the lake annually (Barnett, 2013;Cannon et al, 2014). However, the understanding of weather and climate dynamics in the LVB remains limited due to, in part, the inability of current climate models to adequately represent weather and climate dynamics over Lake Victoria (e.g., Finney et al, 2020;Ridder et al, 2021;Vanderkelen et al, 2018b).…”