Evaluating CMIP6 Model Fidelity at Simulating Non-Gaussian Temperature Distribution Tails

Abstract

<p>Under global warming, changes in extreme temperatures will manifest in more complex ways in locations where temperature distribution tails deviate from Gaussian. For example, uniform warming applied to a temperature distribution with a shorter-than-Gaussian warm tail would lead to greater exceedances in warm-side temperature extremes compared with a Gaussian distribution. Confidence in projections of future temperature extremes and associated impacts under global warming therefore relies on the ability of global climate models (GCMs) to realistically simulate observed temperature distribution tail behavior. This presentation examines the ability of the latest state-of-the-art ensemble of GCMs from the Coupled Model Intercomparison Project phase six (CMIP6) to capture historical global surface temperature distribution tail shape in hemispheric winter and summer seasons. Comparisons between the multi-model ensemble mean and a reanalysis product reveal strong agreement on coherent spatial patterns of longer- and shorter-than-Gaussian tails for the cold and warm sides of the temperature distribution, suggesting that CMIP6 is broadly capturing tail behavior for plausible physical and dynamical reasons. Most individual GCMs are also reasonably skilled at capturing historical tail shape on a global scale, but a division of the domain into sub-regions reveals considerable model and spatial variability. To explore potential mechanisms driving these differences, a back trajectory analysis examining patterns in the origin of air masses on days experiencing extreme temperatures is also discussed.</p>

Diagnosing Non‐Gaussian Temperature Distribution Tails Using Back‐Trajectory Analysis

“…This highlights the utility of using the shift ratio to examine tails specifically. ERA5 shift ratio maps for both warm‐side and cold‐side tails closely resemble other observation‐based and model datasets (Catalano et al., 2020; Loikith & Neelin, 2019; Loikith et al., 2018).…”

“…(2016) demonstrated that trends in summer extremes over the historical period include significant changes in kurtosis and skewness, which could have an appreciable effect on the frequency of extremes as temperatures rise (Ballester et al., 2010; Clark et al., 2006). The latest ensemble of global climate models, CMIP6, skillfully captures coherent regions of non‐Gaussian tails (Catalano et al., 2020), so additional work analyzing the complexity of projected changes in the distribution of temperature extremes would be beneficial.…”

Evaluation of Temperature and Precipitation Simulations in CMIP6 Models Over the Tibetan Plateau

“…The new phase CMIP has incorporated further improved climate modules and thus is expected to yield improved representation of the Earth's climate system (Eyring et al, 2016). Evaluation of these new simulations with respect to the observed mean and extreme climates is drawing extensive research efforts (e.g., Catalano et al, 2020;…”

“…The new phase CMIP has incorporated further improved climate modules and thus is expected to yield improved representation of the Earth's climate system (Eyring et al., 2016). Evaluation of these new simulations with respect to the observed mean and extreme climates is drawing extensive research efforts (e.g., Catalano et al., 2020; Chen et al., 2020; D. Jiang et al., 2020; Rivera & Arnould, 2020; Xin et al., 2020), as it can inform the extent to which these models can be trusted for projecting future climate change and how they can be further improved. These studies demonstrated that the CMIP6 GCMs have an improvement in simulating the temporal and spatial patterns of the climate variables comparing to the previous phase (i.e., CMIP5).…”

Inconsistent variation of return periods of temperature extremum in China and its projection based on CMIP6 results

“…Since the Coupled Model Intercomparison Project in Phase 6 (CMIP6) results are available, the extreme temperature events are widely evaluated and projected based on the multi-model simulations but most work focus on the change of climate extreme indices by the ETCCDI [3,12,22,36]. In this study, we aim to draw a risk map of extreme temperature events under climate change by estimating and comparing annual extreme high and low temperature values based on return level.…”