A diagnostic to evaluate CMIP6 model fidelity at simulating non-Gaussian temperature distribution tails

Abstract:

Under future warming, changes in extreme temperatures may be manifested in more complex ways depending on the shape of the distribution tail. For example, uniform warming applied to a temperature distribution with a shorter-than-Gaussian cold tail would lead to a more rapid decline in extreme cold temperatures than were the underlying distribution Gaussian. Confidence in global climate model (GCM) projections of temperature extremes and associated impacts therefore relies on the realism of simulated temperature distribution tail behavior under current climate conditions. The ability of CMIP6 GCMs to capture coherent regions of non-Gaussianity in the tails of the temperature distribution is evaluated using a process-oriented diagnostic. Comparisons with a global reanalysis product reveal strong agreement on coherent spatial patterns of longer- and shorter-than-Gaussian tails for both sides of the temperature distribution, suggesting that CMIP6 GCMs are broadly capturing tail behavior for plausible physical and dynamical reasons. On a global scale, most GCMs are reasonably skilled at simulating historical tail shape, exhibiting high pattern correlations with reanalysis and low values of normalized centered root mean square difference, with multi-model mean values generally outperforming individual GCMs in these metrics. A division of the domain into sub-regions exhibiting temperature distributions with notable departures from Gaussianity further quantifies individual model skill at a regional-scale. Overall, results highlight the utility of this diagnostic for evaluating model fidelity at simulating seasonal temperature distribution tail deviations from Gaussianity, which provides insight into the complexity of future changes in extreme warm and cold temperatures. A module containing this and other related diagnostics has been developed for the Model Diagnostics Task Force (MDTF) framework to facilitate model development and improvement.