Evaluation of Extreme Temperatures Over Australia in the Historical Simulations of CMIP5 and CMIP6 Models. Issue 7 (2nd July 2021)

• Record Type: Journal Article
• Authors: Deng, Xu; Perkins‐Kirkpatrick, Sarah E.; Lewis, Sophie C.; Ritchie, Elizabeth A.
• Is Part Of: Earth's future. Volume 9:Issue 7(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-02
• Subjects: CMIP6 -- CMIP5 -- historical simulations -- extreme temperatures -- Australia -- model performance -- ensemble simulations -- internal variability; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2020EF001902
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract: Historical simulations of models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are evaluated over 10 Australian regions for their performance in simulating extreme temperatures, among which three models with initial‐condition large ensembles (LEs) are used to estimate the effects of internal variability. Based on two observational data sets, the Australian Water Availability Project (AWAP) and the Berkeley Earth Surface Temperatures (BEST), we first analyze the models' abilities in simulating the probability distributions of daily maximum and minimum temperature (TX and TN), followed by the spatial patterns and temporal variations of the extreme indices, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). Overall, the CMIP6 models are comparable to CMIP5, with modest improvements shown in CMIP6. Compared to CMIP5, the CMIP6 ensemble tends to have narrower interquartile model ranges for some cold extremes, as well as narrower ensemble ranges in temporal trends for most indices. Over southeast, tropical, and southern regions, both CMIP ensembles generally exhibit relatively large deficiencies in simulating temperature extremes. We also confirm that internal variability can affect the trends of the extremes and there is uncertainty in representing the irreducible variability among different LEs in CMIP6. Furthermore, the evaluation based on Perkins' skill score (PSS) and root‐mean‐square error (RMSE) in the three LEs does not directly correlate with the ranges of the trends for extreme temperatures. The findings of this study are useful in informing and interpreting future projections of temperature‐related extremes over Australia. Key Points: The assessment on the probability distributions of daily maximum and minimum temperature makes the evaluation of extremes more robust Temperature extremes over Australia are broadly similar in CMIP5 and CMIP6 There are differences in estimating internal variability across multiple CMIP6 models