1119 Evaluation of the CMIP6 Multimodel Ensemble for Climate Extreme Indices

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Yeon-Hee Kim, Pohang Univ. of Science and Technology, Pohang, Korea, Korea, Republic of (South); and S. K. Min, X. Zhang, and J. Sillmann

Handout (971.7 kB)

This study evaluates the performance of global climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) in terms of their performances in simulating the climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCD). First, we analyze overall performance of individual models (currently 13 models) for the global climatology patterns of climate extreme indices through comparison with HadEX3 observations and reanalyses datasets, which is summarized in a portrait diagram. For more detailed regional evaluations, we consider new 36 regions defined by the IPCC sixth assessment report and compare CMIP6 model skills to those from CMIP5 models. In particular, the 20-yr return values (20RV) of warmest day and coldest night temperatures (TXx and TNn) and annual maximum of daily precipitation (Rx1day) are evaluated for the regional mean biases by utilizing the generalized extreme value analysis. Initial results show that the CMIP6 models are generally able to capture the observed global and regional patterns of temperature extremes, but exhibit systematic biases like a cold bias in cold extremes over high-latitude areas, consistent with CMIP5 results. The CMIP6 model skills for the precipitation intensity and frequency indices are also largely comparable to those of CMIP5 models. The GEV analysis results indicate that biases in 20RV of temperature extremes are dominated by mean intensity (GEV location parameter) with relatively small contribution from inter-annual variability (GEV scale parameter). 20RV of Rx1day is characterized by dry biases over tropics and subtropical rain band areas, for which biases in both mean intensity and inter-annual variability are found to be important. With more models available, we will further examine important modeling factors that can affect model performances, including the influence of spatial resolution on precipitation extreme simulations.
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