A Performance Evaluation of Potential Intensity over the Tropical Cyclone Passage to South Korea Simulated by CMIP5 and CMIP6 Models

Potential intensity (PI) is a metric for climate model evaluation of TC-related thermodynamic conditions. However, PI is utilized usually for assessing basin-wide TC-related thermodynamic conditions, and not for evaluating TC passage to a certain region. Here we evaluate model-simulated PI over the passage of TCs affecting South Korea (KOR PI) as well as the PI over the entire western North Pacific basin (WNP PI) using 25 CMIP5 and 27 CMIP6 models. In terms of pattern correlations and bias-removed root mean square errors, CMIP6 model performances for KOR PI are found to be noticeably improved over CMIP5 models in contrast to negligible improvement for WNP PI, although it is not in terms of normalized standard deviations. This implies that thermodynamic condition on the route of TCs affecting South Korea is likely better captured by CMIP6 models than CMIP5 models.

[1]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[2]  S. Camargo,et al.  Natural and Forced North Atlantic Hurricane Potential Intensity Change in CMIP5 Models , 2014 .

[3]  K. Boo,et al.  Performance Evaluation of CMIP5 and CMIP6 Models on Heatwaves in Korea and Associated Teleconnection Patterns , 2020, Journal of Geophysical Research: Atmospheres.

[4]  S. Camargo.  Global and Regional Aspects of Tropical Cyclone Activity in the CMIP5 Models , 2013 .

[5]  Chang‐Hoi Ho,et al.  Dependency of tropical cyclone risk on track in South Korea , 2018, Natural Hazards and Earth System Sciences.

[6]  Chang‐Hoi Ho,et al.  Highlighting socioeconomic damages caused by weakened tropical cyclones in the Republic of Korea , 2016, Natural Hazards.

[7]  Chang‐Hoi Ho,et al.  Evidence of reduced vulnerability to tropical cyclones in the Republic of Korea , 2015 .

[8]  K. Emanuel,et al.  Low frequency variability of tropical cyclone potential intensity 1. Interannual to interdecadal variability , 2002 .

[9]  Veronika Eyring,et al.  Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .

[10]  Chang‐Hoi Ho,et al.  Strong landfall typhoons in Korea and Japan in a recent decade , 2011 .

[11]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[12]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[13]  Nils Wedi,et al.  Future Changes in the Western North Pacific Tropical Cyclone Activity Projected by a Multidecadal Simulation with a 16-km Global Atmospheric GCM , 2014 .

[14]  K. Emanuel.  The dependence of hurricane intensity on climate , 1987, Nature.

[15]  Lei Wang,et al.  Tropical cyclone genesis potential index over the western North Pacific simulated by CMIP5 models , 2015, Advances in Atmospheric Sciences.

[16]  M. Tippett,et al.  Human influence on tropical cyclone intensity , 2016, Science.

[17]  Ming Zhao,et al.  The Response of Tropical Cyclone Statistics to an Increase in CO2 with Fixed Sea Surface Temperatures , 2011 .

[18]  Xuebin Zhang,et al.  Evaluation of the CMIP6 multi-model ensemble for climate extreme indices , 2020 .

[19]  G. Vecchi,et al.  Dynamical Downscaling Projections of Twenty-First-Century Atlantic Hurricane Activity: CMIP3 and CMIP5 Model-Based Scenarios , 2013 .

[20]  K. Emanuel,et al.  Hurricanes and Climate: The U.S. CLIVAR Working Group on Hurricanes , 2015 .

[21]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .