A Quantitative Method to Evaluate Tropical Cyclone Tracks in Climate Models

The ability to simulate tropical cyclones (TCs) realistically is an important factor in the performance evaluation of climate models. In previous studies, indirect evaluation methods have been proposed that are based on the comparison of TC-related background circulation between model results and observations. Direct model evaluation methods, in most cases, are limited to the model skill in simulating the TC frequency, intensity, and track density. Here we propose a new method to quantitatively and directly evaluate the ability of climate models in simulating TC tracks. The method consists of two indicators that account for the model performance in simulating TC track density and the geographic properties of TC tracks, respectively. This method is applied to evaluate the skill of climate models in simulating TC tracks over the western North Pacific Ocean. The explicit models include seven from phase 5 of the Coupled Model Intercomparison Project and eight from the U.S. CLIVAR Hurricane Working Group (HWG), as well as four downscaled HWG models. Our results indicate the order of these 15 explicit models according to their ability to simulate TC tracks. In addition, we show that, for one of the models, the TC track simulation is greatly improved by using downscaling.

[1]  Chang‐Hoi Ho,et al.  2010 Western North Pacific Typhoon Season: Seasonal Overview and Forecast Using a Track-Pattern-Based Model , 2012 .

[2]  Liguang Wu,et al.  Influence of future tropical cyclone track changes on their basin-wide intensity over the western North Pacific: Downscaled CMIP5 projections , 2015, Advances in Atmospheric Sciences.

[3]  M. Tippett,et al.  A Poisson Regression Index for Tropical Cyclone Genesis and the Role of Large-Scale Vorticity in Genesis , 2011 .

[4]  K. Emanuel.  Climate and tropical cyclone activity : A new model downscaling approach , 2006 .

[5]  Zhou Bo-Tao.  Model Evaluation and Projection on the Linkage between Hadley Circulation and Atmospheric Background Related to the Tropical Cyclone Frequency over the Western North Pacific , 2012 .

[6]  Shian-Jiann Lin,et al.  Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. , 2009 .

[7]  Sai Ravela,et al.  A STATISTICAL DETERMINISTIC APPROACH TO HURRICANE RISK ASSESSMENT , 2006 .

[8]  Liguang Wu,et al.  Growing typhoon influence on east Asia , 2005 .

[9]  Tracking Hurricanes , 1966 .

[10]  Tim Li,et al.  Influence of Model Biases on Projected Future Changes in Tropical Cyclone Frequency of Occurrence , 2014 .

[11]  K. Emanuel,et al.  Past and Projected Changes in Western North Pacific Tropical Cyclone Exposure , 2016 .

[12]  Michael K. Tippett,et al.  An Environmentally Forced Tropical Cyclone Hazard Model , 2018 .

[13]  Padhraic Smyth,et al.  Cluster Analysis of Typhoon Tracks. Part II: Large-Scale Circulation and ENSO , 2007 .

[14]  Arun Kumar,et al.  Characteristics of tropical cyclones in high‐resolution models in the present climate , 2014 .

[15]  Kenneth R. Knapp,et al.  Quantifying Interagency Differences in Tropical Cyclone Best-Track Wind Speed Estimates , 2010 .

[16]  Padhraic Smyth,et al.  Probabilistic clustering of extratropical cyclones using regression mixture models , 2007 .

[17]  W. M. Gray,et al.  Tropical Cyclones and Global Climate Change: A Post-IPCC Assessment , 1998 .

[18]  C. J. Neumann,et al.  The International Best Track Archive for Climate Stewardship (IBTrACS): unifying tropical cyclone data. , 2010 .

[19]  Bin Wang,et al.  Assessing Impacts of Global Warming on Tropical Cyclone Tracks , 2004 .

[20]  Padhraic Smyth,et al.  Cluster Analysis of Typhoon Tracks. Part I: General Properties , 2007 .

[21]  Ming Zhao,et al.  Tracking Scheme Dependence of Simulated Tropical Cyclone Response to Idealized Climate Simulations , 2014 .

[22]  K. Emanuel,et al.  Cluster Analysis of Downscaled and Explicitly Simulated North Atlantic Tropical Cyclone Tracks , 2015 .

[23]  A statistical assessment of tropical cyclone activity in atmospheric general circulation models , 2005 .

[24]  K. Emanuel,et al.  The poleward migration of the location of tropical cyclone maximum intensity , 2014, Nature.

[26]  Christopher G. DesAutels,et al.  Environmental Control of Tropical Cyclone Intensity , 2004 .

[27]  G. Holland.  Tropical Cyclone Motion: Environmental Interaction Plus a Beta Effect , 1983 .

[28]  Hiroyuki Murakami,et al.  Future Changes in Tropical Cyclone Activity Projected by the New High-Resolution MRI-AGCM* , 2012 .

[29]  S. Camargo,et al.  The Effect of Regional Climate Model Domain Choice on the Simulation of Tropical Cyclone-Like Vortices in the Southwestern Indian Ocean , 2005 .

[30]  Ming Ying,et al.  Comparison of Three Western North Pacific Tropical Cyclone Best Track Datasets in a Seasonal Context , 2011 .

[31]  S. Camargo,et al.  Impact of ocean warming on tropical cyclone track over the western north pacific: A numerical investigation based on two case studies , 2017 .

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

[33]  Daniel J. Halperin,et al.  Observed versus GCM-Generated Local Tropical Cyclone Frequency: Comparisons Using a Spatial Lattice , 2013 .

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

[35]  James B. Elsner,et al.  Spatial grids for hurricane climate research , 2012, Climate Dynamics.

[36]  Ying Xu,et al.  How the “best” CMIP5 models project relations of Asian–Pacific Oscillation to circulation backgrounds favorable for tropical cyclone genesis over the western North Pacific , 2017, Journal of Meteorological Research.

[37]  M. Esch,et al.  Tropical cyclones in a T159 resolution global climate model: comparison with observations and re-analyses , 2007 .

[38]  Robert F. Rogers,et al.  Convective-Scale Structure and Evolution during a High-Resolution Simulation of Tropical Cyclone Rapid Intensification , 2010 .

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

[40]  余锦华,et al.  K-MEANS CLUSTERING FOR CLASSIFICATION OF THE NORTHWESTERN PACIFIC TROPICAL CYCLONE TRACKS , 2016 .

[41]  Upmanu Lall,et al.  Classifying North Atlantic Tropical Cyclone Tracks by Mass Moments , 2009 .

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