Climatology of Tropical Cyclone Precipitation in the S2S Models

Author:

García-Franco Jorge L.1,Lee Chia-Ying1,Camargo Suzana J.1,Tippett Michael K.2,Kim Daehyun3,Molod Andrea4,Lim Young-Kwon45

Affiliation:

1. a Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

2. b Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York

3. c Department of Atmospheric Sciences, University of Washington, Seattle, Washington

4. d Global Modeling and Assimilation Office, NASA/Goddard Space Flight Center, Greenbelt, Maryland

5. e University of Maryland, Baltimore County, Baltimore, Maryland

Abstract

Abstract This study evaluates the representation of tropical cyclone precipitation (TCP) in reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project. The global distribution of precipitation in S2S models shows relevant biases in the multimodel mean ensemble that are characterized by wet biases in total precipitation and TCP, except for the Atlantic. The TCP biases can contribute more than 50% of the total precipitation biases in basins such as the southern Indian Ocean and South Pacific. The magnitude and spatial pattern of these biases exhibit little variation with lead time. The origins of TCP biases can be attributed to biases in the frequency of tropical cyclone occurrence. The S2S models simulate too few TCs in the Atlantic and western North Pacific and too many TCs in the Southern Hemisphere and eastern North Pacific. At the storm scale, the average peak precipitation near the storm center is lower in the models than observations due to a too high proportion of weak TCs. However, this bias is offset in some models by higher than observed precipitation rates at larger radii (300–500 km). An analysis of the mean TCP for each TC at each grid point reveals an overestimation of TCP rates, particularly in the near-equatorial Indian and western Pacific Oceans. These findings suggest that the simulation of TC occurrence and the storm-scale precipitation require better representation in order to reduce TCP biases and enhance the subseasonal prediction skill of mean and extreme total precipitation.

Funder

National Aeronautics and Space Administration

Publisher

American Meteorological Society

Subject

Atmospheric Science

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