Tropical cyclone precipitation skill in S2S models
- 1Columbia University, Lamont-Doherty Earth Observatory, Oceanography and Climate Physics, (jorgegf@ldeo.columbia.edu)
- 2Department of Atmospheric Sciences, University of Washington, Seattle, Washington
- 3Goddard Space Flight Center, NASA, Greenbelt, MD, USA
Tropical cyclone precipitation (TCP) contributes a significant fraction of total annual rainfall and also is a frequent cause of extreme precipitation in many parts of the tropics. The climatology of TCP in the S2S models is characterized by dry biases in the North Atlantic and wet biases in most other basins, specially in the Southern Indian Ocean and Australia.
Biases in total precipitation (P), TCP and their ratio (TCP/P) are mostly positive in the multi-model ensemble mean and change very little with lead time. in these models the frequency biases are the dominant contribution to TCP biases. However, in some models, there are positive biases in average precipitation per each TC which contribute significantly to TCP biases at equatorial latitudes.
The prediction skill of these reforecasts is evaluated using skill scores such as the ranked probability skill score for TCP and the Brier Skill score for genesis and occurrence. The implication of these results is discussed for their relevance to mean and extreme precipitation prediction skill using S2S models.
How to cite: Garcia-Franco, J. L., Lee, C.-Y., Camargo, S., Tippett, M., Kim, D., Molod, A., and Lim, Y.-K.: Tropical cyclone precipitation skill in S2S models, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7102, https://doi.org/10.5194/egusphere-egu23-7102, 2023.
