Seasonal prediction of tropical cyclones over the North Atlantic and Western North Pacific: dependence on model, resolution and stochastic physics

Befort, D. J., Hodges, K. I. ORCID: https://orcid.org/0000-0003-0894-229X and Weisheimer, A. (2022) Seasonal prediction of tropical cyclones over the North Atlantic and Western North Pacific: dependence on model, resolution and stochastic physics. Journal of Climate, 35 (5). pp. 1385-1397. ISSN 1520-0442 doi: 10.1175/JCLI-D-21-0041.1

Abstract/Summary

In this study, Tropical Cyclones (TC) over the Western North Pacific (WNP) and North Atlantic (NA) basins are analysed in seasonal forecasting models from five European centres. Most models are able to capture the observed seasonal cycle of TCs over both basins, however, large differences for spatial track densities are found. Besides shortcomings in TC characteristics, significant positive skill (deterministic and probabilistic) in predicting TC numbers and accumulated cyclone energy is found over both basins. Furthermore, all seasonal forecasting models are skillful in predicting TC variability over a region covering the Caribbean and North American coastline, suggesting that the models carry useful information, e.g. for planning purposes ahead of the upcoming TC season. It is found that TC numbers for most models are reliable over the NA basin, whereas prediction of TC numbers over the WNP are mostly unreliable. Similar to previous studies, TC numbers are often underestimated, which is probably related to coarse model resolutions. Motivated by recent studies showing the positive impact of stochastic schemes, its specific influence on TCs is assessed using ECMWF’s coupled model. Results show that stochastic physics increases TC numbers over the Northern Hemisphere to a similar extent as increasing horizontal resolution. Furthermore, indications for slight improvements in TC intensity and partly predictability are found. Given the low computational costs of stochastic physics, these schemes provide a low-cost alternative compared to increased resolution to enhance TC characteristics in seasonal forecasts.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/96742
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Meteorological Society
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