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Margaret M. Hurwitz, S. Baxter, B. Brown, J. Carman, J. Dale, C. Draper, F. Horsfall, M. Hughes, J. Gerth, S. Kapnick, C. Olheiser, M. Olsen, C. Stachelski, M. Vincent, R. S. Webb, and J. Zdrojewski
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G. A. Vecchi, T. Delworth, R. Gudgel, S. Kapnick, A. Rosati, A. T. Wittenberg, F. Zeng, W. Anderson, V. Balaji, K. Dixon, L. Jia, H.-S. Kim, L. Krishnamurthy, R. Msadek, W. F. Stern, S. D. Underwood, G. Villarini, X. Yang, and S. Zhang

Abstract

Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system; therefore, understanding and predicting TC location, intensity, and frequency is of both societal and scientific significance. Methodologies exist to predict basinwide, seasonally aggregated TC activity months, seasons, and even years in advance. It is shown that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basinwide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal time scales, and comprises high-resolution (50 km × 50 km) atmosphere and land components as well as more moderate-resolution (~100 km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux adjustment.” A suite of 12-month duration retrospective forecasts is performed over the 1981–2012 period, after initializing the climate model to observationally constrained conditions at the start of each forecast period, using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basinwide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally aggregated regional TC activity months in advance are feasible.

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