Multilevel vector autoregressive prediction of sea surface temperature in the North Tropical Atlantic Ocean and the Caribbean Sea
We use a multilevel vector autoregressive model (VAR-L), to forecast sea surface temperature anomalies (SSTAs) in the Atlantic hurricane Main Development Region (MDR). VAR-L is a linear regression model using global SSTA data from L prior months as predictors. In hindcasts for the recent 30 years, the multilevel VAR-L outperforms a state-of-the-art dynamic forecast model, as well as the commonly used linear inverse model (LIM). The multilevel VAR-L model shows skill in 6–12 month forecasts, with its greatest skill in the months of the active hurricane season. The optimized model for the best long-range skill score in the MDR, chosen by a cross-validation procedure, has 12 time levels and 12 empirical orthogonal function modes. We investigate the optimal initial conditions for MDR SSTA prediction using a generalized singular vector decomposition of the propagation matrix. We find that the added temporal degrees of freedom for the predictands in VAR12 as compared with a LIM model, which allow the model to capture both the local wind–evaporation–SST feedback in the Tropical Atlantic and the impact on the Atlantic of an improved medium-range ENSO forecast, elevate the long-range forecast skill in the MDR.
KeywordsData-driven model Sea surface temperature prediction Atlantic hurricane Main Development Region
The authors would like to express our deep appreciation to the anonymous reviewer for the constructive comments. This research was supported by the Office of Naval Research under the Grant No. N00014-12-1-0911.
- Kravtsov S, Kondrachov D, Ghil M (2009) Empirical model reduction and the modeling hierarchy in climate dynamics. In: Palmer T, Williams T (eds) Stochastic physics and climate modeling. Cambridge University Press, Cambridge, pp 35–72Google Scholar