Using all data to improve seasonal sea surface temperature predictions: A combination‐based model forecast with unequal observation lengths

Abstract

One way to reduce model uncertainty in climate predictions is to combine forecasts from several models. Recent multi‐model combination approaches combine model forecasts by pooling data for a time period, common across all the models, thus ignoring the additional data available or discarding altogether the models with the shorter time period. This results in the loss of some information which could otherwise be used while combining the models to possibly improve forecast skill. Our research explores this issue in the context of multi‐model sea surface temperature (SST) models predictions and proposes a novel concept that allows a framework for combining models with unequal time period. Here, the unequal time periods imply different range of start and end dates of available model forecasts. A qualitative standpoint of our multi‐model forecasting strategy is to reduce the uncertainty and improve the forecast skill. The utility of the approach is demonstrated by combining the global seasonal NDJ (November–January) SST predictions of two models and also as many as eight models, obtained using both equal and unequal time periods. The proposed approach shows improvement over 62–69% grid cells around the entire globe over the case when the common period of data length across the models is considered.