Room for Improvement in Seasonal-to-Decadal Climate Prediction

The hindcast skill, for leads from a season to a decade, of the current generation of coupled GCMs (CGCMs) is assessed and benchmarked with skill of an empirical model, a Linear Inverse Model (LIM). Both systems produce sea surface temperature (SST) and sea surface height (SSH) forecasts. For seasonal forecast leads, the CGCM ensemble mean hindcasts come from the North American Multimodel Ensemble (NMME), while for interannual-to-decadal leads the CMIP5 hindcasts are analyzed. The LIM, constructed from near global (60^oS-65^oN) observed monthly anomalies during the period 1961-2010, produces forecasts from 1 month to 9 year lead.

The LIM skill is comparable to or better than the CGCM ensemble mean, as well as local univariate AR(1) process, at all timescales and all locations. Notably, the LIM is significantly more skillful than the CGCM ensemble mean over the extratropics, especially at longer forecast lead time. For example, significant skill in the Pacific Decadal Oscillation (PDO), in both PDO phase and amplitude, up to 6-9 yr lead in LIM are well compared to relatively low insignificant skills in CMIP5 models.

Analysis of the LIM suggests that possible error in representing the SST-SSH coupling, rather than uncertainty in forecast initialization, is a major cause of reduced SST and SSH skill in the CGCMs. This also suggests that reducing this model error should improve model prediction skill of seasonal-to-decadal SST and SSH anomalies.