Dynamically downscaled seasonal ocean forecasts for North American East Coast ecosystems

Abstract

Abstract. Using a 1/12° regional model of the Northwest Atlantic Ocean (MOM6-NWA12), we downscale an ensemble of retrospective seasonal forecasts from a 1° global forecast model. To evaluate whether downscaling improved the forecast skill for surface temperature and salinity and bottom temperature, the global and downscaled forecasts are compared with each other and with a reference forecast of persistence using anomaly correlation. Both sets of forecasts are also evaluated on the basis of mean bias and ensemble spread. We find that downscaling significantly improved the forecast skill for monthly sea surface temperature anomalies in the Northeast U.S. Large Marine Ecosystem, a region that global models have historically struggled to predict skillfully. The downscaled SST predictions for this region were also more skillful than the persistence baseline across most initialization months and lead times. Although some of the SST prediction skill in this region stems from the recent, rapid warming trend, prediction skill above persistence is generally maintained after removing the contribution of the trend, and patterns of skill suggestive of predictable processes are also preserved. While downscaling mainly improved SST skill in the Northeast U.S. region, it improved bottom temperature and sea surface salinity skill across many of the marine ecosystems along the North American East Coast. Downscaling generally reduced the mean surface salinity biases found in the global model, particularly in regions with sharp salinity gradients (the Northern Gulf of Mexico and the Northeast U.S.). In some cases, however, downscaling amplified the surface and bottom temperature biases found in the global predictions. We discuss several processes that are better resolved in the regional model and contribute to the improved skill, including the autumn re-emergence of temperature anomalies and advection of water masses by coastal currents. Overall, the results show that a downscaled, high resolution model can produce improved seasonal forecast skill by representing fine-scale processes that drive predictability.