How does assimilating satellite SSTs affected by strong diurnal warming impact higher latitude ocean forecasts?

Abstract

Infrared satellite sea surface temperature (SST) observations capture the strong warming signal at the surface of the ocean during diurnal warming events. Assimilating these observations into ocean forecast models might introduce forecast errors, as the models do not fully resolve the strong diurnal cycle captured by SST observations. A standard approach is to remove affected SST observations from the assimilated dataset using a simplistic filter. However, this approach also removes observations not affected by diurnal warming. For high northern latitudes, where diurnal warming events are less frequent than at lower latitudes, it is unclear whether observations affected by strong warming negatively impact the forecasts, which would justify applying the filter. Here, we explore the consequences of both assimilating diurnal warming-affected SSTs and applying the filter using an ocean forecast model covering the seas off Norway. Through data assimilation experiments assimilating synthetic SSTs, we find that the strong warming signal in observations impacts forecasts in undesirable ways. The surface warming spreads below the mixed layer, decreasing its depth, and changes made to the model below the mixed layer persist into subsequent forecasts. Applying the filter reduces assimilated observations by 46 % without degrading forecasts, suggesting a redundancy in satellite SSTs.