Argo data assimilation and its effect on climate state estimation and forecasting in the western North Pacific using a coupled model

Abstract In this study, we investigated the effects of ocean subsurface data (Argo data) on climate state estimation and forecasting, focusing on the reproduction of North Pacific subtropical mode water (STMW) using a four‐dimensional variational data assimilation system with a coupled model. We produced two reanalysis plus forecast data sets for the ocean and atmosphere in 2010 using a 3 month assimilation period: the first including Argo data (Argo case) and the second did not include Argo data (control case). In the control case, the Kuroshio, Kuroshio Extension front, and recirculation gyres along the front were not adequately reproduced. Consequently, there were large biases in temperature and salinity in the western North Pacific. The assimilation of Argo data effectively corrected these biases and significantly improved reproduction of the Kuroshio fronts and recirculation gyres, resulting in a more realistic reproduction of the winter mixed layer and STMW. The correction of these biases is critical to the 1–3 year predictions of the STMW core properties, and the assimilation of Argo data enables prediction of these properties for more than a year. We showed that assimilation of Argo data affects the surface atmospheric temperature above the STMW formation region.