Combining Argo and remote-sensing data to estimate the ocean three-dimensional temperature fields—a first approach based on simulated observations

The study aims to analyze the contribution of the combination of high-resolution sea level and sea surface temperature satellite data with accurate but sparse in situ temperature profile data as given by Argo to the reconstruction of the large-scale, monthly mean, 200-m depth temperature fields. The main issue is to reconstruct instantaneous temperature fields at high temporal and spatial resolution and thus improve the representation of the large-scale and low-frequency temperature fields at the given depth. The method is developed and presented for the temperature field at 200-m depth but can be applied to any depth and also to the salinity field. The study uses outputs and profiling float simulations derived from a state-of-the-art, eddy-resolving (1/6°-resolution) primitive equation model of the North Atlantic. Synthetic 200-m temperatures are first derived from simulated altimeter and SST data through a multiple linear regression; they are then combined with individual Argo 200-m simulated temperatures. The optimal merging uses an objective analysis method that takes into account analyzed errors on the observations and, particularly, correlated errors on synthetic temperatures deduced from remote-sensing data. Results indicate that the optimal combination is instrumental in reducing the aliasing due to the mesoscale variability and in adjusting the high-resolution combined fields to the in situ data. The rms of mapping error of the large-scale and low-frequency temperature fields at 200-m depth is largely reduced (by a factor of 4 in large mesoscale variability regions) when combining both data types, as compared to the results obtained using only in situ profiles.