A Statistical Method for Improving Continental Shelf and Nearshore Marine Climate Predictions

Spatially and temporally homogeneous measurements of ocean temperature variability at high resolution on the continental shelf are scarce. Daily estimates of large-scale ocean properties are readily available from global ocean reanalysis products. However, the ocean models that underpin these reanalysis products tend not to have been designed for the simulation of complex coastal ocean variability. Hence, across-shelf values are often poorly represented. This study involved developing a statistical approach to more accurately and robustly represent SST on the continental shelf informed by large-scale satellite observations and reanalysis data or model output. Using the southeastern Australian shelf region as a case study, this paper demonstrates that this statistical model approach generates more accurate estimates of the inshore SST using (i) offshore SST from Bluelink Reanalysis (BRAN) and (ii) the statistical relationship between inshore and offshore SST in observations from the Advanced Very High Resolution Radiometer. SST is separated into the mean, seasonal cycle, and residual variability, and separate models are developed for each component. The offshore locations used to inform the model are determined by taking into account (i) the quality of BRAN at each location, (ii) the strength between the inshore and offshore variability, and (iii) the proximity of the inshore and offshore locations. Model predictions are made for the continental shelf around southeastern Australia. The role of the mean circulation in providing connectivity between the shelf and the offshore regions is discussed, and how this information can be used to better inform the choice of model predictor locations, leading to a hybrid statistical-connectivity model.