Estimation of global satellite-derived chlorophyll-a as function of physical drivers using shallow neural networks

Artificial Neural Networks (ANN) are applied to estimate the interannual variability of monthly-mean satellite-derived chlorophyll-a (CHL) at a global scale in the 1997-2025 period, as function of different physical variables and climate teleconnection indices. Among other variables, satellite-derived sea-surface height (SSH) proved to be a good single predictor for the CHL, showing significant CHL-SSH correlation in most of the world ocean between 60°S and 60°N (where the most continuous data series are available). This correlation, generally low for a linear estimation, opens the possibility to CHL reconstruction using higher-performance non-linear techniques like ANN. The ANN-model successfully reproduces the CHL interannual variability: 59% of the modeled CHL present correlations > 0.90. Then, the ANN-model can be used to predict CHL beyond the training period, showing a good predictability at least one season ahead. On the other hand, a similar exercise for the reconstruction/predictability of CHL is subsequently carried out using selected teleconnection indices as predictors, presenting an alternative simpler method to estimate the CHL variability in key regions along the world ocean. Thus, the proposed methods open the possibility to predict not only CHL but other related biogeochemical variables.