Taming the Heterogeneous Dynamics of Oceanic Chlorophyll‐a Concentration With a Novel Deep Learning Model to Improve Prediction

Abstract

Chlorophyll‐a (Chl_a) concentration in the ocean is a critical indicator of primary production and plays a pivotal role in the global carbon cycle. Its accurate predictions are essential for reliable projections of future climate change. However, Chl_a is controlled by myriads of interactive physical and biogeochemical processes, posing a substantial challenge for its diagnosis and prognosis through numerical modeling. Machine learning (ML) approaches offer a promising venue to face the challenge, yet existing ML models often struggle in handling Chl_a's significant spatiotemporal variability. We propose a novel machine learning model, Spatiotemporal Dynamics Hunter (STD‐Hunter), to tame spatiotemporally heterogeneous dynamics of Chl_a and improve its prediction. STD‐Hunter effectively accommodates the heterogeneity in principal dynamics characterized by multiple basis ML models. By integrating these basis models with Chl_a's spatiotemporal characteristics, STD‐Hunter forms task‐specific predictive models that harvest the underlying dynamics. STD‐Hunter optimizes the trade‐off between effectively leveraging data and preserving idiosyncratic dynamics. Based on remotely sensing data, we demonstrate the superior performance of STD‐Hunter in predicting highly variable Chl_a in an active marginal sea. The extracted characteristics are also well aligned with Chl_a's intrinsic dynamics. By bridging between highly nonuniform observations and their underlying dynamics, STD‐Hunter offers an interpretable tool to obtain physically meaningful spatiotemporal characteristics from a data‐driven perspective.