Deep learning model emulators for marine biogeochemistry forecasting from days to decades

Abstract. There has been a major surge of activity in the development of deep-learning emulators for Earth System Models. These emulators offer the potential to substantially reduce computational costs, when used to run short-range, seasonal, and climate-scale predictions. When trained on reanalysis data, they may even outperform conventional numerical model forecasts. Marine biogeochemistry has so far lagged somewhat behind these developments, although a rapid expansion of activity in this area is already happening. We have used a simplified one-dimensional water column model, coupling a physical ocean model with a high complexity marine biogeochemistry model (European Regional Seas Ecosystem Model, ERSEM), and have demonstrated that ERSEM can be successfully emulated using deep learning techniques. We explored two key emulator architectures, Long Short-Term Memory (LSTM) Neural Networks that emulate a selected subset of ERSEM variables at daily time-step resolution, and physics-informed 1D Convolutional Neural Networks (CNN) that emulate the full pelagic ERSEM system throughout the entire water column. We show that by using ocean physics simulator inputs, these emulators remain largely stable over multi-decadal timescales and are highly skilful in reproducing ERSEM simulations in both decadal climate projections and short-range (10-day) forecasting applications. The former includes the emulator's ability to accurately predict the timing of phytoplankton Spring blooms several years in advance. Furthermore, we show that, when trained on reanalysis data, the emulators can outperform ERSEM forecast skill score for several key variables, including phytoplankton and zooplankton, by 50–60 %. If similar performance can be achieved in three-dimensional regional applications, the emulators could deliver substantially higher-quality predictions at a fraction of the computational cost. We also apply novel explainability techniques, which can help provide insights into emulated system's emergent behaviour and feed important information to marine biogeochemistry model builders. The emulator performance is evaluated using a range of metrics, including the ability to reproduce daily anomalies and extreme events. We anticipate that these emulator techniques will have wide applicability in the future, including operational forecasting and in marine autonomous systems. We conclude by discussing key challenges and opportunities for further development.