Stammer, D., Amrhein, D.E., Alonso Balmaseda, M., Bertino, L., Bonavita, M., Buontempo, C., Caltabiano, N., Counillon, F., Fenty, I., Ferrari, R., Fujii, Y., et al. 2024: Earth System Reanalysis in Support of Climate Model Improvements. Bull. Amer. Meteor. Soc.. https://doi.org/10.1175/BAMS-D-24-0110.1

Summary: A 3-day workshop took place from 12 to 14 June 2023, at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, focusing on data assimilation (DA) and machine learning (ML) in the context of Earth system reanalysis and climate model improvements.
The workshop, organized 25 years after the inception of the Estimating the Circulation and Climate of the Ocean (ECCO), was an effort to lay out the roadmap for future development of DA in support of climate modeling and climate knowledge improvements, or “climate DA.” The following is a summary of the workshop outcomes and recommendations arising to move the field of DA forward in the context of climate modeling.
Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial time scales.
Further model improvements are expected to arise specifically from the improved representation of physical processes realized through model–data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble, and ML methods might be what is required in the future. In this context, ML could be important to handle nonlinear responses and to better approximate non-Gaussian distributions.

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