Recent climate model developments, established through increased model resolution, have led to improvements in model simulations of the coupled Earth system. However, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. Further model improvements are expected to arise specifically from 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 data assimilation (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. Climate DA must aim to enhance climate knowledge through the improved ability to simulate and predict the real world by optimally combining Earth system models and most available global observations from different Earth system components and domains. For this, climate DA needs to bring the simulations of climate models into consistency with the natural world as observed by the global climate observing system, and to produce a dynamically balanced climate estimate in support of initialized climate predictions, investigation of climate processes, and the identification and reduction of model bias. In the future, arguably the most important aspects of climate DA and ML in support of model improvement and enhancing predictive capabilities might become optimizing initial conditions, model parameters and model structure to mitigate model biases and thereby improve models’ skill in simulating the observed climate, as well as to enhance model skill for climate projections. To realize the full capabilities of climate DA we need to advance science and technologies for analyzing and merging global observations and Earth system model data in the context of Earth system DA. A close collaboration of DA scientists with model developers is needed to enable parameter and model structure improvements and model bias correction by applying this new technology. Automatic Differentiation (AD) can be the backbone for the integration of modern climate model code and its optimization. An optimal fusion of models and observations will also enable to determine where observations are most needed to reduce uncertainty and enable enhancement of the observation system. The talk will summarize recommendations for actions required to reaching the full potential of climate data assimilation. Recommendations will be discussedunder the six headings of (1) Exploitation of Earth Observations; (2) Development of AD and ML Infrastructure; (3) Advances in Ensemble and Variational DA and ML Methods; (4) Model Improvements through Parameter Estimation; (5) Performing Earth System Reanalysis; (6) Understanding, Prediction, and Projection of Earth System Evolution.

Detlef Stammer is Chair of the WCRP Joint Scientific Committee. Detlef received a Ph.D. in Physical Oceanography from the Institute of Oceanography, Kiel. In 1993 he took a postdoctoral position at MIT, where he subsequently became Principal Research Scientist. In 1999 he was appointed to a tenured faculty position at the Scripps Institute of Oceanography at the University of California, San Diego. Detlef remained in America until 2003, when he returned to Germany to take up a Professorship at the Institue for Oceanography at the University of Hamburg. He is now a senior Professor at the Center for Earth System Research and Sustainability at the University of Hamburg, Germany. Detlef's research interests include the role of the ocean in climate variability and sea level change.