Atmospheric nudging in weather and climate modeling: Bias reduction, mechanism isolation, and attribution

Abstract Atmospheric nudging refers to the relaxation of prognostic meteorological fields towards specified values and involves the addition of empirical damping terms to the model's dynamics that adjust the model state toward a prescribed analysis. Originally developed in the 1970s as a way to avoid numerical instabilities in data assimilation, the applications of nudging have broadened significantly over recent decades within both the weather and climate modeling communities. To capture some of its more salient applications in recent years, here we highlight how nudging has contributed considerable insight into (1) how model biases affect processes, (2) the mechanisms coupling different components of the Earth system, and (3) attribution of weather and climate to specific atmospheric regions and/or processes. We also address longstanding caveats associated with the implementation and interpretation of nudging and propose recommendations for how to resolve them, based on past lessons learned. We conclude by proposing a recommended research path forward for using nudging to tackle emerging questions in the climate science and machine‐learning research communities, emphasizing the need for improved coordination across research domains in developing nudging strategies that are more robust and generalizable across applications.