“…In the context of dynamical climate models, DL algorithms have proven effective tools for deriving model parameterizations directly from numerical simulations. For example, many past studies have focused on learning subgrid parameterizations from high resolution experiments and/or observations of the ocean (Bolton & Zanna, 2019; Sane et al., 2023; Zanna & Bolton, 2020; Zhu et al., 2022), atmosphere (Brenowitz & Bretherton, 2018; Gentine et al., 2018; O’Gorman & Dwyer, 2018; Rasp et al., 2018; P. Wang et al., 2022; Yuval & O’Gorman, 2020), and sea ice (Finn et al., 2023). In the context of DA‐based approaches, some recent studies have relied on iterative sequences of DA and ML to infer unresolved scale parameterizations from sparse and noisy observations (Brajard et al., 2021), or to learn state‐dependent model error from analysis increments (Farchi et al., 2021) and nudging tendencies (Bretherton et al., 2022; Watt‐Meyer et al., 2021), while others have combined DA with equation discovery to extract interpretable structural model errors (Mojgani et al., 2022).…”