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Abstract There is compelling evidence that atmospheric moisture may either increase or decrease midlatitude eddy kinetic energy (EKE). We reconcile these findings by using a hierarchy of idealized atmospheric models to demonstrate that moisture energizes individual eddies given fixed large-scale background winds and temperatures but makes those background conditions less favorable for eddy growth. For climates similar to the present day, the latter effect wins out, and moisture weakens midlatitude eddy activity. The model hierarchy includes a moist two-layer quasigeostrophic (QG) model and an idealized moist general circulation model (GCM). In the QG model, EKE increases when moisture is added to simulations with fixed baroclinicity, closely following a previously derived scaling. But in both models, moisture decreases EKE when environmental conditions are allowed to vary. We explain these results by examining the models’ mean available potential energy (MAPE) and by calculating terms in the models’ Lorenz energy cycles. In the QG model, the EKE decreases because precipitation preferentially forms on the poleward side of the jet, releasing latent heat where the model is relatively cold and decreasing the MAPE, hence the EKE. In the moist GCM, the MAPE primarily decreases because the midlatitude stability increases as the model is moistened, with reduced meridional temperature gradients playing a secondary role. Together, these results clarify moisture’s role in driving the midlatitude circulation and also highlight several drawbacks of QG models for studying moist processes in midlatitudes. Significance StatementDry models of the atmosphere have played a central role in the study of large-scale atmospheric dynamics. But we know that moisture adds much complexity, associated with phase changes, its effect on atmospheric stability, and the release of latent heat during condensation. Here, we take an important step toward incorporating moisture into our understanding of midlatitude dynamics by reconciling two diverging lines of literature, which suggest that atmospheric moisture can either increase or decrease midlatitude eddy kinetic energy. We explain these opposing results by showing that moisture not only makes individual eddies more energetic but also makes the environment in which eddies form less favorable for eddy growth. For climates similar to the present day, the latter effect wins out such that moisture decreases atmospheric eddy kinetic energy. We demonstrate this point using several different idealized atmospheric models, which allow us to gradually add complexity and to smoothly vary between moist and dry climates. These results add fundamental understanding to how moisture affects midlatitude climates, including how its effects change in warmer and moisture climates, while also highlighting some drawbacks of the idealized atmospheric models. 

Abstract The mechanisms by which clouds impact the variability of the mid‐latitude atmosphere are poorly understood. We use an idealized, dry atmospheric model to investigate the relationship between Atmospheric Cloud Radiative Effects (ACRE) and annular mode persistence. We force the model with time‐varying diabatic heating that mimics the observed ACRE response to the Southern Annular Mode (SAM). Realistic ACRE forcing reduces annular mode persistence by 5 days (−16%), which we attribute to a weakening of low‐frequency eddy forcing via modified low‐level temperature gradients, though this effect is partly compensated by reduced frictional damping due to zonal wind anomalies becoming more top‐heavy. The persistence changes are nonlinear with respect to the amplitude of ACRE forcing, reflecting nonlinearities in the response of the eddy forcing. These results highlight the ACRE's impact on low‐frequency eddy forcing as the dominant cause of changes in annular mode persistence. 

Abstract We synthesize knowledge from numerical weather prediction, inverse theory, and statistics to address the problem of estimating a high‐dimensional covariance matrix from a small number of samples. This problem is fundamental in statistics, machine learning/artificial intelligence, and in modern Earth science. We create several new adaptive methods for high‐dimensional covariance estimation, but one method, which we call Noise‐Informed Covariance Estimation (NICE), stands out because it has three important properties: (a) NICE is conceptually simple and computationally efficient; (b) NICE guarantees symmetric positive semi‐definite covariance estimates; and (c) NICE is largely tuning‐free. We illustrate the use of NICE on a large set of Earth science–inspired numerical examples, including cycling data assimilation, inversion of geophysical field data, and training of feed‐forward neural networks with time‐averaged data from a chaotic dynamical system. Our theory, heuristics and numerical tests suggest that NICE may indeed be a viable option for high‐dimensional covariance estimation in many Earth science problems.