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Air‐sea flux variability has contributions from both ocean and atmosphere at different spatio‐temporal scales. Atmospheric synoptic scales and the air‐sea turbulent heat flux that they drive are well represented in climate models, but ocean mesoscales and their associated variability are often not well resolved due to non‐eddy‐resolving spatial resolutions of current climate models. We deploy a physics‐based stochastic subgrid‐scale parameterization for ocean density, that reinforces the lateral density variations due to oceanic eddies, and examine its effect on air‐sea heat flux variability in a comprehensive coupled climate model. The stochastic parameterization substantially modifies sea surface temperature (SST) and latent heat flux (LHF) variability and their co‐variability, primarily at scales near the resolution of the ocean model grid. Enhancement in the SST‐LHF anomaly covariance, and correlations, indicate that the ocean‐intrinsic component of the air‐sea heat flux variability is more consistent with high‐resolution satellite observations, especially in Gulf Stream region. Plain Language Summary: Variations in air‐sea heat fluxes arise from both ocean and atmosphere at different space and time scales. Studies suggest that at large scales, for example, thousands of kilometers, atmospheric processes drive the ocean variability at the surface, such as sea‐surface temperature. However, at smaller spatial scales, for example, [100−1,000] km, the oceans control the atmosphere variability near the air‐sea interface. These local air‐sea feedbacks influence both oceans and the atmosphere on various levels and are of significant dynamical importance. However, climate models typically use large grid spacing and fail to represent the air‐sea interaction mechanism inherent to these small scales. We address this problem by modifying the ocean density using random noise at multiple places in the model before coupling it to the atmosphere. We chose density because it is used for multiple purposes in ocean models, and imperfections in it arise due to the missing subgrid‐scale effects that can have a major impact all over the oceans, especially the upper ocean which interacts the most with the atmosphere. The proposed approach led to significant improvement in the air‐sea interaction properties at various spatial scales compared to satellite observations. Key Points: The ability of a subgrid‐scale parameterization to improve the ocean‐intrinsic air‐sea flux variability in a climate model is assessedThe parameterization modifies the sea surface temperature and latent heat flux variability at the grid‐scale level and boosts their simultaneous co‐variabilityThe stochastic parameterization improves consistency with the observations of air‐sea interaction [ABSTRACT FROM AUTHOR]

Current climate models systematically underestimate the strength of oceanic fronts associated with strong western boundary currents, such as the Kuroshio and Gulf Stream Extensions, and have difficulty simulating their positions at the mid-latitude ocean's western boundaries (1). Even with an enhanced grid resolution to resolve ocean mesoscale eddies--energetic circulations with horizontal scales of about a hundred kilometres that strongly interact with the fronts and currents--the bias problem can still persist (2); to improve climate models we need a better understanding of the dynamics governing these oceanic frontal regimes. Yet prevailing theories about the western boundary fronts are based on ocean internal dynamics without taking into consideration the intense air-sea feedbacks in these oceanic frontal regions. Here, by focusing on the Kuroshio Extension Jet east of Japan as the direct continuation of the Kuroshio, we show that feedback between ocean mesoscale eddies and the atmosphere (OME-A) is fundamental to the dynamics and control of these energetic currents. Suppressing OME-A feedback in eddy-resolving coupled climate model simulations results in a 20-40 per cent weakening in the Kuroshio Extension Jet. This is because OME-A feedback dominates eddy potential energy destruction, which dissipates more than 70 per cent of the eddy potential energy extracted from the Kuroshio Extension Jet. The absence of OME-A feedback inevitably leads to a reduction in eddy potential energy production in order to balance the energy budget, which results in a weakened mean current. The finding has important implications for improving climate models' representation of major oceanic fronts, which are essential components in the simulation and prediction of extratropical storms and other extreme events (3-6), as well as in the projection of the effect on these events of climate change., With the availability of high-resolution satellite observations and climate model simulations, it has become increasingly evident that ocean mesoscale eddies and fronts can have a profound influence on the atmospheric [...]

The water mass transformation (WMT) framework describes how water of one class, such as a discrete interval of density, is converted into another class via air-sea fluxes or interior mixing processes. This paper investigates how this process is modified at the surface when mesoscale ocean eddies are present, using a state-of-the-art high-resolution climate model with reasonable fidelity in the Southern Ocean. The method employed is to coarse-grain the high-resolution model fields to remove eddy signatures, and compare the results with those from the full model fields. This method shows that eddies reduced the WMT by 2-4 Sv (10%-20%; 1 Sv ≡ 106 m³ s-1) over a wide range of densities, from typical values of 20 Sv in the smoothed case. The corresponding water mass formation was reduced by 40%at one particular density increment, namely, between 1026.4 and 1026.5 kg m-3, which corresponds to the lighter end of the range of Indian Ocean ModeWater in the model. The effect of eddies on surfaceWMT is decomposed into three terms: direct modulation of the density outcrops, then indirectly, by modifying the air-sea density flux, and the combined effect of the two, akin to a covariance. It is found that the first and third terms dominate, i.e., smoothing the outcrops alone has a significant effect, as does the combination of smoothing both outcrops and density flux distributions, but smoothing density flux fields alone has little effect. Results from the coarse-graining method are compared to an alternative approach of temporally averaging the data. Implications for climate model resolution are also discussed. [ABSTRACT FROM AUTHOR]