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Understanding and predicting “droughts” in wind and solar power availability can help the electric grid operator planning and operation toward deep renewable penetration. We assess climate models' ability to simulate these droughts at different horizontal resolutions, ∼100 and ∼25 km, over Western North America and Texas. We find that these power droughts are associated with the high/low pressure systems. The simulated wind and solar power variabilities and their corresponding droughts during historical periods are more sensitive to the model bias than to the model resolution. Future climate simulations reveal varied future change of these droughts across different regions. Although model resolution does not affect the simulation of historical droughts, it does impact the simulated future changes. This suggests that regional response to future warming can vary considerably in high‐ and low‐resolution models. These insights have important implications for adapting power system planning and operations to the changing climate.

We investigate the role of ocean heat transport (OHT) in driving the decadal variability of the Arctic climate by analyzing the pre‐industrial control simulation of a high‐resolution climate model. While the OHT variability at 65°N is greater in the Atlantic, we find that the decadal variability of Arctic‐wide surface temperature and sea ice area is much better correlated with Bering Strait OHT than Atlantic OHT. In particular, decadal Bering Strait OHT variability causes significant changes in local sea ice cover and air‐sea heat fluxes, which are amplified by shortwave feedbacks. These heat flux anomalies are regionally balanced by longwave radiation at the top of the atmosphere, without compensation by atmospheric heat transport (Bjerknes compensation). The sensitivity of the Arctic to changes in OHT may thus rely on an accurate representation of the heat transport through the Bering Strait, which is difficult to resolve in coarse‐resolution ocean models.

In this paper we summarize improvements in climate model simulation of eastern boundary upwelling systems (EBUS) when changing the forcing dataset from the Coordinated Ocean-Ice Reference Experiments (CORE; ∼2° winds) to the higher-resolution Japanese 55-year Atmospheric Reanalysis for driving ocean–sea ice models (JRA55-do, ∼0.5°) and also due to refining ocean grid spacing from 1° to 0.1°. The focus is on sea surface temperature (SST), a key variable for climate studies, and which is typically too warm in climate model representation of EBUS. The change in forcing leads to a better-defined atmospheric low-level coastal jet, leading to more equatorward ocean flow and coastal upwelling, both in turn acting to reduce SST over the upwelling regions off the west coast of North America, Peru, and Chile. The refinement of ocean resolution then leads to narrower and stronger alongshore ocean flow and coastal upwelling, and the emergence of strong across-shore temperature gradients not seen with the coarse ocean model. Off northwest Africa the SST bias mainly improves with ocean resolution but not with forcing, while in the Benguela, JRA55-do with high-resolution ocean leads to lower SST but a substantial bias relative to observations remains. Reasons for the Benguela bias are discussed in the context of companion regional ocean model simulations. Finally, we address to what extent improvements in mean state lead to changes to the monthly to interannual variability. It is found that large-scale SST variability in EBUS on monthly and longer time scales is largely governed by teleconnections from climate modes and less sensitive to model resolution and forcing than the mean state.

The Kuroshio Extension (KE) has far-reaching influences on climate as well as on local marine ecosystems. Thus, skillful multi-year to decadal prediction of the KE state and understanding sources of skill are valuable. Retrospective forecasts using the high-resolution Community Earth System Model (CESM) show exceptional skill in predicting KE variability up to lead year 4, substantially higher than the skill found in a similarly configured low-resolution CESM. The higher skill is attained because the high-resolution system can more realistically simulate the westward Rossby wave propagation of initialized ocean anomalies in the central North Pacific and their expression within the sharp KE front, and does not suffer from spurious variability near Japan present in the low-resolution CESM that interferes with the incoming wave propagation. These results argue for the use of high-resolution models for future studies that aim to predict changes in western boundary current systems and associated biological fields.

Ocean warming is a key factor impacting future changes in climate. Here we investigate vertical structure changes in globally averaged ocean heat content (OHC) in high‐ (HR) and low‐resolution (LR) future climate simulations with the Community Earth System Model (CESM). Compared with observation‐based estimates, the simulated OHC anomalies in the upper 700 and 2,000 m during 1960–2020 are more realistic in CESM‐HR than ‐LR. Under RCP8.5 scenario, the net surface heat into the ocean is very similar in CESM‐HR and ‐LR. However, CESM‐HR has a larger increase in OHC in the upper 250 m compared to CESM‐LR, but a smaller increase below 250 m. This difference can be traced to differences in eddy‐induced vertical heat transport between CESM‐HR and ‐LR in the historical period. Moreover, our results suggest that with the same heat input, upper‐ocean warming is likely to be underestimated by most non‐eddy‐resolving climate models.

It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model.

The impact of increased model horizontal resolution on climate prediction performance is examined by comparing results from low-resolution (LR) and high-resolution (HR) decadal prediction simulations conducted with the Community Earth System Model (CESM). There is general improvement in global skill and signal-to-noise characteristics, with particularly noteworthy improvements in the eastern tropical Pacific, when resolution is increased from order 1° in all components to order 0.1°/0.25° in the ocean/atmosphere. A key advance in the ocean eddy-resolving HR system is the reduction of unrealistic warming in the Southern Ocean (SO) which we hypothesize has global ramifications through its impacts on tropical Pacific multidecadal variability. The results suggest that accurate representation of SO processes is critical for improving decadal climate predictions globally and for addressing longstanding issues with coupled climate model simulations of recent Earth system change.

Accurate prediction of sea surface temperatures (SSTs) in the tropical North Atlantic on multiyear timescales is of paramount importance due to its notable impact on tropical cyclone activity. Recent advances in high-resolution climate predictions have demonstrated substantial improvements in the skill of multiyear SST prediction. This study reveals a notable enhancement in high-resolution tropical North Atlantic SST prediction that stems from a more realistic representation of the Atlantic Meridional Mode and the associated wind-evaporation-SST feedback. The key to this improvement lies in the enhanced surface wind response to changes in cross-equatorial SST gradients, resulting from Intertropical Convergence Zone bias reduction when atmospheric model resolution is increased, which, in turn, amplifies the positive feedback between latent and sensible surface heat fluxes and SST anomalies. These advances in high-resolution climate prediction hold promise for extending tropical cyclone forecasts at multiyear timescales.