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Our study investigates the global impact of midlatitude cyclones on extreme wind speed events in both hemispheres under a warmer climate. Using the latest version of the high-resolution ≈ 50 km grid-spacing atmospheric climate model AM4, developed by the Geophysical Fluid Dynamics Laboratory, we conducted simulations covering the 71-years period 1949–2019 for both the present-day climate and an idealised future global warming climate scenario with a homogeneous Sea Surface Temperature (SST) increase by 2 K. Our findings reveal that extreme near-surface wind speeds increase by up to 3% K⁻¹towards the poles while decrease by a similar amount in the lower midlatitudes. When considering only extreme wind speed events objectively attributed to midlatitude cyclones, we observe a migration by the same amount towards higher latitudes both in percentage per degree SST warming and absolute value. The total number of midlatitude cyclones decreases by roughly 4%, but the proportion of cyclone-associated extreme wind speed events increases by 10% in a warmer climate. Finally, Northwestern Europe, the British Isles, and the West Coast of North America are identified as hot spots with the greatest socio-economic impacts from increased cyclone-associated extreme winds.

 

Serving deep learning models from relational databases brings significant benefits. First, features extracted from databases do not need to be transferred to any decoupled deep learning systems for inferences, and thus the system management overhead can be significantly reduced. Second, in a relational database, data management along the storage hierarchy is fully integrated with query processing, and thus it can continue model serving even if the working set size exceeds the available memory. Applying model deduplication can greatly reduce the storage space, memory footprint, cache misses, and inference latency. However, existing data deduplication techniques are not applicable to the deep learning model serving applications in relational databases. They do not consider the impacts on model inference accuracy as well as the inconsistency between tensor blocks and database pages. This work proposed synergistic storage optimization techniques for duplication detection, page packing, and caching, to enhance database systems for model serving. Evaluation results show that our proposed techniques significantly improved the storage efficiency and the model inference latency, and outperformed existing deep learning frameworks in targeting scenarios. 

The atmospheric Green's function method is a technique for modeling the response of the atmosphere to changes in the spatial field of surface temperature. While early studies applied this method to changes in atmospheric circulation, it has also become an important tool to understand changes in radiative feedbacks due to evolving patterns of warming, a phenomenon called the “pattern effect.” To better study this method, this paper presents a protocol for creating atmospheric Green's functions to serve as the basis for a model intercomparison project, GFMIP. The protocol has been developed using a series of sensitivity tests performed with the HadAM3 atmosphere‐only general circulation model, along with existing and new simulations from other models. Our preliminary results have uncovered nonlinearities in the response of the atmosphere to surface temperature changes, including an asymmetrical response to warming versus cooling patch perturbations, and a change in the dependence of the response on the magnitude and size of the patches. These nonlinearities suggest that the pattern effect may depend on the heterogeneity of warming as well as its location. These experiments have also revealed tradeoffs in experimental design between patch size, perturbation strength, and the length of control and patch simulations. The protocol chosen on the basis of these experiments balances scientific utility with the simulation time and setup required by the Green's function approach. Running these simulations will further our understanding of many aspects of atmospheric response, from the pattern effect and radiative feedbacks to changes in circulation, cloudiness, and precipitation.