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ABSTRACT The aftermath of the North American fur trade resulted in the depletion of many furbearing mammal populations in their native North American range while simultaneously creating invasive populations of these species through translocations worldwide. Here, we document the ongoing results of this mass ecological experiment by describing the natural history of a remnant fur colony of muskrats (Ondatra zibethicus) putatively introduced to the Isles of Shoals archipelago in the Gulf of Maine in the early 20th century. Through a combination of intensive surveys and camera trapping, we document how muskrats have been influenced by insular conditions under expectations of island biogeographic theory. Unlike other translocated muskrats that have produced successful wetland‐restricted populations in continental Europe and Asia, the Shoals muskrats appear to have shifted their habitat use and lodge building behavior and have encountered a new predator: gulls (Laridae). This Nature Note formalizes decades of anecdotal observations and provides important insight into the ecological flexibility of muskrats given the paradox of a species that is apparently now declining in its native range but expanding outside of it. 

Abstract Cloud microphysics is one of the most time‐consuming components in a climate model. In this study, we port the cloud microphysics parameterization in the Community Atmosphere Model (CAM), known as Parameterization of Unified Microphysics Across Scales (PUMAS), from CPU to GPU to seek a computational speedup. The directive‐based methods (OpenACC and OpenMP target offload) are determined as the best fit specifically for our development practices, which enable a single version of source code to run either on the CPU or GPU, and yield a better portability and maintainability. Their performance is first examined in a PUMAS stand‐alone kernel and the directive‐based methods can outperform a CPU node as long as there is enough computational burden on the GPU. A consistent behavior is observed when we run PUMAS on the GPU in a practical CAM simulation. A 3.6× speedup of the PUMAS execution time, including data movement between CPU and GPU, is achieved at a coarse horizontal resolution (8 NVIDIA V100 GPUs against 36 Intel Skylake CPU cores). This speedup further increases up to 5.4× at a high resolution (24 NVIDIA V100 GPUs against 108 Intel Skylake CPU cores), which highlights the fact that GPU favors larger problem size. This study demonstrates that using GPU in a CAM simulation can save noticeable computational costs even with a small portion of code being GPU‐enabled. Therefore, we are encouraged to port more parameterizations to GPU to take advantage of its computational benefit. 

Remote sensing observational instruments are critical for better understanding and predicting severe weather. Observational data from such instruments, such as Doppler radar data, for example, are often processed for assimilation into numerical weather prediction models. As such instruments become more sophisticated, the amount of data to be processed grows and requires efficient variational analysis tools. Here we examine the code that implements the popular SAMURAI (Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation) technique for estimating the atmospheric state for a given set of observations. We employ a number of techniques to significantly improve the code’s performance, including porting it to run on standard HPC clusters, analyzing and optimizing its single-node performance, implementing a more efficient nonlinear optimization method, and enabling the use of GPUs via OpenACC. Our efforts thus far have yielded more than 100x improvement over the original code on large test problems of interest to the community.