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Abstract The response of severe convective storms to a warming climate is poorly understood outside of a few well studied regions. Here, projections from seven global climate models from the CMIP6 archive, for both historical and future scenarios, are used to explore the global response in variables that describe favorability of conditions for the development of severe storms. The variables include convective available potential energy (CAPE), convection inhibition (CIN), 0–6 km vertical wind shear (S06), storm relative helicity (SRH), and covariate indices (i.e., severe weather proxies) that combine them. To better quantify uncertainty, understand variable sensitivity to increasing temperature, and present results independent from a specific scenario, we consider changes in convective variables as a function of global average temperature increase across each ensemble member. Increases to favorable convective environments show an overall frequency increases on the order of 5%–20% per °C of global temperature increase, but are not regionally uniform, with higher latitudes, particularly in the Northern Hemisphere, showing much larger relative changes. The driving mechanism of these changes is a strong increase in CAPE that is not offset by factors that either resist convection (CIN), or modify the likelihood of storm organization (S06, SRH). Severe weather proxies are not the same as severe weather events. Hence, their projected increases will not necessarily translate to severe weather occurrences, but they allow us to quantify how increases in global temperature will affect the occurrence of conditions favorable to severe weather.more » « less
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Stern, Charles; Abernathey, Ryan; Hamman, Joseph; Wegener, Rachel; Lepore, Chiara; Harkins, Sean; Merose, Alexander (, Frontiers in Climate)Pangeo Forge is a new community-driven platform that accelerates science by providing high-level recipe frameworks alongside cloud compute infrastructure for extracting data from provider archives, transforming it into analysis-ready, cloud-optimized (ARCO) data stores, and providing a human- and machine-readable catalog for browsing and loading. In abstracting the scientific domain logic of data recipes from cloud infrastructure concerns, Pangeo Forge aims to open a door for a broader community of scientists to participate in ARCO data production. A wholly open-source platform composed of multiple modular components, Pangeo Forge presents a foundation for the practice of reproducible, cloud-native, big-data ocean, weather, and climate science without relying on proprietary or cloud-vendor-specific tooling.more » « less
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Abernathey, Ryan P.; Augspurger, Tom; Banihirwe, Anderson; Blackmon-Luca, Charles C.; Crone, Timothy J.; Gentemann, Chelle L.; Hamman, Joseph J.; Henderson, Naomi; Lepore, Chiara; McCaie, Theo A.; et al (, Computing in Science & Engineering)null (Ed.)
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