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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. 

Abstract During 2013, multiple tornadoes occurred across Australia, leading to 147 injuries and considerable damage. This prompted speculation as to the frequency of these events in Australia, and whether 2013 constituted a record year. Leveraging media reports, public accounts, and the Bureau of Meteorology observational record, 69 tornadoes were identified for the year in comparison to the official count of 37 events. This identified set and the existing historical record were used to establish that, in terms of spatial distribution, 2013 was not abnormal relative to the existing climatology, but numerically exceeded any year in the bureau’s record. Evaluation of the environments in which these tornadoes formed illustrated that these conditions included tornado environments found elsewhere globally, but generally had a stronger dependence on shear magnitude than direction, and lower lifting condensation levels. Relative to local environment climatology, 2013 was also not anomalous. These results illustrate a range of tornadoes associated with cool season, tropical cyclone, east coast low, supercell tornado, and low shear/storm merger environments. Using this baseline, the spatial climatology from 1980 to 2019 as derived from the nonconditional frequency of favorable significant tornado parameter environments for the year is used to highlight that observations are likely an underestimation. Applying the results, discussion is made of the need to expand observing practices, climatology, forecasting guidelines for operational prediction, and improve the warning system. This highlights a need to ensure that the general public is appropriately informed of the tornado hazard in Australia, and provide them with the understanding to respond accordingly. 

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.