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  1. ; ; ; ; ; ; ; ; ( , Geophysical Research Letters)
    Free, publicly-accessible full text available May 16, 2023
  2. ; ; ( , Geophysical Research Letters)
    Free, publicly-accessible full text available February 16, 2023
  3. ; ; ( , Geophysical Research Letters)
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  4. ; ; ; ; ; ( , Journal of Climate)
    Abstract Machine-learning-based methods that identify drought in three-dimensional space–time are applied to climate model simulations and tree-ring-based reconstructions of hydroclimate over the Northern Hemisphere extratropics for the past 1000 years, as well as twenty-first-century projections. Analyzing reconstructed and simulated drought in this context provides a paleoclimate constraint on the spatiotemporal characteristics of simulated droughts. Climate models project that there will be large increases in the persistence and severity of droughts over the coming century, but with little change in their spatial extent. Nevertheless, climate models exhibit biases in the spatiotemporal characteristics of persistent and severe droughts over parts of themore »Northern Hemisphere. We use the paleoclimate record and results from a linear inverse modeling-based framework to conclude that climate models underestimate the range of potential future hydroclimate states. Complicating this picture, however, are divergent changes in the characteristics of persistent and severe droughts when quantified using different hydroclimate metrics. Collectively our results imply that these divergent responses and the aforementioned biases must be better understood if we are to increase confidence in future hydroclimate projections. Importantly, the novel framework presented herein can be applied to other climate features to robustly describe their spatiotemporal characteristics and provide constraints on future changes to those characteristics.« less
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  5. ; ; ; ; ; ; ; ; ; ; et al ( , American Journal of Physics)
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  6. ; ; ; ; ; ; ; ( , JOM)
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  7. ; ; ; ; ; ; ; ( , Paleoceanography and Paleoclimatology)
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  8. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Advances in Modeling Earth Systems)
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  9. ; ; ; ; ; ; ; ; ; ; et al ( , Monthly notices of the Royal Astronomical Society)
    Gravitational waves provide a unique tool for observational astronomy. While the first LIGO–Virgo catalogue of gravitational wave transients (GWTC-1) contains 11 signals from black hole and neutron star binaries, the number of observations is increasing rapidly as detector sensitivity improves. To extract information from the observed signals, it is imperative to have fast, flexible, and scalable inference techniques. In a previous paper, we introduced BILBY: a modular and user-friendly Bayesian inference library adapted to address the needs of gravitational-wave inference. In this work, we demonstrate that BILBY produces reliable results for simulated gravitational-wave signals from compact binary mergers, and verifymore »that it accurately reproduces results reported for the 11 GWTC-1 signals. Additionally, we provide configuration and output files for all analyses to allow for easy reproduction, modification, and future use. This work establishes that BILBY is primed and ready to analyse the rapidly growing population of compact binary coalescence gravitational-wave signals.« less
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  10. ; ; ; ; ; ; ; ; ; ; et al ( , Earth system science data)
    Paper was published in Earth System Science Data Discussions on Feb 5, 2020.
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