Abstract
Atmospheric rivers (ARs) are narrow jets of integrated water vapor transport that are important for the global water cycle and also have large impacts on local weather and regional hydrology. Uniformly weighted multi-model averages have been used to describe how ARs will change in the future, but this type of estimate does not consider skill or independence of the climate models of interest. Here, we utilize information from various model averaging approaches, such as Bayesian model averaging (BMA), to evaluate 21 global climate models from the Coupled Model Intercomparison Project Phase 5. Model ensemble weighting strategies are based on model independence and AR performance skill relative to ERA-Interim reanalysis data and result in higher accuracy for the historic period, for example, root mean square error for AR frequency (in % of time steps) of 0.69 for BMA versus 0.94 for the multi-model ensemble mean. Model weighting strategies also result in lower uncertainties in the future estimates, for example, only 20–25% of the total uncertainties seen in the equal weighting strategy. These model averaging methods show, with high certainty, that globally the frequency of ARs is expected to have average relative increases of ~50% (and ~25% in AR intensity) by the end of the century.
Original language | English |
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Pages (from-to) | 1136-1151 |
Number of pages | 16 |
Journal | Earth's Future |
Volume | 7 |
Issue number | 10 |
DOIs | |
State | Published - Oct 1 2019 |
Externally published | Yes |
Funding
E. C. M., V. E., and D. E. W.'s contribution to this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration, Copyright 2019. All data used in this study are publicly available. The ERA‐Interim reanalysis data set is available online ( http://apps.ecmwf.int/datasets/data/interim‐full‐daily/ ), and the CMIP5 model output data are also available online ( https://cmip.llnl.gov/cmip5/data_portal.html ). The derived data used for estimating the historic and future AR frequencies are available online ( https://figshare.com/articles/Global_Atmospheric_Rivers_Historic_and_Future_/8317079 ).
Funders | Funder number |
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National Aeronautics and Space Administration | |
Jet Propulsion Laboratory | |
California Institute of Technology |
Keywords
- atmospheric rivers
- bayesian model averaging
- climate change
- extreme weather
- model averaging
- skill and independence