Articles | Volume 13, issue 12
Geosci. Model Dev., 13, 6131–6148, 2020
Geosci. Model Dev., 13, 6131–6148, 2020
Model description paper
03 Dec 2020
Model description paper | 03 Dec 2020

Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1

Travis A. O'Brien et al.

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Cited articles

Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., and Richmond, M.: Predictability of Extreme Precipitation in Western U.S. Watersheds Based on Atmospheric River Occurrence, Intensity, and Duration, Geophys. Res. Lett., 45, 11693–11701,, 2018. a
Chen, X., Leung, L. R., Wigmosta, M., and Richmond, M.: Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds, J. Geophys. Res.-Atmos., 124, 8896–8916,, 2019. a
Dettinger, M.: Climate change, atmospheric rivers, and floods in California – a multimodel analysis of storm frequency and magnitude changes, J. Am. Water Resour. Assoc., 47, 514–523,, 2011. a
Dong, L., Leung, L. R., Song, F., and Lu, J.: Roles of SST versus internal atmospheric variability in winter extreme precipitation variability along the U.S. West Coast, J. Climate, 32, JCLI–D–18–0062.1,, 2018. a, b
Espinoza, V., Waliser, D. E., Guan, B., Lavers, D. A., and Ralph, F. M.: Global Analysis of Climate Change Projection Effects on Atmospheric Rivers, Geophys. Res. Lett., 45, 4299–4308,, 2018. a
Short summary
Researchers utilize various algorithms to identify extreme weather features in climate data, and we seek to answer this question: given a plausible weather event detector, how does uncertainty in the detector impact scientific results? We generate a suite of statistical models that emulate expert identification of weather features. We find that the connection between El Niño and atmospheric rivers – a specific extreme weather type – depends systematically on the design of the detector.