Global evaluation of atmospheric river subseasonal prediction skill
Subseasonal-to-Seasonal (S2S) forecasts of weather and climate extremes are being increasingly demanded by water resource managers, operational forecasters, and other users in the applications community. This study uses hindcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) S2S forecast system to evaluate global subseasonal prediction skill of atmospheric rivers (ARs), which are intense lower tropospheric plumes of moisture transport that often project strongly onto extreme precipitation. An aggregate quantity is introduced to assess AR subseasonal prediction skill, defined as the number of AR days occurring over a week-long period (AR1wk occurrence). The observed pattern of seasonal mean AR1wk occurrence strongly resembles the general pattern of daily AR frequency. The ECMWF S2S forecast system generally shows positive (negative) biases relative to reanalysis in the mid-latitude regions in summer (winter) of up to 0.5–1.0 AR days in AR1wk occurrence in regions of highest AR activity. ECMWF AR1wk occurrence forecast skill outperforms a reference forecast based on monthly climatology of AR1wk occurrence at week-3 (14–20 days) lead over a number of subtropical to midlatitude regions, with slightly better skill evident in wintertime. The magnitude and subseasonal forecast skill of AR1wk occurrence are shown to vary interannually, and both quantities are modulated during certain phases of the El Niño–Southern Oscillation, Arctic Oscillation, Pacific–North America teleconnection pattern, and Madden–Julian Oscillation.
We gratefully acknowledge the availability of the S2S hindcast database which makes this work possible. S2S is a joint initiative of the World Weather Research Programme (WWRP) and the World Climate Research Program (WCRP). The original S2S database is hosted at ECMWF as an extension of the “TIGGE database”. We would like to acknowledge support from the NASA Energy and Water Cycle Program and the California Department of Water Resources. MD’s and DW’s contributions to this study were carried out on behalf of the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The authors thank Dillon Amaya (UCSD-Scripps) for assistance in obtaining ERSSTV3b data.
- Lavers DA, Waliser D, Ralph F, Dettinger M (2016a) Predictability of horizontal water vapor transport relative to precipitation: enhancing situational awareness for forecasting western US extreme precipitation and flooding. Geophys Res Lett 43:2275–2282. https://doi.org/10.1002/2016GL067765 CrossRefGoogle Scholar
- Lorenz E (1982) Atmospheric predictability experiments with a large numerical model. Tellus 34:505–513. https://doi.org/10.1111/j.2153-3490.1982.tb01839.x CrossRefGoogle Scholar
- National Academies of Sciences, Engineering, and Medicine (Baltimore) (2016) Next generation earth system prediction: strategies for subseasonal to seasonal forecasts. National Academies, WashingtonGoogle Scholar
- National Research Council (NRC) (2010) Assessment of intraseasonal to interannual climate prediction and predictability. The National Academies, Washington, p 192 (ISBN-10:0-309-15183-X) Google Scholar
- Neiman PJ, Ralph F, Wick G, Lundquist J, Dettinger M (2008b) Meteorological characteristics and overland precipitation impacts of atmospheric rivers affecting the west coast of North America based on eight years of SSM/I satellite observations. J Hydrometeorol 9(1):22–47. https://doi.org/10.1175/2007JHM855.1 CrossRefGoogle Scholar
- Wheeler MC, Hendon HH (2004) An all-season real-time multivariate MJO index: development of an index for monitoring and prediction. Mon Wea Rev 132:1917–1932. https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2 CrossRefGoogle Scholar