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Global evaluation of atmospheric river subseasonal prediction skill

  • Michael J. DeFlorio
  • Duane E. Waliser
  • Bin Guan
  • F. Martin Ralph
  • Frédéric Vitart
Article
  • 163 Downloads

Abstract

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.

Notes

Acknowledgements

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.

Supplementary material

382_2018_4309_MOESM1_ESM.docx (4 mb)
Supplementary material 1 (DOCX 4117 KB)

References

  1. Baggett CF, Barnes E, Maloney E, Mundhenk B (2017) Advancing atmospheric river forecasts into subseasonal-to-seasonal time scales. Geophys Res Lett 44:7528–7536.  https://doi.org/10.1002/2017GL074434 CrossRefGoogle Scholar
  2. Becker E, H den Dool Q, Van Zhang (2014) Predictability and forecast skill in NMME. J Clim 27:5891–5906.  https://doi.org/10.1175/JCLI-D-13-00597.1 CrossRefGoogle Scholar
  3. Cordeira JM, Ralph F, Moore B (2013) The development and evolution of two atmospheric rivers in proximity to western North Pacific tropical cyclones in October 2010. Mon Weather Rev 141:4234–4255.  https://doi.org/10.1175/MWR-D-13-00019.1 CrossRefGoogle Scholar
  4. Dee D et al (2011a) The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597.  https://doi.org/10.1002/qj.828 CrossRefGoogle Scholar
  5. DeFlorio MJ, Waliser D, Guan B, Lavers D, Ralph F, Vitart F (2018) Global assessment of atmospheric river prediction skill. J Hydrometeorol 19:409–426.  https://doi.org/10.1175/JHM-D-17-0135.1 CrossRefGoogle Scholar
  6. Dettinger MD, Ralph F, Das T, Neiman P, Cayan D (2011) Atmospheric rivers, floods and the water resources of California. Water 3:445–478.  https://doi.org/10.3390/w3020445 CrossRefGoogle Scholar
  7. Eiras-Barca J, Brands S, Miguez-Macho G (2016) Seasonal variations in North Atlantic atmospheric river activity and associations with anomalous precipitation over the Iberian Atlantic Margin. J Geophys Res Atmos 121:931–948.  https://doi.org/10.1002/2015JD023379 CrossRefGoogle Scholar
  8. Gershunov A, Shulgina T, Ralph F, Lavers D, Rutz J (2017) Assessing the climate-scale variability of atmospheric rivers affecting western North America. Geophys Res Lett 44:7900–7908.  https://doi.org/10.1002/2017GL074175 CrossRefGoogle Scholar
  9. Gorodetskaya IV, Tsukernik M, Claes K, Ralph F, Neff W, Van Lipzig N (2014) The role of atmospheric rivers in anomalous snow accumulation in East Antarctica. Geophys Res Lett 41:6199–6206.  https://doi.org/10.1002/2014GL060881 CrossRefGoogle Scholar
  10. Guan B, Waliser D (2015) Detection of atmospheric rivers: evaluation and application of an algorithm for global studies. J Geophys Res Atmos 120:12514–512535.  https://doi.org/10.1002/2015JD024257 CrossRefGoogle Scholar
  11. Guan B, Molotch N, Waliser D, Fetzer E, Neiman PJ (2010) Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements. Geophys Res Lett 37:L20401.  https://doi.org/10.1029/2010GL044696 CrossRefGoogle Scholar
  12. Guan B, Waliser D, Molotch N, Fetzer E, Neiman P (2012) Does the Madden–Julian Oscillation influence wintertime atmospheric rivers and snowpack in the Sierra Nevada? Mon Weather Rev 140:325–342.  https://doi.org/10.1175/MWR-D-11-00087.1 CrossRefGoogle Scholar
  13. Guan B, Molotch N, Waliser D, Fetzer E, Neiman P (2013) The 2010/2011 snow season in California’s Sierra Nevada: role of atmospheric rivers and modes of large-scale variability. Water Resour Res 49:6731–6743.  https://doi.org/10.1002/wrcr.20537 CrossRefGoogle Scholar
  14. Hatchett BJ, Burak S, Rutz J, Oakley N, Bair E, Kaplan M (2017) Avalanche fatalities during atmospheric river events in the western United States. J Hydrometeorol 18(5):1359–1374.  https://doi.org/10.1175/JHM-D-16-0219.1 CrossRefGoogle Scholar
  15. Hauke J, Kossowski T (2011) Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaest Geogr 30(2):87–93.  https://doi.org/10.2478/v10117-011-0021-1 Google Scholar
  16. Hu H, Dominguez F, Wang Z, Lavers D, Zhang G, Ralph F (2017) Linking atmospheric river hydrological impacts on the US West Coast to Rossby wave breaking. J Clim.  https://doi.org/10.1175/JCLI-D-16-0386.1 Google Scholar
  17. Huang B, Thorne PW, Banzon VF et al (2017) Extended reconstructed sea surface temperature version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J Clim 30:8179–8205.  https://doi.org/10.1175/JCLI-D-16-0836.1 CrossRefGoogle Scholar
  18. Kamae Y, Mei W, Xie S-P, Naoi M, Ueda H (2017) Atmospheric rivers over the Northwestern Pacific: climatology and interannual variability. J Clim.  https://doi.org/10.1175/JCLI-D-16-0875.1 Google Scholar
  19. Khouakhi A, Villarini G (2016) On the relationship between atmospheric rivers and high sea water levels along the US West Coast. Geophys Res Lett 43:8815–8822.  https://doi.org/10.1002/2016GL070086 CrossRefGoogle Scholar
  20. Kim J, Waliser D, Neiman P, Guan B, Ryoo JM, G Wick (2013a) Effects of atmospheric river landfalls on the cold season precipitation in California. Clim Dyn 40:465.  https://doi.org/10.1007/s00382-012-1322-3 CrossRefGoogle Scholar
  21. Lavers DA, Villarini G (2013) The nexus between atmospheric rivers and extreme precipitation across Europe. Geophys Res Lett 40:3259–3264.  https://doi.org/10.1002/grl.50636 CrossRefGoogle Scholar
  22. Lavers DA, Pappenberger F, Zsoter E (2014) Extending medium-range predictability of extreme hydrological events in Europe. Nat Commun 5:5382.  https://doi.org/10.1038/ncomms6382 CrossRefGoogle Scholar
  23. 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
  24. Leung LR, Qian Y (2009) Atmospheric rivers induced heavy precipitation and flooding in the western US simulated by the WRF regional climate model. Geophys Res Lett 36:L03820.  https://doi.org/10.1029/2008GL036445 CrossRefGoogle Scholar
  25. Liu X, Ren X, Yang X-Q (2016) Decadal changes in multiscale water vapor transport and atmospheric river associated with the Pacific Decadal Oscillation and the North Pacific Gyre Oscillation. J Hydrometeorol.  https://doi.org/10.1175/JHM-D-14-0195.1 Google Scholar
  26. Liu X et al (2017) MJO Prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center. Clim Dyn 48:3283–3307.  https://doi.org/10.1007/s00382-016-3264-7 CrossRefGoogle Scholar
  27. 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
  28. Mahoney K, Jackson D, Neiman P, Hughes M, Darby L, Wick G, White A, Sukovich E, Cifelli R (2016) Understanding the role of atmospheric rivers in heavy precipitation in the southeast United States. Mon Weather Rev 144(4):1617–1632.  https://doi.org/10.1175/MWR-D-15-0279.1 CrossRefGoogle Scholar
  29. Mundhenk B, Barnes E, Maloney E (2016) All-season climatology and variability of atmospheric river frequencies over the North Pacific. J Clim 29 4885–4903.  https://doi.org/10.1175/JCLI-D-15-0655.1 CrossRefGoogle Scholar
  30. Mundhenk B, Barnes E, Maloney E, Baggett C (2018) Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden–Julian oscillation and quasi-biennial oscillation. NPJ Clim Atmos Sci 1:7.  https://doi.org/10.1038/s41612-017-0008-2 CrossRefGoogle Scholar
  31. National Academies of Sciences, Engineering, and Medicine (Baltimore) (2016) Next generation earth system prediction: strategies for subseasonal to seasonal forecasts. National Academies, WashingtonGoogle Scholar
  32. 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
  33. Nayak MA, Villarini G (2017) A long-term perspective of the hydroclimatological impacts of atmospheric rivers over the central United States. Water Resour Res 53:1144–1166.  https://doi.org/10.1002/2016WR019033 CrossRefGoogle Scholar
  34. Nayak MA, Villarini G, Lavers D (2014) On the skill of numerical weather prediction models to forecast atmospheric rivers over the central United States. Geophys Res Lett 41:4354–4362.  https://doi.org/10.1002/2014GL060299 CrossRefGoogle Scholar
  35. Neff W, Compo G, Ralph F, Shupe M (2014) Continental heat anomalies and the extreme melting of the Greenland ice surface in 2012 and 1889. J Geophys Res Atmos 119:6520–6536.  https://doi.org/10.1002/2014JD021470 CrossRefGoogle Scholar
  36. 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
  37. Neiman PJ, Schick LJ, Ralph F, Hughes M, Wick G (2011) Flooding in western Washington: the connection to atmospheric rivers. J Hydrometeorol 12:1337–1358.  https://doi.org/10.1175/2011JHM1358.1 CrossRefGoogle Scholar
  38. Oakley NS, Lancaster J, Kaplan M, Ralph F (2017) Synoptic conditions associated with cool season post-fire debris flows in the transverse ranges of southern California. Nat Hazards 88:327–354.  https://doi.org/10.1007/s11069-017-2867-6 CrossRefGoogle Scholar
  39. Osman M, Alvarez M (2017) Subseasonal prediction of the heat wave of December 2013 in Southern South America by the POAMA and BCC-CPS models. Clim Dyn.  https://doi.org/10.1007/s00382-016-3474-z Google Scholar
  40. Paltan H, Waliser D, Lim W, Guan B, Yamazaki D, Pant R, Dadson S (2017) Global floods and water availability driven by atmospheric rivers. Geophys Res Lett 44:10387–10395.  https://doi.org/10.1002/2017GL074882 CrossRefGoogle Scholar
  41. Ralph FM, P Neiman, G Wick (2004) Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North-Pacific Ocean during the El Niño winter of 1997/98. Mon Weather Rev 132 1721–1745. https://doi.org/10.1175/1520-0493(2004)132<1721:SACAOO>2.0.CO;2CrossRefGoogle Scholar
  42. Vitart F (2017) Madden–Julian Oscillation prediction and teleconnections in the S2S database. QJR Meteorol Soc 143:2210–2220.  https://doi.org/10.1002/qj.3079 CrossRefGoogle Scholar
  43. Vitart F et al (2017) The subseasonal to seasonal (S2S) prediction project database. Bull Am Meteor Soc 98(1):163–176.  https://doi.org/10.1175/BAMS-D-16-0017.1 CrossRefGoogle Scholar
  44. Waliser DE, Guan B (2017) Extreme winds and precipitation during landfall of atmospheric rivers. Nat Geosci 10:179–183.  https://doi.org/10.1038/ngeo2894 CrossRefGoogle Scholar
  45. Wang X, Zheng Z, Feng G (2018) Effects of air-sea interaction on extended-range prediction of geopotential height at 500 hPa over the northern extratropical region. J Theor Appl Climatol 132:31.  https://doi.org/10.1007/s00704-017-2071-3 CrossRefGoogle Scholar
  46. 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
  47. Wick GA, Neiman P, Ralph F, Hamill T (2013) Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models. Weather Forecast 28(6):1337–1352.  https://doi.org/10.1175/WAF-D-13-00025.1 CrossRefGoogle Scholar
  48. Yang Y, Zhao T, Ni G, Sun T (2018) Atmospheric rivers over the Bay of Bengal lead to northern Indian extreme rainfall. Int J Climatol 38:1010–1021.  https://doi.org/10.1002/joc.5229 CrossRefGoogle Scholar
  49. Zhu Y, R Newell (1998) A proposed algorithm for moisture fluxes from atmospheric rivers. Mon Weather Rev 126 725–735. https://doi.org/10.1175/15200493(1998)126<0725:APAFMF>2.0.CO;2CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  1. 1.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  2. 2.Joint Institute for Regional Earth System Science and EngineeringUniversity of California, Los AngelesLos AngelesUSA
  3. 3.Center for Western Weather and Water Extremes, Scripps Institution of OceanographyUniversity of California, San DiegoLa JollaUSA
  4. 4.European Centre for Medium-Range Weather ForecastsReadingUK

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