Insights into the Causes and Predictability of the 2022/23 California Flooding

Siegfried D. Schubert aGlobal Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland
bScience Systems and Applications, Inc., Lanham, Maryland

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Yehui Chang aGlobal Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland
cGoddard Earth Sciences Technology and Research II, Morgan State University, Baltimore, Maryland

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Anthony M. DeAngelis aGlobal Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland
bScience Systems and Applications, Inc., Lanham, Maryland

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Young-Kwon Lim aGlobal Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland
dGoddard Earth Sciences Technology and Research II, University of Maryland Baltimore County, Baltimore, Maryland

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Natalie P. Thomas aGlobal Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland
dGoddard Earth Sciences Technology and Research II, University of Maryland Baltimore County, Baltimore, Maryland

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Randal D. Koster aGlobal Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland

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Michael G. Bosilovich aGlobal Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland

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Andrea M. Molod aGlobal Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland

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Allison Collow aGlobal Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland
dGoddard Earth Sciences Technology and Research II, University of Maryland Baltimore County, Baltimore, Maryland

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Amin Dezfuli aGlobal Modeling and Assimilation Office, NASA GSFC, Greenbelt, Maryland
dGoddard Earth Sciences Technology and Research II, University of Maryland Baltimore County, Baltimore, Maryland

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Abstract

In late December of 2022 and the first half of January 2023, an unprecedented series of atmospheric rivers (ARs) produced near-record heavy rains and flooding over much of California. Here, we employ the NASA GEOS AGCM run in a “replay” mode, together with more idealized simulations with a stationary wave model, to identify the remote forcing regions, mechanisms, and underlying predictability of this flooding event. In particular, the study addresses the underlying causes of a persistent positive Pacific–North American (PNA)-like circulation pattern that facilitated the development of the ARs. We show that the pattern developed in late December as a result of vorticity forcing in the North Pacific jet exit region. We further provide evidence that this vorticity forcing was the result of a chain of events initiated in mid-December with the development of a Rossby wave (as a result of forcing linked to the MJO) that propagated from the northern Indian Ocean into the North Pacific. As such, both the initiation of the event and the eventual development of the PNA depended critically on internally generated Rossby wave forcings, with the North Pacific jet playing a key role. This, combined with contemporaneous SST (La Niña) forcing that produced a circulation response in the AGCM that was essentially opposite to the positive PNA, underscores the fundamental lack of predictability of the event at seasonal time scales. Forecasts produced with the GEOS-coupled model suggest that useful skill in predicting the PNA and extreme precipitation over California was in fact limited to lead times shorter than about 3 weeks.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Siegfried D. Schubert, siegschu2002@yahoo.com

Abstract

In late December of 2022 and the first half of January 2023, an unprecedented series of atmospheric rivers (ARs) produced near-record heavy rains and flooding over much of California. Here, we employ the NASA GEOS AGCM run in a “replay” mode, together with more idealized simulations with a stationary wave model, to identify the remote forcing regions, mechanisms, and underlying predictability of this flooding event. In particular, the study addresses the underlying causes of a persistent positive Pacific–North American (PNA)-like circulation pattern that facilitated the development of the ARs. We show that the pattern developed in late December as a result of vorticity forcing in the North Pacific jet exit region. We further provide evidence that this vorticity forcing was the result of a chain of events initiated in mid-December with the development of a Rossby wave (as a result of forcing linked to the MJO) that propagated from the northern Indian Ocean into the North Pacific. As such, both the initiation of the event and the eventual development of the PNA depended critically on internally generated Rossby wave forcings, with the North Pacific jet playing a key role. This, combined with contemporaneous SST (La Niña) forcing that produced a circulation response in the AGCM that was essentially opposite to the positive PNA, underscores the fundamental lack of predictability of the event at seasonal time scales. Forecasts produced with the GEOS-coupled model suggest that useful skill in predicting the PNA and extreme precipitation over California was in fact limited to lead times shorter than about 3 weeks.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Siegfried D. Schubert, siegschu2002@yahoo.com

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  • Bosilovich, M. G., R. Lucchesi, and M. Suarez, 2016: MERRA-2: File specification. GMAO Office Note 9 (version 1.1), 81 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf.

  • Chang, Y., S. D. Schubert, R. D. Koster, A. M. Molod, and H. Wang, 2019: Tendency bias correction in coupled and uncoupled global climate models with a focus on impacts over North America. J. Climate, 32, 639661, https://doi.org/10.1175/JCLI-D-18-0598.1.

    • Search Google Scholar
    • Export Citation
  • Chen, J., R. Li, S. Xie, J. Wei, and J. Shi, 2023: Characteristics and mechanisms of long-lasting 2021–2022 summer Northeast Pacific warm blobs. Front. Mar. Sci., 10, 1158932, https://doi.org/10.3389/fmars.2023.1158932.

    • Search Google Scholar
    • Export Citation
  • Collow, A. B. M., H. Mersiovsky, and M. G. Bosilovich, 2020: Large-scale influences on atmospheric river–induced extreme precipitation events along the coast of Washington State. J. Hydrometeor., 21, 21392156, https://doi.org/10.1175/JHM-D-19-0272.1.

    • Search Google Scholar
    • Export Citation
  • DeAngelis, A. M., S. D. Schubert, Y. Chang, Y.-K. Lim, R. D. Koster, H. Wang, and A. B. Marquardt Collow, 2023: Dynamical drivers of the exceptional warmth over Siberia during the spring of 2020. J. Climate, 36, 48374861, https://doi.org/10.1175/JCLI-D-22-0387.1.

    • Search Google Scholar
    • Export Citation
  • DeFlorio, M. J., and Coauthors, 2023: From California’s extreme drought to major flooding: Evaluating and synthesizing experimental seasonal and subseasonal forecasts of landfalling atmospheric rivers and extreme precipitation during winter 2022/23. Bull. Amer. Meteor. Soc., 105, E84E104, https://doi.org/10.1175/BAMS-D-22-0208.1.

    • Search Google Scholar
    • Export Citation
  • Fish, M. A., J. M. Done, D. L. Swain, A. M. Wilson, A. C. Michaelis, P. B. Gibson, and F. M. Ralph, 2022: Large-scale environments of successive atmospheric river events leading to compound precipitation extremes in California. J. Climate, 35, 15151536, https://doi.org/10.1175/JCLI-D-21-0168.1.

    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Search Google Scholar
    • Export Citation
  • Gimeno, L., R. Nieto, M. Vázquez, and D. A. Lavers, 2014: Atmospheric rivers: A mini-review. Front. Earth Sci., 2, 2, https://doi.org/10.3389/feart.2014.00002.

    • Search Google Scholar
    • Export Citation
  • GMAO, 2015a: MERRA-2 tavg1_2d_slv_Nx: 2d, 1-hourly, time-averaged, single-level, assimilation, single-level diagnostics, V5.12.4. Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), accessed 1 February 2023, https://doi.org/10.5067/VJAFPLI1CSIV.

  • GMAO, 2015b: MERRA-2 tavg1_2d_flx_Nx: 2d, 1-hourly, time-averaged, single-level, assimilation, surface flux diagnostics, V5.12.4. Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), accessed 1 February 2023, https://doi.org/10.5067/7MCPBJ41Y0K6.

  • GMAO, 2015c: MERRA-2 inst3_3d_asm_Np: 3d, 3-hourly, instantaneous, pressure-level, assimilation, assimilated meteorological fields, V5.12.4. Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), accessed 1 February 2023, https://doi.org/10.5067/QBZ6MG944HW0.

  • GMAO, 2015d: MERRA-2 tavg3_3d_tdt_Np: 3d, 3-hourly, time-averaged, pressure-level, assimilation, temperature tendencies, V5.12.4. Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), accessed 1 February 2023, https://doi.org/10.5067/9NCR9DDDOPFI.

  • Goss, M., S. Lee, S. B. Feldstein, and N. S. Diffenbaugh, 2018: Can ENSO-like convection force an ENSO-like extratropical response on subseasonal time scales? J. Climate, 31, 83398349, https://doi.org/10.1175/JCLI-D-17-0771.1.

    • Search Google Scholar
    • Export Citation
  • Griffies, S. M., 2012: Elements of the Modular Ocean Model (MOM). GFDL Ocean Group Tech. Rep. 7, NOAA/GFDL, 645 pp., https://mom-ocean.github.io/assets/pdfs/MOM5_manual.pdf.

  • Griffies, S. M., and Coauthors, 2005: Formulation of an ocean model for global climate simulations. Ocean Sci., 1, 4579, https://doi.org/10.5194/os-1-45-2005.

    • Search Google Scholar
    • Export Citation
  • Guirguis, K., A. Gershunov, T. Shulgina, R. E. S. Clemesha, and F. M. Ralph, 2019: Atmospheric rivers impacting Northern California and their modulation by a variable climate. Climate Dyn., 52, 65696583, https://doi.org/10.1007/s00382-018-4532-5.

    • Search Google Scholar
    • Export Citation
  • Jong, B.-T., M. Ting, and R. Seager, 2016: El Niño’s impact on California precipitation: Seasonality, regionality, and El Niño intensity. Environ. Res. Lett., 11, 054021, https://doi.org/10.1088/1748-9326/11/5/054021.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., M. J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar, 2000: A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure. J. Geophys. Res., 105, 24 80924 822, https://doi.org/10.1029/2000JD900327.

    • Search Google Scholar
    • Export Citation
  • Liu, A. Z., M. Ting, and H. Wang, 1998: Maintenance of circulation anomalies during the 1988 drought and 1993 floods over the United States. J. Atmos. Sci., 55, 28102832, https://doi.org/10.1175/1520-0469(1998)055<2810:MOCADT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mariotti, A., and Coauthors, 2020: Windows of opportunity for skillful forecasts subseasonal to seasonal and beyond. Bull. Amer. Meteor. Soc., 101, E608E625, https://doi.org/10.1175/BAMS-D-18-0326.1.

    • Search Google Scholar
    • Export Citation
  • Molod, A., L. Takacs, M. Suarez, and J. Bacmeister, 2015: Development of the GEOS-5 atmospheric general circulation model: Evolution from MERRA to MERRA2. Geosci. Model Dev., 8, 13391356, https://doi.org/10.5194/gmd-8-1339-2015.

    • Search Google Scholar
    • Export Citation
  • Molod, A., and Coauthors, 2020: GEOS-S2S version 2: The GMAO high-resolution coupled model and assimilation system for seasonal prediction. J. Geophy. Res. Atmos., 125, e2019JD031767, https://doi.org/10.1029/2019JD031767.

    • Search Google Scholar
    • Export Citation
  • Pegion, K., and Coauthors, 2019: The subseasonal experiment (SubX): A multi-model subseasonal prediction experiment. Bull. Amer. Meteor. Soc., 100, 20432060, https://doi.org/10.1175/BAMS-D-18-0270.1.

    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., Q. Liu, R. D. Koster, C. S. Draper, S. P. P. Mahanama, and G. S. Partyka, 2017: Land surface precipitation in MERRA-2. J. Climate, 30, 16431664, https://doi.org/10.1175/JCLI-D-16-0570.1.

    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2008: The GEOS-5 data assimilation system: Documentation of versions 5.0.1 and 5.1.0, and 5.2.0. NASA Tech. Rep. NASA/TM-2008-104606, 118 pp., https://ntrs.nasa.gov/api/citations/20120011955/downloads/20120011955.pdf.

  • Sardeshmukh, P. D., and B. J. Hoskins, 1988: The generation of global rotational flow by steady idealized tropical divergence. J. Atmos. Sci., 45, 12281251, https://doi.org/10.1175/1520-0469(1988)045<1228:TGOGRF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schubert, S., H. Wang, and M. Suarez, 2011: Warm season subseasonal variability and climate extremes in the Northern Hemisphere: The role of stationary Rossby waves. J. Climate, 24, 47734792, https://doi.org/10.1175/JCLI-D-10-05035.1.

    • Search Google Scholar
    • Export Citation
  • Schubert, S. D., M. J. Suarez, Y. Chang, and G. Branstator, 2001: The impact of ENSO on extratropical low-frequency noise in seasonal forecasts. J. Climate, 14, 23512365, https://doi.org/10.1175/1520-0442(2001)014<2351:TIOEOE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schubert, S. D., A. Borovikov, Y.-K. Lim, and A. Molod, 2019a: Ensemble generation strategies employed in the GMAO GEOS-S2S forecast system. NASA Tech. Rep. NASA/TM-2019-104606/Vol. 53, 75 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Schubert1183.pdf.

  • Schubert, S. D., Y. Chang, H. Wang, R. D. Koster, and A. M. Molod, 2019b: A systematic approach to assessing the sources and global impacts of errors in climate models. J. Climate, 32, 83018321, https://doi.org/10.1175/JCLI-D-19-0189.1.

    • Search Google Scholar
    • Export Citation
  • Schubert, S. D., Y. Chang, A. M. DeAngelis, H. Wang, and R. D. Koster, 2021: On the development and demise of the fall 2019 Southeast U.S. flash drought: Links to an extreme positive IOD. J. Climate, 34, 17011723, https://doi.org/10.1175/JCLI-D-20-0428.1.

    • Search Google Scholar
    • Export Citation
  • Schubert, S. D., Y. Chang, A. M. DeAngelis, R. D. Koster, Y.-K. Lim, and H. Wang, 2022: Exceptional warmth in the Northern Hemisphere during January–March of 2020: The roles of unforced and forced modes of atmospheric variability. J. Climate, 35, 25652584, https://doi.org/10.1175/JCLI-D-21-0291.1.

    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., J. M. Wallace, and G. W. Branstator, 1983: Barotropic wave propagation and instability, and atmospheric teleconnection patterns. J. Atmos. Sci., 40, 13631392, https://doi.org/10.1175/1520-0469(1983)040<1363:BWPAIA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Straus, D. M., and J. Shukla, 2002: Does ENSO force the PNA? J. Climate, 15, 23402358, https://doi.org/10.1175/1520-0442(2002)015<2340:DEFTP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ting, M., and L. Yu, 1998: Steady response to tropical heating in wavy linear and nonlinear baroclinic models. J. Atmos. Sci., 55, 35653582, https://doi.org/10.1175/1520-0469(1998)055<3565:SRTTHI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Toride, K., and G. J. Hakim, 2022: What distinguishes MJO events associated with atmospheric rivers? J. Climate, 35, 61356149, https://doi.org/10.1175/JCLI-D-21-0493.1.

    • Search Google Scholar
    • Export Citation
  • Uccellini, L. W., and D. R. Johnson, 1979: The coupling of upper and lower tropospheric jet streaks and implications for the development of severe convective storms. Mon. Wea. Rev., 107, 682703, https://doi.org/10.1175/1520-0493(1979)107<0682:TCOUAL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vitart, F., 2017: Madden—Julian oscillation prediction and teleconnections in the S2S database. Quart. J. Roy. Meteor. Soc., 143, 22102220, https://doi.org/10.1002/qj.3079.

    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, B., F. Liu, and G. Chen, 2016: A trio-interaction theory for Madden–Julian oscillation. Geosci. Lett., 3, 34, https://doi.org/10.1186/s40562-016-0066-z.

    • Search Google Scholar
    • Export Citation
  • Wang, H., and M. Ting, 1999: Seasonal cycle of the climatological stationary waves in the NCEP–NCAR reanalysis. J. Atmos. Sci., 56, 38923919, https://doi.org/10.1175/1520-0469(1999)056<3892:SCOTCS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, J., H. Kim, D. Kim, S. A. Henderson, C. Stan, and E. D. Maloney, 2020: MJO teleconnections over the PNA region in climate models. Part I: Performance- and process-based skill metrics. J. Climate, 33, 10511067, https://doi.org/10.1175/JCLI-D-19-0253.1.

    • Search Google Scholar
    • Export Citation
  • Zhou, Y., H. Kim, and D. E. Waliser, 2021: Atmospheric river lifecycle responses to the Madden-Julian oscillation. Geophys. Res. Lett., 48, e2020GL090983, https://doi.org/10.1029/2020GL090983.

    • Search Google Scholar
    • Export Citation
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