• Barth, N. A., G. Villarini, M. A. Nayak, and K. White, 2017: Mixed populations and annual flood frequency estimates in the western United States: The role of atmospheric rivers. Water Resour. Res., 53, 257269, https://doi.org/10.1002/2016WR019064.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., and Coauthors, 2020: SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection. J. Adv. Model. Earth Syst., 12, e2019MS001895, https://doi.org/10.1029/2019MS001895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., 2011: Climate change, atmospheric rivers, and floods in California—A multimodel analysis of storm frequency and magnitude changes. J. Amer. Water Resour. Assoc., 47, 514523, https://doi.org/10.1111/j.1752-1688.2011.00546.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M. D., 2013: Atmospheric rivers as drought busters on the U.S. West Coast. J. Hydrometeor., 14, 17211732, https://doi.org/10.1175/JHM-D-13-02.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunne, J. P., and Coauthors, 2020: The GFDL Earth System Model version 4.1 (GFDL-ESM4.1): Overall coupled model description and simulation characteristics. J. Adv. Model. Earth Syst., https://doi.org/10.1029/2019MS002015, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Espinoza, V., D. E. Waliser, B. Guan, D. A. Lavers, and F. M. Ralph, 2018: Global analysis of climate change projection effects on atmospheric rivers. Geophys. Res. Lett., 45, 42994308, https://doi.org/10.1029/2017GL076968.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. Meehl, C. A. Senior, B. Stevens, R. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Y., J. Lu, L. R. Leung, Q. Yang, S. Hagos, and Y. Qian, 2015: Dynamical and thermodynamical modulations on future changes of landfalling atmospheric rivers over western North America. Geophys. Res. Lett., 42, 71797186, https://doi.org/10.1002/2015GL065435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, Y., J. Lu, and L. R. Leung, 2016: Uncertainties in projecting future changes in atmospheric rivers and their impacts on heavy precipitation over Europe. J. Climate, 29, 67116726, https://doi.org/10.1175/JCLI-D-16-0088.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gorodetskaya, I. V., M. Tsukernik, K. Claes, M. F. Ralph, W. D. Neff, and N. P. M. V. Lipzig, 2014: The role of atmospheric rivers in anomalous snow accumulation in East Antarctica. Geophys. Res. Lett., 41, 61996206, https://doi.org/10.1002/2014GL060881.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., and D. E. Waliser, 2015: Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies. J. Geophys. Res. Atmos., 120, 12 51412 535, https://doi.org/10.1002/2015JD024257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guan, B., and D. E. Waliser, 2017: Atmospheric rivers in 20 year weather and climate simulations: A multimodel, global evaluation. J. Geophys. Res. Atmos., 122, 55565581, https://doi.org/10.1002/2016JD026174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haarsma, R. J., and Coauthors, 2016: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6. Geosci. Model Dev., 9, 41854208, https://doi.org/10.5194/gmd-9-4185-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S., L. R. Leung, J.-H. Yoon, J. Lu, and Y. Gao, 2016: A projection of changes in landfalling atmospheric river frequency and extreme precipitation over western North America from the large ensemble CESM simulations. Geophys. Res. Lett., 43, 13571363, https://doi.org/10.1002/2015GL067392.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, https://doi.org/10.1175/JCLI3990.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Held, I. M., and Coauthors, 2019: Structure and performance of GFDL’s CM4.0 climate model. J. Adv. Model. Earth Syst., 11, 36913727, https://doi.org/10.1029/2019MS001829.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2015: Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4). Part I: Upgrades and intercomparisons. J. Climate, 28, 911930, https://doi.org/10.1175/JCLI-D-14-00006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., and G. Villarini, 2013: The nexus between atmospheric rivers and extreme precipitation across Europe. Geophys. Res. Lett., 40, 32593264, https://doi.org/10.1002/grl.50636.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., and G. Villarini, 2015: The contribution of atmospheric rivers to precipitation in Europe and the United States. J. Hydrol., 522, 382390, https://doi.org/10.1016/j.jhydrol.2014.12.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lavers, D. A., R. P. Allan, G. Villarini, B. Lloyd-Hughes, D. J. Brayshaw, and A. J. Wade, 2013: Future changes in atmospheric rivers and their implications for winter flooding in Britain. Environ. Res. Lett., 8, 034010, https://doi.org/10.1088/1748-9326/8/3/034010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nayak, M. A., and G. Villarini, 2017: A long-term perspective of the hydroclimatological impacts of atmospheric rivers over the central United States. Water Resour. Res., 53, 11441166, https://doi.org/10.1002/2016WR019033.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neff, W., G. P. Compo, F. M. Ralph, and M. D. Shupe, 2014: Continental heat anomalies and the extreme melting of the Greenland ice surface in 2012 and 1889. J. Geophys. Res. Atmos., 119, 65206536, https://doi.org/10.1002/2014JD021470.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, G. A. Wick, Y. Kuo, T. Wee, Z. Ma, G. H. Taylor, and M. D. Dettinger, 2008: Diagnosis of an intense atmospheric river impacting the Pacific Northwest: Storm summary and offshore vertical structure observed with COSMIC satellite retrievals. Mon. Wea. Rev., 136, 43984420, https://doi.org/10.1175/2008MWR2550.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., L. J. Schick, F. M. Ralph, M. Hughes, and G. A. Wick, 2011: Flooding in western Washington: The connection to atmospheric rivers. J. Hydrometeor., 12, 13371358, https://doi.org/10.1175/2011JHM1358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Payne, A. E., and G. Magnusdottir, 2015: An evaluation of atmospheric rivers over the North Pacific in CMIP5 and their response to warming under RCP 8.5. J. Geophys. Res. Atmos., 120, 11 17311 190, https://doi.org/10.1002/2015JD023586.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Payne, A. E., and Coauthors, 2020: Responses and impacts of atmospheric rivers to climate change. Nat. Rev. Earth Environ., 1, 143157, https://doi.org/10.1038/s43017-020-0030-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pierce, D. W., and Coauthors, 2013: The key role of heavy precipitation events in climate model disagreements of future annual precipitation changes in California. J. Climate, 26, 58795896, https://doi.org/10.1175/JCLI-D-12-00766.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Radić, V., A. J. Cannon, B. Menounos, and N. Gi, 2015: Future changes in autumn atmospheric river events in British Columbia, Canada, as projected by CMIP5 global climate models. J. Geophys. Res. Atmos., 120, 92799302, https://doi.org/10.1002/2015JD023279.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, and G. A. Wick, 2004: Satellite and CALJET aircraft observations of atmospheric rivers over the eastern North Pacific Ocean during the winter of 1997/98. Mon. Wea. Rev., 132, 17211745, https://doi.org/10.1175/1520-0493(2004)132<1721:SACAOO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., P. J. Neiman, G. A. Wick, S. I. Gutman, M. D. Dettinger, D. R. Cayan, and A. B. White, 2006: Flooding on California’s Russian River: Role of atmospheric rivers. Geophys. Res. Lett., 33, L13801, https://doi.org/10.1029/2006GL026689.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., J. J. Rutz, J. M. Cordeira, M. Dettinger, M. Anderson, D. Reynolds, L. J. Schick, and C. Smallcomb, 2019: A scale to characterize the strength and impacts of atmospheric rivers. Bull. Amer. Meteor. Soc., 100, 269289, https://doi.org/10.1175/BAMS-D-18-0023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramos, A. M., R. Tome, R. M. Trigo, M. L. R. Liberato, and J. G. Pinto, 2016: Projected changes in atmospheric rivers affecting Europe in CMIP5 models. Geophys. Res. Lett., 43, 93159323, https://doi.org/10.1002/2016GL070634.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rutz, J. J., W. J. Steenburgh, and F. M. Ralph, 2014: Climatological characteristics of atmospheric rivers and their inland penetration over the western United States. Mon. Wea. Rev., 142, 905921, https://doi.org/10.1175/MWR-D-13-00168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shields, C. A., and J. T. Kiehl, 2016a: Atmospheric river landfall-latitude changes in future climate simulations. Geophys. Res. Lett., 43, 87758782, https://doi.org/10.1002/2016GL070470.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shields, C. A., and J. T. Kiehl, 2016b: Simulating the Pineapple Express in the half degree Community Climate System Model, CCSM4. Geophys. Res. Lett., 43, 77677773, https://doi.org/10.1002/2016GL069476.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shields, C. A., and Coauthors, 2018: Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project goals and experimental design. Geosci. Model Dev., 11, 24552474, https://doi.org/10.5194/gmd-11-2455-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., I. M. Held, and S.-J. Lin, 2012: Some counterintuitive dependencies of tropical cyclone frequency on parameters in a GCM. J. Atmos. Sci., 69, 22722283, https://doi.org/10.1175/JAS-D-11-0238.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., and Coauthors, 2018a: The GFDL global atmosphere and land model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTs. J. Adv. Model. Earth Syst., 10, 691734, https://doi.org/10.1002/2017MS001208.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., and Coauthors, 2018b: The GFDL global atmosphere and land model AM4.0/LM4.0: 2. Model description, sensitivity studies, and tuning strategies. J. Adv. Model. Earth Syst., 10, 735769, https://doi.org/10.1002/2017MS001209.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., and R. E. Newell, 1998: A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Wea. Rev., 126, 725735, https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Simulations of Atmospheric Rivers, Their Variability, and Response to Global Warming Using GFDL’s New High-Resolution General Circulation Model

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  • 1 Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
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Abstract

A 50-km-resolution GFDL AM4 well captures many aspects of observed atmospheric river (AR) characteristics including the probability density functions of AR length, width, length–width ratio, geographical location, and the magnitude and direction of AR mean vertically integrated vapor transport (IVT), with the model typically producing stronger and narrower ARs than the ERA-Interim results. Despite significant regional biases, the model well reproduces the observed spatial distribution of AR frequency and AR variability in response to large-scale circulation patterns such as El Niño–Southern Oscillation (ENSO), the Northern and Southern Hemisphere annular modes (NAM and SAM), and the Pacific–North American (PNA) teleconnection pattern. For global warming scenarios, in contrast to most previous studies that show a large increase in AR length and width and therefore the occurrence frequency of AR conditions at a given location, this study shows only a modest increase in these quantities. However, the model produces a large increase in strong ARs with the frequency of category 3–5 ARs rising by roughly 100%–300% K−1. The global mean AR intensity as well as AR intensity percentiles at most percent ranks increases by 5%–8% K−1, roughly consistent with the Clausius–Clapeyron scaling of water vapor. Finally, the results point out the importance of AR IVT thresholds in quantifying modeled AR response to global warming.

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ming Zhao, ming.zhao@noaa.gov

Abstract

A 50-km-resolution GFDL AM4 well captures many aspects of observed atmospheric river (AR) characteristics including the probability density functions of AR length, width, length–width ratio, geographical location, and the magnitude and direction of AR mean vertically integrated vapor transport (IVT), with the model typically producing stronger and narrower ARs than the ERA-Interim results. Despite significant regional biases, the model well reproduces the observed spatial distribution of AR frequency and AR variability in response to large-scale circulation patterns such as El Niño–Southern Oscillation (ENSO), the Northern and Southern Hemisphere annular modes (NAM and SAM), and the Pacific–North American (PNA) teleconnection pattern. For global warming scenarios, in contrast to most previous studies that show a large increase in AR length and width and therefore the occurrence frequency of AR conditions at a given location, this study shows only a modest increase in these quantities. However, the model produces a large increase in strong ARs with the frequency of category 3–5 ARs rising by roughly 100%–300% K−1. The global mean AR intensity as well as AR intensity percentiles at most percent ranks increases by 5%–8% K−1, roughly consistent with the Clausius–Clapeyron scaling of water vapor. Finally, the results point out the importance of AR IVT thresholds in quantifying modeled AR response to global warming.

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ming Zhao, ming.zhao@noaa.gov
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