• Ban, N., J. Schmidli, and C. Schar, 2015: Heavy precipitation in a changing climate: Does short term summer precipitation increase faster? Geophys. Res. Lett., 42, 11651172, https://doi.org/10.1002/2014GL062588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., and Coauthors, 2019: Daily evaluation of 26 precipitation datasets using Stage-IV gauge radar data for the CONUS. Hydrol. Earth Syst. Sci., 23, 207224, https://doi.org/10.5194/hess-23-207-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corringham, T. W., F. M. Ralph, A. Gershunov, D. R. Cayan, and C. A. Talbot, 2019: Atmospheric rivers drive flood damages in the western United States. Sci. Adv., 5, eaax4631, https://doi.org/10.1126/sciadv.aax4631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., R. M. Rasmussen, C. Liu, K. Ikeda, and A. F. Prein, 2017: A new mechanism for warm season precipitation response to global warming based on convection-permitting simulations. Climate Dyn., 55, 343368, https://doi.org/10.1007/S00382-017-3787-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical–topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Das, T. M., M. D. Dettinger, D. R. Cayan, and H. G. Hidalog, 2011: Potential increase in floods in California’s Sierra Nevada under future climate projections. Climatic Change, 109, 7194, https://doi.org/10.1007/S10584-011-0298-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davenport, F. V., J. E. Herrera-Estrada, M. Burke, and N. S. Diffenbaugh, 2020: Flood size increases nonlinearly across the western United States in response to lower snow precipitation ratios. Water Resour. Res., 56, e2019WR025571, https://doi.org/10.1029/2019WR025571.

    • 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., F. M. Ralph, T. Das, P. J. Neiman, and D. R. Cayan, 2011: Atmospheric rivers, floods, and the water resources of California. Water, 3, 445478, https://doi.org/10.3390/w3020445.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doswell, C. A., III, H. E. Brooks, and R. A. Maddox, 1996: Flash flood forecasting: An ingredients-based methodology. Wea. Forecasting, 11, 560581, https://doi.org/10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dougherty, E., and K. L. Rasmussen, 2019: Climatology of flood-producing storms and their associated rainfall characteristics in the United States. Mon. Wea. Rev., 147, 38613877, https://doi.org/10.1175/MWR-D-19-0020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dougherty, E., and K. L. Rasmussen, 2020: Changes in flash flood-producing storms in the United States. J. Hydrometeor., 21, 22212236, https://doi.org/10.1175/JHM-D-20-0014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn, 2011: Regional climate models add value to global model data: A review and selected examples. Bull. Amer. Meteor. Soc., 92, 11811192, https://doi.org/10.1175/2011BAMS3061.1.

    • 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
  • Guan, B., N. P. Molotch, D. E. Waliser, E. J. Fetzer, and P. J. Neiman, 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gutmann, E. D., and Coauthors, 2018: Changes in hurricanes from a 13-yr convection-permitting pseudo global warming simulation. J. Climate, 31, 36433657, https://doi.org/10.1175/JCLI-D-17-0391.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S. M., 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 American from the large ensemble CESM simulations. Geophys. Res. Lett., 43, 13571363, https://doi.org/10.1002/2015GL067392.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hatchett, B., B. Daudert, C. Garner, N. Oakley, A. Putnam, and A. White, 2017: Winter snow level rise in the northern Sierra Nevada from 2008 to 2017. Water, 9, 899, https://doi.org/10.3390/w9110899.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, X., A. D. Hall, and N. Berg, 2018: Anthropogenic warming impacts on today’s Sierra Nevada snowpack and flood risk. Geophys. Res. Lett., 45, 62156222, https://doi.org/10.1029/2018GL077432.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, X., D. L. Swain, and D. B. Walton, 2020: Simulation and evaluating atmospheric river-induced precipitation extremes along the U.S. Pacific Coast: Case studies from 1980–2017. J. Geophys. Res. Atmos., 125, e2019JD031554, https://doi.org/10.1029/2019JD031554.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Junker, N. W., R. H. Grumm, R. Hart, L. F. Bosart, K. M. Bell, and F. J. Pereira, 2008: Use of normalized anomaly fields to anticipate extreme rainfall in the mountains of Northern California. Wea. Forecasting, 23, 336356, https://doi.org/10.1175/2007WAF2007013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kendon, E., N. M. Roberts, H. J . Fowler, M. J. Roberts, S. C. Chan, and C. A. Senior, 2014: Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat. Climate Change, 4, 570576, https://doi.org/10.1038/nclimate2258.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lackmann, G. M., 2013: The south-central U.S. flood of May 2010: Present and future. J. Climate, 26, 46884709, https://doi.org/10.1175/JCLI-D-12-00392.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., and K. E. Mitchell, 2005: The NCEP Stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2, https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.

  • Liu, C., and Coauthors, 2017: Continental-scale convection-permitting modeling of the current and future climate of North America. Climate Dyn., 49, 7195, https://doi.org/10.1007/s00382-016-3327-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorente-Plazas, R., T. P. Mitchell, G. Mauger, and E. P. Salathé Jr., 2018: Local enhancement of extreme precipitation during atmospheric rivers as simulated in a regional climate model. J. Hydrometeor., 19, 14291446, https://doi.org/10.1175/JHM-D-17-0246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., C. F. Chappell, and L. R. Hoxit, 1979: Synoptic and meso-α scale aspects of flash flood events. Bull. Amer. Meteor. Soc., 60, 115123, https://doi.org/10.1175/1520-0477-60.2.115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., F. Canova, and L. R. Hoxit, 1980: Meteorological characteristics of flash flood events over the western United States. Mon. Wea. Rev., 108, 18661877, https://doi.org/10.1175/1520-0493(1980)108<1866:MCOFFE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahoney, K., M. Alexander, J. D. Scott, and J. Barsugli, 2013: High-resolution downscaled simulations of warms-season extreme precipitation events in the Colorado Front Range under past and future climates. J. Climate, 26, 86718689, https://doi.org/10.1175/JCLI-D-12-00744.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahoney, K., D. Swales, M. J. Mueller, M. Alexander, M. Hughes, and K. Malloy, 2018: An examination of inland-penetrating atmospheric river flood event under potential future thermodynamic conditions. J. Climate, 31, 62816297, https://doi.org/10.1175/JCLI-D-18-0118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Musselman, K. N., F. Lehner, K. Ikeda, M. P. Clark, A. F. Prein, C. Liu, M. Barlage, and R. Rasmussen, 2018: Projected increases and shifts in rain-on-snow flood risk over western North America. Nat. Climate Change, 8, 808812, https://doi.org/10.1038/s41558-018-0236-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, A. B. White, D. E. Kingsmill, and P. O. G. Persson, 2002: The statistical relationship between upslope flow and rainfall in California’s coastal mountains: Observations during CALJET. Mon. Wea. Rev., 130, 14681492, https://doi.org/10.1175/1520-0493(2002)130<1468:TSRBUF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, G. A. Wick, J. D. Lundquist, and M. D. Dettinger, 2008: 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. Hydrometeor., 9, 2247, https://doi.org/10.1175/2007JHM855.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, B. J. Moore, M. Hughes, K. M. Mahoney, J. M. Cordeira, and M. D. Dettinger, 2013: The landfall and inland penetration of a flood-producing atmospheric river in Arizona. Part I: Observed synoptic-scale, orographic, and hydrometeorological characteristics. J. Hydrometeor., 14, 460484, https://doi.org/10.1175/JHM-D-12-0101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G. Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., R. M. Rasmussen, K. Ikeda, C. Liu, M. P. Clark, and G. J. Holland, 2017a: The future intensification of hourly precipitation extremes. Nat. Climate Change, 7, 4852, https://doi.org/10.1038/nclimate3168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., C. Liu, K. Ikeda, R. Bullock, R. M. Rasmussen, G. J. Holland, and M. Clark, 2017b: Simulating North American mesoscale convective systems with a convection-permitting climate model. Climate Dyn., 55, 95110, https://doi.org/10.1007/s00382-017-3993-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., C. Liu, K. Ikeda, S. B. Trier, R. M. Rasmussen, and G. J. Holland, 2017c: Increased rainfall volume from future convective storms in the US. Nat. Climate Change, 7, 880884, https://doi.org/10.1038/s41558-017-0007-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., and M. D. Dettinger, 2012: Historical and national perspectives on extreme West Coast precipitation associated with atmospheric rivers during December 2010. Bull. Amer. Meteor. Soc., 93, 783790, https://doi.org/10.1175/BAMS-D-11-00188.1.

    • 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., P. J. Neiman, G. N. Kiladis, K. Weickman, and D. W. Reynolds, 2011: A multi-scale observational case study of a Pacific atmospheric river exhibiting tropical-extratropical connections and a mesoscale frontal wave. Mon. Wea. Rev., 139, 11691189, https://doi.org/10.1175/2010MWR3596.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., T. Coleman, P. J. Neiman, R. J. Zamora, and M. D. Dettinger, 2013: Observed impacts of duration and seasonality of atmospheric-river landfalls on soil moisture and runoff in coastal Northern California. J. Hydrometeor., 14, 443459, https://doi.org/10.1175/JHM-D-12-076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., and Coauthors, 2019: A scale to characterize the strength and impacts of atmospheric rivers. Bull. Amer. Meteor. Soc., 100, 269288, https://doi.org/10.1175/BAMS-D-18-0023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, K. L., A. F. Prein, R. M. Rasmussen, K. Ikeda, and C. Liu, 2017: Changes in the convective population and thermodynamic environments in convection-permitting regional climate simulations over the United States. Climate Dyn., 55, 383408, https://doi.org/10.1007/S00382-017-4000-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and Coauthors, 2011: High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate. J. Climate, 24, 30153048, https://doi.org/10.1175/2010JCLI3985.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and Coauthors, 2014: Climate change impacts on the water balance of the Colorado Headwaters: High-resolution regional climate model simulations. J. Hydrometeor., 15, 10911116, https://doi.org/10.1175/JHM-D-13-0118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • 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
  • Saharia, M., P. Kirsteetter, H. Vergara, J. J. Gourley, Y. Hong, and M. Giroud, 2017b: Characterization of floods in the United States. J. Hydrol., 548, 524535, https://doi.org/10.1016/j.jhydrol.2017.03.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, M., A. Hall, F. Sun, D. Walton, and N. Berg, 2017: Significant and inevitable end-of-twenty-first-century advances in surface runoff timing in California’s Sierra Nevada. J. Hydrometeor., 18, 31813197, https://doi.org/10.1175/JHM-D-16-0257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shen, S., Y. Mei, and E. M. Anagnostou, 2017: A comprehensive database of flood events in the contiguous United States from 2002 to 2013. Bull. Amer. Meteor. Soc., 98, 14931502, https://doi.org/10.1175/BAMS-D-16-0125.1.

    • 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
  • Thompson, G., and T. Eidhammer, 2014: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci., 71, 36363658, https://doi.org/10.1175/JAS-D-13-0305.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 12051218, https://doi.org/10.1175/BAMS-84-9-1205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vano, J. A., K. Miller, M. D. Dettinger, R. Cifelli, D. Curtis, A. Dufour, J. R. Olsen, and A. M. Wilson, 2018: Hydroclimatic extremes as challenges for the water management community: Lessons from Oroville Dam and Hurricane Harvey. Bull. Amer. Meteor. Soc., 100, S9S14, https://doi.org/10.1175/BAMS-D-18-0219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Z., A. C. Bovik, H. R. Sheikh, and E. P. Simoncellli, 2004: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., 13, 600612, https://doi.org/10.1109/TIP.2003.819861.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, A. B., B. J. Moore, D. J. Gottas, and P. J. Neiman, 2019: Winter storm conditions leading to excessive runoff above California’s Oroville Dam during January and February 2017. Bull. Amer. Meteor. Soc., 100, 5570, https://doi.org/10.1175/BAMS-D-18-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, A. M., K. T. Skelly, and J. M. Cordeira, 2017: High-impact hydrologic events and atmospheric rivers in California: An investigation using the NCEI Storm Events Database. Geophys. Res. Lett., 44, 33933401, https://doi.org/10.1002/2017GL073077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    The study domain over California and its elevation to highlight the complex terrain. Also shown are boxes around three different regions in California: Northern California (purple), central California (red), and Southern California (pink).

  • View in gallery

    Probability density function of the structural similarity index from Wang et al. (2004) used to compute the structural differences in rainfall accumulation among 45 cool-season floods in California in CTRL vs PGW simulations. The mean value is indicated by the dashed vertical line.

  • View in gallery

    The accumulated rainfall in (a) CTRL simulations and (b) PGW simulations for a slow-rise flood that occurred from 0000 to 2200 UTC 20 Jan 2010. This flood had the lowest structural similarity index of 0.88 among all 45 cool-season floods in California examined in this study.

  • View in gallery

    Average cool-season (October–April) precipitation over California in (a) CTRL simulations, (b) PGW simulations, and (c) PGW − CTRL difference, where blue indicates increased PGW precipitation and red indicates a decrease.

  • View in gallery

    As in Fig. 4, but for SWE.

  • View in gallery

    As in Fig. 4, but for runoff.

  • View in gallery

    PGW − CTRL difference in cool-season precipitation, runoff, and SWE shown in Figs. 46 at each grid cell in as a function of altitude over California.

  • View in gallery

    Seasonality from October to May of percent future change in precipitation (turquoise), SWE (purple), and runoff (green). The dashed line at zero indicates no change, while values above (below) the line indicate a future percent increase (decrease). Note that the percent change is computed as (PGW − CTRL)/CTRL × 100.

  • View in gallery

    As in Fig. 4, but for average precipitation in 45 cool-season flood-producing storms in California between 2002 and 2013.

  • View in gallery

    As in Fig. 5, but for average SWE in 45 cool-season flood-producing storms in California between 2002 and 2013.

  • View in gallery

    PGW − CTRL differences in precipitation (red dots) and SWE (blue dots) in 45 flood-producing storms in California as a function of altitude. Turquoise (dark blue) colored dots indicate SWE changes in storms where the temperature is below (above) freezing (273 K) in the PGW simulations.

  • View in gallery

    As in Fig. 6, but for average runoff in 45 cool-season flood-producing storms in California between 2002 and 2013.

  • View in gallery

    As in Fig. 12, but for IVT.

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Future Changes in the Hydrologic Cycle Associated with Flood-Producing Storms in California

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  • 1 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
  • | 2 Department of Atmospheric Science, Texas A&M University, College Station, Texas
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Abstract

California receives much of its precipitation from cool-season atmospheric rivers, which contribute to water resources and flooding. In winter 2017, a large number of atmospheric rivers caused anomalous winter precipitation, near-saturated soils, and a partial melting of snowpack, which led to excessive runoff that damaged the emergency spillway of the Oroville Dam. Given the positive and negative impacts ARs have in California, it is necessary to understand how they will change in a future climate. While prior studies have examined future changes in the frequency of atmospheric rivers impacting the West Coast of the United States, these studies primarily use coarse global climate models that are unable to resolve the complex terrain of this region. Such a limitation is overcome by using a high-resolution convection-permitting regional climate model, which resolves complex topography and orographic rainfall processes that are the main drivers of heavy precipitation in landfalling atmospheric rivers. This high-resolution model is used to examine changes to precipitation and runoff in California’s cool season from 2002 to 2013, particularly in flood-producing storms associated with atmospheric rivers, in a future, warmer climate using a pseudo–global warming approach. In 45 flood-producing storms, precipitation and runoff increase by 21%–26% and 15%–34%, respectively, while SWE decreases by 32%–90%, with the greatest changes at mid-elevations. These trends are consistent with future precipitation changes during the entire cool season. Results suggest more intense floods and less snowpack available for water resources in the future, which should be carefully considered in California’s future water management plans.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Erin Dougherty, edough@rams.colostate.edu

Abstract

California receives much of its precipitation from cool-season atmospheric rivers, which contribute to water resources and flooding. In winter 2017, a large number of atmospheric rivers caused anomalous winter precipitation, near-saturated soils, and a partial melting of snowpack, which led to excessive runoff that damaged the emergency spillway of the Oroville Dam. Given the positive and negative impacts ARs have in California, it is necessary to understand how they will change in a future climate. While prior studies have examined future changes in the frequency of atmospheric rivers impacting the West Coast of the United States, these studies primarily use coarse global climate models that are unable to resolve the complex terrain of this region. Such a limitation is overcome by using a high-resolution convection-permitting regional climate model, which resolves complex topography and orographic rainfall processes that are the main drivers of heavy precipitation in landfalling atmospheric rivers. This high-resolution model is used to examine changes to precipitation and runoff in California’s cool season from 2002 to 2013, particularly in flood-producing storms associated with atmospheric rivers, in a future, warmer climate using a pseudo–global warming approach. In 45 flood-producing storms, precipitation and runoff increase by 21%–26% and 15%–34%, respectively, while SWE decreases by 32%–90%, with the greatest changes at mid-elevations. These trends are consistent with future precipitation changes during the entire cool season. Results suggest more intense floods and less snowpack available for water resources in the future, which should be carefully considered in California’s future water management plans.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Erin Dougherty, edough@rams.colostate.edu

1. Introduction

The duality of California’s Mediterranean climate from wet winters to dry summers poses a challenge in managing water resources, especially when combined with an ever-growing population (Dettinger et al. 2011). While the wet winters in California provide most of the state’s water resources, they can also lead to floods and natural disasters. For example, the Oroville Dam catastrophe in February 2017 was caused by anomalous winter precipitation, near-saturated soils, and partial melting of snowpack that contributed to excessive runoff (an entire year’s worth in January and February 2017; White et al. 2019). The anomalous precipitation in winter 2017 in California was due to an especially active season of atmospheric rivers (ARs)—the main precipitation and flood-producing systems along the U.S. West Coast that cause $1.1 billion in damage annually in the West (Corringham et al. 2019).

ARs impacting California are narrow corridors of enhanced water vapor plumes in the warm sector of extratropical cyclones coming off the Pacific Ocean (Neiman et al. 2008). They are defined in a variety of ways, based upon their scale and intensity. Neiman et al. (2008) defined them as narrow plumes of integrated water vapor (IWV) exceeding 2 cm and over 2000 km long and less than 1000 km wide. They are also defined by integrated water vapor transport (IVT), with one common form of this equation given by Gao et al. (2015):
IVT=(1g1000500qudp)2+(1g1000500qυdp)2,
where g is the gravitational acceleration, q is the specific humidity, and u and υ are the zonal and meridional winds, respectively. This variable quantifies the amount of water vapor transport in the lower troposphere, where a majority of the moisture resides in ARs (Neiman et al. 2008). Typically, IVT values exceeding 250 kg m−1 s−1 are used to indicate ARs (Ralph et al. 2011; Rutz et al. 2014; Young et al. 2017; Ralph et al. 2019), sometimes in conjunction with a spatial requirement that this IVT threshold covers a contiguous region over 2000 km long (Rutz et al. 2014). While there are a variety of ways in which ARs are defined, using an IVT threshold (whether absolute or relative) is a common method, though using different forms of this definition can yield a range in the detection of these atmospheric systems (Shields et al. 2018).

ARs are associated with heavy rainfall along the West Coast due to the shallow, moist neutral air in extratropical cyclones impinging upon mountainous terrain which results in strong forcing for orographic rainfall, particularly in the cool season from October through March (Neiman et al. 2008). In California, ARs contribute 20%–50% of California’s annual streamflow and precipitation, with only a few storms necessary to produce this contribution (Dettinger et al. 2011). One-third to one-half of all precipitation in California falls in 5–10 days, highlighting the importance of ARs in delivering crucial precipitation to the state (Dettinger et al. 2011). This also includes snowfall, as ARs contribute 40% of the total snow water equivalent (SWE) in California, particularly in the Sierra Nevada (Neiman et al. 2008; Guan et al. 2010).

In addition to making important contributions to rainfall and snowpack in California, ARs also contribute to flooding and landslides. Ralph et al. (2006) found that AR conditions were present in all seven floods on California’s Russian River from 1997 to 2006 and contributed to those floods due to associated heavy orographic rainfall. In December 2010, ARs produced over 670 mm of rain in the San Bernardino Mountains outside of Los Angeles and led to floods in California as well as Washington (Ralph and Dettinger 2012). Young et al. (2017) specifically examined the relationship between ARs and floods, flash floods, and debris flows in California based on storm reports and a climatology of landfalling ARs from Rutz et al. (2014). They found that floods and debris flow were associated with ARs during the cool season in Northern California and flash floods not associated with ARs during the warm season in Southern California. Along the West Coast, the top 11 out of 20 counties with the highest proportion of flood damage due to ARs are located in California (Corringham et al. 2019).

Given the impact ARs have on California’s precipitation, floods, and water resources, current research has investigated how ARs might change in a future climate. The frequency of ARs along the West Coast is projected to increase anywhere from 2.5 days yr−1 (Dettinger 2011) to double the number of AR days per season (Gao et al. 2015) to 35% in the mean number of AR days (Hagos et al. 2016). Dettinger (2011) showed that IWV increased in future ARs and storms were 1.8°C warmer, possibly explaining the increased number of AR days. The findings of Gao et al. (2015) demonstrate that increased AR frequency in the future is mainly due to thermodynamic rather than dynamic effects, supporting the results of Dettinger (2011). Due to the changing thermodynamic effects on ARs in a future climate, future AR extreme precipitation is projected to increase by 100%–200% in the fall and winter along the West Coast (Gao et al. 2015), and upward of 200% in a case study of a flood-producing AR in the Pacific Northwest due to more orographic enhancement in a future climate (Mahoney et al. 2018). These thermodynamic changes in a future climate also affect the future hydrology in California’s Sierra Nevada, with a projected decline in snowpack of 67% and increased frequency of days with runoff above 20 mm nearly tripling (Huang et al. 2018). This decrease in snowpack in a future warmer climate is due to more precipitation falling as rain rather than snow (Das et al. 2011). A future change from snow to rain leads to a projected increase in the magnitude and frequency of floods in the Sierra Nevada when using downscaled temperature and precipitation from GCMs to drive a Variable Infiltration Capacity (VIC) model (Das et al. 2011). Davenport et al. (2020) found that historical rainfall-driven floods in the western CONUS produce larger streamflow magnitudes than snowmelt-driven floods, suggesting a heightened flood risk as snow changes to rain with future warming, particularly at mid-elevation sites that receive a mix of rain- and snowfall-driven floods. Thus, ARs impacting the West Coast in a future warmer climate are predicted to be more frequent and intense, producing more rainfall, less snowpack, and more runoff, suggesting an enhanced future flood risk.

While these previous studies highlight many important changes to ARs and hydrology in the Sierra Nevada in a future, warmer climate, most of these studies utilized global climate models (GCMs) to simulate future changes. GCMs are helpful for understanding the large-scale changes to future ARs, including their frequency, but due to their coarse resolution, they cannot accurately simulate orographic precipitation processes and mesoscale storm dynamics over complex terrain. This is a serious limitation for investigating changes to AR precipitation because AR precipitation is orographically enhanced and requires scales on the order of several kilometers to accurately depict the topographic effects on atmospheric flows (Rasmussen et al. 2011). In addition, improvements in simulated precipitation of up to 40%–60% in nine historical ARs were found when increasing model resolution from 27 to 3 km (Huang et al. 2020). Though some studies like Das et al. (2011) used downscaled precipitation and temperature data from GCMs, the 12-km spatial resolution is still too coarse to properly depict precipitation and snowfall characteristics over mountainous terrain due to the topography resolution (Rasmussen et al. 2011) and use of a convective parameterization to simulate cloud and mesoscale dynamics. Mahoney et al. (2018) is one of the few studies to investigate changes to a landfalling AR event and its associated rainfall using high-resolution simulations of a future climate under a pseudo–global warming (PGW) framework. This is a useful demonstration of a case study but studying how a broad spectrum of AR events change using high-resolution simulations is necessary to gain confidence in future projected changes.

The present study seeks to address these limitations and build upon the understanding of how ARs will change in a future climate by using high-resolution convection-permitting simulations that more accurately resolve complex terrain, orographic precipitation processes, and mesoscale storm dynamics along the West Coast. Specifically, this study investigates changes to cool-season precipitation and flood-producing storms associated with ARs in California in a future, warmer climate. In addition, the specific focus on flood-producing storms associated with ARs and how the precipitation, SWE, and runoff changes in these storms in a future climate has not been conducted before and is necessary to understand future changes to extremes in the hydrologic cycle.

2. Data and methods

a. Convection-permitting simulations

To examine California’s cool-season precipitation and floods in a future climate, 4-km convection-permitting simulations run by scientists at the National Center for Atmospheric Research (NCAR) are utilized (Liu et al. 2017). These simulations (hereafter called WRF-CONUS) were run with the Weather Research and Forecasting (WRF) Model v3.4.1 over a 1360 × 1016 grid point domain covering the CONUS and parts of Canada and Mexico. The horizontal grid spacing is 4 km and there are 51 stretched vertical levels up to 50 hPa. The WRF-CONUS parameterizations include the Thompson aerosol-aware microphysics (Thompson and Eidhammer 2014), the Yonsei University (YSU) planetary boundary scheme (Hong et al. 2006), the Rapid Radiative Transfer Model for GCMs (RRTMG) (Iacono et al. 2008), and the Noah-MP land surface model (Niu et al. 2011), which was improved for these simulations (Liu et al. 2017). For the purposes of this study, only simulations over California are considered (Fig. 1).

Fig. 1.
Fig. 1.

The study domain over California and its elevation to highlight the complex terrain. Also shown are boxes around three different regions in California: Northern California (purple), central California (red), and Southern California (pink).

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

The WRF-CONUS simulations were forced by 6-hourly ERA-Interim reanalysis data. Two sets of simulations were run over this time period—a current (CTRL) simulation that reproduced the present climate, and a future climate simulated using a PGW approach. The PGW approach adds a climate delta signal to the ERA-Interim data, as given by the following equation:
PGW=ERA-I+ΔCMIP5RCP8.5,
where ΔCMIP5RCP8.5 is the 19-model ensemble monthly mean change between the current and future climate under the RCP8.5 emissions scenario:
ΔCMIP5RCP8.5=CMIP520712100CMIP519762005.
The PGW simulations perturb the horizontal wind, geopotential, temperature, specific humidity, sea surface temperature, soil temperature, sea level pressure, and sea ice. Large-scale spectral nudging was applied to these simulations above the planetary boundary layer on horizontal scales greater than 2000 km in order to minimize long-term climate drift (Feser et al. 2011). Nudging also allows for specific synoptic weather events to be reproduced, but subsynoptic scales can freely evolve, thus allowing for an investigation of how storms will change in a future climate (Liu et al. 2017).

The PGW approach applied to convection-permitting simulations has been utilized in numerous studies investigating how storm processes will change in a future climate. Heavy precipitation in the CONUS (Mahoney et al. 2013; Prein et al. 2017a; Dai et al. 2017), the European Alps (Ban et al. 2015), and the United Kingdom (Kendon et al. 2014) has been studied using this method. Case studies of historical floods have also been examined from this perspective in the south-central United States (Lackmann 2013) and Pacific Northwest (Mahoney et al. 2018) to better understand how certain flood “ingredients” (Doswell et al. 1996) will change in the future. The WRF-CONUS simulations have recently been used to understand how the convective population (Rasmussen et al. 2017), mesoscale convective systems (Prein et al. 2017b,c), hurricanes (Gutmann et al. 2018), rain-on-snow events (Musselman et al. 2018), and flash flood–producing storms (Dougherty and Rasmussen 2020) will change in a future climate. These simulations compare well with observations of the diurnal cycle of convection (Rasmussen et al. 2017), and outperform other gridded rainfall datasets, particularly over the mountainous West (Beck et al. 2019). This is especially critical for understanding future changes to cool-season rainfall and floods in California, most of which are orographically enhanced. The large-scale nudging used in the simulations also provides confidence that flood-producing storms are being represented in the future climate, since cool-season floods in California are largely driven by synoptic-scale ARs (Dettinger et al. 2011; Saharia et al. 2017b; Dougherty and Rasmussen 2019), and the nudging reproduces these synoptic conditions in flood-producing storms, as shown by Dougherty and Rasmussen (2020) in the case of the 2008 Arkansas flash flood.

However, there are limitations to the WRF-CONUS simulations. Due to the use of large-scale nudging and taking ensemble mean monthly differences, future changes in large-scale dynamics, including changes in storm track, are weak and unable to be explored. Within the context of ARs, this weak large-scale dynamical change means that changes in future moisture flux are likely due to future increases in moisture rather than stronger winds, since the PGW simulations focus more on how the future thermodynamic change will affect similar weather patterns in the future (Liu et al. 2017). Yet, because the mesoscale is allowed to freely evolve, detailed changes in precipitation from a variety of weather systems can be assessed on fine horizontal resolutions that omit the need for a convective parameterization. Due to this high-resolution and the computational constraint of running the WRF-CONUS simulations, the climate change uncertainty cannot be addressed from these simulations using multimember ensembles. Taking the 19-model ensemble monthly mean from CMIP5 simulations was done in order to partially address this limitation, rather than using a single model run as in Rasmussen et al. (2011, 2014). While these limitations are notable, the purpose of the WRF-CONUS simulations to understand how today’s weather might change in a warmer and moister future climate is well suited to understand detailed precipitation changes associated with ARs in California.

b. Cool-season climatology

A majority of California’s annual precipitation is concentrated in the cool season from October through March, due to enhanced baroclinic cyclogenesis in the Pacific Ocean and a prevalence of landfalling ARs during this time period (Neiman et al. 2008; Dettinger et al. 2011). Therefore, most of California’s water resources and also floods emanate from cool-season precipitation (Dettinger et al. 2011). To understand how California’s precipitation will change in a future climate, the cool-season (October–April) hydrologic components are examined in the WRF-CONUS CTRL and PGW simulations, similar to Rasmussen et al. (2014). Future changes in precipitation are examined using the hourly CTRL and PGW data, while changes in SWE, and total (surface + subsurface) runoff are examined using monthly totals. All quantities are presented in terms of cool-season averages and provide a holistic understanding of how California’s water resources are changing in a warmer, future climate, and not just the precipitation delivered.

c. Cool-season floods

A majority of the cool-season precipitation in California is associated with flooding, particularly when delivered by ARs (Ralph et al. 2006; Young et al. 2017). In addition, as ARs make landfall on the West Coast, complex interactions with the topography and associated mesoscale processes are important factors in determining the intensity and hydrological impact of each event (Neiman et al. 2008; Lorente-Plazas et al. 2018). Thus, to understand how high-impact precipitation affecting California will change in a future climate, we focus specifically on flood-producing storms. The flood-producing storm climatology created in Dougherty and Rasmussen (2019) is utilized to identify cool-season (October–April) flash and slow-rise flood-producing storms in California from 2002 to 2013. This climatology merges flood reports from the NCEI Storm Events Database with streamflow-indicated floods from Shen et al. (2017) and their associated rainfall characteristics from the Stage-IV precipitation dataset (Lin and Mitchell 2005). To isolate the likely flood-contributing rainfall associated with each flood, the largest contiguous object with accumulated rainfall at or above the 75th percentile within a ±5° latitude/longitude grid box from the flood centroid was identified. Flash floods in the Dougherty and Rasmussen (2019) flood-climatology are shorter duration, high intensity events usually associated with convection, while slow-rise floods are longer duration, moderate intensity events associated with larger-scale synoptic events. Young et al. (2017) showed that a majority of flash and slow-rise floods in California during the cool season are associated with ARs. Thus, the cool-season floods in California identified from Dougherty and Rasmussen (2019) are likely associated with ARs.

Sixty-nine flood-producing storms are identified in California from 2002 to 2013. The date and location of floods from the Dougherty and Rasmussen (2019) climatology are used to subset the hourly CTRL and PGW precipitation data, again only using the largest contiguous area with rainfall accumulations meeting or exceeding the 75th percentile within the flood domain (±5°). These heavy rainfall objects are computed separately in the CTRL and PGW simulations to take into account that the amount and location of heavy rainfall within each flood domain could change slightly in a future, warmer climate. However, the accumulated rainfall (as well as SWE, temperature, and runoff) are computed using the same dates from Dougherty and Rasmussen (2019) in the CTRL and PGW simulations. While this is a possible limitation of the study, due to the possibility of rainfall within a flood event being longer or shorter in duration in a future climate, the use of spectral nudging and minimal circulation changes ensures similar synoptic situations and durations in the PGW simulations. Given that flood events associated with ARs are synoptically driven, it was expected that the CTRL flood location, rainfall structure, and duration would compare well with observations and to PGW simulations.

To test this expectation, each of the 69 floods in the CTRL simulations are compared to Stage-IV observations and PGW simulations. Between CTRL simulations and Stage IV, if the area-averaged rainfall difference exceeds 50% within a flood, a manual comparison occurs. This manual comparison is conducted using observed daily archived precipitation data over California from the Advanced Hydrologic Prediction Center.1 In the western CONUS, the daily archived precipitation data is derived from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM; Daly et al. 1994) dataset, due to issues in Stage-IV coverage over the mountainous West. Based on a qualitative comparison of CTRL simulated precipitation with PRISM precipitation, 24 floods are omitted, leaving 27 (18) flash (slow-rise) flood-producing storms for a total of 45 cool-season floods in California.

Among these 45 cool-season floods, the similarity between rainfall amount and structure in CTRL and PGW simulations is quantified using the structural similarity index (SSIM) from Wang et al. (2004):
SSIM(x,y)=(2μxμy+c1)(2σxy+c2)(μx2+μy2+c1)(σx2+σy2+c2),
where x, y is the pixel location, μ is the mean pixel intensity, σ is the variance of intensity, and c1 and c2 are constants to avoid instability when the other terms in the denominator are close to zero. SSIM is often used in the image-processing community to compare the difference in two images luminance, contrast, and structural differences. In this study, SSIM is utilized to compute differences in accumulated rainfall amount and structure between each flood in the CTRL and PGW simulations like in Dougherty and Rasmussen (2020), who computed this metric for 584 flash flood–producing storms over the CONUS and found good agreement between both the observations and CTRL simulations, and the CTRL and PGW simulations. This metric quantifies the similarity of the rainfall structure between CTRL and PGW simulations over the same time period and location for each of the 45 floods in California to ensure that a similar flood occurs in the future.

Computing the SSIM between the CTRL and PGW accumulated rainfall for all 45 floods in California results in a mean value of 0.94, with values ranging from 0.88 to 0.99 (Fig. 2). A value of 1 indicates exact similarity, so a mean value of 0.94 suggests that the CTRL and PGW precipitation is remarkably similar in flood-producing storms. Even for the flood with the lowest SSIM of 0.88, the rainfall structure and location is similar (Fig. 3). Though exact agreement is not expected between CTRL and PGW flood precipitation due to a different thermodynamic environment in PGW simulations, the high SSIM values are likely due to the PGW simulations’ ability to reproduce the synoptic environment in the CTRL simulations.

Fig. 2.
Fig. 2.

Probability density function of the structural similarity index from Wang et al. (2004) used to compute the structural differences in rainfall accumulation among 45 cool-season floods in California in CTRL vs PGW simulations. The mean value is indicated by the dashed vertical line.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

Fig. 3.
Fig. 3.

The accumulated rainfall in (a) CTRL simulations and (b) PGW simulations for a slow-rise flood that occurred from 0000 to 2200 UTC 20 Jan 2010. This flood had the lowest structural similarity index of 0.88 among all 45 cool-season floods in California examined in this study.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

Maddox et al. (1979, 1980) showed that similar synoptic environments are associated with floods, including ARs, which are, by definition, synoptically driven. Therefore, while remarkable, it is not surprising that the PGW simulated rainfall in floods associated with ARs in California is similar to the CTRL simulations, based on the simulation design and the types of floods examined. These results provide confidence that flood-producing storms in the current climate are being reproduced in the PGW simulations in terms of location, timing, and structural similarity of precipitation. However, future changes in soil moisture, land use, and streamflow are not assessed in this study, which could modulate the occurrence of floods in a future climate. Therefore, it cannot be said with certainty that storms associated with floods in the current climate also produce floods in the future.

However, work by Young et al. (2017) showed that nearly 80% of floods and debris flow were associated with ARs in California. The Rutz et al. (2014) AR database using Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011) reanalysis data from 1980 to 2019 is utilized to confirm that the flood-producing storms in this study were associated with AR conditions. If the start date of the flood-producing storm is less than or equal to two days from the end date of an AR event from Rutz et al. (2014), then that storm is classified as being associated with an AR. Based on this definition, all 45 flood-producing storms are associated with ARs. Given the association with AR conditions, it is likely that these flood-producing storms result in floods in the future.

In addition to precipitation, changes in SWE and runoff are examined using the CTRL and PGW hourly data among the 45 floods. IVT is also compared in CTRL and PGW flood-producing storms using Eq. (1), since this quantity is often used to define ARs (Rutz et al. 2014; Gao et al. 2015; Ralph et al. 2019). IVT has a particularly important link to precipitation, as it is strongly related to precipitation over complex terrain (Junker et al. 2008; Neiman et al. 2002, 2013; Ralph et al. 2013). Therefore, an understanding of how IVT changes in future California floods associated with ARs is crucial to explain how rainfall in these flood-producing storms might change.

3. Results and discussion

a. Cool-season climatology

Average cool-season precipitation over California increases almost everywhere in a future climate (Fig. 4), especially over the complex terrain of the Coastal Range and Sierra Nevada (refer to Fig. 1 for topography). Precipitation increases from 40 to 120 mm in most of the state, with increases of up to 180 mm over the Sierra Nevada. Localized areas of decreased precipitation occur on the leeside of the southern Coastal Range, but these decreases are minimal (40-mm decrease) and occur in a climatologically dry area. The statewide increase in future precipitation is 49 mm (11%). Such an increase in future precipitation during California’s cool-season is not unexpected, as water vapor increases in a warmer climate according to the Clausius–Clapeyron equation (Trenberth et al. 2003). The greatest future increase in precipitation over complex terrain is also not surprising, as orography enhances forcing via mechanical lifting for precipitation.

Fig. 4.
Fig. 4.

Average cool-season (October–April) precipitation over California in (a) CTRL simulations, (b) PGW simulations, and (c) PGW − CTRL difference, where blue indicates increased PGW precipitation and red indicates a decrease.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

Future changes in cool-season SWE show the opposite change compared to precipitation, with an average decrease of 57.5 mm (47.5%; Fig. 5). Decreased SWE is greatest over the Sierra Nevada, which receive the climatological maximum snowfall in California (Huang et al. 2018). These decreases are up to 360 mm over the mountains, and no locations display an increase in average cool-season SWE. Such a result is particularly concerning, as California relies on most of its water resources from seasonal snowpack accumulations (Dettinger et al. 2011). Changes in snow have already been observed in the Sierra Nevada, with an increase in the observed mean snow line over the last 15 years (Hatchett et al. 2017). Future decreases in snow have also been shown in the Colorado Rockies and high-elevation locations in the Pacific Northwest, with future warming causing a shift from snow to rain (Rasmussen et al. 2014; Mahoney et al. 2018). Furthermore, Huang et al. (2018) showed that for the 2016/17 season, maximum SWE decreases by 67% under RCP8.5 using a 9-km downscaled simulation of the future climate. However, the results in this study span a longer timeframe than Huang et al. (2018) over a 13-yr timeframe, include all of California, and properly resolve orographic precipitation processes (Rasmussen et al. 2014), which represents a more comprehensive understanding of future changes to SWE in California. Given that this study uses the highest-resolution future simulations over the longest continuous period currently available, the 47.5% average decrease in California’s cool-season SWE should be seriously considered.

Fig. 5.
Fig. 5.

As in Fig. 4, but for SWE.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

The above changes in future cool-season precipitation and SWE due to warming contribute to changes in future cool-season runoff magnitude (Fig. 6), which has not been examined over multiple years using high-resolution convective-permitting simulations over the entire state of California before. On average, cool-season runoff increases 51.8 mm (27.1%), with a majority of California experiencing increased runoff, except for some localized areas in southern California that experience slight decreases in runoff less than 50 mm. Increased runoff is greatest over the Sierra Nevada, due to the greatest increase (decrease) in precipitation (SWE) here, with average increased runoff up to 450 mm. This is consistent with Rasmussen et al. (2014) and Schwartz et al. (2017) who found more precipitation falling as rain rather snow in a future climate, which led to increased winter runoff in the Colorado Rockies (Rasmussen et al. 2014), and earlier snowmelt and runoff in the Sierra Nevada (Schwartz et al. 2017). Huang et al. (2018) also found that runoff increases under RCP8.5 in a future simulation of the 2016/17 wet year in the Sierra Nevada, with days of runoff exceeding 20 mm nearly tripling. The future increases in cool-season runoff in California due to increased precipitation and decreased SWE not only suggest a shift in the water balance, but also an enhanced future flood risk, which will be explored further in section 3b.

Fig. 6.
Fig. 6.

As in Fig. 4, but for runoff.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

To further understand the nuances of future changes in cool-season precipitation, SWE, and runoff, the dependence on elevation in California is explored (Fig. 7). Consistent with Figs. 46, future precipitation increases at all elevations, up to 200 mm. SWE decreases at all elevations in the future, as seen in Fig. 5, with a maximum decrease of nearly 800 mm between 1500 and 2000 m in elevation, and smaller decreases in SWE above and below this elevation. This large decrease in SWE at mid-elevations is consistent with projected future changes in the 2016/17 1 April SWE in the Sierra Nevada, during which SWE between 1500 and 2500 m almost disappears, and high elevation (>2500 m) SWE is halved (Huang et al. 2018). The maximum decrease in SWE at mid-elevations is likely due to higher freezing levels in a warmer climate that converts snow to rain (Rasmussen et al. 2014; Mahoney et al. 2018). Interestingly, Rasmussen et al. (2014) found that at the highest elevations in the Colorado Rockies, temperatures were still cold enough to support increased snow in the future, which is not the case in California. This could be due to most of the cool-season precipitation in California being delivered by ARs, which tend to be associated with warmer than normal conditions and thus, high snowlines (Neiman et al. 2008; Dettinger et al. 2011). Due to the overall increase in precipitation and decrease in SWE, future runoff increases at and above mid-elevations (1500 m), where snow is likely falling as rain in a future warmer climate. This is consistent with future increased runoff at mid- and high elevations in the Sierra Nevada for the 2016/17 season (Huang et al. 2018), and increased runoff in the spring in the Colorado Rockies (Rasmussen et al. 2014). Such a result suggests an increased flood risk at mid-elevations as snow changes to rain in a future climate, since rainfall-driven events have higher streamflow magnitudes than snowmelt-driven events (Davenport et al. 2020).

Fig. 7.
Fig. 7.

PGW − CTRL difference in cool-season precipitation, runoff, and SWE shown in Figs. 46 at each grid cell in as a function of altitude over California.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

The seasonality of precipitation, SWE, and runoff are explored for the cool season in California to examine if there were any shifts in the seasonality of these components, and thus, the water balance. Future precipitation and runoff increase through the entire cool season (October through the end of April), with maximum increases in January–March ranging from 15% to 20% and from 20% to 40%, respectively (Fig. 8). SWE decreases in the future from October through April, with the greatest decreases of over 60% in the early season (October–December). Not much SWE exists in California in October (the statewide average is 4 mm in CTRL simulations), so the future decrease is only a few mm averaged over the state, despite the large percentage decrease. However, the large future decrease of nearly 70%–80% in November and December SWE is likely robust, as California climatologically experiences large gains in SWE during these months (Neiman et al. 2008).

Fig. 8.
Fig. 8.

Seasonality from October to May of percent future change in precipitation (turquoise), SWE (purple), and runoff (green). The dashed line at zero indicates no change, while values above (below) the line indicate a future percent increase (decrease). Note that the percent change is computed as (PGW − CTRL)/CTRL × 100.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

For most of the cool season in California, there does not appear to be a future shift in the timing of precipitation, SWE, or runoff, but simply increased (decreased) precipitation and runoff (SWE). The only exception occurs on the fringes of the cool season, where precipitation and runoff slightly decrease in the future in December and April. The decrease in precipitation and runoff in April could indicate earlier snowmelt, especially since SWE decreases more in April than the previous months. Such a result would be consistent with Huang et al. (2018) who found that early-season gains in Sierra Nevada runoff (January–February) in a future climate came at the expense of decreased late-season runoff (April–June). The present study only explores the cool season, however, so any changes in the seasonality beyond this timeframe cannot be stated. Yet, these results are consistent with future changes in water balance components in the Colorado Rockies (Rasmussen et al. 2014) and Sierra Nevada during the 2016/17 season (Huang et al. 2018)—namely, the future increases (decreases) in precipitation and runoff during the middle (edges) of the cool season, and largest decreases in SWE on the edges of the cool season in California. However, the present study goes beyond demonstrating consistency with previous studies by showing concurrent changes in precipitation, SWE, and runoff over all of California using high resolution future projections over a continuous 13-yr period. This provides confidence in the expected future changes in the water balance and water resources in California in a warmer climate.

b. Cool-season flood-producing storms

Future cool-season flood-producing storms in California show an increase in precipitation everywhere (Fig. 9), similar to changes in overall cool-season precipitation (Fig. 4). However, the future percent increase in flood-producing storm precipitation exceeds the cool-season precipitation, with increases of 25.4% (7.1 mm) in Northern California (NorCal), 25.8% (5.3 mm) in central California (CenCal), and 21.7% (3.3 mm) in Southern California (SoCal; Tables 12 ). These increases are less than the projected future precipitation increases in ARs simulated by Mahoney et al. (2018) and Gao et al. (2015), who found increased precipitation up to 200%. These differences could be due to Mahoney et al. (2018) only considering one landfalling AR case in the Pacific Northwest, which are different in nature than landfalling ARs in California (Neiman et al. 2008). Additionally, Gao et al. (2015) concentrated on future changes to landfalling ARs on the West Coast and their extreme (>95th percentile) precipitation using GCMs, rather than the entire distribution of precipitation using a convection-permitting model, as is considered in this study. Using future ensemble changes in CMIP5 members rather than just the mean change, as is considered in the present study, is another reason why the projected increase in precipitation in the current study is possibly less than that in Gao et al. (2015).

Fig. 9.
Fig. 9.

As in Fig. 4, but for average precipitation in 45 cool-season flood-producing storms in California between 2002 and 2013.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

Table 1.

Future percent change in precipitation, SWE, and runoff in 45 flood-producing storms in California from 2002 to 2013. The future percent change is (PGW − CTRL)/CTRL × 100 in different California regions (Northern, central, and Southern).

Table 1.
Table 2.

As in Table 1, but for the raw future change (PGW − CTRL) in temperature, precipitation, SWE, and runoff.

Table 2.

Future changes in SWE among California flood-producing storms, which has not been examined in much detail before, display an overall decrease (Fig. 10). This decrease is largest in SoCal of −89.6% (Table 1), but SoCal does not receive much SWE in a current climate (Fig. 10b), so the absolute decrease of 0.3 mm in SWE in a future climate is minimal (Table 2). However, the future decrease in NorCal SWE of −68.3% (−4.4 mm) is notable, as it receives 50–130 mm of SWE on average from cool-season flood events in the CTRL simulations. Interestingly, CenCal exhibits the least future decrease in SWE in a future climate, with a −32% (−1.7 mm) average decrease. This region contains some of the highest mountains, and the highest SWE in the CTRL simulations, with large areas of SWE exceeding 100 mm (Fig. 10a). The reason for the lesser decrease in SWE in CenCal is because some areas in the Sierra Nevada exhibit a future increase in SWE, up to 20%–30%. This is along the highest terrain of the Sierra Nevada (Fig. 1), and the increase is due to future temperatures in this region staying below freezing (<273 K; Fig. 11), thus allowing for frozen precipitation to continue despite a warmer climate. Some mid-elevation (1500–2500 m) locations are also below freezing in the future, but these places show a 0–25-mm decrease in SWE. The reason for the future increases in SWE at the high elevations that are below freezing in the future climate, but not mid-elevations is unclear, but results are consistent with Rasmussen et al. (2014), and could be due to mid-elevations sites receiving a mix of rain and snow, depending on the individual flood-producing storm characteristics.

Fig. 10.
Fig. 10.

As in Fig. 5, but for average SWE in 45 cool-season flood-producing storms in California between 2002 and 2013.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

Fig. 11.
Fig. 11.

PGW − CTRL differences in precipitation (red dots) and SWE (blue dots) in 45 flood-producing storms in California as a function of altitude. Turquoise (dark blue) colored dots indicate SWE changes in storms where the temperature is below (above) freezing (273 K) in the PGW simulations.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

The future increase in precipitation and decrease in SWE in flood-producing storms in California results in increased future runoff over the entire state (Fig. 12), similar to the cool-season climatology. Runoff increases range from 15.3% (1.2 mm) in SoCal to 34.4% (4.4 mm) in NorCal, with the greatest increases along complex terrain. The increased runoff is consistent with future changes in the Sierra Nevada (Huang et al. 2018) and the Colorado Rockies (Rasmussen et al. 2014), due to the change from snow to rain, and overall increased precipitation. Given that ARs are associated with anomalously warm conditions in the present climate that exacerbate rain-on-snow flood risks (Neiman et al. 2008), and future temperatures in cool-season flood-producing storms increase by approximately 4 K (Table 2), future warming likely enhances rain-on-snow flood risk (Musselman et al. 2018) or purely rainfall-driven flood risk (Davenport et al. 2020), and contributes to increased runoff. Thus, cool-season flood-producing storms in California in a future climate appear to be more intense due to increased precipitation and runoff, and decreased SWE, consistent with future projections of floods in the Sierra Nevada that show increased streamflow magnitudes due to more frequent storms and precipitation falling as rain rather than snow (Das et al. 2011).

Fig. 12.
Fig. 12.

As in Fig. 6, but for average runoff in 45 cool-season flood-producing storms in California between 2002 and 2013.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

To understand why precipitation and runoff increase in future cool-season flood-producing storms in California, future changes to IVT are examined. IVT is not only used to indicate AR occurrence (Ralph et al. 2011; Rutz et al. 2014; Young et al. 2017; Ralph et al. 2019) but is also strongly related to orographic precipitation (Junker et al. 2008; Neiman et al. 2002, 2013; Ralph et al. 2013). In the CTRL simulations, the average IVT plume during flood-producing storms in California is 250 kg m−1 s−1, with a defined IVT plume in the lower half exceeding values of 290 kg m−1 s−1 (Fig. 13). Given that the IVT threshold for AR identification is at or equal to 250 kg m−1 s−1 (Ralph et al. 2011; Rutz et al. 2014; Young et al. 2017; Ralph et al. 2019), this confirms the identification of 45 flood-producing storms being associated with ARs based on the Rutz et al. (2014) database. In the future, the average IVT plume increases by 100 to 350 kg m−1 s−1 (Fig. 12), which is a 10.7% increase per degree of warming. Dettinger et al. (2011) similarly found that future ARs become more intense, with increases in IWV. Gao et al. (2015) discovered that this thermodynamic (water vapor) effect dominates over the dynamical (wind) effect in explaining the 50%–600% increase in future AR days along western North America. This thermodynamic effect of increased water vapor, which is simulated using the PGW methodology in this study, likely explains much of why IVT increases in a future climate in association with flood-producing storms. Therefore, stronger future ARs associated with cool-season floods in California is consistent with prior work, with the present study additionally showing that the stronger ARs are associated with increased precipitation and runoff due to enhanced water vapor transport in California flood-producing storms.

Fig. 13.
Fig. 13.

As in Fig. 12, but for IVT.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0067.1

4. Conclusions

High-resolution convection-permitting simulations are utilized to understand future changes to California’s precipitation, SWE, and runoff in 45 AR flood-producing events as well as the entire cool season. Given the ability of the PGW simulations to reproduce the precipitation structure and amount in these flood-producing storms over the same duration and location as CTRL simulations, there is confidence that storms producing floods in the current climate also occur in the future, likely resulting in a flood due to their association with AR conditions. While studies have previously investigated future changes to ARs impacting the West Coast, including a flood caused by an AR (Mahoney et al. 2018), this is the first study to examine changes in a large number of flood-producing storms and the cool-season’s hydrologic components using a convection-permitting regional climate model framework. Such information is critical to understand how California’s water resources will change in a future climate, and the use of high-resolution simulations are necessary for accurately depicting the complex orographic processes and storm dynamics leading to such changes in a future, warmer climate.

Future changes in California’s cool-season flood-producing storms associated with ARs are similar to changes in the cool-season climatology of precipitation, SWE, and runoff. This result suggests that changes in future AR flood-producing storms will significantly affect California’s water resources, given that a majority of the state’s water resources are due to cool-season precipitation from ARs (Neiman et al. 2008; Dettinger et al. 2011). Flood-producing storm precipitation increases 22%–26% in the future likely due to increased water vapor via the Clausius–Clapeyron relationship (Trenberth et al. 2003) and increased average IVT of 100 kg m−1 s−1. Thus, the ARs associated with floods will likely become more intense in a future climate, leading to enhanced precipitation. However, due to the approximately 4°C warmer temperatures in these storms (Table 2), flood-producing storm SWE decreases by 32%–90% in the future, mostly over the Sierra Nevada. Only at the highest elevations (i.e., above 3000 m) does SWE increase in the future, due to temperatures staying below freezing. The overall increase (decrease) in flood-producing precipitation (SWE) results in increased future runoff between 15% and 34%.

Increases in future precipitation and runoff suggests that flood-producing storms will become more intense in a future climate in California. Decreases in future SWE imply decreased available water resources available for California, given that this provides approximately 60% of the state’s developed water (Huang et al. 2018). While extending this analysis through the entire year would allow for a more accurate determination of changes in future SWE (especially snowmelt during May and June) and water resources, the results of the present study have serious implications for California. More intense future flood-producing storms and less future water resources in California are two detrimental impacts of climate change that must be taken into account in future water management strategies. Vano et al. (2018) specifically highlighted the need of water managers to understand how flood-producing storms will change in the future, and this study directly addresses those needs. Thus, such results should be considered in water management plans in California in order to effectively capture the excessive precipitation and runoff projected in future flood-producing storms, especially since the main current water source—snowpack—might not be able to provide for the state’s ever-growing population in a warmer climate.

Acknowledgments

This work has been supported by the National Science Foundation Research Experiences for Undergraduates Site in Climate Science at Colorado State University under Cooperative Agreement AGS-1461270.

Data availability statement

The WRF-CONUS dataset was accessed through Cheyenne, with an online listing through the Research Data Archive site (https://rda.ucar.edu/datasets/ds612.0/). Flood cases over California are from Dougherty and Rasmussen (2019) and are available upon request. Hourly runoff and snow water equivalent data were obtained through personal communication with Kyoko Ikeda. The Rutz et al. (2014) database is available from the following website: http://www.inscc.utah.edu/~rutz/ar_catalogs/.

REFERENCES

  • Ban, N., J. Schmidli, and C. Schar, 2015: Heavy precipitation in a changing climate: Does short term summer precipitation increase faster? Geophys. Res. Lett., 42, 11651172, https://doi.org/10.1002/2014GL062588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., and Coauthors, 2019: Daily evaluation of 26 precipitation datasets using Stage-IV gauge radar data for the CONUS. Hydrol. Earth Syst. Sci., 23, 207224, https://doi.org/10.5194/hess-23-207-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corringham, T. W., F. M. Ralph, A. Gershunov, D. R. Cayan, and C. A. Talbot, 2019: Atmospheric rivers drive flood damages in the western United States. Sci. Adv., 5, eaax4631, https://doi.org/10.1126/sciadv.aax4631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., R. M. Rasmussen, C. Liu, K. Ikeda, and A. F. Prein, 2017: A new mechanism for warm season precipitation response to global warming based on convection-permitting simulations. Climate Dyn., 55, 343368, https://doi.org/10.1007/S00382-017-3787-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical–topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Das, T. M., M. D. Dettinger, D. R. Cayan, and H. G. Hidalog, 2011: Potential increase in floods in California’s Sierra Nevada under future climate projections. Climatic Change, 109, 7194, https://doi.org/10.1007/S10584-011-0298-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davenport, F. V., J. E. Herrera-Estrada, M. Burke, and N. S. Diffenbaugh, 2020: Flood size increases nonlinearly across the western United States in response to lower snow precipitation ratios. Water Resour. Res., 56, e2019WR025571, https://doi.org/10.1029/2019WR025571.

    • 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., F. M. Ralph, T. Das, P. J. Neiman, and D. R. Cayan, 2011: Atmospheric rivers, floods, and the water resources of California. Water, 3, 445478, https://doi.org/10.3390/w3020445.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doswell, C. A., III, H. E. Brooks, and R. A. Maddox, 1996: Flash flood forecasting: An ingredients-based methodology. Wea. Forecasting, 11, 560581, https://doi.org/10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dougherty, E., and K. L. Rasmussen, 2019: Climatology of flood-producing storms and their associated rainfall characteristics in the United States. Mon. Wea. Rev., 147, 38613877, https://doi.org/10.1175/MWR-D-19-0020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dougherty, E., and K. L. Rasmussen, 2020: Changes in flash flood-producing storms in the United States. J. Hydrometeor., 21, 22212236, https://doi.org/10.1175/JHM-D-20-0014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn, 2011: Regional climate models add value to global model data: A review and selected examples. Bull. Amer. Meteor. Soc., 92, 11811192, https://doi.org/10.1175/2011BAMS3061.1.

    • 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
  • Guan, B., N. P. Molotch, D. E. Waliser, E. J. Fetzer, and P. J. Neiman, 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gutmann, E. D., and Coauthors, 2018: Changes in hurricanes from a 13-yr convection-permitting pseudo global warming simulation. J. Climate, 31, 36433657, https://doi.org/10.1175/JCLI-D-17-0391.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagos, S. M., 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 American from the large ensemble CESM simulations. Geophys. Res. Lett., 43, 13571363, https://doi.org/10.1002/2015GL067392.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hatchett, B., B. Daudert, C. Garner, N. Oakley, A. Putnam, and A. White, 2017: Winter snow level rise in the northern Sierra Nevada from 2008 to 2017. Water, 9, 899, https://doi.org/10.3390/w9110899.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, X., A. D. Hall, and N. Berg, 2018: Anthropogenic warming impacts on today’s Sierra Nevada snowpack and flood risk. Geophys. Res. Lett., 45, 62156222, https://doi.org/10.1029/2018GL077432.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, X., D. L. Swain, and D. B. Walton, 2020: Simulation and evaluating atmospheric river-induced precipitation extremes along the U.S. Pacific Coast: Case studies from 1980–2017. J. Geophys. Res. Atmos., 125, e2019JD031554, https://doi.org/10.1029/2019JD031554.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Junker, N. W., R. H. Grumm, R. Hart, L. F. Bosart, K. M. Bell, and F. J. Pereira, 2008: Use of normalized anomaly fields to anticipate extreme rainfall in the mountains of Northern California. Wea. Forecasting, 23, 336356, https://doi.org/10.1175/2007WAF2007013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kendon, E., N. M. Roberts, H. J . Fowler, M. J. Roberts, S. C. Chan, and C. A. Senior, 2014: Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat. Climate Change, 4, 570576, https://doi.org/10.1038/nclimate2258.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lackmann, G. M., 2013: The south-central U.S. flood of May 2010: Present and future. J. Climate, 26, 46884709, https://doi.org/10.1175/JCLI-D-12-00392.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., and K. E. Mitchell, 2005: The NCEP Stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2, https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.

  • Liu, C., and Coauthors, 2017: Continental-scale convection-permitting modeling of the current and future climate of North America. Climate Dyn., 49, 7195, https://doi.org/10.1007/s00382-016-3327-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorente-Plazas, R., T. P. Mitchell, G. Mauger, and E. P. Salathé Jr., 2018: Local enhancement of extreme precipitation during atmospheric rivers as simulated in a regional climate model. J. Hydrometeor., 19, 14291446, https://doi.org/10.1175/JHM-D-17-0246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., C. F. Chappell, and L. R. Hoxit, 1979: Synoptic and meso-α scale aspects of flash flood events. Bull. Amer. Meteor. Soc., 60, 115123, https://doi.org/10.1175/1520-0477-60.2.115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., F. Canova, and L. R. Hoxit, 1980: Meteorological characteristics of flash flood events over the western United States. Mon. Wea. Rev., 108, 18661877, https://doi.org/10.1175/1520-0493(1980)108<1866:MCOFFE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahoney, K., M. Alexander, J. D. Scott, and J. Barsugli, 2013: High-resolution downscaled simulations of warms-season extreme precipitation events in the Colorado Front Range under past and future climates. J. Climate, 26, 86718689, https://doi.org/10.1175/JCLI-D-12-00744.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahoney, K., D. Swales, M. J. Mueller, M. Alexander, M. Hughes, and K. Malloy, 2018: An examination of inland-penetrating atmospheric river flood event under potential future thermodynamic conditions. J. Climate, 31, 62816297, https://doi.org/10.1175/JCLI-D-18-0118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Musselman, K. N., F. Lehner, K. Ikeda, M. P. Clark, A. F. Prein, C. Liu, M. Barlage, and R. Rasmussen, 2018: Projected increases and shifts in rain-on-snow flood risk over western North America. Nat. Climate Change, 8, 808812, https://doi.org/10.1038/s41558-018-0236-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, A. B. White, D. E. Kingsmill, and P. O. G. Persson, 2002: The statistical relationship between upslope flow and rainfall in California’s coastal mountains: Observations during CALJET. Mon. Wea. Rev., 130, 14681492, https://doi.org/10.1175/1520-0493(2002)130<1468:TSRBUF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, G. A. Wick, J. D. Lundquist, and M. D. Dettinger, 2008: 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. Hydrometeor., 9, 2247, https://doi.org/10.1175/2007JHM855.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neiman, P. J., F. M. Ralph, B. J. Moore, M. Hughes, K. M. Mahoney, J. M. Cordeira, and M. D. Dettinger, 2013: The landfall and inland penetration of a flood-producing atmospheric river in Arizona. Part I: Observed synoptic-scale, orographic, and hydrometeorological characteristics. J. Hydrometeor., 14, 460484, https://doi.org/10.1175/JHM-D-12-0101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G. Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., R. M. Rasmussen, K. Ikeda, C. Liu, M. P. Clark, and G. J. Holland, 2017a: The future intensification of hourly precipitation extremes. Nat. Climate Change, 7, 4852, https://doi.org/10.1038/nclimate3168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., C. Liu, K. Ikeda, R. Bullock, R. M. Rasmussen, G. J. Holland, and M. Clark, 2017b: Simulating North American mesoscale convective systems with a convection-permitting climate model. Climate Dyn., 55, 95110, https://doi.org/10.1007/s00382-017-3993-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., C. Liu, K. Ikeda, S. B. Trier, R. M. Rasmussen, and G. J. Holland, 2017c: Increased rainfall volume from future convective storms in the US. Nat. Climate Change, 7, 880884, https://doi.org/10.1038/s41558-017-0007-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., and M. D. Dettinger, 2012: Historical and national perspectives on extreme West Coast precipitation associated with atmospheric rivers during December 2010. Bull. Amer. Meteor. Soc., 93, 783790, https://doi.org/10.1175/BAMS-D-11-00188.1.

    • 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., P. J. Neiman, G. N. Kiladis, K. Weickman, and D. W. Reynolds, 2011: A multi-scale observational case study of a Pacific atmospheric river exhibiting tropical-extratropical connections and a mesoscale frontal wave. Mon. Wea. Rev., 139, 11691189, https://doi.org/10.1175/2010MWR3596.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., T. Coleman, P. J. Neiman, R. J. Zamora, and M. D. Dettinger, 2013: Observed impacts of duration and seasonality of atmospheric-river landfalls on soil moisture and runoff in coastal Northern California. J. Hydrometeor., 14, 443459, https://doi.org/10.1175/JHM-D-12-076.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., and Coauthors, 2019: A scale to characterize the strength and impacts of atmospheric rivers. Bull. Amer. Meteor. Soc., 100, 269288, https://doi.org/10.1175/BAMS-D-18-0023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, K. L., A. F. Prein, R. M. Rasmussen, K. Ikeda, and C. Liu, 2017: Changes in the convective population and thermodynamic environments in convection-permitting regional climate simulations over the United States. Climate Dyn., 55, 383408, https://doi.org/10.1007/S00382-017-4000-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and Coauthors, 2011: High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate. J. Climate, 24, 30153048, https://doi.org/10.1175/2010JCLI3985.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and Coauthors, 2014: Climate change impacts on the water balance of the Colorado Headwaters: High-resolution regional climate model simulations. J. Hydrometeor., 15, 10911116, https://doi.org/10.1175/JHM-D-13-0118.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • 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
  • Saharia, M., P. Kirsteetter, H. Vergara, J. J. Gourley, Y. Hong, and M. Giroud, 2017b: Characterization of floods in the United States. J. Hydrol., 548, 524535, https://doi.org/10.1016/j.jhydrol.2017.03.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, M., A. Hall, F. Sun, D. Walton, and N. Berg, 2017: Significant and inevitable end-of-twenty-first-century advances in surface runoff timing in California’s Sierra Nevada. J. Hydrometeor., 18, 31813197, https://doi.org/10.1175/JHM-D-16-0257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shen, S., Y. Mei, and E. M. Anagnostou, 2017: A comprehensive database of flood events in the contiguous United States from 2002 to 2013. Bull. Amer. Meteor. Soc., 98, 14931502, https://doi.org/10.1175/BAMS-D-16-0125.1.

    • 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
  • Thompson, G., and T. Eidhammer, 2014: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci., 71, 36363658, https://doi.org/10.1175/JAS-D-13-0305.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 12051218, https://doi.org/10.1175/BAMS-84-9-1205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vano, J. A., K. Miller, M. D. Dettinger, R. Cifelli, D. Curtis, A. Dufour, J. R. Olsen, and A. M. Wilson, 2018: Hydroclimatic extremes as challenges for the water management community: Lessons from Oroville Dam and Hurricane Harvey. Bull. Amer. Meteor. Soc., 100, S9S14, https://doi.org/10.1175/BAMS-D-18-0219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Z., A. C. Bovik, H. R. Sheikh, and E. P. Simoncellli, 2004: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., 13, 600612, https://doi.org/10.1109/TIP.2003.819861.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, A. B., B. J. Moore, D. J. Gottas, and P. J. Neiman, 2019: Winter storm conditions leading to excessive runoff above California’s Oroville Dam during January and February 2017. Bull. Amer. Meteor. Soc., 100, 5570, https://doi.org/10.1175/BAMS-D-18-0091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, A. M., K. T. Skelly, and J. M. Cordeira, 2017: High-impact hydrologic events and atmospheric rivers in California: An investigation using the NCEI Storm Events Database. Geophys. Res. Lett., 44, 33933401, https://doi.org/10.1002/2017GL073077.

    • Crossref
    • Search Google Scholar
    • Export Citation
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