• Beobide-Arsuaga, G., T. Bayr, A. Reintges, and M. Latif, 2021: Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models. Climate Dyn., 56, 38753888, https://doi.org/10.1007/s00382-021-05673-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beusch, L., L. Gudmundsson, and S. I. Seneviratne, 2020: Emulating Earth system model temperatures with MESMER: From global mean temperature trajectories to grid-point-level realizations on land. Earth Syst. Dyn., 11, 139159, https://doi.org/10.5194/esd-11-139-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Box, G. E., and D. R. Cox, 1964: An analysis of transformations. J. Roy. Stat. Soc., B26, 211243, https://doi.org/10.1111/J.2517-6161.1964.TB00553.X.

    • Search Google Scholar
    • Export Citation
  • Castruccio, S., Z. Hu, B. Sanderson, A. Karspeck, and D. Hammerling, 2019: Reproducing internal variability with few ensemble runs. J. Climate, 32, 85118522, https://doi.org/10.1175/JCLI-D-19-0280.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J., F. P. Brissette, and R. Leconte, 2010: A daily stochastic weather generator for preserving low-frequency of climate variability. J. Hydrol., 388, 480490, https://doi.org/10.1016/j.jhydrol.2010.05.032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, J., H. Chen, and S. Guo, 2018: Multi-site precipitation downscaling using a stochastic weather generator. Climate Dyn., 50, 19751992, https://doi.org/10.1007/s00382-017-3731-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, X., and J. M. Wallace, 2016: Orthogonal PDO and ENSO indices. J. Climate, 29, 38833892, https://doi.org/10.1175/JCLI-D-15-0684.1.

  • Dai, A., J. C. Fyfe, S.-P. Xie, and X. Dai, 2015: Decadal modulation of global surface temperature by internal climate variability. Nat. Climate Change, 5, 555559, https://doi.org/10.1038/nclimate2605.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., F. Zeng, L. Zhang, R. Zhang, G. A. Vecchi, and X. Yang, 2017: The central role of ocean dynamics in connecting the North Atlantic Oscillation to the extratropical component of the Atlantic multidecadal oscillation. J. Climate, 30, 37893805, https://doi.org/10.1175/JCLI-D-16-0358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. S. Phillips, and J. W. Hurrell, 2004: Pacific interdecadal climate variability: Linkages between the tropics and the North Pacific during boreal winter since 1900. J. Climate, 17, 31093124, https://doi.org/10.1175/1520-0442(2004)017<3109:PICVLB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. S. Phillips, V. Bourdette, and H. Teng, 2012: Uncertainty in climate change projections: The role of internal variability. Climate Dyn., 38, 527546, https://doi.org/10.1007/s00382-010-0977-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. S. Phillips, M. A. Alexander, and B. V. Smoliak, 2014: Projecting North American climate over the next 50 years: Uncertainty due to internal variability. J. Climate, 27, 22712296, https://doi.org/10.1175/JCLI-D-13-00451.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., I. R. Simpson, A. S. Phillips, and K. A. McKinnon, 2018: How well do we know ENSO’s climate impacts over North America, and how do we evaluate models accordingly? J. Climate, 31, 49915014, https://doi.org/10.1175/JCLI-D-17-0783.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., and Coauthors, 2020: Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Climate Change, 10, 277286, https://doi.org/10.1038/s41558-020-0731-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, L., L. R. Leung, F. Song, and J. Lu, 2018: Roles of SST versus internal atmospheric variability in winter extreme precipitation variability along the U.S. West Coast. J. Climate, 31, 80398058, https://doi.org/10.1175/JCLI-D-18-0062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durack, P. J., S. E. Wijffels, and R. J. Matear, 2012: Ocean salinities reveal strong global water cycle intensification during 1950 to 2000. Science, 336, 455458, https://doi.org/10.1126/science.1212222.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and X. Bi, 2009: Time of emergence (TOE) of GHG-forced precipitation change hot-spots. Geophys. Res. Lett., 36, L06709, https://doi.org/10.1029/2009GL037593.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harding, B., A. Wood, and J. Prairie, 2012: The implications of climate change scenario selection for future streamflow projection in the upper Colorado River basin. Hydrol. Earth Syst. Sci., 16, 39894007, https://doi.org/10.5194/hess-16-3989-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., H. von Storch, K. Hasselmann, B. D. Santer, U. Cubasch, and P. D. Jones, 1996: Detecting greenhouse-gas-induced climate change with an optimal fingerprint method. J. Climate, 9, 22812306, https://doi.org/10.1175/1520-0442(1996)009<2281:DGGICC>2.0.CO;2.

    • 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
  • Kay, J. E., and Coauthors, 2015: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleiber, W., R. W. Katz, and B. Rajagopalan, 2012: Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes. Water Resour. Res., 48, W01523, https://doi.org/10.1029/2011WR011105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kooperman, G. J., Y. Chen, F. M. Hoffman, C. D. Koven, K. Lindsay, M. S. Pritchard, A. L. Swann, and J. T. Randerson, 2018: Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land. Nat. Climate Change, 8, 434440, https://doi.org/10.1038/s41558-018-0144-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamarque, J.-F., and Coauthors, 2010: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application. Atmos. Chem. Phys., 10, 70177039, https://doi.org/10.5194/acp-10-7017-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • L’Heureux, M. L., M. K. Tippett, and A. G. Barnston, 2015: Characterizing ENSO coupled variability and its impact on North American seasonal precipitation and temperature. J. Climate, 28, 42314245, https://doi.org/10.1175/JCLI-D-14-00508.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maher, N., D. Matei, S. Milinski, and J. Marotzke, 2018: ENSO change in climate projections: Forced response or internal variability? Geophys. Res. Lett., 45, 11 39011 398, https://doi.org/10.1029/2018GL079764.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maher, N., and Coauthors, 2019: The Max Planck Institute Grand Ensemble: Enabling the exploration of climate system variability. J. Adv. Model. Earth Syst., 11, 20502069, https://doi.org/10.1029/2019MS001639.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahlstein, I., R. W. Portmann, J. S. Daniel, S. Solomon, and R. Knutti, 2012: Perceptible changes in regional precipitation in a future climate. Geophys. Res. Lett., 39, L05701, https://doi.org/10.1029/2011GL050738.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mankin, J. S., F. Lehner, S. Coats, and K. A. McKinnon, 2020: The value of initial condition large ensembles to robust adaptation decision-making. Earth’s Future, 8, e2012EF001610, https://doi.org/10.1029/2020EF001610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martin, E. R., C. Thorncroft, and B. B. Booth, 2014: The multidecadal Atlantic SST–Sahel rainfall teleconnection in CMIP5 simulations. J. Climate, 27, 784806, https://doi.org/10.1175/JCLI-D-13-00242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKinnon, K. A., and C. Deser, 2018: Internal variability and regional climate trends in an observational large ensemble. J. Climate, 31, 67836802, https://doi.org/10.1175/JCLI-D-17-0901.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKinnon, K. A., A. Poppick, E. Dunn-Sigouin, and C. Deser, 2017: An “observational large ensemble” to compare observed and modeled temperature trend uncertainty due to internal variability. J. Climate, 30, 75857598, https://doi.org/10.1175/JCLI-D-16-0905.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meinshausen, M., and Coauthors, 2011: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109, 213241, https://doi.org/10.1007/s10584-011-0156-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newman, M., and Coauthors, 2016: The Pacific decadal oscillation, revisited. J. Climate, 29, 43994427, https://doi.org/10.1175/JCLI-D-15-0508.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., R. Knutti, F. Lehner, C. Deser, and B. M. Sanderson, 2017: Precipitation variability increases in a warmer climate. Sci. Rep., 7, 17966, https://doi.org/10.1038/s41598-017-17966-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Persad, G. G., D. L. Swain, C. Kouba, and J. P. Ortiz-Partida, 2020: Inter-model agreement on projected shifts in California hydroclimate characteristics critical to water management. Climatic Change, 162, 14931513, https://doi.org/10.1007/s10584-020-02882-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Phillips, A. S., C. Deser, and J. Fasullo, 2014: Evaluating modes of variability in climate models. Eos, Trans. Amer. Geophys. Union, 95, 453455, https://doi.org/10.1002/2014EO490002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N., D. E. Parker, E. Horton, C. K. Folland, L. V. Alexander, D. Rowell, E. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roberts, J., and T. D. Roberts, 1978: Use of the Butterworth low-pass filter for oceanographic data. J. Geophys. Res., 83, 55105514, https://doi.org/10.1029/JC083iC11p05510.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., and M. S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon. Wea. Rev., 115, 16061626, https://doi.org/10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruprich-Robert, Y., T. Delworth, R. Msadek, F. Castruccio, S. Yeager, and G. Danabasoglu, 2018: Impacts of the Atlantic multidecadal variability on North American summer climate and heat waves. J. Climate, 31, 36793700, https://doi.org/10.1175/JCLI-D-17-0270.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sarojini, B. B., P. A. Stott, and E. Black, 2016: Detection and attribution of human influence on regional precipitation. Nat. Climate Change, 6, 669675, https://doi.org/10.1038/nclimate2976.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scaife, A. A., and D. Smith, 2018: A signal-to-noise paradox in climate science. npj Climate Atmos. Sci., 1, 28, https://doi.org/10.1038/s41612-018-0038-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, U., T. Fuchs, A. Meyer-Christoffer, and B. Rudolf, 2008: Global precipitation analysis products of the GPCC. DWD Global Precipitation Climatology Centre, 112 pp.

  • Schreiber, T., and A. Schmitz, 1996: Improved surrogate data for nonlinearity tests. Phys. Rev. Lett., 77, 635638, https://doi.org/10.1103/PhysRevLett.77.635.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simpson, I. R., C. Deser, K. A. McKinnon, and E. A. Barnes, 2018: Modeled and observed multidecadal variability in the North Atlantic jet stream and its connection to sea surface temperatures. J. Climate, 31, 83138338, https://doi.org/10.1175/JCLI-D-18-0168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simpson, I. R., S. G. Yeager, K. A. McKinnon, and C. Deser, 2019: Decadal predictability of late winter precipitation in western Europe through an ocean–jet stream connection. Nat. Geosci., 12, 613619, https://doi.org/10.1038/s41561-019-0391-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steinschneider, S., and C. Brown, 2013: A semiparametric multivariate, multisite weather generator with low-frequency variability for use in climate risk assessments. Water Resour. Res., 49, 72057220, https://doi.org/10.1002/wrcr.20528.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Suarez-Gutierrez, L., N. Maher, and S. Milinski, 2020: Evaluating the internal variability and forced response in large ensembles. US CLIVAR Variations, 18, 27–35, https://doi.org/10.5065/0DSY-WH17.

    • Crossref
    • Export Citation
  • Sun, C., J. Li, and S. Zhao, 2015: Remote influence of Atlantic multidecadal variability on Siberian warm season precipitation. Sci. Rep., 5, 16853, https://doi.org/10.1038/srep16853.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swain, D. L., B. Langenbrunner, J. D. Neelin, and A. Hall, 2018: Increasing precipitation volatility in twenty-first-century California. Nat. Climate Change, 8, 427433, https://doi.org/10.1038/s41558-018-0140-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tél, T., T. Bódai, G. Drótos, T. Haszpra, M. Herein, B. Kaszás, and M. Vincze, 2020: The theory of parallel climate realizations. J. Stat. Phys., 179, 1496–1530, https://doi.org/10.1007/s10955-019-02445-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W., E. A. Barnes, C. Deser, W. E. Foust, and A. S. Phillips, 2015: Quantifying the role of internal climate variability in future climate trends. J. Climate, 28, 64436456, https://doi.org/10.1175/JCLI-D-14-00830.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 2011: Changes in precipitation with climate change. Climate Res., 47, 123138, https://doi.org/10.3354/cr00953.

  • Trenberth, K. E., and D. J. Shea, 2006: Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett., 33, L12704, https://doi.org/10.1029/2006GL026894.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Oldenborgh, G. J., and G. Burgers, 2005: Searching for decadal variations in ENSO precipitation teleconnections. Geophys. Res. Lett., 32, L15701, https://doi.org/10.1029/2005GL023110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verdin, A., B. Rajagopalan, W. Kleiber, and R. W. Katz, 2015: Coupled stochastic weather generation using spatial and generalized linear models. Stochastic Environ. Res. Risk Assess., 29, 347356, https://doi.org/10.1007/s00477-014-0911-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • von Trentini, F., E. E. Aalbers, E. M. Fischer, and R. Ludwig, 2020: Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe. Earth Syst. Dyn., 11, 10131031, https://doi.org/10.5194/esd-11-1013-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S., J. Huang, Y. He, and Y. Guan, 2014: Combined effects of the Pacific Decadal Oscillation and El Niño–Southern Oscillation on global land dry–wet changes. Sci. Rep., 4, 6651, https://doi.org/10.1038/srep06651.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1997: Resampling hypothesis tests for autocorrelated fields. J. Climate, 10, 6582, https://doi.org/10.1175/1520-0442(1997)010<0065:RHTFAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., and R. L. Wilby, 1999: The weather generation game: A review of stochastic weather models. Prog. Phys. Geogr., 23, 329357, https://doi.org/10.1177/030913339902300302.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wills, R. C., T. Schneider, J. M. Wallace, D. S. Battisti, and D. L. Hartmann, 2018: Disentangling global warming, multidecadal variability, and El Niño in Pacific temperatures. Geophys. Res. Lett., 45, 24872496, https://doi.org/10.1002/2017GL076327.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H., R. Seager, J. He, H. Diao, and S. Pascale, 2021: Quantifying atmosphere and ocean origins of North American precipitation variability. Climate Dyn., 56, 40514074, https://doi.org/10.1007/s00382-021-05685-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, L., and T. L. Delworth, 2015: Analysis of the characteristics and mechanisms of the Pacific decadal oscillation in a suite of coupled models from the Geophysical Fluid Dynamics Laboratory. J. Climate, 28, 76787701, https://doi.org/10.1175/JCLI-D-14-00647.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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The Inherent Uncertainty of Precipitation Variability, Trends, and Extremes due to Internal Variability, with Implications for Western U.S. Water Resources

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  • 1 aDepartment of Statistics and Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, California
  • | 2 bNational Center for Atmospheric Research, Boulder, Colorado
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Abstract

The approximately century-long instrumental record of precipitation over land reflects a single sampling of internal variability. Thus, the spatiotemporal evolution of the observations is only one realization of “what could have occurred” given the same climate system and boundary conditions but different initial conditions. While climate models can be used to produce initial-condition large ensembles that explicitly sample different sequences of internal variability, an analogous approach is not possible for the real world. Here, we explore the use of a statistical model for monthly precipitation to generate synthetic ensembles based on a single record. When tested within the context of the NCAR Community Earth System Model version 1 Large Ensemble (CESM1-LE), we find that the synthetic ensemble can closely reproduce the spatiotemporal statistics of variability and trends in winter precipitation over the extended contiguous United States and that it is difficult to infer the climate change signal in a single record given the magnitude of the variability. We additionally create a synthetic ensemble based on the Global Precipitation Climatology Centre (GPCC) dataset, termed the GPCC-synth-LE; comparison of the GPCC-synth-LE with the CESM1-based ensembles reveals differences in the spatial structures and magnitudes of variability, highlighting the advantages of an observationally based ensemble. We finally use the GPCC-synth-LE to analyze three water resource metrics in the upper Colorado River basin: frequency of dry, wet, and whiplash years. Thirty-one-year “climatologies” in the GPCC-synth-LE can differ by over 20% in these key water resource metrics due to sampling of internal variability, and individual ensemble members in the GPCC-synth-LE can exhibit large near-monotonic trends over the course of the last century due to sampling of internal variability alone.

© 2021 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: Karen A. McKinnon, kmckinnon@ucla.edu

Abstract

The approximately century-long instrumental record of precipitation over land reflects a single sampling of internal variability. Thus, the spatiotemporal evolution of the observations is only one realization of “what could have occurred” given the same climate system and boundary conditions but different initial conditions. While climate models can be used to produce initial-condition large ensembles that explicitly sample different sequences of internal variability, an analogous approach is not possible for the real world. Here, we explore the use of a statistical model for monthly precipitation to generate synthetic ensembles based on a single record. When tested within the context of the NCAR Community Earth System Model version 1 Large Ensemble (CESM1-LE), we find that the synthetic ensemble can closely reproduce the spatiotemporal statistics of variability and trends in winter precipitation over the extended contiguous United States and that it is difficult to infer the climate change signal in a single record given the magnitude of the variability. We additionally create a synthetic ensemble based on the Global Precipitation Climatology Centre (GPCC) dataset, termed the GPCC-synth-LE; comparison of the GPCC-synth-LE with the CESM1-based ensembles reveals differences in the spatial structures and magnitudes of variability, highlighting the advantages of an observationally based ensemble. We finally use the GPCC-synth-LE to analyze three water resource metrics in the upper Colorado River basin: frequency of dry, wet, and whiplash years. Thirty-one-year “climatologies” in the GPCC-synth-LE can differ by over 20% in these key water resource metrics due to sampling of internal variability, and individual ensemble members in the GPCC-synth-LE can exhibit large near-monotonic trends over the course of the last century due to sampling of internal variability alone.

© 2021 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: Karen A. McKinnon, kmckinnon@ucla.edu

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