• Athanasiadis, P. J., J. M. Wallace, and J. J. Wettstein, 2010: Patterns of wintertime jet stream variability and their relation to the storm tracks. J. Atmos. Sci., 67, 13611381, https://doi.org/10.1175/2009JAS3270.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Black, J., N. C. Johnson, S. Baxter, S. B. Feldstein, D. S. Harnos, and M. L. L’Heureux, 2017: The predictors and forecast skill of Northern Hemisphere teleconnection patterns for lead times of 3–4 weeks. Mon. Wea. Rev., 145, 28552877, https://doi.org/10.1175/MWR-D-16-0394.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bladé, I., D. Fortuny, G. J. van Oldenborgh, and B. Liebmann, 2012: The summer North Atlantic Oscillation in CMIP3 models and related uncertainties in projected summer drying in Europe. J. Geophys. Res., 117, D16104, https://doi.org/10.1029/2012JD017816.

    • Search Google Scholar
    • Export Citation
  • Butler, A. H., D. W. J. Thompson, and R. Heikes, 2010: The steady-state atmospheric circulation response to climate change–like thermal forcings in a simple general circulation model. J. Climate, 23, 34743496, https://doi.org/10.1175/2010JCLI3228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Z., B. Gan, L. Wu, and F. Jia, 2018: Pacific–North American teleconnection and North Pacific Oscillation: Historical simulation and future projection in CMIP5 models. Climate Dyn., 50, 43794403, https://doi.org/10.1007/s00382-017-3881-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cook, B. I., J. S. Mankin, K. Marvel, A. P. Williams, J. E. Smerdon, and K. J. Anchukaitis, 2020: Twenty-first century drought projections in the CMIP6 forcing scenarios. Earth’s Future, 8, e2019EF001461, https://doi.org/10.1029/2019EF001461.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., and T. M. L. Wigley, 2000: Global patterns of ENSO-induced precipitation. Geophys. Res. Lett., 27, 12831286, https://doi.org/10.1029/1999GL011140.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. 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. 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., J. W. Hurrell, and A. S. Phillips, 2017: The role of the North Atlantic Oscillation in European climate projections. Climate Dyn., 49, 31413157, https://doi.org/10.1007/s00382-016-3502-z.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fiedler, S., and et al. , 2020: Simulated tropical precipitation assessed across three major phases of the Coupled Model Intercomparison Project (CMIP). Mon. Wea. Rev., 148, 36533680, https://doi.org/10.1175/MWR-D-19-0404.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garfinkel, C. I., O. Adam, E. Morin, Y. Enzel, E. Elbaum, M. Bartov, D. Rostkier-Edelstein, and U. Dayan, 2020: The role of zonally averaged climate change in contributing to intermodel spread in CMIP5 predicted local precipitation changes. J. Climate, 33, 11411154, https://doi.org/10.1175/JCLI-D-19-0232.1.

    • 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
  • Grise, K. M., and L. M. Polvani, 2016: Is climate sensitivity related to dynamical sensitivity? J. Geophys. Res., 121, 51595176, https://doi.org/10.1002/2015JD024687.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grise, K. M., S. Son, and J. R. Gyakum, 2013: Intraseasonal and interannual variability in North American storm tracks and its relationship to equatorial Pacific variability. Mon. Wea. Rev., 141, 36103625, https://doi.org/10.1175/MWR-D-12-00322.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harvey, B. J., L. C. Shaffrey, and T. J. Woollings, 2014: Equator-to-pole temperature differences and the extra-tropical storm track responses of the CMIP5 climate models. Climate Dyn., 43, 11711182, https://doi.org/10.1007/s00382-013-1883-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor. Soc., 90, 10951108, https://doi.org/10.1175/2009BAMS2607.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hazeleger, W., B. van den Hurk, E. Min, G. J. van Oldenborgh, A. C. Petersen, D. A. Stainforth, E. Vasileiadou, and L. A. Smith, 2015: Tales of future weather. Nat. Climate Change, 5, 107113, https://doi.org/10.1038/nclimate2450.

    • 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
  • Hersbach, H., and et al. , 2019: ERA5 monthly averaged data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Accessed 6 April 2021, https://doi.org/10.24381/cds.6860a573.

    • Crossref
    • Export Citation
  • Hersbach, H., and et al. , 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

  • Hoerling, M., J. Eischeid, and J. Perlwitz, 2010: Regional precipitation trends: Distinguishing natural variability from anthropogenic forcing. J. Climate, 23, 21312145, https://doi.org/10.1175/2009JCLI3420.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoerling, M., J. Eischeid, J. Perlwitz, X. Quan, K. Wolter, and L. Cheng, 2016: Characterizing recent trends in U.S. heavy precipitation. J. Climate, 29, 23132332, https://doi.org/10.1175/JCLI-D-15-0441.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscillation: Regional temperatures and precipitation. Science, 269, 676679, https://doi.org/10.1126/science.269.5224.676.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., Y. Kushnir, G. Ottersen, and M. Visbeck, 2003: An overview of the North Atlantic Oscillation. The North Atlantic Oscillation: Climatic Significance and Environmental Impact, J. W. Hurrell et al., Eds., American Geophysical Union, 1–35, https://doi.org/10.1029/134GM01.

    • Crossref
    • Export Citation
  • Karpechko, A. Y., and E. Manzini, 2012: Stratospheric influence on tropospheric climate change in the Northern Hemisphere. J. Geophys. Res., 117, D05133, https://doi.org/10.1029/2011JD017036.

    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., F. Zeng, and A. T. Wittenberg, 2013: Multimodel assessment of regional surface temperature trends: CMIP3 and CMIP5 twentieth-century simulations. J. Climate, 26, 87098743, https://doi.org/10.1175/JCLI-D-12-00567.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leathers, D. J., B. Yarnal, and M. A. Palecki, 1991: The Pacific/North American teleconnection pattern and United States climate. Part I: Regional temperature and precipitation associations. J. Climate, 4, 517528, https://doi.org/10.1175/1520-0442(1991)004<0517:TPATPA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lenderink, G., B. van den Hurk, A. Klein Tank, G. J. van Oldenborgh, E. van Meijgaard, H. de Vries, and J. J. Beersma, 2014: Preparing local climate change scenarios for the Netherlands using resampling of climate model output. Environ. Res. Lett., 9, 115008, https://doi.org/10.1088/1748-9326/9/11/115008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manzini, E., and et al. , 2014: Northern winter climate change: Assessment of uncertainty in CMIP5 projections related to stratosphere–troposphere coupling. J. Geophys. Res., 119, 79797998, https://doi.org/10.1002/2013JD021403.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., C. A. Senior, V. Eyring, G. Flato, J.-F. Lamarque, R. J. Stouffer, K. E. Taylor, and M. Schlund, 2020: Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv., 6, eaba1981, https://doi.org/10.1126/sciadv.aba1981.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mindlin, J., T. G. Shepherd, C. S. Vera, M. Osman, G. Zappa, R. W. Lee, and K. I. Hodges, 2020: Storyline description of Southern Hemisphere midlatitude circulation and precipitation response to greenhouse gas forcing. Climate Dyn., 54, 43994421, https://doi.org/10.1007/s00382-020-05234-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Notaro, M., W.-C. Wang, and W. Gong, 2006: Model and observational analysis of the northeast U.S. regional climate and its relationship to the PNA and NAO patterns during early winter. Mon. Wea. Rev., 134, 34793505, https://doi.org/10.1175/MWR3234.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Neill, B. C., and et al. , 2016: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev., 9, 34613482, https://doi.org/10.5194/gmd-9-3461-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pithan, F., and T. Mauritsen, 2014: Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat. Geosci., 7, 181184, https://doi.org/10.1038/ngeo2071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheff, J., and D. Frierson, 2012: Twenty-first-century multimodel subtropical precipitation declines are mostly midlatitude shifts. J. Climate, 25, 43304347, https://doi.org/10.1175/JCLI-D-11-00393.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seager, R., N. Naik, and G. A. Vecchi, 2010: Thermodynamic and dynamic mechanisms for large-scale changes in the hydrological cycle in response to global warming. J. Climate, 23, 46514668, https://doi.org/10.1175/2010JCLI3655.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shepherd, T. G., 2019: Storyline approach to the construction of regional climate change information. Proc. Roy. Soc., 475A, 20190013, https://doi.org/10.1098/rspa.2019.0013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shepherd, T. G., and et al. , 2018: Storylines: An alternative approach to representing uncertainty in physical aspects of climate change. Climatic Change, 151, 555571, https://doi.org/10.1007/s10584-018-2317-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and J. M. Caron, 2000: The Southern Oscillation revisited: Sea level pressures, surface temperatures, and precipitation. J. Climate, 13, 43584365, https://doi.org/10.1175/1520-0442(2000)013<4358:TSORSL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • USGCRP, 2018: Impacts, Risks, and Adaptation in the United States. Vol. II, Fourth National Climate Assessment, D. R. Reidmiller et al., Eds., U.S. Global Change Research Program, https://doi.org/10.7930/NCA4.2018.

    • Crossref
    • Export Citation
  • van den Hurk, B., G. J. van Oldenborgh, G. Lenderink, W. Hazeleger, R. Haarsma, and H. de Vries, 2014a: Drivers of mean climate change around the Netherlands derived from CMIP5. Climate Dyn., 42, 16831697, https://doi.org/10.1007/s00382-013-1707-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van den Hurk, B., P. Siegmund, and A. Klein Tank, Eds., 2014b: KNMI’14: Climate change scenarios for the 21st century—A Netherlands perspective. KNMI Tech. Rep. WR 2014-01, 120 pp, http://bibliotheek.knmi.nl/knmipubWR/WR2014-01.pdf.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., Q. Fu, B. V. Smoliak, P. Lin, and C. M. Johanson, 2012: Simulated versus observed patterns of warming over the extratropical Northern Hemisphere continents during the cold season. Proc. Natl. Acad. Sci. USA, 109, 14 33714 342, https://doi.org/10.1073/pnas.1204875109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wold, H., 1966: Estimation of principal components and related models by iterative least squares. Multivariate Analysis, P. R. Krishnaiah, Ed., Academic Press, 391–420.

  • Yeh, S.-W., D.-W. Yi, M.-K. Sung, and Y. H. Kim, 2018: An eastward shift of the North Pacific Oscillation after the mid-1990s and its relationship with ENSO. Geophys. Res. Lett., 45, 66546660, https://doi.org/10.1029/2018GL078671.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zappa, G., 2019: Regional climate impacts of future changes in the mid-latitude atmospheric circulation: A storyline view. Curr. Climate Change Rep., 5, 358371, https://doi.org/10.1007/s40641-019-00146-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zappa, G., and T. G. Shepherd, 2017: Storylines of atmospheric circulation change for European regional climate impact assessment. J. Climate, 30, 65616577, https://doi.org/10.1175/JCLI-D-16-0807.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., T. A. Myers, D. T. McCoy, S. Po-Chedley, P. M. Caldwell, P. Ceppi, S. A. Klein, and K. E. Taylor, 2020: Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 261 261 48
Full Text Views 78 78 14
PDF Downloads 116 116 24

Drivers of Twenty-First-Century U.S. Winter Precipitation Trends in CMIP6 Models: A Storyline-Based Approach

View More View Less
  • 1 a Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Climate change during the twenty-first century has the potential to substantially alter geographic patterns of precipitation. However, regional precipitation changes can be very difficult to project, and in some regions, global climate models do not even agree on the sign of the precipitation trend. Since some of this uncertainty is due to internal variability rather than model bias, models cannot be used to narrow the possibilities to a single outcome, but they can usefully quantify the range of plausible outcomes and identify the combination of dynamical drivers that would be likely to produce each. This study uses a storylines approach—a type of regression-based analysis—to identify some of the key dynamical drivers that explain the variance in twenty-first-century U.S. winter precipitation trends across CMIP6 models under the SSP3–7.0 emissions scenario. This analysis shows that the spread in precipitation trends is not primarily driven by differences in modeled climate sensitivity. Key drivers include global-mean surface temperature, but also tropical upper-troposphere temperature, El Niño–Southern Oscillation (ENSO), the Pacific–North America (PNA) pattern, and the east Pacific (EP) dipole (a dipole pattern in geopotential heights over North America’s Pacific coast). Combinations of these drivers can reinforce or cancel to produce various high- or low-impact scenarios for winter precipitation trends in various regions of the United States. For example, the most extreme winter precipitation trends in the southwestern United States result from opposite trends in ENSO and EP, whereas the wettest winter precipitation trends in the midwestern United States result from a combination of strong global warming and a negative PNA trend.

SIGNIFICANCE STATEMENT

The newest generation of climate models (CMIP6) is now available, but despite some improvements, models still disagree on future precipitation changes over North America. In some ways, this is to be expected: precipitation changes (both in the real world and in the models) depend partly on climate change, but also partly on random natural variability, so they will never be completely predictable. Thus, instead of trying to pin down future precipitation changes to a specific outcome, we show a range of plausible outcomes that policymakers should be prepared for. We also explore the reasons for the differences between model runs. For example, precipitation trends on the U.S. West Coast differ between models partly because precipitation in that region is affected by El Niño events (as well as other factors that we identify), and future changes to El Niño differ between models, even for the same amount of warming.

© 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: Daniel F. Schmidt, dfs2uc@virginia.edu

Abstract

Climate change during the twenty-first century has the potential to substantially alter geographic patterns of precipitation. However, regional precipitation changes can be very difficult to project, and in some regions, global climate models do not even agree on the sign of the precipitation trend. Since some of this uncertainty is due to internal variability rather than model bias, models cannot be used to narrow the possibilities to a single outcome, but they can usefully quantify the range of plausible outcomes and identify the combination of dynamical drivers that would be likely to produce each. This study uses a storylines approach—a type of regression-based analysis—to identify some of the key dynamical drivers that explain the variance in twenty-first-century U.S. winter precipitation trends across CMIP6 models under the SSP3–7.0 emissions scenario. This analysis shows that the spread in precipitation trends is not primarily driven by differences in modeled climate sensitivity. Key drivers include global-mean surface temperature, but also tropical upper-troposphere temperature, El Niño–Southern Oscillation (ENSO), the Pacific–North America (PNA) pattern, and the east Pacific (EP) dipole (a dipole pattern in geopotential heights over North America’s Pacific coast). Combinations of these drivers can reinforce or cancel to produce various high- or low-impact scenarios for winter precipitation trends in various regions of the United States. For example, the most extreme winter precipitation trends in the southwestern United States result from opposite trends in ENSO and EP, whereas the wettest winter precipitation trends in the midwestern United States result from a combination of strong global warming and a negative PNA trend.

SIGNIFICANCE STATEMENT

The newest generation of climate models (CMIP6) is now available, but despite some improvements, models still disagree on future precipitation changes over North America. In some ways, this is to be expected: precipitation changes (both in the real world and in the models) depend partly on climate change, but also partly on random natural variability, so they will never be completely predictable. Thus, instead of trying to pin down future precipitation changes to a specific outcome, we show a range of plausible outcomes that policymakers should be prepared for. We also explore the reasons for the differences between model runs. For example, precipitation trends on the U.S. West Coast differ between models partly because precipitation in that region is affected by El Niño events (as well as other factors that we identify), and future changes to El Niño differ between models, even for the same amount of warming.

© 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: Daniel F. Schmidt, dfs2uc@virginia.edu

Supplementary Materials

    • Supplemental Materials (PDF 1.13 MB)
Save