• Abghari, H., H. Tabari, and P. Hosseinzadeh Talaee, 2013: River flow trends in the west of Iran during the past 40 years: Impact of precipitation variability. Global Planet. Change, 101, 5260, https://doi.org/10.1016/j.gloplacha.2012.12.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 228232, https://doi.org/10.1038/nature01092.

    • Search Google Scholar
    • Export Citation
  • Ault, T. R., J. E. Cole, and S. S. George, 2012: The amplitude of decadal to multidecadal variability in precipitation simulated by state-of-the-art climate models. Geophys. Res. Lett., 39, L21705, https://doi.org/10.1029/2012GL053424.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barlow, M., S. Nigam, and E. H. Berbery, 2001: ENSO, Pacific decadal variability, and U.S. summertime precipitation, drought, and stream flow. J. Climate, 14, 21052128, https://doi.org/10.1175/1520-0442(2001)014<2105:EPDVAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellenger, H., E. Guilyardi, J. Leloup, M. Lengaigne, and J. Vialard, 2014: ENSO representation in climate models: from CMIP3 to CMIP5. Climate Dyn., 42, 19992018, https://doi.org/10.1007/s00382-013-1783-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Biasutti, M., 2013: Forced Sahel rainfall trends in the CMIP5 archive. J. Geophys. Res. Atmos., 118, 16131623, https://doi.org/10.1002/jgrd.50206.

  • Brown, J. R., A. F. Moise, and R. A. Colman, 2017: Projected increases in daily to decadal variability of Asian-Australian monsoon rainfall. Geophys. Res. Lett., 44, 56835690, https://doi.org/10.1002/2017GL073217.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Castruccio, S., D. J. McInerney, M. L. Stein, F. L. Crouch, R. L. Jacob, and E. J. Moyer, 2014: Statistical emulation of climate model projections based on precomputed GCM runs. J. Climate, 27, 18291844, https://doi.org/10.1175/JCLI-D-13-00099.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, W., M. L. Stein, J. Wang, V. R. Kotamarthi, and E. J. Moyer, 2016: Changes in spatiotemporal precipitation patterns in changing climate conditions. J. Climate, 29, 83558376, https://doi.org/10.1175/JCLI-D-15-0844.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, W., J. Wang, J. Marohnic, V. R. Kotamarthi, and E. J. Moyer, 2018: Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking. Climate Dyn., 55, 175192, https://doi.org/10.1007/s00382-018-4294-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheung, A. H., M. E. Mann, B. A. Steinman, L. M. Frankcombe, M. H. England, and S. K. Miller, 2017: Comparison of low-frequency internal climate variability in CMIP5 models and observations. J. Climate, 30, 47634776, https://doi.org/10.1175/JCLI-D-16-0712.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Covey, C., P. J. Gleckler, C. Doutriaux, D. N. Williams, A. Dai, J. Fasullo, K. Trenberth, and A. Berg, 2016: Metrics for the diurnal cycle of precipitation: Toward routine benchmarks for climate models. J. Climate, 29, 44614471, https://doi.org/10.1175/JCLI-D-15-0664.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 46054630, https://doi.org/10.1175/JCLI3884.1.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Davini, P., and C. Cagnazzo, 2014: On the misinterpretation of the North Atlantic Oscillation in CMIP5 models. Climate Dyn., 43, 14971511, https://doi.org/10.1007/s00382-013-1970-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 2013: Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc. Natl. Acad. Sci. USA, 110, 12 21912 224, https://doi.org/10.1073/pnas.1301293110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eyring, V., and Coauthors, 2019: Taking climate model evaluation to the next level. Nat. Climate Change, 9, 102110, https://doi.org/10.1038/s41558-018-0355-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Frost, C., and S. G. Thompson, 2000: Correcting for regression dilution bias: Comparison of methods for a single predictor variable. J. Roy. Stat. Soc., 163A, 173189, https://doi.org/10.1111/1467-985X.00164.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fuentes-Franco, R., F. Giorgi, E. Coppola, and F. Kucharski, 2016: The role of ENSO and PDO in variability of winter precipitation over North America from twenty first century CMIP5 projections. Climate Dyn., 46, 32593277, https://doi.org/10.1007/s00382-015-2767-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gornall, J., R. Betts, E. Burke, R. Clark, J. Camp, K. Willett, and A. Wiltshire, 2010: Implications of climate change for agricultural productivity in the early twenty-first century. Philos. Trans. Roy. Soc. London, 365B, 29732989, https://doi.org/10.1098/rstb.2010.0158.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goyal, M. K., C. S. P. Ojha, and D. H. Burn, 2012: Nonparametric statistical downscaling of temperature, precipitation, and evaporation in a semiarid region in India. J. Hydrol. Eng., 17, 615627, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000479.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Groisman, P. Ya., and Coauthors, 1999: Changes in the probability of heavy precipitation: Important indicators of climatic change. Climatic Change, 42, 243283, https://doi.org/10.1023/A:1005432803188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamlet, A. F., P. W. Mote, M. P. Clark, and D. P. Lettenmaier, 2005: Effects of temperature and precipitation variability on snowpack trends in the western United States. J. Climate, 18, 45454561, https://doi.org/10.1175/JCLI3538.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, C., and T. Li, 2018: Does global warming amplify interannual climate variability? Climate Dyn., 52, 26672684, https://doi.org/10.1007/s00382-018-4286-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hennessy, K. J., J. M. Gregory, and J. F. B. Mitchell, 1997: Changes in daily precipitation under enhanced greenhouse conditions. Climate Dyn., 13, 667680, https://doi.org/10.1007/s003820050189.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hunt, B. G., and T. I. Elliott, 2004: Interaction of climatic variability with climatic change. Atmos.–Ocean, 42, 145172, https://doi.org/10.3137/ao.420301.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ines, A. V. M., and J. W. Hansen, 2006: Bias correction of daily GCM rainfall for crop simulation studies. Agric. For. Meteor., 138, 4453, https://doi.org/10.1016/j.agrformet.2006.03.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Katz, R. W., and B. G. Brown, 1992: Extreme events in a changing climate: Variability is more important than averages. Climatic Change, 21, 289302, https://doi.org/10.1007/BF00139728.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klavans, J. M., A. Poppick, S. Sun, and E. J. Moyer, 2016: The influence of model resolution on temperature variability. Climate Dyn., 48, 30353045, https://doi.org/10.1007/s00382-016-3249-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutti, R., D. Masson, and A. Gettelman, 2013: Climate model genealogy: Generation CMIP5 and how we got there. Geophys. Res. Lett., 40, 11941199, https://doi.org/10.1002/grl.50256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kripalani, R. H., J. H. Oh, A. Kulkarni, S. S. Sabade, and H. S. Chaudhari, 2007: South Asian summer monsoon precipitation variability: Coupled climate model simulations and projections under IPCC AR4. Theor. Appl. Climatol., 90, 133159, https://doi.org/10.1007/s00704-006-0282-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, X.-Z., W.-C. Wang, and M. P. Dudek, 1995: Interannual variability of regional climate and its change due to the greenhouse effect. Global Planet. Change, 10, 217238, https://doi.org/10.1016/0921-8181(94)00027-B.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, R., and Y. Fu, 2010: Intensification of East Asian summer rainfall interannual variability in the twenty-first century simulated by 12 CMIP3 coupled models. J. Climate, 23, 33163331, https://doi.org/10.1175/2009JCLI3130.1.

    • 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
  • Mearns, L. O., F. Giorgi, L. McDaniel, and C. Shields, 1995: Analysis of daily variability of precipitation in a nested regional climate model: Comparison with observations and doubled CO2 results. Global Planet. Change, 10, 5578, https://doi.org/10.1016/0921-8181(94)00020-E.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., M. Wheeler, and W. M. Washington, 1994: Low-frequency variability and CO2 transient climate change. Part 3. Intermonthly and interannual variability. Climate Dyn., 10, 277303, https://doi.org/10.1007/BF00228028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Gorman, P. A., and T. Schneider, 2009: The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc. Natl. Acad. Sci. USA, 106, 14 77314 777, https://doi.org/10.1073/pnas.0907610106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., and D. L. Hartmann, 2014: Two modes of change of the distribution of rain. J. Climate, 27, 83578371, https://doi.org/10.1175/JCLI-D-14-00182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., and R. Knutti, 2018: The uneven nature of daily precipitation and its change. Geophys. Res. Lett., 45, 11 98011 988, https://doi.org/10.1029/2018GL080298.

    • 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
  • Polade, S. D., A. Gershunov, D. R. Cayan, M. D. Dettinger, and D. W. Pierce, 2013: Natural climate variability and teleconnections to precipitation over the Pacific–North American region in CMIP3 and CMIP5 models. Geophys. Res. Lett., 40, 22962301, https://doi.org/10.1002/grl.50491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Räisänen, J., 2002: CO2-induced changes in interannual temperature and precipitation variability in 19 CMIP2 experiments. J. Climate, 15, 23952411, https://doi.org/10.1175/1520-0442(2002)015<2395:CICIIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riha, S. J., D. S. Wilks, and P. Simoens, 1996: Impact of temperature and precipitation variability on crop model predictions. Climatic Change, 32, 293311, https://doi.org/10.1007/BF00142466.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rind, D., R. Goldberg, and R. Ruedy, 1988: Change in climate variability in the 21st century. Climatic Change, 14, 537, https://doi.org/10.1007/BF00140173.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rugenstein, M., and Coauthors, 2019: LongRunMIP: Motivation and design for a large collection of millennial-length AO-GCM simulations. Bull. Amer. Meteor. Soc., 100, 22512570, https://doi.org/10.1175/BAMS-D-19-0068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rupp, D. E., J. T. Abatzoglou, K. C. Hegewisch, and P. W. Mote, 2013: Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. J. Geophys. Res. Atmos., 118, 10 88410 906, https://doi.org/10.1002/jgrd.50843.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Semenov, V., and L. Bengtsson, 2002: Secular trends in daily precipitation characteristics: Greenhouse gas simulation with a coupled AOGCM. Climate Dyn., 19, 123140, https://doi.org/10.1007/s00382-001-0218-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH..

    • Crossref
    • Export Citation
  • Sun, F., M. L. Roderick, and G. D. Farquhar, 2012: Changes in the variability of global land precipitation. Geophys. Res. Lett., 39, L19402, https://doi.org/10.1029/2012GL053369.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2006: How often does it rain? J. Climate, 19, 916934, https://doi.org/10.1175/JCLI3672.1.

  • Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2007: How often will it rain? J. Climate, 20, 48014818, https://doi.org/10.1175/JCLI4263.1.

  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2011: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • 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
  • Trenberth, K. E., Y. Zhang, and M. Gehne, 2017: Intermittency in precipitation: Duration, frequency, intensity, and amounts using hourly data. J. Hydrometeor., 18, 13931412, https://doi.org/10.1175/JHM-D-16-0263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Vuuren, D. P., and Coauthors, 2011: The representative concentration pathways: An overview. Climatic Change, 109, 531, https://doi.org/10.1007/s10584-011-0148-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., A. W. Robertson, and D. L. T. Anderson, 2012: Subseasonal to Seasonal Prediction Project: Bridging the gap between weather and climate. WMO Bull., 61 (2), https://public.wmo.int/en/resources/bulletin/subseasonal-seasonal-prediction-project-bridging-gap-between-weather-and-climate.

  • Wang, J., and V. R. Kotamarthi, 2015: High-resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America. Earth’s Future, 3, 2015EF000304, https://doi.org/10.1002/2015EF000304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wasko, C., A. Sharma, and S. Westra, 2016: Reduced spatial extent of extreme storms at higher temperatures. Geophys. Res. Lett., 43, 40264032, https://doi.org/10.1002/2016GL068509.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wetherald, R. T., 2010: Changes of time mean state and variability of hydrology in response to a doubling and quadrupling of CO2. Climatic Change, 102, 651670, https://doi.org/10.1007/s10584-009-9701-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yokoyama, C., and Y. N. Takayabu, 2008: A statistical study on rain characteristics of tropical cyclones using TRMM satellite data. Mon. Wea. Rev., 136, 38483862, https://doi.org/10.1175/2008MWR2408.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zwiers, F. W., and V. V. Kharin, 1998: Changes in the extremes of the climate simulated by CCC GCM2 under CO2 doubling. J. Climate, 11, 22002222, https://doi.org/10.1175/1520-0442(1998)011<2200:CITEOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 271 271 135
Full Text Views 85 85 45
PDF Downloads 90 90 42

Changes in Future Precipitation Mean and Variability across Scales

View More View Less
  • 1 Center for Robust Decision-Making on Climate and Energy Policy, The University of Chicago, Chicago, Illinois
  • 2 Department of Mathematics and Statistics, Carleton College, Northfield, Minnesota
  • 3 Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
  • 4 National Centre for Atmospheric Science, University of Reading, Reading, England
  • 5 Environmental Science Division, Argonne National Laboratory, Lemont, Illinois
  • 6 Department of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia, Australia
  • 7 Department of Geophysical Sciences, The University of Chicago, Chicago, Illinois
© Get Permissions
Restricted access

Abstract

Changes in precipitation variability can have large societal consequences, whether at the short time scales of flash floods or the longer time scales of multiyear droughts. Recent studies have suggested that in future climate projections, precipitation variability rises more steeply than does its mean, leading to concerns about societal impacts. This work evaluates changes in mean precipitation over a broad range of spatial and temporal scales using a range of models from high-resolution regional simulations to millennial-scale global simulations. Results show that changes depend on the scale of aggregation and involve strong regional differences. On local scales that resolve individual rainfall events (hours and tens of kilometers), changes in precipitation distributions are complex and variances rise substantially more than means, as is required given the well-known disproportionate rise in precipitation intensity. On scales that aggregate across many events, distributional changes become simpler and variability changes smaller. At regional scale, future precipitation distributions can be largely reproduced by a simple transformation of present-day precipitation involving a multiplicative shift and a small additive term. The “extra” broadening is negatively correlated with changes in mean precipitation: in strongly “wetting” areas, distributions broaden less than expected from a simple multiplicative mean change; in “drying” areas, distributions narrow less. Precipitation variability changes are therefore of especial concern in the subtropics, which tend to dry under climate change. Outside the tropics, variability changes are similar on time scales from days to decades (i.e., show little frequency dependence). This behavior is highly robust across models, suggesting it may stem from some fundamental constraint.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0001.s1.

© 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: Elisabeth J. Moyer, moyer@uchicago.edu

Abstract

Changes in precipitation variability can have large societal consequences, whether at the short time scales of flash floods or the longer time scales of multiyear droughts. Recent studies have suggested that in future climate projections, precipitation variability rises more steeply than does its mean, leading to concerns about societal impacts. This work evaluates changes in mean precipitation over a broad range of spatial and temporal scales using a range of models from high-resolution regional simulations to millennial-scale global simulations. Results show that changes depend on the scale of aggregation and involve strong regional differences. On local scales that resolve individual rainfall events (hours and tens of kilometers), changes in precipitation distributions are complex and variances rise substantially more than means, as is required given the well-known disproportionate rise in precipitation intensity. On scales that aggregate across many events, distributional changes become simpler and variability changes smaller. At regional scale, future precipitation distributions can be largely reproduced by a simple transformation of present-day precipitation involving a multiplicative shift and a small additive term. The “extra” broadening is negatively correlated with changes in mean precipitation: in strongly “wetting” areas, distributions broaden less than expected from a simple multiplicative mean change; in “drying” areas, distributions narrow less. Precipitation variability changes are therefore of especial concern in the subtropics, which tend to dry under climate change. Outside the tropics, variability changes are similar on time scales from days to decades (i.e., show little frequency dependence). This behavior is highly robust across models, suggesting it may stem from some fundamental constraint.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0001.s1.

© 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: Elisabeth J. Moyer, moyer@uchicago.edu

Supplementary Materials

    • Supplemental Materials (PDF 88.8 MB)
Save