• Adler, R. F., and Coauthors, 2003: The version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., G. Gu, M. Sapiano, J.-J. Wang, and G. J. Huffman, 2017: Global precipitation: Means, variations and trends during the satellite era (1979–2014). Surv. Geophys., 38, 679699, https://doi.org/10.1007/s10712-017-9416-4.

    • Search Google Scholar
    • Export Citation
  • Allan, R. P., C. Liu, M. Zahn, D. A. Lavers, E. Koukouvagias, and A. Bodas-Salcedo, 2014: Physically consistent responses of the global atmospheric hydrological cycle in models and observations. Surv. Geophys., 35, 533552, https://doi.org/10.1007/s10712-012-9213-z.

    • Search Google Scholar
    • Export Citation
  • Allan, R. P., and Coauthors, 2020: Advances in understanding large-scale responses of the water cycle to climate change. Ann. N. Y. Acad. Sci., 1472, 4975, https://doi.org/10.1111/nyas.14337.

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

    • Search Google Scholar
    • Export Citation
  • Barlow, M., H. Cullen, and B. Lyon, 2002: Drought in central and southwest Asia: La Niña, the warm pool, and Indian Ocean precipitation. J. Climate, 15, 697700, https://doi.org/10.1175/1520-0442(2002)015<0697:DICASA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • DeAngelis, A. M., X. Qu, M. D. Zelinka, and A. Hall, 2015: An observational radiative constraint on hydrologic cycle intensification. Nature, 528, 249253, https://doi.org/10.1038/nature15770.

    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2019: 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.

    • Search Google Scholar
    • Export Citation
  • Fläschner, D., T. Mauritsen, and B. Stevens, 2016: Understanding the intermodel spread in global-mean hydrological sensitivity. J. Climate, 29, 801817, https://doi.org/10.1175/JCLI-D-15-0351.1.

    • Search Google Scholar
    • Export Citation
  • Flynn, C. M., and T. Mauritsen, 2020: On the climate sensitivity and historical warming evolution in recent coupled model ensembles. Atmos. Chem. Phys., 20, 78297842, https://doi.org/10.5194/acp-20-7829-2020.

    • Search Google Scholar
    • Export Citation
  • Gu, G., and R. F. Adler, 2013: Interdecadal variability/long-term changes in global precipitation patterns during the past three decades: Global warming and/or Pacific decadal variability? Climate Dyn., 40, 30093022, https://doi.org/10.1007/s00382-012-1443-8.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Hoell, A., and C. Funk, 2013: The ENSO-related west Pacific sea surface temperature gradient. J. Climate, 26, 95459562, https://doi.org/10.1175/JCLI-D-12-00344.1.

    • Search Google Scholar
    • Export Citation
  • Kosaka, Y., and S.-P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501, 403407, https://doi.org/10.1038/nature12534.

    • Search Google Scholar
    • Export Citation
  • Kramer, R., and B. J. Soden, 2016: The sensitivity of the hydrological cycle to internal climate variability versus anthropogenic climate change. J. Climate, 29, 36613673, https://doi.org/10.1175/JCLI-D-15-0408.1.

    • Search Google Scholar
    • Export Citation
  • L’Heureux, M. L., S. Lee, and B. Lyon, 2013: Recent multidecadal strengthening of the Walker circulation across the tropical Pacific. Nat. Climate Change, 3, 571576, https://doi.org/10.1038/nclimate1840.

    • Search Google Scholar
    • Export Citation
  • Lindzen, R. S., M.-D. Chou, and A. Y. Hou, 2001: Does the Earth have an adaptive infrared iris? Bull. Amer. Meteor. Soc., 82, 417432, https://doi.org/10.1175/1520-0477(2001)082<0417:DTEHAA>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mariotti, A., 2007: How ENSO impacts precipitation in southwest central Asia. Geophys. Res. Lett., 34, L16706, https://doi.org/10.1029/2007GL030078.

    • Search Google Scholar
    • Export Citation
  • Mason, S. J., and L. Goddard, 2001: Probabilistic precipitation anomalies associated with ENSO. Bull. Amer. Meteor. Soc., 82, 619638, https://doi.org/10.1175/1520-0477(2001)082<0619:PPAAWE>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mauritsen, T., and B. Stevens, 2015: Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nat. Geosci., 8, 346351, https://doi.org/10.1038/ngeo2414.

    • Search Google Scholar
    • Export Citation
  • Merrifield, M. A., 2011: A shift in western tropical Pacific sea level trends during the 1990s. J. Climate, 24, 41264138, https://doi.org/10.1175/2011JCLI3932.1.

    • Search Google Scholar
    • Export Citation
  • Moore, S. M., and Coauthors, 2017: El Niño and the shifting geography of cholera in Africa. Proc. Natl. Acad. Sci. USA, 114, 44364441, https://doi.org/10.1073/pnas.1617218114.

    • Search Google Scholar
    • Export Citation
  • Morice, C. P., and Coauthors, 2021: An updated assessment of near-surface temperature change from 1850: The HadCRUT5 data set. J. Geophys. Res. Atmos., 126, e2019JD032361, https://doi.org/10.1029/2019JD032361.

    • Search Google Scholar
    • Export Citation
  • Nazemosadat, M. J., and A. R. Ghasemi, 2004: Quantifying the ENSO-related shifts in the intensity and probability of drought and wet periods in Iran. J. Climate, 17, 40054018, https://doi.org/10.1175/1520-0442(2004)017<4005:QTESIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • O’Gorman, P. A., R. P. Allan, M. P. Byrne, and M. Previdi, 2012: Energetic constraints on precipitation under climate change. Surv. Geophys., 33, 585608, https://doi.org/10.1007/s10712-011-9159-6.

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

    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., 2020: The global-mean precipitation response to CO2-induced warming in CMIP6 models. Geophys. Res. Lett., 47, e2020GL089964, https://doi.org/10.1029/2020GL089964.

    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., and D. L. Hartmann, 2012: Global-mean precipitation and black carbon forcing in AR4 simulations. Geophys. Res. Lett., 39, L01703, https://doi.org/10.1029/2011GL050067.

    • Search Google Scholar
    • Export Citation
  • Richter, I., and S.-P. Xie, 2008: Muted precipitation increase in global warming simulations: A surface evaporation perspective. J. Geophys. Res. Atmos., 113, D24118, https://doi.org/10.1029/2008JD010561.

    • Search Google Scholar
    • Export Citation
  • Su, H., and Coauthors, 2017: Tightening of tropical ascent and high clouds key to precipitation change in a warmer climate. Nat. Commun., 8, 15771, https://doi.org/10.1038/ncomms15771.

    • Search Google Scholar
    • Export Citation
  • Tebaldi, C., and Coauthors, 2021: Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth Syst. Dyn., 12, 253293, https://doi.org/10.5194/esd-12-253-2021.

    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., and B. J. Soden, 2007: Global warming and the weakening of the tropical circulation. J. Climate, 20, 43164340, https://doi.org/10.1175/JCLI4258.1.

    • Search Google Scholar
    • Export Citation
  • Watanabe, M., Y. Kamae, H. Shiogama, A. M. DeAngelis, and K. Suzuki, 2018: Low clouds link equilibrium climate sensitivity to hydrological sensitivity. Nat. Climate Change, 8, 901906, https://doi.org/10.1038/s41558-018-0272-0.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will global warming bring? Science, 317, 233235, https://doi.org/10.1126/science.1140746.

    • Search Google Scholar
    • Export Citation
  • Yeh, S.-W., S.-Y. Song, R. P. Allan, S.-I. An, and J. Shin, 2021: Contrasting response of hydrological cycle over land and ocean to a changing CO2 pathway. npj Climate Atmos. Sci., 4, 53, https://doi.org/10.1038/s41612-021-00206-6.

    • Search Google Scholar
    • Export Citation
  • Ying, J., P. Huang, and R. Huang, 2016: Evaluating the formation mechanisms of the equatorial Pacific SST warming pattern in CMIP5 models. Adv. Atmos. Sci., 33, 433441, https://doi.org/10.1007/s00376-015-5184-6.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., S.-P. Xie, L.-H. Lv, and Z.-Q. Zhou, 2016: Inter-model uncertainty in ENSO amplitude change tied to Pacific Ocean warming pattern. J. Climate, 29, 72657279, https://doi.org/10.1175/JCLI-D-16-0039.1.

    • Search Google Scholar
    • Export Citation
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Evaluating Hydrologic Sensitivity in CMIP6 Models: Anthropogenic Forcing versus ENSO

Jesse NorrisaAtmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Alex HallaAtmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Chad W. ThackerayaAtmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Di ChenaAtmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Gavin D. MadakumburaaAtmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Abstract

Large uncertainty exists in hydrologic sensitivity (HS), the global-mean precipitation increase per degree of warming, across global climate model (GCM) ensembles. Meanwhile, the global circulation and hence global precipitation are sensitive to variations of surface temperature under internal variability. El Niño–Southern Oscillation (ENSO) is the most dominant mode of global temperature variability and hence of precipitation variability. Here we show in phase 6 of the Coupled Model Intercomparison Project (CMIP6) that the strength of HS under ENSO is predictive of HS in the climate change context (r = 0.56). This correlation increases to 0.62 when only central Pacific ENSO events are considered, suggesting that they are a better proxy for HS under future warming than east Pacific ENSO events. GCMs with greater HS are associated with greater weakening of the Walker circulation and expansion of the Hadley circulation under ENSO. Observations of HS under ENSO suggest that it is significantly underestimated by the GCMs, with the lower bound of observational uncertainty almost double even the highest-HS GCMs. The ENSO-related transformation of the tropical circulation holds clues into how the GCMs may be improved in order to more reliably simulate future hydrological cycle intensification.

© 2022 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: Jesse Norris, jessenorris@ucla.edu

Abstract

Large uncertainty exists in hydrologic sensitivity (HS), the global-mean precipitation increase per degree of warming, across global climate model (GCM) ensembles. Meanwhile, the global circulation and hence global precipitation are sensitive to variations of surface temperature under internal variability. El Niño–Southern Oscillation (ENSO) is the most dominant mode of global temperature variability and hence of precipitation variability. Here we show in phase 6 of the Coupled Model Intercomparison Project (CMIP6) that the strength of HS under ENSO is predictive of HS in the climate change context (r = 0.56). This correlation increases to 0.62 when only central Pacific ENSO events are considered, suggesting that they are a better proxy for HS under future warming than east Pacific ENSO events. GCMs with greater HS are associated with greater weakening of the Walker circulation and expansion of the Hadley circulation under ENSO. Observations of HS under ENSO suggest that it is significantly underestimated by the GCMs, with the lower bound of observational uncertainty almost double even the highest-HS GCMs. The ENSO-related transformation of the tropical circulation holds clues into how the GCMs may be improved in order to more reliably simulate future hydrological cycle intensification.

© 2022 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: Jesse Norris, jessenorris@ucla.edu

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