An observational radiative constraint on hydrologic cycle intensification

  • A Corrigendum to this article was published on 23 March 2016

Abstract

Intensification of the hydrologic cycle is a key dimension of climate change, with substantial impacts on human and natural systems1,2. A basic measure of hydrologic cycle intensification is the increase in global-mean precipitation per unit surface warming, which varies by a factor of three in current-generation climate models (about 1–3 per cent per kelvin)3,4,5. Part of the uncertainty may originate from atmosphere–radiation interactions. As the climate warms, increases in shortwave absorption from atmospheric moistening will suppress the precipitation increase. This occurs through a reduction of the latent heating increase required to maintain a balanced atmospheric energy budget6,7. Using an ensemble of climate models, here we show that such models tend to underestimate the sensitivity of solar absorption to variations in atmospheric water vapour, leading to an underestimation in the shortwave absorption increase and an overestimation in the precipitation increase. This sensitivity also varies considerably among models due to differences in radiative transfer parameterizations, explaining a substantial portion of model spread in the precipitation response. Consequently, attaining accurate shortwave absorption responses through improvements to the radiative transfer schemes could reduce the spread in the predicted global precipitation increase per degree warming for the end of the twenty-first century by about 35 per cent, and reduce the estimated ensemble-mean increase in this quantity by almost 40 per cent.

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Figure 1: The atmospheric energy budget.
Figure 2: Relationship between temperature-mediated LvP and SWA responses.
Figure 3: The sensitivity of solar absorption to varying atmospheric water vapour.
Figure 4: Shortwave parameterization schemes.

References

  1. 1

    Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011)

    Article  Google Scholar 

  2. 2

    Pendergrass, A. G. & Hartmann, D. L. Changes in the distribution of rain frequency and intensity in response to global warming. J. Clim. 27, 8372–8383 (2014)

    Article  ADS  Google Scholar 

  3. 3

    Kharin, V. V., Zwiers, F. W., Zhang, X. & Wehner, M. Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim. Change 119, 345–357 (2013)

    Article  ADS  Google Scholar 

  4. 4

    Pendergrass, A. G. & Hartmann, D. L. Global-mean precipitation and black carbon in AR4 simulations. Geophys. Res. Lett. 39, L01703 (2012)

    Article  ADS  CAS  Google Scholar 

  5. 5

    Andrews, T. & Forster, P. M. The transient response of global-mean precipitation to increasing carbon dioxide levels. Environ. Res. Lett. 5, 025212 (2010)

    Article  ADS  CAS  Google Scholar 

  6. 6

    O’Gorman, P. A., Allan, R. P., Byrne, M. P. & Previdi, M. Energetic constraints on precipitation under climate change. Surv. Geophys. 33, 585–608 (2012)

    Article  ADS  Google Scholar 

  7. 7

    Takahashi, K. The global hydrological cycle and atmospheric shortwave absorption in climate models under CO2 forcing. J. Clim. 22, 5667–5675 (2009)

    Article  ADS  Google Scholar 

  8. 8

    Arkin, P. A., Smith, T. M., Sapiano, M. R. P. & Janowiak, J. The observed sensitivity of the global hydrological cycle to changes in surface temperature. Environ. Res. Lett. 5, 035201 (2010)

    Article  ADS  CAS  Google Scholar 

  9. 9

    Allen, M. R. & Ingram, W. J. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 224–232 (2002)

    CAS  PubMed  ADS  Google Scholar 

  10. 10

    Lambert, F. H. & Webb, M. J. Dependency of global mean precipitation on surface temperature. Geophys. Res. Lett. 35, L16706 (2008)

    Article  ADS  CAS  Google Scholar 

  11. 11

    Manabe, S. & Wetherald, R. T. The effects of doubling the CO2 concentration on the climate of a general circulation model. J. Atmos. Sci. 32, 3–15 (1975)

    CAS  Article  ADS  Google Scholar 

  12. 12

    Pendergrass, A. G. & Hartmann, D. L. The atmospheric energy constraint on global-mean precipitation change. J. Clim. 27, 757–768 (2014)

    Article  ADS  Google Scholar 

  13. 13

    Mitchell, F. B., Wilson, C. A. & Cunnington, W. M. On CO2 climate sensitivity and model dependence of results. Q. J. R. Meteorol. Soc. 113, 293–322 (1987)

    CAS  Article  ADS  Google Scholar 

  14. 14

    Previdi, M. Radiative feedbacks on global precipitation. Environ. Res. Lett. 5, 025211 (2010)

    Article  ADS  CAS  Google Scholar 

  15. 15

    Yang, F., Kumar, A., Schlesinger, M. E. & Wang, W. Intensity of hydrological cycles in warmer climates. J. Clim. 16, 2419–2423 (2003)

    Article  ADS  Google Scholar 

  16. 16

    Dong, B., Gregory, J. M. & Sutton, R. T. Understanding land–sea warming contrast in response to increasing greenhouse gases. Part I: Transient adjustment. J. Clim. 22, 3079–3097 (2009)

    Article  ADS  Google Scholar 

  17. 17

    Bony, S. et al. Robust direct effect of carbon dioxide on tropical circulation and regional precipitation. Nature Geosci. 6, 447–451 (2013)

    CAS  Article  ADS  Google Scholar 

  18. 18

    Bala, G., Caldeira, K. & Nemani, R. Fast versus slow response in climate change: implications for the global hydrological cycle. Clim. Dyn. 35, 423–434 (2010)

    Article  Google Scholar 

  19. 19

    Andrews, T., Forster, P. M., Boucher, O., Bellouin, N. & Jones, A. Precipitation, radiative forcing and global temperature change. Geophys. Res. Lett. 37, L14701 (2010)

    Article  ADS  CAS  Google Scholar 

  20. 20

    Gregory, J. M. et al. A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett. 31, L03205 (2004)

    ADS  Google Scholar 

  21. 21

    Wielicki, B. A. et al. Clouds and the Earth's Radiant Energy System (CERES): An Earth observing system experiment. Bull. Am. Meteorol. Soc. 77, 853–868 (1996)

    ADS  Google Scholar 

  22. 22

    Kato, S. et al. Surface irradiances consistent with CERES-derived top-of-atmosphere shortwave and longwave irradiances. J. Clim. 26, 2719–2740 (2013)

    Article  ADS  Google Scholar 

  23. 23

    Collins, W. D. et al. Radiative forcing by well-mixed greenhouse gases: Estimates from climate models in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). J. Geophys. Res. 111, D14317 (2006)

    Article  ADS  CAS  Google Scholar 

  24. 24

    Oreopoulos, L. et al. The continual intercomparison of radiation codes: results from phase I. J. Geophys. Res. 117, D06118 (2012)

    Article  ADS  Google Scholar 

  25. 25

    Pincus, R. et al. Radiative flux and forcing parameterization error in aerosol-free clear skies. Geophys. Res. Lett. 42, 5485–5492 (2015)

    PubMed  PubMed Central  Article  ADS  Google Scholar 

  26. 26

    Mauritsen, T. & Stevens, B. Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nature Geosci. 8, 346–351 (2015)

    CAS  Article  ADS  Google Scholar 

  27. 27

    Zhang, Y., Rossow, W. B., Lacis, A. A., Oinas, V. & Mishchenko, M. I. Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data. J. Geophys. Res. 109, D19105 (2004)

    Article  ADS  Google Scholar 

  28. 28

    Voldoire, A. et al. The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dyn. 40, 2091–2121 (2013)

    Article  Google Scholar 

  29. 29

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experimental design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012)

    Article  ADS  Google Scholar 

  30. 30

    Andrews, T., Forster, P. M. & Gregory, J. M. A surface energy perspective on climate change. J. Clim. 22, 2557–2570 (2009)

    Article  ADS  Google Scholar 

  31. 31

    Lu, J. & Cai, M. Stabilization of the atmospheric boundary layer and the muted global hydrological cycle response to global warming. J. Hydrometeorol. 10, 347–352 (2009)

    Article  ADS  Google Scholar 

  32. 32

    Allan, R. P. et al. Physically consistent responses of the global atmospheric hydrological cycle in models and observations. Surv. Geophys. 35, 533–552 (2014)

    Article  ADS  Google Scholar 

  33. 33

    Devore, J. L. in Probability and Statistics for Engineering and the Sciences 7th edn (eds Crockett, C., Gershman, B. & Broyer, R. ) 12.446–12.499 (Duxbury Press, Belmont, California, 2008)

  34. 34

    Donner, L. J. et al. The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J. Clim. 24, 3484–3519 (2011)

    Article  ADS  Google Scholar 

  35. 35

    Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part I: Physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012)

    Article  ADS  Google Scholar 

  36. 36

    Freidenreich, S. M. & Ramaswamy, V. A new multiple-band solar radiative parameterization for general circulation models. J. Geophys. Res. 104, 31389–31409 (1999)

    Article  ADS  Google Scholar 

  37. 37

    The GFDL Global Atmospheric Model Development Team. The new GFDL global atmosphere and land model AM2–LM2: evaluation with prescribed SST simulations. J. Clim. 17, 4641–4673 (2004)

  38. 38

    Briegleb, B. P. Delta-eddington approximation for solar radiation in the NCAR Community Climate Model. J. Geophys. Res. 97(D7), 7603–7612 (1992)

    Article  ADS  Google Scholar 

  39. 39

    Galin, V. Y. Parametrization of radiative processes in the DNM atmospheric model. Izvest. Atmos. Ocean. Phys. 34, 339–347 (1998)

    Google Scholar 

  40. 40

    Yukimoto, S. et al. Meteorological Research Institute-Earth System Model Version 1 (MRI-ESM1) — Model description. Tech. Rep. No. 64 (Meteorological Research Institute, Tsukuba-city, Ibaraki, Japan, 2011)

  41. 41

    Kato, S., Ackerman, T. P., Mather, J. H. & Clothiaux, E. E. The k-distribution method and correlated-k approximation for a shortwave radiative transfer model. J. Quant. Spectrosc. Radiat. Trans. 62, 109–121 (1999)

    CAS  Article  ADS  Google Scholar 

  42. 42

    Kato, S. et al. Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties. J. Geophys. Res. 116, D19209 (2011)

    Article  ADS  Google Scholar 

  43. 43

    Sekiguchi, M. & Nakajima, T. A k-distribution-based radiation code and its computational optimization for an atmospheric general circulation model. J. Quant. Spectrosc. Radiat. Trans. 109, 2779–2793 (2008)

    CAS  Article  ADS  Google Scholar 

  44. 44

    Rose, F. et al. CERES Proto-Edition 3 radiative transfer: tests and radiative closure over surface validation sites. In 12th Conference on Atmospheric Radiationhttps://ams.confex.com/ams/pdfpapers/112358.pdf (2006)

  45. 45

    Ferraro, R. R., Weng, F., Grody, N. C. & Basist, A. An eight-year (1987–1994) time series of rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Am. Meteorol. Soc. 77, 891–905 (1996)

    Article  ADS  Google Scholar 

  46. 46

    Remote Sensing Systems. Monthly mean total precipitable water data set on a 1 degree grid made from Remote Sensing Systems Version-7 microwave radiometer data. http://www.remss.com/measurements/atmospheric-water-vapor/tpw-1-deg-product (2012, updated July 2014, accessed August 2014)

  47. 47

    Kidwell, K. NOAA Polar Orbiter Data Products Users Guide (National Environmental Satellite Data and Information Service, Silver Spring, Maryland, 1995)

  48. 48

    Schmidt, G. A. et al. Present-day atmospheric simulations using GISS ModelE: comparison to in situ, satellite, and reanalysis data. J. Clim. 19, 153–192 (2006)

    Article  ADS  Google Scholar 

  49. 49

    Schmidt, G. A. et al. Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst. 6, 141–184 (2014)

    Article  ADS  Google Scholar 

  50. 50

    Lacis, A. A. & Hansen, J. E. A parameterization for the absorption of solar radiation in the Earth’s atmosphere. J. Atmos. Sci. 31, 118–133 (1974)

    Article  ADS  Google Scholar 

  51. 51

    Hansen, J. et al. Efficient three-dimensional global models for climate studies: Models I and II. Mon. Weath. Rev. 111, 609–662 (1983)

    Article  ADS  Google Scholar 

  52. 52

    Lacis, A. A. & Oinas, V. A description of the correlated k distribution method for modeling nongray gaseous absorption, thermal emission, and multiple scattering in vertically inhomogeneous atmospheres. J. Geophys. Res. 96(D5), 9027–9063 (1991)

    Article  ADS  Google Scholar 

  53. 53

    Fouquart, Y. & Bonnel, B. Computations of solar heating of the Earth’s atmosphere: a new parameterization. Contrib. Atmos. Phys. 53, 35–62 (1980)

    Google Scholar 

  54. 54

    Morcrette, J.-J. Radiation and cloud radiative properties in the European Centre for Medium Range Weather Forecasts forecasting system. J. Geophys. Res. 96(D5), 9121–9132 (1991)

    Article  ADS  Google Scholar 

  55. 55

    Collins, W. D., Lee-Taylor, J. M., Edwards, D. P. & Francis, G. L. Effects of increased near-infrared absorption by water vapor on the climate system. J. Geophys. Res. 111, D18109 (2006)

    Article  ADS  Google Scholar 

  56. 56

    Paynter, D. & Ramaswamy, V. Investigating the impact of the shortwave water vapor continuum upon climate simulations using GFDL global models. J. Geophys. Res. Atmos. 119, 10720–10737 (2014)

    Article  ADS  Google Scholar 

  57. 57

    Hewitt, H. T. et al. Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system. Geosci. Model Dev. 4, 223–253 (2011)

    Article  ADS  Google Scholar 

  58. 58

    Bi, D. et al. The ACCESS coupled model: description, control climate and evaluation. Austral. Meteorol. Oceanogr. J. 63, 41–64 (2013)

    Article  Google Scholar 

  59. 59

    Edwards, J. M. & Slingo, A. Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Q. J. R. Meteorol. Soc. 122, 689–719 (1996)

    Article  ADS  Google Scholar 

  60. 60

    Cusack, S., Edwards, J. M. & Crowther, J. M. Investigating k distribution methods for parameterizing gaseous absorption in the Hadley Centre climate model. J. Geophys. Res. 104, 2051–2057 (1999)

    CAS  Article  ADS  Google Scholar 

  61. 61

    Wu, T. et al. The Beijing Climate Center atmospheric general circulation model: description and its performance for the present-day climate. Clim. Dyn. 34, 123–147 (2010)

    Article  Google Scholar 

  62. 62

    Collins, W. D. et al. Description of the NCAR Community Atmosphere Model (CAM 3.0). Tech. Rep. NCAR/TN-464+STR (National Center for Atmospheric Research, Boulder, Colorado, 2004)

  63. 63

    Chylek, P., Li, J., Dubey, M. K., Wang, M. & Lesins, G. Observed and model simulated 20th century Arctic temperature variability: Canadian Earth System Model CanESM2. Atmos. Chem. Phys. Discuss. 11, 22893–22907 (2011)

    Article  ADS  Google Scholar 

  64. 64

    Li, J. & Barker, H. W. A radiation algorithm with correlated-k distribution. Part I: Local thermal equilibrium. J. Atmos. Sci. 62, 286–309 (2005)

    Article  ADS  Google Scholar 

  65. 65

    Gent, P. R. et al. The Community Climate System Model Version 4. J. Clim. 24, 4973–4991 (2011)

    Article  ADS  Google Scholar 

  66. 66

    Neale, R. B. et al. Description of the NCAR Community Atmosphere Model (CAM 4.0). Tech. Rep. NCAR/TN-485+STR (National Center for Atmospheric Research, Boulder, Colorado, 2010)

  67. 67

    Martin, G. M. et al. The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part I: Model description and global climatology. J. Clim. 19, 1274–1301 (2006)

    Article  ADS  Google Scholar 

  68. 68

    Collins, W. J. et al. Development and evaluation of an Earth-System model — HadGEM2. Geosci. Model. Dev. 4, 1051–1075 (2011)

    Article  ADS  Google Scholar 

  69. 69

    Volodin, E. M., Dianskii, N. A. & Gusev, A. V. Simulating present day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izvest. Atmos. Ocean. Phys. 46, 414–431 (2010)

    Article  ADS  Google Scholar 

  70. 70

    Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013)

    Article  Google Scholar 

  71. 71

    Hourdin, F. et al. LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection. Clim. Dyn. 40, 2193–2222 (2013)

    Article  Google Scholar 

  72. 72

    Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872 (2011)

    Article  ADS  Google Scholar 

  73. 73

    Watanabe, M. et al. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010)

    Article  ADS  Google Scholar 

  74. 74

    Jungclaus, J. H. et al. Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model. J. Adv. Model. Earth Syst. 5, 422–446 (2013)

    Article  ADS  Google Scholar 

  75. 75

    Stevens, B. et al. Atmospheric component of the MPI-M Earth system model: ECHAM6. J. Adv. Model. Earth Syst. 5, 146–172 (2013)

    Article  ADS  Google Scholar 

  76. 76

    Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. & Clough, S. A. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. 102(D14), 16663–16682 (1997)

    CAS  Article  ADS  Google Scholar 

  77. 77

    Mlawer, E. J. & Clough, S. A. On the extension of rapid radiative transfer model to the shortwave region. In Proceedings of the 6th Atmospheric Radiation Measurement Science Team Meetinghttps://www.arm.gov/publications/proceedings/conf06/extended_abs/mlawer_ej.pdf (US Department of Energy, CONF-9603149, 1997)

  78. 78

    Iacono, M. J. et al. Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J. Geophys. Res. 113, D13103 (2008)

    Article  ADS  CAS  Google Scholar 

  79. 79

    Yukimoto, S. et al. A new global climate model of the Meteorological Research Institute: MRI-CGCM3. J. Meteorol. Soc. Jpn 90A, 23–64 (2012)

    Article  Google Scholar 

  80. 80

    Bentsen, M. et al. The Norwegian Earth System Model, NorESM1-M—Part 1: Description and basic evaluation of the physical climate. Geosci. Model Dev. 6, 687–720 (2013)

    Article  ADS  Google Scholar 

Download references

Acknowledgements

All authors were supported by the Regional and Global Climate Modeling Program of the Office of Science of the US Department of Energy. The work of M.D.Z. was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Extended Data Table 1) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We acknowledge use of the CERES-EBAF flux data obtained from the National Aeronautics and Space Administration (NASA) Langley Research Center (http://ceres.larc.nasa.gov/order_data.php), the ISCCP-FD data obtained from the NASA Goddard Institute for Space Studies (http://isccp.giss.nasa.gov/projects/flux.html), the SSM/I data obtained from the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (http://www.ncdc.noaa.gov/oa/rsad/ssmi/gridded/index.php), and the RSS data obtained from Remote Sensing Systems (http://www.remss.com/measurements/atmospheric-water-vapor/tpw-1-deg-product). We thank M. Previdi for providing the radiative kernels. We also thank S. A. Klein, K. E. Taylor, P. M. Caldwell, A. A. Lacis, R. Pincus and A. J. Broccoli for discussion on the topic.

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A.M.D., X.Q. and A.H. designed the methodology. A.M.D. performed the analysis and wrote the paper. M.D.Z. provided the kernel-derived temperature-mediated shortwave absorption response estimates. All authors discussed the results and edited the manuscript.

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Correspondence to Anthony M. DeAngelis.

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Extended data figures and tables

Extended Data Figure 1 Demonstration of the Gregory method for the GFDL-CM3.

Global-mean annual anomalies (abrupt4 × CO2—piControl; see Methods) in atmospheric energy budget terms (latent heat release from precipitation (LvP), net longwave cooling (LWC), shortwave absorption (SWA), and sensible heating (SH)) are regressed against those in 2-m air temperature (see Methods). For LvP, precipitation anomalies are multiplied by the latent heat of vaporization, Lv. Radiative terms are computed with all-sky fluxes. The statistics of the linear regression (slope (temperature-mediated response in W m−2 K−1), y-intercept (rapid adjustment in W m−2), and correlation coefficient, r) are displayed in the key. Source data

Extended Data Figure 2 Summary of model spread in temperature-mediated responses.

a, The temperature-mediated response of each atmospheric energy budget term (equation (1) in main text) is shown for each model as blue circles and the model mean as a red cross. Responses of the radiative terms (dLWC/dT and dSWA/dT) computed with all-sky fluxes or with clear-sky fluxes are denoted with all- or clr-, respectively. The numbers above the abscissa are the cross-model correlations between LvdP/dT and each other temperature-mediated response. b, Scatterplot of the all-sky versus clear-sky temperature-mediated SWA response and corresponding linear fit. Source data

Extended Data Figure 3 Total changes and rapid adjustments for LvP and SWA.

a, The total change in LvP per unit warming (mean over years 131–150 of the abrupt4 × CO2 simulation minus the corresponding mean of the piControl simulation, normalized by 2-m air temperature change, LvΔPT) versus total change in clear-sky SWA per unit warming (clr-ΔSWA/ΔT). b, The rapid adjustment of LvP versus rapid adjustment of clear-sky (clr-) SWA. A linear fit is shown in both cases. Source data

Extended Data Figure 4 Contributions of water vapour change to model spread in temperature-mediated SWA response.

a, The Gregory method (Methods) is applied to anomalies of globally averaged specific humidity (q) at standard atmospheric levels, total column precipitable water (PW), and upper tropospheric precipitable water (PW(500–200), computed by vertically integrating q between 500 and 200 mbar) to quantify the temperature-mediated response of atmospheric water vapour for each model. The natural log was taken before computing annual anomalies. For each quantity, the symbols (circle, diamond, square) represent the model mean and the whiskers represent the full model spread. The globally averaged annual-mean clear-sky shortwave atmospheric q kernel (Methods) is overlaid (blue curve). b, The cross-model correlation between the responses of water vapour in a and the temperature-mediated clear-sky SWA response (clr-dSWA/dT) computed with model-produced fluxes (black) or radiative kernels (blue). Filled symbols are statistically significant at the 5% level based on a two-tailed t-test33, with degrees of freedom corresponding to the number of participating modelling institutions (14) within the 25 model ensemble. Source data

Extended Data Figure 5 The SWA sensitivity curve for each model.

Shown are normalized bin-mean SWA versus PW and corresponding linear fit (as in Fig. 3a) for each model (black dots/line); models are sorted from (top left) smallest dSWA/dPW ((% kg−1 m2), printed on every panel) to (bottom right) largest dSWA/dPW. Dashed lines depict the 10th–90th percentile spread of SWA within each PW bin, demonstrating the tight constraint PW places on SWA. Numbers next to model names are those from Extended Data Table 1. On every panel, the SWA versus PW curve and linear fit based on CERES-EBAF fluxes and SSM/I water vapour are also shown (blue triangles/line); the 10th–90th percentile spread is shown only on the second panel for visual clarity. Source data

Extended Data Figure 6 Methodology for parameterizing absorption of solar radiation by water vapour.

Shown is the relationship between dSWA/dPW and methodology used to parameterize solar absorption by water vapour in a cloud-free atmosphere, with colours for each model referring to different parameterization procedures as documented in the references listed in the key (see also Extended Data Table 1). (Boxes outlined in black indicate that water vapour continuum absorption in the shortwave is accounted for in the parameterization.) Model numbers, printed on each box, are identified on the ordinate axis as in Fig. 4. The width of the horizontal shading for each model represents the 95% CI of the regression slope to the SWA versus PW curve, as in Fig. 4. Statistical uncertainty of dSWA/dPW derived from CERES-EBAF fluxes and three PW data sets (Obs.) and from ISCCP-FD is represented with vertical dashed lines, as described in Figs 3 and 4, respectively. (refs 36, 38, 43, 50, 51, 52, 53, 55, 59, 60, 64, 76).

Extended Data Figure 7 Constraining the spread in late twenty-first century precipitation change.

a, The relationship between temperature-mediated clear-sky SWA response (clr-dSWA/dT) and SWA sensitivity to varying PW (dSWA/dPW) (as in Fig. 3b), showing an estimate of the ‘true’ dSWA/dT (blue line/star), and how it is quantified. b, The temperature-mediated LvP response (LvdP/dT) versus dSWA/dT (as in Fig. 2b), showing how the ‘true’ dSWA/dT in a is used to quantify a bias in LvdP/dT originating from a bias in dSWA/dT; the bias for an example model (no. 13, GISS-E2-R) is displayed. c, The full (abscissa) versus constrained (with bias in b removed, ordinate) total change (indicated with Δ) in LvP normalized by change in 2-m warming at the end of the twenty-first century under RCP8.5 (see Methods and equation (2) in Methods). d, As in c but for total LvP change not normalized by warming. Model numbers are defined in Extended Data Table 1. Two models (no. 8, CNRM-CM5-2 and no. 23, MPI-ESP-P) are excluded from c and d owing to unavailable RCP8.5 output. Source data

Extended Data Table 1 CMIP5 models analysed in this paper

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DeAngelis, A., Qu, X., Zelinka, M. et al. An observational radiative constraint on hydrologic cycle intensification. Nature 528, 249–253 (2015). https://doi.org/10.1038/nature15770

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