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An observational radiative constraint on hydrologic cycle intensification

Nature volume 528, pages 249–253 (2015)Cite this article

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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.

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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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Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences, University of California Los Angeles, Los Angeles, California, 90095, USA

    Anthony M. DeAngelis, Xin Qu & Alex Hall

  2. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, 94550, California, USA

    Mark D. Zelinka

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Contributions

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, LvΔP/ΔT) 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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