Abstract
Climate models simulate an increase in global precipitation at a rate of approximately 1–3% per Kelvin of global surface warming. This change is often interpreted through the lens of the atmospheric energy budget, in which the increase in global precipitation is mostly offset by an increase in net radiative cooling. Other studies have provided different interpretations from the perspective of the surface, where evaporation represents the turbulent transfer of latent heat to the atmosphere. Expanding on this surface perspective, here we derive a version of the Penman–Monteith equation that allows the change in ocean evaporation to be partitioned into a thermodynamic response to surface warming, and additional diagnostic contributions from changes in surface radiation, ocean heat uptake, and boundary-layer dynamics/relative humidity. In this framework, temperature is found to be the primary control on the rate of increase in global precipitation within model simulations of greenhouse gas warming, while the contributions from changes in surface radiation and ocean heat uptake are found to be secondary. The temperature contribution also dominates the spatial pattern of global evaporation change, leading to the largest fractional increases at high latitudes. In the surface energy budget, the thermodynamic increase in evaporation comes at the expense of the sensible heat flux, while radiative changes cause the sensible heat flux to increase. These tendencies on the sensible heat flux partly offset each other, resulting in a relatively small change in the global mean, and contributing to an impression that global precipitation is radiatively constrained.
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Notes
On long timescales, \(T_s-T_a\) is generally positive over the ocean, implying a transfer of both sensible and latent heat from the surface to the atmosphere.
Equilibrium simulations were not part of CMIP5.
The contribution from \(\varDelta R_s\) is negative at high latitudes in the Southern Hemisphere, reflecting a decrease in shortwave absorption as a result of increased cloud cover.
These percentages are based on a comparison of the ocean-mean values that appear in the top left of each panel in Fig. 2.
References
Allen M, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419(6903):224–232
Andrews T, Forster PM (2010) The transient response of global-mean precipitation to increasing carbon dioxide levels. Environ Res Lett 5(2):025212. https://doi.org/10.1088/1748-9326/5/2/025212
Andrews T, Forster PM, Gregory JM (2009) A surface energy perspective on climate change. J Clim 22(10):2557–2570. https://doi.org/10.1175/2008JCLI2759.1
Andrews T, Forster P, Boucher O, Bellouin N, Jones A (2010) Precipitation, radiative forcing and global temperature change. Geophys Res Lett 37(14):L14701. https://doi.org/10.1029/2010GL043991
Bala G, Duffy PB, Taylor KE (2008) Impact of geoengineering schemes on the global hydrological cycle. Proc Natl Acad Sci USA 105(22):7664–9. https://doi.org/10.1073/pnas.0711648105
Boer G (1993) Climate change and the regulation of the surface moisture and energy budgets. Clim Dyn 8:225–239
DeAngelis AM, Qu X, Zelinka MD, Hall A (2015) An observational radiative constraint on hydrologic cycle intensification. Nature 528(7581):249–253. https://doi.org/10.1038/nature15770
Fläschner D, Mauritsen T, Stevens B (2016) Understanding the intermodel spread in global-mean hydrological sensitivity. J Clim 29(2):801–817. https://doi.org/10.1175/JCLI-D-15-0351.1
Frieler K, Meinshausen M, Schneider von Deimling T, Andrews T, Forster P (2011) Changes in global-mean precipitation in response to warming, greenhouse gas forcing and black carbon. Geophys Res Lett 38(4). https://doi.org/10.1029/2010GL045953
Fu Q, Feng S (2014) Responses of terrestrial aridity to global warming. J Geophys Res Atmos 119(13):7863–7875. https://doi.org/10.1002/2014JD021608
Gregory JM, Ingram WJ, Palmer MA, Jones GS, Stott PA, Thorpe RB, Lowe JA, Johns TC, Williams KD (2004) A new method for diagnosing radiative forcing and climate sensitivity. Geophys Res Lett 31(3):L03205. https://doi.org/10.1029/2003GL018747
Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19(21):5686–5699
Kleidon A, Renner M (2013a) A simple explanation for the sensitivity of the hydrologic cycle to surface temperature and solar radiation and its implications for global climate change. Earth Syst Dyn 4(2):455–465. https://doi.org/10.5194/esd-4-455-2013
Kleidon A, Renner M (2013b) Thermodynamic limits of hydrologic cycling within the Earth system: concepts, estimates and implications. Hydrol Earth Syst Sci 17(7):2873–2892. https://doi.org/10.5194/hess-17-2873-2013
Lambert FH, Faull NE (2007) Tropospheric adjustment: the response of two general circulation models to a change in insolation. Geophys Res Lett 34(3):L03701. https://doi.org/10.1029/2006GL028124
Lambert FH, Webb MJ (2008) Dependency of global mean precipitation on surface temperature. Geophys Res Lett 35(16):L16706. https://doi.org/10.1029/2008GL034838
Lambert FH, Allen MR, Lambert FH, Allen MR (2009) Are changes in global precipitation constrained by the tropospheric energy budget? J Clim 22(3):499–517. https://doi.org/10.1175/2008JCLI2135.1
Le Hir G, Donnadieu Y, Goddéris Y, Pierrehumbert RT, Halverson GP, Macouin M, Nédélec A, Ramstein G (2009) The snowball earth aftermath: exploring the limits of continental weathering processes. Earth Planet Sci Lett 277(3–4):453–463. https://doi.org/10.1016/j.epsl.2008.11.010
Lorenz DJ, DeWeaver ET, Vimont DJ (2010) Evaporation change and global warming: the role of net radiation and relative humidity. J Geophys Res Atmos 115(D20):D20118. https://doi.org/10.1029/2010JD013949
Manabe S, Wetherald RT (1975) The effects of doubling the CO\(\_2\) concentration on the climate of a general circulation model. J Atmos Sci 32(1):3–15. https://doi.org/10.1175/1520-0469(1975) 032<0003:TEODTC>2.0.CO;2
McInerney D, Moyer E (2012) Direct and disequilibrium effects on precipitation in transient climates. Atmos Chem Phys Discuss 12(8):19649–19681. https://doi.org/10.5194/acpd-12-19649-2012. http://www.atmos-chem-phys-discuss.net/12/19649/2012/
Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE, Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) THE WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88(9):1383–1394. https://doi.org/10.1175/BAMS-88-9-1383
Ming Y, Ramaswamy V, Persad G (2010) Two opposing effects of absorbing aerosols on global-mean precipitation. Geophys Res Lett 37(13):L13701. https://doi.org/10.1029/2010GL042895
Monteith JL (1981) Evaporation and surface temperature. Q J R Meterol Soc 107(451):1–27. https://doi.org/10.1002/qj.49710745102
O’Gorman PA, Schneider T (2008) The hydrological cycle over a wide range of climates simulated with an idealized GCM. J Clim 21(15):3815–3832. https://doi.org/10.1175/2007JCLI2065.1
O’Gorman PA, Allan RP, Byrne MP, Previdi M (2012) Energetic constraints on precipitation under climate change. Surv Geophys 33(3–4):585–608. https://doi.org/10.1007/s10712-011-9159-6
Pendergrass AG, Hartmann DL (2014) The atmospheric energy constraint on global-mean precipitation change. J Clim 27(2):757–768. https://doi.org/10.1175/JCLI-D-13-00163.1
Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc A Math Phys Eng Sci 193(1032):120–145. https://doi.org/10.1098/rspa.1948.0037
Pierrehumbert RT (1999) Subtropical water vapor as a mediator of rapid global climate change. In: Mechanisms of global climate change at millennial time scales, pp 339–361. https://doi.org/10.1029/GM112p0339
Pierrehumbert RT (2002) The hydrologic cycle in deep-time climate problems. Nature 419(6903):191–8. https://doi.org/10.1038/nature01088
Pierrehumbert RT (2010) Principles of planetary climate. Cambridge University Press, Cambridge
Previdi M (2010) Radiative feedbacks on global precipitation. Environ Res Lett 5(2):025211. https://doi.org/10.1088/1748-9326/5/2/025211
Priestly CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100(2):81–92. https://doi.org/10.1175/1520-0493(1972) 100<0081:OTAOSH>2.3.CO;2
Richter I, Xie SP (2008) Muted precipitation increase in global warming simulations: a surface evaporation perspective. J Geophys Res Atmos 113(D24):D24118. https://doi.org/10.1029/2008JD010561
Samset BH, Myhre G, Forster PM, Hodnebrog Ø, Andrews T, Faluvegi G, Fläschner D, Kasoar M, Kharin V, Kirkevåg A, Lamarque JF, Olivié D, Richardson T, Shindell D, Shine KP, Takemura T, Voulgarakis A (2016) Fast and slow precipitation responses to individual climate forcers: a PDRMIP multimodel study. Geophys Res Lett 43(6):2782–2791. https://doi.org/10.1002/2016GL068064
Scheff J, Frierson DMW (2014) Scaling potential evapotranspiration with greenhouse warming. J Clim 27(4):1539–1558. https://doi.org/10.1175/JCLI-D-13-00233.1
Stephens GL, Ellis TD (2008) Controls of global-mean precipitation increases in global warming GCM experiments. J Clim 21(23):6141–6155. https://doi.org/10.1175/2008JCLI2144.1
Takahashi K (2009) Radiative constraints on the hydrological cycle in an idealized radiativeconvective equilibrium model. J Atmos Sci 66(1):77–91. https://doi.org/10.1175/2008JAS2797.1
Taylor KE, Stouffer RJ, Meehl GA, Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498. https://doi.org/10.1175/BAMS-D-11-00094.1
Trenberth K (1999) Conceptual framework for changes of extremes of the hydrological cycle with climate change. Clim Change 42(1):327–339. https://doi.org/10.1023/a:1005488920935
Trenberth K (2011) Changes in precipitation with climate change. Clim Res 47(1):123–138. https://doi.org/10.3354/cr00953
Trenberth KE, Dai A (2007) Effects of Mount Pinatubo volcanic eruption on the hydrological cycle as an analog of geoengineering. Geophys Res Lett 34(15). https://doi.org/10.1029/2007GL030524
Trenberth KE, Smith L, Qian T, Dai A, Fasullo J, Trenberth KE, Smith L, Qian T, Dai A, Fasullo J (2007) Estimates of the global water budget and its annual cycle using observational and model data. J Hydrometeorol 8(4):758–769. https://doi.org/10.1175/JHM600.1
Van Der Ent RJ, Tuinenburg OA (2017) The residence time of water in the atmosphere revisited. Hydrol Earth Syst Sci 21:779–790. https://doi.org/10.5194/hess-21-779-2017
Wetherald RT, Manabe S, Wetherald RT, Manabe S (1975) The effects of changing the solar constant on the climate of a general circulation model. J Atmos Sci 32(11):2044–2059. https://doi.org/10.1175/1520-0469(1975) 032<2044:TEOCTS>2.0.CO;2
Acknowledgements
We are very grateful to Ray Pierrehumbert and four anonymous reviewers for their excellent comments that greatly improved the paper.
Funding
This work was supported by the National Science Foundation (AGS-1752796 [KCA] and AGS-1524569 [NF]).
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Appendices
Appendix 1: Calculating the contributions to evaporation change in Eq. (14)
The terms in Eq. (14) were calculated as follows: LE and \(R_s\) were taken directly from model output; G was determined from \(R_s\), LE, and H based on the surface energy budget (Eq. 4); \(\eta \) was calculated from the two-meter air temperature using Eqs. (11) and (13). Finally, given LE, \(\eta \), \(R_s\), and G, we then solved for \(\kappa \) in Eq. (10). The contributions were calculated from ensemble-mean output over the last 5 years of the simulation period. In the equilibrium warming simulations, this was typically 21–25 years after \(\hbox {CO}_2\) doubling. In the transient warming simulations, we used years 96–100 after \(\hbox {CO}_2\) quadrupling. The contributions were first calculated for each month, and then the monthly contributions were averaged to arrive at an annual-mean value. However, the results were essentially unchanged when the contributions were calculated from annual-mean output.
To understand the global impact of the fractional contributions in Fig. 1, we must account for spatial variability in the magnitude of the mean-state evaporation and surface-air warming. To do so, we multiply each term in Eq. (14) by the following (dimensionless) weighting function,
where the overbars in the denominator indicate the ocean-mean values of each variable. These results are then averaged in space, yielding the ocean-mean contributions given in the top left of each panel in Fig. 1.
Appendix 2: Estimating \(R_s-G+\kappa \) in the idealized simulations of O’Gorman and Schneider (2008)
To estimate the value of \(R_s-G+\kappa \) in O’Gorman and Schneider (2008) simulations, we use the fact that their control climate exhibits a global-mean surface-air temperature of \(\overline{T_a}=288\) K, and a global-mean precipitation of 4.3 mm/day, which equates to \(L{\overline{E}}=124\)\(\hbox {Wm}^{-2}\). Given \(\eta \approx 0.63\) at \(\overline{T_a}= 288\) K, this implies a combined value of \(R_s-G+\kappa =197\)\(\hbox {Wm}^{-2}\). If we assume that this sum is constant, global precipitation is directly proportional to \(\eta \), resulting in the gray curve in Fig. 4.
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Siler, N., Roe, G.H., Armour, K.C. et al. Revisiting the surface-energy-flux perspective on the sensitivity of global precipitation to climate change. Clim Dyn 52, 3983–3995 (2019). https://doi.org/10.1007/s00382-018-4359-0
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DOI: https://doi.org/10.1007/s00382-018-4359-0