TY - JOUR
T1 - Weak hydrological sensitivity to temperature change over land, independent of climate forcing
AU - Samset, B. H.
AU - Myhre, G.
AU - Forster, P. M.
AU - Hodnebrog,
AU - Andrews, T.
AU - Boucher, O.
AU - Faluvegi, G.
AU - Fläschner, D.
AU - Kasoar, M.
AU - Kharin, V.
AU - Kirkevåg, A.
AU - Lamarque, J. F.
AU - Olivié, D.
AU - Richardson, T. B.
AU - Shindell, D.
AU - Takemura, T.
AU - Voulgarakis, A.
N1 - Funding Information:
B.H.S., G.M. and O.H. were funded by the Research Council of Norway, through the grant NAPEX (229778). Supercomputer facilities were generously provided by NOTUR. D.S. thanks the NASA High-End Computing Program through the NASA Center for Climate Simulation at Goddard Space Flight Center for computational resources. M.K. and A.V. are supported by the Natural Environment Research Council under grant number NE/K500872/1. Simulations with HadGEM3-GA4 were performed using the MONSooN system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, which is a strategic partnership between the Met Office and the Natural Environment Research Council. T. T. was supported by the supercomputer system of the National Institute for Environmental Studies, Japan, the Environment Research and Technology Development Fund (S-12-3) of the Ministry of the Environment, Japan and JSPS KAKENHI Grant Number JP15H01728 and JP15K12190. D.J.L.O. and A.K. were supported by the Norwegian Research Council through the projects EVA (grant no. 229771) and EarthClim (207711/E10), and NOTUR (nn2345k) and NorStore (ns2345k) projects. T.R. was supported by NERC training award NE/K007483/1, and acknowledges use of the MONSooN system. Computing resources for J.F.L. (ark:/85065/d7wd3xhc) were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies. Computing resources for the simulations with the MPI model were provided by the German Climate Computing Center (DKRZ), Hamburg. Computing resources for O.B. were provided by GENCI (project gencmip6). T.A. was supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil).
Publisher Copyright:
© 2018, The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - We present the global and regional hydrological sensitivity (HS) to surface temperature changes, for perturbations to CO2, CH4, sulfate and black carbon concentrations, and solar irradiance. Based on results from ten climate models, we show how modeled global mean precipitation increases by 2–3% per kelvin of global mean surface warming, independent of driver, when the effects of rapid adjustments are removed. Previously reported differences in response between drivers are therefore mainly ascribable to rapid atmospheric adjustment processes. All models show a sharp contrast in behavior over land and over ocean, with a strong surface temperature-driven (slow) ocean HS of 3–5%/K, while the slow land HS is only 0–2%/K. Separating the response into convective and large-scale cloud processes, we find larger inter-model differences, in particular over land regions. Large-scale precipitation changes are most relevant at high latitudes, while the equatorial HS is dominated by convective precipitation changes. Black carbon stands out as the driver with the largest inter-model slow HS variability, and also the strongest contrast between a weak land and strong sea response. We identify a particular need for model investigations and observational constraints on convective precipitation in the Arctic, and large-scale precipitation around the Equator.
AB - We present the global and regional hydrological sensitivity (HS) to surface temperature changes, for perturbations to CO2, CH4, sulfate and black carbon concentrations, and solar irradiance. Based on results from ten climate models, we show how modeled global mean precipitation increases by 2–3% per kelvin of global mean surface warming, independent of driver, when the effects of rapid adjustments are removed. Previously reported differences in response between drivers are therefore mainly ascribable to rapid atmospheric adjustment processes. All models show a sharp contrast in behavior over land and over ocean, with a strong surface temperature-driven (slow) ocean HS of 3–5%/K, while the slow land HS is only 0–2%/K. Separating the response into convective and large-scale cloud processes, we find larger inter-model differences, in particular over land regions. Large-scale precipitation changes are most relevant at high latitudes, while the equatorial HS is dominated by convective precipitation changes. Black carbon stands out as the driver with the largest inter-model slow HS variability, and also the strongest contrast between a weak land and strong sea response. We identify a particular need for model investigations and observational constraints on convective precipitation in the Arctic, and large-scale precipitation around the Equator.
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U2 - 10.1038/s41612-017-0005-5
DO - 10.1038/s41612-017-0005-5
M3 - Article
AN - SCOPUS:85047013259
SN - 2397-3722
VL - 1
JO - npj Climate and Atmospheric Science
JF - npj Climate and Atmospheric Science
IS - 1
M1 - 20173
ER -