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Understanding the intermodel spread in global-mean hydrological sensitivity

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Fläschner,  Dagmar
Director’s Research Group AES, The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;
IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society;

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Mauritsen,  Thorsten
Climate Dynamics, The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

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Stevens,  Bjoern       
Director’s Research Group AES, The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

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jcli-d-15-0351.1.pdf
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Citation

Fläschner, D., Mauritsen, T., & Stevens, B. (2016). Understanding the intermodel spread in global-mean hydrological sensitivity. Journal of Climate, 29, 801-817. doi:10.1175/JCLI-D-15-0351.1.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0028-4836-7
Abstract
This paper assesses intermodel spread in the slope of global-mean precipitation change ΔP with respect to surface temperature change. The ambiguous estimates in the literature for this slope are reconciled by analyzing four experiments from phase 5 of CMIP (CMIP5) and considering different definitions of the slope. The smallest intermodel spread (a factor of 1.5 between the highest and lowest estimate) is found when using a definition that disentangles temperature-independent precipitation changes (the adjustments) from the slope of the temperature-dependent precipitation response; here this slope is referred to as the hydrological sensitivity parameter η. The estimates herein show that η is more robust than stated in most previous work. The authors demonstrate that adjustments and η estimated from a steplike quadrupling CO2 experiment serve well to predict ΔP in a transient CO2 experiment. The magnitude of η is smaller in the coupled ocean–atmosphere quadrupling CO2 experiment than in the noncoupled atmosphere-only experiment. The offset in magnitude due to coupling suggests that intermodel spread may undersample uncertainty. Also assessed are the relative contribution of η, the surface warming, and the adjustment on the spread in ΔP on different time scales. Intermodel variation of both η and the adjustment govern the spread in ΔP in the years immediately after the abrupt forcing change. In equilibrium, the uncertainty in ΔP is dominated by uncertainty in the equilibrium surface temperature response. A kernel analysis reveals that intermodel spread in η is dominated by intermodel spread in tropical lower tropospheric temperature and humidity changes and cloud changes.