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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 5 | Copyright
Hydrol. Earth Syst. Sci., 18, 1575-1589, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 May 2014

Research article | 06 May 2014

A general framework for understanding the response of the water cycle to global warming over land and ocean

M. L. Roderick1,2,3,**, F. Sun2,3, W. H. Lim2,3,*, and G. D. Farquhar2,3 M. L. Roderick et al.
  • 1Research School of Earth Sciences, The Australian National University, Canberra, ACT 0200, Australia
  • 2Research School of Biology, The Australian National University, Canberra, ACT 0200, Australia
  • 3Australian Research Council Centre of Excellence for Climate System Science, Canberra, Australia
  • *Currently at: Department of Civil Engineering, Tokyo Institute of Technology, Tokyo, 152-8552, Japan
  • **Invited contribution by M. L. Roderick, recipient of the EGU John Dalton Medal 2013.

Abstract. Climate models project increases in globally averaged atmospheric specific humidity that are close to the Clausius–Clapeyron (CC) value of around 7% K−1 whilst projections for mean annual global precipitation (P) and evaporation (E) are somewhat muted at around 2% K−1. Such global projections are useful summaries but do not provide guidance at local (grid box) scales where impacts occur. To bridge that gap in spatial scale, previous research has shown that the "wet get wetter and dry get drier" relation, Δ(PE) ∝ PE, follows CC scaling when the projected changes are averaged over latitudinal zones. Much of the research on projected climate impacts has been based on an implicit assumption that this CC relation also holds at local (grid box) scales but this has not previously been examined. In this paper we find that the simple latitudinal average CC scaling relation does not hold at local (grid box) scales over either ocean or land. This means that in terms of PE, the climate models do not project that the "wet get wetter and dry get drier" at the local scales that are relevant for agricultural, ecological and hydrologic impacts. In an attempt to develop a simple framework for local-scale analysis we found that the climate model output shows a remarkably close relation to the long-standing Budyko framework of catchment hydrology. We subsequently use the Budyko curve and find that the local-scale changes in PE projected by climate models are dominated by changes in P while the changes in net irradiance at the surface due to greenhouse forcing are small and only play a minor role in changing the mean annual PE in the climate model projections. To further understand the apparently small changes in net irradiance we also examine projections of key surface energy balance terms. In terms of global averages, we find that the climate model projections are dominated by changes in only three terms of the surface energy balance: (1) an increase in the incoming long-wave irradiance, and the respective responses (2) in outgoing long-wave irradiance and (3) in the evaporative flux, with the latter change being much smaller than the former two terms and mostly restricted to the oceans. The small fraction of the realised surface forcing that is partitioned into E explains why the hydrologic sensitivity (2% K−1) is so much smaller than CC scaling (7% K−1). Much public and scientific perception about changes in the water cycle has been based on the notion that temperature enhances E. That notion is partly true but has proved an unfortunate starting point because it has led to misleading conclusions about the impacts of climate change on the water cycle. A better general understanding of the potential impacts of climate change on water availability that are projected by climate models will surely be gained by starting with the notion that the greater the enhancement of E, the less the surface temperature increase (and vice versa). That latter notion is based on the conservation of energy and is an underlying basis of climate model projections.

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