Land-ocean shifts in tropical precipitation linked to surface temperature and humidity change
Journal of Climate
American Meteorological Society
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Reason for embargo
A compositing scheme that predicts changes in tropical precipitation under climate change from changes in near-surface relative humidity (RH) and temperature is presented. As shown by earlier work, regions of high tropical precipitation in general circulation models (GCMs) are associated with high near-surface RH and temperature. Under climate change, we find that high precipitation continues to be associated with the highest surface RH and temperatures in most CMIP5 GCMs, meaning that it is the “rank” of a given GCM gridbox with respect to others that determines how much precipitation falls rather than the absolute value of surface temperature or RH change, consistent with the weak temperature gradient approximation. Further, we demonstrate that the majority of CMIP5 GCMs are close to a threshold near which reductions in land RH produce large reductions in the RH-ranking of some land regions, causing reductions in precipitation over land, particularly South America, and compensating increases over ocean. Recent work on predicting future changes in specific humidity allows us to predict the qualitative sense of precipitation change in some GCMs when land surface humidity changes are unknown. However, the magnitudes of predicted changes are too small. Further study, perhaps into the role of radiative and land-atmosphere feedbacks that we neglect, is necessary.
We are grateful to Richard Allan, whose suggestions substantially improved results. We acknowledge the World Climate Research Programmes Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating sup- port and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank the JASMIN and CEDA team for making available the JASMIN computing resource (Lawrence et al. 2013). FHL was part supported by the UK-China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Part- nership (CSSP) China as part of the Newton Fund; AJF was supported by the NERC PROBEC project NE/K016016/1; RC was supported by the Newton Fund through the Met Office CSSP Brazil
This is the final version of the article. Available from American Meteorological Society via the DOI in this record.
Vol. 30 No. 12, June 2017, pp. 4527–4545