4.7 Article

Sensitivity of terrestrial precipitation trends to the structural evolution of sea surface temperatures

Journal

GEOPHYSICAL RESEARCH LETTERS
Volume 42, Issue 4, Pages 1190-1196

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1002/2014GL062593

Keywords

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Funding

  1. Department of Energy [DE-SC0004975]
  2. National Science Foundation [NSF-AGS-1312865]
  3. UK National Environment Research Council
  4. NSF [AGS-1102838]
  5. NOAA [NA11OAR4310099, NA14OAR4310274]
  6. U.S. Department of Energy (DOE) [DE-SC0004975] Funding Source: U.S. Department of Energy (DOE)
  7. Natural Environment Research Council [ncas10009] Funding Source: researchfish
  8. Directorate For Geosciences [1102838] Funding Source: National Science Foundation
  9. Directorate For Geosciences
  10. Div Atmospheric & Geospace Sciences [1312865] Funding Source: National Science Foundation
  11. Div Atmospheric & Geospace Sciences [1102838] Funding Source: National Science Foundation
  12. Div Atmospheric & Geospace Sciences
  13. Directorate For Geosciences [1103209] Funding Source: National Science Foundation

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Pronounced intermodel differences in the projected response of land surface precipitation (LSP) to future anthropogenic forcing remain in the Coupled Model Intercomparison Project Phase 5 model integrations. A large fraction of the intermodel spread in projected LSP trends is demonstrated here to be associated with systematic differences in simulated sea surface temperature (SST) trends, especially the representation of changes in (i) the interhemispheric SST gradient and (ii) the tropical Pacific SSTs. By contrast, intermodel differences in global mean SST, representative of differing global climate sensitivities, exert limited systematic influence on LSP patterns. These results highlight the importance to regional terrestrial precipitation changes of properly simulating the spatial distribution of large-scale, remote changes as reflected in the SST response to increasing greenhouse gases. Moreover, they provide guidance regarding which region-specific precipitation projections may be potentially better constrained for use in climate change impact assessments.

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