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Climatic Change

, Volume 60, Issue 1–2, pp 7–35 | Cite as

Climate Scenarios for the Southeastern U.S. Based on GCM and Regional Model Simulations

  • L. O. Mearns
  • F. Giorgi
  • L. McDaniel
  • C. Shields
Article

Abstract

We analyze the control runs and 2 × CO2 projections (5-yearlengths) of the CSIRO Mk 2 GCM and the RegCM2 regional climate model, which was nested in the CSIRO GCM, over the Southeastern U.S.; and we present the development of climate scenarios for use in an integrated assessment of agriculture. The RegCM exhibits smaller biases in both maximum and minimum temperature compared to the CSIRO. Domain average precipitation biases are generally negative and relatively small in winter, spring, and fall, but both models produce large positive biases in summer, that of the RegCM being the larger. Spatial pattern correlations of the model control runs and observations show that the RegCM reproduces better than the CSIRO the spatial patterns of precipitation, minimum and maximum temperature in all seasons. Under climate change conditions, the most salient feature from the point of view of scenarios for agriculture is the large decreases in summer precipitation, about 20% in the CSIRO and 30% in the RegCM. Increases in springprecipitation are found in both models, about 35% in the CSIRO and 25% in theRegCM. Precipitation decreases of about 20% dominate in winter in the CSIRO,while a more complex pattern of increases and decreases is exhibited by the regional model. Temperature increases by 3 to 5 °C in the CSIRO, the higher values dominating in winter and spring. In the RegCM, temperature increases are much more spatially and temporally variable, ranging from 1 to 7 °C acrossall months and grids. In summer large increases (up to 7 °C) in maximum temperature are found in the northeastern part of the domain where maximum drying occurs.

Keywords

Spatial Pattern Regional Climate Model Regional Model Climate Scenario Summer Precipitation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • L. O. Mearns
    • 1
  • F. Giorgi
    • 2
  • L. McDaniel
    • 1
  • C. Shields
    • 1
  1. 1.National Center for Atmospheric ResearchBoulderU.S.A
  2. 2.Abdus Salam International Centre for Theoretical PhysicsTriesteItaly

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