Evaluating Climate Change Effects Using Downscaling Methods: a Case Study

EasyChair Preprint 14893

Abstract

Climate change poses significant challenges to regional environments and economies, necessitating detailed local assessments to inform adaptation strategies. This study evaluates the effects of climate change on a semi-arid region in the southwestern United States using downscaling techniques. Two primary downscaling methods—statistical and dynamical—were applied to translate global climate model projections into high-resolution, region-specific forecasts. The statistical downscaling involved developing regression models to relate large-scale climate variables with local climate observations, while the dynamical downscaling utilized regional climate models to simulate detailed climate processes. Results from both methods indicated substantial increases in temperature and variable changes in precipitation patterns. Statistical downscaling predicted a rise in average temperatures by 2-4°C by mid-century and a potential decrease in annual precipitation, whereas dynamical downscaling highlighted more pronounced regional temperature increases and an increase in extreme precipitation events. The findings underscore the importance of localized climate assessments and provide insights for regional adaptation strategies. Recommendations for future research include improving downscaling techniques, expanding case studies, and integrating climate projections with impact models for more comprehensive assessments.

Keyphrases: Downscaling, Precipitation patterns, Regional Climate Models, Statistical downscaling, adaptation strategies, climate change, climate impact assessment, dynamical downscaling, semi-arid region, temperature projections

@booklet{EasyChair:14893,
  author    = {Kayode Sheriffdeen},
  title     = {Evaluating Climate Change Effects Using Downscaling Methods: a Case Study},
  howpublished = {EasyChair Preprint 14893},
  year      = {EasyChair, 2024}}