Examining the Impacts of Climate Change via Downscaling Approaches: a Case Study

EasyChair Preprint 14895

Abstract

Climate change's impacts are increasingly evident and pose significant risks at regional scales, necessitating high-resolution climate projections for effective adaptation strategies. This study examines climate change impacts through downscaling approaches by focusing on a mountainous region in the Pacific Northwest of the United States, chosen for its sensitivity to temperature fluctuations and precipitation variability. We employed both statistical and dynamical downscaling methods to generate localized climate projections from global climate models. Statistical downscaling involved creating regression-based models to translate large-scale climate variables into regional forecasts, while dynamical downscaling utilized high-resolution regional climate models to simulate localized climate processes. The results indicate substantial warming trends with average temperatures projected to increase by 2-6°C by mid-century, alongside changes in precipitation patterns including increased frequency of heavy snowfall and altered seasonal distributions. The findings emphasize the necessity for high-resolution climate information for regional planning and risk management. Future research directions include improving downscaling accuracy, expanding studies to other vulnerable regions, and integrating climate projections with impact models for comprehensive assessments.

Keyphrases: Downscaling, Precipitation patterns, Regional Climate Models, Snowfall, Statistical downscaling, climate change, climate impact assessment, dynamical downscaling, mountainous region, temperature projections

@booklet{EasyChair:14895,
  author    = {Alakitan Samad},
  title     = {Examining the Impacts of Climate Change via Downscaling Approaches: a Case Study},
  howpublished = {EasyChair Preprint 14895},
  year      = {EasyChair, 2024}}