Downscaling Maximum Temperatures to Subkilometer Resolutions in the Shenandoah National Park of Virginia, USA

Temple R Lee,Stephan F J De Wekker,John E B Wofford

doi:10.1155/2014/594965

Abstract

Downscaling future temperature projections to mountainous regions is vital for many applications, including ecological and water resource management. In this study, we demonstrate a method to downscale maximum temperatures to subkilometer resolutions using the Parameter-elevation Regression on Independent Slopes Model (PRISM). We evaluate the downscaling method with observations from a network of temperature sensors deployed along western and eastern slopes of Virginia’s Shenandoah National Park in the southern Appalachian Mountains. We find that the method overestimates mean July maximum temperatures by about 2°C (4°C) along the western (eastern) slopes. Based on this knowledge, we introduce corrections to generate maps of current and future maximum temperatures in the Shenandoah National Park.

Highlights

General circulation models (GCMs) predict changing temperature and moisture patterns in many regions of the world due to increases in atmospheric carbon dioxide (CO2) concentration [1]
The largest overestimates occur along the east slope of Shenandoah National Park (SNP), where the mean bias error (MBE) between downscaled Parameter-elevation Regression on Independent Slopes Model (PRISM) and the slope observations of mean Tmax is 3.5–4.7∘C (Figure 3)
We have presented a new method to downscale maximum temperatures in a mountainous region using PRISM and have evaluated our method with temperature observations from mountain slopes in SNP

Summary

Introduction

General circulation models (GCMs) predict changing temperature and moisture patterns in many regions of the world due to increases in atmospheric carbon dioxide (CO2) concentration [1]. Even regional climate models (RCMs) with resolutions of a few tens of kilometers are still too coarse to assess the effects of climate change on the scale of local habitats. A 50 km grid spacing typical of RCMs [7, 8] averages the elevation changes across the grid, thereby smoothing the topography in mountainous regions [9] including the Blue Ridge Mountains in and around SNP. This topographic smoothing makes it very difficult to understand projected climate changes in these mountainous regions

Methods

Results

Conclusion