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06:27
Simulating Impacts of Ice Storms on Forest Ecosystems
Published on: June 30, 2020
Assessing uncertainty in high-resolution spatial climate data across the US Northeast.
Daniel A Bishop1, Colin M Beier
1Department of Forest and Natural Resources Management, College of Environmental Science and Forestry, State University of New York, Syracuse, New York, USA. dbishop@syr.edu
Plos One
|August 13, 2013
Summary
Understanding climate data uncertainty is crucial for adaptation. This study ground-truthed two high-resolution gridded historical climate (GHC) temperature products in the US Northeast, revealing errors influenced by elevation and digital elevation model quality.
Area of Science:
- Climate science
- Environmental modeling
- Geospatial analysis
Background:
- Accurate local and regional climate data are essential for modeling ecosystem responses, assessing climate change vulnerabilities, and developing adaptation strategies.
- High-resolution gridded historical climate (GHC) products provide valuable data but often contain unquantified uncertainties.
- Users need a better understanding of GHC uncertainty, especially in regions with complex climates.
Purpose of the Study:
- To conduct a ground-truthing analysis of two 4 km GHC temperature products (PRISM and NRCC) in the US Northeast.
- To estimate prediction errors for monthly temperature means and trends (1980-2009) for these GHC products.
- To evaluate the influence of landscape factors on GHC prediction errors.
Main Methods:
- Utilized 51 Cooperative Network (COOP) weather stations across the US Northeast for ground-truthing.
- Estimated prediction errors for monthly temperature means and trends for PRISM and NRCC GHC products.
- Analyzed the correlation between prediction errors and landscape characteristics like elevation and distance from the coast.
Main Results:
- Both PRISM and NRCC GHC products exhibited similar magnitudes of station-based prediction errors.
- NRCC generally predicted cooler temperatures and trends than observed, while PRISM was cooler for means but warmer for trends.
- Prediction errors were largest at high elevations, and errors in coarse-scale digital elevation models correlated with temperature prediction errors.
Conclusions:
- Uncertainty in spatial climate data arises from multiple sources and varies across landscapes.
- Users should assess GHC uncertainty at scales relevant to their specific applications.
- A method using weather stations can help evaluate local GHC uncertainty and inform product selection.
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