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Erratum: Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA
- Source :
- Scientific Data
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies.
- Subjects :
- Statistics and Probability
Training set
010504 meteorology & atmospheric sciences
0208 environmental biotechnology
Climate change
02 engineering and technology
Library and Information Sciences
01 natural sciences
Projection and prediction
020801 environmental engineering
Computer Science Applications
Education
Impact studies
Climatology
Environmental science
Precipitation
Statistics, Probability and Uncertainty
Climate-change impacts
Climate and Earth system modelling
Analysis
0105 earth and related environmental sciences
Information Systems
Downscaling
Subjects
Details
- ISSN :
- 20524463
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- Scientific Data
- Accession number :
- edsair.doi.dedup.....0684698fd179edd550f2e3c34b6f8fca
- Full Text :
- https://doi.org/10.1038/sdata.2018.50