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Local and regional-scale knowledge of climate change is needed to model ecosystem responses, assess vulnerabilities and devise effective adaptation strategies. High-resolution gridded historical climate (GHC) products address this need, but come with multiple sources of uncertainty that are typically not well understood by data users. To better understand this uncertainty in a region with a complex climatology, we conducted a ground-truthing analysis of two 4 km GHC temperature products (PRISM and NRCC) for the US Northeast using 51 Cooperative Network (COOP) weather stations utilized by both GHC products. We estimated GHC prediction error for monthly temperature means and trends (1980–2009) across the US Northeast and evaluated any landscape effects (e.g., elevation, distance from coast) on those prediction errors. Results indicated that station-based prediction errors for the two GHC products were similar in magnitude, but on average, the NRCC product predicted cooler than observed temperature means and trends, while PRISM was cooler for means and warmer for trends. We found no evidence for systematic sources of uncertainty across the US Northeast, although errors were largest at high elevations. Errors in the coarse-scale (4 km) digital elevation models used by each product were correlated with temperature prediction errors, more so for NRCC than PRISM. In summary, uncertainty in spatial climate data has many sources and we recommend that data users develop an understanding of uncertainty at the appropriate scales for their purposes. To this end, we demonstrate a simple method for utilizing weather stations to assess local GHC uncertainty and inform decisions among alternative GHC products. [ABSTRACT FROM AUTHOR]

Human mortality exhibits a strong seasonal pattern with deaths in winter far exceeding those in the summer. While the pattern itself is clear, there have been very few studies examining whether the magnitude or timing of seasonal mortality varies considerably across space. Thus, the goal of this study is to conduct a comprehensive geographic analysis of seasonal mortality across the United States and to uncover systematic regional differences in such mortality. Unique seasonal mortality curves were created for 28 metropolitan statistical areas across the United States, and the amplitude and timing of mortality peaks were determined. The findings here indicate that the seasonality of mortality exhibits strong spatial variation with the largest seasonal mortality amplitudes found in the southwestern United States and the smallest in the North, along with South Florida. In addition, there were strong intra-regional similarities that exist among the examined cities, implying that environmental factors are more important than social factors in determining seasonal mortality response. This work begins to fill a large gap within the scientific literature concerning the geographic variation and underlying causes of seasonal mortality across the United States. [ABSTRACT FROM AUTHOR]