Assessing Uncertainty in High-Resolution Spatial Climate Data across the US Northeast

Colin M Beier,Juan A Añel,Daniel A Bishop

doi:10.1371/journal.pone.0070260

Colin M Beier, Juan A Añel + Show 1 more

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https://doi.org/10.1371/journal.pone.0070260

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Abstract

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.

Highlights

A growing demand for high-resolution spatial climate information by researchers, educators and decision-makers has led to the creation of various gridded historical climate (GHC) data products (e.g., [1,2,3,4,5,6,7,8,9])
Prediction Error – Mean Temperatures Both the PRISM and NRCC 4 km products predicted lower than observed mean temperatures across the US Northeast during 1980–2009, based on seasonal and annual averages of errors calculated on a monthly basis at 51 Cooperative Observer Network (COOP) weather stations (Table 4)
NRCC errors were consistently larger for TMin relative to TMax (Table 4), ranging from 23.501uC (PN-New Hampshire (NH)) to +0.746uC (L-New York (NY)) for mean TMax (Fig. 2) and from 22.756uC (PN-NH) to +0.857uC (L-NY) for mean TMin (Fig. 3)

Summary

Introduction

A growing demand for high-resolution spatial climate information by researchers, educators and decision-makers has led to the creation of various gridded historical climate (GHC) data products (e.g., [1,2,3,4,5,6,7,8,9]) These GHC products are often freely available and incorporated in geographic information systems (GIS) and are being widely used for mapping and estimating climate conditions at local and regional scales. Instead of treating the data as model outputs with associated uncertainty, it appears many GHC users treat them as ‘true’ climate information – i.e., an accurately measured independent variable – in statistical, spatial and simulation models These GHC products are model outputs that typically have increasing uncertainty at higher resolutions, creating a ‘resolution vs realism tradeoff’, in which finer grain maps appear more intuitively accurate but cannot be validated [14]. Model errors are not uniform across space or time, but tend to be spatially and temporally complex, representing error propagation from sources related to both measurement and modeling [14], [16], [17]

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