Assessments of downscaled climate data with a high‐resolution weather station network reveal consistent but predictable bias

• Published in: International journal of climatology Vol. 39; no. 6; pp. 3091 - 3103
• Main Authors: Roberts, David R., Wood, Wendy H., Marshall, Shawn J.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.05.2019; Wiley Subscription Services, Inc
• Subjects: Accuracy; Agricultural land; Algorithms; Bias; bias correction; Climate change; Climate models; Climatic data; Data processing; Ecological models; Ecological monitoring; Environment models; Environmental assessment; Foothills; Humidity; lapse rate; Mathematical models; mesonet; Modelling; Mountains; Prairies; Products; Regression analysis; Resolution; Rocky Mountains; Slope; Temperature; topography; validation; Weather; Weather stations
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.6005

Ecological analyses often incorporate high‐resolution environmental data to capture species‐environment relationships in modelling applications, and downscaled climate data are increasingly being used for such analyses. While such data products provide high precision, the accuracy of these data is seldom directly tested. Consequently, introduced bias from downscaling algorithms may propagate through analyses that incorporate these data products. Here, we utilize data from the Foothills Climate Array (FCA), a mesoscale grid of 232 weather stations in the prairies and eastern slopes of the Rocky Mountains in southern Alberta, Canada, to evaluate several publicly available downscaled climate products. We consider daily, monthly, and annual records for a suite of temperature and humidity variables. The FCA data are ideal to evaluate climate downscaling because they contain multi‐year observations and cover a range of topographic conditions, from flat prairie grass‐ and croplands to mountainous terrain. We find that the downscaling algorithms improve the accuracy of climate variables over simple interpolations of low‐resolution data, but errors are often large at validation locations (e.g., several °C for temperature variables), and downscaled datasets show notable elevational and seasonal bias for all variables. A bias adjustment analysis demonstrates that such bias can be greatly reduced with relatively simple regression‐based models, even when only a small subset of observational data are used, provided they cover a relatively large spread of elevations. We discuss our findings in the context of climate change and ecological modelling and make general recommendations for consumers of downscaled climate data products. Compared to a mesoscale network of observation stations in the Canadian Rocky Mountains, climate downscalings consistently demonstrate elevational and seasonal bias in residuals. For average temperatures, downscalings are generally warmer than observations at low and high elevations and cooler at middle elevations, particularly through winter months, though patterns vary across individual variables and datasets. Bias adjustment models improve accuracy and can be developed even with a small number of observation stations, provided those stations cover a wide breadth of elevation.