Abstract. Near-surface air temperatures were monitored from 2005 to 2010 in a mesoscale network of 230 sites in the foothills of the Rocky Mountains in southwestern Alberta, Canada. The monitoring network covers a range of elevations from 890 to 2880 m above sea level and an area of about 18 000 km 2 , sampling a variety of topographic settings and surface environments with an average spatial density of one station per 78 km 2 . This paper presents the multiyear temperature dataset from this study, with minimum, maximum, and mean daily temperature data available at https://doi.org/10.1594/PANGAEA.880611. In this paper, we describe the quality control and processing methods used to clean and filter the data and assess its accuracy. Overall data coverage for the study period is 91 %. We introduce a weather-system-dependent gap-filling technique to estimate the missing 9 % of data. Monthly and seasonal distributions of minimum, maximum, and mean daily temperature lapse rates are shown for the region.

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.

Abstract. Hourly near-surface relative humidity and temperature were monitored from 2005 to 2010 in a mesoscale network of 232 sites in the foothills of the Rocky Mountains in southwestern Alberta, Canada. The monitoring network covers a range of elevations from 890 to 2880 m above sea level and an area of about 18 000 km2, sampling a variety of topographic settings and surface environments with an average spatial density of one station per 78 km2. Having been combined with air pressure measurements from Calgary International Airport and adjusted for the site elevation, the hourly data form the basis of estimates of daily mean specific humidity, vapour pressure, and relative humidity at each site, available at https://doi.org/10.1594/PANGAEA.889435. Overall data coverage for the study period is 89 %. This paper describes the processing methods used to quality control and gap fill the data. Inverse-distance weighting techniques are used to estimate the missing 11 % of data, based on neighbourhood values of daily mean specific humidity. We also report monthly mean lapse rates of specific and relative humidity. Plots of seasonal and spatial humidity patterns in the region illustrate the relations between humidity variables and temperature, elevation, and longitude.

No abstract

Abstract. Near-surface humidity was monitored from 2005 to 2010 in a mesoscale network of 232 sites in the foothills of the Rocky Mountains in southwestern Alberta, Canada. The monitoring network covers a range of elevations from 890 to 2880 m above sea level and an area of about 18,000 km2, sampling a variety of topographic settings and surface environments with an average spatial density of one station per 78 km2. Hourly screen-level temperature and relative humidity were recorded over the study period, forming the basis for daily mean relative humidity and vapour pressure estimates. Hourly air pressure measurements at Calgary Airport are adjusted for elevation to calculate specific humidity from the vapour pressure. Daily mean specific humidity, relative humidity, and vapour pressure from the multi-year study are available at https://doi.pangaea.de/10.1594/PANGAEA.889435. This manuscript describes the processing methods used to quality-control and gap-fill the data. Overall data coverage for the study period is 89 %. Inverse-distance weighting techniques are used to estimate the missing 11 % of data, based on neighourhood values of daily mean specific humidity. We report monthly mean lapse rates of specific and relative humidity. Plots of seasonal and spatial humidity patterns illustrate the relations between humidity variables and temperature, elevation, and longitude in the region.