A Long-Term, 1-km Resolution Daily Meteorological Dataset for Modeling and Mapping Permafrost in Canada

• Published in: Atmosphere
• Main Authors: Yu Zhang, Budong Qian, Gang Hong
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2020
• Subjects: meteorological dataset; permafrost; land-surface modeling; long-term; high resolution; Meteorology. Climatology; QC851-999; Canada
• Online Access: https://doi.org/10.3390/atmos11121363; https://doaj.org/article/b201c6190a6a4635948e420cb19eb3c4

Climate warming is causing permafrost thaw and there is an urgent need to understand the spatial distribution of permafrost and its potential changes with climate. This study developed a long-term (1901–2100), 1-km resolution daily meteorological dataset (Met1km) for modeling and mapping permafrost at high spatial resolutions in Canada. Met1km includes eight climate variables (daily minimum, maximum, and mean air temperatures, precipitation, vapor pressure, wind speed, solar radiation, and downward longwave radiation) and is suitable to drive process-based permafrost and other land-surface models. Met1km was developed based on four coarser gridded meteorological datasets for the historical period. Future values were developed using the output of a new Canadian regional climate model under medium-low and high emission scenarios. These datasets were downscaled to 1-km resolution using the re-baselining method based on the WorldClim2 dataset as spatial templates. We assessed Met1km by comparing it to climate station observations across Canada and a gridded monthly anomaly time-series dataset. The accuracy of Met1km is similar to or better than the four coarser gridded datasets. The errors in long-term averages and average seasonal patterns are small. The error occurs mainly in day-to-day fluctuations, thus the error decreases significantly when averaged over 5 to 10 days. Met1km, as a data generating system, is relatively small in data volume, flexible to use, and easy to update when new or improved source datasets are available. The method can also be used to generate similar datasets for other regions, even for the entire global landmass.