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TitleA long-term, 1-km resolution daily meteorological dataset for modeling and mapping permafrost in Canada
 
AuthorZhang, YORCID logo; Qian, B; Hong, G
SourceAtmosphere vol. 11, issue 12, 1363, 2020 p. 1-27, https://doi.org/10.3390/atmos11121363 Open Access logo Open Access
Image
Year2020
Alt SeriesNatural Resources Canada, Contribution Series 20200726
PublisherMDPI
Documentserial
Lang.English
Mediaon-line; digital
File formatpdf; html
ProvinceCanada; British Columbia; Alberta; Saskatchewan; Manitoba; Ontario; Quebec; New Brunswick; Nova Scotia; Prince Edward Island; Newfoundland and Labrador; Northwest Territories; Yukon; Nunavut; Canada
NTS1; 2; 3; 10; 11; 12; 13; 14; 15; 16; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 52; 53; 54; 55; 56; 57; 58; 59; 62; 63; 64; 65; 66; 67; 68; 69; 72; 73; 74; 75; 76; 77; 78; 79; 82; 83; 84; 85; 86; 87; 88; 89; 92; 93; 94; 95; 96; 97; 98; 99; 102; 103; 104; 105; 106; 107; 114O; 114P; 115; 116; 117; 120; 340; 560
Lat/Long WENS-141.0000 -50.0000 90.0000 41.7500
Subjectsgeophysics; surficial geology/geomorphology; Nature and Environment; Science and Technology; Recent; permafrost; meteorology; climate effects; modelling; mapping techniques; correlations; Met1km; Climate change; permafrost thaw; Methodology; Phanerozoic; Cenozoic; Quaternary
Illustrationstime series; sketch maps; profiles; plots; bar graphs; tables
ProgramCanada Centre for Remote Sensing Optical methods and applications
Released2020 12 16
AbstractClimate 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.
Summary(Plain Language Summary, not published)
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, called 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 permafrost models and other climate impact analysis. 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 down-scaled to 1-km resolution using a 1-km resolution 31-year average monthly dataset as spatial templates. We assessed Met1km by comparing it to climate station observations across Canada and a gridded monthly 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.
GEOSCAN ID328114

 
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