ArticleScientific data2020
North American historical monthly spatial climate dataset, 1901-2016.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Updated plant hardiness zones for Canada and assessment of change over time.Scientific reports · 2025Article
- A 4 km daily gridded meteorological dataset for China from 2000 to 2020.Scientific data · 2024Article
- Spatial datasets of 30-year (1991-2020) average monthly total precipitation and minimum/maximum temperature for Canada and the United States.Data in brief · 2024Article
- Arctic cyanobacterial mat community diversity decreases with latitude across the Canadian Arctic.FEMS microbiology ecology · 2024Article
- Heating degree day spatial datasets for Canada.Data in brief · 2023Article
- North American historical monthly spatial climate dataset, 1901-2016.Scientific data · 2020Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present historical monthly spatial models of temperature and precipitation generated from the North American dataset version "j" from the National Oceanic and Atmospheric Administration's (NOAA's) National Centres for Environmental Information (NCEI). Monthly values of minimum/maximum temperature and precipitation for 1901-2016 were modelled for continental United States and Canada. Compared to similar spatial models published in 2006 by Natural Resources Canada (NRCAN), the current models show less error. The Root Generalized Cross Validation (RTGCV), a measure of the predictive error of the surfaces akin to a spatially averaged standard predictive error estimate, averaged 0.94 °C for maximum temperature models, 1.3 °C for minimum temperature and 25.2% for total precipitation. Mean prediction errors for the temperature variables were less than 0.01 °C, using all stations. In comparison, precipitation models showed a dry bias (compared to recorded values) of 0.5 mm or 0.7% of the surface mean. Mean absolute predictive errors for all stations were 0.7 °C for maximum temperature, 1.02 °C for minimum temperature, and 13.3 mm (19.3% of the surface mean) for monthly precipitation.
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