RESEARCH HIGHLIGHT – Spatial Datasets of 30-year (1991-2020) Average Monthly Total Precipitation and Minimum/Maximum Temperature for Canada and the United States
MacDonald, H., McKenney, D. W., Pedlar, J., Lawrence, K., de Boer, K., & Hutchinson, M. F. (2024). Spatial Datasets of 30-year (1991-2020) Average Monthly Total Precipitation and Minimum/Maximum Temperature for Canada and the United States. In Data in Brief (p. 110561). Elsevier BV. https://doi.org/10.1016/j.dib.2024.110561
We are very pleased to highlight new research published by Canada1Water collaborators at the Canadian Forestry Service and partially funded through the Canada1Water (C1W) project, a collaboration between Natural Resources Canada, Agriculture and Agri-Food Canada, and Aquanty Inc.
Click here to read the article.
This study, led by researchers utilizing thin plate smoothing spline models, examines 30-year (1991-2020) average monthly total precipitation and minimum/maximum temperature datasets for Canada and the United States. These datasets, developed using ANUSPLIN, integrate weather station values from the North American dataset published by NOAA's National Centers for Environmental Information. Maximum temperature mean absolute errors (MAEs) ranged between 0.54°C and 0.64°C, while minimum temperature MAEs varied from 0.87°C to 1.0°C. Thirty-year precipitation estimates were accurate to within approximately 10% of total precipitation levels, with higher error rates in Canada compared to the United States due to station network density disparities. These 30-year gridded datasets, available at ∼2km resolution, hold significant value for diverse applications, serving as baseline data for evaluating climate trends and events.
The findings reveal climatic trends, with maximum temperature mean absolute errors (MAEs) ranging between 0.54°C and 0.64°C and minimum temperature MAEs varying from 0.87°C to 1.0°C. Notably, thirty-year precipitation estimates exhibit a remarkable accuracy, typically within approximately 10% of total precipitation levels. However, the study highlights discernible differences in error rates between Canada and the United States, attributed to variances in station network density. Despite these variances, minimal bias is observed overall, highlighting the robustness of the datasets.
This research aligns with the goals of the Canada1Water project, which aims to address critical water-related challenges in Canada. By providing comprehensive analyses of climate data, including snowmelt and precipitation trends, this study contributes valuable insights into hydrological processes. Understanding the impacts of climate change on snowpack and water availability is crucial for informing water management strategies and policy decisions.
Click here to read the research paper.
Abstract
Thin plate smoothing spline models, covering Canada and the continental United States, were developed using ANUSPLIN for 30-year (1991-2020) monthly mean maximum and minimum temperature and precipitation. These models employed monthly weather station values from the North American dataset published by National Oceanic and Atmospheric Administration's (NOAA's) National Centers for Environmental Information (NCEI). Maximum temperature mean absolute errors (MAEs) ranged between 0.54°C and 0.64°C (approaching measurement error), while minimum temperature MAEs were slightly higher, varying from 0.87°C to 1.0°C. On average, thirty-year precipitation estimates were accurate to within approximately 10% of total precipitation levels, ranging from 9.0% in the summer to 12.2% in the winter. Error rates were higher in Canada compared to estimates in the United States, consistent with a less dense station network in Canada relative to the United States. Precipitation estimates in Canada exhibited MAEs representing 14.7% of mean total precipitation compared to 9.7% in the United States. The datasets exhibited minimal bias overall; 0.004°C for maximum temperature, 0.01°C for minimum temperature, and 0.5% for precipitation. Winter months showed a greater dry bias (0.8% of total winter precipitation) compared to other seasons (-0.4% of precipitation). These 30-year gridded datasets are available at ∼2km resolution.