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John H. Pedlar, Daniel W. McKenney, Kevin Lawrence, Pia Papadopol, Michael F. Hutchinson, and David Price

Abstract

This study produced annual spatial models (or grids) of 27 growing-season variables for Canada that span two centuries (1901–2100). Temporal gaps in the availability of daily climate data—the typical and preferred source for calculating growing-season variables—necessitated the use of two approaches for generating these growing-season grids. The first approach, used only for the 1950–2010 period, employed a computer script to directly calculate the suite of growing-season variables from existing daily climate grids. Since daily grids were not available for the remaining years, a second approach, which employed a machine-learning method called boosted regression trees (BRT), was used to generate statistical models that related each growing-season variable to a suite of climate and water-related predictors. These BRT models were used to generate grids of growing-season variables for each year of the study period, including the 1950–2010 period to allow comparison between the two approaches. Mean absolute errors associated with the BRT-based grids were approximately 30% higher than those associated with the daily-based grids. The two approaches were also compared by calculating trends in growing-season length over the 1950–2010 period. Significant increases in growing-season length were obtained for nearly all ecozones across Canada, and there were no significant differences in the trends obtained from the two approaches. Although the daily-based approach tended to have lower errors, the BRT approach produced comparable map products that should be valuable for periods and regions for which daily data are not available.

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Ron F. Hopkinson, Michael F. Hutchinson, Daniel W. McKenney, Ewa J. Milewska, and Pia Papadopol

Abstract

Spatial models of 1971–2000 monthly climate normals for daily maximum and minimum temperature and total precipitation are required for many applications. The World Meteorological Organization’s recommended standard for the calculation of a normal value is a complete 30-yr record with a minimal amount of missing data. Only 650 stations (~16%) in Canada meet this criterion for the period 1971–2000. Thin-plate smoothing-spline analyses, as implemented by the Australian National University Splines (ANUSPLIN) package, are used to assess the utility of differing amounts of station data in estimating nationwide monthly climate normals. The data include 1) only those stations (1169) with 20 or more years of data, 2) all stations (3835) with 5 or more years of data in at least one month, and 3) as in case 2 but with data adjusted through the most statistically significant linear-regression relationship with a nearby long-term station to 20 or more years (3983 stations). Withheld-station tests indicate that the regression-adjusted normals as in dataset 3 generally yield the best results for all three climatological elements, but the unadjusted normals as in dataset 2 are competitive with the adjusted normals in spring and autumn, reflecting the known longer spatial correlation scales in these seasons. The summary mean absolute differences between the ANUSPLIN estimates and the observations at 48 spatially representative withheld stations for dataset 3 are 0.36°C, 0.66°C, and 4.7 mm, respectively, for maximum temperature, minimum temperature, and precipitation. These are respectively 18%, 7%, and 18% smaller than the summary mean absolute differences for the long-term normals in dataset 1.

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Ron F. Hopkinson, Daniel W. McKenney, Ewa J. Milewska, Michael F. Hutchinson, Pia Papadopol, and Lucie A. Vincent

Abstract

On 1 July 1961, the climatological day was redefined to end at 0600 UTC at all principal climate stations in Canada. Prior to that, the climatological day at principal stations ended at 1200 UTC for maximum temperature and precipitation and 0000 UTC for minimum temperature and was similar to the climatological day at ordinary stations. Hutchinson et al. reported occasional larger-than-expected residuals at 50 withheld stations when the Australian National University Spline (ANUSPLIN) interpolation scheme was applied to daily data for 1961–2003, and it was suggested that these larger residuals were in part due to the existence of different climatological days. In this study, daily minimum and maximum temperatures at principal stations were estimated using hourly temperatures for the same climatological day as local ordinary climate stations for the period 1953–2007. Daily precipitation was estimated at principal stations using synoptic precipitation data for the climatological day ending at 1200 UTC, which, for much of the country, was close to the time of the morning observation at ordinary climate stations. At withheld principal stations, the climatological-day adjustments led to the virtual elimination of large residuals in maximum and minimum temperature and a marked reduction in precipitation residuals. Across all 50 withheld stations the climatological day adjustments led to significant reductions, by around 12% for daily maximum temperature, 15% for daily minimum temperature, and 22% for precipitation, in the residuals reported by Hutchinson et al.

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Heather MacDonald, Daniel W. McKenney, Xiaolan L. Wang, John Pedlar, Pia Papadopol, Kevin Lawrence, Yang Feng, and Michael F. Hutchinson

Abstract

This study presents spatial models (i.e., thin-plate spatially continuous spline surfaces) of adjusted precipitation for Canada at daily, pentad (5 day), and monthly time scales from 1900 to 2015. The input data include manual observations from 3346 stations that were adjusted previously to correct for snow water equivalent (SWE) conversion and various gauge-related issues. In addition to the 42 331 models for daily total precipitation and 1392 monthly total precipitation models, 8395 pentad models were developed for the first time, depicting mean precipitation for 73 pentads annually. For much of Canada, mapped precipitation values from this study were higher than those from the corresponding unadjusted models (i.e., models fitted to the unadjusted data), reflecting predominantly the effects of the adjustments to the input data. Error estimates compared favorably to the corresponding unadjusted models. For example, root generalized cross-validation (GCV) estimate (a measure of predictive error) at the daily time scale was 3.6 mm on average for the 1960–2003 period as compared with 3.7 mm for the unadjusted models over the same period. There was a dry bias in the predictions relative to recorded values of between 1% and 6.7% of the average precipitations amounts for all time scales. Mean absolute predictive errors of the daily, pentad, and monthly models were 2.5 mm (52.7%), 0.9 mm (37.4%), and 11.2 mm (19.3%), respectively. In general, the model skill was closely tied to the density of the station network. The current adjusted models are available in grid form at ~2–10-km resolutions.

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Daniel W. McKenney, Michael F. Hutchinson, Pia Papadopol, Kevin Lawrence, John Pedlar, Kathy Campbell, Ewa Milewska, Ron F. Hopkinson, David Price, and Tim Owen

Over the past two decades, researchers at Natural Resources Canada's Canadian Forest Service, in collaboration with the Australian National University (ANU), Environment Canada (EC), and the National Oceanic and Atmospheric Administration (NOAA), have made a concerted effort to produce spatial climate products (i.e., spatial models and grids) covering both Canada and the United States for a wide variety of climate variables and time steps (from monthly to daily), and across a range of spatial resolutions. Here we outline the method used to generate the spatial models, detail the array of products available and how they may be accessed, briefly describe some of the usage and impact of the models, and discuss anticipated further developments. Our initial motivation in developing these models was to support forestry-related applications. They have since been utilized by a wider range of agencies and researchers. This article is intended to further raise awareness of the strengths and weaknesses of these climate models and to facilitate their wider application.

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