The application of trivariate thin-plate smoothing splines to the interpolation of daily weather data is investigated. The method was used to develop spatial models of daily minimum and maximum temperature and daily precipitation for all of Canada, at a spatial resolution of 300 arc s of latitude and longitude, for the period 1961-2003. Each daily model was optimized automatically by minimizing the generalized cross validation. The fitted trivariate splines incorporated a spatially varying dependence on ground elevation and were able to adapt automatically to the large variation in station density over Canada. Extensive quality control measures were performed on the source data. Error estimates for the fitted surfaces based on withheld data across southern Canada were comparable to, or smaller than, errors obtained by daily interpolation studies elsewhere with denser data networks. Mean absolute errors in daily maximum and minimum temperature averaged over all years were 1.18 and 1.68C, respectively. Daily temperature extremes were also well matched. Daily precipitation is challenging because of short correlation length scales, the preponderance of zeros, and significant error associated with measurement of snow. A two-stage approach was adopted in which precipitation occurrence was estimated and then used in conjunction with a surface of positive precipitation values. Daily precipitation occurrence was correctly predicted 83% of the time. Withheld errors in daily precipitation were small, with mean absolute errors of 2.9 mm, although these were relatively large in percentage terms. However, mean percent absolute errors in seasonal and annual precipitation totals were 14% and 9%, respectively, and seasonal precipitation upper 95th percentiles were attenuated on average by 8%. Precipitation and daily maximum temperatures were most accurately interpolated in the autumn, consistent with the large well-organized synoptic systems that prevail in this season. Daily minimum temperatures were most accurately interpolated in summer. The withheld data tests indicate that the models can be used with confidence across southern Canada in applications that depend on daily temperature and accumulated seasonal and annual precipitation. They should be used with care in applications that depend critically on daily precipitation extremes.

The Haliburton forest region in Ontario, Canada, with cumulus cloud formations. Photo by Mark Primavera, Natural Resources Canada. C limate is a fundamental driver of life. Plant and animal distribution, abundance, and productivity are all closely tied to environmental regimes driven by temperature, precipitation, and solar radiation patterns. Critical biological processes, such as plant bud burst, flowering, and migration, both of animal populations and vegetation communities, are also linked to climate and weather conditions. Furthermore, human activities in many sectors, including food production, building construction, recreation, and power generation (solar, wind, hydroelectric), are closely connected to climate.Not surprisingly, given the pervasive influence of climate, there is a high demand for reliable spatial climate data [indeed, this was very much the theme at the recent World Climate Conference 3: Better climate information for a better future (see www.wmo.int/wcc3/page_en.php); Munang et al. 2010]. In forestry and many other sectors, there is often a need for estimates well away from meteorological stations, which tend to be clustered near agricultural and urban areas. This need is met by "spatial" climate models, which can provide  Natural resources Canada, Canadian forest service, and their partners have developed spatial spline models and gridded datasets for North america for a wide variety of variables, time steps, and spatial resolutions.

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