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.

[1] This study presents a second generation of homogenized monthly mean surface air temperature data set for Canadian climate trend analysis. Monthly means of daily maximum and of daily minimum temperatures were examined at 338 Canadian locations. Data from co-located observing sites were sometimes combined to create longer time series for use in trend analysis. Time series of observations were then adjusted to account for nation-wide change in observing time in July 1961, affecting daily minimum temperatures recorded at 120 synoptic stations; these were adjusted using hourly temperatures at the same sites. Next, homogeneity testing was performed to detect and adjust for other discontinuities. Two techniques were used to detect non-climatic shifts in de-seasonalized monthly mean temperatures: a multiple linear regression based test and a penalized maximal t test. These discontinuities were adjusted using a recently developed quantile-matching algorithm: the adjustments were estimated with the use of a reference series. Based on this new homogenized temperature data set, annual and seasonal temperature trends were estimated for Canada for 1950-2010 and Southern Canada for 1900-2010. Overall, temperature has increased at most locations. For 1950-2010, the annual mean temperature averaged over the country shows a positive trend of 1.5 C for the past 61 years. This warming is slightly more pronounced in the minimum temperature than in the maximum temperature; seasonally, the greatest warming occurs in winter and spring. The results are similar for Southern Canada although the warming is considerably greater in the minimum temperature compared to the maximum temperature over the period 1900-2010.Citation: Vincent, L. A., X. L. Wang, E. J. Milewska, H. Wan, F. Yang, and V. Swail (2012), A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis,

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.

Trends in Canada's climate are analyzed using recently updated data to provide a comprehensive view of climate variability and long-term changes over the period of instrumental record. Trends in surface air tem perature, precipitation, snow cover, and streamflow indices are examined along with the potential impact of lowfrequency variability related to large-scale atmospheric and oceanic oscillations on these trends. The results show that temperature has increased significantly in most regions of Canada over the period 1948-2012, with the largest warming occurring in winter and spring. Precipitation has also increased, especially in the north.Changes in other climate and hydroclimatic variables, including a decrease in the amount of precipitation falling as snow in the south, fewer days with snow cover, an earlier start of the spring high-flow season, and an increase in April streamflow, are consistent with the observed warming and precipitation trends. For the period 1900-2012, there are sufficient temperature and precipitation data for trend analysis for southern Canada (south of 60°N) only. During this period, temperature has increased significantly across the region, precipitation has increased, and the amount of precipitation falling as snow has decreased in many areas south of 55°N. The results also show that modes of low-frequency variability modulate the spatial distribution and strength of the trends; however, they alone cannot explain the observed long-term trends in these climate variables.

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.