A data set of observed daily precipitation, maximum and minimum temperature, gridded to a 1/16° (~6 km) resolution, is described that spans the entire country of Mexico, the conterminous U.S. (CONUS), and regions of Canada south of 53° N for the period 1950-2013. The dataset improves previous products in spatial extent, orographic precipitation adjustment over Mexico and parts of Canada, and reduction of transboundary discontinuities. The impacts of adjusting gridded precipitation for orographic effects are quantified by scaling precipitation to an elevation-aware 1981-2010 precipitation climatology in Mexico and Canada. Differences are evaluated in terms of total precipitation as well as by hydrologic quantities simulated with a land surface model. Overall, orographic correction impacts total precipitation by up to 50% in mountainous regions outside CONUS. Hydrologic fluxes show sensitivities of similar magnitude, with discharge more sensitive than evapotranspiration and soil moisture. Because of the consistent gridding methodology, the current product reduces transboundary discontinuities as compared with a commonly used reanalysis product, making it suitable for estimating large-scale hydrometeorologic phenomena.

Two Long-Term Ecological Research (LTER) sites now include urban areas (Baltimore, Maryland and Phoenix, Arizona). A goal of LTER in these cities is to blend physical and social science investigations to better understand urban ecological change. Research monitoring programs are underway to investigate the effects of urbanization on ecosystems. Climate changes in these urban areas reflect the expanding population and associated land surface modifications. Long-term urban climate effects are detectable from an analysis of the GHCN (Global Historical Climate Network) database and a comparison of urban versus rural temperature changes with decadal population data. The relation of the urban versus rural minimum temperatures (Tminu-r) to population changes is pronounced and non-linear over time for both cities. The Tmaxu-r data show no well-defined temporal trends.