Customized Spatial Climate Models for North America

Abstract

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.

Investigation of the 2013 Alberta flood from weather and climate perspectives

“…To this end, the simulated precipitation is compared to Environment Canada's six-hourly Canadian Precipitation Analysis (CaPA; Mahfouf et al 2007), available at 15 km spatial resolution, and to the daily ANUSPLIN gridded dataset (McKenney et al 2011), available at 10 km spatial resolution. Hourly rainfall accumulations from 30 rain gauges of Alberta's AgroClimatic Information Service, located in and around the western BRB are also used (Fig.…”

Evaluation of extreme rainfall and temperature over North America in CanRCM4 and CRCM5

“…3). Differences between reanalysis products and ANUSPLIN + Livneh highlights observational uncertainty that may stem from a lack of stations in northwestern Canada (McKenney et al 2006(McKenney et al , 2011. Compared to ANUSP-LIN + Livneh, the warm bias in all CanRCM4 simulations covers most of the continent, extending over the central plains, California and southeastern Canada (Fig.…”

“…The ANUSPLIN + Livneh data set combines two data sets. First is the Canada-wide 1/12th degree ANUSPLIN data set that has daily gridded precipitation and temperature values (McKenney et al 2011). Second is the 1/16th degree daily gridded precipitation and temperature product for the continental United States (Livneh et al 2013).…”

The impact of ENSO and the NAO on extreme winter precipitation in North America in observations and regional climate models

“…ANUSPLIN + Livneh is based directly on station observations in Canada (McKenney et al 2011) and the continental United States (Livneh et al 2013), which are combined with bilinear interpolation at the border; see Whan and Zwiers (2015) for more information on this data set. We classify NARR as 'observationally-based' as it assimilates precipitation observations over North America, although this assimilation finishes in 2003 for Canada (Rapaić et al 2015).…”