ANUSPLIN is a gridded observational dataset produced by Natural Resources Canada (NRCan), available at 300 arc second spatial resolution (1/12° grids, ~10 km) over Canada. The bulk of the daily minimum and maximum temperature, and precipitation amounts for the period 1950-2012 were produced circa 2011 by Hopkinson et al. (2011) and McKenney et al. (2011) on behalf of the Canadian Forest Service (CFS), NRCan. The dataset was updated in 2013 to correct for issues in the Churchill River area. Gridding was accomplished with the Australian National University Spline (ANUSPLIN) implementation of the trivariate thin plate splines interpolation method (Hutchinson et al., 2009) with latitude, longitude and elevation as predictors. Precipitation occurrence and square-root transformed precipitation amounts were interpolated separately on each day, combined, and transformed back to original units.
Quality-controlled, but unadjusted, station data from the National Climate Data Archive of Environment and Climate Change Canada data (Hutchinson et al., 2009) were interpolated onto the high-resolution grid using thin plate splines. Station density varies over time with changes in station availability, peaking in the 1970s with a general decrease towards the present day (Hutchinson et al., 2009). Thus, the number of stations active across Canada between 1950 and 2011 ranged from 2000 to 3000 for precipitation and 1500 to 3000 for air temperature (Hopkinson et al., 2011).
BCCAQ is a method developed at the Pacific Climate Impacts Consortium for downscaling daily climate model projections of temperature and precipitation, including indices of extremes. This methodology, a hybrid of BCCA (Maurer et al. 2010) and QMAP (Gudmundsson et al. 2012), combines quantile-mapping bias correction with a constructed analogues approach using daily large-scale temperature and precipitation fields. The method was developed to correct the bias in daily precipitation series from climate models so that the distributional properties, e.g., means, variances and quantiles, more closely match those of the historical observations (provided in this case by the ANUSPLIN dataset). The robustness of the methodology was tested by examining three criteria: the day-to-day sequencing of precipitation events, the distribution characteristics, and spatial correlation. BCCAQv2 is a modification of BCCAQ which preserves the coarse-scale projected changes at each quantile during the quantile mapping step, which other quantile mapping methods have a tendency to amplify (the “inflation” problem), including the method used in BCCAQv1. Preserving the precipitation change signal is important for maintaining the physical scaling relationships with model-projected temperature changes.
For more information see:
Cannon, A.J., S.R. Sobie, and T.Q. Murdock, 2015: Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? Journal of Climate, 28(17), 6938-6959, doi:10.1175/JCLI-D-14-00754.1.
Gudmundsson, L., J. Bremnes, J. Haugen and T. Engen-Skaugen, 2012: Technical note: Downscaling RCM precipitation to the station scale using statistical transformations – A comparison of methods. Hydrol. Earth Syst. Sci., 16, 3383-3390, doi:10.5194/hess-16-3383-2012.
Maurer, E.P., H. Hidalgo, T. Das, M. Dettinger and D. Cayan, 2010: The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrol. Earth Syst. Sci., 14, 1125-1138, doi:10.5194/hess-14-1125-2010.
Hiebert, J., A. Cannon, A. Schoeneberg, Stephen Sobie, and T. Murdock, 2018: ClimDown: Climate Downscaling in R. The Journal of Open Source Software, 3(22), 360.