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.
The SPEI data available from ClimateData.ca are described in Tam et al. (2018). Data are for a 29-member ensemble of CMIP5 global climate models for three RCPs (2.6, 4.5 and 8.5) for the period 1900-2100. Monthly mean maximum and minimum daily temperature and monthly total precipitation from each climate model were regridded to a common 1° x 1° grid.
For a number of reasons, biases in model output still exist when compared to observations. Prior to calculating SPEI, multivariate bias correction was undertaken for precipitation and maximum and minimum temperature (Cannon, 2016). For these three variables, their marginal distributions and inter-variable correlations were corrected to match observed values in the historical calibration period (1950-2005). GCM-projected changes in the quantiles of each variable were also preserved in future time periods. The observational dataset used as the target over the calibration period in the multivariate bias correction process was the Canadian Gridded Dataset (CANGRD; Vincent et al., 2015). Bias correction was applied to each GCM simulation for the 1900-2100 time period.
After bias correction, the difference between precipitation (P) and potential evapotranspiration (PET) was calculated for each month for the whole time period, 1900-2100 for each GCM simulation. PET was calculated using the modified Hargreaves method (Droogers and Allen, 2002), which exhibits similar performance to the more data-intensive Penman-Monteith method, but requires only monthly total precipitation and monthly mean minimum and maximum daily temperature as input. The difference, P-PET, can then be aggregated over different time scales (generally between 1 and 48 months) to investigate the multi-scalar nature of drought. Following the methodology outlined in Vicente-Serrano et al. (2010) and Tam et al. (2018), SPEI was derived from the log-logistic distribution. As in the multivariate bias correction process, 1950-2005 was used as the reference period to fit this distribution and estimate the distribution parameters for PPET at each time scale under consideration. These distribution parameters were then applied to the future period (2006-2100). SPEI values were calculated for time scales of 3 (SPEI-3) and 12 (SPEI-12) months. SPEI-3 corresponds to SPEI of one month and the previous 2 months, while SPEI-12 corresponds to SPEI of one month and the previous 11 months. Seasonal values were extracted from SPEI-3 datasets. The seasons shown on ClimateData.ca correspond to the standard seasons: winter (December, January, February), spring (March, April, May), summer (June, July, August), fall (September, October, November).
On ClimateData.ca you can view maps and time series of SPEI for SPEI-3 (the standard seasons) and also for SPEI-12.
For further details, see:
Canadian Climate Data and Scenarios: http://climate-scenarios.canada.ca/?page=spei-technical-notes
Cannon AJ (2016): Multivariate bias correction of climate model outputs: matching marginal distributions and inter-variable dependence structure. Journal of Climate 29: 7045-7064.
Droogers P, Allen RG (2002): Estimating reference evapotranspiration under inaccurate data conditions. Irrigation and Drainage Systems 16: 33-45.
Tam BY, Szeto K, Bonsal B, Flato G, Cannon AJ, Rong R (2018): CMIP5 drought projections in Canada based on the Standardised Precipitation Evapotranspiration Index. Canadian Water Resources Journal 44: 90-107.
Vicente-Serrano SM, Beguería S, Lopez-Moreno JI (2010): A multiscalar drought index sensitive to global warming: the Standardised Precipitation Evapotranspiration Index. Journal of Climate 23(7): 1696-1718.
Vincent LA, Zhang X, Brown RD, Feng Y, Mekis E, Milewska EJ, Wan H, Wang XL (2015): Observed trends in Canada’s climate and influence of low-frequency variability modes. Journal of Climate 28: 4545-4560.