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Statistically Downscaled Climate Scenarios

PCIC offers statisically downscaled daily Canada-wide climate scenarios, at a gridded resolution of 300 arc-seconds (0.0833 degrees, or roughly 10 km) for the simulated period of 1950-2100. The variables available include minimum temperature, maximum temperature, and precipitation. Users may access the scenarios using an interactive map interface that allows users to zoom, pan and select their region of interest using a rectangular-selection tool. They can select and download scenarios for the Representative Concentration Pathways (RCPs) (Meinshausen et al., 2011) and model combinations that are of interest to them and for the time period of their choosing. In order to download the downscaled climate change scenarios, users are required to log-in to the page using an OpenID account.

Access and download Statistically Downscaled GCM Scenarios.

These downscaling outputs are based on Global Climate Model (GCM) projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012) and historical daily gridded climate data for Canada (McKenney et al., 2011; Hopkinson et al., 2011).​​ Statistical properties and spatial patterns of the downscaled scenarios are based on this gridded observational dataset, which represents one approximation of the actual historical climate. Gridded values may differ from climate stations and biases may be present at high elevations or in areas with low station density (Eum et al., 2014).

Note that for the historical 1950-2005 period, which was used to calibrate the downscaling models, statistical properties of the downscaled outputs will, by design, tend to match those of the gridded observational dataset. The day-by-day, month-by-month, year-by-year, etc. sequencing of values, however, will not correspond to observations, since climate models solve a “boundary value problem” and are not constrained to reproduce the timing of natural climate variability (e.g., El Niño-Southern Oscillation) in the observational record.

The ensemble of 12 climate models selected for downscaling is provided in the table below. The ordering, which differs by region (see map of Giorgi regions, Giorgi and Francisco, 2000), is selected to provide the widest spread in projected future climate for smaller subsets of the full ensemble following Cannon (2015).

 

Model Ensembles and Giorgi Regions

Order

WNA

ALA

CNA

ENA

GRL

1

CNRM-CM5-r1

CSIRO-Mk3-6-0-r1

CanESM2-r1

MPI-ESM-LR-r3

MPI-ESM-LR-r3

2

CanESM2-r1

HadGEM2-ES-r1

ACCESS1-0-r1

inmcm4-r1

inmcm4-r1

3

ACCESS1-0-r1

inmcm4-r1

inmcm4-r1

CNRM-CM5-r1

CanESM2-r1

4

inmcm4-r1

CanESM2-r1

CSIRO-Mk3-6-0-r1

CSIRO-Mk3-6-0-r1

CNRM-CM5-r1

5

CSIRO-Mk3-6-0-r1

ACCESS1-0-r1

MIROC5-r3

HadGEM2-ES-r1

ACCESS1-0-r1

6

CCSM4-r2

MIROC5-r3

HadGEM2-ES-r1

CanESM2-r1

CSIRO-Mk3-6-0-r1

7

MIROC5-r3

HadGEM2-CC-r1

MPI-ESM-LR-r3

MRI-CGCM3-r1

HadGEM2-ES-r1

8

MPI-ESM-LR-r3

MRI-CGCM3-r1

CNRM-CM5-r1

CCSM4-r2

MIROC5-r3

9

HadGEM2-CC-r1

CCSM4-r2

CCSM4-r2

MIROC5-r3

HadGEM2-CC-r1

10

MRI-CGCM3-r1

CNRM-CM5-r1

GFDL-ESM2G-r1

ACCESS1-0-r1

CCSM4-r2

11

GFDL-ESM2G-r1

MPI-ESM-LR-r3

HadGEM2-CC-r1

HadGEM2-CC-r1

MRI-CGCM3-r1

12

HadGEM2-ES-r1

GFDL-ESM2G-r1

MRI-CGCM3-r1

GFDL-ESM2G-r1

GFDL-ESM2G-r1

 

Giorgi regions that intersect with Canada: Alaska (ALA), Western North America (WNA), Central North America (CNA), Greenland (GRL), Eastern North America (ENA) and Central America (CAM).

Statistical Downscaling Methods

Data from the GCM sources described above is downscaled to a finer resolution using two different methods. The first is Bias-Correction Spatial Disaggregation (BCSD) (Wood et al., 2004) following Maurer et al., (2008) with the following modifications: the incorporation of monthly minimum and maximum temperature instead of monthly mean temperature, as suggested by Bürger et al., (2012) and bias correction using detrended quantile mapping with delta method extrapolation following Bürger et al., (2013). For past validation and analysis of the BCSD downscaling algorithm for British Columbia, see Werner (2011) and Bürger et al. (2012, 2013) and Werner and Cannon (2015).

In addition to BCSD, projections are also available using Bias Correction/Constructed Analogues with Quantile mapping reordering (BCCAQ). BCCAQ is a hybrid method that combines results from BCCA (Maurer et al. 2010) and quantile mapping  (QMAP) (Gudmundsson et al. 2012). BCCA uses similar spatial aggregation and quantile mapping steps as BCSD but obtains spatial information from a linear combination of historical analogues for daily large-scale fields, avoiding the need for monthly aggregates. QMAP applies quantile mapping to daily climate model outputs that have been interpolated to the high-resolution grid using the climate imprint method of Hunter and Meentemeyer (2005). BCCAQ combines outputs from these two methods. For more information on BCCAQ, see Werner and Cannon (2015).

We thank the Landscape Analysis and Applications section of the Canadian Forest Service, Natural Resources Canada for developing and making available the Canada-wide historical daily gridded climate dataset used as the downscaling target. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP5, and we thank the climate modeling groups for producing and making available their GCM output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. PCIC gratefully acknowledges support from Environment Canada for the development of the statistically downscaled GCM scenarios that are distributed from this data page.

Data citations

When referring to the Statistically Downscaled Climate Scenarios produced by PCIC, whether retrieved from this website or found otherwise, the source must be clearly stated:

Pacific Climate Impacts Consortium, University of Victoria, (Jan. 2014). Statistically Downscaled Climate  Scenarios. Downloaded from <Permalink> on <date>.

Please include downscaling method (BCSD or BCCAQ), specific GCMs, RCP emissions scenarios used, domain of download, and variables in text where citation is made. Citation of relevant references, as provided below, is also recommended where appropriate.

Terms of use

In addition to PCIC's terms of use, the data for each individual data set is subject to the terms of use of each source organization. For further details please refer to:

No Warranty

The Statistically Downscaled Climate Scenarios are provided by the Pacific Climate Impacts Consortium with an open licence on an “AS IS” basis without any warranty or representation, express or implied, as to its accuracy or completeness. Any reliance you place upon the information contained here is your sole responsibility and strictly at your own risk. In no event will the Pacific Climate Impacts Consortium be liable for any loss or damage whatsoever, including without limitation, indirect or consequential loss or damage, arising from reliance upon the data or derived information.

References:

Bürger, G., T.Q. Murdock, A.T. Werner, S.R. Sobie, and A.J. Cannon, 2012: Downscaling extremes - an intercomparison of multiple statistical methods for present climate. Journal of Climate, 25, 4366–4388. doi:10.1175/JCLI-D-11-00408.1.

Bürger, G., S.R. Sobie, A.J. Cannon, A.T. Werner, and T.Q. Murdock, 2013: Downscaling extremes - an intercomparison of multiple methods for future climate. Journal of Climate, 26, 3429-3449. doi:10.1175/JCLI-D-12-00249.1.

Cannon, A.J., 2015: Selecting GCM scenarios that span the range of changes in a multimodel ensemble: application to CMIP5 climate extremes indices. Journal of Climate, 28(3): 1260-1267. doi:10.1175/JCLI-D-14-00636.1

Giorgi, F. and Francisco, R., 2000: Evaluating uncertainties in the prediction of regional climate change. Geophysical Research Letters, 27(9), 1295-1298.

Gudmundsson, L., J. B. Bremnes, J. E. Haugen, and T. Engen-Skaugen, 2012: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrology and Earth System Sciences, 16, 3383–3390, doi:10.5194/hess-16-3383-2012.

Hopkinson, R.F., D.W. McKenney, E.J. Milewska, M.F. Hutchinson, P. Papadopol, and L.A. Vincent, 2011: Impact of Aligning Climatological Day on Gridding Daily Maximum–Minimum Temperature and Precipitation over Canada. Journal of Applied Meteorology and Climatology, 50, 1654–1665. doi:10.1175/2011JAMC2684.1.

Hunter, R. D., and R. K. Meentemeyer, 2005: Climatologically Aided Mapping of Daily Precipitation and Temperature. Journal of Applied Meteorology, 44, 1501–1510, doi:10.1175/JAM2295.1.

Eum, H.-I., Dibike, Y., Prowse, T. and Bonsal, B., 2014, Inter-comparison of high-resolution gridded climate data sets and their implication on hydrological model simulation over the Athabasca Watershed, Canada. Hydrol. Process., 28, 4250–4271. doi: 10.1002/hyp.10236  

Maurer, E.P., and H.G. Hidalgo, 2008: Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods. Hydrology and Earth System Sciences, 12, 2, 551-563. doi:10.5194/hess-12-551-2008.

Maurer, E., 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. Hydrology and Earth System Sciences, 14, 6, 1125–1138, doi:10.5194/hess-14-1125-2010.

McKenney, D.W., M.F. Hutchinson, P. Papadopol, K. Lawrence, J. Pedlar, K. Campbell, E. Milewska, R. Hopkinson, D. Price, and T. Owen, 2011: Customized spatial climate models for North America. Bulletin of the American Meteorological Society, 92, 12, 1611-1622. doi:10.1175/2011BAMS3132.1.

Meinshausen, M., et al., 2011: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109(1-2), 213-241.

Taylor, K.E., R.J. Stouffer, and G.A. Meehl, 2012: An Overview of CMIP5 and the Experiment Design. Bulletin of the American Meteorological Society, 93, 485–498. doi: 10.1175/BAMS-D-11-00094.1.

Werner, A.T., 2011: BCSD downscaled transient climate projections for eight select GCMs over British Columbia, Canada. Pacific Climate Impacts Consortium, University of Victoria, Victoria, BC, 63 pp.

Werner, A.T. and A.J. Cannon, 2015: Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods. Hydrology and Earth System Sciences Discussion, 12, 6179-6239, doi:10.5194/hessd-12-6179-2015.

Wood, A.W., L.R Leung, V. Sridhar, and D.P. Lettenmaier, 2004: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, 189–216, doi:10.1023/B:CLIM.0000013685.99609.9e.