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Statistically Downscaled Climate Scenarios

PCIC offers statistically 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.

CLIMATE MODELS AND SCENARIOS

A. Coupled Model Intercomparison Project Phase 5 (CMIP5)

Downscaled scenarios were constructed from 27 Global Climate Models (GCMs) and 3 Representative Concentration Pathways (RCPs) (van Vuuren et al., 2011) from CMIP5 (Taylor et al., 2012) using the BCCAQv2 downscaling method described below. Scenarios can be selected from any combination of models, RCPs, and time period. This data set is now referred to as Canadian Downscaled Climate Scenarios – Univariate (CMIP5), or CanDCS-U5 for short.

B. Coupled Model Intercomparison Project Phase 6 (CMIP6)

Downscaled scenarios were constructed from 26 GCMs and 3 Shared Socioeconomic Pathways (SSPs) (Riahi et al., 2017) from CMIP6 (Eyring et al., 2016) using the BCCAQv2 downscaling method described below. Scenarios can be selected from any combination of models, SSPs, and time period. This data set is now referred to as Canadian Downscaled Climate Scenarios – Univariate (CMIP6), or CanDCS-U6 for short.

STATISTICAL DOWNSCALING METHODS

Data from each climate model is downscaled to a finer resolution using one or more statistical methods and a gridded “target” observation-based dataset, which constitutes a reconstruction of the actual historical climate over Canada.

Note that for the historical period used to calibrate the downscaling methods (1950-2005 for CMIP5, 1950-2014 for CMIP6), statistical properties of the downscaled results 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.

A. Bias Correction/Constructed Analogues with Quantile Mapping Reordering (BCCAQv2)

BCCAQv2 (Bias Correction/Constructed Analogues with Quantile delta mapping reordering) is a hybrid method developed at PCIC that combines results from Bias Corrected Constructed Analogs (BCCA; Maurer et al. 2010) and Quantile Delta Mapping (QDM; Cannon et al. 2015). BCCA uses spatial aggregation from a linear combination of historical analogues for daily large-scale fields. QDM applies a form of quantile mapping where relative changes in GCM quantiles are preserved to avoid inflationary effects that can occur with standard quantile mapping. BCCAQv2 is an updated version of BCCAQ (version 1), which employed standard quantile mapping.

B. Other Downscaling Methods 

Other methods used at PCIC for previous version of our downscaled products include the following. 

BCCAQv1: The original version of BCCAQ differs from BCCAQv2 (see above) in that it uses QMAP for the quantile mapping step (Gudmundsson et al., 2012). 

Bias-corrected Spatial Disaggregation (BCSD): BCSD (Wood et al., 2004; Maurer et al., 2008) was used 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), Bürger et al. (2012, 2013) and Werner and Cannon (2016).

MODEL SELECTION (CMIP5 ONLY)

Analysis and storage of the full ensemble is often not feasible. For the CMIP5 ensemble, users are encouraged to select models according to the Table below. The 12 model runs listed capture 90% of the range of projected changes in temperature and precipitation in all seasons for a suite of indices of extremes under RCP4.5 (Cannon, 2015). Recommended subsets of models are provided for a number of geographic sub-regions of North America, known as Giorgi regions (Giorgi and Francisco, 2000), shown in the map below the Table. Note, however, that data are only available for the parts of the Georgi regions that lie within Canada.  If fewer than 12 GCM runs are desired, they should be chosen following the order listed for each sub-region in the Table. Note that only 9 of the 12 GCM runs are available for RCP2.6.

The 12 GCM runs listed under the WNA region are used in PCIC’s Plan2Adapt and PCIC’s Climate Explorer online tools (PCEX). 

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).

 

DOWNSCALING TARGET DATASET

The main observational dataset used to calibrate BCCAQv2 (as well as BCCAQv1 and BCSD), NRCANMet, was produced by Natural Resources Canada (NRCan) and is available at 300 arc second spatial resolution (1/12° grids, ~10 km) over Canada. Daily minimum and maximum temperature, and precipitation amounts for the period 1950-2012 were produced by Hopkinson et al. (2011) and McKenney et al. (2011) on behalf of the Canadian Forest Service (CFS), NRCan.  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. This dataset is also available via the PCIC Daily Gridded Meteorological Datasets page. Note that 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).

ACKNOWLEDGEMENTS

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 CMIP6, 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 and Climate Change 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. For the most recently released data, the following citation is recommended:

Pacific Climate Impacts Consortium, University of Victoria, (Feb. 2019). Statistically Downscaled Climate Scenarios. Downloaded from https://data.pacificclimate.org/portal/downscaled_gcms/map/ on <date>. Method: BCCAQ v2. (Please also include 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

Cannon, A.J., S.R. Sobie, and T.Q. Murdock, 2015: 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.

Giorgi, F. and Francisco, R., 2000: Evaluating uncertainties in the prediction of regional climate change. Geophysical Research Letters, 27(9), 1295-1298, doi:10.1029/1999GL011016.

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.

Hiebert, J., A. Cannon, A. Schoeneberg, S. Sobie, and T. Murdock, 2018: ClimDown: Climate Downscaling in R. The Journal of Open Source Software, 3(22), 360, doi:10.21105/joss.00360.

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., Y. Dibike, T. Prowse and B. Bonsal, 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.

Eyring, V., S. Bony, G.A. Meehl, C. Senior, B. Stevens, R.J. Stouffer and K.E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organizationGeoscientific Model Development9(5), 1937-1958.

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.

Riahi, K. et al. 2017:  The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overviewGlobal Environmental Change42, 153-168, doi:10.1016/j.gloenvcha.2016.05.009.

Sobie, S.R., and T.Q. Murdock, 2017: High-Resolution Statistical Downscaling in Southwestern British Columbia. Journal of Applied Meteorology and Climatology, 56(6), 1625–1641, doi:10.1175/JAMC-D-16-0287.1.

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.

van Vuuren, D.P., et al., 2011: The Representative Concentration Pathways: An OverviewClimatic Change109 (1-2), 5-31, doi:10.1007/s10584-011-0148-z.

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, A. J., 2016: Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods, Hydrololgy and Earth System Sciences, 20, 1483-1508, doi:10.5194/hess-20-1483-2016.

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.