There are many options available for accessing raw data for climate projections from the CMIP5 and CMIP6 experiments (see for example Table 3.1), but only a limited number of options for accessing bias-adjusted and downscaled datasets for Canada in easy-to-use formats for different applications.
The primary source of the BCCAQv2 downscaled CMIP5 and CMIP6 datasets is the Pacific Climate Impacts Consortium. The BCCAQv2 CMIP5 ensemble consists of daily bias-corrected and downscaled output from 24 GCMs for RCPs 2.6, 4.5, and 8.5, for the whole of Canada, and has been used as the basis of the climate projections currently available from the Canadian Centre for Climate Services (CCCS), ClimateData.ca[1] and the Climate Atlas of Canada (RCPs 4.5 and 8.5 only). This dataset is supplied at its original 6 x 10 km resolution on the CCCS website and on ClimateData.ca, but is offered at two map scales (1:250,000 and 1:50,000) on the Climate Atlas of Canada. The data was ‘post processed’, so that it can be delivered in manageable formats and file sizes. For example, the information from the ensemble of models is summarised by providing figures, graphics and data for the median and percentiles of the ensembles. This form is useful for a preliminary exploration of risk, and for communicating risk.
Both ClimateData.ca and the Climate Atlas of Canada have used the BCCAQv2 CMIP5 dataset to calculate a number of climate indices based on downscaled maximum and minimum temperature and precipitation. Both web portals provide map-based visualisation and allow users to search by location to view time series charts for the available climate variables, indices and RCPs. The downscaled data are presented in summary format, either as ensemble-mean (Climate Atlas) or ensemble-median values (ClimateData.ca) accompanied by information about the data range (10th and 90th percentile values). ClimateData.ca is planning to develop projections for climate impact drivers such as fire weather index and humidex.
Recognising that pre-defined thresholds for specific climate indices are not always meaningful, ClimateData.ca has developed the Analyze page which allows users to input custom threshold for a wide variety of temperature- and precipitation-based indices, including heat wave indices, and the number of days above or below specific maximum and minimum temperature or precipitation thresholds. ClimateData.ca also provides access to ECCC’s station data catalogue, historical Intensity-Duration-Frequency (IDF) curves (and guidance on updating these curves for future conditions) and CMIP5-derived projections of relative sea level change for RCPs 2.6, 4.5 and 8.5.
The Climate Atlas of Canada has recently added a map layer detailing First Nations, Inuit and Métis communities. ClimateData.ca also provides data aggregated by health and census regions as well as watersheds. Both climate data portals will be updated to include the downscaled CMIP6 dataset in the next few months.
The BCCAQv2 dataset can also be accessed via the CCCS web page, which in addition provides access to (non-downscaled) annual and seasonal projections of mean temperature, precipitation, wind speed, sea ice thickness and concentration, and snow depth for the CMIP5 ensemble at 1°x1° resolution. Median and percentiles values are available for this CMIP5 ensemble.
(a)
(b)
Figure 4.7: Examples of the (a) ClimateData.ca interface, and (b) the Climate Atlas of Canada
[1] ClimateData.ca is a partnership between Environment and Climate Change Canada, the major regional climate service providers in Canada and Centre de Recherche Informatique de Montréal and HabitatSeven.
References - Overview of climate models for the Canadian North
Barrow, E.M. and D.J. Sauchyn, 2019: Uncertainty in climate projections and time of emergence of climate signals in the western Canadian prairies. International Journal of Climatology, 39(11), 4358-4371, doi:10.1002/joc.6079.
Bush, E. and D.S. Lemmen (editors), 2019: Canada’s Changing Climate Report. Government of Canada, Ottawa, ON. 444 pp.
Cannon, A.J., 2016: Multivariate bias correction of climate model output: Matching marginal distributions and intervariable dependence structure. Journal of Climate, 29(19), 7045-7064, doi:10.1175/JCLI-D-15-0679.1.
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.
Cannon, A.J., D.I. Jeong, X. Zhang, and F.W. Zwiers, 2020: Climate-Resilient Buildings and Core Public Infrastructure: An Assessment of the Impact of Climate Change on Climatic Design Data in Canada. Government of Canada, Ottawa, ON. 106 pp.
Flato, G., J. Marotzke, B. Abiodun, P. Braconnot, S.C. Chou, W. Collins, P. Cox, F. Driouech, S. Emori, V. Eyring, C. Forest, P. Gleckler, E. Guilyardi, C. Jakob, V. Kattsov, C. Reason, and M. Rummukainen, 2013: Evaluation of Climate Models. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Gutowski, W.J., F. Giorgi, B. Timbal, A. Frigon, D. Jacob, H.-S. Kang, K. Raghavan, B. Lee, C. Lennard, G. Nikulin, E. O’Rourke, M. Rixen, S. Solman, T. Stephenson, and F. Tangang, 2016: WCRP Coordinated Regiona Downscaling Experiment (CORDEX): A diagnostic MIP for CMIP6. Geoscientific Model Development, 9(11), 4087-4095, doi:10.5194/gmd-9-4087-2016.
Hawkins, E. and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bulletin of the American Meteorological Society, 90(8), 1095–1107. doi:10.1175/2009BAMS2607.1.
Hawkins, E. and R. Sutton, 2011: The potential to narrow uncertainty in projections of regional precipitation change. Climate Dynamics, 37(1), 407-418, doi:10.1007/s00382-010-0810-6.
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(8), 1654-1665, doi:10.1175/2011JAMC2684.1.
IPCC, 2013a: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. and Midgley, P.M. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
IPCC, 2013b: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group 1 to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. In Press.
Kendon, E.J., N. Ban, N.M. Roberts, H.J. Fowler, M.J. Roberts, S.C. Chan, J.P. Evans, G. Fosser, and J.M. Wilkinson, 2021: Do convection-permitting regional climate models improve projections of future precipitation change? Bulletin of the American Meteorological Society, 98(1): 79-93, doi:.10.1175/BAMS-D-15-0004.1.
Lee, J.Y., J. Marotzke, G. Bala, L. Cao, S. Corti, J.P. Dunne, F. Engelbrecht, E. Fischer, J.C. Fyfe, C. Jones, A. Maycock, J. Mutemi, O. Ndiaye, S. Panickal, and T. Zhou, 2021: Future Global Climate: Scenario-Based Projections and Near-Term Information. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.
Li, G., X. Zhang, A.J. Cannon, T. Murdock, S. Sobie, F.W. Zwiers, K. Anderson, and B. Qian, 2018: Indices of Canada’s future climate for general and agricultural adaptation applications. Climatic Change, 148(1), 249-263.
Iizumi, T., H. Takikawa, Y. Hirabayashi, N. Hanasaki, and M. Nishimori, 2017: Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes. Journal of Geophysical Research: Atmospheres, 122(15), 7800-7819.
Lange, S., 2018: Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth System Dynamics, 9(2), 627-645.
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. 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.F. Hopkinson, D. Price, and T. Owen, 2011: Customized spatial climate models for North America. Bulletin of the American Meteorological Society, 92(12), 1611-1622.
Moss, R.H., J.A. Edmonds, K.A. Hibbard, M.R. Manning, S.K. Rose, D.P. van Vuuren, T.R. Carter, S. Emori, M. Kainuma, T. Kram, G.A. Meehl, J.F.B Mitchell, N. Nakicenovic, K. Riahi, S.J. Smith, R.J. Stouffer, A.M. Thomson, J.P. Weyant, and T.J. Wilbanks, 2010: The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747-756.
Notz, D. and SIMIP Community, 2020: Arctic sea ice in CMIP6. Geophysical Research Letters, 47(10), e2019GL086749, doi:10.1029/2019GL086749.
Notz, D., A. Jahn, M. Holland, E. Hunke, F. Massonnet, J. Stroeve, B. Tremblay, and M. Vancoppenolle, 2016: The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): Understanding sea ice through climate-model simulations. Geoscientific Model Development, 9(9), 3427–3446, doi:10.5194/gmd-9-3427-2016.
O’Neill, B.C., E. Kriegler, K.L. Ebi, E. Kemp-Benedict, K. Riahi, D.S. Rothman, B.J. van Ruijven, D.P. van Vuuren, J. Birkmann, K. Kok, M. Levy, and W. Solecki, 2017: The roads ahead: Narratives for Shared Socioeconomic Pathways describing world futures in the 21st century. Global Environmental Change, 42, 169-180, doi:10.1016/j.gloenvcha.2015.01.004.
Riahi, K., D.P. van Vuuren, E. Kriegler, J. Edmonds, B.C. O’Neill, S. Fujimori, N. Bauer, K. Calvin, R. Dellink, O. Fricko, W. Lutz, A. Popp, J. Crespo Cuaresma, K.C. Samir, M. Leimback, J. Leiwen, T. Kram, S. Rao, J. Emmerling, K. Ebi, T. Hasegawa, P. Havlik, F. Humpenöder, L.A. Da Silva, S. Smith, E. Stehfest, V. Bosetti, J. Eom, D. Gernaat, T. Masui, J. Rogelj, J. Strefler, L. Drouet, V. Krey, G. Luderer, M. Harmsen, K. Takahashi, L. Baumstark, J.C. Doelman, M. Kainuma, Z. Klimont, G. Marangoni, H. Lotze-Campen, M/ Obersteiner, A. Tabeau, and M. Tavoni, 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153-168.
Schwingshackl, C., J. Sillmann, A.M. Vicedo-Cabrera, M. Sandstad, and K. Aunan, 2021: Heat stress indicators in CMIP6: Estimating future trends and exceedances of impact-relevant thresholds. Earth's Future, 9(3), e2020EF001885. doi:10.1029/2020EF001885.
Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477-483, doi:10.1038/nature16542.
Sobie, S.R., F.W. Zwiers, and C.L. Curry, 2021: Climate Model Projections for Canada: A Comparison of CMIP5 and CMIP6. Atmosphere-Ocean, 59(4-5), 269-284, doi:10.1080/07055900.2021.2011103.
Tapiador, F.J., A. Navarro, R. Moreno, J.L. Sánchez, and E. García-Ortega, 2020: Regional climate models: 30 years of dynamical downscaling. Atmospheric Research, 235, 104785, doi:10.1016/j.atmosres.2019.104785.
Tebaldi, C., K. Debeire, V. Eyring, E. Fischer, J. Fyfe, P. Friedlingstein, R. Knutti, J. Lowe, B. O'Neill, B. Sanderson, D. van Vuuren, K. Riahi, M. Meinshausen, Z. Nicholls, K.B. Tokarska, G. Hurtt, E. Kriegler, J.-F. Lamarque, G. Meehl, R. Moss, S.E. Bauer, O. Boucher, V. Brovkin, Y.-H. Byun, M. Dix, S. Gualdi, H. Guo, J.G. John, S. Kharin, Y. Kim, T. Koshiro, L. Ma, D. Olivié, S. Panickal, F. Qiao, X. Rong, N. Rosenbloom, M. Schupfner, R. Séférian, A. Sellar, T. Semmler, X. Shi, Z. Song, C. Steger, R. Stouffer, N. Swart, K. Tachiiri, Q. Tang, H. Tatebe, A. Voldoire, E. Volodin, K. Wyser, X. Xin, S. Yang, Y. Yu, and T. Ziehn, 2021: Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth System Dynamics, 12(1), 253–293, doi:10.5194/esd-12-253-2021.
Teutschbein, C. and J. Seibert, 2012: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456-457, 12-29, doi:10.1016/j.hydrol.2012.05.052.
Tokarska, K.B., M.B. Stolpe, S. Sippel, E.M. Fischer, C.J. Smith, F. Lehner, and R. Knutti, 2020: Past Warming Trend Constrains Future Warming in CMIP6 Models. Science Advances, 6(12), eaaz9549, doi:10.1126/sciadv.aaz9549, 2020.
Van Vuuren, D.P., J. Edmonds, A. Thomson, K. Riahi, M. Kainuma, T. Matsui, G.C. Hurtt, J.-F. Lamarque, M. Meinshausen, S. Smith, C. Granier, S.K. Rose, and K.A. Hibbard, 2011: Representative concentration pathways: an overview. Climatic Change, 109(1), 5–31.
Van Vuuren, D.P., E. Kriegler, B.C. O’Neill, K.L. Ebi, K. Riahi, T.R. Carter, J. Edmonds, S. Hallegatte, T. Kram, R. Mathur, and H. Winkler, 2014: A new scenario framework for climate change research: Scenario matrix architecture. Climatic Change, 122(3), 373-386.