ClimateData.ca is a collaboration between Environment and Climate Change Canada (ECCC), the Computer Research Institute of Montréal (CRIM), CLIMAtlantic, Ouranos, the Pacific Climate Impacts Consortium (PCIC), the Prairie Climate Centre (PCC), and HabitatSeven.
Canada’s climate is changing rapidly. Canada’s Changing Climate Report illustrates that Canada’s climate will continue to warm due to global emissions of greenhouse gases (GHGs). Concerningly, this warming will lead to increases in extreme heat and extreme precipitation events, raise sea-levels, and cause further declines in snow and ice cover. All sectors of society and the economy are at risk from these climate changes. It is therefore vital for Canadians to incorporate climate change considerations in their decision-making processes.
Online data portals are an important way of providing climate data openly and freely to all Canadians. Readily available future climate projections and historical data via an online data portal helps with the development and implementation of climate change adaptation plans and improves the ability for them to make, and have confidence in, specific recommendations. Even for experts, tasks like accessing large amounts of reliable data, conducting a thorough analysis and computing key variables for a specific region can take months and cost several thousands of dollars. To support and enable Canada’s climate change adaptation planning, and improve access to relevant climate data, we are proud to present ClimateData.ca.
ClimateData.ca enables Canadians to access, visualize, and analyze climate data, and provides related information and tools to support adaptation planning and decision-making. Our collaborative approach to providing climate services to Canadians aims to foster the development of a network of national and regional climate services providers which will support the ongoing provision of specialized information tailored to specific industry sectors.
Learn more and explore the portal’s resources by downloading the full outreach package.
ClimateData.ca is a collaboration between:
Each and every one of the various aspects of the development of ClimateData.ca are conducted in a collaborative, participative, transparent and neutral manner. This is the case for requirements gathering, user engagement, issue prioritization, content production, data quality control, project management, and so on. The partners of this project tackle each challenge as a Pan-Canadian, focused multidisciplinary team.
ClimateData.ca is part of an ecosystem of made-in-Canada climate data portals and platforms. The Continuum of Portals collaboratively disseminate climate information targeting different audiences. These include the general public, the media, policy analysts, and decision-makers, as well as researchers and scientists.
Information flow across these portals will be enabled to allow users to access information applicable to their needs and encourage climate service providers to work together, share lessons learned and best practices, and leverage resources and expertise.
The Canadian Downscaled Climate Scenarios-multivariate dataset for CMIP6 (Phase 6 of the Coupled Model Intercomparison Project) provides projected indices of temperature and precipitation, for three emission scenarios at a ~6x10km resolution. The 10th, 50th and 90th percentiles of an ensemble of 26 climate models are provided. Change values are calculated with respect to the 1971-2000 reference period.
Downscaling method: MBCn
Downscaling target: PCIC-Blend
Citing this dataset: Citing ClimateData.ca
N-Dimensional Multivariate Bias Correction (MBCn) is a statistical methodology for downscaling daily Global Climate Model (GCM) data, i.e., it is used to transform coarse-resolution GCM information to more locally relevant spatial scales. It is a multivariate method which downscales multiple variables at once.
For more information see:
Cannon, A. J., 2018: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50, 31-49, doi:10.1007/s00382-017-3580-6.
PCIC-Blend is a daily, gridded observational dataset which has been used in multivariate downscaling techniques such as MBCn, and was used in the development of CanDCS-M6. PCIC-Blend is based on three existing data sets. Two of these are recently updated versions of the NRCANmet dataset used to create CanDCS-U5 and CanDCS-U6: NRCANmet-Adjusted Precipitation, which spans Canada (MacDonald et al. 2021), and NRCANmetV2 Temperature, spanning North America (MacDonald et al. 2020). The third dataset, PNWNAmet, covers Western Canada and the Pacific Northwest, and comprises minimum and maximum temperature and precipitation. While the updated NRCANmet data sets display notable improvements over the earlier version over central and eastern Canada, their performance is inferior to PNWNAmet over western Canada when compared to high quality station observations. PNWNAmet values in western Canada were combined with the NRCANmet V2 (temperature) and NRCANmet-Adjusted (precipitation) values in central and eastern Canada to produce PCIC-Blend.
The Canadian Downscaled Climate Scenarios-univariate dataset for CMIP5 (Phase 5 of the Coupled Model Intercomparison Project) provides projected indices of temperature and precipitation, for three emission scenarios at a ~6x10km resolution. The 10th, 50th and 90th percentiles of an ensemble of 24 climate models are provided. Change values are calculated with respect to the 1971-2000 reference period.
Downscaling method: BCCAQv2
Downscaling target: NRCANmet
Citing this dataset: Citing ClimateData.ca
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.
Additional references:
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 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) by the Canadian Forest Service (CFS), Natural Resources Canada. The grids are available at 300 arc second spatial resolution (1/12° grids, ~10 km) over Canada. These models were produced using ANUSPLIN, a thin plate spline-based spatial climate modeling tool developed by the Australian National University (Xu & Hutchinson, 2013) using latitude, longitude and scaled 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 used as the source data for the models. Station density varies over time with changes in station availability, peaking in the 1970s with a general decrease towards the present day. 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).
Hopkinson RF, Mckenney DW, Milewska EJ, Hutchinson MF, Papadopol P, Vincent LA (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
Hutchinson MF, McKenney DW, Lawrence K, Pedlar JH, Hopkinson RF, Milewska E, Papadopol P (2009): Development and testing of Canada-wide interpolated spatial models od daily minimum-maximum temperature and precipitation for 1961-2003. Journal of Applied Meteorology and Climatology 48: 725-741.
McKenney DW, Hutchinson MF, Papadopol P, Lawrence K, Pedlar J, Campbell K, Milewska E, Hopkinson RF, Price D, Owen T (2011): Customized spatial climate models for North America. Bulletin of the American Meteorological Society 92: 1611-1622.
Xu T, Hutchinson MF (2013): New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package. Environmental Modelling and Software 40: 267-279. https://doi.org/10.1016/j.envsoft.2012.10.003
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
References
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.
Humidex combines the temperature and humidity into one number to reflect the perceived temperature. Because it takes into account the two most important factors that affect summer comfort, it can be a better measure of how the weather affects the human body than either temperature or humidity alone.
Humidex is widely used in Canada. In the past, extremely high values were rare except in the southern regions of Ontario, Manitoba and Quebec, as well as southern sections of Alberta and Saskatchewan. Generally, the Humidex decreases as latitude increases.
Projections are available at a resolution of 0.1° (approximately 9 km) from 1950-2100.
Reference:
Chow, K.K.C., Sankaré, H., Diaconescu, E.P., Murdock, T.Q. & Cannon, A.J. (2024) Bias-adjusted and downscaled humidex projections for heat preparedness and adaptation in Canada. Geoscience Data Journal, 11, 680–698. Available from: https://doi.org/10.1002/gdj3.241
This term is not used to refer to confidence (or lack thereof) in projections but is rather a quantitative description of the elements that make up the range of plausible future projected values. Sources of this “uncertainty” include natural climate variability (which will always be present and is considered irreducible), differences between climate models (which could be potentially reduced with improvements in knowledge and in the models’ representation of the Earth-atmosphere system), and differences in future human emissions of greenhouse gases (which could be potentially reduced with better estimates of the future evolution of population, energy use, technology and political choices, which remain somewhat speculative). This latter source of uncertainty can be explored to some extent through the use of different scenarios of future greenhouse gas emissions.
All results displayed are from an ensemble consisting of multiple climate models. Each climate model simulates the climate for the historical period, and for plausible futures in response to emissions scenarios representing different atmospheric concentrations of greenhouse gases.
Bold lines on the time-series plots represent median values (50th percentile) of the climate model ensemble. The data range (coloured area) is defined by the 10th and 90th percentile values of the climate model ensemble. Colours correspond to the emissions scenarios, as shown at the top of the plot. The Canada-wide maps show the 50th percentile of 30-year averages.