The ClimateData.ca team aims to provide accessible, transparent, trustworthy, and well-documented datasets.
Climate data is increasingly used by users across Canada for consequential decisions around climate change risk, adaptation and resilience. ClimateData.ca and its partners are developers and distributors of this information. In this role, the ClimateData.ca team aims to provide accessible, transparent, trustworthy, and well-documented datasets. To this end, all data under consideration for ClimateData.ca, whether originating from federal, regional, or provincial partners, academia, the private sector, or multi-stakeholder groups, undergoes a thorough evaluation against stringent data standards and requires consensus between all involved partners before addition to the data portal is approved.
ClimateData.ca Data Standards.
When a dataset or tool is proposed to ClimateData.ca, it enters a process (outlined below) designed to evaluate not only the suitability of the data, but also its presentation and accompanying guidance and documentation. Every step of this process involves representatives from all partners to ensure the product meets the requisite standards. Where data standards are not fully met, clear justification is required. Reflection against these standards ensures that all new products distributed by ClimateData.ca support our goal:“to help Canadians make decisions in a changing climate.”
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 emissions 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. See About CanDCS-U5 for citations and more details.
Downscaling method: BCCAQv2
Downscaling target: NRCANmet
Citing this dataset: Citing ClimateData.ca
The Canadian Downscaled Climate Scenarios-univariate dataset for CMIP6 (Phase 6 of the Coupled Model Intercomparison Project) provides projected indices of temperature and precipitation, for three emissions 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. See About CanDCS-U6 for citations and more details.
Downscaling method: BCCAQv2
Downscaling target: NRCANmet
Citing this dataset: Citing ClimateData.ca
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 four emissions 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. See About CanDCS-M6 for citations and more details.
Downscaling method: MBCn
Downscaling target: PCIC-Blend
Citing this dataset: Citing ClimateData.ca
The Standardised Precipitation Evapotranspiration Index (SPEI) is a drought index based on the difference between precipitation (P) and potential evapotranspiration (PET). Negative (positive) values indicate water deficit (surplus). SPEI only speaks to relative drought – i.e., the difference between future conditions compared to historical conditions – and is not an absolute measure of water availability. An SPEI value of zero indicates no change relative to the historical values.
The projections on ClimateData.ca are provided at a 1 degree (~100km) resolution for three emissions scenarios, with the 10th, 50th (median) and 90th percentiles being calculated from an ensemble of 29 climate models. Prior to calculating SPEI, the temperature and precipitation data were bias-adjusted using the multivariate MBCn method with the CanGRD dataset as the target. See About SPEI for more details and an example of interpretation.
Downscaling method: MBCn
Downscaling target: CanGRD
Citing this dataset: Citing ClimateData.ca
Relative Sea Level Change is the change in ocean level relative to land. Whereas global sea-level change can be attributed to thermal expansion of water and meltwater from glaciers, ice caps, and ice sheets, relative sea-level change is the combination of the effects from global sea-level change and the vertical motion of the land.
CMIP6 projected relative sea level change data is available for every decade from 2020-2100, relative to 1994-2015 conditions.
CMIP5 projected relative sea level change data is available for 2006 and for every decade from 2010-2100, relative to 1986-2005 conditions.
See the Relative Sea Level Change variable page for more information.
Downscaling method: See report
Downscaling target: N/A
Citing this dataset: Citing ClimateData.ca
The Humidex index was developed by the Meteorological Service of Canada to describe how hot and humid the weather feels to the average person. In Canada, it is recommended that outdoor activities be moderated when the humidex exceeds 30, and that all unnecessary activities cease when it passes 40. Projections of daily maximum humidex have been used to calculate the three indices available on ClimateData.ca – the number of days when maximum humidex exceeds 30, 35 and 40.
Projections are available a resolution of 0.1° (approximately 9 km) from 1950-2100.Change values are calculated with respect to the 1971-2000 reference period. Uncertainty in the amount of greenhouse gases that will be emitted over the coming century is represented by providing results for multiple emissions scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Climate model uncertainty is represented by providing the 10th, 50th, and 90th percentile of results across a 19-member model ensemble. See the Humidex variable page for more information.
Downscaling method: MBCn
Downscaling target: ERA5-Land
Citing this dataset: Citing ClimateData.ca
Future rainfall rates are obtained by scaling the historical IDF values according to the Clausius Clapeyron relationship. This states that the water-holding capacity of the atmosphere increases by about 7% for every 1°C of warming. The difference in projected future temperature and historical temperature is combined with historical rainfall rates via the Clausius Clapeyron relationship to scale the rainfall rates for future climate conditions. The annual mean temperature projections used for scaling are from the CanDCS-U5 ensemble of 24 climate models for CMIP5, and from the CanDCS-U6 ensemble of 26 climate models for CMIP6. Changes are relative to 1974-2005. After the scaling is completed, the 10th, 50th (median) and 90th percentile values of the ensemble are calculated as well as the 95% confidence limits. See the ReadMe attached to the downloads for more details.
Temperature data downscaled with BCCAQv2
Temperature data downscaling target is NRCANmet
Citing this dataset: Citing ClimateData.ca
Vertical allowance is defined as the amount by which an asset (e.g., building, wharf) should be raised under rising sea levels so that the present frequency of coastal flooding does not increase for a chosen future period (Zhai et al., 2023). These data incorporate current statistics of tides and storm surges, as well as relative sea-level change projections and the uncertainties in those projections.
Projected vertical allowances (in cm) from CMIP6 are available at a resolution of 0.1° (approximately 11 km latitude, 4-8 km longitude) for the coasts of British Columbia, Atlantic Canada and eastern Arctic south of 70°N for every decade from 2020-2100, relative to 2010 conditions. Vertical allowances up to 2150 are available upon request. The data are available for four Shared Socio-economic Pathways (SSP) emissions scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5).
See the Vertical Allowance variable page for more information.
Downscaling method: See Relative Sea Level Change report
Downscaling target: N/A
Citing this dataset: Citing ClimateData.ca
Produced by Natural Resources Canada (NRCan), NRCANmet is a gridded dataset derived from station observations and is available at a ~6×10 km resolution over Canada. Daily minimum and maximum temperature, and precipitation amounts for the period 1950-2012 were produced by Hopkinson et al. (2011 – https://doi.org/10.1175/2011JAMC2684.1) and McKenney et al. (2011 – https://doi.org/10.1175/2011BAMS3132.1) on behalf of the Canadian Forest Service (CFS), NRCan. Gridding was accomplished with the Australian National University Spline (ANUSPLIN) software which uses a trivariate thin plate splines interpolation method (Hutchinson et al., 2009 – https://doi.org/10.1175/2008JAMC1979.1) with latitude, longitude and elevation as predictors. 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., 2019 – https://doi.org/10.5194/hess-23-5151-2019). See About ANUSPLIN for citations and more details.
Type: Interpolated observations
Citing this dataset: Citing ClimateData.ca
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 – https://doi.org/10.1175/JAMC-D-20-0041.1), and NRCANmetV2 Temperature, spanning North America (MacDonald et al. 2020 – https://doi.org/10.1038/s41597-020-00737-2). 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.
Type: Interpolated observations
Citing this dataset: Citing ClimateData.ca
Adjusted and Homogenized Canadian Climate Data (AHCCD) consists of weather station datasets (temperature and precipitation only) and has been developed by Environment and Climate Change Canada for use in climate research, including climate change studies. The station records in AHCCD have been adjusted using statistical techniques to detect and remove discrepancies in long-term data records caused by non-climatic factors (e.g., changes in instrumentation, observing procedures, and weather station location or site exposure). Longer time series were sometimes created by combining data from nearby stations. Monthly, Seasonal and Annual data for these variables and more can be found at the following link: AHCCD Open Data.
Type: Station
Citing this dataset: Citing ClimateData.ca
Station Data is historical daily observed weather station data from the Meteorological Service of Canada and Environment and Climate Change Canada.
Type: Station
Citing this dataset: Citing ClimateData.ca
Climate Normals describe the average climate conditions of a particular location over a 30-year period.
At the end of each decade, Environment and Climate Change Canada calculates a new set of climate normals using station observations from that decade. All member countries of the World Meteorological Organisation calculate climate normals. As they describe the most recent average climate conditions for a location, they are often used to put extreme events into context.
The climate normals offered here are based on Canadian climate stations with at least 15 years of data available during the current 30-year normal period.
Type: Station
Citing this dataset: Citing ClimateData.ca
Intensity Duration Frequency (IDF) curves relate short-duration rainfall intensity with its frequency of occurrence and are often used for flood forecasting and urban drainage design.
Intense precipitation events can deliver large amounts of rain over short periods of time. This rain, as well as related flooding, can overwhelm storm drains, flood basements, wash out bridges and roads, and trigger landslides. To reduce the risk of these impacts, engineers, hydrologists, planners and decision makers need accurate information about extreme rainfall events. IDF curves are one important source of this information.
Climate change is expected to increase extreme rainfall in Canada. Because of this, IDF curves based on historical observations alone are inappropriate for long-term decision-making. To account for climate change impacts to extreme rainfall and IDF curves, Environment and Climate Change Canada recommends use of a scaling methodology. ClimateData.ca provides historical and climate change-scaled IDF data for all ECCC IDF stations in Canada.
Additional guidance about integrating climate change into IDF curves can be found on the Learning Zone. For further technical information on how IDF Curves are produced, please refer to Environment and Climate Change Canada’s Engineering Climate Datasets page or contact the Engineering Climate Services Unit at [email protected].
Type: Station
Citing this dataset: Citing ClimateData.ca
CanGRD is a dataset provided by Environment and Climate Change Canada (ECCC), which offers gridded meteorological data covering Canadian regions. It includes information on various climate variables such as temperature, precipitation, wind speed, and humidity, presented in a gridded format at a ~50km spatial resolution. CanGRD data is widely used for climate analysis, research, and modeling, providing valuable insights into historical climate conditions and trends across Canada. CanGRD was used to bias-adjust the SPEI CMIP5 dataset.
Type: Interpolated observations
Citing this dataset: Citing ClimateData.ca
ERA5-Land is a reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), which offers high-resolution (~9 km) atmospheric reanalysis data specifically focused on land surfaces. It includes information on various meteorological variables such as temperature, precipitation, wind speed, and surface pressure, covering the entire globe. ERA5-Land was used to downscale the Humidex data.
Type: Reanalysis
Citing this dataset: Citing ClimateData.ca
Bias Correction/ Constructed Analogues with Quantile mapping reordering, Version 2 (BCCAQv2) is a bias correction and statistical downscaling method for global climate model (GCM) data, developed at the Pacific Climate Impacts Consortium (PCIC). It is used for downscaling daily climate model projections of temperature and precipitation, i.e., it is used to transform coarse-resolution GCM information to more locally relevant spatial scales. It is a univariate method and so downscales one variable at a time. See About BCCAQv2 for more information regarding BCCAQv2.
Citing this method: 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. See About MBCn for more information regarding MBCn.
Citing this method: Citing ClimateData.ca
Australian National University Spline (ANUSPLIN) is an interpolation method used to create gridded observational datasets. This method uses a curve fitting technique, along with latitude, longitude and elevation to ensure that the gridded data is spatially continuous. ANUSPLIN is used to produce the NRCANmet dataset. See the About ANUSPLIN page for more information.
Citing this method: Citing ClimateData.ca
This project was undertaken with the support of:
