Drought is a deficiency in precipitation over an extended period, usually a season or more, resulting in a water shortage that has adverse impacts on vegetation, animals and/or people. The Climate Moisture Index (CMI) was calculated as the difference between annual precipitation and potential evapotranspiration (PET) – the potential loss of water vapour from a landscape covered by vegetation. Positive CMI values indicate wet or moist conditions and show that precipitation is sufficient to sustain a closed-canopy forest. Negative CMI values indicate dry conditions that, at best, can support discontinuous parkland-type forests. The CMI is well suited to evaluating moisture conditions in dry regions such as the Prairie Provinces and has been used for other ecological studies. Mean annual potential evapotranspiration (PET) was estimated for 30-year periods using the modified Penman-Monteith formulation of Hogg (1997), based on monthly 10-km gridded temperature data. Data shown on maps are 30-year averages. Historical values of CMI (1981-2010) were created by averaging annual CMI calculated from interpolated monthly temperature and precipitation data produced from climate station records. Future values of CMI were projected from downscaled monthly values of temperature and precipitation simulated using the Canadian Earth System Model version 2 (CanESM2) for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century. Multiple layers are provided. First, the mean annual Climate Moisture Index is shown across Canada for a reference period (1981-2010). Projected mean annual Climate Moisture Index is available for the short- (2011-2040), medium- (2041-2070), and long-term (2071-2100) under the RCP 8.5 (continued emissions increases) and, for the long-term (2071-2100), under RCP 2.6 (rapid emissions reductions). Reference: Hogg, E.H. 1997. Temporal scaling of moisture and the forest-grassland boundary in western Canada. Agricultural and Forest Meteorology 84,115–122.

This dataset provides 1/36-degree monthly mean ocean current climatology (October - March) in the Northeast Pacific. The climatological fields are derived from hourly ocean currents for the perid from 1993 to 2020, simulated using a high-resolution Northeast Pacific Ocean Model (NEPOM).

Under the Regulation Respecting the Mandatory Reporting of Certain Contaminant Emissions into the Atmosphere (RDOECA), the Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks collects data on greenhouse gases (GHGs) emitted by Quebec businesses in particular. Thus, any person or municipality operating an establishment that emits GHGs into the atmosphere in a quantity equal to or greater than 10,000 metric tons in CO2 equivalent (t eq. CO2) is required to report its emissions no later than June 1 of each year. The data is presented in separate files: * Total biogenic and CO2 emissions per establishment; * Emissions per establishment and per greenhouse gas and per establishment. Total emissions files include the total quantity of GHGs, the total quantity of GHGs excluding CO2 from biomass, the quantity of CO2 from the combustion of biomass, and the quantity of CO2 from other uses of biomass (for example fermentation). The emission files by establishment and by greenhouse gas include the quantity emitted of each of the GHGs in metric tons and t eq. CO2. Note that CO2 emissions include those from biomass. The data presented in this dataset includes emissions from mandatory and voluntary reporting.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This dataset provides 1/36-degree monthly-mean ocean current climatology (April - September) in the Northeast Pacific. The climatological fields are derived from hourly ocean currents for the period from 1993 to 2020, simulated using a high-resolution Northeast Pacific Ocean Model (NEPOM).

Drought is a deficiency in precipitation over an extended period, usually a season or more, resulting in a water shortage that has adverse impacts on vegetation, animals and/or people. The Climate Moisture Index (CMI) was calculated as the difference between annual precipitation and potential evapotranspiration (PET) – the potential loss of water vapour from a landscape covered by vegetation. Positive CMI values indicate wet or moist conditions and show that precipitation is sufficient to sustain a closed-canopy forest. Negative CMI values indicate dry conditions that, at best, can support discontinuous parkland-type forests. The CMI is well suited to evaluating moisture conditions in dry regions such as the Prairie Provinces and has been used for other ecological studies. Mean annual potential evapotranspiration (PET) was estimated for 30-year periods using the modified Penman-Monteith formulation of Hogg (1997), based on monthly 10-km gridded temperature data. Data shown on maps are 30-year averages. Historical values of CMI (1981-2010) were created by averaging annual CMI calculated from interpolated monthly temperature and precipitation data produced from climate station records. Future values of CMI were projected from downscaled monthly values of temperature and precipitation simulated using the Canadian Earth System Model version 2 (CanESM2) for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century. Provided layer: mean annual Climate Moisture Index across Canada for a reference period (1981-2010). Reference: Hogg, E.H. 1997. Temporal scaling of moisture and the forest-grassland boundary in western Canada. Agricultural and Forest Meteorology 84,115–122.

Canadian Homogenized Surface Air Temperature – Version 3.1 (CanHomT V3.1) This dataset is an update of the Third Generation Homogenized Temperature dataset (Vincent et al. (2020), extended through 2023. It was developed for climate trend analysis and includes long-term daily maximum, minimum, and mean temperature series for 780 stations (776 locations) in Canada. The key enhancement in V3.1 is the inclusion of both Original and Adjusted data, together with their associated data flags and the source station climate ID, providing improved traceability, reproducibility, and transparency. Key processing steps include: • Merging observations from nearby sites to create long records • Quality control of data • Adjusting daily minimum temperatures since July 1961 for the observing-time (climatological day definition) change at 96 principal stations (Vincent et al., 2009) • Detecting non-climatic shifts in annual and seasonal average temperature data series using homogeneity tests with reference and station metadata. • Applying Quantile-Matching (QM) adjustments with reference data (including parallel observations) to correct non-climatic changes (Vincent et al., 2018; Wang et al., 2010). Parallel observations —simultaneous measurements from old and new instruments or locations—were used whenever available because they provide the most reliable basis for data homogenization. Adjustments made without suitable reference data carry greater uncertainty, and this increased uncertainty should be quantified and clearly communicated (WMO, 2020). The QM method adjusts the entire distribution of data in one segment to match another (Wang et al., 2026; Vincent et al., 2018; Wang & Feng, 2013; Wang et al. 2010), ensuring that distribution changes—including variance shifts—at identified changepoints are homogenized. The gridded version of monthly CanHomT V3.1, called CanGridT mlyV3.1 [https://open.canada.ca/data/en/dataset/781e02cc-6c1b-462e-b61b-f96c607b23bd], was found to represent Canada’s warming trend reasonably well since 1900, despite changes in data availability over time (Wang et al., 2026, see their Figure S3). References Vincent, L.A., M.M. Hartwell and X.L. Wang, 2020: A Third Generation of Homogenized Temperature for Trend Analysis and Monitoring Changes in Canada’s Climate. Atmosphere-Ocean, 58(3), 173–191. https://doi.org/10.1080/07055900.2020.1765728. Vincent, L.A., E.J. Milewska, R. Hopkinson and L. Malone, 2009: Bias in minimum temperature introduced by a redefinition of the climatological day at the Canadian synoptic stations. J. Appl. Meteor. Climatol, 48, 2160-2168. DOI: 10.1175/2009JAMC2191.1. Vincent, L.A., E.J. Milewska, X. L. Wang, and M. M. Hartwell, 2018. Uncertainty in homogenized daily temperatures and derived indices of extremes illustrated using parallel observations in Canada, Intl. J. Climatol., 38:2, 692-707. DOI: 10.1002/JOC.5203. Wang, X. L. and Y. Feng, published online July 2013: RHtestsV4 User Manual. Climate Research Division, Atmospheric Science and Technology Directorate, Science and Technology Branch, Environment Canada. 28 pp. [Available online at https://github.com/ECCC-CDAS] DOI: 10.13140/RG.2.2.17309.17125. Wang, X. L., H. Chen, Y. Wu, Y. Feng, and Q. Pu, 2010: New techniques for detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol., 49, 2416-2436. DOI: 10.1175/2010JAMC2376.1. WMO. (2020). Guidelines on Homogenization (2020 edition). World Meteorological Organization. WMO-No. 1245. https://library.wmo.int/records/item/57130-guidelines-on-homogenization?offset=1. Wang, X. L., Feng, Y., Zwiers, F. W., & Cheng, V. Y. S. (2026). Precipitation trends in version 2 of the Canadian homogenized monthly precipitation dataset. Atmosphere-Ocean, 1–16, https://doi.org/10.1080/07055900.2026.2617861.

The fire regime describes the patterns of fire seasonality, frequency, size, spatial continuity, intensity, type (e.g., crown or surface fire) and severity in a particular area or ecosystem. Annual area burned is the average surface area burned annually in Canada by large fires (greater than 200 hectares (ha)). Changes in annual area burned were estimated using Homogeneous Fire Regime (HFR) zones. These zones represent areas where the fire regime is similar over a broad spatial scale (Boulanger et al. 2014). Such zonation is useful in identifying areas with unusual fire regimes that would have been overlooked if fires had been aggregated according to administrative and/or ecological classifications. Fire data comes from the Canadian National Fire Database covering 1959–1999 (for HFR zones building) and 1959-1995 (for model building). Multivariate Adaptive Regression Splines (MARS) modeling was used to relate monthly fire regime attributes with monthly climatic/fire-weather in each HFR zone. Future climatic data were simulated using the Canadian Earth System Model version 2 (CanESM2) and downscaled at a 10 Km resolution using ANUSPLIN for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century. Provided layer: projected annual area burned by large fires (>200 ha) across Canada for the long-term (2071-2100) under the RCP 2.6 (rapid emissions reductions). Reference: Boulanger, Y., Gauthier, S., et al. 2014. A refinement of models projecting future Canadian fire regimes using homogeneous fire regime zones. Canadian Journal of Forest Research 44, 365–376.