Multi-model ensembles of mean temperature based on projections from twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1901-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of mean temperature (°C) are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Multi-model ensembles of surface wind speed based on projections from twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of surface wind speed (m/s) are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better-projected climate change information.

This dataset provides refined monthly climatological projections for six key ocean variables: surface and bottom temperature, surface and bottom salinity, mixed layer depth, and bottom current speed. The projections are provided for six Maritime Marine Protected Areas: the Gully, the Laurentian Channel, St. Anns Bank, Musquash Estuary, Banc-des-Américains, and Basin Head. These projections are based on downscaled outputs from 22 CMIP6 models under four emissions scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for four 20-year periods spanning 2020 to 2099 (2020–2039, 2040–2059, 2060–2079, and 2080–2099).For each model and scenario, 20-year climatological means were computed for each future period. These climatologies were then aggregated to produce ensemble means and ensemble standard deviations for all variables. The released dataset includes only these ensemble summary fields and is provided as gridded data with dimensions longitude × latitude × scenario × time.Cite this data as: Wang., Z. Data of High-resolution climate projection data for Marine Protected Areas in the Maritimes. Published: May 2026. Ocean Ecosystems Science Division, Maritimes Region, Fisheries and Oceans Canada, Dartmouth NS. https://open.canada.ca/data/en/dataset/fb4b3212-bca3-4192-8401-687faccc534b

The data shared are spatially explicit projections of wildfire burn probability across Canada’s forested ecozones under multiple future climate scenarios at a 30-m spatial resolution. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). Four future climate scenarios were used to examine the spatiotemporal distribution of burn probability in the 21st century based on climate, vegetation, and topographic conditions ( Mulverhill et al. 2024). Projected burn probability is provided for four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and four future time periods, including 2021-2040, 2041-2060, 2061-2080, and 2081-2100, along with a baseline period representing average climate conditions and burn probability between 1991 and 2020. Outputs represent the probability that the conditions (climate, vegetation, topography) of a given pixel resemble those of historically burned areas. All non-climate variables were held static; therefore, projections represent burn probability under future climate scenarios given contemporary (2020) forest conditions. When using this dataset, please cite Mulverhill et al. (2025), as below.Mulverhill, C., Coops, N. C., Wulder, M. A., Hermosilla, T., White, J. C., & Bater, C. W. (2025). Projected Future Changes in Burn Probability in Canada’s Forests and Communities Under Different Climate Change Scenarios. Canadian Journal of Remote Sensing, 51(1). https://doi.org/10.1080/07038992.2025.2560347(Mulverhill et al. 2025).For a detailed description of the source data and methods applied to the baseline period to enable the Mulverhill et al. (2025) projections, see:Mulverhill, C., Coops, N.C., Wulder, M.A., White, J.C., Hermosilla, T., and Bater, C.W. 2024. “Multidecadal mapping of status and trends in annual burn probability over Canada’s forested ecosystems.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 209 pp. 279–295. https://doi.org/10.1016/j.isprsjprs.2024.02.006(Mulverhill et al. 2024).

Multi-model ensembles of mean precipitation based on projections from twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1901-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of mean precipitation (mm/day) are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Multi-model ensembles of sea ice thickness based on projections from twenty-six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of sea ice thickness (m) are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Multi-model ensembles of snow depth based on projections from twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of snow depth (m) are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Multi-model ensembles for a suite of variables based on projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) are available for 1850-2100 on a common 1x1 degree global grid. Climate projections vary across GCMs due to differences in the representation and approximation of earth systems and processes, and natural variability and uncertainty regarding future climate drivers. Thus, there is no single best climate model. Rather, using results from an ensemble of models (e.g., taking the average) is best practice, as an ensemble takes model uncertainty into account and provides more reliable climate projections.Provided on Canadian Climate Data and Scenarios (CCDS) are four types of products based on the CMIP6 multi-model ensembles: time series datasets and plots, maps and associated datasets, tabular datasets, and global gridded datasets. Monthly, seasonal, and annual ensembles are available for up to six Shared Socioeconomic Pathways (SSPs) (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0, and SSP5-8.5), four future periods (near-term (2021-2040), mid-term (2041-2060 and 2061-2080), end of century (2081-2100)), and up to five percentiles (5th, 25th, 50th (median), 75th, and 95th) of the CMIP6 ensemble distribution. The number of models in each ensemble differs according to model availability for each SSP and variable, see the model list resource for details on the models included in each ensemble. The majority of products show projected changes expressed as anomalies according to a historical reference period of 1995-2014. The products provided include global, national, and provincial/territorial datasets and graphics. For more information on the CMIP6 multi-model ensembles, see the technical documentation resource.

Multi-model ensembles of sea ice concentration based on projections from twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of sea ice concentration as represented as the percentage (%) of grid cell area, are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

This series of projected future flood susceptibility maps were generated using an XGBoost machine learning model trained on major floods from 2005 to 2023. The trained model was applied to future climate scenarios for 2050, 2070, and 2100, under two SSP scenarios: 245 and 585. The model uses temperature and precipitation time series to estimate potential future flood susceptibility. These maps represent model projections and should be interpreted as indicators of potential flood susceptibility, not precise forecasts.This dataset forms part of a broader collection of flood susceptibility datasets, offering related information and analyses. The collection includes an overview page with associated publications, historic susceptibility values, temporal trends, and future projections.- [Collection – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc)- [Historic - Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/ea1384df-bf4a-4743-97bb-870dc43f8d77)- [Trends and Extremes – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/3202e0a0-0afb-4120-b102-b0c41f0fb9eb)