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

Climate Normals and Averages are used to summarize or describe the average climatic conditions of a particular location. At the completion of each decade, Environment and Climate Change Canada updates its Climate Normals for as many locations and as many climatic characteristics as possible. The Climate Normals, Averages and Extremes offered here are based on Canadian climate stations with at least 15 years of data between 1981 to 2010.

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.

Polygons representing the areas most sensitive to heavy rains and heat waves. Three matrices were produced as part of component 2 of the Oasis program of the Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks (MELCCFP) in order to obtain financial assistance for the development of green infrastructures adapted to current and future climate realities. The methodology developed is based on an adaptation of the one used in the context of the analysis of [Vulnerability to climate change] (https://donnees.montreal.ca/fr/dataset/vulnerabilite-changements-climatiques).The data can also be consulted on the [interactive map of vulnerabilities to climate hazards in the Montreal agglomeration] (https://experience.arcgis.com/experience/944e0b7104bd491591ccca829da24670/page/Page).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This dataset contains the outputs for Bute Inlet from two simulations shown in the publication "Fjord circulation permits persistent subsurface water mass in a long, deep mid-latitude inlet" by Laura Bianucci et al., DFO Ocean Sciences Division, Pacific Region (published in the journal Ocean Science in 2024). The Finite Volume Community Ocean Model (FVCOM v4.1) was run with two different sets of initial conditions for the Discovery Islands region of British Columbia, Canada, from May 24 to June 27, 2019. The "Baseline" simulation used observed initial conditions, while the "Sensitivity" simulation removed the observed cold subsurface water mass from the initial profiles. Here in this dataset, we provide 29-day averages of the following variables in a transect along Bute Inlet: potential temperature, density, along-inlet velocity, and Brunt-Väisälä frequency (N^2). The averaging properly removes the tidal effects.

Accumulated Precipitation represents the amount of total precipitation in mm (solid and/or liquid) which has been recorded over a given month or ISO week.Accumulation of precipitation values serve to inform users about the total amount of precipitation that has fallen in a specific region during a set time period. The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

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).

Difference from Normal Temperature values are computed by subtracting the normal monthly average temperature from the average monthly temperature of the month. The average monthly temperature is computed by obtaining the mean value of average daily temperatures for a month. If the month was colder than normal the value computed will be negative and if it was warmer the value will be positive.Long-term average is 1991-2020.The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

CANGRD is a set of Canadian gridded annual, seasonal, and monthly temperature and precipitation anomalies, which were interpolated from stations in the Adjusted and Homogenized Canadian Climate Data (AHCCD); it is used to produce the Climate Trends and Variations Bulletin (CTVB).

Precipitation percentiles represents the accumulated precipitation (mm) for the time period compared to historical information for the same time period. This comparison ranks the current precipitation amount and assigns it a percentile value based on a historic record.Time periods calculated for monthly precipitation percentiles are 1, 2, 3, 6, 9, 12, 18, 24 months. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday.Historical record goes back to 1980.The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.