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 The majority of products show projected changes expressed as anomalies according to a historical reference period of 1995-2014.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.

Level curves with an equidistance of 1 m derived from a lidar survey conducted in 2024.attributes:ID - Unique IDSubtype - Master (1) or secondary (2) level curve SCORE - Elevation value (m) The High Resolution Digital Elevation Model (m) product The High Resolution Digital Elevation Model (HRDM) product is available on the Open Government website.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Statistically downscaled multi-model ensembles of mean temperature are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Downscaled daily mean temperature was calculated by averaging downscaled daily minimum and maximum temperature. Daily minimum and maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). Historical gridded minimum and maximum temperature datasets of Canada (ANUSPLIN) were used as the respective downscaling targets. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled mean temperature (°C) are available for the historical time period, 1951-2005, 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 model is derived from geological, geophysical and other forms of geodata. Feature extraction used deep learning. Predictive modelling made use of the deep ensemble method. Displayed is a Pan-Canadian probability map of mineral potential of graphite. This map was generated using known graphite deposits and occurrences and their associated features. Higher probability values highlight areas with an increased probability of graphite mineral systems.

This series of historic flood susceptibility maps comes from an XGBboost machine learning model trained on major floods from 2005 to 2023. The trained model is then run for each year from 2000 to 2023, including unique temporal characteristics of temperature, precipitation, land use land cover and Normalized Difference Vegetation Index (NDVI), to predict the flood susceptibility of any given year.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)- [Trends and Extremes – Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/3202e0a0-0afb-4120-b102-b0c41f0fb9eb)- [Future - Flood Susceptibility Mapping]( https://open.canada.ca/data/en/dataset/c00f95a3-7bab-4d28-b9cc-b30f06b5afd2)

Prospectivity model highlights areas of Canada with the greatest potential for magmatic nickel deposits. The preferred prospectivity model is based on public geological, geochemical, and geophysical datasets that were spatially indexed using the H3 discrete global grid system. Each H3 cell is associated with a prospectivity value, or class probability, calculated from the best-performing gradient boosting machines model. Model results are filtered to include the top 20% of prospectivity values for visualization purposes.

The numerical canopy model (MNC) is a representation of the altitude of the canopy. This 3D representation of the arboreal vegetation corresponds to the 2015 2D canopy.If necessary, the MNC can be coupled with the [Numerical Surface Model (MNS)] (/city-of-montreal/numeric-surface-model) 2015 in order to obtain more detailed coverage.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Prospectivity model highlights areas of Canada with the greatest potential for clastic-dominated zinc deposits. The preferred prospectivity model is based on public geological, geochemical, and geophysical datasets that were spatially indexed using the H3 discrete global grid system. Each H3 cell is associated with a prospectivity value, or class probability, calculated from the best-performing gradient boosting machines model. Model results are filtered to include the top 20% of prospectivity values for visualization purposes.

Prospectivity model highlights areas of Canada with the greatest potential for Mississippi Valley-type zinc deposits. The preferred prospectivity model is based on public geological, geochemical, and geophysical datasets that were spatially indexed using the H3 discrete global grid system. Each H3 cell is associated with a prospectivity value, or class probability, calculated from the best-performing gradient boosting machines model. Model results are filtered to include the top 20% of prospectivity values for visualization purposes.

Summer, Winter Alpine, and Winter Forest-Dwelling habitat model for caribou in the Itcha, Ilgachuz, and Rainbow Mountains of West-Central BC. This habitat model was developed using telemetry from the Itcha-Ilgachuz, Rainbow, and Charlotte Alplands Herds. [Season] field should be used to split the data out into separate summer, winter alpine, and winter forest-dwelling habitat models. Model development is detailed in _Apps, C. D., T. A. Kinley, and J. A. Young. 2001. Multi-scale habitat modeling for woodland caribou in the Itcha, Ilgachuz, and Rainbow mountains of west-central British Columbia.Wildlife Section, Ministry of Water, Land and Air Protection, Williams Lake, British Columbia, Canada_. See also: https://catalogue.data.gov.bc.ca/dataset/caribou-habitat-model-for-the-western-cariboo-region-2017-. __Note: The 2017 habitat model covers a similar area, but does not supersede the 2001 habitat model.__