The Strait of Belle Isle connects the Labrador Shelf and Gulf of St. Lawrence. Few observations of currents in the Strait of Belle Isle exist despite its important contribution to the heat, salt, and mass budgets of the Gulf of St. Lawrence. This is because the deployment of instruments is complicated by the Strait’s remote location, its strong currents, and the presence of thick winter sea ice and icebergs. The present data set aims to provide a long-term time series of currents in the Strait of Belle Isle. Data were collected using a moored Teledyne RDI Workhorse 300 KHz acoustic Doppler current profiler (ADCP). The ADCP was mounted on a subsurface buoy anchored 5 m from the sea floor, in water approximately 70 m deep near the north shore of the Strait (56° 37.2 W, 51° 34.7 N). This instrument provides three-dimensional current profiles every 30 minutes at a vertical resolution of 4 m. Backscatter intensity is also collected at the same resolution. Raw data were processed using the Magtogoek software (https://github.com/iml-gddaiss/magtogoek), developed by the Department of Fisheries and Oceans Canada. Quality flags have been assigned to the data based on beam sidelobe contamination and required thresholds for extreme velocities, beam correlation and percentage of good four-beam transformations. The ancillary data used to apply this quality control are included in the data set. Reference : Shaw, J.-L., & Galbraith, P. S. (2023). Climatology of transport in the Strait of Belle Isle. Journal of Geophysical Research: Oceans, 128, e2022JC019084. https://doi.org/10.1029/2022JC019084

Polar cod (Boreogadus saida), Atlantic cod (Gadus morhua), and Greenland cod (Gadus macrocephalus) are prominent gadid species within the northwest Atlantic Ocean in terms of their ecological and socio-economic importance but it is unclear how climate-induced changes in ocean temperature may alter their distributions by the end of the century (2100). We used physiologically based species distribution models to predict how ocean warming will influence the availability of suitable habitat for early life-stages in these marine gadids. We applied CMIP5 ocean temperature projections to egg survival and juvenile growth models for Polar cod, Atlantic cod, and Greenland cod to create predicted suitability raster surfaces for these metrics across four climatology periods (1981–2005, 2026–2050, 2051–2075, 2076–2100). The analysis focused on the projected changes in temperature in ocean shelf areas where ocean depth is ≤400 m. We created an integrated habitat suitability index by combining the suitability surfaces for egg survival and growth potential to predict areas and periods where thermal conditions were suitable for both life stages. The resulting surfaces indicate that suitable thermal habitat for the juvenile life stages of all three species will shift poleward, but the magnitude of the shift and the overall area of thermally suitable habitat remaining will differ across species and life stages through time. Modelled layers are provided in NetCDF format by metric (egg survival, growth potential, habitat suitability). Data layers for Polar cod, Atlantic cod, and Greenland cod are included within each NetCDF file as variables across time. Note that in this study we refer to Gadus macrocephalus/ogac as Greenland cod since Gadus ogac is thought to be a junior synonym of Gadus macrocephalus (Carr et al., 1999). For more details on the methods and results for this analysis see Cote et al. (2021). References: Carr, S. M., Kivlichan, D. S., Pepin, P., & Crutcher, D. C. (1999). Molecular systematics of gadid fishes: implications for the biogeographic origins of Pacific species. Canadian Journal of Zoology, 77(1), 19–26. https://doi.org/10.1139/cjz-77-1-19 Cote, D., Konecny, C. A., Seiden, J., Hauser, T., Kristiansen, T., & Laurel, B. J. (2021). Forecasted Shifts in Thermal Habitat for Cod Species in the Northwest Atlantic and Eastern Canadian Arctic. Frontiers in Marine Science, 8(November), 1–15. https://doi.org/10.3389/fmars.2021.764072

The Operational Hydrodynamic Prediction System (OHPS) is a 2D hydrodynamic prediction system for the St. Lawrence River and fluvial estuary. It helps to better understand flows impacting the St. Lawrence ecosystem and serves as a decision-making tool for the integrated management of the St-Lawrence. Three components are integrated in OHPS system. The first one "steadysol" conducts daily steady-state flow analyses, 4 times per day at 00Z, 06Z, 12Z and 18Z, respectively, over a simulation domain extending from Montreal to Trois-Rivières. The second and third components, i.e. "analysis" and "forecast", provide continuous analyses and 48-hrs forecasts, respectively, for unsteady flows over an extended St. Lawrence domain of which the upstream boundaries locate in Carillon and Beauharnois while the downstream tidal boundary is near Saint-Joseph-de-la-Rive, respectively, 4 times a day at 00Z, 06Z, 12Z and 18Z. The system provides high-resolution outcomes for various parameters such as water levels, depth-averaged velocities and derived attributes, over the simulated domains. The products are available in the NetCDF format, which provides datasets. The published datasets of "steadysol" is over an irregular triangulated mesh, while the datasets of "analysis" and "forecast" are over a Polar Stereographic grid.

Environment and Climate Change Canada’s (ECCC) CMIP6 statistically downscaled agroclimatic indices are an updated version of the CMIP5 agroclimatic indices dataset making use of two different sets of downscaled scenarios created by the Pacific Climate Impacts Consortium (PCIC): 1. Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6), and 2. Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). To address the needs of different user groups in Canada, 49 indices, including agroclimatic indices, were proposed by the Canadian adaptation community through a series of consultations. Please see the definition list for the equations of each index. In 2025, PCIC expanded the CMIP6 agroclimatic indices, by adding CanDCS-M6, which includes SSP3-7.0 for most GCMs. Additionally, PCIC introduced 18 new indices to the previous 49. The 67 indices are available for both the CanDCS-U6 and CanDCS-M6. The range of impact-relevant climate indices available for download includes, indices representing counts of the number of days when temperature or precipitation exceeds (or is below) a threshold value; the episode length when a particular weather/climate condition occurs; and indices that accumulate temperature departures above or below a fixed threshold. The statistically downscaled climate indices are available for individual models and ensembles, historical simulations (1951-2014) and three emissions scenarios called “Shared Socioeconomic Pathways” (SSPs), SSP1-2.6, SSP2-4.5, and SSP5-8.5 (2015-2100), at a 10 x 10 km degree grid resolution. The CanDCS-M6 agroclimatic indices dataset also includes SSP3-7.0 results. Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impact assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with a finer spatial scale. Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

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.

Gridded monthly, seasonal and annual anomalies derived from daily total precipitation is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The anomalies are the percentage difference between the value for a given year or season and a baseline value (defined as the average over 1961-1990 as the reference period). The yearly and seasonal relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

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.

Statistically downscaled multi-model ensembles of total precipitation 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). Daily precipitation (mm/day) from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded precipitation dataset of Canada (ANUSPLIN) was used as the downscaling target. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled total precipitation (mm/day) 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.

Statistically downscaled multi-model ensembles of minimum 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). Daily minimum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded minimum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled minimum 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.

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.