Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset). Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios. 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 impacts 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 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.

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.

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.

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in 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. Projected relative change in total precipitation is with respect to the reference period of 1986-2005 and expressed as a percentage (%). Seasonal and annual averages of projected precipitation change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of projected precipitation change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled total precipitation (%) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of downscaled CMIP5 climate models is provided. 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.

HREPA is part of the NSRPS (National Surface and River Prediction System) experimental system dependent on two other systems. It uses surface station observations and radar QPEs pre-processed by HRDPA and disturbed trial fields generated by the Canadian Land Data Assimilation System (CaLDAS). HREPA produces four precipitation analyses per day on 6-hour accumulations valid at synoptic times (00, 06, 12, and 18 UTC). Each analysis set contains 24 members plus the control member. A quality index (confidence index) is also available on the same grid as the precipitation fields. Finally, two percentiles, 25th and 75th, estimated on these sets are also provided for each synoptic hour. Currently, there is only a high-resolution version of the system, whose domain covers Canada and the northern United States with a horizontal resolution of about 2.5km.

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.

Seasonal and annual trends of mean surface air temperature change (degrees Celsius) for 1948-2016 based on Canadian gridded data (CANGRD) are available at a 50km resolution across Canada. Temperature trends represent the departure from a mean reference period (1961-1990). CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Homogenized climate data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

Gridded monthly, seasonal and annual mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) is available at a 50km resolution across Canada. The Canadian gridded data (CANGRD) are interpolated from homogenized temperature (i.e., AHCCD datasets). Homogenized temperatures 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 difference between the temperature 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 temperature anomalies were computed for the years 1948 to 2017. The data will continue to be updated every year.

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.

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