This dataset consists of three simulations from the Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) which is a configuration of the Nucleus for European Modelling of the Ocean (NEMO) V3.6. The historical simulation is an estimate of the 1986-2005 mean climate. The future simulations project the 2046-2065 mean climate for representative concentration pathways (RCP) 4.5 (moderate mitigation scenario) and 8.5 (no mitigation scenario). Each simulation is forced by a climatology of atmospheric forcing fields calculated over these 20 year periods and the winds are augmented with high frequency variability, which introduces a small amount of interannual variability. Model outputs are averaged over 3 successive years of simulation (the last 3, following an equilibration period); standard deviation among the 3 years is available upon request. For each simulation, the dataset includes the air-sea carbon dioxide flux, monthly 3D fields for potential temperature, salinity, potential density, total alkalinity, dissolved inorganic carbon, nitrate, oxygen, pH, total chlorophyll, aragonite saturation state, total primary production, and monthly maximum and minimum values for oxygen, pH, and potential temperature. The data includes 50 vertical levels at a 1/36 degree spatial resolution and a mask is provided that indicates regions where these data should be used cautiously or not at all. For a more detailed description please refer to Holdsworth et al. 2021. The data available here are the outputs of NEP36-CanOE_historical; an estimate of the 1986-2005 climate. For inquiries about this dataset, if Amber M. Holdsworth is unavailable, please contact James R. Christian (Principal Investigator) at: Email: james.christian@dfo-mpo.gc.ca Phone: 1 (250) 686-6081 Fisheries and Oceans Canada Institute of Ocean Sciences 9860 West Saanich Road P.O. Box 6000 Sidney, BC V8L 4B2 Canada

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 the new set of downscaled scenarios (Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6)) created by the Pacific Climate Impacts Consortium (PCIC). 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. 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 historical simulations (1951-2014) and three new 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. 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.

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). The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and the 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 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.

Multi-model ensembles for a suite of ocean variables based on projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) are available for 1900-2100 on a common 1x1 degree global grid. All ocean variables currently available contain data for the top level (sea surface) of the ocean. 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 CCDS are multi-model ensembles as well as individual model simulations. Multi-model output is available for historical simulations and 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 eight percentiles (maximum, minimum, mean, 5th, 25th, 50th (median), 75th, and 95th) of the CMIP6 ensemble distribution. Datasets are available as both actual and anomaly values. Anomalies of projected changes are expressed with respect to a historical reference period of 1995-2014. 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. For more information on the CMIP6 multi-model ocean datasets, see the technical documentation resource.

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

WCPS-coupled forecast is the component in the Water Cycle Prediction System (WCPS) that provides the coupled atmosphere-ocean-sea ice forecasts at a 1km resolution (0.008 x 0.008 degree) over the Great Lakes, St. Lawrence River and the Gulf of St. Lawrence. It launches 4 times a day at 00, 06, 12, and 18 UTC and produces 84 hours forecast, based on the atmospheric model GEM, coupled with the ocean-ice model NEMO-CICE. The products from WCPS-coupled forecasts are (1) GEM : surface air temperatures, surface wind velocities, and surface runoff (2) NEMO-CICE : variety of lake/ocean sea ice variables, for example, lake levels and temperatures. They are designed to help forecasters issuing bulletins and warnings in ice-infestested waters for navigation, water level alert, emergency response, Search and Rescue, and CIS Sea Ice forecast.