Environment and Climate Change Canada provides global sea-ice analysis fields produced by its operational Global Deterministic Prediction System (GDPS) and interpolated to a rotated latitude-longitude grid with 0.09 x 0.09 degree resolution. The data files are in NetCDF format (NetCDF-4 classic model) and comply with the Climate and Forecast Conventions. The Global Sea-Ice Analysis System is an analysis system based on 3D-Var assimilation covering all waters (ocean and lakes) at a 10km horizontal resolution on a YIN-YANG grid and using a 6 hours persistence forecast for the background state. This analysis assimilates 4 times a day satellite remote sensing data and Canadian Ice Service ice charts.

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.

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 projected change (also known as anomalies) in mean temperature (°C) 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. Projected change in mean temperature (°C) is with respect to the reference period of 1986-2005. Seasonal and annual averages of projected mean temperature change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of mean temperature 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 minimum mean temperature (°C) 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.

Statistically downscaled multi-model ensembles of maximum 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 maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded maximum 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 maximum 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.

The Regional Ensemble storm Surge Prediction System (RESPS) produces storm surge forecasts using the DalCoast ocean model. DalCoast (Bernier and Thompson 2015) is a storm surge forecast system for the east coast of Canada based on the depth-integrated, barotropic and linearized form of the Princeton Ocean Model. The model is forced by the 10 meters winds and sea level pressure from the Global Ensemble Prediction System (GEPS).

Description: 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. Methods: This study uses a multi-stage downscaling approach to dynamically downscale global climate projections at a 1/36° (1.5 − 2.25 km) resolution. We chose to use the second-generation Canadian Earth System model (CanESM2) because high-resolution downscaled projections of the atmosphere over the region of interest are available from the Canadian Regional Climate Model version 4 (CanRCM4). We used anomalies from CanESM2 with a resolution of about 1° at the open boundaries, and the regional atmospheric model, CanRCM4 (Scinocca et al., 2016) for the surface boundary conditions. CanRCM4 is an atmosphere only model with a 0.22° resolution and was used to downscale climate projections from CanESM2 over North America and its adjacent oceans. The model used is computationally expensive. This is due to the relatively high number of points in the domain (715 × 1,021 × 50) and the relatively complex biogeochemical model (19 tracers). Therefore, rather than carrying out interannual simulations for the historical and future periods, we implemented a new method that uses atmospheric climatologies with augmented winds to force the ocean. We show that augmenting the winds with hourly anomalies allows for a more realistic representation of the surface freshwater distribution than using the climatologies alone. Section 2.1 describes the ocean model that is used to estimate the historical climate and project the ocean state under future climate scenarios. The time periods are somewhat arbitrary; 1986–2005 was chosen because the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations end in 2005 as no community-accepted estimates of emissions were available beyond that date (Taylor et al., 2009); 2046–2065 was chosen to be far enough in the future that changes in 20 year mean fields are unambiguously due to changing GHG forcing (as opposed to model internal variability) (e.g., Christian, 2014), but near enough to be considered relevant for management purposes. While it is true that 30 years rather than 20 is the canonical value for averaging over natural variability, in practice the difference between a 20 and a 30 year mean is small (e.g., if we average successive periods of an unforced control run, the variance among 20 year means will be only slightly larger than for 30 year means). Also, there is concern that longer averaging periods are inappropriate in a non-stationary climate (Livezey et al., 2007; Arguez and Vose, 2011). We chose 20 year periods because they are adequate to give a mean annual cycle with little influence from natural variability, while minimizing aliasing of the secular trend into the means. As the midpoints of the two time periods are separated by 60 years, the contribution of natural variability to the differences between the historical and future simulations is negligible e.g., (Hawkins and Sutton, 2009; Frölicher et al., 2016). Section 2.2 describes how climatologies derived from observations were used for the initialization and open boundary conditions for the historical simulations and pseudo-climatologies were used for the future scenarios. The limited availability of observations means that the years used for these climatologies differs somewhat from the historical and future periods. Section 2.3 details the atmospheric forcing fields and the method that we developed to generate winds with realistic high-frequency variability while preserving the daily climatological means from the CanRCM4 data. Section 2.4 shows the equilibration of key modeled variables to the forcing conditions Data Sources: Model output Uncertainties: The historical climatologies were evaluated using observational climatologies generated from stations with a long time series of data over the time period including CTDs, nutrient profiles, lighthouse and satellite SST, and buoy data. The model is able to represent the historical conditions with an acceptable bias. The resolution of this model is insufficient to represent the narrow straits and channels of this region so the dataset includes a cautionary mask to exclude these regions.

Multi-model ensembles of snow depth based on projections from twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of snow depth (m) are available for the historical time period, 1900-2005, 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 relative total precipitation change (%) for 1948-2012 based on Canadian gridded data (CANGRD) are available, at a 50km resolution across Canada. The relative trends reflect the percent change in total precipitation over a period from the baseline value (defined as the average over 1961-1990 as the reference period). CANGRD data are interpolated from adjusted and homogenized climate station data (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.

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in maximum temperature (°C) 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 maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded maximum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. Projected change in maximum temperature (°C) is with respect to the reference period of 1986-2005. Seasonal and annual averages of projected maximum temperature change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of maximum temperature 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 maximum temperature (°C) 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.