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 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.

Argo is a key component of the Global Ocean Observing System (GOOS) with an array of about 4,000 autonomous instruments reporting on ocean conditions. These floats collect data on ocean temperature and salinity, and in some cases, additional properties that characterize the ocean’s biological and chemical processes. Established in 1999, Argo represents an international collaboration involving contribution from more than 30 nations. Data from Argo floats are made publicly available within 24 hours of collection time, for free. The data provide valuable information on changes to the Earth's climate and hydrological cycle. They are used for a variety of purposes, such as assessing climate change, improving weather forecasts and developing ocean models. Argo Canada, led by Fisheries and Oceans Canada, has been a key contributor to the International Argo Program since its inception in 2001 . The program has been supported by contributions from Department of Environment and Climate Change Canada, Department of National Defense, Dalhousie University, University of Victoria and Ocean Networks Canada.

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in sea ice thickness, based on an ensemble of twenty-six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Projected change in sea ice thickness is with respect to the reference period of 1986-2005 and expressed as a percentage (%). The 5th, 25th, 50th, 75th and 95th percentiles of the ensemble of sea ice thickness change are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in sea ice thickness (%) 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 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.

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.

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 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.

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.

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.

Multi-model ensembles of sea ice concentration 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 sea ice concentration as represented as the percentage (%) of grid cell area, 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.