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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).

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.

The Blended Index (BI) is a model which employs multiple potential indicators of drought and excess moisture, such as the Palmer drought index, rolling precipitation amounts and soil moisture, and combines them into a weighted, normalized value between 0 and 100. The inputs and weights used in this model are subject to change periodically as it is optimized to best represent extent, duration and severity of impactful weather conditions. The blended index is deployed as two variations; short term (st) focusing on 1 to 3 months, and long term (lt) focusing on 6 months to 5 years.

The "Agri-Environmental Indicator Risk of Soil Salinization" dataset estimates the risk of accumulation of soluble salts on agricultural lands in the Canadian Prairies. At high levels, the accumulation of these salts in soil and groundwater in the landscape can inhibit the growth of many plant species.

The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and 1 degree and for a few selected vertical levels. In addition, forecast probabilities for below, near, and above normal temperature and precipitation are available at both resolutions. Predictions and corresponding hindcast are made available monthly.

Precipitation (moisture availability) establishes the economic yield potential and product quality of field crops. Both dry and wet precipitation extremes have the ability to inhibit proper crop growth. The maximum daily precipitation index covers the risk of excessive precipitation in the short term, while the other indices pertain to longer term moisture availability. Agriculture is an important primary production sector in Canada. Agricultural production, profitability, sustainability and food security depend on many agrometeorological factors. Extreme weather events in Canada, such as drought, floods, heat waves, frosts and high intensity storms, have the ability to significantly impact field crop production. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.

Frost free days are the number of days in the forecast period with a minimum temperature above the frost temperature; the temperature at which frost damage occurs. This temperature is 0°C for warm season crops (ffd_warm). Week 1 and week 2 forecasted index is available daily from April 1 to October 31. Week 3 and week 4 forecasted index is available weekly (Thursday) from April 1 to October 31. Warm season crops require a relatively warm temperature condition. Typical examples include bean, soybean, corn and sweet potato. They normally grow during the summer season and early fall, then ripen in late fall in southern Canada only. Other agricultural regions in Canada do not always experience sufficiently long growing seasons for these plants to achieve maturity. The optimum temperature for such crops is 30°C. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily and weekly basis.

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.

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.