Gögnin innhalda staðsetningu veðurstöðva sem eru í eigu Vegagerðarinnar og staðsettar eru við þjóðvegi en einnig veðurstöðvar í eigu Veðurstofunnar og annarra.

The probability of effective growing season degree days above 100 for cool season crops. This condition must be maintained for at least 5 consecutive days in order for EGDD to be accumulated (egdd_cool_100prob). Week 1 and week 2 forecasted probability is available daily from April 1 to October 31. Week 3 and week 4 forecasted probability is available weekly (Thursday) from April 1 to October 31. Cumulative heat-energy satisfies the essential requirement of field crop growth and development towards a high yield and good quality of agricultural crop products. 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.

The Global Ice-Ocean Prediction System (GIOPS) produces global sea ice and ocean analyses and 10 day forecasts daily. This product contains time-mean sea ice and ocean forecast fields interpolated to two grids. One of the grids is a 0.2° resolution regular latitude-longitude grid covering the global ocean (north of 80° S). The other grid is in north-polar stereographic projection with a 5-km spacing at the standard parallel 60° N and covers the Arctic Ocean and the neighbouring sub-polar seas. Data is available for 50 depths. The data files are in netCDF format and comply with the Climate and Forecast Conventions.

Multi-model ensembles for a suite of variables based on projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) are available for 1850-2100 on a common 1x1 degree global grid. 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 Canadian Climate Data and Scenarios (CCDS) are four types of products based on the CMIP6 multi-model ensembles: time series datasets and plots, maps and associated datasets, tabular datasets, and global gridded datasets. Monthly, seasonal, and annual ensembles are available for up to 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 five percentiles (5th, 25th, 50th (median), 75th, and 95th) of the CMIP6 ensemble distribution. 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. The majority of products show projected changes expressed as anomalies according to a historical reference period of 1995-2014. The products provided include global, national, and provincial/territorial datasets and graphics. For more information on the CMIP6 multi-model ensembles, see the technical documentation resource.

The fire regime describes the patterns of fire seasonality, frequency, size, spatial continuity, intensity, type (e.g., crown or surface fire) and severity in a particular area or ecosystem. Annual area burned is the average surface area burned annually in Canada by large fires (greater than 200 hectares (ha)). Changes in annual area burned were estimated using Homogeneous Fire Regime (HFR) zones. These zones represent areas where the fire regime is similar over a broad spatial scale (Boulanger et al. 2014). Such zonation is useful in identifying areas with unusual fire regimes that would have been overlooked if fires had been aggregated according to administrative and/or ecological classifications. Fire data comes from the Canadian National Fire Database covering 1959–1999 (for HFR zones building) and 1959-1995 (for model building). Multivariate Adaptive Regression Splines (MARS) modeling was used to relate monthly fire regime attributes with monthly climatic/fire-weather in each HFR zone. Future climatic data were simulated using the Canadian Earth System Model version 2 (CanESM2) and downscaled at a 10 Km resolution using ANUSPLIN for two different Representative Concentration Pathways (RCP). RCPs are different greenhouse gas concentration trajectories adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report. RCP 2.6 (referred to as rapid emissions reductions) assumes that greenhouse gas concentrations peak between 2010-2020, with emissions declining thereafter. In the RCP 8.5 scenario (referred to as continued emissions increases) greenhouse gas concentrations continue to rise throughout the 21st century. Provided layer: projected annual area burned by large fires (>200 ha) across Canada for the medium-term (2041-2070) under the RCP 8.5 (continued emissions increases). Reference: Boulanger, Y., Gauthier, S., et al. 2014. A refinement of models projecting future Canadian fire regimes using homogeneous fire regime zones. Canadian Journal of Forest Research 44, 365–376.

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.

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.

First Fall Frost (0 °C) is defined as the average day, during the second half of the year, of the first occurrence of a minimum temperature at or below 0 °C. These values are calculated across Canada in 10x10 km cells.

The Regional Ensemble Prediction System (REPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the current day out to 3 days into the future. The probabilistic predictions are based on 20 ensemble members that are perturbed through their initial and boundary conditions as well as physical tendencies. A control member that is not perturbed is also available. Atmospheric elements include temperature, precipitation, cloud cover, wind speed and direction, humidity and others. This product contains raw numerical results of these calculations. Geographical coverage includes Canada and the United States. Data is available at a horizontal resolution of 10 km. Data is available on ten vertical levels. Predictions are performed four times a day.

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.