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

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in snow depth based on an ensemble of twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Projected change in snow depth 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 snow depth 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 snow depth (%) 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 Indicator of Risk of Water Contamination by nitrogen (IROWC-N) estimates the risk of water contamination by nitrogen leaching on agricultural lands in Canada from 1981 to 2021. High nitrate level ( > 10 mg N/L) in drinking water may lead to various health impacts including methemoglobinemia (blue baby syndrome) and non-Hodgkin’s lymphoma. High nitrate levels in surface waters can also contribute to algal growth and eutrophication.

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.

Daily climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground. Only a subset of the total stations is shown due to size limitations. The criteria for station selection are listed as below. The priorities for inclusion are as follows: (1) Station is currently operational, (2) Stations with long periods of record, (3) Stations that are co-located with the categories above and supplement the period of record.

Wildlife habitat capacity is the extent and quality of habitat that can support a diversity of species. When we convert wilderness to agricultural land we lose a great deal of wildlife habitat capacity. However, we can manage agricultural land to regain some of this capacity. Agricultural land includes not only fields for food production but also other types of land cover. Wooded areas, wetlands, shoreline areas and natural pastures on agricultural land are important habitats for wildlife. The indicator shows how well wildlife habitat is maintained for feeding and reproduction while producing the food we need. Products in this data series present results for predefined areas as defined by the Soil Landscapes of Canada (SLC v.3.2) data series, uniquely identified by SOIL_LANDSCAPE_ID values. Data is provided for the following years: 2000, 2005, 2010, 2015 and 2020. The annual results respect provincial boundaries which can be of use when analyzing results per province.

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. The number of large fires refers to the annual number of fires greater than 200 hectares (ha) that occur per units of 100,000 ha. It was calculated per Homogeneous Fire Regime (HFR) zones. These HFR 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: the number of large fires (>200 ha) across Canada for a reference period (1981-2010). 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.

The Global Ensemble Wave Prediction System (GEWPS) uses the third generation spectral wave model WaveWatch III® (WW3) to arrive at probabilistic predictions of wave elements from the current day out to 16 days into the future. The probabilistic predictions are based on 20 ensemble members and a control member that are forced by the 10 meters winds from the Global Ensemble Prediction System (GEPS). The GEPS forecast is a coupled atmosphere-ice-ocean model, its sea ice forecast is used by the GEWPS to dampen or suppress wave growth in areas covered respectively with 25% to 75% and more than 75% ice. WW3 (WAVEWATCH III® Development Group, WW3DG 2019) is a third generation spectral wave prediction model that solves the evolution of the energy balance equation for the 2-D wave energy spectrum without any prior assumptions on the shape of the spectrum. The WW3 model has been implemented by a growing number of national operational forecasting centres over the last several years.