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

Drought is a deficiency in precipitation over an extended period, usually a season or more, resulting in a water shortage that has adverse impacts on vegetation, animals and/or people. The Climate Moisture Index (CMI) was calculated as the difference between annual precipitation and potential evapotranspiration (PET) – the potential loss of water vapour from a landscape covered by vegetation. Positive CMI values indicate wet or moist conditions and show that precipitation is sufficient to sustain a closed-canopy forest. Negative CMI values indicate dry conditions that, at best, can support discontinuous parkland-type forests. The CMI is well suited to evaluating moisture conditions in dry regions such as the Prairie Provinces and has been used for other ecological studies. Mean annual potential evapotranspiration (PET) was estimated for 30-year periods using the modified Penman-Monteith formulation of Hogg (1997), based on monthly 10-km gridded temperature data. Data shown on maps are 30-year averages. Historical values of CMI (1981-2010) were created by averaging annual CMI calculated from interpolated monthly temperature and precipitation data produced from climate station records. Future values of CMI were projected from downscaled monthly values of temperature and precipitation simulated using the Canadian Earth System Model version 2 (CanESM2) for multiple RCP radiative forcing scenarios. Provided layer: Climate moisture index (CMI) - Future projections using RCP 8.5 for 2011-2040. Reference: Hogg, E.H. 1997. Temporal scaling of moisture and the forest-grassland boundary in western Canada. Agricultural and Forest Meteorology 84,115–122.

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.

Snow survey administrative basin areas, which are components of the BC snow survey network. Basin codes are used as basis of snow survey station names, and for some reporting purposes.

Frost Day Count (-2 °C) is defined as the count of the number of days in a calendar month where the minimum daily temperature for the climate day was at or below -2 °C. These values are calculated across Canada in 10x10 km cells.

The Coastal Ice Ocean Prediction System (CIOPS) provides a 48 hour ocean and ice forecast over different domains (East, West, Salish Sea) four times a day at 1/36° resolution. A pseudo-analysis component is forced at the ocean boundaries by the Regional Ice Ocean Prediction System (RIOPS) forecasts and spectrally nudged to the RIOPS solution in the deep ocean. Fields from the pseudo-analysis are used to initialize the 00Z forecast, whilst the 06, 12 and 18Z forecasts use a restart files saved at hour 6 from the previous forecast. The atmospheric fluxes for both the pseudo-analysis and forecast components are provided by the High Resolution Deterministic Prediction System (HRDPS) blended both spatially and temporally with either the Global Deterministic Prediction System (GDPS) (for CIOPS-East) or an uncoupled component of the Global Deterministic Prediction System (GDPS) at 10km horizontal resolution (for CIOPS-West) for areas not covered by the HRDPS.

This file contains output from ensemble. Contains vertical profiles and cross sections.

The monthly mean is the average of daily mean values for a given month.

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 number of days during the forecast period with an average wind speed greater than 30 km/h (nswd_prob). Week 1 and week 2 forecasted probability is available daily from September 1 to August 31. Week 3 and week 4 forecasted probability is available weekly (Thursday) from September 1 to August 31. Winds can significantly influence crop growth and yield mainly due to mechanical damage of plant vegetative and reproductive organs, an imbalance of plant-soil-atmosphere water relationships, and pest and disease distributions in agricultural fields. The maximum wind speed and the number of strong wind days over the forecast period represent short term and extended strong wind events respectively. 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.