A Virtual Climate station is the result of threading together climate data from proximate current and historical stations to construct a long term threaded data set. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. The length of the time series of virtual stations is often greater than 100 years. A Virtual Climate station is always named for an “Area” rather than a point, e.g. Winnipeg Area, to indicate that the data are drawn from that area (within a 20km radius from the urban center) rather than a single precise location.

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.

Point locations of water and weather monitoring stations used by the [Surface Water Monitoring Centre](http://www.ontario.ca/page/surface-water-monitoring-centre) to assess flood and drought conditions across Ontario. Monitoring station types include: * streamflow gauge stations * Environment and Climate Change Canada climate stations * Ministry of Transportation road weather stations * Ministry of Natural Resources (MNR) fire weather stations * MNR snow network stations (wildlife) * MNR snow survey stations (weather) * Ontario Power Generation snow survey stations (weather)

The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

A cross-country summary of the averages and extremes for the month, including precipitation totals, max-min temperatures, and degree days. This data is available from stations that produce daily data.

The daily climate records database, also known as Long Term Climate Extremes (LTCE), was developed to address the fragmentation of climate information due to station changes (opening, closing, relocation, etc.) over time. For approximately 750 locations in Canada, "virtual" climate stations have been developed by joining (threading) climate data for an urban location, from nearby stations to make long-term records. Each long-term record consists of the extremes (record values) of daily maximum/minimum temperatures, total precipitation and snowfall for each day of the year. Many of the longest data sets of extremes date as far back as the 1800s. This data provides the daily extremes of record for Snowfall for each day of the year. Daily elements include: Greatest Snowfall.

The HOT2000 software contains monthly and annual climate data for 403 locations in Canada. Boundary lines for HOT2000 climate zones were defined through spatial interpolation of the annual Celsius heating degree-days for each weather station. In a number of instances, the positions of boundary lines may not be representative of the local climate conditions due to lack of appropriate climate data. Each HOT2000 climate zone contains one weather station to be used for all locations within the zone. Climate data represent 20-year averaged data from 1998 to 2017 for locations south of 58° latitude and 13-year averaged data from 2005 to 2017 for locations north of 58° latitude. Note that Whistler, BC uses 13 years of data.The following information is available in the climate map:o Location: the name of the weather station.o Region: the provincial or territorial location of the weather station.o Latitude: measured in degrees north of the equator.o Annual heating degree-days using a base of 18 °C.o Design heating dry bulb temperature (°C): the 2.5% January design temperature used to calculate the design heat loss for the house.o Design cooling dry bulb temperature (°C): the 2.5% July design temperature used to calculate the design cooling load for the house.o Design cooling wet bulb temperature (°C): the 2.5% July design temperature used to calculate the design cooling load for the house.The climate map is intended to be used by all users of the HOT2000 software under the EnerGuide Rating System, including energy advisors, service organizations, regulatory agencies, builders, utilities, and all levels of government.The weather locations and climate data are based on Environment and Climate Change Canada data, specifically the Canadian Weather Energy and Engineering Datasets (CWEEDS).

The impact of climatic variability on the environment is of great importance to the agricultural sector in Canada. Monitoring the impacts on water supplies, soil degradation and agricultural production is essential to the preparedness of the region in dealing with possible drought and other agroclimate risks. Derived normal climate data represent 30-year averages (1961-1990, 1971-2000, 1981-2010, 1991-2020) of climate conditions observed at a particular location. The derived normal climate data represents 30-year averages or “normals” for precipitation, temperature, growing degree days, crop heat units, frost, and dry spells. These normal trends are key to understanding agroclimate risks in Canada. These normal can be used as a baseline to compare against current conditions, and are particularly useful for monitoring drought risk.

This dataset contains the surface temperature and salinity data of the enlarged coastal thermograph network of the St. Lawrence river, estuary and gulf system. It includes data from the Canadian Hydrographic Service water level network (SINECO), the Department of Fisheries and Oceans (DFO)-Quebec long-termed thermograph monitoring program network and the oceanographic buoy network.Each station is linked with a .png file showing the temperature and salinity time series and with a .csv file containing the surface temperature and salinity data themselves (columns : Station,Latitude,Longitude,Date(UTC),Depth/Profondeur(m),Temperature/Température(ºC),Salinity/Salinité(psu)).Supplemental InformationA detailed description of the networks (SINECO, oceanographic buoys and the DFO-Quebec thermograph monitoring program) is available at the St. Lawrence Global Observatory (SLGO) portal :SINECO : https://ogsl.ca/en/tide-gauges-dfo-chs/Oceanographic buoys : https://ogsl.ca/en/marine-conditions-buoys-dfo/Thermographs: https://ogsl.ca/en/marine-conditions-thermographs-dfo/Technical Reports related to the Thermograph Network (the last one is also available at the same hypertext link mentionned above) :Pettigrew, B., Gilbert, D. and Desmarais R. 2016. Thermograph network in the Gulf of St. Lawrence. Can. Tech. Rep. Hydrogr. Ocean Sci. 311: vi + 77 p.Pettigrew, B., Gilbert, D. and Desmarais R. 2017. Thermograph network in the Gulf of St. Lawrence: 2014-2016 update. Can. Tech. Rep. Hydrogr. Ocean Sci. 317: vii + 54 p.

This dataset is used for national and coarse-scale provincial reporting such as analyses of climate, demographics and watersheds.