CANGRD is a set of Canadian gridded annual, seasonal, and monthly temperature and precipitation anomalies, which were interpolated from stations in the Adjusted and Homogenized Canadian Climate Data (AHCCD); it is used to produce the Climate Trends and Variations Bulletin (CTVB).

Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

This web site provides adjusted and homogenized climate data for many climatological stations in Canada. These data were created for use in climate research including climate change studies. They incorporate a number of adjustments applied to the original station data to address shifts due to changes in instruments and in observing procedures. Sometimes the observations from several stations were joined to generate a long time series. Users are strongly cautioned to determine the data suitability for their application. They should also be aware that ongoing research on adjustment techniques may result in future revisions of the datasets. The datasets are updated annually with the most recent data. The adjusted and homogenized data are provided for four climate elements: Surface air temperature, Precipitation, Surface pressure, and Surface wind speed. References Mekis, É. and L.A. Vincent, 2011: An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmosphere-Ocean, 49(2), 163-177. Vincent, L. A., M. M. Hartwell, and X. L. Wang, 2020: A third generation of homogenized temperature for trend analysis and monitoring changes in Canada’s climate. Atmosphere-Ocean., 58:3, 173-191, doi:10.1080/07055900.2020.1765728. Wan, H., X. L. Wang, V. R. Swail, 2010: Homogenization and trend analysis of Canadian near-surface wind speeds. Journal of Climate, 23, 1209-1225. Wan, H., X. L. Wang, V. R. Swail, 2007: A quality assurance system for Canadian hourly pressure data. J. Appl. Meteor. Climatol., 46, 1804-1817.

Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

The Homogenized Surface Wind Speed data consist of monthly, seasonal and annual means of hourly wind speed (kilometres per hour) at standard 10 metre level for 156 locations in Canada. Homogenized climate data incorporate adjustments (derived from statistical procedures) to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The time periods of the data vary by location, with the oldest data available from 1953 at some stations to the most recent update in 2014. Data availability over most of the Canadian Arctic is restricted to 1953 to present. The data will continue to be updated every few years (as time permits).

Climate change in the future will continue to bring about unprecedented climate and climate extremes, and buildings and infrastructure will be exposed to such conditions. To ensure that new and existing buildings deliver satisfactory performance over their design lives, their performance under current and future projected climates needs to be assessed by undertaking building simulations. Here, climate data important for building simulations has been prepared for 564 Canadian locations by bias correcting the Canadian Regional Climate Model version 4 (CanRCM4) large ensemble (LE) simulations with reference to observations. Technical validation results show that bias-correction effectively reduces the bias associated with CanRCM4-LE simulations in terms of their marginal distributions, and inter-relationship between climate variables. As a consequence of global warming, the mean global solar irradiance, averaged across all locations, is projected to decrease by 1-10 kJ/m2, annual rainfall is expected to increase by 13-73 mm, mean temperature is expected to increase by 1-5ºC, atmospheric pressure is expected to increase by 7-64 Pa, and total number of days in a year with snow are expected to decrease by 2-22 days under a global warming of 0.5-3.5 ºC. The climate files generated are for a historical time-period (1991-2021) and future time-periods commensurate with 0.5 ºC, 1.0 ºC, 1.5 ºC, 2.0 ºC, 2.5 ºC, 3.0 ºC, and 3.5 ºC global warming from the historical time-period.

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.

This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.

The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.

The historical and future global warming thresholds and their corresponding time periods are as follows:

Global Warming Scenario

Time Period

Historical

1991-2021

Global Warming 0.5ºC

2003-2033

Global Warming 1.0ºC

2014-2044

Global Warming 1.5ºC

2024-2054

Global Warming 2.0ºC

2034-2064

Global Warming 2.5ºC

2042-2072

Global Warming 3.0ºC

2051-2081

Global Warming 3.5ºC

2064-2094

The following variables are included in the files:

Variable Description

RUN Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data

YEAR Year associated with the record

MONTH Month associated with the record

DAY Day of the month associated with the record

HOUR Hour associated with the record

YDAY Day of the year associated with the record

DRI_kJPerM2 Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

DHI_kJperM2 Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

DNI_kJperM2 Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

GHI_kJperM2 Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

TCC_Percent Instantaneous total cloud cover at the HOUR in % (range: 0-100)

RAIN_Mm Total rainfall in mm (total from previous HOUR to the HOUR indicated)

WDIR_ClockwiseDegFromNorth Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)

WSP_MPerSec Instantaneous wind speed at the HOUR in meters/sec

RHUM_Percent Instantaneous relative humidity at the HOUR in %

TEMP_K Instantaneous temperature at the HOUR in Kelvin

ATMPR_Pa Instantaneous atmospheric pressure at the HOUR in Pascal

SnowC_Yes1No0 Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)

SNWD_Cm Instantaneous snow depth at the HOUR in cm

This dataset has been compiled from public sources. The dataset consists of daily temperatures and precipitation from 13 Canadian centres. Precipitation is either rain or snow (likely snow in winter months). In 1940, there is daily data for seven out of the 13 centres, but by 1960 there is daily data from all 13 centres, with the occasional missing value.

Few of Canada’s weather stations have been operating continuously, so we did need to patch together the data. Our source data is from https://climate-change.canada.ca/climate-data/#/daily-climate-data and here are the weather stations that we queried: CALGARY INTL A CALGARY INT'L A EDMONTON INTL A EDMONTON INT'L A HALIFAX STANFIELD INT'L A HALIFAX STANFIELD INT'L A MONCTON A MONCTON A MONTREAL/PIERRE ELLIOTT TRUDEAU INTL MONTREAL/PIERRE ELLIOTT TRUDEAU INTL A OTTAWA INTL A OTTAWA MACDONALD-CARTIER INT'L A QUEBEC/JEAN LESAGE INTL QUEBEC/JEAN LESAGE INTL A SASKATOON DIEFENBAKER INT'L A SASKATOON INTL A ST JOHN'S A ST JOHNS WEST CLIMATE TORONTO INTL A TORONTO LESTER B. PEARSON INT'L A VANCOUVER INTL A VANCOUVER INT'L A WHITEHORSE A WHITEHORSE A WINNIPEG RICHARDSON INT'L A WINNIPEG THE FORKS

Suggested uses: The data is suitable for time series forecasting. At Penny Analytics, we are using this dataset to demonstrate outlier detection (anomaly detection) in multiple time series and here is the relevant blogpost: https://pennyanalytics.com/2020/01/28/climate-change-canadian-weather-anomalies-are-now-warmer-and-wetter/

Hourly historical climate data from Environment Canada collected from the weather stations across Canada.

This report is a product of Northern Climate Data Working Group, which brings together representatives from government, universities and professional organizations in climate data.

The Ontario in-filled climate data collection includes information from 339 monitoring stations maintained by the Meteorological Service of Canada. Historical climate data commonly has missing hourly and daily records due to equipment malfunctions, temporary site maintenance or other reasons. “In-filling” is a technical process that draws on data from nearby stations to fill in these missing records. This collection contains fully in-filled precipitation and temperature records for Ontario from 1950 to 2005. It is organized into eight separate databases in Microsoft Access format. One of the databases contains daily in-filled climate records. The remaining seven databases contain hourly in-filled climate records divided into regions for manageability.

Climate Data Products at Environment Canada comprise of four different datasets: Almanac Averages and Extremes, Monthly Climate Summaries, Canadian Climate Normals, and Canadian Historical Weather Radar. Almanac Averages and Extremes provides average and extreme temperature and precipitation values for a particular station over its entire period of record. Monthly Climate Summaries contains values of various climatic parameters, including monthly averages and extremes of temperature, precipitation amounts, degree days, sunshine hours, days without precipitation, etc. Canadian Climate Normals are used to summarize or describe the average climatic conditions of a particular location. Data is available for stations with at least 15 years of data between the periods of 1961-1990, 1971-2000 and 1981-2010. Canadian Historical Weather Radar compirses of historical images from the radar network providing a national overview of where percipitation is occuring.

On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the climate data described in the Atlas publications were interpolated to the grid-point locations defined by the original 3-decimal place latitude and longitude values. Interpolating the data to the 6-decimal place latitude and longitude values would in many cases not result in changes to the reported values and for other grid points the changes would be small and insignificant. Similarly, if the digitized Little (1971, 1976, 1977) taxa distribution maps were regridded using the 6-decimal place latitude and longitude values, the changes to the gridded distributions would be minor, with a small number of grid points along the edge of a taxa's digitized distribution potentially changing value from taxa "present" to taxa "absent" (or vice versa). These changes should be considered within the spatial margin of error for the taxa distributions, which are based on hand-drawn maps with the distributions evidently generalized, or represented by a small, filled circle, and these distributions were subsequently hand digitized. Users wanting to use data that exactly match the data in the Atlas volumes should use the 3-decimal place latitude and longitude data provided in the .csv files in this data release to represent the center point of each grid cell. Users for whom an offset of up to 144.2 m from the original grid-point location is acceptable (e.g., users investigating continental-scale questions) or who want to easily visualize the data may want to use the data associated with the 6-decimal place latitude and longitude values in the netCDF files. The variable names in the netCDF files generally match those in the data release .csv files, except where the .csv file variable name contains a forward slash, colon, period, or comma (i.e., "/", ":", ".", or ","). In the netCDF file variable short names, the forward slashes are replaced with an underscore symbol (i.e., "_") and the colons, periods, and commas are deleted. In the netCDF file variable long names, the punctuation in the name matches that in the .csv file variable names. The "country", "state, province, or territory", and "county" data in the .csv files are not included in the netCDF files. Data included in this release: - Geographic scope. The gridded data cover an area that we labelled as "CANUSA", which includes Canada and the USA (excluding Hawaii, Puerto Rico, and other oceanic islands). Note that the maps displayed in the Atlas volumes are cropped at their northern edge and do not display the full northern extent of the data included in this data release. - Elevation. The elevation data were regridded from the ETOPO5 data set (National Geophysical Data Center, 1993). There were 35 coastal grid points in our CANUSA study area grid for which the regridded elevations were below sea level and these grid points were assigned missing elevation values (i.e., elevation = 9999). The grid points with missing elevation values occur in five coastal areas: (1) near San Diego (California, USA; 1 grid point), (2) Vancouver Island (British Columbia, Canada) and the Olympic Peninsula (Washington, USA; 2 grid points), (3) the Haida Gwaii (formerly Queen Charlotte Islands, British Columbia, Canada) and southeast Alaska (USA, 9 grid points), (4) the Canadian Arctic Archipelago (22 grid points), and (5) Newfoundland (Canada; 1 grid point). - Climate. The gridded climatic data provided here are based on the 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and include annual and monthly temperature and precipitation. The CRU CL 2.0 data were interpolated onto the approximately 25-km grid using geographically-weighted regression, incorporating local lapse-rate estimation and correction. Additional bioclimatic variables (growing degree days on a 5 degrees Celsius base, mean temperatures of the coldest and warmest months, and a moisture index calculated as actual evapotranspiration divided by potential evapotranspiration) were calculated using the interpolated CRU CL 2.0 data. Also included are absolute minimum and maximum temperatures for 1951-1980 interpolated in a similar fashion from climate-station data (WeatherDisc Associates, 1989). These climate and bioclimate data were used in Atlas volumes F and G (see Thompson and others, 2015, for a description of the methods used to create the gridded climate data). Note that for grid points with missing elevation values (i.e., elevation values equal to 9999), climate data were created using an elevation value of -120 meters. Users may want to exclude these climate data from their analyses (see the Usage Notes section in the data release readme file). - Plant distributions. The gridded plant distribution data align with Atlas volume G (Thompson and others, 2015). Plant distribution data on the grid include 690 species, as well as 67 groups of related species and genera, and are based on U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977), regional atlases (e.g., Benson and Darrow, 1981), and new maps based on information available from herbaria and other online and published sources (for a list of sources, see Tables 3 and 4 in Thompson and others, 2015). See the "Notes" column in Table 1 (https://pubs.usgs.gov/pp/p1650-g/table1.html) and Table 2 (https://pubs.usgs.gov/pp/p1650-g/table2.html) in Thompson and others (2015) for important details regarding the species and grouped taxa distributions. - Ecoregions. The ecoregion gridded data are the same as in Atlas volumes D and E (Thompson and others, 2006, 2007), and include three different systems, Bailey's ecoregions (Bailey, 1997, 1998), WWF's ecoregions (Ricketts and others, 1999), and Kuchler's potential natural vegetation regions (Kuchler, 1985), that are each based on distinctive approaches to categorizing ecoregions. For the Bailey and WWF ecoregions for North America and the Kuchler potential natural vegetation regions for the contiguous United States (i.e.,

Gridded monthly mean temperature anomalies derived from daily minimum, maximum and mean surface air temperatures (degrees Celsius) and anomalies derived from daily total precipitation 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. For precipitation, the Canadian gridded data (CANGRD) are interpolated from adjusted precipitation (i.e., AHCCD datasets). Adjusted precipitation data 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 percentage difference between the value 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 relative precipitation anomalies were computed for the years 1948 to 2014. The data will be updated as time permits.

Climate change in the future will continue to bring about unprecedented climate and climate extremes, and buildings and infrastructure will be exposed to them. To ensure that new and existing buildings deliver satisfactory performance over their design lives, their performance under current and future projected climates needs to be assessed by undertaking building simulations. Reference years are one year (or a few years) prepared from the climate time series to capture aspects of interest from the long-term climate datasets. This database provides access to the following building simulation reference year files for 564 locations in Canada. 1. Typical Meteorological Year data for building energy applications are prepared using Sandia method (Hall et al. 1978; NREL 2008) by concatenating twelve typical meteorological months selected based on Finkelstein‐Schafer (FS) statistics. 2. Temperature reference years: Typical Downscaled Year, Extreme Cold Year, and Extreme Warm Year data are prepared following Nik (2016; 2017) by concatenating twelve typical, extreme cold, and extreme warm months respectively to capture the variability within the ensemble of climate model simulations. 3. Moisture Reference year data are prepared for hygrothermal applications. The median ranked year in terms of MI is selected as the conditioning year and the 10% level year is selected as the extreme year for hygrothermal applications. The data are provided for a historical time-period: 1991-2021 and seven future time-periods coinciding with 0.5ºC, 1ºC, 1.5ºC, 2ºC, 2.5ºC, 3ºC, 3.5ºC of global warming.

The data consist of homogenized daily maximum, minimum and mean surface air temperatures for more than 330 locations in Canada; adjusted daily rainfall, snowfall and total precipitation for more than 460 locations. The data are given for the entire period of observation. Please refer to the papers below for detailed information regarding the procedures for homogenization and adjustment.

References: Mekis, É. and L.A. Vincent, 2011: An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmosphere-Ocean, 49(2), 163-177. Vincent, L. A., X. L. Wang, E. J. Milewska, H. Wan, F. Yang, and V. Swail, 2012. A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis, J. Geophys. Res., 117, D18110, doi:10.1029/2012JD017859. Wang, X.L, Y. Feng, L. A. Vincent, 2013. Observed changes in one-in-20 year extremes of Canadian surface air temperatures. Atmosphere-Ocean. Doi:10.1080/07055900.2013.818526.

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.

The U.S. Daily Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters for thousands of U.S. stations located across the 50 states, as well as U.S. territories, commonwealths, the Compact of Free Association nations, and one station in Canada. NOAA Climate Normals are a large suite of data products that provide users with many tools to understand typical climate conditions for thousands of locations across the United States. As many NWS stations as possible are used, including those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the Climate Reference Network (CRN). The comprehensive U.S. Climate Normals dataset includes various derived products including daily air temperature normals (including maximum and minimum temperature normal, heating and cooling degree day normal, and others), precipitation normals (including snowfall and snow depth, percentiles, frequencies and other), and hourly normals (all normal derived from hourly data including temperature, dew point, heat index, wind chill, wind, cloudiness, heating and cooling degree hours, pressure normals). Users can access the data either by product or by station. Included in the dataset is extensive documentation to describe station metadata, filename descriptions, and methodology of producing the data. All data utilized in the computation of the 1981-2010 Climate Normals were taken from the ISD Lite (a subset of derived Integrated Surface Data), the Global Historical Climatology Network-Daily dataset, and standardized monthly temperature data (COOP). These source datasets (including intermediate datasets used in the computation of products) are also archived at the NOAA NCDC.

The data consist of monthly, seasonal and annual means of homogenized daily maximum, minimum and mean surface air temperatures for more than 330 locations in Canada; monthly, seasonal and annual totals of adjusted daily rainfall, snowfall and total precipitation for more than 460 locations; homogenized monthly, seasonal and annual means of hourly surface wind speed at more than 110 locations; monthly, seasonal and annual means of hourly station and sea level pressure adjusted for more than 630 locations. The data are given for the entire period of observation. Please refer to the papers below for detailed information regarding the procedures for homogenization and adjustment. References Mekis, É. and L.A. Vincent, 2011: An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmosphere-Ocean, 49(2), 163-177. Vincent, L. A., X. L. Wang, E. J. Milewska, H. Wan, F. Yang, and V. Swail, 2012. A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis, J. Geophys. Res., 117, D18110, doi:10.1029/2012JD017859. Wan, H., X. L. Wang, V. R. Swail, 2010: Homogenization and trend analysis of Canadian near-surface wind speeds. Journal of Climate, 23, 1209-1225. Wan, H., X. L. Wang, V. R. Swail, 2007: A quality assurance system for Canadian hourly pressure data. J. Appl. Meteor. Climatol., 46, 1804-1817. Wang, X.L, Y. Feng, L. A. Vincent, 2013. Observed changes in one-in-20 year extremes of Canadian surface air temperatures. Atmosphere-Ocean. Doi:10.1080/07055900.2013.818526.

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in surface wind speed based on an ensemble of twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Projected change in wind speed 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 wind speed 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 wind speed (%) 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.