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The Agri-Environmental Indicator of Risk of Water Contamination by Phosphorus dataset estimates the relative risk of phosphorus loss from Soil Landscapes of Canada agricultural areas to surface water. The data series for this indicator consists of four (4) datasets: Annual P-Balance, Soil-P-Source, Edge of Field and IROWC-P. 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.

The probability (likelihood) of ice freeze days, the number of days in the forecast period with a minimum temperature below the frost temperature, -5°C for herbaceous crops over the non-growing season (ifd_herb_nogrow_prob). Week 1 and week 2 forecasted probability is available daily from November 1 to March 31. Week 3 and week 4 forecasted probability is available weekly (Thursday) from November 1 to March 31. Over-wintering crops are biennial and perennial field crops such as herbaceous plants (strawberry, alfalfa, timothy, and many other forage crops) and woody fruit trees (apple, pear, peach, cherry, plum, apricot, chestnut, pecan, grape, etc.). These crops normally grow and develop in the growing season and become dormant in the non-growing season. However, extreme weather and climate events such as cold waves in the growing season and ice freezing events during the winter are a major constraint for their success of production and survival in Canada. The winter survival of these plants depends largely on agrometeorological conditions from late autumn to early spring, especially ice-freezing damage during the winter season. The optimum temperature for such crops is 25°C. 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.

The Agri-Environmental Indicator Particulate Matter dataset provides an estimated net emissions of particulate matter from agricultural lands.

30 Year Spatial Climate Averages are used to describe the average climatic conditions for an area and include variables for maximum temperature, minimum temperature, precipitation, and climate moisture index. At the end of each decade, scientists at Natural Resources Canada have been creating the newest models for as many climate variables as possible. Using a program called ANUSPLIN and climate data points, models for Canada and the United States are created. The NRCan Climate Averages are a large suite of datasets that can be used to compare weather of the past and present to help predict the future climate. The 30 year averages are computed for a uniform 30 year period and consists of the 12 monthly averages computed over the 30 year time period. The 30-year periods included in this series are: 1901-1930; 1921-1950; 1931-1960; 1951-1980; 1961-1990; 1971-2000; 1981-2010; 1991-2020. These are standard 30-year WMO (World Meteorological Organization) periods. Although this data has been processed successfully on a computer system at the Canadian Forest Service, no warranty expressed or implied is made regarding the accuracy or utility of the data on any other system or for general scientific purposes, nor shall the act of distribution constitute any such warranty. The disclaimer applies both to individual use of the data and aggregate use with other data. It is strongly recommended that careful attention be paid to the contents of the metadata file associated with these data. The Canadian Forest Service shall not be held liable for improper or incorrect use of the data described and/or contained herein.

Statistically downscaled multi-model ensembles of maximum temperature are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded maximum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled maximum temperature (°C) are available for the historical time period, 1951-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. 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.

Statistically downscaled multi-model ensembles of mean temperature are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Downscaled daily mean temperature was calculated by averaging downscaled daily minimum and maximum temperature. Daily minimum and maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). Historical gridded minimum and maximum temperature datasets of Canada (ANUSPLIN) were used as the respective downscaling targets. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled mean temperature (°C) are available for the historical time period, 1951-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. 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 Global Ensemble Prediction System (GEPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the current day out to 16 days into the future (up to 39 days twice a week on Mondays and Thursdays at 00UTC for calculating forecast anomalies). The GEPS produces different outlooks (scenarios) to estimate the forecast uncertainties due to the nonlinear (chaotic) behavior of the atmosphere. The probabilistic predictions are based on an ensemble of 20 scenarios that differ in their initial conditions, their physics parameters which are randomly perturbed by a Stochastic Parameter Perturbation (SPP) method, and the stochastic perturbations (kinetic energy). A control member that is not perturbed is also available. Weather elements include temperature, precipitation, cloud cover, wind speed and direction, humidity and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on some fifteen vertical levels on a global latitude-longitude uniform grid with 0.5 degree horizontal resolution (about 39km). Predictions are performed twice a day.

The "Agri-Environmental Indicator Risk of Soil Salinization" dataset estimates the risk of accumulation of soluble salts on agricultural lands in the Canadian Prairies. At high levels, the accumulation of these salts in soil and groundwater in the landscape can inhibit the growth of many plant species.

Seasonal and annual trends of relative total precipitation change (%) for 1948-2012 based on Canadian gridded data (CANGRD) are available, at a 50km resolution across Canada. The relative trends reflect the percent change in total precipitation over a period from the baseline value (defined as the average over 1961-1990 as the reference period). CANGRD data are interpolated from adjusted and homogenized climate station data (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.