The Standardized Precipitation Index (SPI) is widely regarded as one of the most accessible tools for quantifying and reporting meteorological drought. At shorter timescales, SPI values are highly correlated with soil moisture, while at extended timescales, they reflect variations in groundwater and reservoir storage. The SPI model derives probability distributions from historical precipitation records, which are then normalized using an incomplete gamma function across multiple timescales. SPI values indicate the number of standard deviations the observed anomaly departs from the long-term period of record, with positive values corresponding to wetter-than-average conditions. Time periods calculated for monthly precipitation percentiles are 1, 2, 3, 6, 9, 12, 18, 24 months. Each ISO week is numbered from 1 to 52 (sometimes 53) within a year. An ISO week starts on Monday and ends on Sunday. Historical record goes back to 1980 Indices values were calculated using the xclim python package The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

Multi-model ensembles of surface wind speed based on projections from twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of surface wind speed (m/s) are available for the historical time period, 1900-2005, 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.

Each pixel value corresponds to the mean historical “Best-quality” Max-NDVI value for a given week, as calculated from the previous 20 years in the MODIS historical record (i.e. does not include data from the current year). These data are also often referred to as “weekly baselines” or “weekly normals”.

Each pixel value corresponds to the difference (anomaly) between the mean “Best-Quality” Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the “Best-Quality” Max-NDVI of the same week in a specific year (e.g. Week 18, 2015). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal. Anomalies > 0 indicate where weekly Max-NDVI is higher than normal. Anomalies close to 0 indicate where weekly Max-NDVI is similar to normal.

The health of individual amphibians, amphibian populations, and their wetland habitats are monitored in the oil sands region and at reference locations. Contaminants assessments are done at all sites. Amphibians developing near oil sands activities may be exposed to concentrations of oil sands-related contaminants, through air emissions as well as water contamination. The focus of field investigations is to evaluate the health of wild amphibian populations at varying distances from oil sands operations. Wood frog (Lithobates sylvaticus) populations are being studied in Alberta, Saskatchewan and the Northwest Territories in order to examine the relationship of proximity to oil sands activities and to prevalence of infectious diseases, malformation rates, endocrine and stress responses, genotoxicity, and concentrations of heavy metals, naphthenic acids and polycyclic aromatic hydrocarbons.

Difference from Normal Temperature values are computed by subtracting the normal monthly average temperature from the average monthly temperature of the month. The average monthly temperature is computed by obtaining the mean value of average daily temperatures for a month. If the month was colder than normal the value computed will be negative and if it was warmer the value will be positive. Long-term average is 1991-2020. The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

30-year Average Number of Days with Temperature above 30 °C are defined as the count of the number of climate days during the time period where the maximum daily temperature was greater than 30 °C. These values are calculated across Canada in 10x10 km cells.

Crop development stage in a numerical scale. All living organisms move from one stage of development to the next over time. For annual crops, it life cycle (growing season) completed within a year. Crop water use differs from one stage to another mostly due to the differences in the amount of green leaves, thus crop stage is closely related to its water consumption and water stress condition. Crop stages are mostly controlled by growing season heat accumulation and regulated by day-length crop some crops. The crop stages provided here are determined by a biometeorlogical time scale model (Robertson, 1968) for cool season crops (wheat, barley etc.) , and a Crop Heat Unit (Brown and Bootsma, 1993) algorithm for warm season crops (corn and soybean etc.).

Description: Seasonal mean total phytoplankton at the surface from the British Columbia continental margin model (BCCM) were averaged over the 1981 to 2010 period to create seasonal mean surface climatology of the Canadian Pacific Exclusive Economic Zone. Methods: Total phytoplankton is the sum of diatoms and flagellates concentration. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain a raster layer of seasonal surface phytoplankton climatology for the Canadian Pacific Exclusive Economic Zone at 3 km spatial resolution. Uncertainties: Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

AIS NL Biofouling Species Fisheries and Oceans Canada's (DFO) National Marine Biofouling Monitoring Program conducts annual field surveys to monitor the introduction, establishment, spread, species richness, and relative abundance of native and some non-native species in Newfoundland and Labrador (NL) Region since 2006. Standardized monitoring protocols employed by DFO's NL, Maritimes, Gulf, and Quebec regions include biofouling collector plates deployed from May to October at georeferenced intertidal and shallow subtidal sites, including public docks, and public and private marinas and nautical clubs. Initially, (2006-2017), the collectors consisted of three 10 cm by 10 cm PVC plates deployed in a vertical array and spaced approximately 40 cm apart, with the shallowest plate suspended at least 1 m below the surface to sample subtidal and shallow intertidal species (McKenzie et al 2016a). Three replicate arrays were deployed at least 5 m apart per site. Since 2018, collector networks have been modified to improve statistical replication, including up to 10 individual collectors deployed per site at 1 m depth and at least 5 m apart (as above) from May to October. Since 2006, seven invasive biofouling organisms have been detected in Newfoundland and Labrador harbours, marinas and coastal areas. Should be cited as follows: DFO Newfoundland and Labrador Region Aquatic Invasive Species Marine Biofouling Monitoring Program. Published March 2024. Coastal and Freshwater Ecology, Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John’s, Newfoundland and Labrador. Reference: Tunicates Vase tunicate (Ciona intestinalis) 2012 The Vase tunicate, is a high impact solitary invader and was first detected by DFO in 2012 on the Burin Peninsula at Ship Cove and Little Bay, Placentia Bay. Various mitigation measures (McKenzie et al. 2016b) contained this invasive tunicate to a small area for six years within Placentia Bay. First detected in Fortune Bay as an established population in 2019, increasing reports of the Vase tunicate have been made along the south coast of Newfoundland. The data provided here indicates the detections of this AIS in coastal NL. From 2018-2022, the Coastal Environmental Baseline Program provided additional support to enhance sampling efforts in Placentia Bay.