A chlorophyll fluorescence time series was collected at various locations around the coast of Vancouver Island, British Columbia, Canada for monitoring phytoplankton concentrations. A Wetlabs ECO fluorometer was deployed every few months on a schedule depending on season and sensor availability. The instrument hung by a chain attached to the side of the buoy, or dock, depending on location, and measured chlorophyll using the fluorescence emission at 695nm. The instrument also measured turbidity by detecting the scattered light at 700nm. The units had internal batteries and data storage and were programmed to make a group of 5 measurements every 30 minutes. A copper wiper covered the sampling window between groups of measurements to reduce fouling. Times are in UTC unless otherwise stated.

Sea level rise increases coastal flooding in many areas of Canada. The Canadian Extreme Water Level Adaptation tool has been developed to accommodate sea level rise. The infrastructure needs to be built higher in order to reduce the risk of flooding. The vertical allowance is the recommended height that the infrastructure to be raised in future years relative to year 2010. The vertical allowance depends on (1) statistics of historical storm surge and tides, and (2) the best estimate and associated uncertainty of future sea level rise. The vertical allowance preserves the frequency of flooding events at some future time under uncertain sea level rise. Vertical allowances are provided for scenarios based on the fifth assessment report (AR5) of IPCC for the period of 2020-2100 and the sixth assessment report (AR6) of IPCC for the period of 2020-2150. Cite this data as: Zhai, L., Greenan, B., Perrie, W. Data of: Vertical allowance gridded dataset for Canada. Published: February 2024. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/5c164079-9785-42fa-8fa5-d886ccbae3b3

This group of maps, which includes the CanMatrix and CanTopo collections, is now a legacy product that is no longer maintained. It may not meet current government standards. Natural Resources Canada's (NRCan) topographic raster maps provide a representation of the topographic phenomena of the Canadian landmass. Several editions of paper maps have been produced over time in order to offer improved products compared to their predecessors in terms of quality and the most up to date information possible. The georeferenced maps can be used in a Geographic Information System (GIS). In all cases, they accurately represent the topographical data available for the date indicated (validity date). The combination of CanMatrix and CanTopo data provides complete national coverage. • CanMatrix - Print Ready: Raster maps produced by scanning topographic maps at scales from 1:25 000 to 1:1 000 000. This product is not georeferenced. Validity dates: 1944 to 2005 (1980 on average). Available formats: PDF and TIFF • CanMatrix - Georeferenced: Raster maps produced by scanning topographic maps at scales of 1:50 000 and 1:250 000. These maps are georeferenced according to the 1983 North American Reference System (NAD 83). Validity dates: 1944 to 2005 (1980 on average). Available format: GeoTIFF • CanTopo: Digital raster maps produced mainly from the GeoBase initiative, NRCan digital topographic data, and other sources. Approximately 2,234 datasets (maps) at scale of 1:50 000, primarily covering northern Canada, are available. CanTopo datasets in GeoPDF and GeoTIFF format are georeferenced according to the 1983 North American Reference System (NAD 83). Validity dates: 1946 to 2012 (2007 on average). Available formats: PDF, GeoPDF, TIFF and GeoTIFF

The National Ecological Framework for Canada's "Land Cover by Ecozone” dataset provides land cover information within the ecozone framework polygon. It provides landcover codes and their English and French language description as well as information about the percentage of the polygon that the component occupies.

The “Agricultural Major Land Practices Groups of the Canadian Prairies” dataset lays out the areas of the 5 Major Land Practices Groups of the agricultural portions of the Canadian Prairies. They are represented by vector polygons amalgamated (dissolved) from the Version 1.9 SLC polygons sharing common water resources, land use and farming practices as developed in the “Agricultural Land Practices Groups of the Canadian Prairies by SLC Polygon” of this series. The dataset is based upon selected attributes from the Soil Landscapes of Canada (SLC) and the 1996 Census of Agriculture. Typical attributes including: land in pasture, land in summerfallow, crop mixture, farm size and the level of chemical and fertilizer inputs.

Description: This dataset consists of three simulations from the Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) which is a configuration of the Nucleus for European Modelling of the Ocean (NEMO) V3.6. The historical simulation is an estimate of the 1986-2005 mean climate. The future simulations project the 2046-2065 mean climate for representative concentration pathways (RCP) 4.5 (moderate mitigation scenario) and 8.5 (no mitigation scenario). Each simulation is forced by a climatology of atmospheric forcing fields calculated over these 20 year periods and the winds are augmented with high frequency variability, which introduces a small amount of interannual variability. Model outputs are averaged over 3 successive years of simulation (the last 3, following an equilibration period); standard deviation among the 3 years is available upon request. For each simulation, the dataset includes the air-sea carbon dioxide flux, monthly 3D fields for potential temperature, salinity, potential density, total alkalinity, dissolved inorganic carbon, nitrate, oxygen, pH, total chlorophyll, aragonite saturation state, total primary production, and monthly maximum and minimum values for oxygen, pH, and potential temperature. The data includes 50 vertical levels at a 1/36 degree spatial resolution and a mask is provided that indicates regions where these data should be used cautiously or not at all. For a more detailed description please refer to Holdsworth et al. 2021. The data available here are the outputs of NEP36-CanOE_RCP 4.5; a projection of the 2046-2065 climate for the moderate mitigation scenario RCP 4.5. Methods: This study uses a multi-stage downscaling approach to dynamically downscale global climate projections at a 1/36° (1.5 − 2.25 km) resolution. We chose to use the second-generation Canadian Earth System model (CanESM2) because high-resolution downscaled projections of the atmosphere over the region of interest are available from the Canadian Regional Climate Model version 4 (CanRCM4). We used anomalies from CanESM2 with a resolution of about 1° at the open boundaries, and the regional atmospheric model, CanRCM4 (Scinocca et al., 2016) for the surface boundary conditions. CanRCM4 is an atmosphere only model with a 0.22° resolution and was used to downscale climate projections from CanESM2 over North America and its adjacent oceans. The model used is computationally expensive. This is due to the relatively high number of points in the domain (715 × 1,021 × 50) and the relatively complex biogeochemical model (19 tracers). Therefore, rather than carrying out interannual simulations for the historical and future periods, we implemented a new method that uses atmospheric climatologies with augmented winds to force the ocean. We show that augmenting the winds with hourly anomalies allows for a more realistic representation of the surface freshwater distribution than using the climatologies alone. Section 2.1 describes the ocean model that is used to estimate the historical climate and project the ocean state under future climate scenarios. The time periods are somewhat arbitrary; 1986–2005 was chosen because the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations end in 2005 as no community-accepted estimates of emissions were available beyond that date (Taylor et al., 2009); 2046–2065 was chosen to be far enough in the future that changes in 20 year mean fields are unambiguously due to changing GHG forcing (as opposed to model internal variability) (e.g., Christian, 2014), but near enough to be considered relevant for management purposes. While it is true that 30 years rather than 20 is the canonical value for averaging over natural variability, in practice the difference between a 20 and a 30 year mean is small (e.g., if we average successive periods of an unforced control run, the variance among 20 year means will be only slightly larger than for 30 year means). Also, there is concern that longer averaging periods are inappropriate in a non-stationary climate (Livezey et al., 2007; Arguez and Vose, 2011). We chose 20 year periods because they are adequate to give a mean annual cycle with little influence from natural variability, while minimizing aliasing of the secular trend into the means. As the midpoints of the two time periods are separated by 60 years, the contribution of natural variability to the differences between the historical and future simulations is negligible e.g., (Hawkins and Sutton, 2009; Frölicher et al., 2016). Section 2.2 describes how climatologies derived from observations were used for the initialization and open boundary conditions for the historical simulations and pseudo-climatologies were used for the future scenarios. The limited availability of observations means that the years used for these climatologies differs somewhat from the historical and future periods. Section 2.3 details the atmospheric forcing fields and the method that we developed to generate winds with realistic high-frequency variability while preserving the daily climatological means from the CanRCM4 data. Section 2.4 shows the equilibration of key modeled variables to the forcing conditions Data Sources: Model output Uncertainties: These climate projections are downscaled from a single global climate model (CanESM2/CanRCM4) because the cost of ensembles is presently prohibitive. Our experimental design uses climatological forcing for each time period so the differences between them are almost entirely due to anthropogenic forcing with little effect of natural variability.

Description: This dataset consists of three simulations from the Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) which is a configuration of the Nucleus for European Modelling of the Ocean (NEMO) V3.6. The historical simulation is an estimate of the 1986-2005 mean climate. The future simulations project the 2046-2065 mean climate for representative concentration pathways (RCP) 4.5 (moderate mitigation scenario) and 8.5 (no mitigation scenario). Each simulation is forced by a climatology of atmospheric forcing fields calculated over these 20 year periods and the winds are augmented with high frequency variability, which introduces a small amount of interannual variability. Model outputs are averaged over 3 successive years of simulation (the last 3, following an equilibration period); standard deviation among the 3 years is available upon request. For each simulation, the dataset includes the air-sea carbon dioxide flux, monthly 3D fields for potential temperature, salinity, potential density, total alkalinity, dissolved inorganic carbon, nitrate, oxygen, pH, total chlorophyll, aragonite saturation state, total primary production, and monthly maximum and minimum values for oxygen, pH, and potential temperature. The data includes 50 vertical levels at a 1/36 degree spatial resolution and a mask is provided that indicates regions where these data should be used cautiously or not at all. For a more detailed description please refer to Holdsworth et al. 2021. Methods: This study uses a multi-stage downscaling approach to dynamically downscale global climate projections at a 1/36° (1.5 − 2.25 km) resolution. We chose to use the second-generation Canadian Earth System model (CanESM2) because high-resolution downscaled projections of the atmosphere over the region of interest are available from the Canadian Regional Climate Model version 4 (CanRCM4). We used anomalies from CanESM2 with a resolution of about 1° at the open boundaries, and the regional atmospheric model, CanRCM4 (Scinocca et al., 2016) for the surface boundary conditions. CanRCM4 is an atmosphere only model with a 0.22° resolution and was used to downscale climate projections from CanESM2 over North America and its adjacent oceans. The model used is computationally expensive. This is due to the relatively high number of points in the domain (715 × 1,021 × 50) and the relatively complex biogeochemical model (19 tracers). Therefore, rather than carrying out interannual simulations for the historical and future periods, we implemented a new method that uses atmospheric climatologies with augmented winds to force the ocean. We show that augmenting the winds with hourly anomalies allows for a more realistic representation of the surface freshwater distribution than using the climatologies alone. Section 2.1 describes the ocean model that is used to estimate the historical climate and project the ocean state under future climate scenarios. The time periods are somewhat arbitrary; 1986–2005 was chosen because the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations end in 2005 as no community-accepted estimates of emissions were available beyond that date (Taylor et al., 2009); 2046–2065 was chosen to be far enough in the future that changes in 20 year mean fields are unambiguously due to changing GHG forcing (as opposed to model internal variability) (e.g., Christian, 2014), but near enough to be considered relevant for management purposes. While it is true that 30 years rather than 20 is the canonical value for averaging over natural variability, in practice the difference between a 20 and a 30 year mean is small (e.g., if we average successive periods of an unforced control run, the variance among 20 year means will be only slightly larger than for 30 year means). Also, there is concern that longer averaging periods are inappropriate in a non-stationary climate (Livezey et al., 2007; Arguez and Vose, 2011). We chose 20 year periods because they are adequate to give a mean annual cycle with little influence from natural variability, while minimizing aliasing of the secular trend into the means. As the midpoints of the two time periods are separated by 60 years, the contribution of natural variability to the differences between the historical and future simulations is negligible e.g., (Hawkins and Sutton, 2009; Frölicher et al., 2016). Section 2.2 describes how climatologies derived from observations were used for the initialization and open boundary conditions for the historical simulations and pseudo-climatologies were used for the future scenarios. The limited availability of observations means that the years used for these climatologies differs somewhat from the historical and future periods. Section 2.3 details the atmospheric forcing fields and the method that we developed to generate winds with realistic high-frequency variability while preserving the daily climatological means from the CanRCM4 data. Section 2.4 shows the equilibration of key modeled variables to the forcing conditions Data Sources: Model output Uncertainties: The historical climatologies were evaluated using observational climatologies generated from stations with a long time series of data over the time period including CTDs, nutrient profiles, lighthouse, satellite SST and buoy data. The model is able to represent the historical conditions with an acceptable bias. The resolution of this model is insufficient to represent the narrow straits and channels of this region so the dataset includes a cautionary mask to exclude these regions. These climate projections are downscaled from a single global climate model (CanESM2/CanRCM4) because the cost of ensembles is presently prohibitive. Our experimental design uses climatological forcing for each time period so the differences between them are almost entirely due to anthropogenic forcing with little effect of natural variability. We caution that our experimental design does not permit analysis of the effects of natural climate variability. We recommend using both of the scenarios (RCP4.5 and RCP8.4) to estimate the scenario uncertainty in these projections.

McElhanney Consulting Services Ltd (MCSL) has performed a LiDAR and Imagery survey in southern Saskatchewan. The acquisition was completed between the 16th and 25th of October, 2009. The survey consisted of approximately 790 square kilometers of coverage. While collecting the LiDAR data, we also acquired aerial photo in RGB and NIR modes consisting of 1649 frames each.

LASer files of a subwatershed in the La Salle watershed, north of Elie, MB. The LiDAR data was collected using Fugro Horizons proprietary FLI-MAP Fx LiDAR system.

Monthly mean currents from Bedford Institute of Oceanography North Atlantic Model (BNAM) results were averaged over 1990 to 2015 period to create monthly mean climatology for the Northwest Atlantic Ocean, which can be considered as a representation of the climatological state of the Northwest Atlantic Ocean. The BNAM model is eddy-resolving, NEMO-based ice-ocean coupled North Atlantic Ocean model developed at the Bedford Institute of Oceanography (BIO) to support DFO monitoring programs. The data available here is monthly climatology for eight selected depths (surface, 110 m, 156 m, 222 m, 318 m, 541 m, 1062 m, bottom) in 1/12 degree spatial resolution. The data for each month from 1990 until present for the entire model domain ( 8°–75°N latitude and 100°W–30°E longitude) and various depths is available upon request. The 1990-2017 model hindcast result is compared with observational data from surface drifter and satellite altimetry. The model demonstrates good skill in simulating surface currents, winter convection events in the Labrador Sea, and the Atlantic Meridional Overturning Circulation as observed at 26.5°N and 41°N. Model results have been used to interpret changes in the Labrador Current and observed warming events on the Scotian Shelf, and are reported through the annual AZMP Canadian Science Advisory Secretariat Process. When using data please cite following: Wang, Z., Lu, Y., Greenan, B., Brickman, D., and DeTracey, B., 2018. BNAM: An eddy resolving North Atlantic Ocean model to support ocean monitoring. Can. Tech. Rep. Hydrogr. Ocean. Sci. 327: vii + 18p