Insights into the North Hemisphere daily snowpack at high resolution from the new Crocus–ERA5 product

A new global daily sea‐surface temperature (SST) analysis system has been developed at Environment and Climate Change Canada (ECCC). All components of the new SST analysis system are implemented within the Modular and Integrated Data Assimilation System (MIDAS) software. MIDAS is already used for the data assimilation component of the main operational numerical weather prediction (NWP) systems at ECCC. The new SST analysis system, integrated together with the global sea‐ice analysis, will be part of the combined ocean surface analysis used for all operational prediction systems at ECCC. The data assimilation method used to compute the new SST analyses is two‐dimensional variational method with a diffusion operator for representing the horizontal background‐error correlations. A new algorithm for satellite data bias estimation has also been developed employing gridded bias estimates computed from a spatial averaging of the differences between collocated satellite and in‐situ data. New algorithms for quality control and thinning of satellite data have also been implemented, making each type of observational dataset more evenly distributed over the globe. The performance of the new SST system is examined relative to the current operational SST system by using independent data. The impact of using the new SST analysis within NWP and ocean prediction systems is also evaluated. When compared with the operational system currently in use, the experiments employing the new SST analysis system produce a nearly neutral impact on the NWP and ocean prediction systems. This validation of the new system is an important first step towards the ability to use MIDAS to perform ensemble‐based three‐dimensional ocean and coupled ocean‐ice–atmosphere data assimilation.

Snow cover trends for Canada over the 1955–2017 period for the daily snow depth–observing network of Environment and Climate Change Canada (ECCC) are presented based on an updated quality-controlled historical daily in situ snow depth dataset. The period since approximately 1995 is characterized by a rapid decline in manual observations (loss of over 800 manual observing sites between 1995 and 2017) and an increasing number of automated stations equipped with sonic snow depth sensors. In 2017 these accounted for approximately 45% of the network and more than 80% of the snow depth–observing network north of latitude 55°N. Automated stations are characterized by more frequent missing and anomalous data than manual ruler observations, particularly at Arctic sites. A comparison of closely located automated sonic and manual ruler observations showed similar numbers of days with snow cover but the sonic sensors detected significantly lower snow depths. For time series analysis of annual snow cover variables, the systematic difference between ruler and sonic snow depth can be removed using a common 2003–2016 reference period to compute snow cover anomalies. The updated trend results are broadly similar to previously published assessments showing long-term decreases in annual snow cover duration (SCD) and snow depth over most of Canada, with the largest decreases observed in spring snow cover and seasonal maximum snow depth (SDmax). Significant declines in SCD and SDmax of −1.7 (±1.1) days decade-1 and −1.8 cm (±0.8) cm decade−1 were observed in the Canada–averaged series over the 1955–2017 period. These trends mainly reflect snow cover conditions over southern Canada where the observing network is concentrated and where there are significant negative correlations between snow cover and winter air temperature. Declining numbers of stations reporting snow depth, issues with sonic sensor data quality, and systematic differences between ruler and sonic sensor measurements are major challenges for continued climate monitoring with the current ECCC snow depth–observing network.

A new ensemble-based land surface data assimilation (DA) system is coupled with the atmospheric four-dimensional ensemble-variational data assimilation (4D-EnVar) system with the goal of improving the analyses within Environment and Climate Change Canada’s Global Deterministic Prediction System. Since 2001, the sequential assimilation of surface variables is used to generate the initial conditions to launch the Global Environmental Multiscale (GEM) coupled forecast model. The work presented here is to replace the sequential DA with an independent surface DA system, the Canadian Land Data Assimilation System (CaLDAS) assimilating screen-level observations, and to compare assimilation experiments with CaLDAS run in uncoupled and weakly coupled modes. In the uncoupled mode, CaLDAS is used to initialize the forecast without interacting with the 4D-EnVar system. In the coupled mode, the analyses generated from CaLDAS and 4D-EnVar are used to initialize the forecast model. The analyses and forecasts from uncoupled and coupled runs are evaluated against surface and radiosonde observations over different subdomains to conclude the impact of coupling CaLDAS with 4D-EnVar. Results indicate a statistically significant reduction in bias and standard deviation at the surface for screen-level temperature and dewpoint temperature on the order of 0.1 K, and in the lower troposphere between 1000 and 500 hPa on the order of 0.1 dam for geopotential height and 0.1 K for air temperature and dewpoint depression in the coupled DA runs. The positive impact persists up to 5 days over some subdomains. It is concluded that the coupled DA approach generally performs better than the uncoupled version.

As part of the National Hydrological Services Transformation Initiative, Environment and Climate Change Canada (ECCC) designed and implemented the National Surface and River Prediction System (NSRPS) in order to provide surface and river flow analysis and forecast products across Canada. Within NSRPS, the Canadian Land Data Assimilation System (CaLDAS) produces snow analyses that are used to initialise the land surface model, which in turn is used to force the river routing component. Originally, CaLDAS was designed to improve atmospheric forecasts with less focus on hydrological processes. When snow data assimilation occurs, the related increments remove/add water from/to the system, which can sometimes be problematic for streamflow forecasting, in particular during the snowmelt period. In this study, a new snow analysis method introduces multiple innovations that respond to the need for higher quality snow analyses for hydrological purposes, including the use of IMS snow cover extent data instead of in situ snow depth observations. The results show that the new snow assimilation methodology brings an overall improvement to snow analyses and substantially enhances water conservation, which is reflected in the generally improved streamflow simulations. This work represents a first step towards a new snow data assimilation process in CaLDAS, with the final objective of producing a reliable snow analysis to initialise and improve NWP as well as environmental predictions, including flood and drought forecasts.

ABSTRACTAn objective analysis is one of the main components of data assimilation. By combining observations with the output of a predictive model we combine the best features of each source of information: the complete spatial and temporal coverage provided by models, with a close representation of the truth provided by observations. The process of combining observations with a model output is called an analysis. To produce an analysis requires the knowledge of observation and model errors, as well as its spatial correlation. This paper is devoted to the development of methods of estimation of these error variances and the characteristic length-scale of the model error correlation for its operational use in the Canadian objective analysis system. We first argue in favor of using compact support correlation functions, and then introduce three estimation methods: the Hollingsworth–Lönnberg (HL) method in local and global form, the maximum likelihood method (ML), and the diagnostic method. We perform one-dimensional (1D) simulation studies where the error variance and true correlation length are known, and perform an estimation of both error variances and correlation length where both are non-uniform. We show that a local version of the HL method can capture accurately the error variances and correlation length at each observation site, provided that spatial variability is not too strong. However, the operational objective analysis requires only a single and globally valid correlation length. We examine whether any statistics of the local HL correlation lengths could be a useful estimate, or whether other global estimation methods such as by the global HL, ML, or should be used. We found in both 1D simulation and using real data that the ML method is able to capture physically significant aspects of the correlation length, while most other estimates give unphysical and larger length-scale values.Implications: This paper describes a proposed improvement of the objective analysis of surface pollutants at Environment and Climate Change Canada (formerly known as Environment Canada). Objective analyses are essentially surface maps of air pollutants that are obtained by combining observations with an air quality model output, and are thought to provide a complete and more accurate representation of the air quality. The highlight of this study is an analysis of methods to estimate the model (or background) error correlation length-scale. The error statistics are an important and critical component to the analysis scheme.

Abstract. The measurement of precipitation in the Environment and Climate Change Canada (ECCC) surface network is a crucial component for climate and weather monitoring, flood and water resource forecasting, numerical weather prediction, and many other applications that impact the health and safety of Canadians. Through the late 1990s and early 2000s, the ECCC surface network modernization resulted in a shift from manual to automated precipitation measurements. Although many advantages to automation are realized, such as enhanced capabilities for monitoring in remote locations and a higher frequency of observations at lower cost, the increased reliance on automated precipitation gauges has also resulted in additional challenges, especially with data quality and homogenization. The automated weighing precipitation gauges used in the ECCC operational network have an increased propensity for wind-induced undercatch of solid precipitation. One outcome of the World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE) was the development of transfer functions for the adjustment of high-frequency solid precipitation measurements made with gauge/wind shield configurations used in the ECCC surface network. Using the SPICE universal transfer function (UTF), hourly precipitation measurements from 397 ECCC automated climate stations were retroactively adjusted for wind undercatch. The data format, quality control, and adjustment procedures are described here. The hourly adjusted data set (2001–2019; version v2019UTF) is available via the ECCC data catalogue at https://doi.org/10.18164/6b90d130-4e73-422a-9374-07a2437d7e52 (ECCC, 2021). A basic spatial impact assessment shows that the highest relative total precipitation adjustments occur in the Arctic, where solid precipitation has an overall higher annual occurrence ratio. The highest adjustments for solid precipitation are shared by the Arctic, Southern Prairies, and the coastal Maritimes, where stations tend to be more exposed and snowfall events occur at higher wind speeds.

<strong class="journal-contentHeaderColor">Abstract.</strong> The measurement of precipitation in the Environment and Climate Change Canada (ECCC) surface network is a crucial component for climate and weather monitoring, flood and water resource forecasting, numerical weather prediction, and many other applications that impact the health and safety of Canadians. Through the late 1990s and early 2000s, the ECCC surface network modernization resulted in a shift from manual to automated precipitation measurements. Although many advantages to automation are realized, such as enhanced capabilities for monitoring in remote locations and a higher frequency of observations at lower cost, the increased reliance on automated precipitation gauges has also resulted in additional challenges, especially with data quality and homogenization. The automated weighing precipitation gauges used in the ECCC operational network have an increased propensity for wind-induced undercatch of solid precipitation. One outcome of the World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE) was the development of transfer functions for the adjustment of high-frequency solid precipitation measurements made with gauge/wind shield configurations used in the ECCC surface network. Using the SPICE universal transfer function (UTF), hourly precipitation measurements from 397 ECCC automated climate stations were retroactively adjusted for wind undercatch. The data format, quality control, and adjustment procedures are described here. The hourly adjusted data set (2001–2019; version v2019UTF) is available via the ECCC data catalogue at <a href="https://doi.org/10.18164/6b90d130-4e73-422a-9374-07a2437d7e52">https://doi.org/10.18164/6b90d130-4e73-422a-9374-07a2437d7e52</a> (ECCC, 2021). A basic spatial impact assessment shows that the highest relative total precipitation adjustments occur in the Arctic, where solid precipitation has an overall higher annual occurrence ratio. The highest adjustments for solid precipitation are shared by the Arctic, Southern Prairies, and the coastal Maritimes, where stations tend to be more exposed and snowfall events occur at higher wind speeds.

The measurement of precipitation in the Environment and Climate Change Canada (ECCC) surface network is a crucial component for climate and weather monitoring, flood and water resource forecasting, numerical weather prediction and many other applications that impact the health and safety of Canadians. Through the late 1990s and early 2000s, ECCC surface network modernization resulted in a shift from manual to automated precipitation measurements. Although many advantages to automation are realized, such as enhanced capabilities for monitoring in remote locations and higher frequency of observations at lower cost, the increased reliance on automated precipitation gauges has also resulted in additional challenges, especially with data quality and homogenization. The automated weighing precipitation gauges used in the ECCC operational network have an increased propensity for wind-induced undercatch of solid precipitation. One outcome of the WMO Solid Precipitation Inter-Comparison Experiment (SPICE) was the development of transfer functions for the adjustment of high frequency solid precipitation measurements made with gauge/wind shield configurations used in the ECCC surface network. Using the SPICE Universal Transfer Function (UTF), hourly precipitation measurements from 397 ECCC automated climate stations were retroactively adjusted for wind undercatch. The data format, quality control and adjustment procedures are described here. The hourly adjusted data set (2001–2019, version v2019UTF) is available via the ECCC data catalogue: https://doi.org/10.18164/6b90d130-4e73-422a-9374-07a2437d7e52 (ECCC, 2021). A basic spatial impact assessment shows that the highest relative total precipitation adjustments occur in the Arctic where solid precipitation has an overall higher annual occurrence ratio. The highest adjustments for solid precipitation are shared by the Arctic, southern Prairies and the coastal Maritimes, where stations tend to be more exposed and snowfall events occur at higher wind speeds.

The measurement of precipitation in the Environment and Climate Change Canada (ECCC) surface network is a crucial component for climate and weather monitoring, flood and water resource forecasting, numerical weather prediction and many other applications that impact the health and safety of Canadians. Through the late 1990s and early 2000s, ECCC surface network modernization resulted in a shift from manual to automated precipitation measurements. Although many advantages to automation are realized, such as enhanced capabilities for monitoring in remote locations and higher frequency of observations at lower cost, the increased reliance on automated precipitation gauges has also resulted in additional challenges, especially with data quality and homogenization. The automated weighing precipitation gauges used in the ECCC operational network have an increased propensity for wind-induced undercatch of solid precipitation. One outcome of the WMO Solid Precipitation Inter-Comparison Experiment (SPICE) was the development of transfer functions for the adjustment of high frequency solid precipitation measurements made with gauge/wind shield configurations used in the ECCC surface network. Using the SPICE Universal Transfer Function (UTF), hourly precipitation measurements from 397 ECCC automated climate stations were retroactively adjusted for wind undercatch. The data format, quality control and adjustment procedures are described here. The hourly adjusted data set (2001–2019, version v2019UTF) is available via the ECCC data catalogue: https://doi.org/10.18164/6b90d130-4e73-422a-9374-07a2437d7e52 (ECCC, 2021). A basic spatial impact assessment shows that the highest relative total precipitation adjustments occur in the Arctic where solid precipitation has an overall higher annual occurrence ratio. The highest adjustments for solid precipitation are shared by the Arctic, southern Prairies and the coastal Maritimes, where stations tend to be more exposed and snowfall events occur at higher wind speeds.

ABSTRACTThe objective of this paper is to provide an overview of the present status and procedures related to surface precipitation observations at Environment and Climate Change Canada (ECCC). This work was done to support the ongoing renewal of observation systems and networks at the Meteorological Service of Canada. The paper focusses on selected parameters, namely, accumulated precipitation, precipitation intensity, precipitation type, rainfall, snowfall, and radar reflectivity. Application-specific user needs and requirements are defined and captured by World Meteorological Organization (WMO) Expert Teams at the international level by Observing Systems Capability Analysis and Review (OSCAR) and WMO Integrated Global Observing System (WIGOS), and by ECCC user engagement initiatives within the Canadian context. The precipitation-related networks of ECCC are separated into those containing automatic instruments, those with human (manual) observers, and the radar network. The unique characteristics and data flow for each of these networks, the instrument and installation characteristics, processing steps, and limitations from observation to data distribution and storage are provided. A summary of precipitation instrument-dependent algorithms that are used in ECCC's Data Management System is provided. One outcome of the analysis is the identification of gaps in spatial coverage and data quality that are required to meet user needs. Increased availability of data, including from long-serving manual sites, and an increase in the availability of precipitation type and snowfall amount are identified as improvements that would benefit many users. Other recognized improvements for in situ networks include standardized network procedures, instrument performance adjustments, and improved and sustained access to data and metadata from internal and external networks. Specific to radar, a number of items are recognized that can improve quantitative precipitation estimates. Increased coverage for the radar network and improved methods for assessing and portraying radar data quality would benefit precipitation users.

Climate change (CC) is already affecting Canada’s hydrologic cycle, posing challenges for water management in mining operations and increasing associated environmental and social risks. However, there is limited research that quantifies the extent of anticipated CC impacts across Canadian watersheds with active mining. This paper aims to fill that gap by assessing CC impacts on key hydroclimatic variables important for Canada’s mine water management. Baseline conditions were established for six key variables: annual precipitation, 24 h intensity–duration–frequency (IDF) precipitation, 10 d extreme precipitation, annual mean temperature, hydrologic drought index like Standardized Precipitation and Evaporation Index (SPEI), and annual snow depth. Date sources included Environment and Climate Change Canada (ECCC) and Coupled Model Intercomparison Project Phase 5 (CMIP5). Future CC projections were generated using ECCC’s transformation equation for 24 h IDF precipitation, the quantile delta mapping (QDM) method for 10 d extreme precipitation, and downscaled, bias corrected CMIP5 ensemble projections for the remaining variables. The assessment considered two greenhouse emission scenarios (RCP4.5 and RCP8.5), and three future timeframes (2020s, 2050s, 2080s). The study reveals projected temperature increases within the case study watersheds of 2.4 °C–3.5 °C by the 2050s and 3 °C–7 °C by the 2080s under median or 50th percentile (p50) conditions. Annual precipitation is expected to rise by 11%–16% (2050s) and 15%–28% (2080s), with more intense shorter-duration events under p50 conditions. For example, the current 100 year 24 h IDF storm is expected to occur more frequently, decreasing to a 27–49 year return period by the 2050s and a 10–40 year return period by the 2080s. Annual average snow depth is projected to decline by 21%–73% (2050s) and 24%–89% (2080s) under p50 conditions. These findings highlights that water management in Canada’s mining regions is set to face escalating hydrological changes under a changing climate. Effective management strategies are therefore essential to prevent intensified environmental and social risks.

To assess the performance of the most recent versions of the Global Precipitation Measurement (GPM) Integrated Multisatellite Retrievals for GPM (IMERG), namely, V05 and V06, in Arctic regions, comparisons with Environment and Climate Change Canada (ECCC) Climate Network stations north of 60°N were performed. This study focuses on the IMERG monthly final products. The mean bias and mean error-weighted bias were assessed in comparison with 25 precipitation gauge measurements at ECCC Climate Network stations. The results of this study indicate that IMERG generally detects higher precipitation rates in the Canadian Arctic than ground-based gauge instruments, with differences ranging up to 0.05 and 0.04 mm h−1 for the mean bias and the mean error-weighted bias, respectively. Both IMERG versions perform similarly, except for a few stations, where V06 tends to agree slightly better with ground-based measurements. IMERG’s tendency to detect more precipitation is in good agreement with findings indicating that weighing gauge measurements suffer from wind undercatch and other impairing factors, leading to lower precipitation estimates. Biases between IMERG and ground-based stations were found to be slightly larger during summer and fall, which is likely related to the increased precipitation rates during these seasons. Correlations of both versions of IMERG with the ground-based measurements are considerably lower in winter and spring than during summer and fall, which might be linked to issues that passive microwave (PMW) sensors encounter over ice and snow. However, high correlation coefficients with medians of 0.75–0.8 during summer and fall are very encouraging for potential future applications.

Triclosan is an antibacterial and antifungal chemical used in a variety of consumer products, including soaps, detergents, moisturizers, and cosmetics. Aquatic ecosystems may be exposed to triclosan following the release of remaining residues in wastewater effluents and biosolids. In December 2017, Environment and Climate Change Canada (ECCC) released a federal environmental quality guideline (FEQG) report that contained a federal water quality guideline (FWQG) for triclosan. This guideline will be used as an adjunct to the risk assessment and risk management of priority chemicals identified under the Government of Canada's Chemicals Management Plan (CMP). The FWQG value for triclosan (0.47 μg/L) was derived by ECCC using a hazardous concentration for 5% of species (HC5) from a species sensitivity distribution (SSD). We recalculated the FWQG after performing an independent analysis and evaluation of the available aquatic toxicity data for triclosan and compared our results with the ECCC FWQG value. Our independent analysis of the available aquatic toxicity data entailed conducting a literature search of all available and relevant studies, evaluating the quality and reliability of all studies considered using thorough and consistent study evaluation criteria, and thereby generating a data set of high-quality toxicity values. The selected data set includes 22 species spanning 5 taxonomic groups. An SSD was developed using this data set following the ECCC approaches. The HC5 from the SSD derived based on our validated data set is 0.76 μg/L. This HC5 value is slightly greater (i.e., less sensitive) than the value presented in ECCC's final FWQG. Integr Environ Assess Manag 2018;14:437-441. © 2018 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

The GEM-MACH-Global model is a global online meteorology-chemistry system currently being developed at the Department of Environment and Climate Change Canada (ECCC). The model is an extension of the Department’s operational, regional GEM-MACH numerical weather and air quality prediction system. The objectives for its development are to improve our understanding of the long range transport and fate of criteria air contaminants, and to improve our forecasting system by providing chemical boundary conditions for the regional air quality forecast system, and background fields for global chemical data assimilation (O3 and NOy species). For this purpose, GEM-MACH-Global was recently updated with a comprehensive photolysis module (JVAL14-MESSy) and a detailed gas-phase chemistry module based on the SAPRC07C mechanism. Compared to its original ADOM2 chemistry mechanism, the revised gas-phase chemistry is more explicit, with new species and ~15 additional reactions important in the upper troposphere and lower stratosphere (UTLS) region. Furthermore, a lightning emission module was incorporated to represent NOx emissions aloft. These changes were evaluated with a 2010 annual simulation on a 400 × 200 global-grid. The simulation included inputs from 2010 HTAP global anthropogenic emissions, GFEDv3 biomass burning emissions and ECCC’s operational meteorological analyses. The presentation will describe the current state-of-science development of GEM-MACH-Global and show comparisons results of the annual simulation.

Abstract. Canada has major sources of atmospheric methane (CH4), with the world's second-largest boreal wetland and the world's fourth-largest natural gas production. However, Canada's CH4 emissions remain uncertain among estimates. Better quantification and characterization of Canada's CH4 emissions are critical for climate mitigation strategies. To improve our understanding of Canada's CH4 emissions, we performed an ensemble regional inversion for 2007–2017 constrained with the Environment and Climate Change Canada (ECCC) surface measurement network. The decadal CH4 estimates show no significant trend, unlike some studies that reported long-term trends. The total CH4 estimate is 17.4 (15.3–19.5) Tg CH4 yr−1, partitioned into natural and anthropogenic sources at 10.8 (7.5–13.2) and 6.6 (6.2–7.8) Tg CH4 yr−1, respectively. The estimated anthropogenic emission is higher than inventories, mainly in western Canada (with the fossil fuel industry). Furthermore, the results reveal notable spatiotemporal characteristics. First, the modelled differences in atmospheric CH4 among the sites show improvement after inversion when compared to observations, implying the CH4 observation differences could help in verifying the inversion results. Second, the seasonal variations show slow onset and a late-summer maximum, indicating wetland CH4 flux has hysteretic dependence on air temperature. Third, the boreal winter natural CH4 emissions, usually treated as negligible, appear quantifiable (≥ 20 % of annual emissions). Understanding winter emission is important for climate prediction, as the winter in Canada is warming faster than the summer. Fourth, the inter-annual variability in estimated CH4 emissions is positively correlated with summer air temperature anomalies. This could enhance Canada's natural CH4 emission in the warming climate.