Climate Change Canada Research Articles

Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer

The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed using the 12-year reforecasts data from five Sub-seasonal to Seasonal (S2S) models, including the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), the National Centers for Environmental Prediction (NCEP), and the Met Office (UKMO). The result shows that, compared with the individual models, our newly derived median multi-model ensemble (MME) can significantly improve the prediction skill of sub-seasonal precipitation in the MC. Both the Temporal Correlation Coefficient (TCC) skill and the Pattern Correlation Coefficient (PCC) skill reached 0.6 in lead week 1, dropped the following week, did not exceed 0.2 in lead week 3, and then lost their significance. The results show higher prediction skill near the Equator than in the north at 10° N. It is difficult to make effective predictions with the models beyond three weeks. The prediction ability of the median MME improves significantly as the total number of model members increases. The prediction performance of the median MME depends not only on the diversity of models but also on the number of model members. Moreover, the prediction skill is particularly sensitive to the intensity and phase of Boreal Summer Intraseasonal Oscillation 1 (BSISO1) with the highest skills appearing at initial phases 1 and 5.

Technique to study poop offers a snapshot of diets, ecosystems, and climate

Hundreds of thousands of eider ducks once nested on the remote, rocky beaches of the Canadian Arctic. But sometime late in the 19th century, eiders began to disappear. Inuit oral traditions recall declines well before most scientific seabird censuses came so far north in the 1970s, says paleolimnologist Jules Blais at the University of Ottawa in Canada. To understand exactly when and why past duck populations crashed, Blais uses an emerging tool: stable isotope analysis of duck poop. The story of Baffin Island’s eider ducks is told in stable isotopes of duck poop stored in years of sediment. Researchers across fields are now using fecal isotope analysis to answer a range of research questions. Image credit: Grant Gilchrist (Environment and Climate Change Canada, Ottawa, Canada). Stable isotope analysis itself is nothing new. An established technique in several fields, it uses isotopes to trace physical and biological processes. For example, because isotopes—atoms of an element that vary in their number of neutrons—have slightly different weights, they exist in different kinds of food in distinctive ratios and therefore move into body tissues in distinctive ratios. These ratios are widely used to work backward from bone or hair to infer the diets of animals or ancient humans. But fecal matter hasn’t generally been a go-to material for this kind of isotope research—until recently. Fecal stable isotope analysis is catching on in ecology, paleoecology, and paleoclimatology in part because samples are easy to collect and offer a quick snapshot of an animal’s diet. Some researchers are applying stable isotope analysis to poop to study modern and ancient diets, whereas others use it to reconstruct past ecology, climates, and environments, says paleoecologist Rachel Reid at the Virginia Polytechnic Institute and State University in Blacksburg. In the eider duck case, Blais analyzed the ratio of stable …

Erratum to: Greenhouse gas mitigation through dairy manure acidification

Erratum to: Greenhouse gas mitigation through dairy manure acidification

Assessment of the quality of TROPOMI high-spatial-resolution NO<sub>2</sub> data products in the Greater Toronto Area

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite (launched on 13 October 2017) is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral ranges. The measured spectra are used to retrieve total columns of trace gases, including nitrogen dioxide (NO2). For ground validation of these satellite measurements, Pandora spectrometers, which retrieve high-quality NO2 total columns via direct-sun measurements, are widely used. In this study, Pandora NO2 measurements made at three sites located in or north of the Greater Toronto Area (GTA) are used to evaluate the TROPOMI NO2 data products, including a standard Royal Netherlands Meteorological Institute (KNMI) tropospheric and stratospheric NO2 data product and a TROPOMI research data product developed by Environment and Climate Change Canada (ECCC) using a high-resolution regional air quality forecast model (in the air mass factor calculation). It is found that these current TROPOMI tropospheric NO2 data products (standard and ECCC) met the TROPOMI design bias requirement (< 10 %). Using the statistical uncertainty estimation method, the estimated TROPOMI upper-limit precision falls below the design requirement at a rural site but above in the other two urban and suburban sites. The Pandora instruments are found to have sufficient precision (< 0.02 DU) to perform TROPOMI validation work. In addition to the traditional satellite validation method (i.e., pairing ground-based measurements with satellite measurements closest in time and space), we analyzed TROPOMI pixels located upwind and downwind from the Pandora site. This makes it possible to improve the statistics and better interpret the high-spatial-resolution measurements made by TROPOMI. By using this wind-based validation technique, the number of coincident measurements can be increased by about a factor of 5. With this larger number of coincident measurements, this work shows that both TROPOMI and Pandora instruments can reveal detailed spatial patterns (i.e., horizontal distributions) of local and transported NO2 emissions, which can be used to evaluate regional air quality changes. The TROPOMI ECCC NO2 research data product shows improved agreement with Pandora measurements compared to the TROPOMI standard tropospheric NO2 data product (e.g., lower multiplicative bias at the suburban and urban sites by about 10 %), demonstrating benefits from the high-resolution regional air quality forecast model.

In Situ Estimates of Freezing/Melting Point Depression in Agricultural Soils Using Permittivity and Temperature Measurements

AbstractWe present a method to characterize soil moisture freeze‐thaw events and freezing/melting point depression using permittivity and temperature measurements, readily available from in situ sources. In cold regions soil freeze‐thaw processes play a critical role in the surface energy and water balance, with implications ranging from agricultural yields to natural disasters. Although monitoring of the soil moisture phase state is of critical importance, there is an inability to interpret soil moisture instrumentation in frozen conditions. To address this gap, we investigated the freeze‐thaw response of a widely used soil moisture probe, the HydraProbe, in the laboratory. Soil freezing curves (SFCs) and soil thawing curves (STCs) were identified using the relationship between soil permittivity and temperature. The permittivity SFC/STC was fit using a logistic growth model to estimate the freezing/melting point depression (Tf/m) and its spread (s). Laboratory results showed that the fitting routine requires permittivity changes greater than 3.8 to provide robust estimates and suggested that a temperature bias is inherent in horizontally placed HydraProbes. We tested the method using field measurements collected over the last 7 years from the Environment and Climate Change Canada and the University of Guelph's Kenaston Soil Moisture Network in Saskatchewan, Canada. By dividing the time series into freeze‐thaw events and then into individual transitions, the permittivity SFC/STC was identified. The freezing and melting point depression for the network was estimated as Tf/m = − 0.35 ± 0.2,with Tf = − 0.41 ± 0.22 °C and Tm = − 0.29 ± 0.16 °C, respectively.

Long-Term Analysis of Sea Ice Drift in the Western Ross Sea, Antarctica, at High and Low Spatial Resolution

The Ross Sea region, including three main polynya areas in McMurdo Sound, Terra Nova Bay, and in front of the Ross Ice Shelf, has experienced a significant increase in sea ice extent in the first four decades of satellite observations. Here, we use Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) to estimate 894 high-resolution sea ice motion fields of the Western Ross Sea in order to explore ice-atmosphere interactions based on sequential high-resolution Advanced Synthetic Aperture Radar (ASAR) images from the Envisat satellite acquired between 2002–2012. Validation of output motion vectors with manually drawn vectors for 24 image pairs show Pearson correlation coefficients of 0.92 ± 0.09 with a mean deviation in direction of −3.17 ± 6.48 degrees. The high-resolution vectors were also validated against the Environment and Climate Change Canada sea ice motion tracking algorithm, resulting in correlation coefficients of 0.84 ± 0.20 and the mean deviation in the direction of −0.04 ± 17.39 degrees. A total of 480 one-day separated velocity vector fields have been compared to an available NSIDC low-resolution sea ice motion vector product, showing much lower correlations and high directional differences. The high-resolution product is able to better identify short-term and spatial variations, whereas the low-resolution product underestimates the actual sea ice velocities by 47% in this important near-coastal region. The large-scale pattern of sea ice drift over the full time period is similar in both products. Improved image coverage is still desired to capture drift variations shorter than 24 h.

A Selective and Sensitive Gas Chromatography-Tandem Mass Spectrometry Method for Quantitation of Synthetic Musks in Human Serum.

Synthetic musk compounds are widely used as fragrances in many consumer products; however, information on human exposure and health effects is limited. Also, analytical methods for their quantification in biological matrices are limited. In this study, an integrated method was developed and validated for the analysis of selected synthetic musk compounds in human serum. The method is based on liquid-liquid extraction (LLE), sample clean-up by solid-phase extraction (SPE), and separation and detection by gas chromatography coupled with tandem mass spectrometry (GC-MS/MS). The method demonstrated good recoveries (86-105%) and high sensitivity, with low method detection limits (MDLs) ranging from 0.04 to 0.17 µg/L. The method was applied to the analysis of 10 synthetic musk compounds in 40 serum samples collected from Canadian women aged 20-44 years (20 individual samples collected in 2014 and 20 pooled samples collected in 2006). The most commonly detected compound was Galaxolide (HHCB), with median concentrations of 0.59 µg/L in samples collected in 2006, and 0.34 µg/L for samples collected in 2014. Musk ketone (MK) was not detected in any of the samples collected in 2006, but was detected in 60% of the samples collected in 2014 with a median concentration of 0.29 µg/L. Tonalide (AHTN) was detected in only one sample above its MDL (0.12 µg/L). This is the first study in Canada to report levels of synthetic musks in human. The data generated from this study has been used in risk screening assessment by Environment and Climate Change Canada and Health Canada.

The 2018 fire season in North America as seen by TROPOMI: aerosol layer height intercomparisons and evaluation of model-derived plume heights

Abstract. Before the launch of the TROPOspheric Monitoring Instrument (TROPOMI), only two other satellite instruments were able to observe aerosol plume heights globally, the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The TROPOMI aerosol layer height is a potential game changer, since it has daily global coverage, and the aerosol layer height retrieval is available in near real time. The aerosol layer height can be useful for aviation and air quality alerts, as well as for improving air quality forecasting related to wildfires. Here, TROPOMI's aerosol layer height product is evaluated with MISR and CALIOP observations for wildfire plumes in North America for the 2018 fire season (June to August). Further, observing system simulation experiments were performed to interpret the fundamental differences between the different products. The results show that MISR and TROPOMI are, in theory, very close for aerosol profiles with single plumes. For more complex profiles with multiple plumes, however, different plume heights are retrieved; the MISR plume height represents the top layer, and the plume height retrieved with TROPOMI tends to have an average altitude of several plume layers. The comparison between TROPOMI and MISR plume heights shows that, on average, the TROPOMI aerosol layer heights are lower, by approximately 600 m, compared to MISR, which is likely due to the different measurement techniques. From the comparison to CALIOP, our results show that the TROPOMI aerosol layer height is more accurate over dark surfaces, for thicker plumes, and plumes between approximately 1 and 4.5 km. MISR and TROPOMI are further used to evaluate the plume height of Environment and Climate Change Canada's operational forecasting system FireWork with fire plume injection height estimates from the Canadian Forest Fire Emissions Prediction System (CFFEPS). The modelled plume heights are similar compared to the satellite observations but tend to be slightly higher with average differences of 270–580 and 60–320 m compared to TROPOMI and MISR, respectively.

What is Effective Research Communication? Towards Cooperative Inquiry with Nunavut Communities

Communication is recognized as the foundation of developing partnerships in science. In this study, we assess the effectiveness of several communication processes, practices, and tools used by wildlife researchers in northern communities in Arctic Canada. A case study was conducted in the communities of Cape Dorset and Coral Harbour (Salliq), Nunavut, Canada, to assess the effectiveness of research communication approaches carried out by the northern marine bird research group of Environment and Climate Change Canada, which has a long-standing research relationship with these two communities. Our objectives were to 1) explore local experiences with research—marine bird research in particular, 2) examine what communication approaches and tools Nunavummiut viewed as most effective for learning about research activities and feeling engaged in the process, and 3) identify new and emerging communication needs in Nunavut communities to support more effective research partnerships. Our findings indicate that several communication methods used by wildlife researchers, such as community meetings, have become less effective because of changing information-sharing practices at the community level. Other communication practices, such as using social media, hold much promise, but as of yet are underutilized by researchers, though of interest to northern communities. Acknowledging that every northern community is unique, with context-specific priorities, capacities, and needs, effective research partnerships should be built upon communication approaches that foster cooperative inquiry and learning. In progress towards this goal, we explore two emerging and related themes: first, access to information and communication technologies in the two communities, and second, the engagement of youth in Arctic research communication and delivery.

Assimilation of SAR Ice and Open Water Retrievals in Environment and Climate Change Canada Regional Ice-Ocean Prediction System

In this article, we evaluate the impact of assimilating spaceborne synthetic aperture radar (SAR) data in an Arctic regional ice analysis system over a year cycle. Ice and water information was automatically extracted from more than 7000 RADARSAT-2 HH-HV ScanSAR Wide images acquired over the Canadian Arctic and adjacent waters throughout the entire year 2013. A quality-control procedure was specifically developed and applied to reduce the number of erroneous SAR retrievals. To assess the impact of SAR ice and water retrievals on the Environment and Climate Change Canada (ECCC) Regional Ice-Ocean Prediction System (RIOPS) ice concentration analyses, we designed a set of data assimilation experiments with and without the inclusion of SAR retrievals. Our verification results suggest that the assimilation of SAR-derived retrievals considerably improves ice concentration analyses in the situations where high spatial resolution is important (e.g., near land and over small inland lakes). Furthermore, SAR retrievals are particularly useful over the areas where the Canadian Ice Service's (CIS) manually derived ice products (such as Image Analyses, daily and weekly ice charts) are not available or have limited coverage. The three-satellite RADARSAT Constellation Mission (RCM) launched in June 2019 will significantly increase the temporal frequency of SAR data. According to the most recent CIS estimate, more than 54 000 RCM images a year will be acquired over the CIS areas of interest. Therefore, the assimilation of SAR retrievals from RCM should further enhance automated ice concentration analyses products.

Evaluation of the Soil, Vegetation, and Snow (SVS) Land Surface Model for the Simulation of Surface Energy Fluxes and Soil Moisture under Snow-Free Conditions

The recently developed Soil, Vegetation, and Snow (SVS) land surface model is being progressively implemented at Environment and Climate Change Canada (ECCC) for operational numerical weather and hydrological predictions. The objective of this study is to evaluate the ability of SVS, in offline point-scale mode and under snow-free conditions, to simulate the surface heat fluxes and soil moisture when compared to flux tower observations and simulations from the Canadian Land Surface Scheme (CLASS), used here as a benchmark model. To do this, we performed point-scale simulations of between 4 and 12 years of data records at six selected sites of the FLUXNET network under arid, Mediterranean and tropical climates. At all sites, SVS shows realistic simulations of latent heat flux, sensible heat flux and net radiation. Soil heat flux is reasonably well simulated for the arid sites and one Mediterranean site and poorly simulated for the tropical sites. On the other hand, surface soil moisture was reasonably well simulated at the arid and Mediterranean sites and poorly simulated at the tropical sites. SVS performance was comparable to CLASS not only for energy fluxes and soil moisture, but also for more specific processes such as evapotranspiration and water balance.

The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally

AbstractThe Canadian Surface Prediction Archive (CaSPAr) is an archive of numerical weather predictions issued by Environment and Climate Change Canada. Among the products archived on a daily basis are five operational numerical weather forecasts, three operational analyses, and one reanalysis product. The products have hourly to daily temporal resolution and 2.5–50-km spatial resolution. To date the archive contains 394 TB of data while 368 GB of new data are added every night. The data are archived in CF-1.6-compliant netCDF-4 format. The archive is available online (https://caspar-data.ca) since June 2017 and allows users to precisely request data according to their needs, that is, spatial cropping based on a standard shape or uploaded shapefile of the domain of interest and selection of forecast horizons, variables, and issue dates. The degree of customization in CaSPAr is a unique feature relative to other publicly accessible numerical weather prediction archives and it minimizes user download requirements and local processing time. We benchmark the processing time and required storage of such requests based on 216 test scenarios. We also demonstrate how CaSPAr data can be employed to analyze extreme rainfall events. CaSPAr provides access to data that are fundamental for evaluating numerical weather prediction models and demonstrating the improvement in products such as flood and energy demand forecasting systems.

Subseasonal Forecast Skill over the Northern Polar Region in Boreal Winter

AbstractPentad (5-day averaged) forecast skill over the Arctic region in boreal winter is evaluated for the subseasonal to seasonal prediction (S2S) systems from three operational centers: the European Centre for Medium-Range Weather Forecasts (ECMWF), the U.S. National Centers for Environmental Prediction (NCEP), and Environment and Climate Change Canada (ECCC). The results indicate that for a lead time longer than about 10 days the forecast skill of 2-m air temperature and 500-hPa geopotential height in the Arctic area is low compared to the tropical and midlatitude regions. The three S2S systems have comparable forecast skill in the northern polar region. Relatively high skill is observed in the Arctic sector north of the Bering Strait in pentads 4–6. Possible sources of S2S predictability in the polar region are explored. The polar forecast skill is found to be dependent on the phase of the Arctic Oscillation (AO) in the initial condition; that is, forecasts initialized with the negative AO are more skillful than those starting from the positive AO. This is likely due to the influence of the stratospheric polar vortex. The tropical MJO is found to also influence the prediction skill in the polar region. Forecasts starting from MJO phases 6–7, which correspond to suppressed convection in the equatorial eastern Indian Ocean and enhanced convection in the tropical western Pacific, tend to be more skillful than those initialized from other MJO phases. To improve the polar prediction on the subseasonal time scale, it is important to have a well-represented stratosphere and tropical MJO and their associated teleconnections in the model.

The Canadian Arctic Weather Science Project: Introduction to the Iqaluit Site

AbstractThe goal of the Canadian Arctic Weather Science (CAWS) project is to conduct research into the future operational monitoring and forecasting programs of Environment and Climate Change Canada in the Arctic where increased economic and recreational activities are expected with enhanced transportation and search and rescue requirements. Due to cost, remoteness and vast geographical coverage, the future monitoring concept includes a combination of space-based observations, sparse in situ surface measurements, and advanced reference sites. A prototype reference site has been established at Iqaluit, Nunavut (63°45'N, 68°33'W), that includes a Ka-band radar, water vapor lidars (both in-house and commercial versions), multiple Doppler lidars, ceilometers, radiation flux, and precipitation sensors. The scope of the project includes understanding of the polar processes, evaluating new technologies, validation of satellite products, validation of numerical weather prediction systems, development of warning products, and communication of their risk to a variety of users. This contribution will provide an overview of the CAWS project to show some preliminary results and to encourage collaborations.

Using CloudSat‐CPR Retrievals to Estimate Snow Accumulation in the Canadian Arctic

AbstractSnow is a critical contributor to our global water and energy budget, with profound impacts for water resource availability and flooding in cold regions. The vast size and remote nature of the Arctic present serious logistical and financial challenges to measuring snow over extended time periods. Satellite observations provided by the Cloud Profiling Radar instrument—installed on the National Aeronautics and Space Administration satellite CloudSat—allow the retrieval of snowfall rates in high‐latitude regions, which have been used to estimate surface snow accumulation. In this study, a validation of CloudSat‐derived terrestrial snow estimates is presented at four Environment and Climate Change Canada weather stations situated in the Arctic for the common period 2007–2015. Comparisons of monthly climatological snow accumulation show mean biases of less than 1.5‐mm snow water equivalent annually. Monthly time series exhibit correlations above 0.5 and root‐mean‐square error below 10‐mm snow water equivalent at the two highest‐latitude stations (Eureka and Resolute Bay) with correlations falling below 0.5 south of 70°N. CloudSat was also found to underestimate annual mean snow accumulation at the majority of sites, suggesting a potential negative bias in CloudSat's accumulation estimates, or underestimation related to sampling. These results imply that CloudSat can provide reliable estimates of snow accumulation across similar high‐latitude regions above 70°N.

The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM–MACH–GHG v.137-reg

Abstract. In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high-spatiotemporal-resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km×10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower-resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate the high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations based on comparisons to surface and aircraft observations. In addition, the bias and standard deviation of forecast error in boreal summer are reduced by the regional model. Better representation of model topography in the regional model results in improved simulation of the CO2 diurnal cycle compared to the global model at Walnut Grove, California. The new regional model will form the basis of a flux inversion system that estimates regional-scale fluxes of GHGs over Canada.

A Multi-Year Evaluation of Doppler Lidar Wind-Profile Observations in the Arctic

Doppler light detection and ranging (lidar) wind profilers have proven their capability to measure vertical wind profiles with an accuracy comparable to anemometers and radiosondes. However, most of these comparisons were performed over short time periods or at mid-latitudes. This study presents a multi-year assessment of the accuracy of Doppler lidar wind-profile measurements in the Arctic by comparing them with coincident radiosonde observations, and excellent agreement was observed. The suitability of the Doppler lidar for verification case studies of operational numerical weather prediction (NWP) models during the World Meteorological Organization’s Year of Polar Prediction is also demonstrated, by using Environment and Climate Change Canada’s (ECCC) global environmental multiscale model (GEM-2.5 km and GEM-10 km). Since 2016, identical scanning Doppler lidars were deployed at two supersites commissioned by ECCC as part of the Canadian Arctic Weather Science project. The supersites are located in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) with a third Halo Doppler lidar located in Squamish (50°N, 123°W). Two lidar wind-profile measurement methodologies were investigated; the velocity-azimuth display method exhibited a smaller average bias (−0.27 ± 0.02 m/s) than the Doppler beam-swinging method (–0.46 ± 0.02 m/s) compared to the sonde. Comparisons to ECCC’s NWP models indicate good agreement, more so during the summer months, with an average bias < 0.71 m/s for the higher-resolution (GEM-2.5 km) ECCC models at Iqaluit. Larger biases were found in the mountainous terrain of Whitehorse and Squamish, likely due to difficulties in the model’s ability to resolve the topography. This provides evidence in favor of using high temporal resolution lidar wind-profile measurements to complement radiosonde observations and for NWP model verification and process studies.

Advances in wind power forecasting and power loss mitigation for cold climate operation

Key challenges related to operating wind farms in cold climates include lost power production due to ice accumulation on wind turbine blades, increased vibrations, and site safety due to the risk of ice throw. This paper presents the results of recent efforts to estimate the overall impact of cold climate impacts on wind energy in Canada, and describes the latest progress in developing an icing forecast model based on meteorological forecasts generated from the Environment and Climate Change Canada GEM-LAM mesoscale model. Two recent studies – an energy production analysis of 89 Canadian wind farms, and an operator survey of 43 wind farms – showed similar results in terms of estimated annual energy production losses due to cold climate, indicating that a good understanding of the magnitude of cold climate impacts on Canadian wind farms has been achieved. Improved forecasting offers the possibility to better manage icing events and other cold climate issues, reducing lost energy and improving overall system efficiency. The GPEO model being developed by Nergica aims to provide accurate forecasts of ice accumulation and energy loss from meteorological input data and is intended to be used by wind farms and utilities in an operational setting.

Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1)

Abstract. A fully coupled atmosphere–ocean–ice model has been used to produce global weather forecasts at Environment and Climate Change Canada (ECCC) since November 2017. Currently, the system relies on four uncoupled data assimilation (DA) components for initializing the fully coupled global atmosphere–ocean–ice forecast model: atmosphere, ocean, sea ice and sea surface temperature (SST). The goal of the present study is to implement a weakly coupled data assimilation (WCDA) between the atmosphere and ocean components and evaluate its performance against uncoupled DA. The WCDA system uses coupled atmosphere–ocean–ice short-term forecasts as background states for the atmospheric and the ocean DA components that independently compute atmospheric and ocean analyses. This system leads to better agreement between the coupled atmosphere–ocean analyses and the coupled atmosphere–ocean–ice forecasts than between the uncoupled analyses and the coupled forecasts. The use of WCDA improves the atmospheric forecast score near the surface, but a slight increase in the atmospheric temperature bias is observed. A small positive impact from using the short-term SST forecast on the satellite radiance observation-minus-forecast statistics is noted. Ocean temperature and salinity forecasts are also improved near the surface. The next steps toward stronger DA coupling are highlighted.

Dimethyl sulfide and its role in aerosol formation and growth in the Arctic summer – a modelling study

Abstract. Atmospheric dimethyl sulfide, DMS(g), is a climatically important sulfur compound and is the main source of biogenic sulfate aerosol in the Arctic atmosphere. DMS(g) production and emission to the atmosphere increase during the summer due to the greater ice-free sea surface and higher biological activity. We implemented DMS(g) in the Environment and Climate Change Canada’s (ECCC) online air quality forecast model, GEM-MACH (Global Environmental Multiscale–Modelling Air quality and CHemistry), and compared model simulations with DMS(g) measurements made in Baffin Bay and the Canadian Arctic Archipelago in July and August 2014. Two seawater DMS(aq) datasets were used as input for the simulations: (1) a DMS(aq) climatology dataset based on seawater concentration measurements (Lana et al., 2011) and (2) a DMS(aq) dataset based on satellite detection (Galí et al., 2018). In general, GEM-MACH simulations under-predict DMS(g) measurements, which is likely due to the negative biases in both DMS(aq) datasets. However, a higher correlation and smaller bias were obtained with the satellite dataset. Agreement with the observations improved when climatological values were replaced by DMS(aq) in situ values that were measured concurrently with atmospheric observations over Baffin Bay and the Lancaster Sound area in July 2014. The addition of DMS(g) to the GEM-MACH model resulted in a significant increase in atmospheric SO2 for some regions of the Canadian Arctic (up to 100 %). Analysis of the size-segregated sulfate aerosol in the model shows that a significant increase in sulfate mass occurs for particles with a diameter smaller than 200 nm due to the formation and growth of biogenic aerosol at high latitudes (>70∘ N). The enhancement in sulfate particles is most significant in the size range from 50 to 100 nm; however, this enhancement is stronger in the 200–1000 nm size range at lower latitudes (<70∘ N). These results emphasize the important role of DMS(g) in the formation and growth of fine and ultrafine sulfate-containing particles in the Arctic during the summertime.