Global Environmental Multiscale Model Research Articles

Sweep interpolation: a cost-effective semi-Lagrangian scheme in the Global Environmental Multiscale model

Abstract. The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach for solving advection and is commonly used in numerical weather prediction (NWP) models. It has a significant impact on the accuracy of the solution and can potentially be the most expensive part of model integration. The sweep algorithm, which was first described by Mortezazadeh and Wang (2017), performs SL interpolation with the same computational cost as a third-order polynomial scheme but at the accuracy of the fourth order. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps. In this paper, we present a new application of the sweep algorithm within the context of global forecasts produced with Environment Climate Change Canada's Global Environmental Multiscale (GEM) model. Results show that the SL scheme with sweep interpolation is computationally more efficient compared to a conventional SL scheme with fourth-order polynomial interpolation, especially when a very large number of passive tracers are advected. An additional advantage of this new approach is that its implementation in a chemical and weather forecast model requires minimum modifications of the interpolation weighting coefficients. An analysis of the computational performance for a set of theoretical benchmarks as well as a global ozone forecast experiment show that up to 15 % reduction in total wall clock time is achieved. Forecasting experiments using the global version of the GEM model and the new interpolation show that the sweep interpolation can perform very well in predicting ozone distribution, especially in the tropopause region, where transport processes play a significant role.

Effect of urban heat island mitigation strategies on precipitation and temperature in Montreal, Canada: Case studies

High-resolution numerical weather prediction experiments using the Global Environmental Multiscale (GEM) model at a 250-m horizontal resolution are used to investigate the effect of the urban land-use on 2-m surface air temperature, thermal comfort, and rainfall over the Montreal (Canada) area. We focus on two different events of high temperatures lasting 2–3 days followed by intense rainfall: one is a large-scale synoptic system that crosses Montreal at night and the other is an afternoon squall line. Our model shows an overall good performance in adequately capturing the surface air temperature, dew-point temperature and rainfall during the events, although the precipitation pattern seems to be slightly blocked upwind of the city. Sensitivity experiments with different land use scenarios were conducted. Replacing all urban surfaces by low vegetation showed an increase of human comfort, lowering the heat index during the night between 2° and 6°C. Increasing the albedo of urban surfaces led to an improvement of comfort of up to 1°C during daytime, whereas adding street-level low vegetation had an improvement of comfort throughout the day of up to 0.5°C in the downtown area. With respect to precipitation, significant differences are only seen for the squall line event, for which removing the city modifies the precipitation pattern. For the large-scale synoptic system, the presence of the city does not seem to impact precipitation. These findings offer insight on the effects of urban morphology on the near-surface atmospheric conditions.

Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture

The development of a computationally efficient machine learning-based framework to understand the underlying causes for biases in climate model simulated fields is presented in this study. The framework consists of a two-step approach, with the first step involving the development of a Random Forest (RF) model, trained on observed data of the climate variable of interest and related predictors. The second step involves emulations of the climate variable of interest with the RF model developed in step one by replacing the observed predictors with those from the climate model one at a time. The assumption is that comparing these emulations with that of a reference emulation driven by all observed predictors can shed light on the contribution of respective predictor biases to the biases in the climate model simulation. The proposed framework is used to understand the biases in the Global Environmental Multiscale (GEM) model simulated surface soil moisture (SSM) for the April–September period, over a domain covering part of north-east Canada. The grid cell-based RF model, trained on daily SSM and related climate predictors (water availability, 2 m temperature, relative humidity, snowmelt, maximum snow water equivalent) from the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), demonstrates great skill in emulating SSM, with root mean square error of 0.036. Comparison of the five RF emulations based on GEM predictors with that based on ERA5 predictors suggests that the biases in the mean April–September SSM can be attributed mainly to biases in three predictors: water availability, 2 m temperature and relative humidity. The regions where these predictors contribute to biases in SSM are mostly collocated with the regions where they are shown to be the among the top three influential predictors through the predictor importance analysis, i.e., 2 m temperature in the southern part of the domain, relative humidity in the northern part of the domain and water availability over rest of the domain. The framework, without having to undertake expensive simulations with the climate model, thus successfully identifies the main causes for SSM biases, albeit with slightly reduced skill for heavily perturbed simulations. Furthermore, identification of the causes for biases, by informing targeted climate model improvements, can lead to additional reductions in computational costs.

Summertime Assessment of an Urban-Scale Numerical Weather Prediction System for Toronto

Urban-scale Numerical Weather Prediction (NWP) systems will be important tools for decision-making in and around large cities in a changing climate exposed to more extreme weather events. Such a state-of-the-art real-time system down to 250-m grid spacing was implemented in the context of the Toronto 2015 Panamerican games, Canada (PanAm). Combined with the Global Environmental Multiscale (GEM) model, attention was brought to the representation of the detailed urban landscape, and to the inclusion of sub-daily variation of the Great Lakes surface temperature. Results show a refined representation of the urban coastal environment micro-meteorology with a strong anisotropy of the urban heat island reaching about 2 °C on average for the summer season, coastal upwelling, and mesoscale features such as cumulus clouds and lake-breeze flow. Objective evaluation at the surface with a dense observational network reveals an overall good performance of the system and a clear improvement in comparison to reference forecasts at 2.5-km grid spacing in particular for standard deviation errors in urban areas up to 0.3 °C for temperature and dew point temperature, and up to 0.5 m s−1 for the wind speed, as well as for precipitation with an increased Equitable Threat Score (ETS) by up to 0.3 for the evening accumulation. The study provides confidence in the capacity of the new system to improve weather forecasts to be delivered to urban dwellers although further investigation of the initialization methods in urban areas is needed.

Regional-scale investigation of pile bearing capacity for Canadian permafrost regions in a warmer climate

Climate change is being experienced particularly intensely in the Arctic and therefore adaptation of engineering systems for this region cannot be further delayed. However, one of the major barriers to studies focused on adapting northern engineering systems is the lack of information at the spatial and temporal scales required for engineering applications. This study investigates pile bearing capacity for selected pile configurations for the Canadian permafrost regions (Nunavut and Northwest Territories), for current and future climates, using the very first ultra-high resolution (4 km) climate change simulation developed for the region using the Global Environmental Multiscale (GEM) model, for a high emission scenario.Comparison of the ultra-high-resolution GEM simulation, driven by reanalysis, with available observations confirms the model's ability in representing near-surface permafrost and related climate variables. The estimated adfreeze contribution to the total bearing capacity, for current climate, informed by the reanalysis-driven GEM simulation, for a 5-m cement pile, is found to be of the order of 15% for regions with shallow bedrock and 80% for regions with deeper bedrock. Application of the GEM climate change simulation outputs, for RCP8.5 scenario, suggest decreases to adfreeze contribution in the 5–30% range by 2040, with the largest differences noted for regions with deeper bedrock. For steel piles of same configuration, although the adfreeze contributions are only about 70% of that for cement piles, the projected relative changes are of similar magnitude.Further downscaling to 250 m resolution using the land model of GEM for the Slave Geological-Grays Bay corridor, where future developments are planned, including an all-season road, enables better estimation of bearing capacity for realistic pile scenarios such as those for bridges (in thick layer of sediments) used for river crossings. Due to the wide variation of pile materials, lengths and installation methods, site specific information can be developed from the framework developed in this study. The results of this study, including the ultra-high resolution climate change information, will thus form the basis for additional detailed investigations on climate-infrastructure interactions and climate resiliency studies.

Objective evaluation of the Global Environmental Multiscale Model (GEM) with precipitation and temperature for Iran

AbstractThe Global Environmental Multiscale Model (GEM) is currently in operational use for data assimilation and forecasting at 25–15 km scales; regional 10 km scales over North America; and 2.5 km scales over Canada. To evaluate the GEM model for forecasting applications in Iran, global daily temperature and precipitation outputs of GEM at a 25 km scale were compared to data sets from hydrometeorological stations and the De Martonne climate classification method was used to demarcate climate zones for comparisons. GEM model outputs were compared to observations in each of these zones. The results show good agreement between GEM outputs and measured daily temperatures with Kling‐Gupta efficiencies of 0.76 for the arid, 0.71 for the semiarid, and 0.78 for the humid regions. There is also an agreement between GEM outputs and measured annual precipitation with differences of 50% for the arid, 36% for the semiarid, and 15% for the humid region. There is a ~13% systematic difference between the elevation of stations and the average elevation of corresponding GEM grid cells; differences in elevation associated with forcing data sets can be potentially corrected using environmental lapse rates. Compared with hydrometeorological data sets, the GEM model precipitation outputs are less accurate than temperature outputs, and this may influence the accuracy of potential Iranian forecasting operations utilizing GEM. The results of this study provide an understanding of the operation and limitations of the GEM model for climate change and hydro‐climatological studies.

Wind Speed Forecasts of a Mesoscale Ensemble for Large-Scale Wind Farms in Northern China: Downscaling Effect of Global Model Forecasts

To facilitate wind power integration for the electric power grid operated by the Inner Mongolia Electric Power Corporation—a major electric power grid in China—a high-resolution (of 2.7 km grid intervals) mesoscale ensemble prediction system was developed that forecasts winds for 130 wind farms in the Inner Mongolia Autonomous Region. The ensemble system contains 39 forecasting members that are divided into 3 groups; each group is composed of the NCAR (National Center for Atmospheric Research) real-time four-dimensional data assimilation and forecasting model (RTFDDA) with 13 physical perturbation members, but driven by the forecasts of the GFS (Global Forecast System), GEM (Global Environmental Multiscale Model), and GEOS (Goddard Earth Observing System), respectively. The hub-height wind predictions of these three sub-ensemble groups at selected wind turbines across the region were verified against the hub-height wind measurements. The forecast performance and variations with lead time, wind regimes, and diurnal and regional changes were analyzed. The results show that the GFS group outperformed the other two groups with respect to correlation coefficient and mean absolute error. The GFS group had the most accurate forecasts in ~59% of sites, while the GEOS and GEM groups only performed the best on 34% and 2% of occasions, respectively. The wind forecasts were most accurate for wind speeds ranging from 3 to 12 m/s, but with an overestimation for low speeds and an underestimation for high speeds. The GEOS-driven members obtained the least bias error among the three groups. All members performed rather accurately in daytime, but evidently overestimated the winds during nighttime. The GFS group possessed the fewest diurnal errors, and the bias of the GEM group grew significantly during nighttime. The wind speed forecast errors of all three ensemble members increased with the forecast lead time, with the average absolute error increasing by ~0.3 m/s per day during the first 72 h of forecasts.

Implementation of a semi-Lagrangian fully-implicit time integration of the unified soundproof system of equations for numerical weather prediction

AbstractAn alternate dynamical core that employs the unified equations of A. Arakawa and C.S. Konor (2009) has been developed within Environment and Climate change Canada’s GEM (Global Environmental Multiscale) atmospheric model. As in the operational GEM dynamical core, the novel core utilizes the same fully-implicit two-time-level semi-Lagrangian scheme for time discretization while the log-pressure-based terrain-following vertical coordinate has been slightly adapted. Overall, the new dynamical core implementation required only minor changes to the existing informatics code of the GEM model and from a computational performance perspective, the new core does not incur any significant additional cost. A broad range of tests – that include both two-dimensional idealized theoretical cases and three-dimensional deterministic forecasts covering both hydrostatic and non-hydrostatic scales–have been carried out to evaluate the performance of the new dynamical core. For all the tested cases, when compared to the operational GEM model, the new dynamical core based on the unified equations has been found to produce statistically equivalent results. These results imply that the unified equations can be adopted for operational numerical weather prediction that would employ a single soundproof system of equations to produce reliable forecasts for all meteorological scales of interest with negligible changes for the computational overhead.

Multi-scale snowdrift-permitting modelling of mountain snowpack

Abstract. The interaction of mountain terrain with meteorological processes causes substantial temporal and spatial variability in snow accumulation and ablation. Processes impacted by complex terrain include large-scale orographic enhancement of snowfall, small-scale processes such as gravitational and wind-induced transport of snow, and variability in the radiative balance such as through terrain shadowing. In this study, a multi-scale modelling approach is proposed to simulate the temporal and spatial evolution of high-mountain snowpacks. The multi-scale approach combines atmospheric data from a numerical weather prediction system at the kilometre scale with process-based downscaling techniques to drive the Canadian Hydrological Model (CHM) at spatial resolutions allowing for explicit snow redistribution modelling. CHM permits a variable spatial resolution by using the efficient terrain representation by unstructured triangular meshes. The model simulates processes such as radiation shadowing and irradiance to slopes, blowing-snow transport (saltation and suspension) and sublimation, avalanching, forest canopy interception and sublimation, and snowpack melt. Short-term, kilometre-scale atmospheric forecasts from Environment and Climate Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic Prediction System (HRDPS) drive CHM and are downscaled to the unstructured mesh scale. In particular, a new wind-downscaling strategy uses pre-computed wind fields from a mass-conserving wind model at 50 m resolution to perturb the mesoscale HRDPS wind and to account for the influence of topographic features on wind direction and speed. HRDPS-CHM was applied to simulate snow conditions down to 50 m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (∼1000 km2) in the Canadian Rockies. Simulations were evaluated using high-resolution airborne light detection and ranging (lidar) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both wind-induced and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of windblown snow on leeward slopes and associated snow cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture lee-side flow recirculation and associated wind speed decreases. A terrain-based metric helped to identify these lee-side areas and improved the wind field and the associated snow redistribution. An overestimation of snow redistribution from windward to leeward slopes and subsequent avalanching was still found. The results of this study highlight the need for further improvements of snowdrift-permitting models for large-scale applications, in particular the representation of subgrid topographic effects on snow transport.

Evaluation of Arctic Water Vapor Profile Observations from a Differential Absorption Lidar

The continuous measuring of the vertical profile of water vapor in the boundary layer using a commercially available differential absorption lidar (DIAL) has only recently been made possible. Since September 2018, a new pre-production version of the Vaisala DIAL system has operated at the Iqaluit supersite (63.74°N, 68.51°W), commissioned by Environment and Climate Change Canada (ECCC) as part of the Canadian Arctic Weather Science project. This study presents its evaluation during the extremely dry conditions experienced in the Arctic by comparing it with coincident radiosonde and Raman lidar observations. Comparisons over a one year period were strongly correlated (r > 0.8 at almost all heights) and exhibited an average bias of +0.13 ± 0.01 g/kg (DIAL-sonde) and +0.18 ± 0.02 g/kg (DIAL-Raman). Larger differences exhibiting distinct artifacts were found between 250 and 400 m above ground level (AGL). The DIAL’s observations were also used to conduct a verification case study of operational numerical weather prediction (NWP) models during the World Meteorological Organization’s Year of Polar Prediction. Comparisons to ECCC’s global environmental multiscale model (GEM-2.5 km and GEM-10 km) indicate good agreement with an average bias < 0.16 g/kg for the higher-resolution (GEM-2.5 km) models. All models performed significantly better during the winter than the summer, likely due to the winter’s lower water vapor concentrations and decreased variability. This study provides evidence in favor of using high temporal resolution lidar water vapor profile measurements to complement radiosonde observations and for NWP model verification and process studies.

High-resolution hydrodynamic modelling to study year-round circulations and inter-basin exchanges in Lake Winnipeg

A new high-resolution (500 × 500 m), three-dimensional hydrodynamic model was applied to Lake Winnipeg to study summer and winter water circulation, temperature, and ice-cover during 2016–17. The model was run with a combination of buoy-based observations and the outputs from the Global Environmental Multiscale model forcing. Four primary riverine inflows and two outflows were considered in the model. The bathymetry from a previous study by the authors was revised using a 2018 survey covering the South Basin and the Narrows. Comparisons of this new model with the previous model setup (2 km resolution) show noticeable improvements in all simulated parameters. In the Narrows, where seiche-driven flows have predominant oscillation periods of ~27 h and ~17 h, the RMSE of simulated currents is 0.1 m s−1, half of that of the previous simulations. The new model was able to reasonably simulate the spatial development of ice-cover over the lake. The ice-free period circulation results show that there are two clockwise and counterclockwise gyres in the North Basin, and a weak seasonal clockwise gyre in the South Basin. Monthly circulation patterns differ from those during short wind events due to spatiotemporal variability of wind patterns. The materials from the Red and the Winnipeg Rivers need ~50 days to reach the Narrows before transported from the South Basin to the North Basin. The daily inter-basin exchange flow oscillations during the ice-free period can range up to ~3.5 × 104 m3 s−1, while under-ice daily exchanges are always northwards with values depending on inflowing riverine discharge up to ~0.5 × 104 m3 s−1.

Adaptation of the Predicted Particles Properties (P3) Microphysics Scheme for Large-Scale Numerical Weather Prediction

AbstractA parameterization for the subgrid-scale cloud and precipitation fractions has been incorporated into the Predicted Particle Properties (P3) microphysics scheme for use in atmospheric models with relatively coarse horizontal resolution. The modified scheme was tested in a simple 1D kinematic model and in the Canadian Global Environmental Multiscale (GEM) model using an operational global NWP configuration with a 25-km grid spacing. A series of 5-day forecast simulations was run using P3 and the much simpler operational Sundqvist condensation scheme as a benchmark for comparison. The effects of using P3 in a global GEM configuration, with and without the modifications, were explored through statistical metrics of common forecast fields against upper-air and surface observations. Diagnostics of state variable tendencies from various physics parameterizations were examined to identify possible sources of errors resulting from the use of the modified scheme. Sensitivity tests were performed on the coupling between the deep convection parameterization scheme and the microphysics, specifically regarding assumptions in the physical properties of detrained ice. It was found that even without recalibration of the suite of moist physical parameterizations, substituting the Sundqvist condensation scheme with the modified P3 microphysics resulted in some significant improvements to the temperature and geopotential height bias throughout the troposphere and out to day 5, but with degradation to error standard deviation toward the end of the integrations, as well as an increase in the positive bias of precipitation quantities. The modified P3 scheme was thus shown to hold promise for potential use in coarse-resolution NWP systems.

Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles

ABSTRACT This study describes a significant upgrade to the particulate matter (PM) size disaggregation profile library used for preparing emissions files for the GEM-MACH (Global Environmental Multiscale-Modelling Air-quality and CHemistry) chemical transport model (CTM). This model uses a sectional (bin) approach to represent the PM size distribution, where one configuration employs 12 size bins to disaggregate PM2.5 and PM10 inventory emissions into the first 10 bins ranging from 0.01 to 10.24 μm in diameter. For the size disaggregation step, a small library of three generic PM size disaggregation profiles is currently applied for three broad source categories (area, mobile, and point). However, as might be expected, these generic profiles are not always representative: for example, emissions from two very different area sources ‒ paved road dust and residential wood combustion ‒ are disaggregated using the same generic size distribution profile. In order to improve the current small PM size disaggregation profile library, a comprehensive literature review was conducted: over 100 relevant publications were identified and PM size distribution profiles for 36 different emission source types were selected and compiled. These 36 source-type-specific PM size distribution profiles were then combined based on process type with corresponding PM speciation profiles to create a library of chemically speciated and size-resolved PM disaggregation profiles. This library can now be used by the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system for the 12-bin version of GEM-MACH to perform PM chemical speciation and size allocation in one step. The size-profile data collected and compiled in this study may also be used for emissions processing for other CTMs with a size-resolved representation of PM. Details of the compilation of the 36 PM size disaggregation profiles are discussed, and the differences in processed PM emissions based on the current and updated PM size disaggregation profile libraries are shown. Implications: A new and expanded particulate matter (PM) size disaggregation profile library covering 36 emission source types has been developed based on an extensive literature review. Its use can produce significant changes in the size allocation of bulk PM inventory emissions processed for input to size-resolved PM chemical transport models. Such models are used to predict atmospheric visibility and to simulate the interactions of aerosol particles with atmospheric radiation and with clouds. The use of more accurate, size-resolved primary PM emissions by these models should improve their predictive skill for atmospheric PM processes affecting air quality, meteorology, and climate.

A note on the potential impact of aviation emissions on jet stream propagation over the northern hemisphere

The goal of the study was to investigate if aviation emissions could influence the climate and weather by modifying the chemical composition of the atmosphere and subsequently, the radiative balance. To carry out the set objective, we used the global environmental multiscale atmospheric chemistry model with comprehensive tropospheric and stratospheric chemistry that is interactive with the radiation calculations. The model was run for two current climate scenarios, with and without aviation emissions. The results of the study indicate that the most significant difference in the jet stream propagation occurred during the winter season, and the smallest was observed during summer. Changes in the jet stream propagation vary by season and region. During the colder time of the year, the eddy-driven jet stream tends to shift poleward, while during the spring season the equatorward shift was observed in a scenario with aviation emissions. Analysis of regional changes shows that the most noticeable differences occurred over the Pacific Ocean, Atlantic Ocean and Asia. The changes over the oceans changed the occurrence of the North Pacific and Bermuda–Azores Highs. Over Asia (Siberia), a stronger and more poleward drift of the eddy-driven jet stream was observed in a scenario without aviation emission. Dissimilarity in the jet stream velocity was found only during the winter seasons when in a scenario with aviation emission, the jet stream velocity was 10 m/s smaller as compared to the scenario without aviation emission.

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.

Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part II: Weak and Strong Coupling

We examine data assimilation coupling between meteorology and chemistry in the stratosphere from both weak and strong coupling strategies. The study was performed with the Canadian operational weather prediction Global Environmental Multiscale (GEM) model coupled online with the photochemical stratospheric chemistry model developed at the Belgian Institute for Space Aeronomy, described in Part I. Here, the Canadian Meteorological Centre’s operational variational assimilation system was extended to include errors of chemical variables and cross-covariances between meteorological and chemical variables in a 3D-Var configuration, and we added the adjoint of tracer advection in the 4D-Var configuration. Our results show that the assimilation of limb sounding observations from the MIPAS instrument on board Envisat can be used to anchor the AMSU-A radiance bias correction scheme. Additionally, the added value of limb sounding temperature observations on meteorology and transport is shown to be significant. Weak coupling data assimilation with ozone–radiation interaction is shown to give comparable results on meteorology whether a simplified linearized or comprehensive ozone chemistry scheme is used. Strong coupling data assimilation, using static error cross-covariances between ozone and temperature in a 3D-Var context, produced inconclusive results with the approximations we used. We have also conducted the assimilation of long-lived species observations using 4D-Var to infer winds. Our results showed the added value of assimilating several long-lived species, and an improvement in the zonal wind in the Tropics within the troposphere and lower stratosphere. 4D-Var assimilation also induced a correction of zonal wind in the surf zone and a temperature bias in the lower tropical stratosphere.

Simulating Canadian Arctic Climate at Convection-Permitting Resolution

Inadequate representation and parameterization of sub-grid scale features and processes are one of the main sources for uncertainties in regional climate change projections, particularly for the Arctic regions where the climate change signal is amplified. Increasing model resolution to a couple of kilometers will be helpful in resolving some of these challenges, for example to better simulate convection and refined land heterogeneity and thus land–atmosphere interactions. A set of multi-year simulations has been carried out for the Canadian Arctic domain at 12 km and 3 km resolutions using limited-area version of the global environmental multi-scale (GEM) model. The model is integrated for five years driven by the fifth generation of the European Centre for medium-range weather forecast reanalysis (ERA-5) at the lateral boundaries. The aim of this study is to investigate the role of horizontal model resolution on the simulated surface climate variables. Results indicate that although some aspects of the seasonal mean values are deteriorated at times, substantial improvements are noted in the higher resolution simulation. The representation of extreme precipitation events during summer and the simulation of winter temperature are better captured in the convection-permitting simulation. Moreover, the observed temperature–extreme precipitation scaling is realistically reproduced by the higher resolution simulation. These results advocate for the use of convective-permitting resolution models for simulating future climate projections over the Arctic to support climate impact assessment studies such as those related to engineering applications and where high spatial and temporal resolution are beneficial.

Ground-based infrared mapping of H2O2 on Mars near opposition

We pursued our ground-based seasonal monitoring of hydrogen peroxide on Mars using thermal imaging spectroscopy, with two observations of the planet near opposition, in May 2016 (solar longitude Ls = 148.5°, diameter = 17 arcsec) and July 2018 (Ls = 209°, diameter = 23 arcsec). Data were recorded in the 1232–1242 cm−1 range (8.1 μm) with the Texas Echelon Cross Echelle Spectrograph (TEXES) mounted at the 3 m Infrared Telescope Facility (IRTF) at the Mauna Kea Observatories. As in the case of our previous analyses, maps of H2O2 were obtained using line depth ratios of weak transitions of H2O2 divided by a weak CO2 line. The H2O2 map of April 2016 shows a strong dichotomy between the northern and southern hemispheres, with a mean volume mixing ratio of 45 ppbv on the north side and less than 10 ppbv on the south side; this dichotomy was expected by the photochemical models developed in the LMD Mars Global Climate Model (LMD-MGCM) and with the recently developed Global Environmental Multiscale (GEM) model. The second measurement (July 2018) was taken in the middle of the MY 34 global dust storm. H2O2 was not detected with a disk-integrated 2σ upper limit of 10 ppbv, while both the LMD-MGCM and the LEM models predicted a value above 20 ppbv (also observed by TEXES in 2003) in the absence of dust storm. This depletion is probably the result of the high dust content in the atmosphere at the time of our observations, which led to a decrease in the water vapor column density, as observed by the PFS during the global dust storm. GCM simulations using the GEM model show that the H2O depletion leads to a drop in H2O2, due to the lack of HO2 radicals. Our result brings a new constraint on the photochemistry of H2O2 in the presence of a high dust content. In parallel, we reprocessed the whole TEXES dataset of H2O2 measurements using the latest version of the GEISA database (GEISA 2015). We recently found that there is a significant difference in the H2O2 line strengths between the 2003 and 2015 versions of GEISA. Therefore, all H2O2 volume mixing ratios up to 2014 from TEXES measurements must be reduced by a factor of 1.75. As a consequence, in four cases (Ls around 80°, 100°, 150°, and 209°) the H2O2 abundances show contradictory values between different Martian years. At Ls = 209° the cause seems to be the increased dust content associated with the global dust storm. The inter-annual variability in the three other cases remains unexplained at this time.

A New Dynamical Core of the Global Environmental Multiscale (GEM) Model with a Height-Based Terrain-Following Vertical Coordinate

Abstract A new dynamical core of Environment and Climate Change Canada’s Global Environmental Multiscale (GEM) atmospheric model is presented. Unlike the existing log-hydrostatic-pressure-type terrain-following vertical coordinate, the proposed core adopts a height-based approach. The move to a height-based vertical coordinate is motivated by its potential for improving model stability over steep terrain, which is expected to become more prevalent with the increasing demand for very high-resolution forecasting systems. A dynamical core with height-based vertical coordinate generally requires an iterative solution approach. In addition to a three-dimensional iterative solver, a simplified approach has been devised allowing the use of a direct solver for the new dynamical core that separates a three-dimensional elliptic boundary value problem into a set of two-dimensional independent Helmholtz problems. The issue of dynamics–physics coupling has also been studied, and incorporating the physics tendencies within the discretized dynamical equations is found to be the most acceptable approach for the height-based vertical coordinate. The new dynamical core is evaluated using numerical experiments that include two-dimensional nonhydrostatic theoretical cases as well as 25-km resolution global forecasts. For a wide range of horizontal grid resolutions—from a few meters to up to 25 km—the results from the direct solution approach are found to be equivalent to the iterative approach for the new dynamical core. Furthermore, results from the different numerical experiments confirm that the new height-based dynamical core is equivalent to the existing pressure-based core in terms of solution accuracy.

Synchronized generation of high-resolution gridded precipitation and temperature fields

In this study, synchronized gridded precipitation and temperature generators were developed and evaluated in terms of their ability to reconstruct the spatial structure and other statistical properties of high-resolution CaPA (Canadian Precipitation Analysis) precipitation and GEM (Global Environmental Multiscale Model) temperature products. The precipitation generator is an extension to the gridded multi-site occurrence-amount model. While the multi-site framework is not efficiently applicable to regions with limited precipitation information and sparse observation network, this paper provides the full mathematical description of estimating the model based on high-resolution gridded products. The temperature model, on the other hand, introduces the first gridded hourly generator in the literature in which cyclic temperature components are conditioned using wet and dry day status information. This was achieved using harmonic functions establishing the connection between the simulated daily precipitation occurrence time-series and the moments of the generated temperature data. One of the key features of the proposed gridded generators is the capacity to produce temporally coherent fields of precipitation and temperature; an essential prerequisite for hydrologic modeling applications. The generators were evaluated in terms of their ability to reproduce the standard, interannual, and extreme-value characteristics of observed gridded products. The gridded precipitation model can preserve key statistical and spatial properties of the gridded CaPA observations to an acceptable level, while similar to the original multi-site framework, it falls short to capture long dry spells and the interannual variability of monthly total precipitation. The temperature model, on the other hand, preserves the majority of key features of the gridded GEM temperature observations, including the first two moments of the hourly data and the cumulative frequency of maximum and minimum temperature thresholds. The only notable defect identified is the departure of the interannual variability of the generated maximum and minimum temperatures from those observed.