Tracking Carbon Dioxide with Lagrangian Transport Simulations: Case Study of Canadian Forest Fires in May 2021

: The large amounts of greenhouse gases, such as carbon dioxide, produced by severe forest fires not only seriously affect the ecosystems in the area where the fires occur but also cause a greenhouse effect that has a profound impact on the natural environment in other parts of the world. Numerical simulations of greenhouse gas transport processes are often affected by uncertainties in the location and timing of the emission sources and local meteorological conditions, and it is difficult to obtain accurate and credible predictions by combining remote sensing satellite data with given meteorological forecasts or reanalyses. To study the regional transport processes and impacts of greenhouse gases produced by sudden large-scale forest fires, this study applies the Lagrangian particle dispersion model Massive-Parallel Trajectory Calculations (MPTRAC) to conduct forward simulations of the CO 2 transport process of greenhouse gases emitted from forest fires in the central region of Saskatchewan, Canada, during the period of 17 May to 25 May 2021. The simulation results are validated with the Orbiting Carbon Observatory-2 Goddard Earth Observing System (OCO-2 GEOS) Level 3 daily gridded CO 2 product over the study area. In order to leverage the high computational costs of the numerical simulations of the model, we implement the forward simulations on the Tianhe-2 supercomputer platform and the JUWELS HPC system, which greatly improves the computational efficiency through parallel computation and makes near-real-time predictions of atmospheric transport processes feasible.


Introduction
Greenhouse gases (GHGs) such as carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O), among others, affect the Earth's energy balance and contribute to the greenhouse effect, which is one of the main factors contributing to climate change [1,2].The international community is highly concerned about the emission of GHGs, especially CO 2 .Normally, the concentration of CO 2 in the atmosphere is at a certain background level, but large-scale human activities and sudden natural events may lead to a sharp increase in the level of CO 2 in the atmosphere [3].The impact of forest fires as a natural disaster cannot be ignored [4], and the subsequent transport of CO 2 usually crosses multiple countries and regions.By studying CO 2 transport pathways and sharing data and information, we can better understand the distribution of CO 2 in the atmosphere, thus laying the foundation for further backtracking and modelling of the dispersion of atmospheric pollutants or GHGs from unknown sources, and contributing positively to the monitoring and management of CO 2 emission issues.
Large-scale trajectory simulations play a crucial role in understanding and predicting the dispersion of trace gases or other atmospheric constituents.One key aspect of largescale trajectory simulations is their significance in environmental studies.By accurately simulating the transport of trace gases, researchers can gain a better understanding of how pollutants disperse in the atmosphere.This knowledge is essential for assessing the impact of industrial emissions, forest fires, or other sources of pollution on the environment and human health.It allows scientists and policymakers to develop effective strategies for mitigating pollution and improving air quality.Furthermore, large-scale trajectory simulations are crucial in assessing the potential risks and consequences of accidental releases or hazardous events.Accurate simulations enable decision-makers to make informed choices and minimize the potential harm to the affected population and the environment.In addition, large-scale trajectory simulations are essential in the field of atmospheric science and climate research.Scientists can study the transport of greenhouse gases, such as CO 2 and CH 4 , which play a significant role in global climate change.Understanding how these trace gases disperse and interact with the atmosphere allows researchers to improve climate models and enhance predictions regarding weather phenomena, climate patterns, and long-term climate trends.
The main types of chemistry transport models used to study transport and dispersion of CO 2 are the Lagrangian particle dispersion model [5], the Eulerian model [6], and the hybrid model [7].The Lagrangian particle dispersion model describes air flow by tracking the position and velocity of individual air parcels or particles.Eulerian models describe air flow based on the advection and diffusion of trace gases on a regular grid.Compared to the Eulerian model, the Lagrangian model is based on a microscopic perspective, assuming that each air parcel or particle has its own unique velocity and pressure, allowing it to capture small-scale features and reduce numerical diffusion.The independent motion of the particles allows us to compute and update particle trajectories independently.At the same time, it facilitates the allocation of particles to different processing units for parallel computation of their positions, velocities, and other properties.This property gives the Lagrangian particle diffusion model excellent parallel performance.
The Lagrangian particle dispersion model (LPDM) has been widely used in recent years for the forward and inverse modelling of atmospheric pollutants and trace gas emissions [8][9][10] due to its scalability, numerical efficiency, capability of retrieving sourcereceptor relationships, and ability to implement stochastic processes representing transport uncertainties [11].A modified version of the Stochastic Time-Inverted Lagrangian Transport (STILT) model, known as "X-STILT" [12], has been developed to simulate urban CO 2 signals using NASA's OCO-2 satellite data.X-STILT incorporates satellite profiles and provides comprehensive uncertainty estimates of urban CO 2 enhancements, successfully reproducing observed CO 2 enhancements and quantifying the contributions of transport and emission uncertainties to the overall signal.This model, X-STILT, is expected to serve as a valuable tool for interpreting column measurements, estimating urban emissions, and enhancing our understanding of urban CO 2 quantification.
Currently, the LPDM is widely used in atmospheric research and environmental studies to understand the transport, dispersion, and deposition of atmospheric constituents on regional and global scales.Commonly used LPDMs include the Flexible Particle Dispersion model (FLEXPART) [13,14], the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) [15,16], the three-dimensional unsteady Lagrangian diffusion model California Puff (Calpuff) [17,18], and Massive-Parallel Trajectory Calculations model (MPTRAC) [19,20] among others.Different models differ in their numerical methods to solve the advection and diffusion problem, as characterized by the complex flow field environment during particle transport and the complexity of the diffusion processes.Compared to other Lagrangian models, MPTRAC is a newly developed LDPM specifically designed for supercomputer platforms.Based on an MPI-OpenMP-OpenACC hybrid parallelization scheme, it provides high computational efficiency, is suitable for large-scale simulations, and has been applied in several volcanic SO 2 dispersion case studies [21].
In this paper, we applied the Lagrangian particle dispersion model MPTRAC [19] mainly on the Tianhe-2 supercomputer.Our aim was to use MPTRAC to test new field applications other than volcanic eruption simulation and to develop a numerical modelling approach for CO 2 transport processes.We focused on a forest fire in the region near Saskatchewan, Canada.The modeling case study covered the period from 17 May to 25 May 2021.We conducted a qualitative comparison of our model's simulation results with a satellite-based data product of daily CO 2 distributions provided by the National Aeronautics and Space Administration (NASA).The objective was to demonstrate the validity of the long-range transport simulations of CO 2 from forest fires provided by MPTRAC.This is the first time that the MPTRAC model has been used to simulate the long-range transport of CO 2 following forest fires.

The MPTRAC Lagrangian Transport Model
The Massive-Parallel Trajectory Calculations (MPTRAC) (version2.6,manufactured by the Jülich Supercomputing Centre, Jülich, Germany)model [19] is a Lagrangian particle dispersion model and an open-source software package developed by the Jülich Supercomputing Center in Germany.It can be utilized to simulate and analyze the transport of trace gases and aerosols in the free troposphere and stratosphere.It is suitable for studying fine-scale structures, filamentary transport, and mixing processes in the atmosphere.The MPTRAC model implements an MPI-OpenMP-OpenACC hybrid parallelization scheme, making it highly suitable for large-scale simulations on high-performance computing systems.
Given the wind and velocity field v(x, t), the MPTRAC model uses the trajectory equation in Equation (1) to calculate the position x(t) of an air parcel at time t, To obtain a reasonably accurate and computationally efficient solution to the trajectory equation [22], the model uses the explicit midpoint method [23] to calculate the air parcel trajectory, where x(t) represents the spatial position of a particle at time t, v(t) represents the velocity of the particle at time t, and ∆t is the time step of the model.Following the approach of the FLEXPART model [13], MPTRAC simulates the diffusion of particles in the troposphere and stratosphere by adding random perturbations to the positions x of the air parcels at each time step ∆t.The random perturbations are characterized by a horizontal diffusivity of 50 m 2 s −1 in the troposphere and a vertical diffusivity of 0.1 m 2 s −1 in the stratosphere.A smooth transition between tropospheric and stratospheric diffusivities is achieved by linearly interpolating the horizontal and vertical diffusivities over a logarithmic pressure altitude range of ±1 km around the tropopause.In addition to the fixed diffusivities, a parameterization for subgrid-scale wind fluctuations based on the Langevin equation is applied.
In our experiments, the "air parcels" were the "CO 2 particles" emitted by the forest fire.Each trajectory corresponded to a single CO 2 parcel.The transport of CO 2 was represented by an ensemble of trajectories, each carrying a specific amount of CO 2 .Large quantities of CO 2 particles released by forest fires can be obtained by calculations using the Lagrangian model.

The OCO-2 GEOS Level 3 Data Product
Forest fires release large amounts of CO 2 into the atmosphere, causing strong anomalies that can be observed by satellite measurements.We used NASA's existing OCO-2 GEOS Level 3 data product to help analyze the simulation results.The OCO-2 GEOS Level 3 daily, 0.5 • × 0.625 • assimilated CO 2 V10r data product [24] provides assimilated gridded daily CO 2 data from OCO-2 satellite measurements and the GEOS Earth system model.The OCO-2 mission provides the highest-quality space-based XCO 2 retrievals to date [25].However, the instrument data are characterized by large gaps in coverage due to OCO-2's narrow 10 km ground track and an inability to see through clouds and dense aerosols.The global gridded Level 3 dataset is produced using a data assimilation technique commonly referred to as state estimation in the geophysical literature.Data assimilation integrates simulations and observations and adjusts the state of atmospheric constituents, such as CO 2 , to reflect observed values.This process effectively fills observational gaps in time and space where data are unavailable, utilizing information from prior observations and short transport simulations conducted by GEOS.Compared to other methods, data assimilation has the advantage of providing estimates based on collective scientific understanding, particularly of the Earth's carbon cycle and atmospheric transport.
OCO-2 GEOS (Goddard Earth Observing System) Level 3 data are produced by ingesting OCO-2 Level 2 retrievals every 6 h with GEOS CoDAS, a modeling and data assimilation system maintained by NASA's Global Modeling and Assimilation Office (GMAO).GEOS CoDAS uses a high-performance computing implementation of the Gridpoint Statistical Interpolation (GSI) approach to solve the state estimation problem.GSI finds the analyzed state that minimizes the three-dimensional variational (3D-Var) cost function formulation of the state estimation problem.Detailed information of the OCO-2 GEOS Level 3 data is shown in Table 1.

Property Description
Shortname OCO2_GEOS_L3CO2_DAY Longname OCO-2 GEOS Level 3 daily, 0.5 Although Canada has mostly a subarctic climate [26], severe droughts in recent years have forced more forest fires [27].According to the statistics from the Canadian National Fire Database (CNFDB), the area affected by forest fires reached a small peak in 2021, and the occurrence of forest fires is mainly concentrated between May and August of that year.In our numerical experiment, to study the long-range transport of CO 2 emissions from severe forest fires, we selected the largest affected area by a forest fire as a case study for simulation.The selected forest fire case occurred in mid-May 2021 in the central region of Saskatchewan (SK), Canada.The fire, numbered 21PA-CLOVERDALE, occurred on 17 May, at a longitude of 105 • 67 ′ W and a latitude of 53 • 24 ′ N. It covered an area of 5470 hectares and was completely extinguished on 10 June 2021.After investigation, it was determined that the fire was human-induced.It was reported on the day of ignition, and the relevant data were last updated in the CNFDB on 11 April 2022.

Preparation of Data and Operating Environment
The data used in our case study are divided into two parts: one is the meteorological data that provide the horizontal wind and vertical velocity fields to calculate the trajectories with MPTRAC, and the other is the OCO-2 GEOS data product of CO 2 concentrations around the forest fire area in Canada.In this case study, the meteorological data were obtained from the ERA5 reanalysis provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) [28], a dataset with a temporal resolution of 1 h, a horizontal resolution of 0.3 • × 0.3 • , and a vertical resolution of 137 model levels from the surface up to 0.01 hPa.The CO 2 concentration data used in the experiment were obtained from the assimilated data product of CO 2 concentrations from the OCO-2 satellite and the GEOS model [24] provided by NASA.The temporal resolution of the data is 24 h, matching the output time interval of the simulation, and the spatial resolution is 0.5 • × 0.625 • .These two datasets used here have undergone years of research and validation, are widely used in fields such as meteorology, climatology, and environmental research, and were chosen to ensure reliability and accuracy.
Most of the transport simulations were conducted on the Tianhe-2 high-performance computing system operated by the National Supercomputing Center in Guangzhou, China.In the Tianhe-2H cluster, the regular compute nodes consist of two Intel Xeon E5-2692 v2 12-core multi-core central processing units (CPUs) with a clock frequency of 2.2 GHz.Each compute node has 64 GByte of memory shared between the two CPUs.The operating system of the Tianhe-2H cluster is Red Hat Enterprise Linux Server release 7.3 (Maipo) (manufactured by Red Hat Inc., Raleigh, North Carolina, United States).Currently, the Tianhe-2H system is configured with GNU and Intel compilation environments, supporting the development of programs in the C, C++, Fortran77, and Fortran90 programming languages.Additionally, the Tianhe-2H system supports two parallel programming models: OpenMP and MPI.OpenMP is a shared memory programming model that enables parallelism within a single compute node, with the maximum number of threads not exceeding the number of physical cores of the regular compute nodes (set at 24).In contrast, MPI is a distributed memory parallel programming model enabling computational tasks to run across multiple nodes, with the maximum number of processes being constrained by the total number of available compute nodes under the user's account.The OpenMP parallelization, which operates on shared memory, is typically supported directly by the compilers.Both the GNU and Intel compiler have implemented support for this standard.In this case, each individual simulation was launched with the full set of CPU threads with OpenMP parallelization to achieve the best runtime.The MPI parallelization, which operates on distributed memory, is enabled by the specific MPI environment and software stack.Here, MPI was utilized to enable sensitivity tests via ensemble simulations.

Baseline Experiment
This study applied the MPTRAC Lagrangian transport model to conduct forward simulations of the transport trajectories of CO 2 released by the Canadian forest fire.The parameters and data that need to be set are mainly the source term, i.e., the total mass of CO 2 being released as well as the number and initial positions and time of the particles, the time range simulated in the experiment, the meteorological data that drive the simulations, and the types of outputs required for further analysis.
The study region covered a longitude range from 120 • W to 0 • and a latitude range from 10 • to 70 • N. The model output was selected as grid output with a grid size of 240 × 120 boxes.Each grid box covered approximately 55 km × 55 km.As described in Section 3.1, the fire was located at (105 • 67 ′ W, 53 • 24 ′ N), and we set the initial position of the particle source term with a Gaussian distribution with full width at half-maximum of 200 km centered on these coordinates.In addition, according to observations provided by the CALIPSO [29] satellite mission, aerosol emissions in the atmosphere after the fire were found to be concentrated at altitudes between 8 and 12 km, as shown in Figure 1.Therefore, we tentatively chose 10 km as the initial altitude of the CO 2 emissions.In this experiment, we qualitatively assigned a total amount of CO 2 of 4 × 10 10 kg released by the fire.The model setup is summarized in Table 2.
We simulated the transport pathways of CO 2 from the forest fire from 17 May, 00:00 UTC, to 25 May, 00:00 UTC.We compared the MPTRAC simulation results of the horizontal CO 2 transport with the NASA OCO-2 Level 3 data product.For the NASA OCO-2 CO 2 data, we calculated the CO 2 anomalies caused by the fire and other sources by subtracting the daily mean value within the observation area to exclude the variability of the background CO 2 concentration for a better comparison.The simulated and observed horizontal dispersion of CO 2 from the fire is shown in Figure 2.
Atmosphere 2024, 15, 429 6 of 13 Therefore, we tentatively chose 10 km as the initial altitude of the CO2 emissions.In this experiment, we qualitatively assigned a total amount of CO2 of 4 10 kg released by the fire.The model setup is summarized in Table 2.We simulated the transport pathways of CO2 from the forest fire from 17 May, 00:00 UTC, to 25 May, 00:00 UTC.We compared the MPTRAC simulation results of the horizontal CO2 transport with the NASA OCO-2 Level 3 data product.For the NASA OCO-2 CO2 data, we calculated the CO2 anomalies caused by the fire and other sources by subtracting the daily mean value within the observation area to exclude the variability of the background CO2 concentration for a better comparison.The simulated and observed horizontal dispersion of CO2 from the fire is shown in Figure 2.  Inspection of the MPTRAC simulation results shows that a significant amount of CO 2 emitted from the fire formed a distinct transport pathway.On May 18th, the main area of CO 2 distribution was between 45 • ~55 • N and 110 • ~80 • W, forming a transport belt roughly oriented to the southeast.On 22 May and 23, the distribution of CO 2 transport showed a narrow elongated area extending eastward.At the same time, a circular transport belt was formed in the area of 75 • ~40 • W and 25 • ~50 • N. As time progresses, CO 2 continues to be transported eastward, while also spreading over a larger area.On May 24th, the distribution of CO 2 transport can be clearly divided into two parts: one part formed a southeastward trajectory in the region of 90 • ∼60 • W, 40 • ∼55 • N, and the other part dispersed over a large area in the region of 60 • ∼0 • , 30 • ∼60 • N. Inspecting the OCO-2 data product, it was found that on 21 May and 22 May, a long and narrow eastward transport belt also formed at the source location, consistent with the MPTRAC simulation results.On 23 May and 24 May, the distribution of CO 2 near the source in the observational data also matched the simulation results.At the same time, during the eastward transport, there was also a phenomenon of large-scale diffusion in the observations.Inspection of the MPTRAC simulation results shows that a significant amount of CO2 emitted from the fire formed a distinct transport pathway.On May 18th, the main area of CO2 distribution was between 45°~55° N and 110°~80° W, forming a transport belt roughly oriented to the southeast.On 22 May and 23, the distribution of CO2 transport showed a narrow elongated area extending eastward.At the same time, a circular transport belt was formed in the area of 75°~40° W and 25°~50° N. As time progresses, CO2 continues to be transported eastward, while also spreading over a larger area.On May 24th, the distribution of CO2 transport can be clearly divided into two parts: one part formed a southeastward trajectory in the region of 90°∼60° W, 40°∼55° N, and the other part dispersed over a large area in the region of 60°∼0°, 30°∼60° N. Inspecting the OCO-2 data product, it was found that on 21 May and 22 May, a long and narrow eastward transport belt also formed at the source location, consistent with the MPTRAC simulation results.On 23 May and 24 May, the distribution of CO2 near the source in the observational data also matched the simulation results.At the same time, during the eastward transport, there was also a phenomenon of large-scale diffusion in the observations.However, we also noticed that the distribution of CO2 in the observational data shows peak anomalies in the region of 120°~60° W, 20°∼40° N, which are not reflected in the simulation results.At the same time, comparing the two sets of maps also reveals differences in CO2 concentration.For these inconsistencies between the simulation results and the observation, we considered several reasons.First, on 18 May and 19 May, the OCO-2 satellite data observations do not show clear CO2 signals around the fire site.Considering cloud index data from the Atmospheric Infrared Sounder (AIRS) [30] on these days, we assumed that significant fractions of clouds and aerosols in the atmosphere led to the blocking of observations near the location of the fire.The AIRS cloud index data within the simulation area are shown in Figure 3 for reference.Second, regarding the CO2 anomalies at lower altitudes that are not present in the simulation results, we assumed that the satellite observations also included CO2 emissions from fires or other sources of CO2 in However, we also noticed that the distribution of CO 2 in the observational data shows peak anomalies in the region of 120 • ~60 • W, 20 • ∼40 • N, which are not reflected in the simulation results.At the same time, comparing the two sets of maps also reveals differences in CO 2 concentration.For these inconsistencies between the simulation results and the observation, we considered several reasons.First, on 18 May and 19 May, the OCO-2 satellite data observations do not show clear CO 2 signals around the fire site.Considering cloud index data from the Atmospheric Infrared Sounder (AIRS) [30] on these days, we assumed that significant fractions of clouds and aerosols in the atmosphere led to the blocking of observations near the location of the fire.The AIRS cloud index data within the simulation area are shown in Figure 3 for reference.Second, regarding the CO 2 anomalies at lower altitudes that are not present in the simulation results, we assumed that the satellite observations also included CO 2 emissions from fires or other sources of CO 2 in other locations that were not considered in the simulations, which caused interference when comparing the results.
Atmosphere 2024, 15, 429 9 of 13 other locations that were not considered in the simulations, which caused interference when comparing the results.

Sensitivity Test on CO2 Release Height
To show the sensitivity of the simulation results to the particle release height, we performed a series of simulations with air parcels released at different altitudes centered at 3, 7, 10, and 13 km, which cover the altitude ranges of aerosol observations provided by

Sensitivity Test on CO 2 Release Height
To show the sensitivity of the simulation results to the particle release height, we performed a series of simulations with air parcels released at different altitudes centered at 3, 7, 10, and 13 km, which cover the altitude ranges of aerosol observations provided by the CALIPSO [29] satellite project.As in the baseline experiment, we disabled the convection parametrization of MPTRAC to avoid any influence of vertical mixing on the CO 2 transport process.In Figure 4, we compare the CO 2 plumes released at different heights on the day of 23 May.The comparison indicates that there is a correlation between the horizontal transport paths of CO 2 and the release height of the particles.The plume moves faster towards the east coast and the Atlantic at higher altitudes, which is related to stronger westerly winds in the upper troposphere.

Sensitivity Test on CO2 Release Height
To show the sensitivity of the simulation results to the particle release height, we performed a series of simulations with air parcels released at different altitudes centered at 3, 7, 10, and 13 km, which cover the altitude ranges of aerosol observations provided by the CALIPSO [29] satellite project.As in the baseline experiment, we disabled the convection parametrization of MPTRAC to avoid any influence of vertical mixing on the CO2 transport process.In Figure 4, we compare the CO2 plumes released at different heights on the day of 23 May.The comparison indicates that there is a correlation between the horizontal transport paths of CO2 and the release height of the particles.The plume moves faster towards the east coast and the Atlantic at higher altitudes, which is related to stronger westerly winds in the upper troposphere.

Comparison of Simulation Results on Different HPC Systems
To evaluate possible differences in the simulation results of the MPTRAC model on different HPC systems, we conducted simulations on the Tianhe-2 supercomputer platform at the National Supercomputer Center in Guangzhou in China and the JUWELS HPC system [31] at the Jülich Supercomputing Centre in Germany.After excluding blank grid boxes outside the particle diffusion area to avoid potential division by zero issues, we used Equation (3) to calculate the average differences between each grid box in the simulation results of these two HPC systems, where  and  represent the simulation results of Tianhe-2 and JUWELS at each data point within the simulation range, respectively, and n represents the total number of non-empty grid boxes on the grid.The spatial distribution of the differences between the simulation results is shown in Figure 5.The main plumes simulated on two HPC systems demonstrate good consistency with each other, with only some differences in small diffusion features.The results on the Tianhe-2 platform show more diffusion features than on

Comparison of Simulation Results on Different HPC Systems
To evaluate possible differences in the simulation results of the MPTRAC model on different HPC systems, we conducted simulations on the Tianhe-2 supercomputer platform at the National Supercomputer Center in Guangzhou in China and the JUWELS HPC system [31] at the Jülich Supercomputing Centre in Germany.After excluding blank grid boxes outside the particle diffusion area to avoid potential division by zero issues, we used Equation (3) to calculate the average differences between each grid box in the simulation results of these two HPC systems, Di f f erence = 1 n ∑ x JUWELS -x Tianhe−2 max x JUWELS , x Tianhe−2 , where x Tianhe−2 and x JUWELS represent the simulation results of Tianhe-2 and JUWELS at each data point within the simulation range, respectively, and n represents the total number of non-empty grid boxes on the grid.The spatial distribution of the differences between the simulation results is shown in Figure 5.The main plumes simulated on two HPC systems demonstrate good consistency with each other, with only some differences in small diffusion features.The results on the Tianhe-2 platform show more diffusion features than on JUWELS.This can be attributed to different random numbers being generated.
system [31] at the Jülich Supercomputing Centre in Germany.After excluding blank grid boxes outside the particle diffusion area to avoid potential division by zero issues, we used Equation (3) to calculate the average differences between each grid box in the simulation results of these two HPC systems, where  and  represent the simulation results of Tianhe-2 and JUWELS at each data point within the simulation range, respectively, and n represents the total number of non-empty grid boxes on the grid.The spatial distribution of the differences between the simulation results is shown in Figure 5.The main plumes simulated on two HPC systems demonstrate good consistency with each other, with only some differences in small diffusion features.The results on the Tianhe-2 platform show more diffusion features than on JUWELS.This can be attributed to different random numbers being generated.

Summary and Outlook
In this study, we used the Lagrangian particle dispersion model MPTRAC to conduct forward simulations of CO2 transport pathways.We focused on a case study of forest fire emissions in Central Saskatchewan, Canada, for the period from 17th May to 25th May 2021.We presented a baseline simulation, for which we qualitatively analyzed the longrange transport and dispersion of CO2.We incorporated the NASA OCO-2 Level 3 data product to facilitate the analysis of the simulation results.We noted that both the meas-

Figure 1 .
Figure 1.CALIPSO Level 2 Lidar Vertical Feature Mask (VFM) on 19 May 2021, 09:26 to 09:39 UTC, of a satellite overpass over the study region near Saskatchewan, Canada.The VFM shows tropospheric aerosol detections (orange color) at about 8 to 10 km of altitude near (49.09°N, 97.50° W), which relate to the forest fire of the case study.

Figure 1 .
Figure 1.CALIPSO Level 2 Lidar Vertical Feature Mask (VFM) on 19 May 2021, 09:26 to 09:39 UTC, of a satellite overpass over the study region near Saskatchewan, Canada.The VFM shows tropospheric aerosol detections (orange color) at about 8 to 10 km of altitude near (49.09• N, 97.50 • W), which relate to the forest fire of the case study.

Figure 2 .
Figure 2. Maps of CO 2 distribution from the Canadian forest fire.(a) Simulation results of MPTRAC.(b) Anomalies calculated from the NASA OCO-2 Level 3 assimilated data product.The red triangle indicates the particle source term position.

Figure 3 .
Figure 3. AIRS/Aqua satellite observations of 8.1-micron brightness temperatures [30] on 18 (left) and 19 (right) of May at about 13:30 local time indicate the presence of clouds and aerosol near the location of the Canadian forest fire, which may lead to blocking of OCO-2 CO2 observations.The red triangle indicates the particle source term position.

Figure 3 .
Figure 3. AIRS/Aqua satellite observations of 8.1-micron brightness temperatures [30] on 18 (left) and 19 (right) of May at about 13:30 local time indicate the presence of clouds and aerosol near the location of the Canadian forest fire, which may lead to blocking of OCO-2 CO 2 observations.The red triangle indicates the particle source term position.

Figure 3 .
Figure 3. AIRS/Aqua satellite observations of 8.1-micron brightness temperatures [30] on 18 (left) and 19 (right) of May at about 13:30 local time indicate the presence of clouds and aerosol near the location of the Canadian forest fire, which may lead to blocking of OCO-2 CO2 observations.The red triangle indicates the particle source term position.

Figure 4 .
Figure 4. Simulated CO2 plumes on 23rd of May considering different release heights of CO2.(a-d) Released altitudes of air parcels centered at 3, 7, 10, and 13 km, respectively.The red triangle indicates the particle source term position.

Figure 4 .
Figure 4. Simulated CO 2 plumes on 23rd of May considering different release heights of CO 2 .(a-d) Released altitudes of air parcels centered at 3, 7, 10, and 13 km, respectively.The red triangle indicates the particle source term position.

Atmosphere 2024, 15 , 429 11 of 13 Figure 5 .
Figure 5. Spatial distribution of differences in CO2 distributions in ppmv between the Tianhe-2 supercomputer platform and the JUWELS HPC system.The red triangle indicates the particle source term position.

Figure 5 .
Figure 5. Spatial distribution of differences in CO 2 distributions in ppmv between the Tianhe-2 supercomputer platform and the JUWELS HPC system.The red triangle indicates the particle source term position.

CO 2 Trajectory Modeling of Forest Fires in Canada
• × 0.625 • assimilated CO 3.1.Problem Description and Selection of Case Study

Table 2 .
MPTRAC model set up for the Canadian forest fire case study.