Global air quality forecasting enables the study of global scale impacts of air pollutants such as long-range transport, the impact on human health and environmental degradation in remote and under-served regions, and the intersection of air quality and climate impacts. This paper will focus on the recent development of the global version of GEM-MACH, which is the operational forecasting model for Environment and Climate Change Canada. We will discuss the model with respect to its use as a global forecasting tool, a regulatory tool and a tool for scientific research. The model’s performance for a 6-year period will be assessed against satellite data, ozonesonde data and ground-based observations, as well as compared against other global air quality forecast models. Its suitability to provide chemical lateral boundary conditions for the regional model are also discussed.

Emissions from aircraft can have a disproportionate impact on atmospheric chemistry compared to other anthropogenic emissions since the bulk of the emissions is at altitude where there are fewer emission sources, and the emitted species have longer atmospheric lifetimes. Using the global version of GEM-MACH, the operational forecasting model for Environment and Climate Change Canada, this paper investigates the impact of non-CO2 emissions from the aviation sector on surface air quality due to local emissions and the downward transport of emissions from aloft. The focus will be on traditional pollutants emissions such as ozone, NOx and particulate matter.

2023 was record-breaking for wildfires in Canada with unprecedented impacts on local ecosystems as well as large scale smoke hazards. Fire smoke plumes are extreme air quality events with exceptionally high concentrations and related uncertainties fall outside statistical ranges. These particular conditions induce specific challenges for data assimilation algorithms, because error estimates need to represent the high uncertainties and spatial gradients. A novel assimilation approach, called parametric Kalman filter (PKF), explicitly propagates the main error parameters in a forecast model to create reasonable uncertainty estimates at very low computational costs. Similar to atmospheric constituents, the accuracy of forecasting error parameters relies on the physical processes that are considered. As a first step, the most important processes to be considered in a variance forecast for fire aerosols are investigated in this case study. It is shown that a variance source term which represents the uncertainties in emissions, vertical diffusion and advection are critical processes for dynamical variance simulation during extreme air quality events. This provides an important step towards parametric aerosol assimilation for improved wildfire smoke forecasts in the operational forecast model GEM-MACH.

The online version of the Regional Air Qual-ity Deterministic Prediction System (RAQDPS) is a chem-ical weather forecast system that has been employed op-erationally by Environment and Climate Change Canada(ECCC) since 2009. It is run twice per day to produce72 h forecasts of hourly 10 km abundance fields of threekey predictands, NO2, O3, and PM2.5total mass, as well asother gas-phase chemical species, PM2.5chemical compo-nents, and dry and wet deposition for Canada, the contigu-ous U.S., and northern Mexico. The forecasts of NO2, O3,and PM2.5are needed to calculate the Air Quality Health In-dex (AQHI), which is used to communicate current and fore-casted pollutant levels to the Canadian public. Version 023of the RAQDPS (RAQDPS023) went into service at ECCCin December 2021 and was replaced by the RAQDPS025in June 2024. This paper provides the first full descrip-tion of any version of the online RAQDPS. After givinga brief history of the ECCC operational air quality fore-casting program, we provide a comprehensive descriptionof the RAQDPS023 forecast system as well as shorter de-scriptions of several upstream and downstream forecast andanalysis systems. The latter include two upstream opera-tional meteorological forecast systems that were based onversion 5.1.0 of the ECCC Global Environmental Multiscale(GEM) numerical weather prediction model, one which useda global configuration, the Global Deterministic PredictionSystem (GDPS 8.0.0), and the other which used a regionalconfiguration, the Regional Deterministic Prediction System(RDPS 8.0.0). An emissions processing system, an Update-able Model Output Statistics-based system for bias-correctedstation-specific pollutant concentration forecasts (UMOS-AQ), and a regional objective analysis system for surfacepollutant concentration fields, the Regional Deterministic AirQuality Analysis system (RDAQA 2.0.0), are also described. The RAQDPS023 itself consisted of version 3.1.0.0 of theGEM-Modelling Air quality and CHemistry (GEM-MACH)chemistry module, which was embedded with one-way cou-pling within GEM 5.1.0, its meteorological host model. Themeteorological configuration of the RAQDPS023 closelyfollowed that of the RDPS 8.0.0. Details covered in thispaper include a summary of the dynamical representationsand physical parameterizations used in the three GEM-basedforecast systems, which are highly harmonized, the chemi-cal species and parameterizations used in the MACH chem-istry module, including gas-phase, aqueous-phase, and in-organic heterogeneous schemes and associated numericalsolvers, system inputs, including both anthropogenic and nat-ural emissions of chemical species, system outputs, and runconfiguration, strategies, and timings. One simplification inaddition to the use of the condensed ADOM-2 gas-phasechemistry scheme that was made to reduce RAQDPS023 ex-ecution time for operational deployment was to represent the particulate matter (PM) size distribution with only twoaerosol particle size bins, one corresponding to particle di-ameters in the 0-2.5 mu m range ("fine particles" or PM2.5) andthe other to the 2.5-10 mu m range ("coarse fraction" or PMcf).A second simplification was to represent the chemical com-position of PM2.5with only nine chemical components, anda third simplification was to use a longer time step (900 s)for the time integration of atmospheric chemistry than thetime step used for time integration of atmospheric dynamicsand physics (300 s). Even so, activating the MACH moduleincreased RAQDPS023 run time by a factor of 4. 4 on aver-age compared to meteorology only, partly due to the cost ofthe integration of chemistry but partly to the increased costof the GEM dynamical core due to the advection with im-posed shape preservation and mass conservation of 57 addi-tional chemical tracers. The role of the RAQDPS-FW023, asecond chemical weather forecast system that was identicalto the operational RAQDPS023 (or RAQDPS-OP023) exceptfor the addition of near-real-time biomass burning emissions,is also described. Biomass burning emissions for Canada andthe U.S. estimated from satellite measurements were first cal-culated by the Canadian Forest Fire Emissions PredictionSystem (CFFEPS) version 4.1 before each RAQDPS-FW023run was launched. Outputs from the two RAQDPS versionswere then used to produce forecasts of wildfire smoke trans-port and diffusion. The paper closes by summarizing the keyupgrades made to the RAQDPS025, the current version ofthe ECCC operational chemical weather forecast system, andthen describing some possible future improvements and up-dates. A companion paper by Moran et al. (2026) presents theresults of a comprehensive, five-year performance evaluationof prospective and retrospective annual air quality simula-tions made with the RAQDPS023.

The 2023 fire season was a record-breaking natural disaster event in Canada with more than 15 million hectares (Mha) of forests consumed by wildfires. Smoke from wildfires resulted in extremely poor air quality across Canada and impacted cities in eastern USA. Smoke plumes were also transported long-distance across the Atlantic impacting cities in Europe. The FireWork air quality forecast modelling system operated by Environment and Climate Change Canada (ECCC) has been demonstrated to be a valuable tool during these extreme smoke episodes. The system captured the timing and duration of the pollution, and adequately forecasted the PM2.5 concentrations in many regions across the country. A new research version of the system has fire emissions modelled within the GEM-MACH chemistry transport model with coupled meteorology feedback. The system accounts for aerosol direct and indirect effects on radiative transfer and cloud microphysics. In regions impacted by smoke, the fully coupled model simulations showed reduced surface air temperatures, reducing forecast biases relative to observations. In this work, we will present operational analysis of the FireWork system for the 2023 fire seasons, at the same time, show research application of the coupled model on the study of wildfire smoke and aerosol effects on regional weather.

The operational online version of the RegionalAir Quality Deterministic Prediction System (RAQDPS)is a chemical weather forecast system that has been em-ployed by Environment and Climate Change Canada (ECCC)since 2009. It is run twice daily to produce 72 h fore-casts of hourly 10 km abundance fields of three key pre-dictands, NO2, O-3, and PM2.5 total mass, as well as othergas-phase chemical species, PM(2.5 )chemical components,and dry and wet deposition for Canada, the contiguous USand Alaska, and northern Mexico. Version 023 of theRAQDPS (RAQDPS023) went into service at ECCC inDecember 2021 and was replaced by the RAQDPS025 inJune 2024. A companion paper by Moran et al. (2026) de-scribes the RAQDPS023 in detail. In this paper we presentthe results of a five-year performance evaluation of prospec-tive and retrospective annual air quality (AQ) simulationsmade with the RAQDPS023. The annual simulations con-sidered were the first year of operational RAQDPS023 fore-casts in 2021/2022 and four years of retrospective annualsimulations for the 2013-2016 period that used historical,year-specific emissions. This version of the RAQDPS023,which did not include biomass burning (BB) emissions,is referred to in the text as the RAQDPS-OP023. Fore-casts made by the RAQDPS-FW023, a duplicate opera-tional system to the RAQDPS-OP023 except for the ad-dition of time-dependent BB emissions, were also evalu-ated for the 2021/2022 period. A near-real-time measure-ment data set consisting of hourly NO2, O-3, and PM(2.5 )surface measurements for Canada and the US was used for the2021/2022 evaluation, whereas a much more extensive setof air-chemistry and precipitation-chemistry measurementswas used for the 2013-2016 RAQDPS-OP023 evaluations.Some evaluation results were also compared with results forthe 2010-2019 period for forecasts made by earlier opera-tional versions of the RAQDPS and with evaluation resultsfor several peer AQ forecast models. In addition to looking ata number of highly aggregated "headline" scores, many strat-ified analyses were also performed, including evaluations bynetwork, season, month, hour of day, region, and land-usetype. Consideration of simulations for multiple years withthe same model but year-specific input emissions helped toidentify systematic model errors by reducing the influenceof year-to-year variations in meteorology and emissions, anda comprehensive evaluation for many additional chemicalspecies for 2013-2016 supported by stratified analyses pro-vided diagnostic insights that allowed the scientific basis forthe RAQDPS-OP023 forecasts to be assessed (e.g., were theright answers obtained for the right reasons?). Although one confounding factor for this study was the sizable reductionin the emissions of some pollutants in North America thatoccurred from 2013 to 2021, it was found that the trendsin AQ observations over this period agreed with the year-specific description of emissions used for the five annual sim-ulations from a rank-ordered perspective. While RAQDPS-OP023 evaluation scores for hourly NO(2 )and O-3 volume mixing ratio forecasts were found tobe competitive with peer models and often met suggestedperformance benchmarks for the five simulation years, an-other key finding was that the RAQDPS-OP023 forecastsconsistently underpredicted hourly PM(2.5 )total mass con-centrations for all months in 2021/2022 and for the major-ity of months in 2013-2016. The largest underpredictionsoccurred in summer and at rural stations, whereas overpre-dictions often occurred in the cold season at urban stations.The model also missed the observed bimodality in monthlyPM(2.5) concentrations and exaggerated the observed diurnalvariations in hourly PM(2.5 )concentrations. Additional evalu-ations with daily PM2.5 chemical composition measurementsand daily gravimetric PM2.5 total mass measurements fromthe US. PM(2.5 )mass monitoring network were also exam-ined to better understand the hourly PM2.5 underpredictions.Consistent overpredictions of elemental carbon and sea saltconcentrations and underpredictions of sulfate concentrationwere identified, but scores for predictions of daily gravi-metric PM(2.5 )total mass were better than those for hourly PM(2.5 )total mass, directing attention to differences in mea-surement methods. SO(2 )and HNO(3 )levels were also foundto be overpredicted in general while NH3 levels were under-predicted: these three gas-phase species are all PM(2.5 )pre-cursors, which raises concerns about some process repre-sentations in the model such as those for sulfur oxidationand gas-phase dry deposition. As well, springtime O-3 levelswere underpredicted while isoprene levels were consistentlyoverpredicted in all seasons. The impact of BB emissions onpredictions of NO2, O-3, and PM2.5 was also characterizedin detail by comparing evaluation results for the 2021/2022RAQDPS-OP023 and RAQDPS-FW023 forecasts. Negligi-ble impact was found for monthly NO2forecasts when BBemissions were included, but monthly O-3 forecast scores forthe RAQDPS-FW023 were modestly improved and monthly PM(2.5 )forecast scores were markedly improved from July toSeptember 2021, as well as summer and annual scores. Takentogether, the results of this comprehensive multi-year evalu-ation point to a number of RAQDPS023 system componentswhere improvements are desirable. These results also pro-vide a strong benchmark against which to compare the per-formance of future versions of the RAQDPS.

The record‐breaking 2023 Canadian wildfire season had large‐scale burning that resulted in wide‐reaching long‐range transport of smoke plumes and their associated trace gases. This paper examines three events (May 16‐23, June 3‐9 and June 17‐30, 2023) during which the composition of smoke was measured over Toronto and Egbert, Ontario. Tropospheric columns (0–10 km) of CO, C 2 H 6 , CH 3 OH, HCN, HCOOH, NH 3 and O 3 were measured using high‐resolution Fourier transform infrared spectrometers. Coincident enhancements of CO and other gases during the events were used to calculate enhancement ratios. Correlations with CO were observed for C 2 H 6 , CH 3 OH, HCN and HCOOH, but not for NH 3 and O 3 . Plume transport was investigated with the Hybrid Single‐Particle Lagrangian Integrated Trajectory model, the GEM‐MACH‐FireWork (GM‐FW) air quality model, and Measurements of Pollution in the Troposphere (MOPITT) CO satellite data. Additional measurements examined were surface CO, O 3 , and PM 2.5 , plume height from a Mini Micro Pulse Lidar, and EM27/SUN XCO columns. GM‐FW model output was compared with ground‐based surface and 0–10 km column measurements, and MOPITT CO maps. Over the 2023 forest fire season (May‐September), the model underestimated background tropospheric columns of CO, NH 3 and O 3 , but generally overestimated enhancements during smoke events. Relative to surface in situ measurements, GM‐FW seasonal averages overestimated CO and underestimated O 3 (which was not generally enhanced during smoke events), while PM 2.5 fluctuated between a positive and negative bias. Compared to MOPITT, the GM‐FW event‐averaged CO columns appropriately represent plume dispersion across the country, with some offsets on the scale of the ground‐based locations that are consistent with the discussed findings.

Accurate fi fire weather forecasting is essential for effective wildfire management, particularly in regions increasingly affected by extreme fi fire activity such as British Columbia and Alberta, Canada. This study evaluates the predictive performance of three ensemble forecasting systems the Ensemble Prediction System (ENS), the Global Ensemble Forecast System (GEFS), and the Canadian Global Ensemble Prediction System (GEPS) and one deterministic model (High Resolution Forecast, HRES) in forecasting components of the Canadian fi fire weather index (FWI) system with 1-15 days lead time during the 2021-23 wildfire seasons. Using ERA5 reanalysis as reference datasets, forecast skill was assessed using mean absolute error (MAE), continuous ranked probability score (CRPS), and precision-recall area under the curve (PR-AUC) metrics. Results show that ENS consistently demonstrates superior performance across all FWI components and weather inputs, with lower MAE and CRPS values across all the forecast lead times. A super ensemble combining all ensemble members from ENS, GEFS, and GEPS further improves long-range forecast reliability. Although deterministic forecasts outperform individual ensemble members, they are generally surpassed by ensemble-mean and ensemble-median forecasts at lead times greater than 5 days. The skill of deterministic forecasts also declines more rapidly with lead time and fails to quantify forecast uncertainty, despite their higher spatial resolution. These fi findings highlight the operational benefits of incorporating ensemble forecasts into fi fire management decision-making. This study also emphasizes the importance of overwintering adjustments and ensemble size in forecast skill and provides insights for improving fi fire weather prediction systems.

Abstract. This study introduces a simple parametric Kalman Filter (PKF) specifically tailored to the requirements of operational air quality data assimilation under highly uncertain emissions like wildfire smoke events. Operational smoke plume assimilation systems require fast, yet accurate error estimations to represent the large, case-dependent and spatio-temporally varying uncertainties. The PKF offers a computationally efficient alternative to existing ensemble approaches, where the dynamics of error parameters (such as error standard deviations) are explicitly evolved numerically at a fraction of the cost of ensemble-based methods. This study focuses on the forecast step of the PKF by evolving error standard deviations in the Canadian operational air quality model GEM-MACH. It includes the following three steps: 1) theoretical derivation of forecast dynamics tailored to near-surface air quality applications with uncertain emissions, 2) implementation into the GEM-MACH modeling system, 3) application to surface PM2.5 in eastern Canada during a wildfire episode in July 2023. The theoretical investigation conducted in this study suggests that error standard deviation is a more suitable parameter than error variance for operational models. This is due to improved process-understanding, numerical accuracy, and a simpler form of the forecast equation that can be implemented with minor modifications of the forecasting model. Implementing diffusion and emission processes of errors in a state-of-the-science atmospheric model for the first-time demonstrates their sensitivity to other error parameters, state error correlation and emission error, respectively. Although the setup of the error forecast remains highly simplified, the case study results show significant impacts on hourly PM2.5 analysis increments compared to the operational setup. These differences can be related to the ability of the simple PKF to attribute large analysis increments to highly uncertain areas like wildfire plumes far away from observation locations. Thus, spreading sparse observation information much more efficiently in a highly case-dependent and anisotropic way only though improved variance fields.

We perform a global inverse modelling analysis to quantify biomass burning emissions of carbon monoxide (CO) from the extreme wildfires in Canada between May and September 2023. Using the GEOS-Chem model, we assimilated observations at 3 d temporal and 2° × 2.5° horizontal resolution from the Tropospheric Monitoring Instrument (TROPOMI) separately and then jointly with Total Carbon Column Observing Network (TCCON) measurements. We also evaluated prior emissions from the Quick Fire Emissions Dataset (QFED), Blended Global Biomass Burning Emissions Product eXtended (GBBEPx), Global Fire Assimilation System (GFAS), and Canadian Forest Fire Emissions Prediction System (CFFEPS). The assimilation of TROPOMI-only measurements estimated posterior North America emissions for QFED, GBBEPx, GFAS, and CFFEPS of 110.4 ± 20, 112.8 ± 20, 127.2 ± 17, and 125.6 ± 18 Tg CO compared to prior estimates of 37.1, 42.7, 91.0, and 90.2 Tg CO, respectively. The joint assimilation of TROPOMI+TCCON reduced the posterior 1σ uncertainty on the North American emission estimates by up to about 30 %, while showing only a modest impact (<5 %) on the mean estimate of the inferred emissions. An evaluation against independent measurements reveals that adding TCCON data increases the correlations and slightly lowers the biases and standard deviations. Additionally, including an experimental TCCON product at East Trout Lake with higher surface sensitivity, we find better agreement of the assimilation results with nearby in situ tall tower and aircraft measurements. This highlights the potential importance of vertical sensitivity in these experimental data for constraining local surface emissions. Our results demonstrate the complementarity of the greater temporal coverage provided by TCCON with the spatial coverage of TROPOMI when these data are jointly assimilated.

This paper presents EnsemFire v1.0, a global ensemble fire emission dataset that provides daily emissions at 0.1° × 0.1° spatial resolution for key air pollutants like fine particulate matter (PM₂.₅), black carbon (BC), organic carbon (OC), carbon monoxide (CO), ammonia (NH₃), nitrogen oxides (NOx), and sulfur dioxide (SO₂), greenhouse gases including carbon dioxide (CO₂) and methane (CH₄), and fire radiative power (FRP). EnsemFire integrates seven widely used biomass burning emission inventories, including five global datasets (GFAS, FINN, FEER, QFED, GBBEPx) and two regional products (EPA and CFFEPS). Our analysis reveals noticeable inconsistencies among these datasets, reflecting the large uncertainty in biomass burning emission estimates. By applying an ensemble approach, EnsemFire reduces this uncertainty and provides a more robust emission estimate. When used as input to the Unified Forecast System (UFS) model, EnsemFire significantly reduces simulation bias and improves the model performance to predict aerosol optical depth (AOD) compared to the control run that uses the default emission input. This dataset offers a valuable resource for atmospheric modeling, air quality forecasting, and climate research.

Emissions from biomass burning are a significant source of air pollution, which can adversely impact air quality and ecosystems thousands of kilometres downwind. These emissions can be estimated by a bottom-up approach that relies on fuel consumed and standardized emission factors. Emissions are also commonly derived with a top-down approach, using satellite-observed fire radiative power (FRP) as a proxy for fuel consumption. Biomass burning emissions can also be estimated directly from satellite trace gas observations, including carbon monoxide (CO). Here, we explore the potential of satellite-derived CO emission rates from biomass burning and provide new insights into the understanding of satellite-derived fire CO emissions globally, with respect to differences in regions and vegetation type. Specifically, we use the TROPOMI (Tropospheric Monitoring Instrument) high-spatial-resolution satellite datasets to derive burning CO emissions directly for individual fires between 2019 and 2021 globally. Using synthetic data (with known emissions), we show that the direct emission estimate methodology has a 34 % uncertainty for deriving CO emissions (and a total uncertainty of 44 % including wind and CO column uncertainty). From the TROPOMI-derived CO emissions, we derive biome-specific emission coefficients (emissions relative to FRP) by combining the direct emission estimates and the satellite-observed FRP from the Moderate Resolution Imaging Spectrometer (MODIS). These emission coefficients are used to establish annual top-down CO emission inventories from biomass burning, showing that Southern Hemisphere Africa has the highest CO biomass burning emissions (over 25 % of global total of 300–390 Mt(CO) yr−1 between 2003–2021), and almost 25 % of global CO biomass burning emissions are from broadleaved evergreen tree fires. A comprehensive comparison between direct estimates, top-down and bottom-up approaches, provides insight into the strengths and weaknesses of each method: FINN2.5 has higher CO emissions, by a factor between 2 and 5, than all other inventories assessed in this study. Trends over the past 2 decades are examined for different regions around the globe, showing that global CO biomass burning emissions have, on the whole, decreased (by 5.1 to 8.7 Mt(CO) yr−1), where some regions experience increased and others decreased emissions.

The 2023 wildfire season in Canada was unprecedented in its scale and intensity, spanning from mid-April to late October and across much of the forested regions of Canada. Here, we summarize the main causes and impacts of this exceptional season. The record-breaking total area burned (similar to 15 Mha) can be attributed to several environmental factors that converged early in the season: early snowmelt, multiannual drought conditions in western Canada, and the rapid transition to drought in eastern Canada. Anthropogenic climate change enabled sustained extreme fire weather conditions, as the mean May-October temperature over Canada in 2023 was 2.2 degrees C warmer than the 1991-2020 average. The impacts were profound with more than 200 communities evacuated, millions exposed to hazardous air quality from smoke, and unmatched demands on fire-fighting resources. The 2023 wildfire season in Canada not only set new records, but highlights the increasing challenges posed by wildfires in Canada.

The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. The model uses a bottom-up approach, based on remotely sensed hotspot locations, and global databases linking burned area per hotspot to ecosystem-type classification at a 1 km resolution. Unlike other global fire emissions models, GFFEPS provides dynamic estimates of fuel consumption, fire behaviour and fire growth based on the Canadian Forest Fire Danger Rating System, plant phenology as calculated from daily global weather and burned-area estimates using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots and historical burned-area statistics. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3 h time steps (in contrast to non-dynamic models that use fixed consumption rates and require a collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in operational forecasting applications as well as historical simulations for which data are available. A study was conducted showing GFFEPS predictions through a 6-year period (2015-2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model's fuel consumption per unit area results clearly distinguished regions dominated by grassland (Africa) from those dominated by forests (boreal regions) and showed high variability in regions affected by El Ni & ntilde;o and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED4.1s) and the Fire INventory from NCAR (FINN 1.5 and 2.5). GFFEPS estimated values lower than GFAS and GFED (80 % and 74 %) and had values similar to FINN 1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. Model evaluation efforts to date are described - an ongoing effort is underway to further validate the model, with further developments and improvements expected in the future.

Abstract. Emissions from wildfires are a significant source of air pollution, which can adversely impact air quality and ecosystems thousands of kilometers downwind. These emissions can be estimated by a bottom-up approach, using inputs such fuel type, burned area, and standardized emission factors. Emissions are also commonly derived with a top-down approach, using satellite observed fire radiative power (FRP) as proxy for fuel consumption. More recently, wildfire emissions have been demonstrated to be estimated directly from satellite observations, including carbon monoxide (CO). Here, we explore the potential of satellite-derived CO emission rates from wildfires and provide new insights into the understanding of satellite-derived fire CO emissions globally, with respect to differences in regions and vegetation type. Specifically, we use the TROPOMI (Tropospheric Monitoring Instrument) high spatial-resolution satellite datasets to create a global inventory database of burning emissions CO emissions between 2019 and 2021. Our retrieval methodology includes an analysis of conditions under which emission estimates may be inaccurate and filters these accordingly. Additionally, we determine biome specific emission coefficients (emissions relative to FRP) and show how combining the satellite derived CO emissions with satellite observed FRP from the Moderate Resolution Imaging Spectrometer (MODIS) establishes an annual CO emission budget from wildfires. The resulting emissions totals are compared to other top-down and bottom-up emission inventories over the past two decades. In general, the satellite-derived emissions inventory values and bottom-up emissions inventories have similar CO emissions totals across different global regions, though the discrepancies may be large for some regions (Southern Hemisphere South America, Southern Hemisphere Africa, Southeast Asia) and for some bottom-up inventories (e.g. FINN2.5, where CO emissions are a factor of 2 to 5 higher than other inventories). Overall, these estimates can help to validate emission inventories and predictive air quality models, and help to identify limitations present in existing bottom-up emissions inventory estimates.

Chapter 9 Profiles of Operational and Research Forecasting of Smoke and Air Quality Around the World Susan M. O'Neill, Susan M. O'Neill Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorPeng Xian, Peng Xian United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorJohannes Flemming, Johannes Flemming European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorMartin Cope, Martin Cope CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlexander Baklanov, Alexander Baklanov World Meteorological Organization, Geneva, SwitzerlandSearch for more papers by this authorNarasimhan K. Larkin, Narasimhan K. Larkin Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorJoseph K. Vaughan, Joseph K. Vaughan Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, USASearch for more papers by this authorDaniel Tong, Daniel Tong Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, USASearch for more papers by this authorRosie Howard, Rosie Howard Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorRoland Stull, Roland Stull Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorDidier Davignon, Didier Davignon Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorRavan Ahmadov, Ravan Ahmadov CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorM. Talat Odman, M. Talat Odman School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USASearch for more papers by this authorJohn Innis, John Innis EPA Tasmania, Hobart, Tasmania, AustraliaSearch for more papers by this authorMerched Azzi, Merched Azzi Department of Planning and Environment, Government of New South Wales, Sydney, AustraliaSearch for more papers by this authorChristopher Gan, Christopher Gan Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorRadenko Pavlovic, Radenko Pavlovic Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorBoon Ning Chew, Boon Ning Chew Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorJeffrey S. Reid, Jeffrey S. Reid United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorEdward J. Hyer, Edward J. Hyer United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorZak Kipling, Zak Kipling European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorAngela Benedetti, Angela Benedetti European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorPeter R. Colarco, Peter R. Colarco NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorArlindo Da Silva, Arlindo Da Silva NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorTaichu Tanaka, Taichu Tanaka Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, JapanSearch for more papers by this authorJeffrey McQueen, Jeffrey McQueen NOAA National Centers for Environmental Prediction, College Park, Maryland, USASearch for more papers by this authorPartha Bhattacharjee, Partha Bhattacharjee I. M. Systems Group, NWS/NCEP/EMC, College Park, Maryland, USASearch for more papers by this authorJonathan Guth, Jonathan Guth Météo-France, Toulouse, FranceSearch for more papers by this authorNicole Asencio, Nicole Asencio Météo-France, Toulouse, FranceSearch for more papers by this authorOriol Jorba, Oriol Jorba Barcelona Supercomputing Center, Barcelona, SpainSearch for more papers by this authorCarlos Pérez García-Pando, Carlos Pérez García-Pando Barcelona Supercomputing Center, Barcelona, Spain Catalan Institution for Research and Advanced Studies, Barcelona, SpainSearch for more papers by this authorRostislav Kouznetsov, Rostislav Kouznetsov Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMikhail Sofiev, Mikhail Sofiev Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMelissa E. Brooks, Melissa E. Brooks Met Office, Exeter, United KingdomSearch for more papers by this authorJack Chen, Jack Chen Environment and Climate Change Canada, Ottawa, Ontario, CanadaSearch for more papers by this authorEric James, Eric James CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorFabienne Reisen, Fabienne Reisen CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlan Wain, Alan Wain Australian Bureau of Meteorology, Melbourne, Victoria, AustraliaSearch for more papers by this authorKerryn McTaggart, Kerryn McTaggart Department of Environment, Land, Water and Planning, Government of Victoria, Melbourne, Victoria, AustraliaSearch for more papers by this authorAngus MacNeil, Angus MacNeil Forest Practices Authority, Hobart, Tasmania, AustraliaSearch for more papers by this author Susan M. O'Neill, Susan M. O'Neill Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorPeng Xian, Peng Xian United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorJohannes Flemming, Johannes Flemming European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorMartin Cope, Martin Cope CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlexander Baklanov, Alexander Baklanov World Meteorological Organization, Geneva, SwitzerlandSearch for more papers by this authorNarasimhan K. Larkin, Narasimhan K. Larkin Pacific Northwest Research Station, United States Forest Service, Seattle, Washington, USASearch for more papers by this authorJoseph K. Vaughan, Joseph K. Vaughan Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, USASearch for more papers by this authorDaniel Tong, Daniel Tong Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia, USASearch for more papers by this authorRosie Howard, Rosie Howard Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorRoland Stull, Roland Stull Earth, Ocean and Atmospheric Sciences Department, The University of British Columbia, Vancouver, British Columbia, CanadaSearch for more papers by this authorDidier Davignon, Didier Davignon Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorRavan Ahmadov, Ravan Ahmadov CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorM. Talat Odman, M. Talat Odman School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USASearch for more papers by this authorJohn Innis, John Innis EPA Tasmania, Hobart, Tasmania, AustraliaSearch for more papers by this authorMerched Azzi, Merched Azzi Department of Planning and Environment, Government of New South Wales, Sydney, AustraliaSearch for more papers by this authorChristopher Gan, Christopher Gan Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorRadenko Pavlovic, Radenko Pavlovic Environment and Climate Change Canada, Dorval, Quebec City, CanadaSearch for more papers by this authorBoon Ning Chew, Boon Ning Chew Centre for Climate Research Singapore, Meteorological Service Singapore, SingaporeSearch for more papers by this authorJeffrey S. Reid, Jeffrey S. Reid United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorEdward J. Hyer, Edward J. Hyer United States Naval Research Laboratory, Marine Meteorology Division, Monterey, California, USASearch for more papers by this authorZak Kipling, Zak Kipling European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorAngela Benedetti, Angela Benedetti European Centre for Medium-Range Weather Forecasts, Reading, United KingdomSearch for more papers by this authorPeter R. Colarco, Peter R. Colarco NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorArlindo Da Silva, Arlindo Da Silva NASA Goddard Space Flight Center, Greenbelt, Maryland, USASearch for more papers by this authorTaichu Tanaka, Taichu Tanaka Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, JapanSearch for more papers by this authorJeffrey McQueen, Jeffrey McQueen NOAA National Centers for Environmental Prediction, College Park, Maryland, USASearch for more papers by this authorPartha Bhattacharjee, Partha Bhattacharjee I. M. Systems Group, NWS/NCEP/EMC, College Park, Maryland, USASearch for more papers by this authorJonathan Guth, Jonathan Guth Météo-France, Toulouse, FranceSearch for more papers by this authorNicole Asencio, Nicole Asencio Météo-France, Toulouse, FranceSearch for more papers by this authorOriol Jorba, Oriol Jorba Barcelona Supercomputing Center, Barcelona, SpainSearch for more papers by this authorCarlos Pérez García-Pando, Carlos Pérez García-Pando Barcelona Supercomputing Center, Barcelona, Spain Catalan Institution for Research and Advanced Studies, Barcelona, SpainSearch for more papers by this authorRostislav Kouznetsov, Rostislav Kouznetsov Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMikhail Sofiev, Mikhail Sofiev Atmospheric Composition Unit, Finnish Meteorological Institute, Helsinki, FinlandSearch for more papers by this authorMelissa E. Brooks, Melissa E. Brooks Met Office, Exeter, United KingdomSearch for more papers by this authorJack Chen, Jack Chen Environment and Climate Change Canada, Ottawa, Ontario, CanadaSearch for more papers by this authorEric James, Eric James CIRES, University of Colorado Boulder, Boulder, Colorado, USA NOAA Global Systems Laboratory, Boulder, Colorado, USASearch for more papers by this authorFabienne Reisen, Fabienne Reisen CSIRO Climate Science Centre, Aspendale, Victoria, AustraliaSearch for more papers by this authorAlan Wain, Alan Wain Australian Bureau of Meteorology, Melbourne, Victoria, AustraliaSearch for more papers by this authorKerryn McTaggart, Kerryn McTaggart Department of Environment, Land, Water and Planning, Government of Victoria, Melbourne, Victoria, AustraliaSearch for more papers by this authorAngus MacNeil, Angus MacNeil Forest Practices Authority, Hobart, Tasmania, AustraliaSearch for more papers by this author Book Editor(s):Tatiana V. Loboda, Tatiana V. LobodaSearch for more papers by this authorNancy H. F. French, Nancy H. F. FrenchSearch for more papers by this authorRobin C. Puett, Robin C. PuettSearch for more papers by this author First published: 20 October 2023 https://doi.org/10.1002/9781119757030.ch9Book Series:Geophysical Monograph Series AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Biomass burning has shaped many of the ecosystems of the planet and for millennia humans have used it as a tool to manage the environment. When widespread fires occur, the health and daily lives of millions of people can be affected by the smoke leading to a range of health consequences such as respiratory issues, cardiovascular issues, and mortality. It is critical to include smoke and its consequences in atmospheric modeling systems to meet needs such as informing and protecting the public during smoke episodes. This chapter profiles many of the global and regional smoke prediction systems available. It is not an exhaustive list, but rather a profile of many of the systems to give examples of the creativity and complexity needed to simulate the phenomenon of smoke. The global smoke prediction systems are advanced, and many are self-organizing into a powerful ensemble. Regional and national systems are being developed independently for example in Europe (11 systems), North America (7 systems), and Australia (3 systems). Finally, the World Meteorological Organization is bringing together global and regional systems to form an ensemble to support countries with smoke issues and who lack resources. For each system we discuss how fire activity information is obtained, how fire emissions are calculated, and how atmospheric transport and chemical transformation of the smoke plume is treated. REFERENCES Adams , C. , McLinden , C. A. , Shephard , M. 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The photolysis module in Environment and Climate Change Canada's online chemical transport model GEM-MACH (GEM: Global Environmental Multi-scale – MACH: Modelling Air quality and Chemistry) was improved to make use of the online size and composition-resolved representation of atmospheric aerosols and relative humidity in GEM-MACH, to account for aerosol attenuation of radiation in the photolysis calculation. We coupled both the GEM-MACH aerosol module and the MESSy-JVAL (Modular Earth Submodel System) photolysis module, through the use of the online aerosol modeled data and a new Mie lookup table for the model-generated extinction efficiency, absorption and scattering cross sections of each aerosol type. The new algorithm applies a lensing correction factor to the black carbon absorption efficiency (core-shell parameterization) and calculates the scattering and absorption optical depth and asymmetry factor of black carbon, sea salt, dust and other internally mixed components. We carried out a series of simulations with the improved version of MESSy-JVAL and wildfire emission inputs from the Canadian Forest Fire Emissions Prediction System (CFFEPS) for 2 months, compared the model aerosol optical depth (AOD) output to the previous version of MESSy-JVAL, satellite data, ground-based measurements and reanalysis products, and evaluated the effects of AOD calculations and the interactive aerosol feedback on the performance of the GEM-MACH model. The comparison of the improved version of MESSy-JVAL with the previous version showed significant improvements in the model performance with the implementation of the new photolysis module and with adopting the online interactive aerosol concentrations in GEM-MACH. Incorporating these changes to the model resulted in an increase in the correlation coefficient from 0.17 to 0.37 between the GEM-MACH model AOD 1-month hourly output and AERONET (Aerosol Robotic Network) measurements across all the North American sites. Comparisons of the updated model AOD with AERONET measurements for selected Canadian urban and industrial sites, specifically, showed better correlation coefficients for urban AERONET sites and for stations located further south in the domain for both simulation periods (June and January 2018). The predicted monthly averaged AOD using the improved photolysis module followed the spatial patterns of MERRA-2 reanalysis (Modern-Era Retrospective analysis for Research and Applications – version 2), with an overall underprediction of AOD over the common domain for both seasons. Our study also suggests that the domain-wide impacts of direct and indirect effect aerosol feedbacks on the photolysis rates from meteorological changes are considerably greater (3 to 4 times) than the direct aerosol optical effect on the photolysis rate calculations.

Wildfire impacts on air quality and climate are expected to be exacerbated by climate change with the most pronounced impacts in the boreal biome. Despite the large geographic coverage, there is limited information on boreal forest wildfire emissions, particularly for organic compounds, which are critical inputs for air quality model predictions of downwind impacts. In this study, airborne measurements of 193 compounds from 15 instruments, including 173 non-methane organics compounds (NMOG), were used to provide the most detailed characterization, to date, of boreal forest wildfire emissions. Highly speciated measurements showed a large diversity of chemical classes highlighting the complexity of emissions. Using measurements of the total NMOG carbon (NMOGT), the ΣNMOG was found to be 50 % ± 3 % to 53 % ± 3 % of NMOGT, of which, the intermediate- and semi-volatile organic compounds (I/SVOCs) were estimated to account for 7 % to 10 %. These estimates of I/SVOC emission factors expand the volatility range of NMOG typically reported. Despite extensive speciation, a substantial portion of NMOGT remained unidentified (47 % ± 15 % to 50 % ± 15 %), with expected contributions from more highly-functionalized VOCs and I/SVOCs. The emission factors derived in this study improve wildfire chemical speciation profiles and are especially relevant for air quality modelling of boreal forest wildfires. These aircraft-derived emission estimates were further linked with those derived from satellite observations demonstrating their combined value in assessing variability in modelled emissions. These results contribute to the verification and improvement of models that are essential for reliable predictions of near-source and downwind pollution resulting from boreal forest wildfires.