Towards an improved understanding of wildfire CO emissions: a satellite remote-sensing perspective
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.
Debora Griffin et al.
Status: open (until 21 Jun 2023)
- RC1: 'Comment on egusphere-2023-649', Anonymous Referee #1, 01 Jun 2023 reply
Debora Griffin et al.
Debora Griffin et al.
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The manuscript entitled "Towards an improved understanding of wildfires CO emissions: a satellite remote-sensing perspective” by Griffin et al aims to derive CO emission coefficients by correlating TROPOMI CO with MODIS FRP and produce a new global CO emission inventory using the emission coefficients and GFAS FRP. The authors first estimate CO emissions directly for forest fires in an hour near the overpass time of S5P and evaluate temporally redistributed fire emissions from a Canada emission model – CFFEPS. By correlating TROPOMI CO flux to MODIS FRP, biome-specific CO emission coefficients are derived over different numbers of fire events. The annual budget of CO emissions is estimated finally by applying the derived CO emission coefficients to GFAS FRP, and further compared with several other inventories.
Overall, the topic of this study fits the scope of ACP well and it has very meaningful goals. As S5P TROPOMI provides CO observations of global fires at the highest spatial resolution yet, it provides a good opportunity to explore CO emission coefficient for global fires, which potentially improves the estimation of biomass burning emissions. Unfortunately, I think the study fails to achieve these goals due to the seriously flawed method for deriving emission coefficients and the failure of assessing the accuracy of CO budgets. First, I acknowledge that using TROPOMI CO observations to directly estimate CO emissions from fires during a specific short period of time is sound, and direct CO estimates are valuable independent emission data for evaluating other emission estimates. Yet, both the idea and CO estimation method are not new as TROPOMI CO has been successfully applied to assess fire emissions in several recently published papers. Now, let me lay out reasons why I think the method to derive emission coefficients is flawed.
Theoretically, emission coefficient (g/J), which represents the mass of emissions per Joule radiative energy emitted from fire, can be derived if continuous, accurate rate of emission and FRP (or emission mass and FRE) are known. One MODIS instrument provides daily up to two observations of fires at the same location at low-mid latitudes. If only daytime Aqua MODIS FRP is used, it only provides one observation as with TROPOMI CO observation. In the method of deriving CO emission coefficient by correlating TROPOMI CO to Aqua MODIS FRP, the underline assumptions are that emission flux based TROPOMI CO and Aqua MODIS FRP are able to represent mean CO flux and mean FRP for a given fire sample during a specific period of time (±30min or several hours?). I would not think these simplified assumptions hold in most cases. For MODIS FRP, it has a strong dependency on MODIS scan angle. In other words, FRP value can be largely different if the instrument observes the same fire at nadir and in large scan angles. In that case, emission coefficient likely changes largely when the scan angle varies. Moreover, the observation gaps between S5P and Aqua can be up to about 60 minutes although they are thought to be in similar orbits. I think this explains the very scattering distribution of samples in Fig 6, not to mention the very pool correlations in evergreen needle leaf dominated by forest wildfires. It looks like the authors are not aware of the characteristics of MODIS FRP except for listing the incapability of detecting very small fires and cloud/smoke contamination. A scientifically sound way would be deriving coefficients based on TROPOMI CO and FRP from the new-generation geostationary satellites, which has been done in several published papers that are never mentioned as background in Introduction nor discussed in Discussion. The accuracy of CO flux also relies on wind directions and speed, which are also a concern.
The accuracy of the new CO inventories depends on the accuracy of the derived CO emission coefficients and that of GFAS FRE. FRE calculation requires continuous FRP observations. Diurnal FRP varies very largely from day to day even for the same fire, especially large forest wildfires, which have been reported in several JGR and RSE papers. I would not expect reliable FRE to be calculated from daily mean GFAS FRP that is averaged using merely up to four daily MODIS FRP observations, although GFAS emissions are used in ECMWF forecast models. Furthermore, simply comparing it with a few other inventories doesn’t tell any information about the accuracy of the new CO inventory. There are more than 10 BB emission inventories for different purposes, and they can differ from each other by a factor of up to 30 in individual fire events although the difference in their annual budget could be much smaller. I don’t see any meaningful contributions of a new inventory to the BB community without knowing its accuracy.
To sum up, I don’t see sound contributions from this study, thus I would not recommend it for publication in ACP.