dataesr/scientific-paragraphs-categorization huggingface dataset:
Abstract. Biomass burning emissions are a major source of trace gases and aerosols. Wildfires being highly variable in time and space, calculating emissions requires a numerical tool able to estimate fluxes at the kilometer scale and with an hourly time step. Here, the APIFLAME model version 2. 0 is presented. It is structured to be modular in terms of input databases and processing methods. The main evolution compared to version 1. 0 is the possibility of merging burned area and fire radiative power (FRP) satellite observations to modulate the temporal variations of fire emissions and to integrate small fires that may not be detected in the burned area product. Accounting for possible missed detection due to small fire results in an increase in burned area ranging from ∼ 5 % in Africa and Australia to ∼ 30 % in North America on average over the 2013-2017 time period based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) Collection 6 fire products.
Introduction Biomass burning is a major perturbation to atmospheric chemistry, strongly contributing to the global budgets of aerosols and trace gases. Emitted compounds significantly alter air quality at regional scales (e. g. ,  Heil and Golhammer, 2001; Keywood et al. , 2015)  and play a major role in the interannual variability of the background atmospheric composition (e. g. ,  Spracklen et al. , 2007; Jaffe et al. , 2008; Monks et al. , 2012). Through the emission of long-lived greenhouse gases and aerosols and their interaction with radiation, they also have an impact on climate. The availability of more and more comprehensive databases of emission factors (e. g. ,  Andreae and Merlet, 2001; Akagi et al. , 2011)  and of satellite observations (e. g. ,  Giglio et al. , 2006)  since the 1990s has allowed for the development of emission inventories for a more systematic integration of this large source in chemistry transport models (CTMs).
It was initially developed for use with the CHIMERE CTM  (Menut et al. , 2013a; Mailler et al. , 2016) , but it may easily be adapted to other model specifications (chemical schemes) without modifications to the source code. A full description of the model in its first version is provided in  Turquety et al. (2014)  with an application to fire emissions in Europe and the Mediterranean area. It has been successfully used in different studies looking at the impact of fires on the regional atmospheric composition over Europe  (Rea et al. , 2015; Majdi et al. , 2019) , Australia  (Rea et al. , 2016) , California  (Mallet et al. , 2017) , and Africa  (Menut et al. , 2018). The burned area processing method provided with the code is based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) fire observations of burned scars  (Giglio et al.
They found a difference of up to a factor of 7 in total aerosol emissions from fires in North America mainly due to differences in the dry matter burned. The study also shows that performance within the CTM was dependent on the event and region considered, so it is not possible to identify a single inventory that provides the best agreement with observations for all cases. They also stress that some inventories were adjusted to allow better agreement between simulations and observations but that it may result in lower performance for other models or case studies (higher emissions can compensate for model biases due to other processes, for example). The flexibility of the APIFLAME model allows an analysis of the sensitivity of the dry matter burned and the resulting emissions to the input databases used in the calculation.
This allows high-resolution calculations that will keep the variability from the fire and vegetation datasets. In addition to the hourly emissions for the model species selected (cf. Sect. 3. 4), the grid cell area and the FRP, which gives an indication of fire intensity, are also provided. Coincident FRP values may be useful for plume height modeling. For each grid cell, the maximum FRP is calculated, as well as the statistical distribution of specified FRP bins. However, burned area and FRP are not always detected at the same time or location in the MODIS datasets. If the burned area dataset (MCD64 product; cf. Sect. 3. 1) is chosen, there may be grid cells with nonzero burned area (nonzero emissions) but zero FRP (or the other way around). Merging both datasets may be an interesting option for some applications, for example, to improve temporal variability or in order to avoid missing small fires that may not have a detectable signature on both products (cf.
Input observations and databases The datasets required to compute fire emissions are briefly described below. Compared to APIFLAME v1. 0, the code has been updated to use the MODIS Collection 6 data, the emission factors table has been updated, and the possibility of using fuel consumption from the literature has been added. https://doi. org/10. 5194/gmd-13-2981-2020 Geosci. Model Dev. , 13, 2981-3009, 2020.
Fire observations Although adaptable to any burned area database, API-FLAME was developed to derive BA from the MODIS fire databases (Collection 6; https://lpdaac. usgs. gov/ dataset_discovery/modis/modis_products_table/, last access: 12 February 2020). The MCD64A1 burned scars product, based on the alteration of the surface reflectance  (Giglio et al. , 2010 (Giglio et al. , , 2015 (Giglio et al. , , 2018)) , provides the date of burning at 500 m horizontal resolution. The active fire products, MOD14 for MODIS/Terra (Equator overpass time 09:30 and 22:30 LT) and MYD14 for MODIS/Aqua (Equator overpass time 13:30 and 01:30 LT), based on thermal anomalies, are also used. These products provide the FRP at 1 km resolution  (Giglio et al. , 2006). Only high confidence active fire detections are considered (quality index > 8), but false detections mainly associated with industrial activity may remain.
These are filtered as described in  Turquety et al. (2014). A fire pixel is rejected if the corresponding vegetation type is more that 50 % urban (fraction may be modified depending on situations), if it is located less than 1 km from an active volcano, or if the frequency of burning is unrealistically high at this location (≥ 40 %) based on the climatology of MODIS active fire detection. On the other hand, burned scars may miss smaller fires which are more easily detected by their thermal signature  (Randerson et al. , 2012). Both MODIS datasets are systematically processed to derive the burned area, either using one dataset alone or merging both datasets as described in Sect. 4, and to allow users to use the FRP for other possible applications in their analysis. For example, it is used as information on fire intensity for the calculation of plume injection heights by pyroconvection in several schemes (e.
g. ,  Sofiev et al. , 2012). In order to access information on the diurnal variability, SEVIRI and MSG data from the geostationary Meteosat Second Generation (MSG) satellite may be used for Europe and Africa (http://landsaf. ipma. pt/en/products/fire-products/ frppixel/, last access: 24 June 2020, full MSG disk database). The active fire products also include the FRP, provided at 15 min temporal resolution, for pixels of ∼ 3 km horizontal resolution at nadir  (Roberts et al. , 2005). For all products, uncertainty is mainly due to cloud cover, which prevents the observation of surface anomalies. The uncertainty of the temporal variability derived from the MCD64A1 burned area is estimated at about 2 d based on coincidences with active fires  (Giglio et al. , 2018). The high temporal coverage of the SEVIRI observation increases the probability of detecting a fire, but the larger pixel size also increases the limit of detection so that small fires may be missed.
Vegetation cover For the calculation of the burned area from fire detection, the MODIS vegetation cover fraction (VCF) product (MOD44B v006) is used. It provides the fraction of tree and non-tree vegetation cover for 250 m×250 m pixels, which is converted to 500 m and 1 km resolutions for compatibility with the fire products. Only the fraction of MODIS fire pixels covered by vegetation is assumed to burn. The vegetation type burned is also important to derive the fuel consumed and attribute the emission factors for each emitted species. Three land cover datasets may be used: the USGS and the CORINE Land Cover (CLC) datasets, constructed at 1 km resolution (cf. Turquety et al. , 2014, for details) , and the MODIS vegetation classification. The MODIS land cover type product (MCD12Q1 v006) provides information on the land cover at 500 m resolution, specific to the year analyzed, which is associated with each burning pixel during the burned area processing.
Correspondence between vegetation types, ecozones, and PFTs Calculation of the emissions requires information on the type of vegetation burned. It is attributed using a given land cover database. The present version of the code allows for the use of two databases at a global scale (MODIS and USGS) and an additional one for Europe (CLC), described in Sect. 3. 2. If a regional database is chosen, it may be complemented by one of the global databases. In the code, when CLC is chosen for Europe, MODIS is taken by default for regions not covered by the CLC database. For this purpose, a matrix of correspondence between the MODIS International Geosphere-Biosphere Program (IGBP) and the CLC vegetation types is provided. When there is no direct correspondence between the two land cover datasets, their description was used.
For example, for IGBP vegetation type "woody savanna", described as 30 %-60 % tree cover, the CLC correspondence chosen is 30 % mixed forest and 70 % natural grassland. The vegetation type is attributed during the burned area preprocessing. Correspondence matrices between vegetation types and ORCHIDEE plant function types (PFTs) and between vegetation types and the ecozone in the emission factor listing are used to allow consistent calculations. These matrices are quite subjective and may be modified for tests or depending on the region considered. Also, if a different database is used for vegetation attribution in the BA processing, new matrices have to be constructed. Depending on the vegetation database, vegetation in regions with chaparral, bushes, or Mediterranean vegetation types (Mediterranean area, California, Australia) may be classified as shrubland, wooded savanna, or savanna.
Merging burned scars and active fire products The fire observations described in Sect. 3. 1 provide a date of burning at a resolution of 500 m (MCD64 burned scars product) or 1 km (MOD14 active fire product). The burned area is calculated for each burning pixel in the database as the pixel area actually covered by vegetation (cf. Sect. 3. 2), as in the first version of APIFLAME  (Turquety et al. , 2014). Differences in the location of burned areas and active fires detected were found for different events in different regions. While the APIFLAME methodology is based on the burned area, a combination of the estimated burned area with the FRP product is also proposed in version 2. This option offers the possibility of relying primarily on the total monthly burned area from the MCD64 product (burned scar) but to redistribute it temporally depending on the fire intensity.
It is then scaled according to the amplitude of the variations of surface reflectance (providing information on fire intensity). To limit the number of datasets required to run APIFLAME, a simple linear modulation based on the FRP is used. Small fires are only included if the maximum FRP in the corresponding grid cell is > 50 MW, and the full pixel (∼ 10 6 m 2 ) is allowed to burn only if the maximum FRP is > 1000 MW (which corresponds to extreme values, ∼ 99th percentile of the global FRP dataset for 2013-2017) so that the burned area from small fires in grid cell i during day d is estimated from the number of active fire detections that are not collocated with burned scars, N out (i, d), as follows: A small (i, d) = N out (i, d) × 10 6 × FRP max (i, d) FRP , (3) with FRP = 1000 MW. A larger burned area is therefore associated with fires of greater radiative intensity.
To further smooth possibly artificially high hourly variability, daily data are processed in three steps: 1. The data are gridded at hourly resolutions on the model grid and a fixed 1 • × 1 • grid. 2. Gaps shorter than 5 h between two detections of more than 1 h are filled using linear interpolation. 3. The resulting hourly distribution is smoothed using a polynomial fit. Examples are shown in Fig. 3  for large fires in Portugal during the summer of 2016. Even after an averaging of the data on larger grid cells, the temporal variability at 15 min temporal resolution seems unrealistic. Averaging at 1 h temporal resolution allows a first smoothing of the dataset, but there are still gaps (FRP geo = 0) between two periods of fire activity. The linear interpolation fills in smaller gaps (≤ 4 h, as seen in panel a), while the smoothing fills larger gaps but can strongly decrease the peak values (panel b).
The daily variation in CO biomass burning emissions is presented in Fig. 6. Emissions using the same burned area processing configurations but different vegetation databases can show significant differences in magnitude and temporal variations. Merging burned area and FRP (BA-FRP) compared to using the burned area data alone results in an increase of 8 %-10 % of emissions. Adding the contribution from small fires to the MCD64 burned area (BA-sf) results in an increase of 33 %-36 %. Using the tabulated fuel consumption has a low impact (±2 %). This option is only available with the MODIS IGBP vegetation type. If the vegetation type is forced to temperate forest, the vegetation type burned according to CLC, the resulting emissions are close to the BA-sf-CLC configuration. This demonstrates the good consistency of the fuel consumption calculation in APIFLAME.
Emissions based on the CLC vegetation database are about 17 % higher than emissions based on the MODIS vegetation for CO. For OCAR, using CLC results in a small decrease in the estimated emissions. For other PPM, emissions are much lower using CLC due to lower emission factors for temperate forest than for pasture maintenance, chaparral, and savanna. The strongest impact during this event corresponds to the choice of emission factor database. Using the values reported for forest fires in Portugal increases total CO emissions by 126 % for CO and 50 % for OCAR. https://doi. org/10. 5194/gmd-13-2981-2020 Geosci. Model Dev. , 13, 2981-3009, 2020  The dispersion of the regional daily total emissions is quantified as the average coefficient of variation (CV = standard deviation / mean value). This provides information on the uncertainty of emissions.
Observations of atmospheric concentrations Measurements of CO, PM 10 , and PM 2. 5 from the European air quality database (AirBase, https://www. eea. europa. eu/data-and-maps/data/ airbase-the-european-air-quality-database-7, last access: 13 July 2018) are used for the validation of simulated surface concentrations. Only rural or suburban background Table  3. Relative impact (%) of factors tested in the calculation of the emissions with APIFLAME on the total emissions of CO and organic carbon and on the resulting concentrations. Differences of simulated concentrations ( CO and PM 10 ) are calculated as (X test -X ref )/X ref and then averaged over points with relative impact from fires > 10 %.
Factor Simulations sites are considered in the statistical comparisons since the resolution chosen is not relevant for urban areas. Satellite observations offer a good complement to surface in situ measurements since they provide daily observations over the full domain. Here, the total CO observations from the Infrared Atmospheric Sounding Interferometer (IASI) instrument  (Clerbaux et al. , 2009; George et al. , 2009) , carried on board the MetOp satellite series since December 2006, are used. IASI is a nadir-viewing infrared sounder with a swath of 2000 km, allowing global coverage twice daily (Equator crossing time 09:30 LST, ascending node) with a horizontal resolution of 12 km (at nadir). In this study, the CO retrievals by the FORLI software  (Hurtmans et al. , 2012)  for measurements on board the MetOp-A and MetOp-B platforms are used.
(2018)  (including the aerosol microphysics, secondary aerosol formation mechanisms, aerosol thermodynamics, and deposition). The evolution of aerosol species (nitrates, sulfates, ammonium, primary organic matter (POM), secondary organic aerosol (SOA), elemental carbon (EC), marine aerosols, and mineral dust) is simulated using a sectional approach with 10 size bins (40 nm to 40 µm). The thermodynamic module ISOR-ROPIA v2. 1  (Fountoukis and Nenes, 2007)  is used for inorganic aerosols, and the module SOAP is used for organic aerosols  (Couvidat and Sartelet, 2015). The optical properties of aerosols are calculated by the Fast-JX module version 7. 0b  (Bian and Prather, 2002)  used in CHIMERE for the online calculation of the photolysis rates. Initial and boundary conditions are derived from a 5-year  (2004) (2005) (2006) (2007) (2008) (2009)  global reanalysis at a resolution of 1.
For small fires, the relative difference is equal to 41 %-48 % on average in the denser part of the plume (relative contribution > 10 %). Using increased emission factors, the CO concentrations are more than doubled (×2. 5 at the surface, ×2. 1 for the column). The impact on PM 10 is lower (lower increase in EF) but still almost as high as the contribution from small fires. Both effects are particularly marked for surface contributions. The influence of the different vegetation types is more nuanced. Using the CLC database results in an increase in CO in Portugal but not in other regions. The effect is lower than that of small fires and emission factors but still very significant, on average 10 %-30 %. These influences are in line with the sensitivity of emissions discussed in Sect. 5. 1. The sensitivity to injection heights is also presented, using either the MISR profile (resulting in ∼ 25 % of emissions injected above the PBL) or the default scheme in CHIMERE  (for this case, all emissions in the PBL).
An underestimate of AOD is diagnosed with the model over the Atlantic and the Mediterranean, as well as over Spain and France. This underestimate may be due to a lack of emissions (anthropogenic, biogenic, fires, or mineral dust) or to the way AOD is calculated in the model (using the Fast-JX online model in CHIMERE). A classic candidate of this kind of underestimation in regional models and in summertime is a missing mineral dust plume coming from Africa. Gama et al. (2020)  show that desert dust contributes significantly to PM concentrations in Portugal and are mixed with the fire contribution in 2016. The analysis of mineral dust concentrations and the Ångström exponent using global databases displayed with Giovanni (https://giovanni. gsfc. nasa. gov/giovanni/, last access: 27 February 2020, Fig.
The maximum values are closer to the observed AOD or significantly higher. A bad timing in emissions and a small shift in transport may explain part of the underestimate. Another explanation is, here again, a missing inflow of dust from long-range transport especially at the beginning of the event. A transport of dust in the free troposphere would also explain that the underestimate obtained for AOD was not obtained for surface PM 10 (which was, on the contrary, overestimated). The altitude of the aerosol plumes is analyzed using the aerosol layer classification from CALIOP (VFM product). Four CALIPSO overpasses are available during the studied time period:    9 ). Results from different CHIMERE simulations are plotted (described in Table  4 ). The spread between the simulated regional average and maximum without collocation with MODIS data is also shown (shaded red area).
Summary and conclusions The APIFLAME biomass burning emissions model allows the calculation of aerosol and trace gas emissions based on observed burned area. The current version of the model (v2. 0) uses the MODIS Collection 6 fire products of burned scars (MCD64A1, providing the date of burning) at 500 m resolution and active fires (MOD14, including hotspot detection and associated FRP) at 1 km resolution. For each fire detected, the vegetation type burned is attributed using the MODIS annual vegetation cover product (MCD12Q1) or the CORINE Land Cover (CLC) or USGS land use databases. The corresponding fuel consumed is derived from either OR-CHIDEE land model simulations or tabulated values from the literature. The carbon consumed is converted to trace gas and aerosol emissions for a list of species for which emission factors are available.
Emission fluxes for model species are then derived using an aggregation matrix. APIFLAME may be used for near real time applications (using MOD14 only). Forecasting the evolution of the emissions remains uncertain. However, the likelihood of a fire being controlled and extinguished increases when weather conditions are less favorable for its spread. A possibility is to modulate emissions with forecasts of fire weather indices computed from the forecasted meteorology, as suggested by Di  Giuseppe et al. (2017). APIFLAME was constructed to be modular in terms of input datasets and processing methods. In addition to the dif-  ferent vegetation databases and the possibility of modifying emission factors as input parameter, different options for the calculation of burned area may be chosen. The main evolution in v2.
using a CTM is illustrated here for the case of the forest fires in Portugal during the summer of 2016 using the CHIMERE CTM. Depending on the burned area processing method, the total burned area ranges from 99 849 ha (MCD64 burned area product) to 144 882 ha (including small fires), while EFFIS reports that 115 788 ha burned. In our method, small fires are included depending on the FRP so that it can represent both the spread and the intensity of burning. The modularity of APIFLAME was used to provide information on uncertainty of the calculated emissions. Several key parameters of the model are tested: burned area, vegetation database, fuel consumption, and emission factor. Emissions with different processing methods of the burned area are used in order to analyze the impact on simulated concentrations: burned area only (BA), FRP daily variability (BA-FRP merged approach), and including small fires (BA-sf).
The impact of attribution of the vegetation burned is tested using either the CLC or MODIS IGBP vegetation classification. The impact of the fuel consumption is tested using either that calculated from ORCHIDEE land model simulations or tabulated values. Finally, emission factors from average values for the fire type or using specific values for the region studied are used. Using the ensemble of emissions calculated, the average variability over 10 km grid cells and at daily resolutions during the event is 80 % for CO emissions, 60 % without considering small fires (variability due to the vegetation database, the daily variability of the burned area, and the calculation of the fuel consumed), and 76 % without experiments on emission factors. Accounting for small fires significantly increases the total emissions during the event (33 %-36 %).
The other critical parameter is the emission factor used. For this case study, the values reported in the literature for the specific vegetation burned are very different from the average values for the fire type/ecozone. In their intercomparison of different inventories,  Carter et al. (2020)  find a low impact of emission factors because all inventories considered use average values for different ecozones. For the case of the fires in Portugal, the impact of the fuel consumption is low, but the impact of the vegetation database can be significant (±30 % on average). It is not similar for all species due to the different emission factors. The resulting impact on surface CO and PM concentrations, as well as total column CO and AOD, is simulated using the CHIMERE CTM driven by the WRF meteorology.