dataesr/scientific-paragraphs-categorization huggingface dataset:
Biomass density To estimate the biomass density in an area affected by fires, simulations by the ORCHIDEE global dynamic vegetation model  (Krinner et al. , 2005; Maignan et al. , 2011)  have been used. ORCHIDEE simulates the interactions between surface and atmosphere, the continental carbon cycle and the longterm evolution of vegetation. It consists of three modules: the SECHIBA model describes the hydrology; the STOMATE model simulates the daily phenology and continental carbon cycle, and the LPJ model is used for the long-term vegetation dynamics. Two ORCHIDEE simulations are used in this study: a global simulation at 70 km and a regional simulation at 30 km for the Euro-Mediterranean area  (Anav et al. , 2010). The global simulation, detailed in  Maignan et al. (2011) , is provided to allow full flexibility of the methodology in terms of area of interest, although the inventory is primarily developed for Europe.
It is based on the ORCHIDEE 1. 8. 2 release, with an improved phenological model for crops. The simulation is forced by ECMWF ERA-Interim meteorological fields  (Berrisford et al. , 2009) , over the 1989-2008 period, starting from an equilibrium state for all carbon reservoirs. The soil map, giving fractions of sand, silt and clay, is derived from  Zobler (1986). The regional simulation was based on a 0. 25 • × 0. 25 • climate forcing by the REMO regional climate model  (Jacob and Podzun, 1997) , provided in the framework of the CEXTREM European project. The simulation is started with a spinup to a neutral net CO 2 exchange in 1901 and then run using changing climate and CO 2 but with fixed land cover. The soil map is derived from the European 1 : 1 000 000 soil database  (Panagos, 2006). Like in other dynamic global vegetation models, vegetation in ORCHIDEE is represented as a set of plant functional types (PFTs).
Each PFT is represented in the model as a unique set of parameterization and parameters. 13 different PFTs are defined in ORCHIDEE mainly splitting vegetation between grass and trees. For trees there is a distinction between phenology (evergreen or deciduous), leaf form (needleleaf or broadleaf), and climate (boreal, temperate and tropical). For grass there is a distinction between natural grassland and crops and a distinction between C3 and C4 n cover for grouped vegetation types and the CLC and MODIS MCD12 databases (re CLC, savanna corresponds to natural grassland and grassland to pasture, for MODIS land, savanna and grassland. White areas to the east of the domain in the CLC map ly burned area on a 0. 1 • × 0. 1 • grid according to the MODIS MCD64 product for f fire occurrence, and average duration of the fire events within each grid cell.
The emissions' model presented here may be used with any burned area database, provided the date of burning, location and corresponding area is known. For the application to Europe and the Mediterranean area, we have chosen to use satellite remote-sensing observations. The fire characteristics will be described in terms of both active fires and burned areas. Both products are briefly described in this section. However, the emissions are calculated based solely on the area burned detection. No combination of active fires and burned area is undertaken in this study.
Following the approach of  Wiedinmyer et al. (2011) , area burned maps are derived by combining the burned pixel detection with the MODIS VCF product  (Hansen et al. , 2003)  in order to determine the fraction of vegetation in each cell. Only that fraction is assumed to have burned (the bare fraction is not burned). In this paper, the different burned area data sets are referred to as MCD45 and MCD64, but correspond to the scaled area burned values. Both MCD45 and MCD64 products have been used in the emission model for uncertainty analysis (Sect. 3). In Sect. 4, the variability of the area burned in Europe is analyzed based on the scaled MCD64 data.
False detections Even using the highest confidence observations in the available data sets, some false detections remain, especially for the active fires. This often corresponds to power plants, gas flares or other industrial activities, or active volcanoes. Previous analyses have used maps of persistent hotspots or high FRP to remove spurious detections, for example  Mu et al. (2011)  for the GFEDv3 inventory or  Kaiser et al. (2012)  for the GFASv1 inventory, both using masks at 0. 5 • resolution. In this work, we also developed a procedure including  -the fire location is within 1 km of an industrial facility using the European Pollutant Release and Transfer Register (e-PRTR http://prtr. ec. europa. eu/) database; -the fire location is within 1 km of an active volcano; -the statistical analysis of MODIS active fires (at 10 km resolution) for the past 10 yr shows an unrealistically high frequency of fires throughout the year (burning ≥ 40 % of the days).
Seasonal and interannual variability Figure  3  shows the averaged total yearly area burned (for each grid cell: the total area burned during the period is divided by the number of years with fires detected), as well as the probability of detecting at least one fire during the year within each grid cell. The main regions affected by vegetation fires are southern countries (the Iberian Peninsula and the Mediterranean area) and eastern countries (eastern Europe, Russia, Ukraine). Large burned areas are mainly observed in the southern countries, but are less frequent than the small fires occurring in the eastern part of the region. Large events (large burned areas, but with low frequency) are also observed in northwestern Russia. The total yearly burned areas by country have been compared to the European Fire Database from the EFFIS/JRC, which reports the forest fire data provided each year by individual countries  (Schmuck et al.
S. France Corsica Sardinia During the summer months (July-August), about 48. 5 % of the fires are detected in croplands, 21. 1 % in forests, 20 % in grasslands, 9 % in shrubland and 1. 4 % in natural grassland. Fires in cropland are dominant in the eastern part of the domain, more specifically eastern Europe, Ukraine, western Russia, and Turkey, but also in southern Italy. Apart from Italy, forest and grassland fires are dominant in the Mediterranean countries. Forest fires contribute 39 % of the fires in Portugal, 30 % in Spain, 25 % in southern France, Corsica and Sardinia, and 22 % in Greece. Mediterranean shrubland only contributes a small fraction according to the CLC database, 10-13 % on average in the Mediterranean area, while grassland contributes about 35-50 %. Fires in spring mainly occur in croplands in eastern Europe and Ukraine.
The fraction of croplands also tends to increase later in the season (September-November). These general features remain consistent if MODIS or USGS land covers are used. The main difference is that MODIS or USGS attribute larger fractions of area burned in shrubland in the Mediterranean countries, that corresponds to grassland in the CLC classes. USGS also tends to attribute more fires in croplands. Peatland burning is not mentioned in Fig. 5  because its contribution on average over the domain is negligible. For the 2010 fires in Russia, the large event in the Moscow area was partly located in peatlands, which contributed to 30 % of the CO emissions according to  Konovalov et al. (2011). For this event, no fires are detected in the MCD12 wetland category. This highlights the need for a more precise database specific to peatlands in this region.
Eq. (  1 ) is applied at fire resolution (depending on the resolution of the area burned or active fire database used): for each fire detection, each parameter of the calculation are estimated to calculate the corresponding emissions for a list of species. The emissions are then binned into a specified grid, with resolution ranging from a few kilometers to several hundreds kilometers, depending on applications. For a given fire location and associated area burned, the steps necessary for the computation of Eq. (  1 ) are described in the following subsections. A general scheme of the emission model is provided in Fig. 7.
Fuel load consumed The fuel load available for burning is calculated by multiplying the biomass density (B p in kg C m -2 ) of each considered carbon pool p in the region of the fire by the fraction of vegetation that is expected to actually burn (burning fraction β). The fuel consumed is then deduced by multiplying the available fuel load by the combustion fraction (C), such that: F v = carbon_pools p=1 C v β p B p,v. (2) The fuel load consumed is converted from kg C m -2 to kg DM m -2 assuming a 48 % carbon content in DM (following van der  Werf et al. , 2010). The biomass density is estimated using the ORCHIDEE model, as described in Sect. 2. 2. Although global data sets of soil  (Nachtergaele et al. , 2012)  and vegetation carbon content  (Gibbs, 2006)  exist at fine spatial resolution, here we used the ORCHIDEE model to estimate the biomass density because these data sets do not discriminate the fraction of each pool (p) contributing to the total carbon content.
Emission factors The species and corresponding emission factors used are listed in Table  5. The values from the recent review of Akagi et al. (  2011 ) are used for most species, complemented with the  Andreae and Merlet (2001)  database for missing values. Emission factors are provided in terms of g species per kg DM burned (g kg -1 ) for all relevant species observed in burning plumes (from field or laboratory measurements) and for different standard vegetation types (tropical forest, temperate and extratropical forest, boreal forest, crop residue, pasture maintenance, savanna). Once the fuel burned is estimated (DM burned), emissions for a large series of trace gases and aerosols can be calculated. The type corresponding to a specific fire is attributed using the vegetation type provided by the landuse classification (CLC, MODIS or USGS).
(2004)  for Europe (not for all regions). For GFED, modeling of the carbon cycle (the CASA-GFED model) is used, which accounts for the impact of observed fires. In this study, offline simulations of the ORCHIDEE model are used, without interaction with the detected fires. GFAS uses a top-down approach, estimating carbon emissions from the fire intensity (FRP measurements). The emission factors are from the  (Andreae and Merlet, 2001)  database for GFED and GFAS, and from the  (Akagi et al. , 2011)  database for FINN. In the following, the area burned estimates are compared to the GFED and FINN area burned, and emissions for the main compounds are compared to GFED, FINN and GFAS.
Regional fire emissions The variability of fire emissions is mainly due to the variability of fire activity itself, as discussed for Europe and the Mediterranean area in Sect. 4. In this section, the monthly CO emissions are presented and compared to the GFED and GFAS inventories. The contribution of fires to the regional pollution budget in terms of average over the past 10 yr is then discussed. Results are presented for the default configuration of the emissions' model, which uses the MODIS MCD64 area burned, the CLC vegetation database, the regional ORCHIDEE simulation with burning fraction varying depending on moisture stress. Sensitivity to the chosen configuration is discussed in Sect. 7. 2.
The combined use of area burned and active fires in GFEDv3 also affects temporal variability. The resulting monthly CO emissions are shown in Fig. 8  for the calculation based on the CLC and the MODIS MCD12 vegetation types, as well as for the GFEDv3 and GFASv1 inventories. The temporal variations are consistent, in particular between this work and GFEDv3 due to the good correlations in area burned products. Correlations with GFASv1 is generally slightly lower (0. 86) due to the different variability of area burned and FRP, discussed in the Sect. 4. The different approaches used in the calculation of emissions result in larger discrepancies in the emitted mass. In the Euro-Mediterranean region, the monthly emissions calculated in this work based on the CLC database are on average 2. 5 times larger than GFEDv3, and 60 % larger than GFASv1.
Burning more forest types would then result in larger emissions in GFEDv3 and GFASv1. On the contrary, the emission factor for cropland burning is larger in the APIFLAME inventory. However, similar differences between the inventories are obtained when comparing carbon emissions (i. e. , fuel consumption, before any emission factor is applied) with GFEDv3 (daily regional values on average 5 times larger for this work) (Fig. 11 ). If the global OR-CHIDEE simulation is used instead of the regional one, results are only slightly lower. However, using a different vegetation database significantly decreases carbon emissions. The Table  8. Average (2003 Average ( -2012) )  total annual emissions in Gg for different pollutants and regions of the Euro-Mediterranean (Euro-Mediterranean: latitudes between 36 and 48 • N, divided into 3 subdomains: West from 10 • W to 5 • E, Central from 5 to 20 • E, and East from 20 to 35 • E.
Ensemble approach Different databases may be used to estimate the key parameters of Eq. (  1 ) controlling emissions estimates. Intercomparing these options shows that significant uncertainty is associated with each of these parameters. However, quantifying the individual uncertainties does not provide a reliable estimate of the resulting uncertainty regarding emissions. For example, for a given burned area in a given region, if the location varies by a few kilometers between fire observation sources, then a different vegetation burned may be attributed, as well as a different fuel consumption, resulting in different emissions. In this study, we have chosen to calculate an ensemble of emissions for the case of the summer of 2007, using different options for each of the parameters of Eq. (  1 ).
11. The emission profiles for the reference configuration and the reference configuration with different vegetation maps are shown along with the GFED carbon emissions for comparison. The range of possible daily emissions appears to be extremely large. The total emitted carbon during July-August in the Euro-Mediterranean region varies between 12 and 63 Tg, with an ensemble average at 30 Tg. The reference inventory gives a total of 42 Tg with the CLC vegetation database, and twice lower with the MODIS vegetation database. GFED indicates a total of 10 Tg emitted. Smallest values of the ensemble are usually obtained when the global ORCHIDEE simulation is used for biomass density with the MODIS vegetation database. Note that the ORCHIDEE simulations were performed with the CLC land cover, so that better consistency is expected and may explain some of these differences.
The cumulative frequency distribution is presented in Fig. 12. The standard deviations associated with sub-ensembles, with only one varying parameter at a time, are also shown. The full ensemble has a standard deviation of 93 % on average, relative to the ensemble mean. It is larger than 100 % for ∼ 50 % of the cases. The sub-ensemble with only area burned varying gives larger dispersions, with standard deviations of 103 % on average, and larger than ∼ 140 % for ∼ 50 % of the cases. The dispersion of this sub-ensemble is on average larger than that of the full ensemble, indicating error compensations. The second largest dispersion comes from the vegetation database used, with an average standard deviation of 44 %. In this analysis, the lowest uncertainty is associated with the fuel consumption, with a standard deviation of 14 % on average if either one of the ORCHIDEE simulations is used, and of only 4 % for the different scenarios of combustion completeness.
They show that uncertainty significantly increases at lower temporal and spatial scales. At daily and kilometric (up to ∼1 km) resolutions, they find uncertainties larger than 133 % for more than 50 % of the CO emissions. At these scales, they find that uncertainty is mainly driven by uncertainties in the burned area. These values are slightly larger but consistent with our results from the ensemble. None of the previous studies addressed the impact of vegetation mapping and attribution in the final uncertainty, but we show that it is also an important factor. More generally,  Wiedinmyer et al. (2011)   ty functions (CDF) of the standard deviation of the ensemble results for daily emissions (relative to the ensemble estimates is indicated in gray. The CDFs for calculations with the reference configuration and one varying pa-, with the following parameters considered individually: area burned (AB), vegetation database (landuse), biomass of combustion completeness (CC scenario).
Code structure and availability Source code for the emission model may be obtained from the following web page: http://www. lmd. polytechnique. fr/ apiflame. The model has been written to allow full flexibility in terms of: -species accounted for: any species may be included provided its emission factor is known; -region analyzed: any domain may be chosen since global databases are provided; -fire information: any list of fire location, date of burning and associated area burned may be used. Several vegetation databases are provided (CLC, MODIS, USGS), but adding a new vegetation database only requires the addition of associated correspondence matrices between the vegetation classes, the ORCHIDEE PFT types and the vegetation types for which emission factors are provided. All user specifications are informed in the main script.
Yearly burned areas are consistent with the forest fire reports from EFFIS/JRC (within 20-30 %) in most countries, but are significantly larger in eastern Europe and Turkey. This is probably due to the fact that agricultural fires are not reported in the forest fires database. Fires affect extended regions in eastern Europe, Ukraine and western Russia with high frequency (every year in some regions), but with small durations and small burned areas on average, indicating many small fires. In the southern countries (Portugal and the Mediterranean areas), fires are less frequent, very localized, but can last 5-10 days with large burned areas. Both types of fires (large events for several days or smaller recurrent fires) have a potentially large impact on regional pollution budgets that needs to be accounted for, especially during summer.
A large fraction of the burned area detected appears in agricultural areas (about half of the detected fires), followed by forests (∼ 21 %), grasslands (∼ 21 %), and shrublands (∼ 9 %). Agricultural fires are particularly frequent in the eastern regions. Fire emissions are calculated using the classical approach introduced by  Seiler and Crutzen (1980)  and used in many inventories. They are derived by multiplying the area burned by the amount of fuel available, combustion completeness, and the emission factors of each included species. Since fuel load and emission factors both depend on the type of vegetation burned, precise knowledge of this parameter is essential for obtaining accurate emission estimates. The model allows the use of several databases. For Europe, the CORINE landcover database (CLC), regridded at 1 km resolution, is recommended.
The MODIS MCD12Q1 database (500 m resolution) is used as a default outside of the CLC domain. Land use (input data) may be modified according to specific applications without modification of the model's core. The fuel load is derived from simulations by the OR-CHIDEE model and depends on the vegetation burned and its location. Monthly mean outputs from global and regional simulations (over Europe) are provided, at 70 and 30 km resolution, respectively. The fuel load in terms of carbon available to burning is estimated depending on the type of vegetation burned, its location, the date of burning, as well as the fuel moisture stress. Emissions factors (g species per kg dry matter burned) then allow the calculation of emissions for a series of trace gases and aerosols. The emissions for inventory species are converted to emissions for model species adapted to specific chemical schemes included in chemistry-transport models using an aggregation matrix.
The standard deviation of the emissions among the different members of the ensemble shows that uncertainty is close to 100 % in the daily carbon emissions, with the dominant contribution from uncertainties in the area burned, and a significant contribution from the vegetation map used (∼ 44 %). This source of uncertainty had not been considered independently in previous uncertainty analyses based on a Monte Carlo approach. Urbanski et al. (2011)  evaluated the impact of different fuel loading databases, implicitly including vegetation type, and van der Werf et al. (  2010 ) considered uncertainty in the fuel load values but not in the vegetation mapping. In our study, uncertainty in the biomass density and fuel load calculation method is low but probably underestimated due to the fact that the same carbon cycle model is used (ORCHIDEE).
The emissions model allows the use of a mean diurnal profile. A future version of the algorithm will include diurnal variations from coincident SEVIRI observations for a more accurate representation of each fire's specificities. Future developments will also include a parameterization of ground layer burning and peatland fires, to allow applications to boreal regions in particular. The APIFLAME model may be applied to the analysis of past events or to the near-real time monitoring of emissions, provided area burned data are available. Daily emissions at a 0. 25 • × 0. 25 • resolution for the 2003-2012 time period over Europe may be obtained from the CHARMEX (Chemistry-Aerosol Mediterranean Experiment) project page of the EC-CAD database (http://eccad. pole-ether. fr). project for including the inventory within the database.
Our analysis relies on a data set of daily sea-level pressure obtained by  Kington (1988). This data set covers the period between 1781 and 1785 and has a regular grid. We use a methodology of windowed pressure analogues  (Yiou et al. , 2013)  to infer sea-level pressure, temperature and wind fields over the North Atlantic. Such unknown fields of the past are reconstructed from a historical sea-level pressure (SLP) data  (Kington, 1988)  to search for analogue fields in the NCEP reanalysis  (Kalnay et al. , 1996). The data and methodology are described in Sect. 2. We shall estimate the meteorological conditions that prevailed after the Laki volcanic eruption in 1783, although we do not model the transport of volcanic gases and aerosols. Hence, we investigate whether the atmospheric circulation of North Atlantic region during the spring of 2010 was similar to the one in spring 1783.
The methodology to produce the gridded data set from station data can also induce biases that are difficult to constrain. For verification purposes, we used observed surface temperature time series in 10 locations in Europe, covering the Laki period. The altitudes of measurements are not always available and are assumed to be near the surface. Although quality checks of the time series were performed, their homogeneity is less constrained than for periods after 1850, when meteorological networks started to thrive in Europe. The French station data (Dijon, La Rochelle, Montmorency and Saint-Malo) come from the French Société Royale de Médecine registers. The meteorological observations organized by the Royal Society were recorded by the correspondents in specially printed forms for recording the daily reports in monthly sets.