dataesr/scientific-paragraphs-categorization huggingface dataset:
2 is based on ERA-Interim meteorology (0. 5 • × 0. 5 • horizontal spatial resolution); the datasets CAMS-GLOB-BIOv3. 0 and v3. 1 were calculated with ERA5 (both 0. 25 • × 0. 25 • horizontal spatial resolution). Furthermore, European isoprene emission potential data were updated using high-resolution land cover maps and detailed information of tree species composition and emission factors from the EMEP MSC-W model system. Updated isoprene emissions are included in the CAMS-GLOB-BIOv3. 1 dataset. The effect of annually changing land cover on BVOC emissions is captured by the CAMS-GLOB-BIOv3. 0 as it was calculated with land cover data provided by the Climate Change Initiative of the European Space Agency (ESA-CCI). The global total annual BVOC emissions averaged over the simulated period vary between the datasets from 424 to 591 Tg (C) yr -1 , with isoprene emissions from 299.
1 to 440. 5 Tg (isoprene) yr -1. Differences between the datasets and variation in their emission estimates provide the emission uncertainty range and the main sources of uncertainty, i. e. meteorological inputs, emission potential data and land cover description. The CAMS-GLOB-BIO time series of isoprene and monoterpenes were compared to other available data. There is a general agreement in an interannual variability in the emission estimates, and the values fall within the uncertainty range.
Emission model The presented emission datasets were calculated using the Model of Emissions of Gases and Aerosols from Nature (MEGANv2. 1; Guenther et al. , 2012). The MEGAN model was developed at the National Center for Atmospheric Research (NCAR, US) and is currently maintained and further improved by the Biosphere Atmosphere Interaction Group at the University of California -Irvine (https://bai. ess. uci. edu/, last access: 18 January 2022). It is an emission model extensively used in the atmospheric modelling community for simulation of biogenic VOC emissions from vegetation and soils at regional and global scales (e. g. Guenther et al. , 2006; Heald et al. , 2008; Arneth et al. , 2011; Sindelarova et al. , 2014; Seco et al. , 2015; Emmerson et al. , 2018; Kaiser et al. , 2018 , Huszar et al.
, 2018 , 2020). Furthermore, the algorithm of the MEGAN model has been embedded into a number of Earth system and chemical transport models (e. g. Emmons et al. , 2010; Lawrence et al. , 2011; Keller et al. , 2014; Henrot et al. , 2017). The model calculates an emission flux F (µg grid cell -1 h -1 ) of specific BVOC species from a model grid cell as follows: F = γ • EP • S, (1) where γ is a dimensionless factor accounting for dependence of emissions on environmental factors (air temperature, solar radiation, ambient CO 2 concentration, leaf age, etc. ); EP (µg m -2 h -1 ) is an emission potential of a grid cell, i. e. a unit emission defined under standardized environmental conditions; and S (m 2 ) is a grid cell surface area. The MEGANv2. 1 was applied with the full canopy module, which calculates meteorological conditions inside the forest canopy (e.
Meteorology Two sources of meteorological data were used for calculation of the emission datasets. CAMS-GLOB-BIOv1. 2 is based on the ERA-Interim  (Dee et al. , 2011)  data, and the datasets CAMS-GLOB-BIOv3. 0 and v3. 1 were calculated with ERA5  (Hersbach et al. , 2020)   (Olofsson et al. , 2007; Jacovides et al. , 2003; Escobedo et al. , 2011). The water mixing ratio was calculated from 2 m dew point temperature following equations from  Lowe and Ficke (1974). Since emissions are calculated on a monthly mean basis, the input meteorological data were synoptic monthly means of analysed and forecasted parameters. ERA-Interim data were available on a global grid with horizontal spatial resolution of 0. 5 • × 0. 5 • with 3 or 6 h time steps. The data were linearly interpolated in time in order to obtain a monthly averaged daily profile of each meteorological variable.
In order to capture the land cover change in MEGAN simulations, we replaced the static CLM4 PFT map with land cover data from the ESA-CCI  (ESA, 2017). ESA-CCI data are provided by the Climate Change Initiative of the European Space Agency. The data consist of time series of global annual mean land cover maps with high horizontal spatial resolution (300 m) available for the period of 1992-2018 based on satellite observations. To be consistent with the MEGAN model, the ESA-CCI land cover categories were converted to PFT classes similar to the CLM4 using the CCI-LC user tool v4. 3  (Poulter et al. , 2015). Emissions calculated with tempo-rally varying land cover are included in the CAMS-GLOB-BIOv3. 0 dataset. Table  1  compares global land areas covered by each PFT category in CLM4 and ESA-CCI (year 2000, converted by the CCI-LC user tool) land cover maps.
Emission potentials from detailed global maps The first option consists of the use of annual mean emission potential maps with high spatial resolution for the main BVOC species, i. e. isoprene, main monoterpenes (α-pinene, β-pinene, myrcene, sabinene, limonene, trans-β-ocimene, 3 -carene) and 2-methyl-3-buten-2-ol (MBO). Emission maps are available together with the MEGANv2. 1 code (https://bai. ess. uci. edu/megan/data-and-code/megan21, last access: 31 May 2021) and were created based on detailed ecoregion description, combining information on species composition with species-specific emission factors and above-canopy flux measurements where available  (Guenther et al. , 2012). Emission potentials for the rest of the modelled species were calculated based on the PFT coverage as described in the following section.
Describing global vegetation by only 16 PFT categories is of course a simplification that inevitably brings inaccuracies, especially for categories such as broadleaf deciduous forest, which can consist of tree species which are very low isoprene emitters but at the same time tree species such as oaks, which are very strong isoprene emitters. On the other hand, such simplifications are often necessary due to lack of detailed information on vegetation composition and/or assignment with emission factor or are simply a result of balance between the level of detail in vegetation description and ability of the model algorithm to digest such data. Calculation of EP from PFT coverage is to some extent inaccurate and on the other hand allows us to change the land cover description dataset (e. g. use ESA-CCI instead of CLM4) and therefore study the impact of land cover on resulting emissions.
Land cover description and emission factors The main basis for European BVOC emissions in Europe in the EMEP system is a map of forest species generated by  Köble and Seufert (2001) , combined with species-specific EFs for each of these species. The forest database provides maps for 115 tree species in 30 (mainly EU) European countries based on a compilation of data from the ICP-forest network (UN-ECE, 1998). These data were further processed to the EMEP grid by the Stockholm Environment Institute at York  (UK, Steven Cinderby, personal communication, 2004 ) in order to add data from other countries in the (2000 era) EMEP domain and for non-forested vegetation. More recently, the EMEP domain was signif-icantly expanded to the east, and data for the expanded area and indeed globally make use of a merger of the GLC_2000 dataset (https://forobs.
Results and discussion The following sections present examples of spatial and temporal distribution of emissions in CAMS-GLOB-BIO inventories on global and regional scales (Sect. 3. 1 and 3. 2). Section 3. 3 focuses in more detail on the impact of land cover change on isoprene emissions, and Sect. 3. 4 focuses on updates of the isoprene emission potential data in Europe, showing differences between emissions calculated with the MEGAN default and updated input EP maps. Section 3. 5 presents a comparison of CAMS-GLOB-BIO isoprene and monoterpene emissions to other available datasets. Table  4  summarizes the data availability in each CAMS-GLOB-BIO inventory as well as the different input parameters used to calculate each dataset.
Global distribution of BVOC emissions The annual global totals averaged over the respective periods are listed in Table  5  for BVOC species available in CAMS-GLOB-BIOv1. 2, v3. 0 and v3. 1 datasets. Though the absolute values differ between the datasets, the species responsible for the majority of the global BVOC total are common to all three inventories. The most abundant species is isoprene (64 %), followed by the sum of monoterpenes (13 %), methanol (7 %), acetone (4 %), ethene (3. 6 %), sesquiterpenes (2. 5 %), propene (2 %), acetaldehyde (1. 4 %) and ethanol (1. 3 %). The numbers in brackets represent the species contribution to the global BVOC total when expressed as Tg (C) yr -1 , averaged over the three datasets. The rest of the species contribute together with less than 10 Tg (C) yr -1 , i.
e. less than 2 %. Note that for monoterpenes we provide emissions of α-pinene, β-pinene and other monoterpenes. Another monoterpene group is a sum of myrcene, sabinene, trans-β-ocimene, limonene and 2carene, following recommendations of  Emmons et al. (2010). The CAMS-GLOB-BIOv3. 1 annual global total BVOC is about 60 Tg (C) yr -1 higher than v1. 2. This difference is mainly due to the use of different meteorological inputs. While v1. 2 was calculated with ERA-Interim reanalysis, v3. 1 is based on ERA5. ERA-Interim data are available with 3 or 6 h time steps, and therefore to obtain hourly input fields for the MEGAN model, the data needed to be temporally interpolated. Such interpolation leads to underestimation of me-  teorological parameters, especially for air temperature and solar radiation, as the interpolated fields do not capture the noon peak hours at locations between the model time steps.
In the case of ERA5, the data are available with hourly time steps, and temporal interpolation is no longer needed. The annual mean ERA5 values of air temperature and solar radiation are therefore higher than ERA-Interim, especially in highly emitting regions of South America, Central Africa, Southeast Asia and Indonesia, which is reflected accordingly in higher modelled emissions. Similar effects of temporal resolution of the input climate data on isoprene emissions were discussed by, for example,  Ashworth et al. (2010). The largest difference in global emission total can be observed between the CAMS-GLOB-BIOv3. 1 and v3. 0 inventories. The v3. 0 emission total is more than 160 Tg (C) yr -1 lower than in v3. 1, with the most significant difference for isoprene estimates, which are more than 140 Tg (isoprene) yr -1 lower in v3.
0 compared to v3. 1. Both inventories are calculated with ERA5 meteorology, but they differ in set-up of the input emission potential data and most importantly in the underlying land cover description. In the calculation of v3. 0 we switched from using static CLM4 land cover maps to annually changing ESA-CCI land cover data in order to capture the effect of land cover change on emissions. To include the effect of changing land cover information in the model, input gridded emission potential maps (described in Sect. 2. 4. 1) had to be replaced by calculation of emission potentials from PFT distributions (described in Sect. 2. 4. 2). As discussed in Sect. 2. 4. 2, such a change in the model set-up leads to a ∼ 10 % decrease in isoprene emissions on a global scale. The rest of the isoprene decrease can be explained by different land cover distribution in the CLM4 and ESA-CCI datasets.
Sensitivity emission model runs using exactly the same input data except for definition of land cover distribution resulted in an isoprene annual global total of 427 Tg (isoprene) yr -1 when using CLM4 and 316 Tg (isoprene) yr -1 when using ESA-CCI, i. e. almost a 30 % difference. As shown in Table  1 , total vegetated area is more than 18 ×10 6 km 2 (19 %) smaller in ESA-CCI than in CLM4 maps. Significant differences between the two vegetation maps are visible in the tropical region, which is a source of ∼ 80 % of global isoprene emissions  (Guenther et al. , 2012; Sindelarova et al. , 2014). The extent of broadleaf evergreen and deciduous tree cover in ESA-CCI is about 25 % lower than in CLM4. The global spatial distribution of CAMS-GLOB-BIOv3. 1 emissions for selected species is presented in Fig.
1. Regions of highest emission are located in the tropical band and include Amazonia, Central Africa, Southeast Asia, Indonesia and northern Australia. Additionally, significant BVOC emission sources are located in the south-eastern part of the US, especially during the summer months of the Northern Hemisphere. Substantial quantities of monoterpenes and methanol are further emitted from the northern temperate and boreal forests. BVOC emissions have strong seasonal variation following local meteorological conditions and a vegetation cycle with the highest emissions during daytime and in the summer season and the lowest emissions during nighttime and winter months. Temporal variations in isoprene, the most abundant BVOC species, for the period of 2000-2019 from the CAMS-GLOB-BIOv3. 1 dataset are presented in Fig.
Impact of land cover change on isoprene emissions The impact of changing land cover on emissions is captured in the CAMS-GLOB-BIOv3. 0 dataset. To illustrate the effect of changing land cover on isoprene, the 20-year time series of isoprene annual totals was fitted with a linear regression trend and compared to data from v3. 1, for which a static CLM4 land cover map was used. When calculated with the static vegetation map, isoprene emissions increase globally by 0. 35 % yr -1 due to temporal changes in meteorology. When annually changing ESA-CCI data are implemented, the trend decreases to 0. 24 % yr -1. A similar observation was made by  Opacka et al. (2021) , who used a modified MODIS land cover data in the MEGAN-MOHYCAN emission model to study the impact of land cover change on isoprene emissions.
They found a 0. 04 to 0. 33 % yr -1 mitigating effect of land cover change on general positive trends of isoprene induced mainly by temperature and solar radiation. Figure  4  presents a comparison of the v3. 0 and v3. 1 isoprene emission trends in selected regions. The regions' definition and spatial extent are given in Table  6. Inclusion of land cover change through ESA-CCI data (v3. 0) reduces isoprene trends in South America (especially in the Amazon) or even causes a negative isoprene trend in Southeast Asia when compared to v3. 1 based on static land cover. Such a trend decline is caused mainly by a retreat of tropical broadleaf forest (broadleaf evergreen and deciduous trees) in these locations. On the other hand, we observe an increase in the isoprene trend in East Africa, North Africa and the Middle East, and Russia, where the ESA-CCI data show an increase in the broadleaf deciduous tree category (tropical and boreal) in the course of the 20-year period.
1). Isoprene monthly totals from these two datasets are compared to CAMS-GLOB-BIOv1. 2 (annual total of 10. 5 Tg yr -1 ) and to isoprene estimated by EMEP (7. 3 Tg yr -1 ) in Fig. 6. The plot shows a clear decrease in emissions after use of the updated EPs and a good agreement between the CAMS-GLOB-BIOv3. 1 and EMEP estimates. The results were extracted for the European domain with latitudes from 30. 5-71. 75 • N and longitudes from 29. 75 • W to 65. 75 • E.
Comparison of CAMS-GLOB-BIO emissions with other inventories Time series of CAMS-GLOB-BIO emissions of isoprene and monoterpenes were compared to other available data. We focus on isoprene and monoterpenes as these are the two most abundant BVOC species and the two species for which time series from other sources are available. The rest of the species unfortunately suffer from lack of available and time-varying data. Datasets gathered for this comparison are listed in Table 7. Each dataset is assigned with basic information such as model used for emission estimation and driving meteorology. Most of the inventories are so-called "bottom-up" inventories, i. e. modelled by an emission model based on meteorology, emission factors and vegetation distribution. There are two "top-down" datasets, IASB-TD-OMI and IASB-TD-GOME2, which were calculated by an emission model and then constrained with satellite observations of formaldehyde (from OMI and GOME2) by applying an inversion technique in the chemical transport model (IMAGESv2)  (Stavrakou et al.
, 2014 (Stavrakou et al. , , 2015)). Most of the inventories were calculated with a "MEGAN-like" emission model algorithm except for the GUESS dataset, which was estimated by a process-based model LPJ-GUESS  (Smith et al. , 2001; Sitch et al. , 2003). The IASB datasets were obtained from the website of the GlobEmission project (http://www. globemission. eu/, last access: 18 January 2022). The rest of the data were obtained from the ECCAD database (http://eccad. aeris-data. fr/, last access: 18 January 2022). Figures  7  and 8  show a comparison of isoprene and monoterpene annual totals within the 2000-2019 period, respectively. In both cases, the CAMS-GLOB-BIO emissions fall well within the range of other estimates. Though both plots show there is quite a large spread between the datasets, with maximal differences up to a factor of 2 to 3 (difference of 320 Tg yr -1 for isoprene and 61 Tg yr -1 for monoterpenes).
There are various reasons for these discrepancies, the most important being selection of the emission model, driving meteorology and input vegetation data (land cover description and emission factors). In this data collection, the highest isoprene and monoterpene emissions are estimated by the MEGAN-MACC dataset. This dataset was calculated based on the MERRA and MERRA2 reanalyses  (Rienecker et al. , 2011)  and therefore differs in the use of meteorological inputs from most of the remaining datasets which used ERA meteorological fields (ERA-Interim and ERA5). Comparison of 2 m temperature and PAR fields from the MERRA and ERA datasets showed higher values of both meteorological parameters in the MERRA dataset mainly in the tropical regions of South America, Central Africa and Australia, i.
e. locations of high BVOC emission potential. Higher temperature and PAR values in MERRA data then result in higher MEGAN-MACC estimates. The key role of meteorology in BVOC estimation is supported also by a relatively good agreement of CAMS-GLOB-BIOv1. 2 isoprene emissions with IASB datasets, especially IASB-BU-OMI and IASB-TD-GOME2, which are both calculated based on ERA-Interim fields. Furthermore, the impact of meteorological inputs can also be observed in the difference between CAMS-GLOB-BIOv1. 2 and v3. 1 estimates based on ERA-Interim and ERA5, respectively, which is discussed in Sect. 3. 1. Interannual variability for most of the datasets is similar as they are mostly driven by the ERA meteorology, again except for MEGAN-MACC, based on MERRA reanalysis, for which the amplitude is higher.
For all datasets there is a clear link between isoprene emissions and El Niño and La Niña phenomena. As also presented in Fig. 2 , isoprene emissions decrease in 2008 and 2011 during strong La Niña and increase in 2002, 2015 and 2019 during El Niño episodes. For monoterpenes the interannual variability is not as profound as for isoprene. Similar to isoprene, monoterpenes are strongly emitted in the tropical region but also have significant sources in the temperate and boreal forests in the Northern Hemisphere. As a result, they are not as susceptible to atmospheric changes in the tropical band as isoprene and keep a rather stable interannual profile.
Conclusions The presented paper describes three new global inventories of biogenic volatile organic compounds emitted from vegetation which are publicly available for use by the air quality and climate models. The datasets are called CAMS-GLOB-BIO v1. 2, v3. 0 and v3. 1 and were calculated with the Model of Emissions of Gases and Aerosols from Nature (MEGANv2. 1) driven by meteorological reanalyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). Inventories include emissions of 25 BVOC species or chemical groups provided as monthly means and monthly averaged daily profiles spanning the period of 2000-2019. The CAMS-GLOB-BIO datasets were developed under the Copernicus Atmosphere Monitoring Service project (CAMS; global and regional emissions) as part of the European Union's Copernicus Earth Observation Programme.
The dataset CAMS-GLOB-BIOv1. 2 is based on ERA-Interim meteorological fields and is available with a horizontal spatial resolution of 0. 5 • × 0. 5 •. The datasets CAMS-GLOB-BIOv3. 1 and v3. 0 were calculated with ERA5 meteorology and are provided with a horizontal spatial resolution of 0. 25 • × 0. 25 •. CAMS-GLOB-BIOv3. 1 estimates global annual total BVOC emission of 591 Tg (C) yr -1 , with isoprene as the main contributing species (440. 5 Tg (isoprene) yr -1 ). Use of ERA5 meteorology in v3. 1 leads to a slight increase in BVOC emissions compared to v1. 2, with a global BVOC total of 532 Tg (C) yr -1 (including 385. 2 Tg (isoprene) yr -1 ). The total emission in the CAMS-GLOB-BIOv3. 0 dataset is 424 Tg (C) yr -1 , with isoprene emissions of 299. 1 Tg (isoprene) yr -1. The difference between v3.
Since the world's vegetation is experiencing significant changes, such as deforestation in the tropical region, replacement of forests by agricultural land and afforestation efforts with fast-growing trees, we aimed to take this effect into account. The CAMS-GLOB-BIOv3. 0 dataset considers changes in global land cover by using the ESA-CCI annual land cover maps for the vegetation description in the MEGAN model. In order to use a new land cover input in the model, the emission potentials had to be calculated from the PFT distribution instead of using the high-resolution emission potential maps. Such a difference in input EP data and a different input land cover map (ESA-CCI instead of CLM4) caused a decrease of ∼ 30 % for annual total isoprene and ∼ 20 % for monoterpenes when compared to CAMS-GLOB-BIOv3.
The different emission estimates in different versions of the CAMS-GLOB-BIO datasets provide the uncertainty's main driving factors, i. e. meteorological inputs, definition of input emission potentials and land cover distribution. The presented CAMS-GLOB-BIO datasets provide highresolution data of global BVOC emissions for the period of 20 recent years based on up-to-date input data. The datasets are suitable for the purposes of air quality modelling, especially for models that do not include their own module for online BVOC emission estimation. Our general recommendation is to use a CAMS-GLOB-BIO dataset which is calculated with the same meteorology as the one that drives the air quality model. If this does not apply, we recommend using the latest CAMS-GLOB-BIOv3. 1 dataset. CAMS-GLOB-BIOv3.
0 should be used for studies focusing on land cover change. Author contributions. KS performed the emission model runs, created emission inventories and analysed the data; JM contributed to the preparation of model inputs and emission processing; DS provided data for the update of isoprene emission potentials in Europe and helped with its conversion to MEGAN model format and analysis of emissions; PH and JK participated in emission modelling; SD and CG performed the emission data formatting and upload to the emission database. KS wrote the manuscript with contributions from all co-authors. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.