DOI: 10.57760/sciencedb.iap.00008
title: Global biogenic volatile organic compound (BVOC) emission inventory during 2001 to 2020
title: Global biogenic volatile organic compound (BVOC) emission inventory during 2001 to 2020

description: 1) ModelWe use the latest Model of Emission of Gases and Aerosols from Nature (MEGANv3.2) to estimate the BVOC emissions from 2001 to 2020 with the input of time-varying satellite-retrieved vegetation and reanalysis meteorology data. Compared to the earlier version MEGANv2.1 (Guenther et al., 2012), MEGANv3.2 estimates vegetation emission factors based on variable plant species measurements instead of on fixed plant functional type (PFT, Guenther et al., 2020). Specifically, MEGANv3.2 uses the so-called Emission Factor Processor (EFP), to estimate the landscape average emission factors, which are based on the following three databases: (1) Growth form datasets for four PFTs: tree, shrub, grass, and crops; (2) Ecotype datasets: composed of a mix of emission-specific tree species/grass associated with specific emission capacities; and (3) Updated tree species/grass datasets corresponding to the biogenic emission classes. These updates can distinguish the differences in vegetation emission factors in regions with the same PFT but with varying plant species. The new version also considers the additional stress factors of emissions by using the simple threshold function, including high/low temperature, and strong wind.2) Model input data (Time-varying vegetation datasets, meteorological datasets, and CO2 concentration)The vegetation parameters driving MEGANv3.2 include LAI (leaf area index), VCF (vegetation cover fraction), and PFT. In this study, the Moderate-resolution Imaging Spectroradiometer (MODIS) vegetation retrievals from 2001 to 2020 were used. LAI data was obtained from Yuan et al. (2011), which improved the MODIS version 6 product MCD15A2H (Myneni et al., 2015) with a temporal resolution of 8 days and a spatial resolution of 0.5°×0.5°. The LAIv calculated in MEGANv3.2 is defined as LAI divided by VCF, representing the leaf area index per unit vegetation area. The VCF was from the yearly MODIS MOD44B version 6 dataset (DiMiceli et al., 2015).The PFT was obtained from the yearly MODIS MCD12C1 product with a spatial resolution of 0.05° (Friedl and Sulla-Menashe, 2015). The selected 17 MODIS IGBP (International Geosphere Biosphere Programme) global vegetation classification types were mapped to four main PFT classification types (i.e., tree, shrub, grass, and crop) in MEGANv3.2 based on methods from Sulla-Menashe and Friedl (2018). The reprocessed datasets were conservatively interpolated to a spatial resolution of 0.5°×0.5° as model inputs.The meteorological parameters driving the MEGANv3.2 model were from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) (Gelaro et al., 2017). The selected variables used in MEGANv3.2 include 2 m temperature, surface downward shortwave radiation, surface soil moisture, water vapor mixing ratio, 10 m wind speed, precipitation, surface air pressure, low-level wind speed, cloud cover, and snow cover. Photosynthetically active radiation (PAR) in MEGANv3.2 was obtained by dividing the surface downward shortwave radiation by two. The temporal resolution of these variables is either 1-hourly or 3-hourly, and the 3-hourly data are linearly interpolated to the uniform 1-hourly data. All selected parameters were further interpolated from the original 0.5° × 0.625° to a spatial resolution of 0.5° × 0.5° (consistent with the resolution of the vegetation datasets) for driving the MEGANv3.2 model. In our study, MERRA-2 data from 2001 to 2020 were used.In addition, the global annual averaged CO2 concentration data are obtained from https://gml.noaa.gov/ccgg/trends/gl_data.html3) SimulationsTo isolate the contribution of different influencing factors (vegetation, meteorology, and CO2) to BVOC emission trends from 2001 to 2020, we have performed nine sensitivity experiments. These experiments consist of two groups.The first group contains four experiments:EMIT_ALL is the control experiment that considers the historical changes of all factors.EMIT_VEG, EMIT_MET, and EMIT_CO2 consider only the historical changes of vegetation parameters, meteorological factors, and CO2 concentration, respectively, while the other factors are fixed as those in 2001.In the second group, five experiments were conducted to isolate the contributions of individual vegetation parameters (i.e., PFT, LAIv) and meteorological factors (i.e., temperature, light, and soil moisture):For vegetation parameters, the experimental setup is the same as EMIT_VEG but with PFT (EMIT_VEG_FIX_PFT) or LAIv (EMIT_VEG_FIX_LAIv) fixed as that in 2001.For meteorological factors, the experimental setup is the same as EMIT_MET but with temperature (EMIT_MET_FIX_T2m), light (EMIT_MET_FIX_RAD), or soil moisture (EMIT_MET_FIX_SM) fixed as that in 2001.The model horizontal resolution is 0.5° × 0.5°, the temporal resolution is 1 hour, and the simulation period is 2001-2020. The input variables include the satellite-retrieved vegetation parameters and MERRA-2 reanalysis data as described above.4) BVOC emission inventory This BVOC emission data are available as monthly mean emission fluxes as well as monthly averaged daily profiles of emissions.Spatial coverage: Global (latmin:-90 latmax:90 lonmin:-180 lonmax:180)Spatial-resolution: 0.5°x0.5°Temporal coverage: 2001-2020Dimensions and their names: monthly:  parameter (20) x month (12) x lat (180) x lon (360)monthly24h: parameter (20) x month (12) x hour (24) x lat (180) x lon (360)Data Format: NetCDFUnits: μg m-2 s-1Biogenic - 20 parameters'ISOP' = isoprene'MBO' = 2-methyl-3-buten-2-ol'MT_PINE' = monoterpenes: pines (alpha and beta)'MT_ACYC' = monoterpenes: acyclic (e.g., myrcene, ocimenes)'MT_CAMP' = monoterpenes: carene, camphene, others'MT_SABI' = monoterpenes: sabinene, limonene, terpinenes, others'MT_AROM' = C10 aromatic: cymenes, cymenenes & C8-C13 oxygenated (e.g., camphor)'MT_OXY' = oxygenated monoterpenes and monoterpenoid-related compounds (e.g., estragole) 'SQT_HR' = highly reactive sesquiterpenes (e.g., caryophyllene)'SQT_LR' = less reactive sesquiterpenes (e.g., longifolene, copaene) and salates'METOH' = methanol'ACTO' = acetone'ETOH' = acetaldehyde and ethanol'ACID' = organic acids: formic acid, acetic acid, pyruvic acid'LVOC' = C2 to C4 HC (e.g., ethene, ethane)'OXPROD' = oxidation products: aldehydes 'STRESS' = stress compounds (e.g., linalool)'OTHER' = other VOC (e.g., indole, pentane, methyl bromide)'CO' = carbon monoxide'NO' = nitric oxideReference:Wang, H., Liu, X., Wu, C., and Lin, G.: Regional to global distributions, trends, and drivers of biogenic volatile organic compound emission from 2001 to 2020, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1830, 2023.
description: 1) ModelWe use the latest Model of Emission of Gases and Aerosols from Nature (MEGANv3.2) to estimate the BVOC emissions from 2001 to 2020 with the input of time-varying satellite-retrieved vegetation and reanalysis meteorology data. Compared to the earlier version MEGANv2.1 (Guenther et al., 2012), MEGANv3.2 estimates vegetation emission factors based on variable plant species measurements instead of on fixed plant functional type (PFT, Guenther et al., 2020). Specifically, MEGANv3.2 uses the so-called Emission Factor Processor (EFP), to estimate the landscape average emission factors, which are based on the following three databases: (1) Growth form datasets for four PFTs: tree, shrub, grass, and crops; (2) Ecotype datasets: composed of a mix of emission-specific tree species/grass associated with specific emission capacities; and (3) Updated tree species/grass datasets corresponding to the biogenic emission classes. These updates can distinguish the differences in vegetation emission factors in regions with the same PFT but with varying plant species. The new version also considers the additional stress factors of emissions by using the simple threshold function, including high/low temperature, and strong wind.2) Model input data (Time-varying vegetation datasets, meteorological datasets, and CO2 concentration)The vegetation parameters driving MEGANv3.2 include LAI (leaf area index), VCF (vegetation cover fraction), and PFT. In this study, the Moderate-resolution Imaging Spectroradiometer (MODIS) vegetation retrievals from 2001 to 2020 were used. LAI data was obtained from Yuan et al. (2011), which improved the MODIS version 6 product MCD15A2H (Myneni et al., 2015) with a temporal resolution of 8 days and a spatial resolution of 0.5°×0.5°. The LAIv calculated in MEGANv3.2 is defined as LAI divided by VCF, representing the leaf area index per unit vegetation area. The VCF was from the yearly MODIS MOD44B version 6 dataset (DiMiceli et al., 2015).The PFT was obtained from the yearly MODIS MCD12C1 product with a spatial resolution of 0.05° (Friedl and Sulla-Menashe, 2015). The selected 17 MODIS IGBP (International Geosphere Biosphere Programme) global vegetation classification types were mapped to four main PFT classification types (i.e., tree, shrub, grass, and crop) in MEGANv3.2 based on methods from Sulla-Menashe and Friedl (2018). The reprocessed datasets were conservatively interpolated to a spatial resolution of 0.5°×0.5° as model inputs.The meteorological parameters driving the MEGANv3.2 model were from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) (Gelaro et al., 2017). The selected variables used in MEGANv3.2 include 2 m temperature, surface downward shortwave radiation, surface soil moisture, water vapor mixing ratio, 10 m wind speed, precipitation, surface air pressure, low-level wind speed, cloud cover, and snow cover. Photosynthetically active radiation (PAR) in MEGANv3.2 was obtained by dividing the surface downward shortwave radiation by two. The temporal resolution of these variables is either 1-hourly or 3-hourly, and the 3-hourly data are linearly interpolated to the uniform 1-hourly data. All selected parameters were further interpolated from the original 0.5° × 0.625° to a spatial resolution of 0.5° × 0.5° (consistent with the resolution of the vegetation datasets) for driving the MEGANv3.2 model. In our study, MERRA-2 data from 2001 to 2020 were used.In addition, the global annual averaged CO2 concentration data are obtained from https://gml.noaa.gov/ccgg/trends/gl_data.html3) SimulationsTo isolate the contribution of different influencing factors (vegetation, meteorology, and CO2) to BVOC emission trends from 2001 to 2020, we have performed nine sensitivity experiments. These experiments consist of two groups.The first group contains four experiments:EMIT_ALL is the control experiment that considers the historical changes of all factors.EMIT_VEG, EMIT_MET, and EMIT_CO2 consider only the historical changes of vegetation parameters, meteorological factors, and CO2 concentration, respectively, while the other factors are fixed as those in 2001.In the second group, five experiments were conducted to isolate the contributions of individual vegetation parameters (i.e., PFT, LAIv) and meteorological factors (i.e., temperature, light, and soil moisture):For vegetation parameters, the experimental setup is the same as EMIT_VEG but with PFT (EMIT_VEG_FIX_PFT) or LAIv (EMIT_VEG_FIX_LAIv) fixed as that in 2001.For meteorological factors, the experimental setup is the same as EMIT_MET but with temperature (EMIT_MET_FIX_T2m), light (EMIT_MET_FIX_RAD), or soil moisture (EMIT_MET_FIX_SM) fixed as that in 2001.The model horizontal resolution is 0.5° × 0.5°, the temporal resolution is 1 hour, and the simulation period is 2001-2020. The input variables include the satellite-retrieved vegetation parameters and MERRA-2 reanalysis data as described above.4) BVOC emission inventory This BVOC emission data are available as monthly mean emission fluxes as well as monthly averaged daily profiles of emissions.Spatial coverage: Global (latmin:-90 latmax:90 lonmin:-180 lonmax:180)Spatial-resolution: 0.5°x0.5°Temporal coverage: 2001-2020Dimensions and their names: monthly:  parameter (20) x month (12) x lat (180) x lon (360)monthly24h: parameter (20) x month (12) x hour (24) x lat (180) x lon (360)Data Format: NetCDFUnits: μg m-2 s-1Biogenic - 20 parameters'ISOP' = isoprene'MBO' = 2-methyl-3-buten-2-ol'MT_PINE' = monoterpenes: pines (alpha and beta)'MT_ACYC' = monoterpenes: acyclic (e.g., myrcene, ocimenes)'MT_CAMP' = monoterpenes: carene, camphene, others'MT_SABI' = monoterpenes: sabinene, limonene, terpinenes, others'MT_AROM' = C10 aromatic: cymenes, cymenenes & C8-C13 oxygenated (e.g., camphor)'MT_OXY' = oxygenated monoterpenes and monoterpenoid-related compounds (e.g., estragole) 'SQT_HR' = highly reactive sesquiterpenes (e.g., caryophyllene)'SQT_LR' = less reactive sesquiterpenes (e.g., longifolene, copaene) and salates'METOH' = methanol'ACTO' = acetone'ETOH' = acetaldehyde and ethanol'ACID' = organic acids: formic acid, acetic acid, pyruvic acid'LVOC' = C2 to C4 HC (e.g., ethene, ethane)'OXPROD' = oxidation products: aldehydes 'STRESS' = stress compounds (e.g., linalool)'OTHER' = other VOC (e.g., indole, pentane, methyl bromide)'CO' = carbon monoxide'NO' = nitric oxideReference:Wang, H., Liu, X., Wu, C., and Lin, G.: Regional to global distributions, trends, and drivers of biogenic volatile organic compound emission from 2001 to 2020, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1830, 2023.