Data from High-latitude vegetation changes will determine future plant volatile impacts on atmospheric organic aerosols

The dataset includes two parts: the part one includes results based on the dynamic ecosystem model, LPJ-GUESS and the other one is based on results from the global chemistry transport model, TM5. Part I: LPJ-GUESS runs for the period 1850-2100, and from 2015-2100, different climate scenarios have been used to drive the model. LPJ-GUESS simulates a variety of ecosystem variables, and in this dataset, we uploaded the data that have been used in the associated paper: including temperature, precipitation and radiation, BVOC emissions, gross primary productivity, leaf area index and aboveground biomass. LPJ-GUESS simulated BVOC emissions and vegetation status (coverage and LAI) from the years 2009 and 2100 were fed into the TM5 to simulate atmospheric chemistry and physics. In this dataset, the model outputs include surface concentration of secondary organic aerosols (SOA, kg m-3) , SOA load from surface to the top layer of atmospheric layer (kg m-2), atmospheric optical thickness and cloud condensation nuclei. LPJ-GUESS outputs The data have been placed in the folders based on their corresponding GCMs. Within each GCM folder, there are five subfolders, corresponding to five SSPs. For all 15 climate scenarios, there are outputs of: Total_aiso_allyears.txt: areal total of annual isoprene emission Total_amon_allyears.txt: and monoterpene emissions (TgC yr-1) Tot_aiso_combined_every5years.out: Sen’s slope of annual isoprene emission for the period 2001-2100 in the trend analysis (mgC m-2 yr-2) Tot_amon_combined_every5years.out: Sen’s slope of annual monoterpene emissions for the period 2001-2100 in the trend analysis (mgC m-2 yr-2) agpp1971to2000.txt & agpp2071to2100.txt: annual GPP during the periods of 1971-2000 and 2071-2100, respectively aiso1971to2000.txt & aiso2071to2100.txt: annual isoprene during the periods of 1971-2000 and 2071-2100, respectively amon1971to2000.txt & amon2071to2100.txt: annual monoterprene during the periods of 1971-2000 and 2071-2100, respectively mtemp_ave.as, mprec_ave.as mrad_ave.as: areal averages of the monthly temperature, precipitation and radiation, respectively Under CanESM5 and GFDL-ESM4 SSP119 & SSP585, there are four subfolders which stores the outputs from factorial simulations. There are some additional files: Tot_aiso_combined_every5years_for_expm.out: Sen’s slope (mgC m-2 yr-2) of annual isoprene emission for the period 2015-2100 in the trend analysis Tot_amon_combined_every5years_for_expm.out: Sen’s slope (mgC m-2 yr-2) of annual monoterpene emission for the period 2015-2100 in the trend analysis Under CanESM5 SSP585, there are also simulation outputs for the historical periods: lai2071to2100.txt: leaf area index (m2 m-2) for the period of 1971-2100 and 2071-2100 agpp****.txt: annual gross primary productivity (KgC m-2) of each year for the period 2001-2014 cmass_leaf****.txt: annual leaf biomass (KgC m-2) of each year for the period 1993-2012 cmass_stem****.txt: annual stem biomass (KgC m-2) of each year for the period 1993-2012 mlai****.txt: monthly LAI of each year for the period 1982-2011 Part II: TM5 outputs are saved in the format of python library in the pickled file: “https://a3s.fi/swift/v1/AUTH_5d61b75ae1f241c982bd17a9e2d23e0f/Paper-Tang_2022_BVOC/all_data-20220723.pkl”. The library keys are the case names, except 'other' which saves general data shared by all the cases. The case names include: cesm585-2009-2009 cesm119-2100-2009 cesm119_noVegDym-2100-2009 cesm119_noCO2Inhibition-2100-2009 cesm119_noCO2-2100-2009 cesm119_noMT_CO2Inhibition-2100-2009 cesm585-2100-2009 cesm585_noVegDym-2100-2009 cesm585_noCO2Inhibition-2100-2009 cesm585_noCO2-2100-2009 cesm585_noMT_CO2Inhibition-2100-2009 cesm119-2100-2100 cesm119_noMT_CO2Inhibition-2100-2100 cesm585-2100-2100 cesm585_noVegDym-2100-2100 cesm585_noCO2Inhibition-2100-2100 cesm585_noCO2-2100-2100 cesm585_noMT_CO2Inhibition-2100-2100 cesm585_noNlim-2100-2009 cesm585_noNlim-2100-2100 Here, cesm119 and cesm585 represent the scenarios of the datasets which were applied as input in the simulation cases. The first-year number (2100 or 2009) represents the year of the vegetation and BVOC emission datasets over the study region (boreal and arctic ecosystems), and the second year number represents the same datasets but for south of the study region. In this study, only the 2100-2009 combination has been analyzed since we would like to remove the impact from south of 45 N, and focus on the differences north of 45 N. Here cesm585-2009-2009 is the control run for present day, so it is not related to the scenario cesm585. The text in the middle of the case names have shown different sensitivity runs. Their meanings are shown below: noVegDym: Using the monthly averages of climate drivers from the period 2005-2014 for driving ecosystem processes, but keeping the predicted future climate for BVOC synthesis in the model for the future period 2015-2100. noCO2Inhibition: Setting CO2 inhibition impacts on BVOC production as in 2014. noCO2: Constant CO2 concentration at year 2014 level for the future period. noMT_CO2Inhibition: Setting CO2 inhibition impacts only on monoterpenes production as in 2014. noNlim: adding 50 kg N/ha/yr to annual nitrogen deposition to reduce nitrogen limitation In each case, individual variable data are saved in a python library structure similarly in the format of xarray DataArray. The variables were selected from the simulation results, their name and meanings are shown below: deltaz3d: layer thickness (m) sconcsoa: surface concentration of secondary organic aerosol (SOA) (kg m-3) loadsoa: load of SOA from surface to the top of the model (kg m-2) od550aer: atmosphere optical thickness (AOC) due to ambient aerosol (dimensionless) od550soa: atmosphere optical thickness (AOC) due to SOA (dimensionless) CCN0.20: cloud condensation nuclei (CCN) at 0.20% CCN1.00: cloud condensation nuclei (CCN) at 1.00% emiisop: emission of isoprene (kg m-2 s-1) emiterp: emission of monoterpenes (kg m-2 s-1) Under the key 'other', several key variables shared by all the cases are saved are saved in a similar python library structure in the format of xarray DataArray except 'area' which is saved as a numpy array. Their names and meanings are shown below: time: date time lev: level index lon: longitude lat: latitude area: surface area of each grid cell