2.2. SOSAA

SOSAA (a model to simulate the concentrations of organic vapors, sulfuric Acid and aerosols) is a 1-D chemistry transport model used to study the atmospheric composition inside the planetary boundary layer. In the past, SOSAA has been applied to study the characteristics of oxidant reactivities,^9,27,28 oxidation of trace gases,⁵⁵ emission of BVOCs,⁶² and vertical exchange and dry deposition of ozone⁶³ and BVOCs,⁶⁴ respectively, as well as new particle formation and growth of sub-3 nm particles.⁶⁵ SOSAA is written in Fortran and parallelized with MPI (Message Passing Interface). In this study, four different modules were used: (1) the meteorological module, which is derived from SCADIS;^66–68 (2) the BVOC emission module, which is a modified version of MEGAN2.04 (Model of Emissions of Gases and Aerosols from Nature);²¹ (3) the chemistry module, which is created by KPP,⁶⁹ with the chemical mechanism generated by MCM3.3.1 (see https://www.mcm.leeds.ac.uk/MCM);^70–72 and (4) the gas dry deposition module, which is modified from MLC-CHEM.^63,64,73 SOSAA describes the atmospheric boundary layer evolution and the vertical mixing of the chemical species in 51 vertical layers, from the surface up to 3 km. The simulation time step is 10 s for the meteorology module and 60 s for other modules.

The meteorological module includes the prognostic equations for the horizontal wind vector, air temperature and absolute humidity. In this study, these prognostic variables at the upper boundary of the model domain were constrained with the ERA-Interim reanalysis data which were provided by the European Centre for Medium-Range Weather Forecast (ECMWF).⁷⁴ In the lower part of the model domain from 4.2 m to 125 m above the ground, the air temperature, wind vector and absolute humidity were nudged to the vertically interpolated measurement data at SMEAR II with a nudging factor of 0.05, which represents the force of regional transport. The incoming short-wave and photosynthetically active radiation (PAR) at the canopy top, as well as the soil properties (soil temperature, soil water content and soil heat flux) were directly taken as input from SMEAR II measurements. The short-wave radiation was provided by the measurement data at SMEAR II, and the radiative transfer module from the ADCHEM model⁷⁵ was used to split the observed radiation into the direct, diffuse, downward and upward radiation components. The radiative transfer module used the quadrature two-stream approximation scheme developed by Toon et al. (1989).⁷⁶ All of the meteorological input data mentioned above were linearly interpolated to 10 s time resolution to match the simulation time step.

The standard emission potentials of the emitted BVOCs at SMEAR II, which were used to calculate the emission rates, refer to the values suggested in Zhou et al. (2017b).⁶⁴ The chemistry scheme was derived from the one used in Zhou et al. (2017b)⁶⁴ but with a newer MCM version 3.3.1. For the reactions of the stabilized Criegee intermediates (sCIs), we diverted from the MCM and instead used newer obtained reaction rates. For the sCIs from α-pinene, β-pinene and limonene, we have used the rates from Mauldin III et al. (2012)⁷⁷ similarly to “Scenario C” in Boy et al. (2013).⁵⁵ For the sCIs from isoprene, we used the rates from Welz et al. (2012)⁷⁸ as done in “Scenario D” in Boy et al. (2013).⁵⁵

The measured mixing ratios of CO, O₃, NO, NO₂ and SO₂ at the height levels 4.2, 8.4, 16.8, 33.6, 50.4, 67.2, 101 and 125 m were averaged and then used as the input values for all the layers in the model. The LODs of SO₂, NO, O₃ and NO₂ were set to 0.06 ppb, 0.05 ppb, 0.3 ppb and 0.1 ppb, respectively (Dr Pasi Kolari, personal discussion). However, for all these four species there exist several long periods when the measured values were below the LOD. In order to prevent the model from noise interference, which has too low values, all the values that are below the LOD are set to the LOD. We also did test runs by setting all values below the LOD to LOD/2. However, the model results showed a stepwise increase in the simulated OH, NO₃ and H₂SO₄ concentrations at all times when the input data went from the LOD to LOD/2. So, we decided to use the LOD as a threshold in case the values are below the LOD for the four gaseous compounds discussed above. There are several other methods used in the literature to overcome this problem such as the “Uniform Fill-In” or the “Log Fill-In” methods discussed and tested by Cohen and Ryan (1998).⁷⁹ However, as all the data below the LOD are unknown, no method predicts their distribution correctly which makes it difficult to choose a single technique that will be best at all times for various parameters. In Table S1 in the ESI,^† we calculated the amount of data points for SO₂ and NO above the LOD (the two parameters with the highest amount of data below the LOD) for different percentile ranges for each year to investigate if a trend in the below LOD data exists.

The measured CH₄ concentrations in 2014 were used as input in SOSAA for the year 2014. For other years, an annual global growth rate of 6 ppb per year was assumed, and the input time series of CH₄ concentrations were thus added (after 2014) or subtracted (before 2014) a multiple times of 6 ppb from the time series in 2014 according to the year difference. The growth rate was chosen from the ‘NASA Earth Observatory’ website and represents the methane increase in 2007–2013 (https://www.earthobservatory.nasa.gov/images/87681/a-global-view-of-methane).

The condensation sinks (CS) for H₂SO₄ and HNO₃ were provided as an input for the model. The CS was calculated based on the particle size distribution measured by using a DMPS (particles with diameters of 3–1000 nm) and an APS (particles with aerodynamic diameters of 0.5–20 μm) system,^52,80 and the hygroscopic growth effect was corrected based on Laakso et al. (2001).⁸¹ Similarly, for the meteorological input data, the input mixing ratios and the CS were also linearly interpolated to 60 s time resolution to match the simulation time step of the emission and chemistry modules.