Integrated evaluation of biogenic secondary organic aerosol formation in a global climate-model
C. ROSE1, R. MAKKONEN1, H. JUNNINEN1, J. KONTKANEN1, P. RANTALA1, M. SIPILÄ1, Q. ZHA1, J.
BÄCK1, T. PETÄJÄ1, V.-M. KERMINEN1 and M. KULMALA1
1
Department of Physics, University of Helsinki, P.O. Box 64, FIN-00014 Helsinki, Finland
Keywords: NEW PARTICLE FORMATION, PRECURSORS, GLOBAL MODEL
Presenting author email: [email protected]
INTRODUCTION
Atmospheric formation of nanometer sized particles through gas to particle conversion processes is a
frequent phenomenon which significantly contributes to global aerosol particle number concentration throughout
the troposphere (Merikanto et al., 2009). Besides their effect on human health, these particles can, after they grow
to larger sizes, act as cloud condensation nuclei (CCN), and impact climate through cloud related radiative
processes (e.g. Kerminen et al., 2012). Global models predict that new particle formation (NPF) could produce a
substantial fraction of the total CCN budget, up to 70% in some regions (Merikanto et al., 2009; Yu and Luo,
2009), and in turn strongly control the present-day climate forcing by aerosol particles.
Despite the fact that instrumentation is continuously being improved, our understanding of the aerosol
formation mechanism still remains uncomplete. For that reason, global models use simplistic representations of
the process which are assumed to cover a large range of atmospheric conditions. Efforts have been put during the
last decades to better constrain predictions to observations in the planetary boundary layer (e.g. Makkonen et al.,
2009), where first attempts to describe nucleation were originally done using the binary nucleation mechanism
derived from the classical nucleation theory (Spracklen et al., 2005). Parameterizations have evolved towards a
more explicit description of the involvement of oxidized organic compounds from biogenic origin in the very
beginning of the NPF process (e.g. Paasonen et al., 2010), as widely suggested by recent observations.
However, when evaluated against observation, the predictions of NPF and its related effects have often
been discussed with respect to the choice of the parameterization for the nucleation step, while the discrepancies
related to the accuracy of the predicted precursors concentrations themselves were most likely left behind further
investigation. Here we report such an analysis for commonly used precursors in models, i.e. monoterpenes and
their oxidation products, hereafter referred as HOMs (Highly Oxidized Multifunctional organic compounds), as
well as sulfuric acid, using simulations conducted with the global model ECHAM5.5-HAM2 (Stier et al., 2005;
Zhang et al., 2012) and measurements performed at the boreal SMEAR II station in Hyytiälä, Finland (Hari and
Kulmala, 2005).
METHODS
Monoterpenes volume mixing ratios (VMRs) were measured in Hyytiälä with a quadrupole Proton
Transfer Reaction Mass Spectrometry (PTR-MS), which allows real-time monitoring of VMRs down to tens of
ppt. Monoterpenes concentrations were derived from the signal obtained at m/z (mass-to-charge-ratio) 137
(Taipale et al., 2008). Sampling was performed close to the top of the forest canopy, i.e. 14 or 16.8 m, every second
or third hour. In order to filter out extremely high monoterpenes concentrations related to the activity of the nearby
sawmill, we applied the modified Thompson tau method, as previously suggested by Liao et al. (2011).
Because of the lack of continuous measurement during the whole period of interest, HOMs and sulfuric
acid concentration were derived from proxies (Kontkanen et al., 2016; Petäjä et al., 2009). However, measurements
performed with a nitrate ion based chemical ionization Atmospheric Pressure interface Time-Of-Flight (CI-APi-
TOF) mass spectrometer (Jokinen et al., 2012) on shorter periods were used in addition. The calibration of the
instrument was based on sulfuric acid detection, and the resulting calibration coefficient was assumed to allow
also for the determination of HOMs concentration. Measurement uncertainty is estimated to be in the range 50%/+100%, because of both calibration and transmission related effects.
More information regarding the oxidation capacity of the atmosphere with respect to monoterpenes was
obtained from measurements and/or estimations of the main oxidant concentrations, i.e. OH, O 3 and NO3. O3
concentration was directly measured with an ozone analyser based on the absorption of UV radiation. In contrast,
OH and NO3 concentrations were calculated from measurements of other parameters, including NO and NOx
concentrations (chemiluminescence analyser) as well as UVB-radiation (SL 501A pyranometer) and temperature
(PT-100 sensor), all performed at 16.8 m except for radiation (18 m). Finally, the identification of NPF event days
as well as the calculation of early particle growth was performed using measurements conducted with AIS (Air
Ion Spectrometer; Mirme et al., 2007) and BSMA (Balance Scanning Mobility Analyser; Tammet, 2006).
Additional particle concentrations were measured using a DMPS (Differential Mobility Particle Sizer).
The ECHAM5.5-HAM2 model was run over the period of 2000-2010 using nudging technique to
assimilate model meteorology towards ERA-Interim. The emissions of dust and sea salt were calculated online,
while anthropogenic emissions of OC, BC and SO2 were taken from ACCMIP emission inventory. The model has
been upgraded with a secondary organic aerosol (SOA) scheme including ELVOCs (Jokinen et al., 2015), and
ELVOCs are participating in the nucleation of new particles (Paasonen et al., 2010). The emissions of BVOC
emissions were either taken from pre-calculated fields from MACC inventory, or calculated online using MEGAN
algorithm. Here we show results from prescribed BVOC emission simulations.
Direct comparison of modelled and measured values was achieved for monoterpenes concentrations,
oxidants concentrations, particle growth rates as well as particle concentrations. In contrast, indirect comparison
was performed for HOMs and sulfuric acid, which concentrations are not explicitly traced in the model. Instead,
their contribution to early particle growth, directly retrieved by the model and deduced from measured or proxyderived concentrations in Hyytiälä, was investigated as an indicator.
CONCLUSIONS
This analysis, based on a long-term dataset, allowed us to investigate how the accuracy of predicted
precursors concentrations might, besides the choice of the parameterization itself, affect prediction of NPF and its
effects, including especially particle concentrations in different diameter ranges up to CCN relevant sizes. As
shown on Figure 1, similar seasonal trends were obtained for modelled and measured monoterpenes concentrations
over the 2007-2010 period. Best agreement was obtained between April and November, suggesting that winter
time predictions could be more especially affected by missing processes in the model. Regarding particle growth
rates in the 1.5-3 nm size range, comparison conducted over an extended period (2003-2010) revealed contrasting
seasonal variations (Figure 2), most likely related to the fraction of growth ascribed to sulfuric acid. Several
reasons are suggested to explain the aforementioned discrepancies, including the strength of precursors emissions
in the model, the calculation of their sinks as well as the treatment of oxidation processes.
While participating in the identification of factors causing uncertainties in the prediction of NPF and its
related effects, this work will help better constraining the process in global models.
Figure 1. Median seasonal variation of
measured and modelled monoterpenes
concentrations derived from the 2007 – 2010
period. Lower and upper limits of the error
bars stand for the 25th and 75th percentile,
respectively.
Figure 2. Mean seasonal variation of
measured and modelled particle growth rates
(1.5-3 nm) derived from the 2003 – 2010
period.
ACKNOWLEDGEMENTS
The EU FP7 BACCHUS project (grant agreement 603445) and Nordic Center of Excellence eSTICC
(Nordforsk grant 57001) are acknowledged for financial support. We thank the tofTools team for providing
tools for mass spectrometry analysis.
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