From the journal Environmental Science: Atmospheres Peer review history

25-Apr-2021

Dear Dr Zhou:

Manuscript ID: EA-ART-03-2021-000020
TITLE: Modelling study of OH, NO₃ and H₂SO₄ in 2007 - 2018 at SMEAR II, Finland: analysis of long-term trends

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Associate Editor, Environmental Sciences: Atmospheres

************

Reviewer 1

This study uses the one-dimensional model SOSAA to analyze the long-term trends of OH, NO3, and H2SO4 at SMEAR II, Finland. This is an important topic as these species are key components that influence air quality and climate. In general, modelling results are carefully validated using observations from multiple campaigns and calculated proxies. However, due to the modelling-observation discrepancies of different meteorological factors as well as chemical compounds, it is hard to conclude on the trends and how uncertain is the modelled results. Also, the writing is not concise and well-organized. A few major issues need to be addressed.

1. The section of Results includes many paragraphs/sentences (e.g., lines 408-416, 545-552, 664-669) that actually serve as introduction. Please be concise by integrating these scatter pieces to the Introduction section.
2. The section about proxy does not contribute too much to the accuracy of the modelled trends. Please consider to integrate the proxy-model comparison to the observation-model comparison.
3. In the Method section, please specify whether multiphase chemistry is used or just gas phase chemistry, and clarify how this would influence the trend analysis. Also, is the simulation time step of 60 s sufficient to capture the fast reactions, especially those related to OH with short lifetime? Also, is MEGAN the only emission inventory used in the study? Where do NOx, CO, SO2, etc come from? Any anthropogenic emissions included?
4. Boundary layer representation could have a large impact on the vertical mixing of oxidants. For example, compared with observations, local-scale models such as a large-eddy simulation model that resolves turbulence can better reproduce the vertical profiles of oxidants and BVOCs compared with the models that use parameterized boundary layer processes. How does SOSAA account for boundary layer evolution? Can turbulence be resolved? Please also specify the typical diurnal variation of boundary layer top height at SMEAR II. Is 3 km enough to capture all the boundary layer changes (lines 151-153)? Also, please elaborate on “the influence by micrometeorology” and the impact of unresolved turbulence (lines 350-351:). More citations are needed.
5. Please add a section to discuss/summarize the uncertainty of modelled trends. Since modelled results rely on different modules, a discussion about the uncertainties induced by each module is necessary. For example, how MEGAN accounts for changes in emissions under different meteorological conditions and how uncertain this calculation is.


More specific comments:

Line 183: please clarify “a jump.”

Line 193: For “an annual growth rate of 6 ppb yr-1,” is it a global rate or a regional rate?

Line 285: If “an overall underestimation of heat fluxes is normal,” why does the underestimation only occur in the winter and autumn months?

Lines 329-330: Please clarify “a stronger decrease” based on “a clear increase from 2007-2018.”

Lines 374-377: There is a discrepancy of about one magnitude. Do the trends really agree well?

Lines 453-458: Most OH is produced during daytime. Can nighttime OH really drive its daily trend?

Lines 490-503: As “CO is the main sink of OH in winter” and “the main factor to explain the long-term modelled OH trend,” please make a connection with the model performance for OH simulation in this season.

Lines 536-538: Please clarify what species this sentence refers to.

Line 547: “Present daytime NPF can almost exclusively be explained by H2SO4 clustering with either ammonia or organic compounds formed from OH-oxidation of monoterpenes.” Please provide a percentage to verify this statement.

Lines 613-615: Confusing sentence. Please rewrite.

Lines 671-672: “By applying this approximation, we derived the monthly median NO3 proxy between 2007-2018.” Is it based on observations or modelled results?

In the section of Summary and Perspectives, please provide some explanations about the impact of NO3 drop at nighttime on air quality or climate.

Lines 720-723: Confusing sentence. Please rewrite.

Reviewer 2

General comments
This manuscript investigates the long-term trend of important atmospheric oxidants (OH, NO3, and O3) utilizing a one-dimensional model. This is a relevant research topic in the field and has important implications for understanding long-term atmospheric composition changes within the context of human activities and climate change. It is generally well-written. The method is appropriate, and the presentation of results is well-organized.

Major comments
One of my major comments is the absence of isoprene evaluation, which I believe is critically important for OH concentration estimation. In addition, one inherent disadvantage of 1D models is the horizontal advection. I wonder how this would affect the comparison between model results and measurements especially for long-lived species. At last, some discussion would enhance the manuscript. Discussions on the implications of this study would be helpful for readers to better understand the significance of the study; discussions of model uncertainties (e.g. the model-measurement discrepancies appear to be large during winter times) will elucidate future studies needed. Please see below for the specific comments.

Minor comments
Line 37. – “Nieminen et al., 2014”. Citation in the abstract should be avoided if possible.

Line 69-70. –“Numerous studies have investigated global OH trends using chemical transport models or retrieval of remote sensing of methyl chloroform (CH3CCl3, MCF) ”. Be more specific with the previous findings from Montzka et al., 2000; Prinn et al., 2001, Kirschke et al., 2013 if possible, increasing or decreasing trend?

Line 139. – Can you comment on 1D chemistry transport model? Does “transport” here refer to horizontal transport? How the 1D model resolve horizontal transport?

Line 177. – “vertically averaged”. Clarify.

Table 2. – Why monoterpene emission rates in winter show an increase?

Line 271. -–How are the vertical distributions of these parameters (i.e. temperature, water vapor, and wind speed) determined in the model domain?

Line 285 - 286. –Stable conditions are hard to deal with! Do you mean the 10 Wm-2 is likely an underestimation by the eddy covariance system? How are the sensible heat fluxes calculated in the model? Is it by the gradient method? How does the potential overestimation (Fig. 1A, B) affect your following results? Please clarify.

Line 296-297. –Is this how you calculated the measured Rnet? How Rnet is calculated in the model then? By Rnet = H+LE-G? Then how G is compared to measurements? If it is a local study for a specific site, why not just constrain the model with measured net radiation?
Section 3.2. –All the relevant tables are in the supplementary, which makes the section a bit hard to read.

Fig. 3. – Can you add a line to indicate the average canopy height?

Line 380-381. – Any reasons for the low measurements in 2012 and 2018?

Line 445-446. – “assuming that the two measurement places were not in the forest”. Clarify.

Line 450-451. – The modeled peaks of OH in spring seem to be the overestimation of the model. Have you tested if [OH] is sensitive to [O3] in the model, even though O3 is the precursor?

Line 463. – “the decrease of the NO concentrations”. Doesn’t [NO] has no trend?

Line 473. – “This is the main source of OH during dark conditions at SMEAR II”. You have a reference for this statement? I find it surprising because the monoterpenes are very low.

Line 481-489. –In section 3.2, CO is said to have an insignificant decrease. Is this the case for winter CO? If so, how the minor decrease in CO leads to a great increase in [OH]?

Line 529-530. – “The reason is that the NO2 concentration is highest during these periods”. Any reasons for the double peak in NO2?

Some figures in the SI seem to be important and could be moved to the main section. I will leave this to the authors to decide.

This text has been copied from the PDF response to reviewers and does not include any figures, images or special characters.

We really appreciate the time and effort that the reviewers dedicated to providing the valuable
comments, which helps us improve our manuscript. We will show a point-by-point reply to
comments and highlight the changes in the modified manuscript.

Notice that according to the comment from Reviewer 2, we moved Fig. S7 to the main section as
Fig. 7, the figure indices after it will be added one in main text and minus one in supplement.

Reviewer 1
Comments to the Author
This study uses the one-dimensional model SOSAA to analyze the long-term trends of OH, NO3,
and H2SO4 at SMEAR II, Finland. This is an important topic as these species are key components
that influence air quality and climate. In general, modelling results are carefully validated using
observations from multiple campaigns and calculated proxies. However, due to the modellingobservation discrepancies of different meteorological factors as well as chemical compounds, it is
hard to conclude on the trends and how uncertain is the modelled results. Also, the writing is not
concise and well-organized. A few major issues need to be addressed.
1. The section of Results includes many paragraphs/sentences (e.g., lines 408-416, 545-552, 664-
669) that actually serve as introduction. Please be concise by integrating these scatter pieces to the
Introduction section.
We agree with the reviewer that the following paragraphs at L408-416, L545-552 and L664-669
will be moved to the introduction section. Some of this information is already in the introduction
and we will merge them with the existing text
L408-416: The text content is overlapped with the introduction part, so it will be deleted:
Delete "The hydroxyl radical, OH is the most important oxidant in the troposphere and it is the
major “cleaning protagonist” in the atmosphere by reacting with nearly all trace gases including the
vast number of VOCs emitted from the boreal forest. OH is also the most important sink term for
methane (CH4), the second most important greenhouse gas, responsible for approximately 20 % of
induced global radiative forcing since pre-industrial times (Turner et al., 2018). It is also crucial for
the sulfuric acid production and in this way, it indirectly influences the formation of secondary
organic aerosols (see Section 3.5). Therefore, it is important to study the trend of the OH
concentrations and to investigate whether increased temperature or changes in the gas-phase
composition in the atmosphere during the last decade at the SMEAR II station had an impact on the
concentration of this radical."
The reference will be deleted: Turner et al., 2018.
L545-552: The text content will be added after L56:
Add after L56: "According to the latest global chemistry-transport model simulations, which used
state-of-the-art new particle formation (NPF) parameterizations from the CLOUD chamber
experiments in CERN (Riccobono et al., 2014; Kirkby et al., 2016), around 96% (Gordon et al.,
2017) or 100% (Dunne et al., 2016) of the present-day NPF can be explained by H2SO4 clustering
with ammonia, ions or organic compounds. Roldin et al. (2019) very recently reproduced observed
NPF by considering sulfuric acid together with ammonia and/or ELVOCs during two periods in
spring 2013 and 2014. Hence it is crucial for all NPF analyses to know the concentrations of H2SO4
and how they have changed in the past and will change in the future."
The references will be added: Kirkby et al., 2016, Dunne et al., 2016.
L664-669: The text content is overlapped with the introduction part, so it will be deleted, but the
references for HUMPPA-COPEC campaign and IBRAIRN campaign will be added in the
introduction part:
Delete "Besides O3, the nitrate radical is the most important oxidant during nighttime and has an
important contribution to BVOC oxidation during this time of the day (Mogensen et al., 2015).
However, until now, NO3 measurements at SMEAR II are rather limited and most of the existing
data achieved during the HUMPPA-COPEC campaign in 2010 (Williams et al., 2011) and the
IBAIRN campaign in 2016 (Liebmann et al., 2018) were below the LOD. Thus, a robust estimate
for NO3 concentration for the whole year is needed."
L91-94:
"At the HUMPPA-COPEC campaing and the IBAIRN (Influence of Biosphere-Atmosphere
Interactions on the Reactive Nitrogen budget) campaignn NO3 concentrations at SMEAR II were
also measured, but most of the time the values were close to the limit of detection (LOD) of the
instrument (Liebmann et al., 2018)."
-->
"At the HUMPPA-COPEC campaign in 2010 (Williams et al., 2011) and the IBAIRN (Influence of
Biosphere-Atmosphere Interactions on the Reactive Nitrogen budget) campaign in 2016 (Liebmann
et al., 2018) NO3 concentrations at SMEAR II were also measured, but most of the time the values
were close to the limit of detection (LOD) of the instrument."
2. The section about proxy does not contribute too much to the accuracy of the modelled trends.
Please consider to integrate the proxy-model comparison to the observation-model comparison.
We agree with the reviewer that the proxy-section does not contribute too much to the accuracy of
the modelled trends, but we want to point out here that this was not the aim of this section. As
mentioned in the text “During the last years, several proxies have been developed for compounds
like the hydroxyl or the nitrate radical, due to absence or sparse long-time observations for these
parameters” (L595-596). And these proxies are more and more applied to estimate the
concentrations of important compounds like OH or H2SO4. Our focus in this section is not to
validate the proxies but to provide for the readers and specifically for the users of the proxies one
additional feedback how the proxies compare with a detailed chemistry-transport model at SMEAR
II. Therefore, we still believe that this extra section could be of interest for the community and it is
more appropriate to not merge this part to the upper sections of the manuscript.
3. In the Method section, please specify whether multiphase chemistry is used or just gas phase
chemistry, and clarify how this would influence the trend analysis. Also, is the simulation time step
of 60 s sufficient to capture the fast reactions, especially those related to OH with short lifetime?
Also, is MEGAN the only emission inventory used in the study? Where do NOx, CO, SO2, etc
come from? Any anthropogenic emissions included?
(1) In this study, only the gas phase chemistry was applied. However, we have taken into account
the condensation sinks for H2SO4 and HNO3 which means the loss of these two compounds to the
particles were also considered in details. Other chemical compounds with low saturation vapour
pressures that might condense on particles could be slightly overestimated in the gas phase,
however, the change in concentrations of the majority of the gas compounds should be negligible
and has not been considered here. For example, Xavier et al. (2019) showed that at temperature
ranges of 258.15-313.15 K the majority of chemical compounds, individually contributed <10% to
the condensed phase. Furthermore, the further oxidation of condensing precursors is mainly related
to autoxidation (Roldin et al., 2019) and thus affect the OH and NO3 concentrations even much less.
Therefore, we can safely say that the general trend analysis of primary compounds (OH, NO3 and
H2SO4) we investigated in the study would not be affected much if the condensed phase is not taken
into account.
(2) In this study Rodas-3 Rosenbrock solver was used to solve the stiff systems of the ordinary
differential equations of the chemistry scheme, including slow reactions and fast reactions (e.g.,
oxidation reactions with OH). This is also the default solver in KPP and proved to be stable and
accurate (Sandu et al., 1997). The same setup of the time step was also applied and tested in our
previous studies (Mogensen et al., 2015; Zhou et al., 2017a, b).
(3) Yes, MEGAN is the only emission inventory in this study and it was applied to calculate BVOC
emissions from canopy.
(4) As described in Section 2.1, the measured concentrations of inorganic compounds, e.g., O3, NOx,
CO, SO2 and CH4, are input into the model, which can be considered to account for the effects of
local and regional transport or large scale variation of these species. Among them, CO and SO2 with
longer lifetime can represent the regional effects of anthropogenic emissions and biomass burning.
The anthropogenic VOCs were not included in this study, but the anthropogenic influence at
SMEAR II is low with the nearest significant city (about 200 000 inhabitants) located 60 km
southwest (Hellén et al., 2018).
The text at L125 will be modified as:
"A detailed description ..."
-->
"The input of O3, NOx, SO2, CO and CH4 can be considered to account for the effects of local and
regional transport or large scale variation of these species. The anthropogenic VOCs were not
included in this study, but the anthropogenic influence at SMEAR II is low with the nearest
significant city (about 200 000 inhabitants) located 60 km southwest (Hellén et al., 2018). A
detailed description ..."
4. Boundary layer representation could have a large impact on the vertical mixing of oxidants. For
example, compared with observations, local-scale models such as a large-eddy simulation model
that resolves turbulence can better reproduce the vertical profiles of oxidants and BVOCs compared
with the models that use parameterized boundary layer processes. How does SOSAA account for
boundary layer evolution? Can turbulence be resolved? Please also specify the typical diurnal
variation of boundary layer top height at SMEAR II. Is 3 km enough to capture all the boundary
layer changes (lines 151-153)? Also, please elaborate on “the influence by micrometeorology” and
the impact of unresolved turbulence (lines 350-351:). More citations are needed.
(1) The boundary layer height is calculated as the lowest layer above the canopy where Richardson
number reaches the critical value 0.25.
(2) The turbulence in SOSAA is not resolved explicitly, but instead it is parametrized. The turbulent
diffusion coefficients are calculated with a TKE-omega scheme as described in more details in Zhou
et al. (2017). Here TKE is turbulent kinetic energy and omega is the specific dissipation of TKE.
(3) A typical diurnal variation of boundary layer height at SMEAR II has been shown in previous
studies, e.g., in Fig. 4 of Ouwersloot et al. (2012) for summer in 2010, in Fig. 3 of Hao et al. (2018)
for spring in 2014.
(4) In Williams at al. (2011) the vertical structure of the atmospheric boundary layer during night
and day was monitored by 175 radiosondes launched throughout the campaign. The analysis of the
data showed that the boundary layer height typically grew from less than 200 m in the early
morning to around 1700 m at the end of the afternoon. This means that the selected domain up to 3
km in SOSAA simulations should be at all times above the atmospheric boundary layer top.
(5) Now the sentence at L349-351 was modified as:
"The different height level of the maximum could be related to the distribution of the emission
inside the model (MEGAN) but also due to the influence by micrometeorology (since small bias in
the turbulence could have a big influence)."
-->
"The different height levels of the maximum could be related to the missing emission sources from
understory vegetation and soil (Mäki et al., 2017, 2019)."
The references will be added: Mäki et al, 2017, 2019.
5. Please add a section to discuss/summarize the uncertainty of modelled trends. Since modelled
results rely on different modules, a discussion about the uncertainties induced by each module is
necessary. For example, how MEGAN accounts for changes in emissions under different
meteorological conditions and how uncertain this calculation is.
The uncertainty of a complex model system like applied in this study depends strongly on the input
variables and the parametrizations used inside the code. As this is an important information for the
readers, we will add one section (2.4 Uncertainties) to the manuscript shown as below.
"
2.4 Uncertainties
The uncertainties in this study are mainly related to the uncertainties of the input variables or the
use of parametrizations inside the model. Concerning the input data, the aerosol condensation sink,
which predicts in our study how rapidly sulfuric acid and nitric acid will condense onto pre-existing
aerosols, has the highest uncertainty. In these calculations we applied measured particle size number
concentrations from SMEAR II. However, the uncertainty of the predicted CS values due to
potentially different hygroscopic growth behaviour depending on the chemical composition of the
particles is difficult to estimate and could have an effect on the simulated acid concentrations.
The second source of uncertainties are related to the meteorology module. It will be validated by
comparison with measurements in following section. The third uncertainty source comes from the
large uncertainties on reaction rate coefficients in the applied chemistry schemes, and when the
same reaction is studied by different groups with different techniques, the reaction rate coefficient
may differ by a factor of 2 or even more (Atkinson et al., 1992). Moreover, for many reactions
between OH and VOCs, no experimental data exist, so the reaction rate coefficients are only
estimations, which increases the uncertainty even further.
The fourth uncertainty source is related to the emission module MEGAN. Guenther et al. (2012)
estimated that the uncertainty associated with the annual global emissions of monoterpenes is a
factor of three, and of methanol, acetone and acetaldehyde is a factor of two. However, in our
simulations MEGAN was constrained by in-situ measurement data, including the relevant
meteorological variables and the standard emission factors, which has decreased the uncertainty.
Moreover, the uncertainty of emission module can also be evaluated by comparing measured and
simulated monoterpene concentrations and fluxes in Section 3.3.
"
The reference will be added: Atkinson et al., 1992.
More specific comments:
Line 183: please clarify “a jump.”
We will change the sentence at L182-184:
"However, the model results showed a jump in the simulated OH, NO3 and H2SO4 concentrations at
all times the input data went from 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."
Line 193: For “an annual growth rate of 6 ppb yr-1,” is it a global rate or a regional rate?
It refers to the annual global growth rate here, which was chosen from "NASA Earth Observatory"
website as explained at L195-196. The text will be modified:
L193: "an annual growth rate" --> "an annual global growth rate"
L194: "the annual growth rate" --> "the annual global growth rate"
Line 285: If “an overall underestimation of heat fluxes is normal,” why does the underestimation
only occur in the winter and autumn months?
The overall underestimation has nothing to do with the season but is related to the very low values
of the fluxes during these periods of the year.
In order to avoid any misunderstanding, we will change the sentence at L284-287:
"We want to point out that the measured fluxes in the winter and autumn months are very low (< 10
W m-2 ) and an overall underestimation of heat fluxes is normal when applying the eddy covariance
technique (Foken, T., 2008) like at SMEAR II; hence making it difficult to form a conclusion on the
accuracy of either the model or measurement during these periods."
-->
"We want to point out that the measured fluxes in the winter and autumn months are very low (< 10
W m-2) and an overall underestimation of heat fluxes during these periods are normal when
applying the eddy covariance technique (Foken, 2008) like at SMEAR II; hence it is difficult to
form a conclusion on the accuracy of either the model or measurement during these periods."
Lines 329-330: Please clarify “a stronger decrease” based on “a clear increase from 2007-2018.”
The sentence at L329-330 is confusing and will be replaced:
"There is a clear increase from 2007 to 2018 for SO2 , which points to a stronger decrease than the
5.43% yr-1 mentioned above."
-->
"There is a clear increase of days with SO2 measurements below LOD from 2007 to 2018, which
points to an even stronger decrease of SO2 concentrations than the 5.43% yr-1 mentioned above."
Lines 374-377: There is a discrepancy of about one magnitude. Do the trends really agree well?
We agree with reviewer and will change the sentence starting at L376-377:
"Therefore, the trends in the model and the measurement agree well, both of which show a
decreasing trend from 2007 to 2013 and a similar change from 2017 to 2018."
-->
"Therefore, the trends in the model and the measurement show both a decreasing trend from 2007 to
2013 and a similar change from 2017 to 2018."
Lines 453-458: Most OH is produced during daytime. Can nighttime OH really drive its daily
trend?
This is an interesting question. We analyzed further the seasonal trends of CO, NO2 and
monoterpenes and improved the explanation about the long-term OH trend. The text at L453-464
and L470-503 will be rewritten as shown below:
L453-464, L470-503:
"During daytime the OH ... sink term for HO2 (Boy et al., 2006).
The increasing trend of the OH ... and stay quite constant"
-->
"Table 1 shows that OH has significant increasing daily and nighttime trends which are +2.39
(+0.95, +3.33) % yr-1 and 3.31 (+2.01, +4.62) % yr-1 respectively with both PMK values below 0.04.
However, during daytime OH concentration only shows a marginal increase of +0.91 (-0.81, +2.10)
% yr-1 with a high statistical uncertainty and the PMK value is 0.24. The trend of OH shown here is
mainly related to its sink terms instead of the source terms, because the main source terms of OH,
e.g., O3 and global short-wave radiation, do not show any significant increasing trends (Table 1).
Among all the sink terms of OH, CO plays a major role and accounts for about 30 - 40% of the
annual removal of OH at SMEAR II (Boy et al., 2006; Praplan et al., 2019). Table 1 shows a
decreasing trend of CO which is consistent with previous studies (Petrenko et al., 2013; Jiang et al.,
2017). Moreover, the time series of mixing ratio of CO in winter also shows opposite trend to OH
concentration (Fig. S6). However, all of the decreasing daily, daytime and nighttime trends of CO
are not significant (Table 1), which indicates that in order to explain the OH trend, other sink terms
and their seasonal trends should be taken into account.
Since the daytime length is shortest in winter and autumn while longest in summer and spring, the
nighttime increasing trend is dominated by the trends of winter and autumn that are +10.25 (+3.95,
+17.32) % yr-1 and +4.54 (+2.39, +6.29) % yr-1, respectively. Figure 7 shows that the strong
increasing trends of OH in these two seasons are caused by the combined effects of decreasing CO
and NO2. The deceasing trend of NO2 is much higher than that of CO with ~ 9.7 times in winter and
~ 5.9 times in autumn, but the OH reactivity due to NO2 is only about half of that due to CO (Fig.
7). The seasonal inter-annual trends of OH reactivities also shows an apparent drop of ROH,CO (OH
reactivity due to CO) and ROH,NO2 (OH reactivity due to NO2) during winter with ROH,NO2 being a
factor of 2-3 lower compared to ROH,CO (Fig. S7). Therefore, as a dominant sink of OH in winter, CO
is the main factor to explain the long-term modelled OH trend, and NO2 also contributes a
comparable portion. Moreover, monoterpenes can also produce OH via ozonolysis reactions, which
is a main source of OH during dark conditions (Zhang and Zhang, 2005; Nguyen et al., 2009; Alam
et al., 2013). So the increasing trend of monoterpenes could enhance the increasing nighttime trend
of OH.
In spring and summer, the monoterpenes and the compounds produced from the second or higher
order reactions (shown as other reactivity in Fig. 7) start to be competitive with or dominant over
CO among the OH reactivity contributors (Figs. 7 and S7). The forest stands near SMEAR II are
dominated by Scots pine, which emits relatively low isoprene (e.g., Rinne et al., 2009 and
references therein). For example, during summer months between 2010-2013 at SMEAR II, the
measured flux of isoprene + MBO (2-methyl-3-buten-2-ol) was usually around one order of
magnitude smaller than that of monoterpenes (Rantala et al., 2015). This indicates that the
compounds produced from the second or higher order reactions are mainly the oxidized products of
monoterpenes. Therefore, the increasing trend of monoterpenes can offset the effect of decreasing
CO, leading to the insignificant daytime trends in spring and summer. And finally, a strong
increasing nighttime trend and an insignificant daytime trend altogether lead to a significant but
weaker daily trend of OH.
In the future, assuming decreasing emissions of CO and NO2 (change in energy production and
lower nitrogen compounds traffic emissions) and increasing monoterpene emissions in boreal
region due to climate warming (Sporre et al., 2019), we would expect an increase of OH during
winter and autumn months in central south Finland. During spring and summer other sink terms are
more relevant and OH should be buffered and stay quite constant."
The references will be added: Jiang et al., 2017, Sporre et al., 2019, Zhang and Zhang, 2005,
Nguyen et al., 2009, Alam et al., 2013.
Lines 490-503: As “CO is the main sink of OH in winter” and “the main factor to explain the longterm modelled OH trend,” please make a connection with the model performance for OH simulation
in this season.
Unfortunately, no OH measurements exist during winter at SMEAR II, which would be required to
validate the model at this season. However, we see no reason why SOSAA should not be able to
predict the OH concentrations in winter as the measured input data and the estimated VOC
concentrations are similarly handled compared to other seasons.
Lines 536-538: Please clarify what species this sentence refers to.
It refers to nighttime NO3 concentration. The sentence at L536-538 are changed:
"The running median also shows an oscillation of 3-3.5 years during the years 2007-2018, but since
this period is relatively short, it is hard to conclude on the reasons."
-->
"The one-year running median of nighttime NO3 concentration also shows an oscillation of 3-3.5
years during the years 2007-2018, but since this period is relatively short, it is hard to conclude on
the reasons (Fig. 9)."
Notice: Fig. 9 was Fig. 8 in the old manuscript.
Line 547: “Present daytime NPF can almost exclusively be explained by H2SO4 clustering with
either ammonia or organic compounds formed from OH-oxidation of monoterpenes.” Please
provide a percentage to verify this statement.
The percentage numbers are added and the sentence is modified as below. And the text at L545-552
are moved to the introduction according to the comments above.
"According to the latest global chemistry-transport model simulations, which used state-of-the-art
new particle formation (NPF) parameterizations from the CLOUD chamber experiments in CERN
(Kirkby et al., 2016; Riccobono et al., 2014), present daytime NPF can almost exclusively be
explained by H2SO4 clustering with either ammonia or organic compounds formed from OHoxidation of monoterpenes (Dunne et al., 2016; Gordon et al., 2017)."
-->
"According to the latest global chemistry-transport model simulations, which used state-of-the-art
new particle formation (NPF) parameterizations from the CLOUD chamber experiments in CERN
(Kirkby et al., 2016; Riccobono et al., 2014), around 96% (Gordon et al., 2017) or 100% (Dunne et
al., 2016) of the present-day NPF can be explained by H2SO4 clustering with ammonia, ions or
organic compounds."
Lines 613-615: Confusing sentence. Please rewrite.
We rewritten the sentence:
"However, we assume that the missing OH-reactivity would not explain the one order difference
during spring and summer but the contribution of OH production through the ozonolysis of terpenes
is a missing factor in the proxies as it is only related to UVB."
-->
"However, we assume that the missing OH-reactivity would not explain the one order difference
during spring and summer between the model and the proxy. Rather, we would expect that the
contribution of OH production through the ozonolysis of terpenes is a missing factor in the proxies
as the proxy is only based on UVB measurements."
Lines 671-672: “By applying this approximation, we derived the monthly median NO3 proxy
between 2007-2018.” Is it based on observations or modelled results?
This refers to our model results which is based on measured NOx concentrations and the applied
chemistry scheme in the simulations, this sentence is deleted to make the statement more clear.
L671-672: Delete "By applying this approximation, we derived the monthly median NO3 proxy
between 2007-2018."
In the section of Summary and Perspectives, please provide some explanations about the impact of
NO3 drop at nighttime on air quality or climate.
We will add the following sentences at the end of L710:
"As pointed out by Mogensen et al. (2015) NO3 is the strongest oxidant during nighttime and can
have an aerosol yield when reacting with monoterpenes up to 65% (Fry et al., 2014). A continuous
drop of the NO3 in the boreal forest could implicate a negative impact on the growth of SOA during
nighttime and decrease the CCN concentrations."
The reference will be added: Fry et al., 2014.
Lines 720-723: Confusing sentence. Please rewrite.
The sentence will be improved as:
"However, quite recently Roldin and co-workers’ (2019) research results counteract this assumption
by stating that the tiniest particles, under some meteorological conditions, are increasing in size at
the expense of the larger aerosol particles over the boreal forest – and it is only the latter that have a
cooling effect on the planet."
-->
"However, recently Roldin and co-workers' (2019) research results counteract this assumption.
Their results showed that under some meteorological conditions high number of new formed
particles are increasing in size at the expense of the larger aerosol particles over the boreal forest –
and it is only the larger aerosol particles that have a cooling effect on the planet."
Reviewer 2
Comments to the Author
General comments
This manuscript investigates the long-term trend of important atmospheric oxidants (OH, NO3, and
O3) utilizing a one-dimensional model. This is a relevant research topic in the field and has
important implications for understanding long-term atmospheric composition changes within the
context of human activities and climate change. It is generally well-written. The method is
appropriate, and the presentation of results is well-organized.
Major comments
One of my major comments is the absence of isoprene evaluation, which I believe is critically
important for OH concentration estimation. In addition, one inherent disadvantage of 1D models is
the horizontal advection. I wonder how this would affect the comparison between model results and
measurements especially for long-lived species. At last, some discussion would enhance the
manuscript. Discussions on the implications of this study would be helpful for readers to better
understand the significance of the study; discussions of model uncertainties (e.g. the modelmeasurement discrepancies appear to be large during winter times) will elucidate future studies
needed. Please see below for the specific comments.
(1) The stand, as well as neighboring stands, are dominated by Scots pine (LUKE Forest resource
maps and municipal statistics), which emits relatively low isoprene (e.g., Rinne et al., 2009 and
references therein). During summer months between 2010-2013 in Hyytiälä, the measured flux of
isoprene + MBO was usually around one order of magnitude smaller than that of monoterpenes
(Rantala et al., 2015). The following is from Aalto et al. (2014): "The mass spectrometer technique
used only distinguished compounds based on their molecular mass, and thus one mass can include
several compounds or their fragments. The protonated 69 amu includes both a dehydrated fragment
of MBO and isoprene (de Gouw and Warneke 2007). Many pine species (including Scots pine) are
known to emit considerable amounts of MBO, but only negligible amounts of isoprene (Zeidler and
Lichtenthaler 2001, Tarvainen et al., 2005; Gray et al., 2006). Based on this result, and the previous
GC-MS measurements from same trees conducted by Tarvainen et al. (2005) and Hakola et al.
(2006), we assume that in this case the measured emission at 69 amu is mainly composed of the
MBO fragment.". The Table 1 in Hellén et al. (2018) includes isoprene concentrations measured by
GC. That shows that the summer mean concentration of isoprene varies from 11-102 ppt between
years. A concentration of 102 ppt leads to an OH reactivity of about 0.24 s-1, which is about 8% of
the total OH reactivity in summertime (Mogensen et al., 2015). Therefore, isoprene does not play a
significant role in modulating OH concentrations at SMEAR II.
(2) We had a discussion about the effects of the long-range transport in the response to Reviewer 1
(please check the response to the major comment 3 from Reviewer 1).
(3) Concerning the implications of this study we will add a discussion on the nitrate radical after
then end of L710 (please check the response to the second last comment from Reviewer 1).
(4) Concerning the uncertainties we have added a new section 2.4 (please check the response to the
major comment 5 from Reviewer 1).
Minor comments
Line 37. – “Nieminen et al., 2014”. Citation in the abstract should be avoided if possible.
The citation was deleted in the modified manuscript.
Line 69-70. –“Numerous studies have investigated global OH trends using chemical transport
models or retrieval of remote sensing of methyl chloroform (CH3CCl3, MCF) ”. Be more specific
with the previous findings from Montzka et al., 2000; Prinn et al., 2001, Kirschke et al., 2013 if
possible, increasing or decreasing trend?
We will add a new sentence at L71 and modify the next sentence:
"Montzka et al. (2011) found a small interannual OH variability, indicating that global OH is
generally well buffered against perturbations."
-->
"Prinn et al. (2001) predicted an overall global negative average OH trend of -0.64% per year
between 1978 and 2000, whereas in a newer study Montzka et al. (2011) found a small interannual
OH variability, indicating that global OH is generally well buffered against perturbations."
Line 139. – Can you comment on 1D chemistry transport model? Does “transport” here refer to
horizontal transport? How the 1D model resolve horizontal transport?
In a 1D chemistry transport model, the vertical transport of chemical species is calculated by a
parametrization method, which is TKE-omega scheme in SOSAA (Zhou et al., 2017a). Here TKE is
turbulent kinetic energy and omega is the specific dissipation of TKE. The horizontal transport is
not considered explicitly in SOSAA, since it is not a Lagrangian model. So here the "transport"
refers to vertical transport. However, as we mentioned in the response above, the effects of
horizontal transport are constrained by using the measurements of long-lived species (e.g., CO, O3,
SO2) as the input and by applying nudging to measured meteorological quantities (e.g., horizontal
wind velocity, air temperature). These approximations are accurate enough to represent the vertical
profiles of chemical species over a homogeneous land cover, which is the case at SMEAR II.
Line 177. – “vertically averaged”. Clarify.
It means the average values of the mixing ratios at different measurement height levels were used as
the input to the model. So here we will delete "vertically" to make it clear.
Table 2. – Why monoterpene emission rates in winter show an increase?
Monoterpene emissions are strongly positively dependent on temperature which shows an apparent
warming trend in winter about 10 times larger than other seasons, so the monoterpene emissions
increased most in winter compared to other seasons.
Line 271. -–How are the vertical distributions of these parameters (i.e. temperature, water vapor,
and wind speed) determined in the model domain?
These meteorological variables in the model domain are calculated from the basic equation set,
including the continuity equation, the momentum equation, the energy equation and the equations
for TKE and omega. Additionally, in order to represent the effects of horizontal transport and the
local weather conditions, the wind speed, temperature, and the specific humidity are also nudged to
the measurement data near the surface (Zhou et al., 2017a).
Line 285 - 286. –Stable conditions are hard to deal with! Do you mean the 10 Wm-2 is likely an
underestimation by the eddy covariance system? How are the sensible heat fluxes calculated in the
model? Is it by the gradient method? How does the potential overestimation (Fig. 1A, B) affect your
following results? Please clarify.
(1) Yes, we agree that under stable conditions it is difficult to simulate or measure the flux
quantities. And here we assume that this underestimation could be related to the error induced by
eddy covariance system that was estimated as 10-30 W m-2 (Foken, 2008).
(2) In SOSAA the sensible heat flux (W m-2) is calculated by the gradient method as:
H = - rho_a * cp * Kh * d_theta/d_z
where rho_a is the air density (kg m-3), cp is the specific heat capacity at constant pressure (J kg-1 K1
), Kh is the turbulent diffusion coefficient for heat (m2
s-1), theta is the potential temperature (K)
and z is the height level (m).
(3) First, from the discussion above, it is difficult to conclude if the model has overestimated the
sensible heat flux or not due to the measurement uncertainties. Secondly, the sensible heat flux is a
diagnostic quantity derived from the coupled meteorological equations, depending on e.g.,
parametrization method of turbulence, air temperature, wind speed, etc. Therefore, it is just an
indicator of the model performance on simulating meteorology and can not directly affect our
current results. And considering that the basic meteorology quantities like air temperature and
absolute humidity are all constrained by measurement data via nudging, as well as the comparisons
between model results and measurement data (Fig. S2), we assume that the discrepancies of
sensible heat fluxes between the model results and measurements does not impose an effect on the
following results.
Line 296-297. –Is this how you calculated the measured Rnet? How Rnet is calculated in the model
then? By Rnet = H+LE-G? Then how G is compared to measurements? If it is a local study for a
specific site, why not just constrain the model with measured net radiation?
(1-4) Yes, and the details are shown here. The incoming (downward) short-wave at the canopy top
was obtained first from the measurement data at SMEAR II, then the radiative transfer module from
ADCHEM model (Roldin et al., 2011) was applied to split it into direct and diffuse radiations which
were finally used as the input in the model (Section 2.2). The long-wave radiation at canopy top
was obtained from ERA-Interim datasets then input into the model. Inside the model, the reflection,
absorption, penetration and emission of the radiation at each layer inside the canopy are explicitly
computed as described in Sogachev et al. (2002), assuming energy balance at each layer including
the soil surface. The measured soil heat flux (G) was an input in the model. So Rnet at the canopy
top was finally calculated as the total incoming short- and long-wave radiation (measured)
subtracting the outgoing short- and long-wave radiation (calculated).
(5) First, the simulated net radiation can be used to validate the model performance on simulating
the energy balance inside the canopy (Fig. 2). Secondly, there were big gaps in the measurement
data of Rnet at SMEAR II, so the measured Rnet was not used for our long-term simulations in this
study.
Section 3.2. –All the relevant tables are in the supplementary, which makes the section a bit hard to
read.
We combined the Tables 1, 2, S2 and S3, and now all the trend data are in a new long Table which is
more convenient for readers to find the values. The digits after the decimal point are reduced to two
to make it more readable and more consistent with the values used in the manuscript. The caption of
the new table is:
"Yearly trends of individual parameters calculated by two different statistical methods (RLM =
robust linear method, MK = Mann Kendall) and seasonal trends of them calculated by RLM. The
numbers in brackets are the 90% confidence intervals (first and second numbers show the 5 th and
95 th percentiles of the trend slopes obtained from 10 000 bootstrapping iterations, respectively) for
the RLM values, and PMK values for MK numbers. The first, second and third rows for each
parameter represent daily, daytime and nighttime values, respectively. Whether the median or mean
values of different parameters were used to calculate the trends are also shown in the bracket after
the parameter names. Except the emissions of the monoterpenes, which were averaged over the
forest canopy height (18m), all other parameters are the median or mean values from the height
level 20m-40m. The significant trends are marked by bold font. Detailed statistical methods are
explained in Section 2.3."
Fig. 3. – Can you add a line to indicate the average canopy height?
A line indicating the canopy height was added in the new Fig. 3 (see also the modified manuscript).
A sentence will be added in the end of the caption:
L359: "The canopy height is marked by a horizontal green line."
Line 380-381. – Any reasons for the low measurements in 2012 and 2018?
The figure below shows that the low measurements in year 2012 and 2017/2018 are caused by low
temperatures on the measurement days. The text at L380-382 will be modified as:
"The lower trends of measurement data may result from several very low measurement
concentrations around the beginning of 2012 and 2018 (Fig. S4)."
-->
"The lower trends of measurement data may result from several very low measurement
concentrations around the beginning of 2012, 2017 and 2018 which were caused by low
temperature during these days (not shown here)."
Line 445-446. – “assuming that the two measurement places were not in the forest”. Clarify.
Because the two measurements were conducted in the forest clearing, which was not inside the
forest canopy. The text at L444-446 will be modified to make it clear:
"For both campaigns we applied here the model height level at 32.8 m assuming that the two
measurement places were not in the forest."
-->
"For both campaigns we applied here the model height level at 32.8 m assuming that the two
measurement places were in the forest clearing instead of inside the forest canopy."
Line 450-451. – The modeled peaks of OH in spring seem to be the overestimation of the model.
Have you tested if [OH] is sensitive to [O3] in the model, even though O3 is the precursor?
As ozone was an input parameter from the measurements and the O3-OH chemistry is well know
and accepted, we do not believe that the overestimation of OH during this time of the year is related
to O3. As we stated in the manuscript we strongly believe that the missing VOCs or missing higher
order reactions between OH and oxidized VOC products are the reason for the overestimation of
OH in the model.
Line 463. – “the decrease of the NO concentrations”. Doesn’t [NO] has no trend?
NO has a decreasing daytime trend, especially in winter. However, Fig. 7 (Notice: Fig. 7 was Fig.
S7 in the old manuscript) shows that NO only plays a minor role in OH reactivity, which indicates
that it may have only little effect on the trend of HOx (OH+HO2). Instead, NO2 plays an important
role in OH reactivity, especially in winter, and its decreasing trend also corresponds to the
increasing trends of HOx. Therefore, the increasing trend of HOx should result from the decreasing
trend of NO2. This is analyzed in the latter part of this section, which will be rewritten as shown
above in the response to "Lines 453-458: Most OH is produced during daytime. Can nighttime OH
really drive its daily trend?" from Reviewer 1.
Line 473. – “This is the main source of OH during dark conditions at SMEAR II”. You have a
reference for this statement? I find it surprising because the monoterpenes are very low.
The monoterpene concentrations at SMEAR II represent the non-methane VOC with the highest
mixing ratios about 5 times higher compared to isoprene. Moreover, the ambient concentration of
monoterpenes in Hyytiälä is higher during nighttime than during daytime, at least in summer (e.g.,
Mogensen et al., 2011; Hellén et al., 2018). Previous studies showed that the ozonolysis of
monoterpenes can produce OH (Zhang and Zhang, 2005; Nguyen et al., 2009), and is an important
source of OH especially during nighttime (Alam et al., 2013). And there are no other important
sources of OH during nighttime which can compete with the OH-recycling by ozonolysis of
monoterpenes, so we conclude that this is the main source of OH during nighttime here. The
references "Zhang and Zhang, 2005; Nguyen et al., 2009; Alam et al., 2013 " are added as shown
above in the new text at L453-464 and L470-503.
Line 481-489. –In section 3.2, CO is said to have an insignificant decrease. Is this the case for
winter CO? If so, how the minor decrease in CO leads to a great increase in [OH]?
CO does not show a significant trend when all the seasons are considered (Table 1), but in winter it
shows a significant decreasing daily trend of -0.89 % yr-1 (Table 1). Moreover, Fig. 7 shows that
NO2 also contributed a major part to OH reactivity in winter and late autumn, and it has a strong
significant decreasing daily trend of -8.49 % yr-1 in winter. Therefore, the great increasing trend of
OH in winter was caused by the combined effects of the decreasing trends of CO and NO2. More
detailed explanation will be added in the new manuscript as shown above in the new text at L453-
464 and L470-503.
Notice: Fig. 7 was Fig. S7 in the old manuscript, and Table 1 is the new table which combines
Tables 1, 2, S2 and S3 of the old manuscript.
Line 529-530. – “The reason is that the NO2 concentration is highest during these periods”. Any
reasons for the double peak in NO2?
At SMEAR II the source of NO2 comes from the oxidation of NO which is emitted from the forest
soil. Also, the NO emission from the forest soil is mainly controlled by the microbial nitrogen
turnover processes (Conrad, 1996; Kesik et al., 2005). Therefore, the high peaks may relate to the
microbial processes when organic litter mass was highest. The low emission in winter can result
from the suppression of the microbial activities by frozen soil. We will modify the text to include
the references:
"The reason is that the NO2 concentration is highest during these periods (see Fig. S2 in
supplementary material)."
-->
"The reason is that the NO2 concentration is highest during these periods (see Fig. S2 in
supplementary material), which may result from the high NO emissions from the microbial
processes in the forest soil (Conrad, 1996; Kesik et al., 2005) when the organic litter mass was
high."
The references will be added: Conrad, 1996; Kesik et al., 2005.
Some figures in the SI seem to be important and could be moved to the main section. I will leave
this to the authors to decide.
This is a good suggestion, since Fig. S7 is important for explaining the long-term OH trend, we will
improve and move this figure to the main section as Fig. 7. Then the indices of figures after this will
be modified, e.g., Fig. 7 will be Fig. 8, Fig. S8 will be Fig. S7, etc.
The caption of Figure 7 is:
"Figure 7: Mean monthly OH reactivity during 2007-2018 contributed by different
compounds/groups with the ±1 standard deviations shown as shadows. Here "other inorganics"
means all the other inorganic compounds that react with OH except the ones already plotted here.
The "other organics" means all the emitted organic compounds which react with OH except
isoprene and monoterpenes. The "other reactivity" means all the other organic compounds which
react with OH except isoprene, monoterpenes and other organics."
Others
1. Add acknowledgement:
"The work was supported by ACCC Flagship, funded by the Academy of Finland grant number
337549."
2. Deleted "and" at L695.
3. Added a missing reference Kirschke et al., 2013:
"Kirschke, S., Bousquet, P., Ciais, P. et al. Three decades of global methane sources and sinks.
Nature Geosci 6, 813–823 (2013). https://doi.org/10.1038/ngeo1955."
4. Since we combined the tables, all the related text will be modified.
5. All the ";" between the confidence intervals inside the brackets are replaced by ",". And all the
trend numbers and their confidence interval values are modified to include two decimals, which
makes it consistent with Table 1.
6. The subplot title font sizes in Fig. S8a and S8b were increased.
Notice: Fig. S8a and Fig. S8b were Fig. S9a and Fig. S9b in the old manuscript.
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13-Jul-2021

Dear Dr Zhou:

Manuscript ID: EA-ART-03-2021-000020.R1
TITLE: Modelling study of OH, NO₃ and H₂SO₄ in 2007 - 2018 at SMEAR II, Finland: analysis of long-term trends

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Reviewer 2

I recommend publishing as is