Research Article Amplified Feedback Mechanism of the ...Research Article Amplified Feedback...

Research ArticleAmplified Feedback Mechanism of theForests-Aerosols-Climate System

Thomas Hede, Caroline Leck, and Jonas Claesson

Department of Meteorology, Stockholm University, 106 91 Stockholm, Sweden

Correspondence should be addressed toThomas Hede; [email protected]

Received 30 December 2014; Revised 9 March 2015; Accepted 10 March 2015

Academic Editor: Alexey V. Eliseev

Copyright © 2015 Thomas Hede et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Climate change very likely has effects on vegetation so that trees grow faster due to carbon dioxide fertilization (a higher partialpressure increases the rate of reactions with Rubisco during photosynthesis) and that trees can be established in new territories ina warmer climate. This has far-reaching significance for the climate system mainly due to a number of feedback mechanisms stillunder debate. By simulating the vegetation using the Lund-Potsdam-Jena guess dynamic vegetation model, a territory in northernRussia is studied during three different climate protocols assuming a doubling of carbon dioxide levels compared to the year 1975.A back of the envelope calculation is made for the subsequent increased levels of emissions of monoterpenes from spruce and pineforests. The results show that the emissions of monoterpenes at the most northern latitudes were estimated to increase with over500% for a four-degree centigrade increase protocol. The effect on aerosol and cloud formation is discussed and the cloud opticalthickness is estimated to increase more than 2%.

1. Introduction

Climate change is a global problem facing humanity and atthe same time it is of anthropogenic origin [1]. However,the mechanisms for global climate change are interlinkedbetween all the components of planet Earth, includingatmosphere, hydrosphere, and biosphere. Although climatechange has begun to become more and more evident, thereare still large uncertainties in future projections of the climate.In order to project the evolution of the climate change, weuse general circulation models (GCMs), but the accuracyof the projection is dependent on both the input data andthe compliance with real processes that affect the climate.One category of processes that is of particular importanceconcerns feedback mechanisms. A feedback mechanism is achain of events that in the end has an impact on the initialstep, either if it is an enhancing effect, so called positivefeedback, or if it has an inhibiting effect, so called negativefeedback.

Three examples of a positive feedback mechanism thatresult from an enhanced greenhouse effect with a subsequentincrease in temperature are given by Mencuccini and Grace[2], Swann et al. [3], and Walter et al. [4] Mencuccini and

Grace [2] discuss the leaf to sapwood area ratio in Scots pineas of a changed difference in water vapor pressure deficit inair. Swann et al. [3] propose one example of an enhancedpositive feedback mechanism. It consists of two components:(1) broad-leaf deciduous trees assumed to invade thewarmingtundra and thereby alter the albedo to less reflective landby replacing snow covered land with darker forests and (2)increased release of water vapour through evapotranspirationcompared to evergreen boreal needle forests contributing tothe greenhouse effect. In a warming climate, the first com-ponent indicates that there is a change in type of vegetationwith new establishment of broad-leaf deciduous trees in thetundra as well as a replacement of evergreen boreal needleforests. The latter has already been mentioned in component2. The two components are interlinked through the changein vegetation type. Walter et al. [4] show the importance ofemissions of methane from thaw lakes in Siberia throughebullition. The thawing permafrost along the lake marginsaccounted for the largest emissions, which was estimated toconstitute an accelerating effect on climate warming.

One example of a negative feedback mechanism wasproposed by Kulmala et al. [5], which links changes in growthof forests and emissions of volatile organic compounds

Hindawi Publishing CorporationJournal of ClimatologyVolume 2015, Article ID 262980, 11 pageshttp://dx.doi.org/10.1155/2015/262980

2 Journal of Climatology

(1)

(2)

(3)

(4)

(5)

Negativefeedback

Negative feedback mechanism (Kulmala et al., 2004)

concentration in the atmosphere

Increase in GPP—boreal forests grow and the gross primaryproduction (GPP) increases as number and size of trees

Increased emissions of VOCs—increase in temperatures andnumber of pine trees increase emitted VOCs

Increased number of CCN—monoterpenes particles emittedgrow and form CCN, allowing cloud drop growth

Brighter clouds form—Twomey effect gives more but smallercloud droplets which are “whiter”

Global warming—increased level of carbon dioxide

Figure 1: Schematic representation of the negative feedback mechanism proposed by Kulmala et al. [5]. GPP is the gross primary productionand increases as the boreal forest grows. VOCs are volatile organic compounds. CCN is cloud condensation nuclei.

(VOCs) with subsequent generation of aerosol particles andpossible changes in the number of particles available for clouddroplet activation called cloud condensation nuclei (CCN)and cloud optical properties. Figure 1 shows a schematicrepresentation.

Number 1 in Figure 1: global warming causally relatedto an increase of atmospheric carbon dioxide (CO

2) and

other so called greenhouse gases [1]. Number 2 in Figure 1:photosynthesis is driven by an increase of atmosphericcarbon dioxide with a subsequent increase in the amount ofchemical energy as biomass that primary producers create ina given length of time called the gross primary production(GPP) [6]. GPP increases when the boreal forest grows byboth tree number and size. Number 3 in Figure 1: borealforests contribute to the emissions of volatile organic com-pounds (VOCs). According toO’Dowd et al. [7] the oxidizingproducts of monoterpenes cis-pinonic and pinic acids areabundant VOCs in the organic fraction of newly formedambient aerosol particles. Number 4 in Figure 1: an increasedfraction of surface-active aerosol particles, which due to adecrease of surface tension [8] become efficientCCN, owed toan increase of VOC emissions. The surface-active propertiesof the VOCs oxidizing products have been discussed inseveral studies [9–12]. Even though Facchini et al. [9] mayhave oversimplified their estimates [13–15], surface-activeorganic material remains to constitute a key component ofatmospheric aerosols in cloud droplet activation [16]. A studyby Dalirian et al. [17] indicates that the partitioning betweenwater soluble and water insoluble matter in activating CCNis as important as the adsorption properties of the surface.Hings et al. [18] suggest that hydrophobic particles may becoated with slightly soluble material and thereby increasethe activation process. The studies of both Dalirian et al.[17] and Hings et al. [18] therefore relate to the activationproperties of the slightly water soluble oxidation productsof monoterpenes. Number 6 in Figure 1: the increase innumber of cloud droplets ultimately could alter cloud optical(microphysical) properties and form brighter clouds [19].

These brighter clouds are proposed to reflect the incomingsolar radiation more effectively and thereby enhance surfacecooling and by that provide a negative feedback mechanism.

In this study we extend the Kulmala et al. [5] study toalso discuss possible implications for the albedo of low-levelclouds in the high Arctic as of an increase of VOCs and forestgrowth in Siberia causally related to global warming due toincrease of levels of atmospheric carbon dioxide. Figure 2gives a schematic representation of the proposed negativefeedback mechanisms under study. Main differences fromthe feedback mechanism proposed by Kulmala et al. [5] aremarked with orange.

To estimate changes of GPP upon a doubling of CO2

levels relative to the year 1975, we used the Lund-Potsdam-Jena (LPJ-GUESS) dynamic vegetation model [21, 22] Thesimulation was started in 1975 and carried out for a periodof 200 years over a terrestrial territory in the Siberian inlandof northern Russia. During the time of simulation a linearincrease in temperature of 2, 3, and 4 degrees centigrade,respectively, resulted after doubling of the atmospheric car-bon dioxide level. From the estimates of GPP subsequentemissions of monoterpenes were calculated. At last, thesimulations and levels of emissions were discussed in termsof cloud formation and possible climate effects.

2. Simulation Method to Estimate GPP

To simulate GPP the dynamic vegetation model LPJ-GUESSwas used [20, 22–29]. LPJ-GUESS can be viewed as a frame-work for ecosystem-modellingwhich has used some elementsfrom the equilibrium terrestrial biosphere model (BIOME)family ofmodels [29].The LPJ-GUESSmodel shows an inter-mediate response and sensitivity to atmosphericCO

2concen-

trations and temperatures [27, 28]. Large-scale dynamics ofterrestrial vegetation and interactions between the biosphereand atmosphere in terms of exchange of carbon dioxide andwater vapour are represented in the model. Processes likephotosynthesis and evapotranspiration are simulated daily

Journal of Climatology 3

Global warming—average global temperatureincrease with an amplification in the Arctic

Melting of permafrost—previous wastelands becomeavailable for plant growth

Increased area of growth of pine trees—pine treegrowth established in new regions

Increased emissions of VOCs—increase in temperaturesand number of pine trees increase emitted VOCs

Increased number of CCN—monoterpenes particlesemitted grow and form CCN, allowing cloud drop growth

Brighter clouds form over the Arctic—Twomey effectgives more but smaller cloud droplets which are “whiter”

(1)

(2)

(3)

(4)

(5)

(6)

Amplifiednegativefeedback

Modified and enhanced negative feedback mechanism

Figure 2: Schematic representation of the negative feedback mechanism proposed in this study. Marked in orange are modifications fromthe negative feedback mechanism proposed by Kulmala et al. [5], which was schematically presented in Figure 1. VOC is volatile organiccompounds; CCN is cloud condensation nuclei.

(a)

E152.25E47.25 E62.25 E77.25 E92.25 E107.25 E122.25 E137.25

N69.25

N66.25

N63.25

N60.25

N57.25

(b)

Figure 3: (a) The permafrost region (white), (b) Siberia and the area under study. Each of the simulated red coordinates does correspond tothe coordinates marked out in Figures 6–11.

while slower processes, like biomass growth, are implementedannually. The input data to the model consist of climateparameters, atmospheric CO

2concentrations, and a soil

code. The climate parameters are monthly or daily values ofmean diurnal air temperature, precipitation, and incomingshortwave radiation (sunshine). The soil code is used forparameters such as hydrology and thermal diffusivity of thesoil. Climate forcing, that is mean monthly temperature,precipitation, and cloud fraction, was using the ClimateResearch Unit (CRU) TS 3.0 data set [26]. Atmospheric CO

2

concentrations were taken from observations [20]. Each sim-ulation was started with a 1000-year initialisation to establishvegetation and soil budgets for the equilibrated climate of year1975. CRU climate data cycled repeatedly to force the modelduring the initialisation using CO

2concentrations of the

year 1975 (330 ppm(v)). After the initialisation, a productionsimulation was applied. During the production simulation,the temperature increased monotonically with adding anoffset of 2–4 degrees centigrade to the baseline climatedataset. The simulations were performed across a 0.5∘× 0.5∘(latitude–longitude) grid [25]. Vegetation in a grid cell isdescribed in terms of the fractional coverage of populations of

different plant functional types (PFTs). Each PFT is assignedbioclimatic limits, which determine whether it can grow andspread under the current condition during the simulation.Weused LPJ-GUESS version 2.1, which includes the PFT set andmodifications described in [24].

2.1. Simulated Area and Global Warming Protocols (1 inFigure 2). The simulated area is located in Siberia in northernRussia. We simulate GPP for a specific geographical coor-dinate. This is done in a grid-box representing the selectedarea. Because of the spherical shape of Earth, the grid-box inthe model is not rectangular, but rather a trapetzoid as a firstsimplification.

This study’s model domain is marked in Figure 3(b).This is a part of the permafrost region, wasteland withpermanently frozen soil; see Figure 3(a). The total areasimulated is 8 424 510 km2. This can be compared to the totalarea of Siberia, which is about 13.1 × 106 km2. The simulatedarea is thus about 64% of the total aerial extent of Siberia.The total simulated area that was forested after the initialequilibration of 1000 years was 6 115 464 km2. This valuecorresponds to the vegetation in the year 1975. Results by


Table 1: The emission protocols assumed in this study.

Emissionprotocol

Carbon dioxidelevel increase Simulated years Temperature

increase

A Doubling 200 2 degreescentigrade

B Doubling 200 3 degreescentigrade

C Doubling 200 4 degreescentigrade

Esser et al. [23] show that the historical carbon storage in theboreal forests is located in the western part of Siberia fromthe years 1860 to 2002.

Three different emission protocols were used to modelthe response in forest growth. We assumed a doubling ofthe CO

2level compared to the year 1975 (330 ppm(v)) for

all of the protocols after the simulated period of 200 years.The temperature increase corresponds to different levels ofclimate sensitivity, namely, 2, 3, or 4 degrees centigradeincrease.The emission protocols are presented in Table 1.Thelevel of CO

2increase could also be varied, so that higher

levels than a doubling can be obtained. It is also possible tokeep the CO

2at a constant level during temperature increase

and thereby determine how much of the effect is attributedto the CO

2doubling. These types of assumptions will not be

considered in this study but could be recommended for futurestudies.

3. Tree Growth Established in New Areas

In the LPJ-GUESS model larch forest is referred to as borealneedle summergreen (BNS) vegetation. In Siberia 46% ofthe forested area is made up by larch forest [30]. Moreover,spruce and pine trees are named boreal needle evergreen(BNE) vegetation in the model. The grass is not contributingsignificantly to the emission ofmonoterpenes and is thereforenot considered in this study. In this study also broadleavedsummer green trees are not considered as a source ofmonoterpene emissions, even though there are exceptionssuch as oak trees [31].

3.1. Simulations Indicating Melted Permafrost (2 in Figure 2).To see whether any new vegetation would be established, as afirst survey we carried out LPJ-GUESS simulations over thepermafrost region using the three emission protocols listed inTable 1. Typical results are shown in Figure 4.The simulationsresulted in the fact that new vegetation was established in thewasteland regions as well as in changes of the composition ofthe forest. We were encouraged by the results from this firstsurvey that a more detailed study could be considered, andthat the primary region of interest should be Siberia, locatedin northern Russia. This was motivated by that the landscapeand vegetation were both highly affected by climate change inthis region.

3.2. Simulations of Forest Growth (3 in Figure 2). Based on thethree emission protocols in Table 1 we calculated the relative

No vegetation, wasteland

Sparse vegetation

Sparse vegetation

Larch tree forests

Pine tree forests

Mixed conifer/boreal forests

Vegetation type transformationsInland, northernRussia

Coastal location,northern Russia

Coastal location,northern Canada

Figure 4:Vegetation establishing in permafrost regions after climatechange.

growth of two types of forests, BNE and BNS, defined as thedifference in biomass (kgC/m2) between the simulated year1975 and after 200 years.

In Figures 6–11, the simulated BNE relative growth inkgC/m2 for northern Russia is listed for each of the protocolsgiven in Table 1. Light shading represents the relative growthas defined; no colour indicates a moderate relative growth(0–3 kgC/m2); yellow colour indicates a substantial relativegrowth (>3 kgC/m2); green colour indicates a very high rela-tive growth (>5 kgC/m2); and red colour indicates a relativedecrease in growth (<0 kgC/m2). Dark shade marks biomasschange in percentage; no colour indicates a moderate changein percent (0–200%); orange colour indicates a substantialchange in percent (>200%); green colour indicates a veryhigh change in percent (>500%); and red colour indicates anegative change in percent (<100%). The symbol “#” markslocations where there was a growth of forest on land withpreviously not established forestation. The relative growth inpercent is given as the percentage of the biomass in the year1975; that is, 100% after 200 years equals the biomass level inthe year 1975. The background image is the box showed inFigure 3(a).

As can be seen from Figures 6, 8, and 10, the relativegrowth (%) of BNE forest is located in the northwest regionof the investigated area, which is in northern Russia. Further,there is a belt of increased growth of BNE forest stretchingfrom the northwest to southeast of the region, and this isat the same time, at least in the southeast part, and at anexpense of BNS forest, which is decreasing in biomass. TheBNE forest is decreasing in biomass in the southwest cornerof the region.This is according to themodel due to a change ofvegetation type where conifer forest is growing at an expenseof BNE forest. Similar trends are shown in all the three climateprotocols: A, B, and C. The trend is however most visible forthe emission protocols with the highest temperature increase(C in Table 1).

From Figures 7, 9, and 11 we can conclude that BNS forestis growing in the northeast region and decreasing in biomassin the southwest region as well as in the belt described above.All in all, the general trend is that the boreal forest of northernRussia is growing and moving northward as a result of anincrease in temperature and atmospheric CO

2. This result is

supported by the findings of Xu et al. [32].


Table 2: Examples of Pinaceae emissions of monoterpenes [20].

Examples of Pinaceae Monoterpene emission(𝜇g gdw

−1 h−1)Cedrus deodara, deodar cedar 0.9Picea sitchensis, Sitka spruce 1.1Pinus halepensis, Aleppo pine 1.0Pinus pinaster, maritime pine 1.0Pinus radiata, Monterey pine 0.7

4. Emissions of Monoterpenes and CloudDroplet Activation

4.1. Estimated Emissions of VOC and CCN (4 in Figure 2).In order to perform a back of the envelope calculation ofthe increase in VOC emissions in the sub-Arctic region thatwould follow forestation of the tundra, VOC emission ratesof spruce, pine, and larch were used. Petersson [33] estimatedthe fraction of needles to total tree weight to be 9.2% forspruce and 4.2% for pine. An average of these numberfractions was used in the simulated BNS and BNE forests.

Lind [34] reported the carbon content of dry massin spruce and pine to be close to 50%. These numbersare used in this study to convert the biomass carbon intoweight of needles per km2. A typical value of 15 kgC/m2would correspond to 30 kg dry mass per m2, which is 3.0 ∗107 kg/km2. This can be compared with the numbers given

by Albrektson [35]; for 194 450 trees per km2 with an averagedry mass of 172 kg makes 3.3 ∗ 107 kg/km2 dry mass.

Ruuskanen et al. [30] report emissions of monoterpenesat 30∘C of 5.2–21𝜇g gdw

−1 h−1 for Siberian Larch (Larixsibirica). Emissions from Scots pine (Pinus sylvestris) andNorwegian spruce (Picea abies) of monoterpenes at 20∘Cwere reported by Janson [36] to be 0.8 ± 0.4 𝜇g gdw

−1 h−1 and0.5 ± 0.7 𝜇g gdw

−1 h−1, respectively. Similar levels of emissionare reported for other members of the Pinaceae family; seeTable 2 [31].

However, emissions of monoterpenes are temperaturedependent. To account for this we used the following equa-tion given by Kesselmeier and Staudt [31]:

𝐸 (𝑡) = 𝐹 × 𝐸 (20) × 10(0.04×Δ𝑡). (1)

Here, Δ𝑡 is the temperature difference between ambienttemperature (𝑡) and 20∘C. The factor 𝐹, which is multipliedwith the emission rate at 20∘C, varies as a function oftemperature.

The factor for calculating the temperature dependencyof monoterpene emissions was estimated from the aver-age monthly temperature at longitudes E47.25, E92.25, andE152.25, respectively. The temperature increase according tothe protocols A, B, and C (the increase in temperature dueto climate change and thereby the deviation from 20∘C) iscompensated by an increase of the factor 𝐹 proportionally by1.20, 1.32, and 1.45, respectively. A typical temperature profileis shown in Figure 5.

DecNovOctSepAugJulJunMayAprMarFebJan

−50

−40

−30

−20

−10

10

20

30

0

N69.25

N66.25

N63.25

N60.25

N57.25

Average monthly temperature at longitude E92.25

Figure 5: Average monthly temperature at longitude E92.25.

The number of days is divided into bins of temperature.Each bin corresponds to a factor (𝐹) for which the emissionrates are corrected, shown in Table 3.

The number of days accounted for in each temperaturebin is based on one month (30 days) for which the temper-ature is within the interval. On average, the model and thetemperature profile are in agreement.

All days when the temperature exceeds 0∘C have beenassumed to emit monoterpenes. The emission rates byRuuskanen et al. [30] and Janson [36] given above were usedin the calculations. To compensate for lower temperatures inthe area the numbers of days were additionally corrected witha factor and done in the same way as for the increased growthlisted in Figures 6–11. The resulting estimates of the annualincrease ofmonoterpenes for the three emission protocols areas follows:

for A: 14.5 Tg y−1,for B: 16.4 Tg y−1,for C: 17.0 Tg y−1.

As a comparison, Laothawornkitkul et al. [37] estimatedthe annual global emission of monoterpenes to range from 33to 480 TgC y−1. Corresponding to the year 1975 (330 ppm(v)CO2) the annual total emission of monoterpenes, in the area

under study, is calculated to be 26.7 Tg.The relative increase is55%, 62%, and 64% for the protocols A, B, andC, respectively.These results were compared well with the estimates by Xuet al. [32] that report a relative greening of the boreal regionfrom 34 to 41%. Most striking is the estimated emissionof monoterpenes at the most northern latitude investigated(N69.25), which increased from 0.54 Tg y−1 with 258% forA protocol, 355% for B protocol, and 496% for C protocol,respectively.

4.2. CCN Production (5) and Cloud Droplet Activation (6) inFigure 2. The number of CCN available for cloud dropletactivation is hard to estimate because the process of con-densational growth of the newly formed particles involv-ing monoterpenes is not fully understood [38]. Given thecomplexity, Spracklen et al. [39] estimated that a factor of


Table 3: Temperature bins and factors for emission rates for different locations in the area under study (Figure 3(b)). For each longitude,shown in paranthesis, the number of days that are applied.

Temp. bin (∘C) Factor Latitude (N)

Number of days forlongitudes

(E47.25, E62.25,E77.25)

Latitude

Number of days forlongitudes

(E92.25, E107.25,E122.25)

Latitude (N)Number of days for

longitudes(E137.25, E152.25)

<0 0

69.25 N/A 69.25 270 69.25 24066.25 210 66.25 240 66.25 24063.25 210 63.25 210 63.25 21060.25 150 60.25 210 60.25 24057.25 150 57.25 180 57.25 N/A

0–5 0.21

69.25 N/A 69.25 30 69.25 3066.25 30 66.25 30 66.25 3063.25 0 63.25 60 63.25 6060.25 60 60.25 30 60.25 6057.25 30 57.25 30 57.25 N/A

5–10 0.30

69.25 N/A 69.25 60 69.25 6066.25 60 66.25 60 66.25 3063.25 60 63.25 0 63.25 060.25 60 60.25 30 60.25 6057.25 30 57.25 60 57.25 N/A

10–15 0.52

69.25 N/A 69.25 0 69.25 3066.25 60 66.25 30 66.25 6063.25 60 63.25 60 63.25 9060.25 30 60.25 60 60.25 057.25 60 57.25 30 57.25 N/A

>15 0.83

69.25 N/A 69.25 0 69.25 066.25 0 66.25 0 66.25 063.25 30 63.25 30 63.25 060.25 60 60.25 30 60.25 057.25 90 57.25 60 57.25 N/A

47.25 62.25 77.25 92.25 107.25 122.25 137.25 152.25 69.25 N/A 4.7 0 0 0 0 0 0

N/A # 0% 0% 0% 0% 0% 0%66.25 2.2 3.9 8.6 0 0 0 0 0

121% 147% 289% 0% 0% 0% 0% 0%63.25 3.9 2.8 9.6 0 0 0 0

132% 123% 82% # 0% 0% 0% 0%60.25 2.1 2.1 4.5 0 0 0 0

97% 117% 120% 139% 0% 0% 0% 0%57.25 3.3 2.2 2.4 0 0.6 N/A

76% 132% 115% 117% 86% # N/A 0%−1.7

−1.7

−0.4

−2.1

Figure 6: BNE relative growth (%) in northern Russia for protocol A after 200-year simulation. The columns show the result for easternlongitudes and the rows for northern latitudes marked in Figure 4(b). Longitudes E47.25, N69.25 and E152.25, N57.25 are not terrestrial andare thus marked with not available (N/A). Light shading represents relative growth and dark shade marks biomass change in percentage. Thesymbol “#” marks locations where there was a growth of forest on a land area with previously not established forestation.

5 increase in monoterpenes yields up to a factor of 3 in theenhancement of CCN from new particle formation. Again, atpresent the production of CCN frommonoterpenes can onlyroughly be estimated as the processes involved are not fullyunderstood.

In the boreal forest Kerminen et al. [40] observednucleating particles, on average <150 cm−3, followed by

subsequent rapid growth. This produced a large numberof particles in sizes larger than 50 nm in diameter. 12hours later, a cloud entered the region and >60% of theparticles in the size range 50–100 nm diameter had beenactivated into cloud droplets. 67% of the cloud dropletswere estimated to originate from particles below ca 15 nm indiameter.


47.25 62.25 77.25 92.25 107.25 122.25 137.25 152.25 69.25 N/A 0 0 0 3.0 5.6 4.7 5.2

N/A # 0% 0% 895% 243% 1522% 341% 66.25 0.1 0.1 3.9 2.6 2.0 4.4 4.5

55% 492% # 449% 300% 128% # 238% 63.25 0.1 0 0.2 1.7 0.6 6.9 2.2

184% 116% 370% 2% 137% 57% 1074% 156% 60.25 0 0.1 0 0 2.6 0.8 5.9 0

118% 189% 195% 109% 262% 109% # 0%57.25 0 0.1 0.1 4.7 0 N/A

23% 60% 136% 229% 171% 181% # N/A −0.2 −0.1

−4.8

−0.1

Figure 7: BNS relative growth (%) in northern Russia for protocol A after 200-year simulation. The columns show the result for easternlongitudes and the rows for northern latitudes marked in Figure 4(b). Longitudes E47.25, N69.25 and E152.25, N57.25 are not terrestrial andare thus marked with not available (N/A). Light shading represents relative growth and dark shade marks biomass change in percentage. Thesymbol “#” marks locations where there was a growth of forest on a land area with previously not established forestation.

47.25 62.25 77.25 92.25 107.25 122.25 137.25 152.25 69.25 N/A 7.9 1.1 0 0 0 0 0

N/A # # 0% 0% 0% 0% 0%66.25 3.5 3.4 6.1 0 0 0 0 0

133% 141% 235% 0% 0% 0% 0% 0%63.25 1.8 1.9 0.7 12.1 0 0 0 0

115% 116% 107% # 0% 0% 0% 0%60.25 0.1 0.7 2.4 0.9 0 0 0.1

92% 101% 107% 121% # 0% 0% # 57.25 1.9 1.4 8.3 N/A

97% 84% 113% 94% 95% # # N/A −1.6−0.2

−1.0

−0.9 −0.6

Figure 8: BNE relative growth (%) in northern Russia for protocol B after 200-year simulation. The columns show the result for easternlongitudes and the rows for northern latitudes marked in Figure 4(b). Longitudes E47.25, N69.25 and E152.25, N57.25 are not terrestrial andare thus marked with not available (N/A). Light shading represents relative growth and dark shade marks biomass change in percentage. Thesymbol “#” marks locations where there was a growth of forest on a land area with previously not established forestation.

47.25 62.25 77.25 92.25 107.25 122.25 137.25 152.25 69.25 N/A 0 0 0 4.7 6.8 5.3

N/A # # 0% 959% 222% 2143% 345% 66.25 0 3.6 3.3

3.3

1.3 7.7 5.3 58% 615% # 432% 356% 118% # 265%

63.25 0 1.4 1.7 6.2 3.9 178% 67% 241% 2% 130% 121% 975% 199%

60.25 0.1

0.1

0.1

0.1 0.0 1.9 1.6 7.8 0 58% 230% 422% 144% 141% 119% # 0%

57.25 0 0 0.1 0.1

0.1

2.7 0 N/A 37% 95% 137% 211% 146% 146% # N/A −0.1

−0.0

−4.9

−0.1

Figure 9: BNS relative growth (%) in northern Russia for protocol B after 200-year simulation. The columns show the result for easternlongitudes and the rows for northern latitudes marked in Figure 4(b). Longitudes E47.25, N69.25 and E152.25, N57.25 are not terrestrial andare thus marked with not available (N/A). Light shading represents relative growth and dark shade marks biomass change in percentage. Thesymbol “#” marks locations where there was a growth of forest on a land area with previously not established forestation.

5. Discussion of the Negative FeedbackMechanisms Postulated in Figure 2

5.1. The Feedback Mechanism. Kulmala et al. [5] proposedthat a global fertilization of atmospheric CO

2with subse-

quent temperature increase would favour photosynthesis and

the growth of Scots pine trees. The increased biomass wouldemit larger amounts of VOCs adding to particle formationand growth into CCN-sizes. A higher aerosol load wouldcontribute to net cooling of the land surface both due toreflection and scattering and to the indirect effect of aerosolparticles [19]. There are however four issues regarding the


47.25 62.25 77.25 92.25 107.25 122.25 137.25 152.25 69.25 N/A 8.6 8.1 0 0 0 0 0

N/A # # 0% 0% 0% 0% 0%66.25 2.6 4.2 8.2 3.4 0 0 0 0

124% 151% 282% # 0% 0% 0% 0%63.25 1.8 3.7 12.8 0 0 0 0

115% 99% 137% # 0% 0% 0% 0%60.25 3.2 3.8 4.7 0 0 3.3

83% 126% 135% 97% # 0% 0% # 57.25 1.6 5.7 10.2 N/A

59% 94% 111% 92% 97% # # N/A −1.1 −0.4

−0.3−2.1

−0.6

−0.1

−3.6

Figure 10: BNE relative growth (%) in northern Russia for protocol C after 200-year simulation. The columns show the result for easternlongitudes and the rows for northern latitudes marked in Figure 4(b). Longitudes E47.25, N69.25 and E152.25, N57.25 are not terrestrial andare thus marked with not available (N/A). Light shading represents relative growth and dark shade marks biomass change in percentage. Thesymbol “#” marks locations where there was a growth of forest on a land area with previously not established forestation.

47.25 62.25 77.25 92.25 107.25 122.25 137.25 152.25 69.25 N/A 0.1 0.1 0 3.8 4.4 8.1 5.6

N/A # # 0% 1106% 212% 2519% 359% 66.25 0.0 0.1 0.1 2.0 4.4 7.6 3.7

125% 554% # 281% 436% 98% # 214% 63.25 0 0 0 1.8 1.6 8.5 5.0

159% 118% 130% 2% 138% 120% 1307% 227% 60.25 0 0 0.1 0.1 2.9 8.0 0.1

95% 129% 230% 228% 77% 132% # # 57.25 0 0 0 0.1 0 0.1 N/A

78% 100% 98% 171% 140% 83% # N/A

−4.9

−0.2

−1.1

−1.0

Figure 11: BNS relative growth (%) in northern Russia for protocol C after 200-year simulation. The columns show the result for easternlongitudes and the rows for northern latitudes marked in Figure 4(b). Longitudes E47.25, N69.25 and E152.25, N57.25 are not terrestrial andare thus marked with not available (N/A). Light shading represents relative growth and dark shade marks biomass change in percentage. Thesymbol “#” marks locations where there was a growth of forest on a land area with previously not established forestation.

feedbackmechanism proposed that we believe they are worthdiscussing in view of the results obtained in this study.

Firstly, the increase in average temperature and theincrease of CO

2in the atmosphere as a result of climate

change not only contribute to an increase of numbers of treesin the forests, as stated by Kulmala et al. [5], but it would alsobe possible for new forests to establish in former wastelandssuch as the subpolar permafrost region in the north of Russia,Scandinavia, Greenland, Canada, and Alaska. Further, if thepermafrost will melt in the region, vegetation can start togrow.This effect would also be amplifying the effect describedby Kulmala et al. [5], since more forests would emit moreVOCs contributing to the population of aerosol particles.

Secondly, the effect on surface tension and thereby thelowering of supersaturation of water vapour needed forcondensational growth by water vapour of droplets accordingto Kohler theory [8] is questioned by Sorjamaa et al. [13]and Kokkola et al. [14]. They state that the partitioningbetween surface and bulk reduces the effect of surface tensionreduction. At a first assumption Li et al. [41] show that thenatural surfactant cis-pinonic acid (an oxidation productof monoterpenes) greatly can reduce the surface tension ofnanosized aerosol particles by accumulating at the surface;

however, Hede et al. [11, 12] showed that cis-pinonic acid canform micelle-like aggregates inside the nanoaerosol, but thesurface tension reduction is not limited by this behaviourin accordance with experimental results by Schwier et al.[42]. Therefore, the effect of surface tension depression isnot irrelevant for cloud formation processes, implying thata greater number of cloud droplets can form as a result ofan increase in the number of available particles consistingof surface tension reducing surfactants. The clouds thatare formed are thus “whiter” (as of changed microphysicalproperties) with a higher albedo and reflecting more ofthe incoming solar radiation. This effect is amplifying thenegative feedback process proposed by Kulmala et al. [5].

Thirdly,monoterpenes evaporated fromboreal trees reactquickly with oxidizing reactants like ozone and hydroxyl rad-icals producing low-volatile substances such as cis-pinonicacid that take place in gas-to-particle conversion processes[43, 44]. According to Boy et al. [45] the growth rate ofnucleation mode particles is from 30 to 50% explained byhydroxyl radical oxidation. According to Riipinen et al. [46]the growth of nanoparticles is governed by the presenceorganics. Given time in the atmosphere newly formed par-ticles may grow to form CCN. Depending on the synoptical


scale meteorology, CCN generated and grown from VOCsoriginating over Siberia could potentially reach the highArctic pack ice region.

In the high Arctic summer there are few of availableparticles for cloud droplet formation, since the air is cleanwith limited influences frommanmade activities [47–49]. Anincrease in particles due to regional transport would havean impact on the microphysical properties of the cloudsbeing formed. Generally, clouds over the pack ice constitutea warming factor [50, 51], but adding particles when CCN >10 cm−3 will have a net cooling effect [52]. This is again anamplification of the negative feedback mechanism.

The fourth issue is the most important. Kulmala et al. [5]use an assumed moderate estimate of the increase of VOCemissions of 10%. This value of VOCs emissions increaseis corresponding to an increase of optical thickness of theclouds by 1 to 2%. Kulmala et al. [5] also state that adoubling (100%) of increase of VOCs emissions correspondsto 20% increase in optical thickness of the clouds. In thepresent study we have found that the increase of emissionsof VOCs is higher than 10%. This result is not based on amoderate estimate but on simulations using the LPJ-GUESSvegetation model and calculations of emission rates reportedin literature. Even though there are approximations includedin both simulations as well as in the calculations, we hopethat the results derived from this study are more accuratethan a moderate estimation.That our estimates of the relativeincrease of VOCs are higher than the estimates by Kulmala etal. [5] would work in the direction towards an amplificationof the feedback mechanism under study.

5.2. Simplifications and Uncertainties. In any kind of attemptto simulate feedback mechanisms there are both simplifi-cations made and uncertainties to consider. For Step 1 inFigure 2, we do not know the magnitude of the globalwarming. The Intergovernmental Panel on Climate Change[1] concludes that there likely is an increase in average globaltemperature, and that the increase is somewhere between0.3∘C and 0.7∘C for the period of 2016–2035 compared tothe period 1986–2005. For the period 2081–2100, differentclimatemodel scenarios (RepresentativeConcentrationPath-ways, RCPs) project the average global temperature increaseas 0.3∘C–1.7∘C (RCP2.6), 1.1∘C–2.6∘C (RCP4.5), 1.4∘C–3.1∘C(RCP6.0), and 2.6∘C–4.8∘C (RCP8.5) [1]. In future studies, theRCPs can be used to model the temperature increase duringthe simulations. For Step 2, the present knowledge is insuffi-cient to know how much warmer the tundra region must befor the permafrost to melt permanently. For Step 3, we relyon the model simulations to be correct, and as mentioned themodel is simplified and the result comes accompanied withuncertainties. For Step 4, the rate of emissions of VOCs istemperature dependent and also different for different typesof vegetation. For Step 5, even if we knew the number ofnewly formed organic particles from emissions of VOCs,the number of particles available as CCN is hard to predictbecause the process of condensational growth of the newlyformed particles is not fully understood [38]. We have seenthat the conventional theory used, Kohler theory [8], lacks in

the description of organic compounds for sizes below 5 nmand complex morphology as the CCN. However, we havepreviously shown that there aremethods for better describingsuch systems [11, 53]. For Step 6, the aerosol-cloud-radiation-albedo relationship over the Arctic pack ice is discussed byLeck and Bigg [49] and was concluded to be more complexthan elsewhere.

For the calculations of increase in emissions of monoter-penes from forest growth, the resolution of the simulated areais of vital importance.Themore accurate the resolution of thecalculated locations is, the higher the degree of informationwould be. Tomeet the need for highly resolved simulationsweuse the 50 km × 50 km horizontal grid resolution provided bythe LPJ-GUESS model. By this we increase the resolution by5 to 10 times relative to using atmospheric GCMs. However,the uncertainties in the assumptions used in the simulationsdescribed above limit the level of accuracy in the calculations.An even higher resolution would therefore not favor the finalresults.

The temperature increase that we set to 2, 3, and 4 degreescentigrade is corresponding to an increase globally in theLPJ-GUESS model evenly distributed. This is, however, notthe case since we can see an amplification of the warming inthe Arctic region and a global mean of 2-degree centigradeincrease could actually correspond to a higher temperaturein the Arctic region. This effect is not considered in oursimulations. This suggests that the enhancement of theforests-aerosol-climate negative feedbackmechanism is mostlikely even more pronounced at a lower global temperatureincrease.

6. Summary and Conclusions

In this study model simulations of the vegetation in northernRussia were carried out for the climate of the year 1975 asa reference and for three possible future projections: two-,three-, and four-degree centigrade increase of mean globaltemperature and with a doubling of the atmospheric carbondioxide concentration over 200 years. The simulations werecarried out using the LPJ-GUESS model. The growth of theforest was converted to emissions of monoterpenes based onliterature values and compensating for the number of days ofsunlight and to a temperature factor.

The results show the following.

(i) Therewas a growth of the boreal forest both for spruceand pine (in the central and western parts of theforest area) and larch (in the north-eastern part ofthe forest), as well as new forestation of previouslywastelands of the tundra. Based on the simulationsthe boreal forest will move northward as a result ofglobal warming.

(ii) The emissions of monoterpenes at the most northernlatitudes were estimated to increase with close to500% for a four-degree centigrade increase protocol.

(iii) The likelihood for organic vapours and particles to betransported northward over the central Arctic Oceanis highest for this northern latitude at the same time


as the projected increase in precursors for particleformation is huge. Kulmala et al. [5] suggested thatthe cloud optical thickness would increase 1 to 2%for an increase in VOC emissions by 10% and thecloud optical thickness would increase 20% for anincrease in VOC emissions by 100%. As we reportan overall increase in VOC emissions by 64%, thecloud optical thickness would then increase morethan 2%. However, a back of the envelope calculationindicates that the optical thickness may be up to9%. For more accurate calculations of cloud opticalthickness, a future study could incorporate a boxmodel for radiation and convection. The preliminaryresults however indicate that the cloud deck to agreater extent would reflect solar radiation back tospace and thereby cool the surface of theArctic regionmore than what was previously expected. Kurten etal. [54] estimate the global contribution from borealaerosol formation to radiative forcing to be from−0.03 to −1.1Wm−2 and therefore an increase ofVOC emissions would be expected to increase theradiative forcing of the same magnitude. This canbe compared to the radiative forcing from surfacealbedo changes which has a global mean value of−0.20Wm−2 or 0.35Wm−2 radiative forcing caused byan atmospheric CO

2increase of 19 ppm(v) since 800

AD [55].

Conflict of Interests

The authors have no conflict of interests to declare.

References

[1] IPCC, “Climate change 2013: the physical science basis,” inContribution of Working Group I to the Fifth Assessment Reportof the Intergovernmental Panel on Climate Change, T. F. Stocker,D. Qin, G.-K. Plattner et al., Eds., p. 1535, Cambridge UniversityPress, Cambridge, UK, 2013.

[2] M. Mencuccini and J. Grace, “Climate influences the leafarea/sapwood area ratio in Scots pine,” Tree Physiology, vol. 15,no. 1, pp. 1–10, 1995.

[3] A. L. Swann, I. Y. Fung, S. Levis, G. B. Bonan, and S. C. Doney,“Changes in arctic vegetation amplify high-latitude warmingthrough the greenhouse effect,” Proceedings of the NationalAcademy of Sciences of the United States of America, vol. 107, no.4, pp. 1295–1300, 2010.

[4] K. M. Walter, S. A. Zimov, J. P. Chanton, D. Verbyla, and F.S. Chapin III, “Methane bubbling from Siberian thaw lakes asa positive feedback to climate warming,” Nature, vol. 443, no.7107, pp. 71–75, 2006.

[5] M. Kulmala, T. Suni, K. E. J. Lehtinen et al., “A new feedbackmechanism linking forests, aerosols, and climate,” AtmosphericChemistry and Physics, vol. 4, no. 2, pp. 557–562, 2004.

[6] R. Valentini, G. Matteucci, A. J. Dolman et al., “Respiration asthe main determinant of carbon balance in European forests,”Nature, vol. 404, no. 6780, pp. 861–865, 2000.

[7] C. D. O’Dowd, P. Aalto, K. Hameri, M. Kulmala, and T. Hoff-mann, “Atmospheric particles from organic vapours,” Nature,vol. 416, no. 6880, pp. 497–498, 2002.

[8] H. Kohler, “The nucleus in and the growth of hygroscopicdroplets,” Transactions of the Faraday Society, vol. 32, pp. 1152–1161, 1936.

[9] M. C. Facchini, S. Decesari, M. Mircea, S. Fuzzi, and G. Loglio,“Surface tension of atmospheric wet aerosol and cloud/fogdroplets in relation to their organic carbon content and chemi-cal composition,” Atmospheric Environment, vol. 34, no. 28, pp.4853–4857, 2000.

[10] H. Rodhe, “Clouds and climate,” Nature, vol. 401, no. 6750, pp.223–225, 1999.

[11] T. Hede, X. Li, C. Leck, Y. Tu, and H. Agren, “Model HULIScompounds in nanoaerosol clusters—investigations of surfacetension and aggregate formation using molecular dynamicssimulations,” Atmospheric Chemistry and Physics, vol. 11, no. 13,pp. 6549–6557, 2011.

[12] T. Hede, C. Leck, L. Sun, Y. Tu, and H. Agren, “A theoreticalstudy revealing the promotion of light-absorbing carbon par-ticles solubilization by natural surfactants in nanosized waterdroplets,” Atmospheric Science Letters, vol. 14, no. 2, pp. 86–90,2013.

[13] R. Sorjamaa, B. Svenningsson, T. Raatikainen, S. Henning, M.Bilde, and A. Laaksonen, “The role of surfactants in Kohlertheory reconsidered,” Atmospheric Chemistry and Physics, vol.4, no. 8, pp. 2107–2117, 2004.

[14] H. Kokkola, R. Sorjamaa, A. Peraniemi, T. Raatikainen, and A.Laaksonen, “Cloud formation of particles containing humic-like substances,” Geophysical Research Letters, vol. 33, no. 10,Article ID L10816, 2006.

[15] R. Sorjamaa and A. Laaksonen, “The influence of surfactantproperties on critical supersaturations of cloud condensationnuclei,” Journal of Aerosol Science, vol. 37, no. 12, pp. 1730–1736,2006.

[16] V. F. McNeill, N. Sareen, and A. N. Schwier, “Surface-activeorganics in atmospheric aerosols,” Topics in Current Chemistry,vol. 339, pp. 201–259, 2013.

[17] M. Dalirian, H. Keskinen, L. Ahlm et al., “CCN activation offumed silica aerosols mixed with soluble pollutants,” Atmo-spheric Chemistry and Physics Discussions, vol. 14, no. 16, pp.23161–23200, 2014.

[18] S. S. Hings, W. C. Wrobel, E. S. Cross, D. R. Worsnop, P.Davidovits, and T. B. Onasch, “CCN activation experimentswith adipic acid: effect of particle phase and adipic acid coatingson soluble and insoluble particles,” Atmospheric Chemistry andPhysics, vol. 8, no. 14, pp. 3735–3748, 2008.

[19] S. Twomey, “The influence of pollution on the shortwave albedoof clouds,” Journal of the Atmospheric Sciences, vol. 34, no. 7, pp.1149–1152, 1977.

[20] A. D. McGuire, S. Sitch, J. S. Clein et al., “Carbon balance of theterrestrial biosphere in the twentieth century: analyses of CO

2,

climate and land use effects with four process-based ecosytemmodels,”Global Biogeochemical Cycles, vol. 15, no. 1, pp. 183–206,2001.

[21] B. Smith, I. C. Prentice, and M. T. Sykes, “Representation ofvegetation dynamics in the modelling of terrestrial ecosystems:comparing two contrasting approaches within European cli-mate space,” Global Ecology and Biogeography, vol. 10, no. 6, pp.621–637, 2001.

[22] S. Sitch, B. Smith, I. C. Prentice et al., “Evaluation of ecosystemdynamics, plant geography and terrestrial carbon cycling in theLPJ dynamic global vegetation model,” Global Change Biology,vol. 9, no. 2, pp. 161–185, 2003.


[23] G. Esser, J. Kattge, and A. Sakalli, “Feedback of carbon andnitrogen cycles enhances carbon sequestration in the terrestrialbiosphere,” Global Change Biology, vol. 17, no. 2, pp. 819–842,2011.

[24] A. Ahlstrom, G. Schurgers, A. Arneth, and B. Smith, “Robust-ness and uncertainty in terrestrial ecosystem carbon responseto CMIP5 climate change projections,” Environmental ResearchLetters, vol. 7, no. 4, Article ID 044008, 2012.

[25] B. Smith, D. Warlind, A. Arneth et al., “Implications of incor-porating N cycling and N limitations on primary production inan individual-based dynamic vegetationmodel,” Biogeosciences,vol. 11, no. 7, pp. 2027–2054, 2014.

[26] T. D. Mitchell and P. D. Jones, “An improved method ofconstructing a database of monthly climate observations andassociated high-resolution grids,” International Journal of Cli-matology, vol. 25, no. 6, pp. 693–712, 2005.

[27] W. Cramer, A. Bondeau, F. I. Woodward et al., “Global responseof terrestrial ecosystem structure and function to CO

2and

climate change: results from six dynamic global vegetationmodels,” Global Change Biology, vol. 7, no. 4, pp. 357–373, 2001.

[28] P. Friedlingstein, P. Cox, R. Betts et al., “Climate-carbon cyclefeedback analysis: results from the C4MIP model intercompar-ison,” Journal of Climate, vol. 19, no. 14, pp. 3337–3353, 2006.

[29] A. Haxeltine and I. C. Prentice, “BIOME3: an equilibrium ter-restrial biosphere model based on ecophysiological constraints,resource availability, and competition among plant functionaltypes,” Global Biogeochemical Cycles, vol. 10, no. 4, pp. 693–709,1996.

[30] T. M. Ruuskanen, H. Hakola, M. K. Kajos, H. Hellen, V.Tarvainen, and J. Rinne, “Volatile organic compound emissionsfrom Siberian larch,” Atmospheric Environment, vol. 41, no. 27,pp. 5807–5812, 2007.

[31] J. Kesselmeier and M. Staudt, “Biogenic volatile organic com-pounds (VOC): an overview on emission, physiology andecology,” Journal of Atmospheric Chemistry, vol. 33, no. 1, pp. 23–88, 1999.

[32] L. Xu, R. B. Myneni, F. S. Chapin et al., “Temperature andvegetation seasonality diminishment over northern lands,”Nature Climate Change, vol. 3, no. 6, pp. 581–586, 2013.

[33] H. Petersson, “Biomassafunktioner for tradfaktorer av tall, granoch bjork i Sverige,” SLUArbetsrapport 59, SLU,Umea, Sweden,1999.

[34] T. Lind, “Kolinnehall i skog och mark i Sverige—Baserat paRiksskogstaxeringens data,” SLU Arbetsrapport 86, SLU, Umea,Sweden, 2001.

[35] A. Albrektson, “Sapwood basal area and needle mass of scotspine (Pinus sylvestris L.) trees in central Sweden,” Forestry, vol.57, no. 1, pp. 35–43, 1984.

[36] R. W. Janson, “Monoterpene emissions from Scots pine andNorwegian spruce,” Journal of Geophysical Research, vol. 98, no.2, pp. 2839–2850, 1993.

[37] J. Laothawornkitkul, J. E. Taylor, N. D. Paul, and C. N. Hewitt,“Biogenic volatile organic compounds in the Earth system,”NewPhytologist, vol. 183, no. 1, pp. 27–51, 2009.

[38] J. Julin, M. Shiraiwa, R. E. H. Miles, J. P. Reid, U. Poschl,and I. Riipinen, “Mass accommodation of water: Bridgingthe gap between molecular dynamics simulations and kineticcondensation models,” Journal of Physical Chemistry A, vol. 117,no. 2, pp. 410–420, 2013.

[39] D. V. Spracklen, K. S. Carslaw, M. Kulmala et al., “Contributionof particle formation to global cloud condensation nuclei

concentrations,” Geophysical Research Letters, vol. 35, no. 6,Article ID L06808, 2008.

[40] V.-M. Kerminen, H. Lihavainen, M. Komppula, Y. Viisanen,and M. Kulmala, “Direct observational evidence linking atmo-spheric aerosol formation and cloud droplet activation,” Geo-physical Research Letters, vol. 32, no. 14, Article ID L14803, pp.1–4, 2005.

[41] X. Li, T. Hede, Y. Tu, C. Leck, and H. Agren, “Surface-activecis-pinonic acid in atmospheric droplets: a molecular dynamicsstudy,”The Journal of Physical Chemistry Letters, vol. 1, no. 4, pp.769–773, 2010.

[42] A. Schwier, D. Mitroo, and V. F. McNeill, “Surface ten-sion depression by low-solubility organic material in aqueousaerosol mimics,” Atmospheric Environment, vol. 54, pp. 490–495, 2012.

[43] R. Atkinson, “Atmospheric chemistry of VOCs and NO𝑥,”

Atmospheric Environment, vol. 34, no. 12–14, pp. 2063–2101,2000.

[44] A. Calogirou, B. R. Larsen, and D. Kotzias, “Gas-phase terpeneoxidation products: a review,”Atmospheric Environment, vol. 33,no. 9, pp. 1423–1439, 1999.

[45] M. Boy, U. Rannik, K. E. J. Lehtinen, V. Tarvainen, H. Hakola,and M. Kulmala, “Nucleation events in the continental bound-ary layer: long-term statistical analyses of aerosol relevantcharacteristics,” Journal of Geophysical ResearchD: Atmospheres,vol. 108, no. 21, article 4667, 2003.

[46] I. Riipinen, T. Yli-Juuti, J. R. Pierce et al., “The contributionof organics to atmospheric nanoparticle growth,” Nature Geo-science, vol. 5, no. 7, pp. 453–458, 2012.

[47] E. K. Bigg and C. Leck, “Cloud-active particles over the centralArctic Ocean,” Journal of Geophysical Research D: Atmospheres,vol. 106, no. 23, pp. 32155–32166, 2001.

[48] C. Leck, M. Norman, E. K. Bigg, and R. Hillamo, “Chemicalcomposition and sources of the high Arctic aerosol relevant forcloud formation,” Journal of Geophysical Research, vol. 107, no.D12, p. 4135, 2002.

[49] C. Leck and E. K. Bigg, “A modified aerosol-cloud-climatefeedback hypothesis,”Environmental Chemistry, vol. 4, no. 6, pp.400–403, 2007.

[50] J.M. Intrieri, C.W. Fairall,M.D. Shupe et al., “An annual cycle ofArctic surface cloud forcing at SHEBA,” Journal of GeophysicalResearch C: Oceans, vol. 107, no. 10, pp. 1–14, 2002.

[51] M. Tjernstrom, “The summer arctic boundary layer duringthe arctic ocean experiment 2001 (AOE-2001),” Boundary-LayerMeteorology, vol. 117, no. 1, pp. 5–36, 2005.

[52] T. Mauritsen, J. Sedlar, M. Tjernstrom et al., “An arctic CCN-limited cloud-aerosol regime,” Atmospheric Chemistry andPhysics, vol. 11, no. 1, pp. 165–173, 2011.

[53] X. Li, T. Hede, Y. Tu, C. Leck, and H. Agren, “Glycine in aerosolwater droplets: a critical assessment of Kohler theory by pre-dicting surface tension from molecular dynamics simulations,”Atmospheric Chemistry and Physics, vol. 11, no. 2, pp. 519–527,2011.

[54] T. Kurten, M. Kulmala, M. Dal Maso et al., “Estimation ofdifferent forest-related contributions to the radiative balanceusing observations in southern Finland,” Boreal EnvironmentResearch, vol. 8, no. 4, pp. 275–285, 2003.

[55] J. Pongratz, C. H. Reick, T. Raddatz, K. Caldeira, and M.Claussen, “Past land use decisions have increased mitigationpotential of reforestation,” Geophysical Research Letters, vol. 38,no. 15, Article ID L15701, 2011.

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