Isoprene emission response to drought and the impact on global atmosphericchemistry
Xiaoyan Jianga,∗, Alex Guenthera, Mark Potosnakb, Chris Geronc, Roger Secoa, Thomas Karld,Saewung Kima, Lianhong Gue, Stephen Pallardyf
a Department of Earth System Science, University of California – Irvine, Irvine, CA 92697, United Statesb Environmental Science and Studies, DePaul University, Chicago, IL 60614, United StatescNational Risk Management Research Laboratory, Air and Energy Management Division, U.S. Environment Protection Agency, Research Triangle Park, NC 27709, UnitedStatesd Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austriae Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, United Statesf School of Natural Resources, University of Missouri, Columbia, MO 65211, United States
Biogenic isoprene emissions play a very important role in atmospheric chemistry. These emissions are stronglydependent on various environmental conditions, such as temperature, solar radiation, plant water stress, am-bient ozone and CO2 concentrations, and soil moisture. Current biogenic emission models (i.e., Model ofEmissions of Gases and Aerosols from Nature, MEGAN) can simulate emission responses to some of the majordriving variables, such as short-term variations in temperature and solar radiation, but the other factors areeither missing or poorly represented. In this paper, we propose a new modelling approach that considers thephysiological effects of drought stress on plant photosynthesis and isoprene emissions for use in the MEGAN3biogenic emission model. We test the MEGAN3 approach by integrating the algorithm into the existingMEGAN2.1 biogenic emission model framework embedded into the global Community Land Model of theCommunity Earth System Model (CLM4.5/CESM1.2). Single-point simulations are compared against availablefield measurements at the Missouri Ozarks AmeriFlux (MOFLUX) field site. The modelling results show that theMEGAN3 approach of using of a photosynthesis parameter (Vcmax) and soil wetness factor (βt) to determine thedrought activity factor leads to better simulated isoprene emissions in non-drought and drought periods. Theglobal simulation with the MEGAN3 approach predicts a 17% reduction in global annual isoprene emissions, incomparison to the value predicted using the default CLM4.5/MEGAN2.1 without any drought effect. This re-duction leads to changes in surface ozone and oxidants in the areas where the reduction of isoprene emissions isobserved. Based on the results presented in this study, we conclude that it is important to simulate the drought-induced response of biogenic isoprene emission accurately in the coupled Earth System model.
1. Introduction
Biogenic volatile organic compounds (BVOCs) emitted from terres-trial ecosystems play a very important role in atmospheric chemistry(Wiedinmyer et al., 2006; Goldstein and Galbally, 2007; Pacifico et al.,2012; Pryor et al., 2014). Thousands of BVOCs have been characterized,and there is growing evidence that many more remain to be identified(Park et al., 2013). Biogenic isoprene, one of the key identified BVOCs,is emitted in large quantities by vegetation. Global estimation of bio-genic isoprene emission is 440–600 TgC per year, approximately half ofthe total BVOC emissions (Guenther et al., 2012). Biogenic isoprene
emissions are highly reactive and contribute to various atmosphericprocesses such as the formation of tropospheric ozone, lifetime of me-thane, and growth of secondary organic aerosols (SOAs), which couldhave important climatic impacts (Claeys et al., 2004; Paulot et al.,2009). Biogenic isoprene emissions not only affect atmospheric com-position and climate, but also are also strongly dependent on climaticconditions (i.e., temperature, solar radiation, plant water stress, am-bient ozone, and CO2 concentrations), landcover, and atmosphericchemistry conditions (i.e., Pacifico et al., 2012). Thus, understandinghow these emissions respond to changes in climate is crucial for pre-dicting important feedback in the biosphere-atmosphere-climate system
https://doi.org/10.1016/j.atmosenv.2018.01.026Received 9 August 2017; Received in revised form 7 January 2018; Accepted 13 January 2018
(Peñuelas and Staudt, 2010).Because of their important air quality and climatic implications,
biogenic isoprene emissions are now routinely included in coupledclimate/chemistry models—such as regional and global air quality andearth system models (i.e., WRF-Chem, [Grell et al., 2005]; CESM [Gentet al., 2011],). However, despite much progress that has been made inestimating biogenic isoprene emissions using numerical models, thereare still large uncertainties in the magnitude and variability of themodel-estimated isoprene emissions (e.g., Folberth et al., 2006;Derwent et al., 2007; Arneth et al., 2008; Guenther, 2013). To betterassess past, present, and future air quality and climate (e.g., Derwentet al., 2007; Folberth et al., 2006; Jiang et al., 2010; Pacifico et al.,2012; Squire et al., 2014) and better simulate the impacts of environ-mental conditions on isoprene emissions and the associated feedbacks,more accurate isoprene emission estimates are needed. Obtaining thesemore accurate estimates requires consideration of environmental dri-vers such as phenology, enzymatic activity, biotic stress, drought andother abiotic stresses.
The sensitivity of isoprene emissions to several environmental fac-tors (i.e. temperature, radiation, CO2) has been well documented(Monson et al., 1994; Sharkey et al., 1999; Petron et al., 2001; Guentheret al., 2006, 2012; Arneth et al., 2007; Heald et al., 2009). The earlystudies of the sensitivity of isoprene emissions to temperature and lightserved as the basis of the first BVOC emission models (Monson et al.,1994; Sharkey et al., 1999; Tingey, 1981; Guenther et al., 1991, 1993;Lamb et al., 1993). Monson et al. (2012), Pacifico et al. (2012), Ungeret al. (2013), Grote et al. (2014) have tried to include a more me-chanistic representation of environmental factors affecting isopreneemissions in BVOC emission models. However, the impacts of climateextremes on biogenic emissions have received little attention because ofa lack of observations. Climate extremes such as droughts are known toimpact ecosystem function severely, including the amount of BVOCsemitted by plants (i.e. Pegoraro et al., 2004). Studies have shown thatclimate extremes and associated changes in ecosystems are increasingin frequency and magnitude (IPCC, 2013). Under future climate sce-narios, more droughts are projected in many parts of the world (Jianget al., 2013; Rauscher et al., 2015; McDowell et al., 2015). Accuratepredictions of future changes in biogenic emissions will need to con-sider drought and other stresses. We therefore urgently need a bettermechanistic understanding to estimate BVOC emissions under differentextreme conditions—such as drought.
Many studies have investigated the impacts of drought on plants(Tingey, 1981; Sharkey and Loreto, 1993; Pegorato et al., 2004; Brilliet al., 2007; Saveyn et al., 2007; De Swaef and Steppe, 2010; Zhouet al., 2014) and have found that photosynthetic rate, stomatal con-ductance, and transpiration rates decline when soil water content de-creases. However, the effects of drought stress on BVOC emissions aremore complex. Drought stress can alter the composition of BVOCs de-pending on the level of stress (Niinemets, 2010). Various studies(Tingey, 1981; Pegoraro et al., 2004; Grote et al., 2009) have shownthat the BVOC emissions can initially be increased or decreased but thisis ultimately followed by a decrease. Tingey (1981) and Pegoraro et al.(2004) found that during short-term drought, isoprene emission ratesstay constant or even increase slightly at the initial stages of drought.They also found a large reduction in isoprene emissions after 12 days ofsevere drought for some oak trees. The shutdown of the physiologicalprocesses of a plant in response to drought can lead to an initial in-crease in emissions, followed by a decrease and then termination ofisoprene emission (Pegoraro et al., 2004; Beckett et al., 2012). In theinitial phase of a drought (mild drought), plants respond by reducingwater loss through transpiration by reducing stomatal conductance.This reduction is accompanied by a decrease in evaporative cooling andan increase in leaf temperature. If the plant has sufficient reducedcarbon resources from stored reserves, this reduction can lead to anincrease in isoprene emissions. However, these reserves will be depletedif the drought condition persists, and isoprene emissions will then
decrease.The observations of Pegoraro et al. (2004) serve as the basis for the
drought effect on isoprene emissions that is currently embedded inModel of Emissions of Gases and Aerosols from Nature version 2.1(MEGAN2.1, Guenther et al., 2006, 2012). Several studies (Seco et al.,2015; Huang et al., 2015) have shown more recently that the currentdrought algorithm (and resulting isoprene emission estimates) inMEGAN 2.1 is highly dependent on the selection of wilting point values.When different wilting point values are used to drive MEGAN, the re-sulting drought impacts on isoprene emissions are substantially dif-ferent. To better understand how drought impacts biogenic isopreneemissions, more field measurements of BVOCs are needed. However,measurements of BVOC emissions under drought conditions are cur-rently very limited, which makes it challenging to develop and test amore mechanistic representation of drought impacts on BVOCs fornumerical biogenic emission models. Soil moisture was also identifiedas a key source of uncertainty in predicting the response of isopreneemission to water stress (Monson et al., 2012; Tawfik et al., 2012;Huang et al., 2015). However, when drought stress occurs, it changesplant physiology as well in complex ways. Šimpraga et al. (2011) andGrote et al. (2014) have attempted to include the linkage betweenphotosynthesis and BVOC emissions in emission models, but the effectof drought on emissions is complex and not easily represented.
Drought responses of photosynthesis are already incorporated intothe vegetation component of most Earth System Models (i.e., Olesonet al., 2013), providing a potential pathway for improving BVOCemission estimates. The aim of this study is to develop a simple me-chanistic representation of the drought response of isoprene emissionand demonstrate the global impacts using an Earth System model – theCommunity Earth System Model (CESM) (Gent et al., 2011). TheCommunity Land Model (CLM4.5) of CESM (Oleson et al., 2013) isequipped with the MEGAN2.1 biogenic emission model. Section 2 de-scribes the MEGAN2.1 and MEGAN3 modelling approaches used torepresent the drought impacts on isoprene emissions. Modelling resultsare given in Section 3 including single point model simulations withvarious drought algorithms that are evaluated against the availablefield measurements. In addition, global simulations with theMEGAN2.1 and MEGAN3 algorithms are carried out to understand thepotential impacts of the new drought algorithm on global estimation ofisoprene emissions. Finally, the drought impacts on ozone chemistrythrough changing biogenic isoprene emissions are analyzed by con-ducting coupled atmosphere–chemistry simulations without and withthe MEGAN3 drought algorithm.
2. Methods
In this section, we present a new modelling approach — MEGAN3drought algorithm — to simulate the impact of drought on biogenicisoprene emissions. The field measurements described below are usedto derive a new approach for calculating the isoprene emission responseto drought. We then examine the impact of drought stress on isopreneemissions from a site representing an important North American Oakecosystem. The effects of the algorithm are then assessed at the globalscale by including the MEGAN3 drought algorithm in the CLM4.5/MEGAN2.1 framework.
2.1. Field measurements
There is growing interest in measurements of isoprene emission thatcan improve the understanding of atmospheric chemistry and climate(Wiedinmyer et al., 2001; Saxton et al., 2007; Liu et al., 2016; Martinet al., 2016). However, there have been no long-term canopy-scaleBVOC flux field measurements that have intentionally focused ondrought except for one study at a Northern Ozarks Tower that includedisoprene emission measurements and was conducted in the US in thesummers of 2011 and 2012. The Northern Ozarks Tower study was
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
70
impacted by mild (2011) to severe (2012) droughts (Potosnak et al.,2014; Seco et al., 2015). Here, we use the biogenic isoprene flux andother measurements (i.e. water flux, CO2 flux, energy fluxes) from a siteof Ameriflux (Baldocchi et al., 2001) to develop a new drought algo-rithm for isoprene emissions for CLM4.5/MEGAN3. The field site,Missouri Ozarks AmeriFlux site (MOFLUX), is located in the BaskettWildlife Research and Education Area (BWREA), and is operated by theUniversity of Missouri near the city of Ashland. MOFLUX is within theOzark Border region of central Missouri. The site is covered by oak-hickory forest dominated by white, post and black oaks, shagbarkhickory, sugar maple, and eastern red cedar. The climate of the area isclassified as warm, humid, and continental (Critchfield, 1966). Siltloam and clay loam are the dominant soil textures at the site. The thinsoils beneath the oak-hickory forests often exacerbate plant water stresswhen droughts occur (Bahari et al., 1985). More details about the sitecharacteristics can be found in Gu et al. (2006, 2007, 2015).
The site experienced a mild drought in mid to late summer of 2011and an extreme to exceptional drought from mid to late summer of2012. It was unusually wet in the spring of 2011, but drought started toappear in June due to lack of rainfall. The temperature also hit therecord high in more than three decades. During the time when theisoprene flux measurements were taken, Leaf Area Index (LAI) was3.7 m2m−2 at the site. The drought condition in 2011 was not as severeas that in 2012. In 2012, the total annual precipitation was the lowest inthe last decade. Soil water content started to decrease at the beginningof May, and dropped to its minimum (Fig. 1a) at the end of August.Plant leaf water potential gradually dropped to −3.79MPa (Seco et al.,2015) by the end of August. When the leaf water potential drops below−1.5MPa, most mesic plants are under drought stress (Bonan, 2002).
LAI, which was measured at the site started to drop in August (Fig. 1b)in response to the drought. The drought also led to decreases in eco-system net CO2 and H2O fluxes (Fig. 1c and d). After rain events oc-curred at the end of August, soil water content, net CO2 flux (NetEcosystem Exchange, or NEE) and H2O flux recovered slightly, but LAIcontinued to decrease, indicating that drought may have caused long-term damage to this canopy. Isoprene emissions increased in May andJune, prior to the onset of severe drought, and peaked in late June asthe drought conditions worsened (Fig. 1). Leaf-level isoprene emissionrates and leaf temperature responses during the pre-drought periodwere similar to the values reported in the literature and used in BVOCemission models (Geron et al., 2016). During the peak of the drought inAugust, isoprene emission rates decreased substantially (Fig. 1). Iso-prene emissions are highly temperature dependent (Singsaas andSharkey, 2000) when there is no drought. In this case, if there was nodrought, isoprene emissions were expected to be very high in summertime. However, the measured isoprene emissions at MOFLUX decreasedin July and August, even as ambient temperature increased. This in-dicates other factors are playing a critical role. Multi-regression analysiscombined canopy-scale isoprene emissions collected in the summers of2011 (mild drought) and 2012 (extreme drought) with CLM4.5drought-related parameters to derive a new drought algorithm based onplant physiology. Due to the limited availability of drought-relatedisoprene emission measurements, our modelling results were comparedagainst the same canopy-scale isoprene flux measurements at the MO-FLUX site.
Isopreneemissions(mg/m2 /hr)
Isopreneemissions(mg/m2 /hr)
Soilmoisture(mm3 /mm3 )
Isopreneemissions(mg/m2 /hr)
CO2
Isopreneemissions(mg/m2 /hr)
HO 2
LAI
Isoprene vs. Soil moisture at 10cm, 20cm, and 100cm Isoprene vs. LAI10 1 10 3.8
Fig. 1. Daily measurements of isoprene emissions (mg m−2hr−1) versus soil moisture (m3m−3), at 10 cm (blue), 20 cm (red), and 100 cm (green) depths; leaf areaindex (LAI) (unitless), CO2 flux (umol m−2s−1), and water flux (umol m−2s−1) from May to September in 2012 at the MOFLUX site. (For interpretation of thereferences to colour in this figure legend, the reader is referred to the Web version of this article.)
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
71
2.2. Isoprene emission algorithms
The biogenic emission model, MEGAN2.1 (Guenther et al., 2012),which has been embedded into CLM4.5 of CESM, is used to estimateisoprene emissions from plants. MEGAN2.1 uses simple mechanisticalgorithms to account for the major known processes controlling bio-genic emissions, including light, temperature, CO2, and empirical ap-proaches to represent the emission response to soil moisture and leafage (Guenther et al., 2006, 2012). The emission activity factor γ2.1 (foreach compound class) accounts for emission response to light (γP),temperature (γT), leaf age (γA), soil moisture (γSM), leaf area index(LAI), and CO2 inhibition (γC):
γ0=CCE LAI γP γT γA γC (1)
γ2.1= γ0 γSM=CCE LAI γP γT γA γC γSMwhere CCE represents a canopy environment parameter and γ0 re-presents the emission activity factor without the drought effect. Formore details, readers are referred to Guenther et al., 2012.
In the current version of MEGAN2.1, only the effects of droughtthrough the use of soil moisture and wilting point on isoprene emissionsare considered. The effects of drought on isoprene emissions inMEGAN2.1 are parameterized using a simple empirical algorithm basedon the observations of Pegoraro et al. (2004) which relates emissionactivity, γSM, isoprene, to soil moisture and wilting point (Guenther et al.,2012).
γSM, isoprene= 1 (θ> θ1)
γSM, isoprene = (θ−θw)/Δθ1 (θw< θ< θ1) (2)
γSM, isoprene= 0 (θ< θw)
where θ is soil moisture (volumetric water content, m3 m−3), θw iswilting point (m3 m−3). Δθ1 (=0.06m3m−3) is an empirical parameterand θ1 is defined as θw + Δθ1. The wilting point is defined as the soilmoisture at which a plant cannot further extract water from soil. Itgenerally occurs at a suction of −1.50MPa and varies with soil texture.The default wilting point values provided with the offline version ofMEGAN2.1 are from Chen and Dudhia (2001) global data set (Fig. 2a).Potosnak et al. (2014) found that when using the Chen and Dudhiawilting point value of 0.08 for the MOFLUX site, the MEGAN2.1 soilmoisture algorithm did not have any impact on modelled isopreneemissions for the years 2011 and 2012 at the MOFLUX site as soilmoisture was always above the threshold needed to trigger the droughtactivity factor in MEGAN 2.1. Seco et al. (2015) used a wilting point
value of 0.23m3m−3, which is a more representative value for theMOFLUX site soil types, and found that this wilting point value couldaccount for much of the observed drought effect on isoprene emissions.Muller et al. (2008) found that when using the ECMWF (EuropeanCentre for Medium-Range Weather Forecasts) global weather model, itwas necessary to use the ECMWF wilting point dataset. These resultsdemonstrate that impact of drought on isoprene emissions is highlydependent on the wilting point values. In CLM4.5/MEGAN2.1, thewilting point is calculated mathematically using:
ψi =ψsat, i(θi/θsat i)(−b) (3)
where i presents individual soil layers, ψi is soil matric potential at soillayer i, ψsat, i is matric potential at saturation for layer i, θi is volumetricsoil moisture (m3 m−3) at soil layer i, θsat, i is water content at sa-turation, and b (unitless) is a soil texture-related parameter from Clappand Hornberger (1978). At wilting point, ψwilt, i =−1.50MPa. Sub-stituting this for ψi in the above formula, one can obtain θw, i, which isdependent on soil texture.
θw,i = θsat,i(ψsat, i/ψwilt, i)(−b) (4)
Since CLM4.5 uses a nested subgrid hierarchy to represent spatialheterogeneity of the land surface, there are up to 15 Plant FunctionalTypes (PFTs) plus bare ground for each model grid in CLM4.5. If onewould like to plot wilting point maps for individual PFTs, there will be15 maps. Thus, for display purpose only, the final wilting point shownin this paper is the lumped value for each grid cell, which is calculatedas a weighted average for all PFTs over the root zone.
There are multiple factors contributing to the difference in thecalculated wilting point values between CLM4.5 and Chen & Dudhia. InChen and Dudhia, only land cover types are used, that means for eachgrid, there is only one type of land cover, while in CLM4.5, there are upto 15 PFTs. The detailed vegetation and soil information and modelledprocesses in CLM4.5 give better vegetation-related soil parameters. TheCLM4.5 calculated wilting point (Fig. 2b) at the MOFLUX site is ap-proximately 0.2m3m−3, which is close to the value of 0.23m3m−3
used in Seco et al. (2015). Comparing the wilting points from Chen andDudhia, used as defaults in the offline MEGAN2.1, and CLM4.5/MEGAN2.1 models (Fig. 2), we can see that the default values used inthe offline MEGAN2.1 (Fig. 2a) are too low, given what is known aboutsoil textures, in many places around the world. The calculated wiltingpoints from CLM4.5/MEGAN2.1 are more comparable to the wiltingpoint data developed by the Global Soil Data Task (http://webmap.ornl.gov/wcsdown/wcsdown.jsp?dg_id=1004_35). We recommend
Fig. 2. Wilting points (m3m−3) used in offline MEGAN2.1 (a) and CLM4.5/MEGAN2.1 (b).
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
these wilting points to be used in offline MEGAN2.1 simulations, in-stead of the Chen and Dudhia values previously used for MEGAN2.1and have made a global dataset available on the MEGAN data portal(sites.google.com/uci.edu/bai/megan).
The existing drought algorithm for isoprene emission in MEGAN2.1does not capture the 2011 or 2012 drought effect on isoprene emissions(Potosnak et al., 2014; Sindelarova et al., 2014; Seco et al., 2015;Huang et al., 2015) when using the default soil data (i.e. soil types andwilting points from Chen and Dudhia, 2001) due to the low wiltingpoint values recommended by Chen and Dudhia (2001). TheMEGAN2.1 framework is embedded in CLM4.5, which has detailedbiogeophysical and hydrological cycles, and biogeochemical compo-nents, and can estimate carbon, water, and energy fluxes (Oleson et al.,2013). We use CLM4.5 to investigate potential approaches for devel-oping a simple mechanistic drought activity factor for isoprene emis-sion. It should be noted that the main purpose of this paper is not tosimply replace the existing drought activity factor in MEGAN2.1; in-stead, we attempt to develop a simple mechanistic representation ofdrought impacts on isoprene emissions by considering photosynthesisand water stress simultaneously. This is intended to be the initial steptowards a more sophisticated mechanistic representation of BVOCemission response to drought and other types of stress that can reducethe carbon substrates available for producing isoprene and otherBVOCs. CLM4.5 uses PFTs to represent vegetation types, and usesmultiple soil layers to simulate hydrological processes. In CLM4.5, thephysiological response of plants to drought stress is implementedthrough imposing stress on photosynthesis by reducing Vcmax with βt,where Vcmax is the maximum rate of carboxylation by the photo-synthetic enzyme Rubisco and βt is the soil water stress function. Re-duction in photosynthesis due to water stress can also limit the demandfor CO2 and indirectly influence the conductance process. The effect ofsoil water on stomatal conductance in CLM4.5 is applied directly bymultiplying the minimum conductance by βt and indirectly through netleaf photosynthesis (Vcmax and respiration, Rd). In this study, we use theCLM4.5 photosynthetic parameter, Vcmax, and the soil water stressfunction, βt, to derive the MEGAN3 algorithm for simulating isopreneemission response to drought.
Stomatal closure is recognized as the main driver of the initialphotosynthetic response to water stress (Zhou et al., 2014). Whendrought occurs, the drought may decrease photosynthetic enzyme ac-tivity (Vcmax) and ribulose-1, 5-bisphosphate (RuBP) regeneration ca-pacity (i.e. the maximum rate of photosynthetic electron transport,Jmax), and triosephosphate isomerase utilization (TPU) (Limousin et al.,2010; Martin-StPaul et al., 2012; Zhou et al., 2014). The three phy-siological processes can simultaneously regulate the decrease in pho-tosynthesis in response to water stress. In this study, we use Vcmax in theinitial attempt to represent the MEGAN3 response to water stress due tochanges in photosynthetic capacity. In CLM4.5, the Ball-Berry con-ductance model as described by Collatz et al. (1991) is used to calculatestomatal conductance and photosynthesis. Photosynthesis processes inC3 and C4 plants are based on the models of Farquhar et al. (1980) andCollatz et al. (1992). More detailed implementation of these processesin CLM4.5 is described in Bonan et al. (2011) and Oleson et al. (2013).Vcmax in CLM4.5 varies with vegetation temperature and drought con-dition. The influence of drought stress on isoprene emission through thereduction in photosynthesis is imposed by multiplying Vcmax by βt. Thefunction βt depends on soil water potential of each soil layer, rootdistribution of PFTs, and a wilting factor, which is based on Clapp andHornberger (1978). The value of βt ranges from one when there is noplant stress to near zero when plants are fully stressed. Thus, βt is ableto capture plant water regulation and limit photosynthesis.
βt = Σwi ri (5)
where wi is the wilting factor, and ri is the fraction of roots in each soillayer. For more details, readers are referred to Oleson et al. (2013).
We define the new isoprene emission activity factor γ3 for MEGAN3using γ0 and the new drought stress activity factor γd, isoprene, which isdifferent from the existing drought activity factor, γSM, isoprene, inMEGAN2.1 and parameterized using the above two key parameters inCLM4.5:
γ3= γ0 γd, isopreneγd, isoprene= 1 (βt > 0.6)
γd, isoprene=Vcmax/α (βt < 0.6, α=37) (6)
γd, isoprene= 0 (βt < 0.6)
where α is an empirical parameter derived from field measurements atthe MOFLUX site. We are limited to the drought-related isopreneemission measurements. Currently, there are no other availabledrought-related whole canopy isoprene flux field measurements exceptfor these data from the MOFLUX site. There are uncertainties associatedwith the parameter α, which needs further evaluation when moremeasurements become available. With this approach, isoprene emis-sions respond to water stress and photosynthesis simultaneously undernon-drought and drought conditions. In the sections that follow, de-tailed description of the development of the MEGAN3 drought activityfactor and evaluation of model results are given.
3. Results
3.1. Single-point simulations
CLM4.5 is equipped with detailed carbon and nitrogen bio-geochemistry. The model version we used for this study is CLM-SPwhere SP stands for satellite phenology indicating that vegetationconditions including LAI, Stem Area Index (SAI), and vegetation heightsare prescribed. We performed single-point simulations driven by site-specific hourly meteorology forcing measured at the MOFLUX site, in-cluding temperature, precipitation, wind, humidity, pressure, anddownward shortwave solar radiation. In the single-point simulations,vegetation and soil parameters including PFTs, LAI, and soil texture, arealso site specific. When CLM4.5 is driven by prescribed satellite-phe-nology, the model does not require a very long spin-up period (Olesonet al., 2013) to reach the steady state. The available meteorology for-cing data for the years 2011 and 2012 were used repeatedly during thespin up simulation. A total of 50 years were run to get the initial con-ditions for CLM4.5. Then, multiple simulations with and withoutdrought effects on isoprene emissions were carried out for 2011 and2012 with the initial conditions obtained from the 50-year spin up run.
We evaluated the CLM4.5 performance using the field measure-ments from the MOFLUX site. Fig. 3a and b shows the comparisonsbetween modelled and measured sensible heat (SH) and latent heat(LH) fluxes on the monthly time scale. The model-simulated SH fluxesmatch well with the observations during the drought period (Fig. 3a).They are slightly overestimated before the onset of drought. For thelatent heat fluxes, the model tends to produce more variations than theobservations, but the overall trend is consistent with the observations.The scatter plots of the two energy fluxes (Fig. 3c and d) show a rela-tively good agreement (see the Fig. 3 caption) between the simulationsand observations. In addition to the analysis of model-simulated energyfluxes, CLM4.5 simulated net CO2 flux (or NEE), which is calculated asthe difference between photosynthesis and respiration, is comparedagainst the net CO2 flux measurement in Fig. 4. On average, CLM4.5underestimates Ameriflux NEE measurements by 15% (Montané et al.,2017). While the performance of CLM4.5 is not optimal, it is acceptablefor the purpose of this study, and improving the NEE performance ofCLM4.5 is beyond the scope of this work.
To assess how different drought algorithms in CLM4.5/MEGAN si-mulate the response of isoprene emission to drought conditions, weperformed several sensitivity experiments at the MOFLUX site. Since
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
73
the wilting point value, 0.08 m3m-3, from Chen & Dudhia is alwaysbelow the soil moisture (Fig. 1) measured at this site, it is expected thatthe use of 0.08 m3m-3 will have no impacts on modelled isopreneemissions. Hence, only three sensitivity experiments were performed tounderstand the impacts of the new drought activity factor in MEGAN:EXP1 (No drought effect is included in MEGAN2.1), EXP2 (CLM4.5-calculated wilting point value is used with the MEGAN2.1 droughtemission activity algorithm), and EXP3 (MEGAN3 drought emissionactivity algorithm is used). When the CLM4.5-calculated wilting point isused in MEGAN2.1 (EXP2), the model produces a significant reductionin isoprene emissions during the start and peak of the drought period in2012 (Fig. 5c and d). The model underestimates isoprene emissionsduring these two periods compared to the observations, suggestingsome missing factors such as photosynthesis, water stress, and heatstress that might contribute to isoprene emission change. This studyattempts to include the effects of photosynthesis and water stress onisoprene emission changes when extreme drought events occur sincethe current MEGAN2.1 already does a reasonable job of re-producingisoprene emissions under non-drought conditions (Seco et al., 2015). InEXP3, the model captures the behavior of isoprene emission well beforeand during the drought periods (Fig. 5e). The simulations were eval-uated in terms of correlation coefficients using the cross-plots of themodelling results and the observations. The correlation coefficient fromEXP3 is the best (0.89) compared to the other experiments (Fig. 5f).
Fig. 3. Top panels show modelled and measured daily sensible heat (SH) (a) and latent heat (LH) (b) from May to September in 2012. Bottom panels show scatterplots of modelled and measured SH (c) and LH (d) from May to September in 2012. For the scatter plots, the calculated correlation coefficients, slopes, and interceptswith a linear fit are 0.86, 0.83, and 17.99 for SH and 0.92, 1.12, and 8.31 for LH respectively.
Fig. 4. Scatter plot of measured (OBS) daily net CO2 flux (umol m−2s−1) andCLM4.5 simulated daily net CO2 flux, which was calculated using photo-synthesis and respiration in May–September 2012. The correlation coefficient,slope, and intercept between them is 0.85, 0.51, and −3.58.
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
74
Other statistics (i.e. slope) of EXP3 also suggest it has a better perfor-mance in simulating isoprene emissions during the drought period. Toevaluate if the new drought activity algorithm works in less severedrought years, the model was also run for 2011. The simulated hourlyand daily isoprene emissions in 2011 from EXP3 match well with theobservations (Fig. 6a and b), suggesting that the new algorithm alsoworks under less severe drought condition. Again, EXP3 reproduces the
diurnal cycle of isoprene emission in 2012 (Fig. 6d).Biogenic isoprene emissions have multiple environmental drivers.
MEGAN represents these multiple environmental drivers using anemission activity factor γ that accounts for multiple emission activityfactors. The relationships among biogenic isoprene emissions, Vcmax,and βt for May–September 2012 are illustrated in Fig. 7. The timeperiod includes both non-drought and drought periods. There is a
Fig. 5. Hourly (a, c, and e) and scatter plots (b, d, and f) of measured and modelled isoprene emissions from May to September in 2012. EXP1 results are shown in (a)and (b), EXP2 results are shown in (c) and (d), and EXP3 results are shown in (e) and (f). EXP1, EXP2, and EXP3 are the CLM4.5/MEGAN2.1 simulations defined inSection 3.1. The calculated correlation coefficients, slopes, and intercepts on the scatter plots with a linear fit are 0.81, 1.48, and 0.96 (EXP1); 0.79, 0.55, and 0.51(EXP2); 0.88, 1.005, and 0.42 (EXP3) respectively.
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
75
strong correlation (correlation coefficient > 0.7) between measuredisoprene emissions and γ0 when βt is larger than 0.6 (Fig. 7a). When βtbecomes smaller than 0.6, the correlation tends to become weaker. Thethreshold value of 0.6 for βt used in equation (6) was selected based ona model sensitivity analysis. A range of threshold values was tested inthe model sensitivity analysis, and the value of 0.6 gave the best fitbetween the measured and modelled isoprene emissions at the site.Therefore, it is used as the threshold for the new drought algorithm forisoprene. When more drought-related isoprene measurements at otherlocations become available, this value needs to be evaluated. Similarrelationships were also plotted for the modelled isoprene emissions(Fig. 7b). Again, when drought effect is not considered in MEGAN2.1,the relationship between modelled isoprene and γ0 becomes weakerwhen βt decreases. The correlations between modelled isoprene emis-sions and γ0 or γ0 x Vcmax are stronger than the correlations between themeasured isoprene emissions and γ0 or γ0 x Vcmax (Fig. 7a). This couldbe explained by the variations in measured biogenic isoprene emissionthat are not accounted for by this simple model, the imperfect re-presentation of physical mechanisms in CLM4.5, and uncertainties inthe observations including variability due to changes in the measure-ment footprint caused by shifts in wind speed and direction. TheMEGAN3 drought response activity factor follows the behavior of theobserved isoprene response to drought expected for the reducedavailability of carbon substrates from photosynthesis. This suggests that
the use of Vcmax and βt in calculating the isoprene emission droughtactivity factor could help improve the accuracy of the total emissionactivity factor in MEGAN during non-drought and drought periods.
Biogenic isoprene emissions are known to be highly temperaturesensitive. Isoprene emission increases and then declines with increasingtemperature. The temperature maximum for isoprene emission is typi-cally between 35 °C and 44 °C (Singsaas and Sharkey, 2000) but can beas high as 50 °C for desert plants growing under hot conditions (Geronet al., 2006). Fig. 7c and d shows that isoprene emissions increase ex-ponentially with vegetation canopy temperature when there is no se-vere drought (βt > 0.6). As the drought intensifies (βt <=0.6), iso-prene emissions start to decline when the vegetation canopytemperature is above 37 °C. The strong exponential relationship be-tween isoprene emission and vegetation temperature also tends to di-minish. Overall, the modelled (Fig. 7d) relationship among isoprene,vegetation canopy temperature, and βt (Fig. 7d) is similar to the re-lationship when measured isoprene emissions are used (Fig. 7c), alsosuggesting satisfactory model agreement with fluxes when the newdrought activity algorithm is used for estimating isoprene emissions.
3.2. Global simulations
To further understand the potential global scale impacts of droughton isoprene emissions, two two-degree offline global CLM4.5/MEGAN
Fig. 6. Top panels show hourly (a) and diurnal (b) isoprene emissions from May to September in 2011 simulated using the new drought activity factor in MEGAN3(EXP3). Bottom panels show hourly (c) and diurnal (d) isoprene emissions in 2012 simulated using the new drought activity factor in MEGAN3 (EXP3).
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
76
Fig. 7. Top panels show scatter plots of measured (a) and modelled (b) biogenic isoprene (mg m−2hr−1) versus MEGAN isoprene emission activity factor γ or γ xVcmax and βt. Bottom panels show scatter plots of measured (c) and modelled (d) isoprene emissions versus vegetation temperature and βt. The modelled biogenicisoprene emissions are from MEGAN2.1 when no drought effect is considered or from MEGAN3.0 when the new drought activity factor is used.
Fig. 8. Model calculated drought activity factor γd (left column, unitless) and changes in isoprene emissions (right column, mg m−2hr−1) in 2007–2013.
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
77
runs (EXP-NO, EXP-NEW) were carried out for the years 2007–2013.This period covers a few severe drought events that occurred in dif-ferent parts of the world. Both runs use CLM4.5 with the MEGAN2.1framework. The EXP-NO run does not include any drought effect, andthe EXP-NEW run considers the drought effect on isoprene using theMEGAN3 drought algorithm. The meteorology forcing data for theglobal runs are CRU-NCEP reanalysis (Piao et al., 2012). The CLM4.5was run in CLM-SP mode. A similar spin-up approach applied for thesingle point simulations was utilized to prepare initial conditions forCLM4.5 in the global simulations.
Fig. 8 shows multi-year (2007–2013) seasonal-averaged droughtactivity factor γd simulated in EXP-NEW and resulting changes in iso-prene emissions. The drought activity factor affects isoprene emissionsin the model when βt falls below 0.6. It can be seen that the drought
impacts on isoprene emissions occur in several regions where a largeamount of isoprene emission is expected (Guenther et al., 2012). Theseareas include the Amazon, the southeast US, Australia, and South Asia.The corresponding changes in isoprene emissions simulated betweenEXP-NO and EXP-NEW show that the predicted drought effect reductionin isoprene emissions in these places can exceed 15% (Fig. 8b). Glob-ally, drought results in an average of ∼17% reduction in isopreneemissions.
To explore how drought impacts isoprene emissions over time, thetime-series plots of isoprene emission with and without drought effectare shown for three regions in the world (Fig. 9). A 42% reduction inisoprene emission is estimated in 2012 for the Missouri area in the US,which was observed in the field measurements. In other years when nodroughts were observed, the modelled isoprene emissions in EXP-NEW
Fig. 9. Time series of monthly isoprene emission (mg m−2hr−1) simulated without the drought effect and with the new drought activity factor in MEGAN inMissouri, US (a), Central Amazon (b), and Australia (c) from 2007 to 2013.
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
78
are the same as those in EXP-NO. The figure shows that the droughtalgorithm reduces year to year variations in isoprene emission incomparison to the no drought effect predictions of much higher iso-prene emissions in hot, sunny and dry years (EXP-NO). This indicatesthat the MEGAN2.1 algorithm without drought effect misses thedrought-induced emission reductions in some regions, leading tooverestimates in isoprene emission. In the central Amazon area, thedrought algorithm results in an 11% reduction in isoprene emissions forall years. The Amazon Basin, which contains the world's largest rainforest, is thought to be the largest global isoprene source. The recentdrying trend in this area (Lewis et al., 2011) does impact model si-mulated isoprene emissions with the new drought algorithm. Anotherarea that has been impacted by droughts is Australia. When the droughteffect is considered in the model, a small reduction in isoprene emis-sions is simulated throughout the simulation years.
3.3. Implications for ozone chemistry
Isoprene is highly reactive with ozone, hydroxyl radicals (OH), andnitrate radicals (NOx). In forested regions, isoprene photooxidation is amajor driver of atmospheric chemistry. Thus changes in isopreneemissions could have profound impacts on tropospheric OH and ozone.Although the influence of isoprene emission on OH and ozone is still inquestion (Liu et al., 2016), the sensitivity simulations presented in thisstudy are used to assess the potential impacts of drought on ozone andOH due to changes in isoprene emissions. Isoprene oxidation occursmostly in the atmospheric mixed layer, although entrainment and re-action in the free troposphere can also be important. Here, only theimpacts on the near surface ozone and OH are examined. We performedtwo six-month (June to December 2010) global simulations using CAM-Chem-CLM4.5-MEGAN2.1 at two-degree spatial resolution(1.9°× 2.5°). The version 4 of CAM-Chem is used in this study(Lamarque et al., 2012). The anthropogenic emissions for 2010 arefrom the POET (Precursors of Ozone and their Effects in the
Fig. 10. Coupled CESM model simulated changes in biogenic isoprene emissions (mg m−2hr−1) from June to November in 2010 with the new drought activity factorin MEGAN.
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
79
Troposphere) database for 2000 (Granier et al., 2005) and fire emis-sions are from FINN (Fire INventory from NCAR) (Wiedinmyer et al.,2011). The two experiments differ only in the use of a drought activityfactor. Experiment Chem-NO does not include the drought effect onisoprene emissions, while experiment Chem-NEW uses the newMEGAN3 drought algorithm. The meteorology forcing for CAM-Chem isgenerated with the Goddard Earth Observing System (GEOS) atmo-spheric model and data assimilation system (Rienecker et al., 2011).Other forcing data include sea surface temperatures, aerosol, andgreenhouse gas emissions for the year 2010. Initial conditions forCLM4.5 are from the offline CLM4.5 spin-up run described above. In thesimulations, no feedback between biogenic emissions and atmosphere isincluded so that the impacts of changes in isoprene emissions on ozoneand OH chemistry can be assessed.
The overall pattern of modelled global changes in biogenic isopreneemissions for the year 2010 (Fig. 10) is similar to the pattern of multi-year averages (Fig. 8). The changes in ozone concentrations due tochanges in biogenic isoprene emissions are shown in Fig. 11. In the
relatively pristine rainforest environment (NOx limited), where ozoneconcentrations are low, the reduction in isoprene emissions is asso-ciated with an increase in ozone in these regions (i.e. rainforests in theAmazon and Congo basins). In Europe and North America (VOC-lim-ited), the reduction in isoprene emissions leads to an approximately3 ppbv reduction in near surface ozone concentrations. The results showthat a reduction in isoprene emission can have a positive or negativeeffect on ozone concentrations. In areas where an increase in ozoneconcentrations is predicted, a negative sensitivity of ozone to isopreneemission is observed owing to NOx-limited and VOC-rich conditions(i.e., Squire et al., 2015) where isoprene is primarily a sink for ozone.The resulting changes in OH (Fig. 12) due to the reduction in isopreneemissions are very similar in the majority of the areas, with slight in-creases in the near surface concentrations associated with lower iso-prene emissions resulting in reduced reaction of isoprene with OH al-lowing an increase in OH. It should be noted that the impacts ofisoprene on the formation of SOA are not included in the current si-mulations. The sensitivity experiments demonstrate the importance of
Fig. 11. CAM-Chem simulated changes in surface O3 (ppbv) due to changes in biogenic isoprene emissions caused by the drought effect from June to November in2010.
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
80
considering the drought stress impact on biogenic isoprene emissionsfor global atmospheric chemistry simulations.
4. Discussion and conclusions
Previous studies have shown that short-term mild drought stressaffects stomatal conductance and thus the rate of photosynthesis butdoes not diminish isoprene emission (Fall and Monson, 1992;Niinemets, 2010), instead mild drought increases isoprene emissionsdue to an increase in leaf temperature and possibly other factors(Pegoraro et al., 2004; Sharkey and Loreto, 1993). Severe drought orprolonged moderate drought does result in significant reductions inisoprene emissions presumably due to decreased leaf carbon avail-ability following sustained reductions in photosynthetic rate(Bruggemann and Schnitzler, 2002; Funk et al., 2005; Sharkey andLoreto, 1993). Clearly, there is a strong connection between photo-synthesis and isoprene emissions (Niinemets et al., 1999; Grote andNiinemets, 2008; Monson et al., 2012; Grote et al., 2014). The new
MEGAN3 drought algorithm presented in this study allows the responseof isoprene to changes in photosynthesis and water stress. The use ofVcmax allows isoprene emission to respond to changes in photosynthesisunder drought conditions. The water stress factor, βt, further controlsthe onset of isoprene emission drop. In this study, the threshold of 0.6for βt is used. The use of the water stress factor allows isoprene emissionto continue the dependence on other emission activity factors when it isstill above the threshold value. Therefore, there is a lag between iso-prene emission and photosynthetic rate. When βt is below the thresholdvalue, isoprene emission is tightly coupled with photosynthesis. Zhenget al. (2015) found that MEGAN with the soil moisture algorithm pro-duces a tight linkage between isoprene emission variability and pho-tosynthesis.
The new method proposed in this study to represent the droughteffect on biogenic isoprene emissions improves the simulation of iso-prene emissions under the severe drought stress, and reproduces theemissions when the drought is mild at MOFLUX. The change in pre-dicted isoprene emissions has important implications for reducing the
Fig. 12. CAM-Chem simulated changes in surface OH (0.01*pptv) due to changes in biogenic isoprene emissions caused by the drought effect from June to Novemberin 2010.
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
81
uncertainties in model simulations and advancing the understanding ofthe interactions between ecosystem functioning and climate change.Despite the lack of field measurements to constrain the isoprene emis-sion, we argue that the isoprene emission variation due to drought canbe simulated as a response to water availability and photosynthesis.Although our understanding of the controlling mechanisms is in-complete, the emission algorithm proposed here could improve modelpredictions. However, there are canopy-scale field data available fromonly one temperate forest site for assessing isoprene emission – droughteffects. Long-term monitoring of isoprene emissions in water-limitedecosystems is needed to improve our understanding of the factorscontrolling biogenic isoprene fluxes. Future canopy-scale studies of theresponse of isoprene to multiple stresses and additional drought stresscases are needed to further improve model simulations of isopreneemission. The MEGAN3 drought algorithm developed in this study hasonly been tested for biogenic isoprene emission, but may be useful formodelling emissions of other BVOCs that are dependent on recentlyfixed carbon. However, additional measurements are needed to assessthis algorithm for other biogenic emissions. It should be noted that thisstudy is intended to be the initial step towards a more mechanisticrepresentation of drought impacts on BVOCs in MEGAN. We expect thatthis will ultimately lead to a better representation of a wide range ofstress response even if there is not a major improvement over the oldalgorithm. The global sensitivity simulations presented in this studyprovide us with some indication of the potential drought impacts onatmospheric chemistry and demonstrate the need to pursue this further.
Author contributions
The manuscript was written through contributions of all authors. Allauthors have given approval to the final version of the manuscript.
Acknowledgments
X. Jiang and A. Guenther were supported by National ScienceFoundation (NSF) Atmospheric Chemistry program award AGS-1643042 and National Aeronautics and Space Administration (NASA)Atmospheric Composition Campaign Data Analysis and Modeling(ACCDAM) program award NNX15AT62G. Isoprene flux measurementsperformed by Mark Potosnak during 2011 were supported by theNational Science Foundation through a Collaborative Research awardentitled Biogenic Volatile Organic Compound Emissions from theTundra and Arctic Atmospheric Chemistry (1025948). We would like toacknowledge the use of computational resources (http://dx.doi.org/10.5065/D6RX99HX) at the NCAR-Wyoming Supercomputing Centerprovided by the National Science Foundation and the State ofWyoming, and supported by NCAR's Computational and InformationSystems Laboratory. The CESM model is supported by the NationalScience Foundation and the Office of Science (BER) of the U.S.Department of Energy. The views expressed in this article are those ofthe author(s) and do not necessarily represent the views or policies ofthe U.S. Environmental Protection Agency.
References
Arneth, A., Niinemets, Ü., Pressley, S., Bäck, J., Hari, P., Karl, T., Noe, S., Prentice, I.C.,Serça, D., Hickler, T., Wolf, A., Smith, B., 2007. Process-based estimates of terrestrialecosystem isoprene emissions: incorporating the effects of a direct CO2-isoprene in-teraction. Atmos. Chem. Phys. 7, 31–53.
Arneth, A., Monson, R.K., Schurgers, G., Niinemets, Ü., Palmer, P.I., 2008. Why are es-timates of global terrestrial isoprene emissions so similar (and why is this not so formonoterpenes)? Atmos. Chem. Phys. 8, 4605–4620.
Bahari, Z., Pallardy, S., Parker, W.C., 1985. Photosynthesis, water relations and droughtadaptation in six woody species of oak-hickory forests in central Missouri. For. Sci.31, 557–569.
Baldocchi, et al., 2001. FLUXNET: a new tool to study the temporal and spatial variabilityof ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am.Meteorol. Soc. 82, 2415–2434.
Beckett, M., Loreto, F., Velikova, V., Brunetti, C., Di Ferdinando, M., Tattini, M., Farrant,
J.M., 2012. Photosynthetic limitations and volatile and non-volatile isoprenoids inthe poikilochlorophyllous resurrection plant Xerophyta humilis during dehydrationand rehydration. Plant Cell Environ. 35, 2061–2074.
Bonan, G.B., 2002. Ecological Climatology: Concepts and Applications. CambridgeUniversity Press, New York, NY.
Bonan, G.B., Lawrence, P.J., Oleson, K.W., Levis, S., Jung, M., Reichstein, M., Lawrence,D.M., Swenson, S.C., 2011. Improving canopy processes in the Community LandModel (CLM4) using global flux fields empirically inferred from FLUXNET data. J.Geophys. Res. 116http://dx.doi.org/10.1029/2010JG001593. G02014.
Brilli, F., Barta, C., Fortunatti, A., Lerdau, M., Loreto, F., Centritto, M., 2007. Response ofisoprene emission and carbon metabolism to drought in white poplar (Populus alba)saplings. New Phytol. 175, 244–254.
Brüggemann, N., Schnitzler, J.P., 2002. Comparison of isoprene emission, intercellularisoprene concentration and photosynthetic performance in water-limited oak(Quercus pubescens Willd. and Quercus robur L.) saplings. Plant Biol. a4, 456–463.
Chen, F., Dudhia, J., 2001. Coupling an advanced land surface–hydrology model with thepenn State–NCAR MM5 modeling system. Part I: model implementation and sensi-tivity. Mon. Wea. Rev. 129, 569–585.
Claeys, M., et al., 2004. Formation of secondary organic aerosols through photooxidationof isoprene. Science 303, 1173–1176.
Clapp, R.B., Hornberger, G.M., 1978. Empirical equations for some soil hydraulic prop-erties. Water Resour. Res. 14, 601–604.
Collatz, G.J., Ball, J.T., Grivet, C., Berry, J.A., 1991. Physiological and environmentalregulation of stomatal conductance, photosynthesis and transpiration: a model thatincludes a laminar boundary layer. Agric. For. Meteorol. 54, 107–136.
Collatz, G.J., Ribas-Carbo, M., Berry, J.A., 1992. Coupled photosynthesisstomatal con-ductance model for leaves of C4 plants. Aust. J. Plant Physiol. 19, 519–538.
Critchfield, H., 1966. General Climatology. Prentice-Hall, Englewood Cliffs, NJ.De Swaef, T., Steppe, K., 2010. Linking stem diameter variations to sap flow, turgor and
water potential in tomato. Funct. Plant Biol. 37, 429–438.Derwent, R.G., Jenkin, M.E., Passant, N.R., Pilling, M.J., 2007. Photochemical ozone
creation potentials (POCPs) for different emission sources of organic compoundsunder European conditions estimated with a Master Chemical Mechanism. Atmos.Environ. 41, 2570–2579.
Fall, R., Monson, R.K., 1992. Isoprene emission rate and intercellular isoprene con-centration as influenced by stomatal distribution and conductance. Plant Physiol.100, 987–992.
Farquhar, G.D., von Caemmerer, S., Berry, J.A., 1980. A biochemical model of photo-synthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90.
Folberth, G.A., Hauglustaine, D.A., Lathiere, J., Brocheton, F., 2006. Interactive chemistryin the Laboratoire de Met' eorologie Dy- namique general circulation model: modeldescription and impact analysis of biogenic hydrocarbons on tropospheric chemistry.Atmos. Chem. Phys. 6, 2273–2319.
Funk, J.L., Jones, C.G., Gray, D.W., Throop, H.L., Hyatt, L.A., Lerdau, M.T., 2005.Variation in isoprene emission from Quercus rubra: sources, causes, and con-sequences for estimating. J. Geophys. Res. Atmos. 110. http://dx.doi.org/10.1029/2004JD005229.
Granier, C., Guenther, A., Lamarque, J., Mieville, A., Muller, J., Olivier, J., Orlando, J.,Peters, J., Petron, G., Tyndall, G., Wallens, S., 2005. POET, a Database of SurfaceEmissions Ofozone Precursors, Available at. http://www.aero.jussieu.fr/projet/ACCENT/POET.php.
Gent, P.R., Danabasoglu, G., Donner, L.J., Holland, M.M., Hunke, E.C., Jayne, S.R.,Lawrence, D.M., Neale, R.B., Rasch, P.J., Vertenstein, M., Worley, P.H., Yang, Z.-L.,Zhang, M., 2011. The community climate system model version 4. J. Clim. 24,4973–4991.
Geron, C., Daly, R., Harley, P., Rasmussen, R., Seco, R., Guenther, A., Karl, T., Gu, L.,2016. Large drought-induced variations in oak leaf volatile organic compoundemissions during PINOT NOIR 2012. Chemosphere 146, 8–21.
Geron, C., Guenther, A., Greenberg, J., Karl, T., Rasmussen, R., 2006. Biogenic volatileorganic compound emissions from desert vegetation of the southwestern US. Atmos.Environ. 40, 1645–1660.
Goldstein, A.H., Galbally, I.E., 2007. Known and unexplored organic constituents in theearth's atmosphere. Environ. Sci. Technol. 41, 1514–1521.
Grell, G.A., Peckham, S.E., Schmitz, R., McKeen, S.A., Frost, G., Skamarock, W.C., Eder,B., 2005. Fully coupled “online” chemistry within the WRF model. Atmos. Environ.39, 6957–6975.
Grote, R., Morfopoulos, C., Niinemets, € U, Sun, Z., Keenan, T.F., Pacifico, F., Butler, T.,2014. A fully integrated isoprenoid emissions model coupling emissions to photo-synthetic characteristics. Plant Cell Environ. 37, 1965–1980.
Grote, R., Niinemets, Ü., 2008. Modeling volatile isoprenoid emissions – a story with splitends. Plant Biol. 10, 8–28.
Grote, R., Lavoir, A.V., Rambal, S., Staudt, M., Zimmer, I., Schnitzler, J.-P., 2009.Modelling the drought impact on monoterpene fluxes from an evergreenMediterranean forest canopy. Oecologia 160, 213–223.
Gu, L., Meyers, T., Pallardy, S.G., et al., 2006. Direct and indirect effects of atmosphericconditions and soil moisture on surface energy partitioning revealed by a prolongeddrought at a temperate forest site. J. Geophys. Res. 111 D16102.
Gu, L., Meyers, T., Pallardy, S.G., et al., 2007. Influences of biomass heat and biochemicalenergy storages on the land surface fluxes and radiative temperature. J. Geophys.Res. 112 D02107.
Gu, L., Pallardy, S.G., Hosman, K.P., Sun, Y., 2015. Drought-influenced mortality of treespecies with different predawn leaf water dynamics in a decade-long study of acentral US forest. Biogeosciences 12, 2831–2845.
Guenther, A., 2013. Biological and chemical diversity of biogenic volatile organic emis-sions into the atmosphere. ISRN Atmos. Sci. 786290.
Guenther, A.B., Jiang, X., Heald, C.L., Sakulyanontvittaya, T., Duhl, T., Emmons, L.K.,Wang, X., 2012. The model of emissions of gases and aerosols from nature version 2.1(MEGAN2.1): an extended and updated framework for modeling biogenic emissions.Geosci. Model Dev. (GMD) 5, 1471–1492.
Guenther, A., Monson, R., Fall, R., 1991. Isoprene and monoterpene emission rate
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
Heald, C.L., Wilkinson, M.J., Monson, R.K., Alo, C.A., Wang, G., Guenther, A., 2009.Response of isoprene emission to ambient CO2 changes and implications for globalbudgets. Global Change Biol. 15, 1127–1140.
Huang, L., McGaughey, G., McDonald-Buller, E., Kimura, Y., Allen, D.T., 2015.Quantifying regional, seasonal and interannual contributions of environmental fac-tors on isoprene and monoterpene emissions estimates over eastern Texas. Atmos.Environ. 106, 120–128.
IPCC (Intergovernmental Panel on Climate Change), 2013. Climate change, the physicalscience basis. Summary for policymakers. In: Alexander et al, L. (Ed.), (IPCCSecretariat, Geneva, Switzerland, http://dx.doi.org/10.1017/CBO9781107415324.
Jiang, X., Rauscher, S.A., Ringler, T.D., Lawrence, D.M., Williams, A.P., Allen, C.D.,Steiner, A.L., Cai, D.M., McDowell, N.G., 2013. Projected future changes in vegeta-tion in Western North America in the twenty-first century. J. Clim. 26, 3671–3687.
Jiang, X.Y., Yang, Z.L., Liao, H., Wiedinmyer, C., 2010. Sensitivity of biogenic secondaryorganic aerosols to future climate change at regional scales an online coupled si-mulation. Atmos. Environ. 44, 4891–4907.
Lamarque, J.-F., Emmons, L.K., Hess, P.G., Kinnison, D.E., Tilmes, S., Vitt, F., Heald, C.L.,Holland, E.A., Lauritzen, P.H., Neu, J., Orlando, J.J., Rasch, P.J., Tyndall, G.K., 2012.CAM-chem: description and evaluation of interactive atmospheric chemistry in theCommunity Earth System Model. Geosci. Model Dev. 5, 369–411.
Lamb, B., Gay, D., Westberg, H., Pierce, T., 1993. A biogenic hydrocarbon emission in-ventory for the United States using a simple forest canopy model. Atmos. Environ.27A, 1673–1690.
Lewis, S.L., Brando, P.M., Phillips, O.L., van der Heijden, G.M.F., Nepstad, D., 2011. The2010 Amazon drought. Science 331, 554.
Limousin, J.M., Misson, L., Lavoir, A.V., Martin, N.K., Rambal, S., 2010. Do photo-synthetic limitations of evergreen Quercus ilex leaves change with long-term in-creased drought severity? Plant Cell Environ. 33, 863–875.
Liu, Y., Brito, J., Dorris, M.R., Rivera-Rios, J.C., Seco, R., Bates, K.H., Artaxo, P., Duvoisin,S., Keutsch, F.N., Kim, S., Goldstein, A.H., Guenther, A.B., Manzi, A.O., Souza, R.A.F.,Springston, S.R., Watson, T.B., McKinney, K.A., Martin, S.T., 2016. Isoprene photo-chemistry over the Amazon rainforest. Proc. Natl. Acad. http://dx.doi.org/10.1073/pnas.1524136113.
Martin-StPaul, N.K., et al., 2012. Photosynthetic sensitivity to drought varies amongpopulations of Quercus ilex along a rainfall gradient. Funct. Plant Biol. 39, 25–37.
Martin, S.T., et al., 2016. The Green ocean Amazon experiment (GoAmazon2014/5)observes pollution affecting gases, aerosols, clouds, and rainfall over the rain forest.Bull. Am. Meteorol. Soc. http://dx.doi.org/10.1175/BAMS-D-15-00221.1. 28.
McDowell, N.G., Williams, A.P., Xu, C., Pockman, W.T., Dickman, L.T., Sevanto, S.,Pangle, R., Limousin, J., Plaut, J., Mackay, D.S., Ogee, J., Domec, J.C., Allen, C.D.,Fisher, R.A., Jiang, X., Muss, J.D., Breshears, D.D., Rauscher, S.A., Koven, C., 2015.Multi-scale predictions of massive conifer mortality due to chronic temperature rise.Nat. Clim. Change. http://dx.doi.org/10.1038/NCLIMATE2873.
Monson, R.K., Grote, R., Niinemets, Ü., Schnitzler, J.-P., 2012. Modeling the isopreneemission rate from leaves. New Phytol. 195, 541–559.
Monson, R.K., Harley, P.C., Litvak, M.E., Wildermuth, M., Guenther, A.B., Zimmerman,P.R., Fall, R., 1994. Environmental and developmental controls over the seasonalpattern of isoprene emission from aspen leaves. Oecologia 99, 260–270.
Montané, F., Fox, A.M., Arellano, A.F., MacBean, N., Alexander, M.R., Dye, A., Bishop,D.A., Trouet, V., Babst, F., Hessl, A.E., Pederson, N., Blanken, P.D., Bohrer, G., Gough,C.M., Litvak, M.E., Novick, K.A., Phillips, R.P., Wood, J.D., Moore, D.J.P., 2017.Evaluating the effect of alternative carbon allocation schemes in a land surface model(CLM4.5) on carbon fluxes, pools, and turnover in temperate forests. Geosci. ModelDev. 10, 3499–3517.
Muller, J.-F., Stavrakou, T., Wallens, S.€, et al., 2008. Global isoprene emissions esti-mated using MEGAN, ECMWF analyses and a detailed canopy environment model.Atmos. Chem. Phys. 8, 1329–1341.
Niinemets, Ü., Tenhunen, J.D., Harley, P.C., Steinbrecher, R., 1999. A model of isopreneemission based on energetic requirements for isoprene synthesis and leaf photo-synthetic properties for Liquidambar and Quercus. Plant Cell Environ. 22,1319–1335.
Niinemets, U., 2010. Mild versus severe stress and BVOCs: thresholds, priming andcon-sequences. Trends Plant Sci. 15, 145–153.
Oleson, K.W., Lawrence, D.M., Bonan, G.B., Drewniak, B., Huang, M., Koven, C.D., Levis,S., Li, F., Riley, W.J., Subin, Z.M., Swenson, S.C., Thornton, P.E., Bozbiyik, A., Fisher,R., Kluzek, E., Lamarque, J.-F., Lawrence, P.J., Leung, L.R., Lipscomb, W., Muszala,S., Ricciuto, D.M., Sacks, W., Sun, Y., Tang, J., Yang, Z.-L., 2013. TechnicalDescription of Version 4.5 of the Community Land Model (CLM). Ncar Technical NoteNCAR/TN-503+STR. National Center for Atmospheric Research, Boulder, CO.http://dx.doi.org/10.5065/D6RR1W7M.
Pacifico, F., Folberth, G.A., Jones, C.D., Harrison, S.P., Collins, W.J., 2012. Sensitivity ofbiogenic isoprene emissions to past, present, and future environmental conditionsand implications for atmospheric chemistry. J. Geophys. Res. 117http://dx.doi.org/10.1029/2012JD018276. D22302.
Park, J.-H., Goldstein, A.H., Timkovsky, J., Fares, S., Weber, R., Karlik, J., Holzinger, R.,2013. Active atmosphere-ecosystem Exchange of the vast majority of detected vola-tile organic compounds. Science 341, 643–647.
Paulot, F., Crounse, J.D., Kjaergaard, H.G., Kürten, A., St Clair, J.M., Seinfeld, J.H.,
Wennberg, P.O., 2009. Unexpected epoxide formation in the gas-phase photooxida-tion of isoprene. Science 325, 730–733.
Pegoraro, E., Rey, A., Greenberg, J., Harley, P., Grace, J., Malhi, Y., Guenther, A., 2004.Effect of drought on isoprene emission rates from leaves of Quercus virginiana Mill.Atmos. Environ. 38, 6149–6156.
Peñuelas, J., Staudt, M., 2010. BVOCs and global change. Trends Plant Sci. 15, 133–144.Petron, G., Harley, P., Greenberg, J., Guenther, A., 2001. Seasonal temperature variations
influence isoprene emission. Geophys. Res. Lett. 28 (9), 1707–1710.Piao, et al., 2012. The carbon budget of terrestrial ecosystems in East Asia over the last
Guenther, A.B., 2014. Observed and modeled ecosystem isoprene fluxes from an oak-dominated temperate forest and the influence of drought stress. Atmos. Environ. 84,314–322.
Rauscher, S.A., Jiang, X., Steiner, A., Williams, A.P., Cai, D.M., McDowell, N.G., 2015. Seasurface temperature warming patterns and future vegetation change. J. Clim. 28,7943–7961.
Rienecker, M.M., Suarez, M.J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich,M.G., Schubert, S.D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty,A., da Silva, A., Gu, W., Joiner, J., Koster, R.D., Lucchesi, R., Molod, A., Owens, T.,Pawson, S., Pegion, P., Redder, C.R., Reichle, R., Robertson, F.R., Ruddick, A.G.,Sienkiewicz, M., Woollen, J., 2011. MERRA: NASA's modern-era retrospective ana-lysis for Research and application. J. Clim. 24, 3624–3648.
Saveyn, A., Steppe, K., Lemeur, R., 2007. Drought and the diurnal patterns of stem CO2efflux and xylem CO2 concentration in young oak (Quercus robur). Tree Physiol. 27,365–374.
Saxton, J.E., Lewis, A.C., Kettlewell, J.H., Ozel, M.Z., Gogus, F., Boni, Y., Korogone,S.O.U., Serça, D., 2007. Isoprene and monoterpene measurements in a secondaryforest in northern Benin. Atmos. Chem. Phys. 7, 4095–4106.
Seco, R., Karl, T., Guenther, A., Hosman, K.P., Pallardy, S.G., Gu, L., Geron, C., Harley, P.,Kim, S., 2015. Ecosystem-scale volatile organic compound fluxes during an extremedrought in a broadleaf temperate forest of the Missouri Ozarks (central USA). Glob.Change Biol. 21, 3657–3674.
Sharkey, T.D., Singsaas, E.L., Lerdau, M.T., Geron, C.D., 1999. Weather effects on iso-prene emission capacity and applications in emissions algorithms. Ecol. Appl. 9,1132–1137.
Sharkey, T.D., Loreto, F., 1993. Water stress, temperature, and light effects on the ca-pacity for isoprene emission and photosynthesis of kudzu leaves. Oecologia 95,328–333.
Šimpraga, M., Verbeeck, H., Demarcke, M., Joo, E., Pokorska, O., Amelynck, C., Schoon,N., Dewulf, J., Van Langenhove, H., Heinesch, B., Aubinet, M., Laffineur, Q., Müller,J.F., Steppe, K., 2011. Clear link between drought stress, photosynthesis and biogenicvolatile organic compounds in Fagus sylvatica L. Atmos. Environ. 45 (30),5254–5259.
Sindelarova, K., Granier, C., Bouarar, I., Guenther, A., Tilmes, S., Stavrakou, T., Müller, J.-F., Kuhn, U., Stefani, P., Knorr, W., 2014. Global data set of biogenic VOC emissionscalculated by the MEGAN model over the last 30 years. Atmos. Chem. Phys. 14,9317–9341.
Singsaas, E.L., Sharkey, T.D., 2000. The effects of high temperature on isoprene synthesisin oak leaves. Plant Cell Environ. 23 (7), 751–757.
Squire, O.J., Archibald, A.T., Abraham, N.L., Beerling, D.J., Hewitt, C.N., Lathière, J.,Pike, R.C., Telford, P.J., Pyle, J.A., 2014. Influence of future climate and croplandexpansion on isoprene emissions and tropospheric ozone. Atmos. Chem. Phys. 14,1011–1024.
Squire, O.J., Archibald, A.T., Griffiths, P.T., Jenkin, M.E., Smith, D., Pyle, J.A., 2015.Influence of isoprene chemical mechanism on modelled changes in troposphericozone due to climate and land use over the 21st century. Atmos. Chem. Phys. 15,5123–5143.
Tawfik, A.B., St€ockli, R., Goldstein, A., Pressley, S., Steiner, A.L., 2012. Quantifying thecontribution of environmental factors to isoprene flux interannual variability. Atmos.Environ. 54, 216–224.
Tingey, D., 1981. The effect of environmental factors on the emission of biogenic hy-drocarbons from live oak and slash pine. Atmos. Biog. Hydrocarbons 53–72.
Unger, N., Harper, K., Zheng, Y., Kiang, N.Y., Aleinov, I., Arneth, A., Schurgers, G.,Amelynck, C., Goldstein, A., Guenther, A., Heinesch, B., Hewitt, C.N., Karl, T.,Laffineur, Q., Langford, B., McKinney, K.A., Misztal, P., Potosnak, M., Rinne, J.,Pressley, S., Schoon, N., Serça, D., 2013. Photosynthesis-dependent isoprene emissionfrom leaf to planet in a global carbon-chemistry-climate model. Atmos. Chem. Phys.13, 10243–10269.
Wiedinmyer, C., et al., 2001. A land use database and examples of biogenic isopreneemission estimates for the state of Texas. USA, Atmos. Environ. 35, 6465–6477.
Wiedinmyer, C., Tie, X., Guenther, A., Neilson, R., Granier, C., 2006. Future changes inbiogenic isoprene emissions: how might they affect regional and global atmosphericchemistry? Earth Interact. 3, 10.
Wiedinmyer, C., Akagi, S.K., Yokelson, R.J., Emmons, L.K., AlSaadi, J.A., Orlando, J.J.,Soja, A.J., 2011. The Fire Inventory from NCAR (FINN), a high resolution globalmodel to estimate the emissions from open burning,. Geosci. Model Dev. 4, 625–641.
Zheng, Y., Unger, N., Barley, M., Yue, X., 2015. Relationships between photosynthesis andformaldehyde as a probe of isoprene emission. Atmos. Chem. Phys. 15, 8559–8576.
Zhou, S.X., Medlyn, B., Sabaté, S., Sperlich, D., Prentice, I.C., 2014. Short-term waterstress impacts on stomatal, mesophyll and biochemical limitations to photosynthesisdiffer consistently among tree species from contrasting climates. Tree Physiol. 34,1035–1046.
X. Jiang et al. Atmospheric Environment 183 (2018) 69–83
