Geophysical Research Letters

RESEARCH LETTER10.1002/2014GL059279

Key Points:• Coupling clouds and vegeta-

tion leads to less extreme valuescloud properties

• Time adjustment of the stomaleads to changes in the surfaceflux variability

• Systematic large eddy simulationscoupled to photosynthesis andstoma aperture

Supporting Information:• Figures S1–S6• Tables S1–S5

Correspondence to:J. Vilà-Guerau de Arellano,jordi.vila@wur.nl

Citation:Vilà-Guerau de Arellano, J., H. G.Ouwersloot, D. Baldocchi, and C. M. J.Jacobs (2014), Shallow cumulus rootedin photosynthesis, Geophys. Res. Lett.,41, doi:10.1002/2014GL059279.

Received 17 JAN 2014

Accepted 10 FEB 2014

Accepted article online 15 FEB 2014

Shallow cumulus rooted in photosynthesisJordi Vilà-Guerau de Arellano1, Huug G. Ouwersloot1,2, Dennis Baldocchi3, and Cor M. J. Jacobs4

1Meteorology and Air Quality Section, Wageningen University, Wageningen, Netherlands, 2Max Planck Institute forChemistry, Mainz, Germany, 3Department of Environmental Science, Policy and Management, University of California,Berkeley, California, USA, 4Climate Change and Adaptive Land and Water Management, Wageningen UR Alterra

Abstract We study the interactions between plant evapotranspiration, controlled by photosynthesis(C3 and C4 grasses), and moist thermals responsible for the formation of shallow cumulus clouds (SCu). Ourfindings are based on a series of systematic numerical experiments at fine spatial and temporal scales usinglarge eddy simulations explicitly coupled to a plant-physiology model. The shading provided by SCu leadsto strong spatial variability in photosynthesis and the surface energy balance. This in turn results in SCucharacterized by less extreme and less skewed values of liquid water path. The larger water use efficiencyof C4 grass leads to two opposite effects that influence boundary layer clouds: more vigorous and deeperthermals due to the larger buoyancy surface flux (positive effect) characterized by less moisture content(negative). We find that under these midlatitude and well-watered soil conditions, SCu are characterized bya larger cloud cover and liquid water path over C4 grass fields.

1. Introduction

Following the pioneer observational study by LeMone and Pennel [1976], who identified moist thermals asthe “roots” of shallow cumulus (SCu), we study the coupling between plant evapotranspiration and SCuformed over land using the large eddy simulation (LES) technique. In doing so, we explicitly capture the rel-evant processes for the interactions between vegetation and atmosphere characterized by temporal scalesranging from seconds to hours. Moist thermals in the convective boundary layer (CBL) control the shallowcumulus dynamics. Over land covered mainly by vegetation, these thermals are highly dependent on thepartitioning of the available energy in latent and sensible heat flux. Photosynthesis affects this partition byregulating the plant loss of water during their uptake of carbon dioxide (CO2). These surface processes play akey role in the transition between clear to cloudy boundary layers, and the development of deep convection[Zhang and Klein, 2010; Garcia-Carreras et al., 2011; Adams et al., 2013]. In regional models, the represen-tation of these processes, and more specifically the transition of the dry CBL to shallow and subsequentlydeep convection, is still a major source of uncertainty [Rio et al., 2009]. This is even more complex to under-stand and represent when accounting for the coupling between vegetation and atmospheric conditions[Doutriaux-Boucher et al., 2008; Vilà-Guerau de Arellano et al., 2012].

We focus on two related research hypotheses concerning the feedback between vegetation and SCucharacteristics. They are as follows:

1. Cloud radiation perturbation and the subsequent stomatal aperture response, either reacting instanta-neously to the perturbation or with a time adjustment, influence the dynamics of SCu.

2. C4 plants have lower conductances to water and CO2 exchange and will generate more sensible heat flux.Consequently, the development of boundary layer clouds over C4 grasslands will differ from C3 grass.

We approach these questions by explicitly resolving the feedbacks between the surface carbon, water andenergy budgets, and the three-dimensional thermodynamic fields calculated by means of LES. We followthe seminal study by Albertson et al. [2001] to describe the coupling through a stomatal resistance thatregulates the carbon-water exchange at plant canopy level. As recently reviewed by Katul et al. [2012],atmospheric LES with submodel representations of canopy processes opens up the possibility of interac-tive studies on how short-term fluctuations (∼ seconds), governed by solar radiation, temperature, andvapor pressure differences, modify the carbon and surface energy balance. Most of these earlier LES studiesfocused on the canopy and just above it [see Katul et al., 2012, and references therein], and little attentionhas been paid to how processes occurring at the top of the CBL influence and are influenced by the vege-tation processes. Previous studies that investigated the relation of moist thermals to the surface conditions,

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Geophysical Research Letters 10.1002/2014GL059279

e.g., Lohou and Patton [2014], did not account explicitly for vegetation feedbacks. Our aim is to extend thesestudies and quantify how the vegetation-cloud coupling yields spatial and temporal changes in the car-bon and surface energy balance, and in cloud properties such as cloud cover (cc) and liquid water path(LWP). By focusing on the differences between the C3 and C4 photosynthesis pathways, we further studyhow the variations of water use efficiency at canopy level [Baldocchi, 1994] leads to changes in the cloudyCBL dynamics.

2. Impact of Cloud Shading

Three numerical experiments, concerning SCu formed over soil that is covered for 90% by C3 grass (10%bare soil) with a leaf area index of 2, enable us to study the role of cloud shading on the surface fluxes. Ourgrass field covers an area of 12.8 × 12.8 km2, and the three-dimensional atmospheric fields are numericallysolved with horizontal and vertical resolutions of 50 m and 12 m, respectively. The vegetation and atmo-spheric conditions are representative of a late summer day at midlatitudes (local time is UTC+2). The threenumerical experiments differ only in the following process: TRA simulates radiatively transparent SCu (bluelines in the figures), SHD activates the shading of SCu (black), and LAG (red) is similar to SHD but now witha noninstantaneous response of the canopy stomatal resistance to atmospheric disturbances (see Table S1in the supporting information). The simulations are made using the Dutch Atmospheric Large Eddy Sim-ulation model (DALES) [Heus et al., 2010] coupled to a submodel for photosynthesis (An) and for canopystomatal resistance (rs). In our simulation framework, we name it the A − rs submodel [Jacobs and de Bruin,1997; Ronda et al., 2001]. Tables S2–S4 in the supporting information include the numerical settings for thesoil, plant, and atmospheric conditions. Cloud water content is calculated using an “all or nothing” micro-physical scheme (see supporting information). All experiments reproduce the diurnal transition from clearto cloudy boundary layers, driven by convective turbulence and a geostrophic wind of 5.1 m s−1. The threesimulations are characterized by a daily averaged Bowen ratio of 0.75. SCu clouds (nonprecipitating) formbefore 11 UTC with typical cloud covers ranging from 10 to 15%. The radiation scheme is one-dimensional;the shortwave incoming component depends on time, but the sun is overhead during the entire simulation.Note that we only account for the direct component of the photosynthetically active radiation.

2.1. Instantaneous Stomatal Aperture ResponseThe impacts of cloud shading on instantaneous photosynthesis uptake, latent (LE), and sensible heat fluxes(SH) are shown in Figure 1 for the TRA and SHD experiments. We represent cloud shading by transmissiv-ity in the incoming shortwave radiation that depends on the broadband cloud optical depth (𝜏) [Joseph etal., 1976]. According to Stephens [1978], 𝜏 is a function of the liquid water path. The fall in incoming short-wave radiation has an instantaneous impact on the surface fluxes (e.g., the cloud shadows on the surfaceat (x,y) = (5,9) km). The calculated 𝜏 values are similar to those observed at the Atmospheric Radiation Mea-surement site (http://www.arm.gov) by McFarlane and Grabowski [2007]. As a result, the stomatal resistancevalues increase from 150 s m−1 in the unshaded regions to 700 s m−1 beneath the SCu (see Figure S1). Vari-ations in the incoming shortwave radiation have a direct impact on the skin temperature and therefore thevapor pressure deficit, leading also to changes in the intercellular CO2 concentration (Ci). Our results alsoshow that due to the rapid cooling of the shaded regions, the Ci/CO2 local ratio increases from 0.82 to 0.85,where CO2 is the atmospheric concentration.

In spite of the larger spatial variability of the An, LE, and SH surface fluxes (over 50% differences), the tem-poral evolution of the spatially averaged surface properties and atmospheric dynamics shows very smalldifferences between the three experiments TRA, SHD, and LAG (see Figures S2 and S3). This is due to fil-tering by averaging the temporary local scale radiation perturbations by SCu fields and the subsequentdisturbances of the surface temperature and vapor pressure deficit. Note that due to the mean geostrophicwind (∼5 m s−1) and the limited size of SCu (diameter ∼500 m), these clouds remain only for a short time(∼ minutes) over a single location.

In contrast, the cloud cover and horizontally averaged integrated liquid water path evolutions are sen-sitive to cloud shading. As Figure 2 shows, the fluctuations in these two quantities are larger in the TRAthan SHD experiment, when clouds are more active (between 13.5 and 15 UTC). Cloud base and topheights also show more extreme values in TRA than SHD (Figure S4). The LWP statistics obtained over theentire simulation (Table S5) further corroborate the larger deviations and more skewed values for the TRA

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Geophysical Research Letters 10.1002/2014GL059279

Figure 1. Instantaneous horizontal cross section (SHD experiment) of (a) cloud optical depth (𝜏), (b) canopy CO2 fluxdensity (CO2 assimilation by photosynthesis) (An), (c) surface latent heat flux (LE), and (d) surface sensible heat flux (SH).All the variables were sampled at 14 UTC (16 LT), the time of maximum cloud activity (see Figure 2).

simulation (6.2 LWP skewness compared to 4.6 for SHD). These findings indicate that the drop in theincoming shortwave radiation below the clouds weakens the moist thermals, and therefore, the verticaltransport of moisture. As a result, the explicit coupling between radiation perturbations by clouds andvegetation acts as a modulating factor in the development of SCu.

Figure 2. Temporal evolution of spatially averaged (a) cloud cover (cc)and (b) liquid water path (LWP) for the simulations with transparent(TRA) and shading clouds (SHD).

2.2. Adjustment in StomatalResponse TimeWe further study the sensitivity of ourexperiments to an adjustment in theresponse time of the stomatal aper-ture to atmospheric changes. In DALES,we represent the retarded openingor closing of the stomata by a nudg-ing function toward the instantaneoussteady state stomatal resistance, fol-lowing the approach suggested bySellers et al. [1996] and Vico et al. [2011].For the sake of simplicity, we assumeequal nudging times for opening andclosing, even though the response canbe asymmetric [Ooba and Takahashi,2003]. In our LAG simulation, this time isset to 14 min. Compared to the instan-taneous responses, shown in Figure 1,the adjustment in the stomatal aperture

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Geophysical Research Letters 10.1002/2014GL059279

Figure 3. Time series of cloud optical depth (𝜏), canopy photosynthesis (An), latent heat (LE) flux, and sensible heat (SH)flux recorded at an arbitrary virtual point from 13.6 UTC to 15.0 UTC for the simulations (a) SHD and (b) LAG.

reduces the spatial variability, although the signature of the SCu shading is still noticeable in the instanta-neous cross-section of 𝜏 (Figure S5).

By comparing experiments SHD and LAG, we show in Figure 3 the impact of the time adjustment on thephotosynthesis, latent, and sensible heat fluxes in a virtual measurement site located at an arbitrary point inthe domain from 13.6 to 14 UTC. Note that although SHD and LAG only differ in the adjustment time and thevariables are sampled at the same location, the magnitude and variation in time of surface fluxes and cloudoptical depth vary considerably. This finding confirms our first working hypothesis that modifications tothe stomatal apertures result in changes in turbulence and in cloud properties at shorter time scales. In theSHD simulation with instantaneous response (Figure 3a), we find a near-perfect (anti)correlation betweenthe passing clouds, quantified by 𝜏 , and the responses of An, LE, and SH. The introduction of a lag in theresponse leads to a reduction of the minimum dip for An and LE (Figure 3b), but also results in a lingeringeffect of instantaneous SCu perturbations as shown by the evolution of An and LE between 14.4 and14.6 UTC. This shows that the retarded response of the stomata damps the local and instantaneousextremes, as found in SHD, but still maintains the coupling effect between grass and SCu.

The order of variability in our results is comparable to the 1 min flux measurements of water vapor and CO2

with a scintillometer [Van Kesteren et al., 2013]. In both SHD and LAG experiment, An, LE, and SH are nearlyperfectly correlated, even though An, LE, and SH are differently dependent on radiation, surface and atmo-spheric temperatures, and the vapor pressure deficit. Our results indicate that the sudden drop in incomingshortwave radiation is the dominant process, leading to increase the stomatal resistance by a factor of 4as shown by both observations [see Van Kesteren et al., 2013, Figure 12b2] and our numerical experiments(Figure S1). The subsequent decrease in the skin temperature in the shaded area (sudden decreases of 1 Kor more) further increases the canopy stomatal resistance. The reduction of the vapor pressure deficit couldcounteract the closing of the stomata [Ronda et al., 2001], but our results show this plays a minor role in thecases under study.

3. Shallow Convection Formed Over C4 Grass

In order to explore the sensitivity of shallow convection to the photosynthesis path, we design the C4Pexperiment (green): this is identical to SHD, but with the C3 grass replaced by C4 grass [Ronda et al., 2001].Figure 4a shows the difference in LE and SH between C3 and C4 grasses. The latent heat flux for C4 vegeta-tion is up to 60 W m−2 lower than for C3 grass, leading to an increase of approximately 50% in SH. Due tothe systematic approach of our experiments, the SHD and C4P experimental analyses enable us to confirmthat the shift in the partitioning of energy is solely due to the higher efficiency of C4 plants in assimilatingCO2. As Figure 4b shows, we find larger values of the canopy water use efficiency (WUE), defined as the ratioof the CO2 assimilation flux to the latent heat flux. This An/LE ratio is 1.5 times larger for C4 grass than for

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Geophysical Research Letters 10.1002/2014GL059279

Figure 4. Temporal evolution of the (a) sensible and latent heat fluxes and (b) water use efficiency (WUE) andevaporative fraction (EF) for the simulations SHD (C3 grass) and C4P (C4 grass).

C3 grass. This means that C4 grass is more efficient in the uptake of CO2 while it additionally loses less waterthrough transpiration. The higher values of WUE have a direct impact on the ratio between SH and LE, asexpressed by the lower values of the evaporative fraction, EF=LE/(SH+LE), in the C4P experiment.

Moist thermals, driven by the SH and LE forcing, are responsible for the transport of heat and moisturetoward the levels at which air parcels become saturated. As a result of the shift in surface energy balancepartitioning toward higher SH, thermals over C4 grass are characterized by being more energetic (higherSH) and drier (lower LE) than the thermals formed above C3 grass. To quantify this, we show in Figure 5 thecloud top (a), and the cloud base and mixed-layer (subcloud layer) height (b). These quantities are spatially(slab) averaged variables over the whole clouded part of the domain and are sampled every 60 s. The com-bination of higher SH and lower LE in the C4P experiment leads to a deeper mixed layer (larger turbulenteddies) and a higher cloud base due to higher lifting condensation level (Figure 5b). The clouds also reachdeeper heights over C4 grass.

Figure 5. Temporal evolution of the (a) cloud top (Ct) and (b) cloud base (Cb) and mixed layer height (zi) for the simula-tions SHD (C3 grass) and C4P (C4 grass). The mixed layer height is defined as the altitude at which the virtual potentialtemperature gradient exceeds 50% of its maximum value [Sullivan et al., 1998; Ouwersloot et al., 2011].

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Table 1. Cloud Properties of SHD and C4Pa

Photosynthesis Path cc (%) Max (cc) (%) LWP (g kg−2) cb (m) ct (m) zi (m) wmax (m s−1)

C3 (SHD) 13.0 21.0 7.2 1799 3371 1600 7.2C4 (C4P) 14.6 24.4 9.2 2015 3567 1794 7.7

aThe statistics are calculated over the clouded grid points, i.e., positions with a liquid water content largerthan 0. We sample the DALES data every 60 s. The values were averaged between 10.8 UTC and 15.8 UTC.The spatially averaged cloud cover is cc and max(cc) is its maximum value. LWP is the liquid water path.Respectively, cb and ct are the cloud base and top heights; zi is the depth of the mixed layer and wmax is themaximum vertical velocity.

It follows that the cloud dynamics are significantly affected by C4 grass in two opposite ways. First, the morebuoyant thermals, driven by the higher surface SH, penetrate to higher altitudes and reach lower absolutetemperatures favoring vapor saturation. Second, these thermals transport less moisture. In order to deter-mine which process is dominant, we calculate the main cloud dynamic properties in Table 1 for the SHDand C4P experiments. All statistical properties of the cloud properties show larger values for the C4P case.C4 grass therefore promotes more vigorous thermals which lead to higher average and maximum cloudcover, and cloud layers that contain more liquid water (see Figure S6). Although SCu formation is alsodependent on free tropospheric conditions, mainly by the lapse rates of potential temperature and spe-cific moisture [Ek and Holstlag, 2004], our findings indicate that C4 grass might yield comparable effects inenhancing boundary-layer clouds as was found for bare soils [Taylor et al., 2011a]. This enhancement maybe further reinforced when boundary-layer clouds form over heterogeneous surfaces [van Heerwaardenand Vilà-Guerau de Arellano, 2008; Taylor et al., 2011b], which in our case could consist of patches of C3 andC4 grasses.

4. Conclusions and Outlook

We quantify the two-way interactions between the carbon, water and energy diurnal cycles, and the for-mation and development of boundary layer clouds. Special emphasis is placed on studying how the rapidperturbations in solar radiation by SCu, and the subsequent increase in the spatial variability of the CO2

plant uptake, latent heat, and sensible heat surface fluxes, modify the cloud properties. We study this inter-active system by means of systematic numerical experiments, using a large eddy simulation model coupledto submodels representing photosynthesis and stomatal resistance. Our findings show that simulated cloudfields that account for the explicit interaction between clouds and vegetation are characterized by lessskewed (less extreme values) behavior than radiative transparent SCu. By studying the impact of the timeresponse of the stomatal aperture (either instantaneous or with an adjustment time to atmospheric pertur-bations), we find differences in the instantaneous values and spatial patterns of the surface fluxes and cloudproperties. However, the spatially averaged values remain similar. Provided that the free tropospheric condi-tions are identical in all simulations, SCu formed over C4 grass, which is more water use efficient, have highercloud covers compared to SCu formed over C3 grass with higher liquid water content due to the more vig-orous buoyant thermals. We demonstrate that the coupling between DALES and the vegetation model is anadequate platform for studying interactions (and their parameterizations) of photosynthesis and stomatalapertures to atmospheric perturbations, and the dependence of cloud formation on vegetation.

ReferencesAdams, D. K., S. I. Gutman, K. L. Holub, and D. S. Pereira (2013), GNSS observations of deep convective time scales in the Amazon,

Geophys. Res. Lett., 40, 2818–2823, doi:10.1002/grl.50573.Albertson, J. D., G. G. Katul, and P. Wiberg (2001), Relative importance of local and regional controls on coupled water, carbon, and

energy fluxes, Adv. Water Resour., 24, 1103–1118.Baldocchi, D. (1994), A comparative study of mass and energy exchange rates over a closed C3 (wheat) and an open C4 (corn) crop. II

CO2 exchange and water use efficiency, Agric. For. Meteorol., 67, 291–321.Doutriaux-Boucher, M., M. J. Webb, J. M. Gregory, and O. Boucher (2008), Carbon dioxide induced stomatal closure increases radiative

forcing via a rapid reduction in low cloud, Geophys. Res. Lett., 36, L02703, doi:10.1029/2008GL036273.Ek, M., and A. A. M. Holstlag (2004), Influence of soil moisture on boundary layer development, J. Hydrometeorol., 5, 86–99.Garcia-Carreras, L., D. J. Parker, and J. H. Marshman (2011), What is the mechanism for the modification of convective cloud distributions

by land surface-induced flows?, J. Atmos. Sci., 68, 619–634.Heus, T., et al. (2010), Formulation and numerical studies by the Dutch Atmospheric Large-Eddy Simulation (DALES), Geosci. Model Dev.,

3, 415–444.

AcknowledgmentsDALES simulations were performedat SURFSara with the financialsupport of project NCF-NWOSH-006-13. J.V.G. acknowledgessupport partly by the SpanishMINECO project CGL2009-08609 andCGL2012-37416-C04-03.

The Editor thanks two anonymousreviewers for their assistance inevaluating this paper.

VILÀ-GUERAU DE ARELLANO ET AL. ©2014. American Geophysical Union. All Rights Reserved. 6

Geophysical Research Letters 10.1002/2014GL059279

Jacobs, C. M. J., and H. A. R. de Bruin (1997), Predicting regional transpiration of elevated atmospheric CO2: Influence of thePBL-vegetation interaction, J. Appl. Meteorol., 36, 1663–1675.

Joseph, J. H., W. H. Wiscombe, and J. A. Weinman (1976), The delta-Eddington approximation for radiative transfer fluxes, J. Atmos. Sci.,33, 2452–2458.

Katul, G. G., R. Oren, S. Manzoni, C. Higgins, and M. B. Parlange (2012), Evapotranspiration: A process driving mass transport and energyexchange in the soil-plant-atmosphere-climate system, Rev. Geophys., 50, RG3002, doi:10.1029/2011RG000366.

LeMone, M. A., and W. T. Pennel (1976), The relationship of trade wind cumulus distribution to subcloud layer fluxes and structure, Mon.Weather Rev., 104, 524–539.

Lohou, F., and E. G. Patton (2014), Surface energy balance and buoyancy response to shallow cumulus shading, J. Atmos. Sci., 71, 665–682.McFarlane, S. A., and W. W. Grabowski (2007), Optical properties of shallow tropical cumuli derived from ARM ground-based remote

sensing, Geophys. Res. Lett., 34, L06808, doi:10.1029/2006GL028767.Ooba, M., and H. Takahashi (2003), Effect of asymmetric stomatal response on gas-exchange dynamics, Ecol. Modell., 164, 65–82.Ouwersloot, H. G., J. Vilà-Guerau de Arellano, C. C. van Heerwaarden, L. N. Ganzeveld, M. C. Krol, and J. Lelieveld (2011), On the

segregation of chemical species in a clear boundary layer over heterogeneous land surfaces, Atmos. Chem. Phys., 11, 10,681–10,704.Rio, C., F. Hourdin, J.-Y. Grandpeix, and J.-P. Lafore (2009), Shifting the diurnal cycle of parameterized deep convection over land, Geophys.

Res. Lett., 36, L07809, doi:10.1029/2008GL036779.Ronda, R. J., H. A. R. de Bruin, and A. A. M. Holtslag (2001), Representation of the canopy conductance in modeling the surface energy

budget for low vegetation, J. Appl. Meteorol., 40, 1431–1444.Sellers, P. J., D. A. Randall, G. J. Collatz, J. A. Berry, C. B. Field, D. A. Dazlich, C. Zhang, G. D. Collelo, and L. Bounoua (1996), A revised land

surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation, J. Clim., 9, 676–705.Stephens, G. L. (1978), Radiation profiles in extended water profiles. II: Parameterization schemes, J. Atmos. Sci., 35, 2123–2132.Sullivan, P. P., C.-H. Moeng, B. Stevens, D. H. Lenschow, and S. D. Mayor (1998), Structure of the entrainment zone capping the convective

atmospheric boundary layer, J. Atmos. Sci., 55, 3042–3064.Taylor, C. M., R. A. M. de Jeu, F. Guichard, P. P. Harris, and W. A. Dorigo (2011a), Afternoon rain more likely over drier soils, Nature, 489,

423–426.Taylor, C. M., A. Gounou, F. Guicahrd, P. P. Harris, R. J. Ellis, F. Couvreux, and M. De Kauwe (2011b), Frequency of Sahelian storm initiation

enhanced over mesoscale soil-moisture patterns, Nat. Geosci., 4, 430–433.van Heerwaarden, C. C., and J. Vilà-Guerau de Arellano (2008), Relative humidity as an indicator for cloud formation over heterogeneous

land surface, J. Atmos. Sci., 65, 3263–3277.Van Kesteren, B., O. K. Hartogensis, D. van Dinther, A. F. Moene, and H. A. R. De Bruin (2013), Measuring H2O and CO2 fluxes at field

scales with scintillometry: Part II—Validation and application of 1-min flux estimates, Agric. For. Meteorol., 178, 88–105.Vico, G., S. Manzoni, S. Palmroth, and G. Katul (2011), Effects of stomatal delay on the economics of leaf gas exchange under intermittent

light regimes, New Phytol., 192, 640–652.Vilà-Guerau de Arellano, J., C. H. van Heerwaarden, and J. Lelieveld (2012), Modelled suppression of boundary-layer clouds by plants in a

CO2-rich atmosphere, Nat. Geosci., 5, 701–704.Zhang, Y., and S. K. Klein (2010), Mechanisms affecting the transition from shallow to deep convection over land: Inferences from

observations of the diurnal cycle collected at the ARM Southern great plains site, J. Atmos. Sci., 67, 2943–2959.

VILÀ-GUERAU DE ARELLANO ET AL. ©2014. American Geophysical Union. All Rights Reserved. 7