supplementary information for ”modelled suppression of ...€¦ · the atmospheric boundary layer...

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Supplementary information for”Modelled suppression of boundary-layer clouds by

plants in a CO2-rich atmosphere ”

Jordi Vila-Guerau de Arellano and 1∗, Chiel C. van Heerwaarden2 and Jos Lelieveld3,4

1Meteorology and Air Quality Section, Wageningen UniversityP.O. Box 47, 6700 AA Wageningen, The Netherlands

2Max Planck Institute for Meteorology, Hamburg, Germany3Max Planck Institute for Chemistry, Mainz, Germany

4The Cyprus Institute, Nicosia, Cyprus

∗To whom correspondence should be addressed; E-mail: [email protected]

1 Soil-Water-Atmosphere-Plant model

The model system consists of interactive representations of the atmosphere and surface. For the

atmosphere, the temporal evolution of the mean structure of the ABL is based on mixed-layer

theory (1). For the surface, the dynamic components incorporate vegetation and soil processes.

Fig. S1a sketches the main components of the modeling system: soil, surface layer including

vegetation, atmospheric surface and boundary layer, and the free troposphere.

The mathematical equation describing the diurnal evolution of an atmospheric state ther-

modynamic variable ψ (potential temperature, specific moisture and wind) and atmospheric

constituents like CO2 can be expressed in the following generic form:

1

© 2012 Macmillan Publishers Limited. All rights reserved.

∂〈ψ〉

∂t=w′ψ′

s − w′ψ′

e

h=

SUR︷︸︸︷

w′ψ′

s +ABL︷︸︸︷we

FT−ABL︷︸︸︷

∆ψ

h︸︷︷︸

ABL

. (1)

The physical interpretation of the budget Eq. S1 is that in the absence of horizontal advective

transport, changes over time of the mixed-layer variable〈ψ〉 depend on the surface and the

entrainment fluxes (w′ψ′

s andw′ψ′

e). Consequently, as shown in Fig. S1a, the mean profiles

are constant with height within the atmospheric boundary layer. Notice that Eq. S1 depends

solely on time,i.e. a slab or 0-order model. Eq. S1 encompasses the interaction of the land

surface processes (SUR) described by the surface fluxes (in Fig. S1a SH, LE, u∗ and Fc) and

the atmospheric boundary layer dynamics, represented by the entrainment fluxw′ψ′

e and the

height h. In the next two sections, we provide a complete description of the formulation and

physical interpretation of both fluxes.

Finally, the boundary layer height (h) modulates the dilution capacity of the ABL shown

by Eq. S1. The rate of air mass exchanged between the ABL and FT is quantified by the

entrainment velocity we = (∂h/∂t) - ws; where ws represents the mean vertical subsidence

velocity (Fig. S1a). In this prototype ABL, ws is a negative velocity opposing the boundary

layer growth and it quantifies the large-scale forcing driven by high-pressure systems.

1.1 Representing the dynamics of the ABL

The diurnal variability of the atmospheric boundary layer is calculated using a mixed-layer

(slab) model. This approach follows the pioneering concept by (1) to study the heat budget in

an ABL over land, though it is extended to include the effects of specific moisture, wind and

carbon dioxide on the ABL dynamics. Its physical basis is that, due to the efficient turbulent

mixing, the thermodynamic variables and CO2 profiles are constant with height (well-mixed)

within the ABL (see Fig. S1a). As a result, the gradients of these variables are constant in time,

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and the turbulent fluxes therefore adopt a linear profile (2), with the bottom and top boundary

conditions imposed by the surface and entrainment fluxes. These assumptions are physically

well-established and reproduce the observed diurnal evolution of temperature, moisture and

carbon dioxide. Eq. S1 is derived by applying these assumptions to the conservation equa-

tions of heat, moisture, momentum and carbon dioxide: vertical integration of these governing

equations to derive a zeroth-order model only depends on time.

An important new component of the atmospheric model is the representation of the FT-

ABL interface exchange. The entrainment flux depends on the boundary layer growth rate,i.e.

the entrainment velocity we, and the difference of the variable between the free troposphere

(ψFT ) and the ABL (〈ψ〉), i.e. the so-called ”jump” of the variable at the entrainment zone:

∆ψ = ψFT − ψABL, defined as a sharp infinitesimally thin interface layer. Fig. S1a depicts a

characteristic prototype ABL where the FT air mass entrains warm (∆θ > 0) and dry (∆q< 0)

air with lower CO2-mixing ratios (∆CO2 < 0) into the ABL.

In summary, for the main variables under study (θ, q, U, V and CO2), the atmospheric model

system comprises eleven coupled equations:

– Five prognostic slab equations for the potential temperature, specific moisture, the two

wind components and carbon dioxide (see generic Eq. S1 and main profiles in Fig. S1a).

– One prognostic boundary layer growth equation that depends on the entrainment buoy-

ancy flux and subsidence vertical velocity. By expressing we as a function of the entrain-

ment and surface flux, we derive the following expression:

∂h

∂t= we + ws

= −

(

w′θ′v)

e

∆θv+ ws (2)

3


=βθv

(

w′θ′v)

s

∆θv+ ws,

whereβθv= 0.2 + (u∗/w∗)

3 and∆θv is the jump of the virtual potential temperature

at the interface. This is a closure assumption that is needed to provide consistency to the

system of eleven equations relating the entrainment buoyancy flux to that at the surface.

Including u∗ takes the effect of mechanical turbulence driven by the wind shear in the

surface layer into account (see the logarithmic u-profile that drives the wind shear above

the surface in Fig. S1a).

– Five prognostic equations for the differences in the potential temperature, specific mois-

ture, wind and carbon dioxide at the entrainment interface. The generic equation reads:

∂∆ψh∂t

=∂ψh+

∂t−∂〈ψ〉

∂t= γψ

(

∂h

∂t− ws

)

−∂〈ψ〉

∂t, (3)

whereγψ represents the variation in height ofψ in the free troposphere, namely the lapse

rates of the respective variables (γψ) in Fig. S1a).

Finally, we define the diagnostic variables, i.e. diurnal temperature range (DTR), diurnal

carbon dioxide range (DCR) and lifting condensation level (LCL). DTR is defined as the max-

imum potential temperature during the day minus the minimum represented by the initial po-

tential temperature (nocturnal)θo. DCR is defined as the difference between the minimum CO2

during the day minus the maximum initial (nocturnal) CO2 mixing ratio represented by CO2o.

The temporal evolution of LCL is calculated diagnostically from the calculated temperature and

specific humidity as the height where a parcel lifted adiabatically reaches a relative humidity of

100%.

This model has been used and tested extensively and successfully to study the profiles and

variability of carbon dioxide measured in tropical forests (3), the role of evapotranspiration

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in ABL dynamics (4), the explanation of different trends in actual evapotranspiration and pan

evaporation under changing climate conditions (6), and the diurnal variability of reactive atmo-

spheric reactive compounds like nitrogen oxides and hydrocarbons (7, 23).

1.2 Representing the soil-plant dynamics

We employ the same land surface model as described in (4), with two new representations

related to the inclusion of carbon dioxide: (a) dynamic representation of plant photosynthesis

and (b) CO2 efflux respiration by soil decomposition processes. Fig. S1b shows the main

processes and variable dependencies of the photosynthesis and CO2-soil respiration model.

In Fig. S1a, the surface or stomatal resistance (the surface resistance rs) is calculated, taking

into account the photosynthesis process by plants (An, see Fig. S1b) and a representation of the

resistance (or conductance) of carbon dioxide and water by the stomata (rs). Therefore the

model accounts for A-rs (or A-gs if the conductance gs is used; where gs=(1/rs)).

In our study we use the same expressions derived and evaluated by (8) closely related to the

the seminal research by (9). Note that we simplify important aspects of plant physiology (10),

however, for the diurnal and the ABL-scales under study, we represent the essential components

of the interactions of the atmosphere-plant-vegetation system. In brief, the following are the

main components of the combined soil-plant model:

– The radiation balance of the four components (Fig. S1a) of the shortwave and longwave

radiation fluxes are calculated according to the standard expressions presented by (11).

This radiation balance provides the net radiation and the photosynthetically active radia-

tion (PAR). As a first approximation, we relate PAR linearly to the incoming shortwave

radiation.

– By connecting the radiation budget to the surface energy balance throughout the net ra-

diation, we used this variable to constrain the surface sensible and latent heat fluxes, and

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the soil heat flux.

– The sensible heat flux is calculated using the expression (see Fig. S1a for the definition

of the variables):

SH =ρcpra

(θs − 〈θ〉) . (4)

where ra is the aerodynamic capacity, being inversely proportional to the wind and a

heat drag coefficient;〈θ〉 is the mixed-layer value calculated by equation 1,θs is the

temperature at the surface,ρ is the density of air and cp is the specific heat capacity.

– The latent heat (LE) flux is calculated using:

LE =ρLv

ra + rs,w(qsat (Ts) − 〈q〉) , (5)

whereqsat (Ts) is the specific moisture saturation at surface temperature (Ts), and rs,w is

the stomata resistance to water vapor.〈q〉 is the mixed-layer value calculated by Eq. S1.

A useful diagnostic variable employed in our study (see Fig. 3 in the main paper) to

quantify the partitioning of the surface energy balance is the evaporative fraction (EF),

which is defined as:

EF =LE

SH + LE. (6)

– As shown in Eq. S5, and as mentioned above, the stomatal resistance (rs) is obtained from

the dynamic vegetation model A-rs (see Fig. S1b). It is important to remember that the

same surface resistance expression is used to calculate the latent heat flux and CO2 uptake

by plants. They are related by the molecular diffusion rates of water and carbon dioxide

as follows: rs,w = 1.6 rs,c, where w and c signify water and carbon dioxide, respectively.

6


– For both resistances, and following (8), we scale up the resistance from a leaf to a canopy

level by integrating the expression of the leaf resistance and consequently introducing the

dependence on the parameter Leaf Area Index (LAI).

– The total CO2-uptake by plants (An) (Fig. S1b) fluxes reads:

An = Ag − Rd =1

ra + rs,c(〈CO2〉 − CO2i). (7)

where Ag is the gross assimilation by plants and Rd accounts for the plant respiration

under dark conditions. It is parameterized as a function of the photosynthetic rate at

infinite light intensity (9, 12). CO2i is the carbon dioxide concentration within the leaf

and〈CO2〉 is the mixed-layer concentration calculated by Eq. S1.

– Thus, and as shown in Fig. S1b, in the A-rs formulation the surface resistance is a function

of PAR, the water vapor pressure deficit, the atmosphere-plant CO2-gradient, the soil

moisture stress, and the LAI.

– To complete the calculation of the net exchange of carbon at the surface, we need to

introduce a soil flux of CO2 to take the plant root and microbial decomposition (Rs) into

account. Here, we follow the expression proposed by (13), used and tested above grass

by (14). It depends on the surface temperature according to an Arrhenius function and a

correction to take soil moisture stress into account. In order to incorporate the influence

of wind in the CO2 soil flux, we add a linear function that depends on the friction velocity:

(1 + (u∗/u∗max), where u∗max= 1 m s−1.

– The final expression for the net exchange of carbon dioxide at the surface, combining

photosynthesis uptake by grass and soil respiration, reads:

7


Fc = An + Rs. (8)

Similar to the Bowen ratio, we define the ratio of the photosynthesis to the soil respiration

(PR) as:

PR =AnRs

. (9)

Notice that in our sign convention, An is a negative flux (indicating a removal of CO2

from the atmosphere), while Rs is positive (emission flux into the atmosphere). The PR-

ratio is used in Fig. 3a in the main paper to quantify the response of photosynthesis and

soil respiration to CO2-rise and warmer temperatures. It is important to note that Fc is the

surface flux in Eq. S1 that enables us to calculate CO2 surface processes and link them to

the dynamic evolution of the atmospheric processes.

– The soil fluxes for heat and moisture are calculated as a function of temperature and

specific moisture at two soil levels by means of a force-restore soil model based on the

formulations of (15) and (16). As shown in Fig. S1a, on the first level we calculate the

variation of time of temperature and soil moisture, while at the deeper level 2, the values

are imposed.

By combining both modules, we obtain a closed system of equations that is solved dynami-

cally in time: (i)θ, q, U, V and CO2; (ii) the surface fluxes of the same quantities (surface energy

balance and the net exchange of carbon dioxide, including photosynthesis and soil respiration),

the boundary layer height evolution and important diagnostic variables related to cloud forma-

tion like the lifting condensation level (LCL). A comprehensive evaluation of the model can be

found in (4).

8


2 Observations of the middle latitude prototype ABL

By evaluating the model results against observations, our aim is to ensure that all components

of the system are satisfactorily represented. By taking this integral approach, we ensure that

the accurate representation of the surface fluxes feeds back on the boundary layer dynamics.

The evolution of the boundary layer depth governs the exchange of thermodynamic variables

between the FT and ABL; which is central to reproduce the diurnal variability ofθ, q, wind

and CO2. We therefore need to demonstrate the ability of the model to reproduce the coupling

within the land-atmosphere system (17), and also show its relevance for carbon dioxide. We

select a comprehensive observational set of surface and upper air measurements taken at the

meteorological site in Cabauw on 25th September 2003 (51o 57’ N, 4 o 54’ E, 0.7 m above

sea level). The case has been thoroughly analyzed (18) and it is representative of a prototype

of a diurnal atmospheric boundary layer developing over grass over a well-watered soil. The

contribution of heat, moisture and carbon dioxide by horizontal advection is smaller than the

divergence of the turbulent vertical fluxes.

The reason for focusing on a single day is that it allows a comprehensive description and

quantification of the entire coupled system and thus including variables that are neither shown

nor measured simultaneously. Moreover, for the same site, the monthly averages of the period

between April and September reproduce similar daily patterns (19). Under different vegeta-

tion and atmospheric conditions, (3) and (20) reported a similar evolution of CO2 shown by

measurements gathered in tropical and mid-latitude forests, respectively.

All observations were collected from the 213-meter tower (21, 24), and the boundary layer

height evolution was measured by means of a wind profiler. As mentioned, the dominant veg-

etation is grass (C3 vegetation type, LAI between 2 and 3) growing in a homogeneous and flat

terrain. The first soil layer is clay (0.7 m) above peat. Table S1 shows the prescribed initial

9


and boundary conditions of the atmospheric variables for theCabauw case for 25th September

2003. Similarly, Table S2 summarizes the initial, boundary conditions and constants needed for

the vegetation-soil module. The A-rs constants are identical to those used by (8) (see Table A1

in their article).

Fig. S2 shows that the model evolution of potential temperature, specific moisture and car-

bon dioxide agrees satisfactorily with the observations, complementing the evaluation shown in

Fig. 2 of the main text. As shown by the measurements at two heights, the well-mixed condi-

tions are reached at 9 UTC. As a matter of importance for the q-evolution, and closely related to

boundary layer growth, we note the ability of the model in reproducing the slow breakdown of

the thermal inversion (until 9.50 UTC) and the the entrainment of free tropospheric dry air after

this time. This is shown by the peak of specific moisture (see Fig. S2b), and the subsequent

decrease of q between 9.50 and 12 UTC due to the entrainment of dry air. Fig. S2c shows that

the model is capable of reproducing the decrease of carbon dioxide during the day due to the

combined effect of surface processes and atmospheric dynamics (see following section for the

calculation of the budget of carbon dioxide).

It is important to mention that to include to boundary layer cloud formation conditions, we

modify three conditions in the entrainment zone and free troposphere. Table S3 includes these

changes. The other conditions are equal to those described in Table S1.

10


Table 1: The prescribed initial variables used for the mixed-layer model to calculate the boundary layer height, the ther-modynamic and the carbon dioxide budget evolution on 25th

September 2003 in Cabauw (The Netherlands). Initial valuesare at 6 UTC. See also Fig. S1a for the variables definition.

Dynamics:Surface pressure P0 [Pa] 102900.Large scale subsidence velocity (ws) [m s−1] 0.Coriolis parameter (fc) [s−1] 1.0x10−4

Boundary layer height [m] 175.

Heat:< θ > [K] 284.∆θ [K] 4.2γθ [K m −1] 0.0036βθv

[-] 0.2+5(u∗/w∗)3

Moisture:< q > [g kg−1] 4.9∆q [g kg−1] -0.8γq [(g kg−1) m−1] -0.0012

Wind:< U > [m s−1] 5∆U [m s−1] 3γU [s−1] 0.002

Carbon dioxide:< CO2 > [ppm] 422∆CO2 [ppm] -44γCO2

[ppm m−1] 0

11


Table 2: Initial and boundary conditions for the vegeta-tion and soil models for 25 September 2003, Cabauw, TheNetherlands. The initial conditions are prescribed at 6 UTC.See Fig. S1b for the representation of the variables.

Variable Description and unit CabauwGeographic and time:

lat latitude [deg] 51.97 Nlon longitude [deg] 4.93 Edoy day of the year [-] 268.time initial time [UTC] 6.

Vegetation:LAI leaf area index of vegetated surface fraction [-] 2.rc,min minimum resistance transpiration [s m−1] 110.rs,soil,min minimum resistance soil evaporation [s m−1] 50.gD VPD correction factor for surface resistance [-] 0.z0m roughness length for momentum [m] 0.05z0h roughness length for heat and moisture [m] 0.01α surface albedo [-] 0.25Wl equivalent water layer depth for wet vegetation [m] 1.4 x 10−4

Soil:Ts initial surface temperature [K] 284.Tsoil1 temperature top soil layer [K] 282.Tsoil2 temperature deeper soil layer [K] 285.wsat saturated volumetric water content [m3 m−3] 0.600wfc volumetric water content field capacity [m3 m−3] 0.491wwilt volumetric water content wilting point [m3 m−3] 0.314wsoil1 volumetric water content top soil layer [m3 m−3] 0.48wsoil2 volumetric water content deeper soil layer [m3 m−3] 0.48cveg vegetation fraction [-] 0.9a Clapp and Hornberger retention curve parameter [-] 0.083b Clapp and Hornberger retention curve parameter [-] 11.4p Clapp and Hornberger retention curve parameter [-] 12.CGsat saturated soil conductivity for heat [K m−2 J−1] 3.6 x 10−6

C1sat Coefficient force term moisture [-] 0.342C2ref Coefficient restore term moisture [-] 0.3Λ Thermal diffusivity skin layer [-] 5.9

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Table 3: Free tropospheric values modified relative to thosein Table S1 for the numerical experiments depicted in Fig. 3.Biomass growth value used for section 4 in SI and free tropo-spheric and soil moisture values for the sensitivity analysisof section 5 of the SI.

Free troposphere conditions (Fig. 3):∆θ [K] 0.5γθ [K m −1] 0.001γq [(g kg−1) m−1] 0.0

Biomass growth (section 4 SI):LAI [-] 3

Warmer free troposphere (section 5 SI):γθ [K m −1] 0.0014

Moister free troposphere (section 5 SI):∆q [g kg−1] -0.7

Lower soil moisture content (section 5 SI):wsoil1 [m3 m−3] 0.47wsoil2 [m3 m−3] 0.47

3 Budgeting the diurnal CO2 evolution

Fig. S3 shows the contribution of each process to the diurnal evolution of carbon dioxide. In

short, we calculate each term of Eq. S1 combined with Eq. S8 to calculate the contribution

of photosynthesis (An term represented by Phot), soil efflux (Rs represented by Resp), CO2-

entrainment (indicated in the figure by Entr) and the tendency (Tend). As shown by the inset in

Fig. S3, the decrease of CO2 (∂CO2/∂t < 0) over time is quantified by the storage term. This

negative tendency is due to the combined contribution of the assimilation of CO2 by grass and

the entrainment of air masses with lower carbon dioxide mixing ratios. The role played by the

latter process was also identified from the surface observations above tropical forests (3) and

aircraft measurements in Cabauw (25). This negative CO2 tendency is partly compensated by

13


the positive soil respiration flux. Notice that all the contributions are very large during the rapid

growth of the boundary layer (until 11 UTC), indicating that a satisfactory representation of the

boundary layer height is necessary to reproduce the daily evolution of CO2. As shown by Fig.

S2c the carbon dioxide dependence on height begins to follow a well-mixed profile at around

10 UTC when all data at different heights are clustered together. The model results reproduce

the CO2 mixing ratio observations after this time very well. The large values of the individual

contributions before 10 UTC points to the importance of the morning transition in reproducing

the CO2 diurnal variability, since it establishes the tendency for the rest of the day. After 12

UTC the positive and negative tendencies balance against observations and∂CO2/∂t ≈ 0.

4 Effect of biomass on boundary-layer cloud formation

We anticipate an increase of the plant biomass in the scenario for the year 2100. To reproduce

this effect, we repeat the same 256 numerical experiments, but now imposing a leaf area index

LAI=3. Hence we explore the response of the soil-water-atmosphere-plant system to a 50%

increase in the leaf area index (from 2 to 3), assuming that the vegetation can freely grow

and that it is not limited by soil water and nutrient availability. Fig. S4 shows the response

of the land-atmosphere system to rises in temperature and carbon dioxide under conditions

characterized by larger plant biomass.

Compared to Fig. 3a and 3b, we find very similar patterns for the evaporative fraction

and the diurnal temperature range. A relevant difference is that the increase in LAI enhances

evaporation and leads to a more rapid reduction in the difference between LCL-h (for instance

compare the 25-meter LCL-h contour in Fig. 3c to the 0-meter LCL-h contour in Fig. S4a).

Similarly, as shown in Fig. S4, we find an enhancement of the photosynthesis at higher LAI,

indicated by the more negative values in the PR-ratio. This thus leads to a reduction in the

release of carbon dioxide into the atmosphere being enhanced by the increase in soil respiration

14


at relatively high temperatures. As expected, progressing to more active grasslands leads to a

decrease in the difference between the lifting condensation level and ABL height (the proxy

LCL-h approaches zero Fig. S4a) than in Fig. 3c) However, the LCL-h patterns obtained

under the conditions LAI=2 and 3 are consistent, and therefore our findings corroborate that the

combined effect of increasing CO2 and temperature leads to less favourable conditions for the

formation of BL clouds.

5 Effect of warmer and moister free tropospheric conditions

Global warming and rising CO2 can also lead to modifications on the free tropospheric con-

ditions (22). Although still with a high degree of uncertainty, it is expected that a future free

troposphere will be characterized by more stable (warmer) and moister conditions. Since these

upper atmospheric conditions exert a strong influence on the surface-ABL system, we perform

a sensitivity analysis imposing a larger lapse rate for the potential temperature and reducing the

value of the difference between the specific moisture in the FT and the ABL (see Table S3).

Notice that the changes are relatively small to be able to compare with Fig. 3 in the main text.

In the first scenario, one can expect that the shallower boundary layer will prevent the formation

of BL clouds, whereas an increase of moisture at the FT optimizes the conditions for BL cloud

development.

Fig. S5 maps the proxy LCL-h as a function of increasing carbon dioxide and tempera-

ture. In the case of a warmer FT (Fig. S5a), LCL is reached at higher altitude whereas the ABL

height grows at a slower rate due to the higher stability, and therefore the proxy LCL-h increases

relative to that in Fig. 3c. In turn, Fig. S5b shows that a moister FT decreases the LCL-h differ-

ences by enhancing the moisture in the ABL. This effect is partly compensated by an increase

of the sensible heat flux because the moister ABL conditions lead to a decrease in the latent

heat flux (17). To complete the analysis, we show in (Fig. S5c) the sensitivity of the system to

15


a reduction of a soil moisture (1%). As studied by (5), a soil moisture decrease can lead to an

increase or decrease of the BL-clouds depending on the free tropospheric potential temperature

lapse rate conditions. Fig. S5c shows that for the ABL prototype formed over grassland the

probability of BL-cloud formation becomes less due to the decrease in surface evaporation that

it is not compensated by a higher ABL depth. However, most important and in support of our

main conclusion, the most favourable conditions for BL-cloud formation occurred under the

current levels of CO2.

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19


SOILLAYER 1

SOILLAYER 2

0.1h

h

0 zom zoh

T Wsoil1 soil1

T Wsoil2 soil2

U q q

SURFACELAYER

ATMOSPHERICBOUNDARYLAYER

Tsq (Ts)sat

qsoil

rsoil

rs

ra

LESH Fc

FREETROPOSPHERE

CO2

DqDCO2DqDU

gU gCO2gqgq Subsidence

Radiation

ws

Entrainmentzone

u*

Entrainment

we

(a)

esat

Ag

R (T ,W ,u )s s s *

A -R =g d

A (e-e ,C-C ,PAR,LAI)n sat i

PAR

Ci

rs(e-e ,C-C ,PAR,LAI)sat i

Rd

T e C

(b)

Photosyntehsis & Dark Respiration

Stomatal resistance

SoilRespiration

Figure 1: (a) Representation of the main components and variables in the atmosphere-vegetation-soilsystem. It consists of two soil layers, dynamic models for both aerodynamic and surface resistance, theatmospheric boundary layer and the free troposphere. (b) Representation of the main components andvariables in the soil-vegetation system to compute the carbon dioxide budget. The dependences of thephotosynthesis (An), surface resistance (rs) and soil respiration (Rs) are shown in parentheses.

20


7 8 9 10 11 12 13 14 15

time UTC [h]284285286287288289290291

�

[K]

(a)

7 8 9 10 11 12 13 14 15time UTC [h]

4.44.64.85.05.25.45.65.8

θ [g

kg�

�]

(b)

7 8 9 10 11 12 13 14 15time UTC [h]

370

380

390

400

410

CO2

[ppm

]

(c)

Figure 2: Diurnal evolution of the the potential temperature (a), specific moisture (b) carbon dioxidemixing ratio (c). Observations are represented by the triangles.

6 8 10 12 14time UTC [h]

�10

�5

0

5

ppm

h

��

PhotRespEntrTend

8 10 12 14time UTC [h]

370

390

410

CO2

[ppm

]

Figure 3: Budget terms of the diurnal evolution of carbon dioxide: photosynthesis (Phot), soil respi-ration (Resp), entrainment (Entr) and CO2-tendency contributions (Tend). The calculated and observedCO2-evolution is shown in the inset.

21


350 400 450 500 550 600 650 700 750CO2� [ppm]

275

280

285

290

295

��

[K]

(a)

0.4

0.5

0.6

0.7

0.8

3.5

3.0

2.5

2.0

1.5

PR [−

]350 400 450 500 550 600 650 700 750

CO2� [ppm]

275

280

285

290

295

��

[K]

(b)1.0

1.5

2.0

2.5

3.0 46

44

42

40

38

DCR

[ppm

]

350 400 450 500 550 600 650 700 750CO2� [ppm]

275

280

285

290

295

��

[K]

(c)

0 25

5020

60

100

140

180

LCL−

h [m

]

Figure 4: Same as Fig. 3 in the main paper, but for LAI=3 to mimic an increase of the biomass. (a)Ratio of photosynthesis to soil respiration PR (full contour) and evaporative fraction EF (solid line), (b)diurnal range of carbon dioxide DCR (full contour) and diurnal temperature range (DTR)(solid line) and(c) difference between lifting condensation level (LCL) and ABL height (h) in meters. The 25 and 50meter isolines are also shown for reference (solid lines).22


350 400 450 500 550 600 650 700 750CO2 [ppm]

275

280

285

290

295

��

[K]

(a)

50

20

60

100

140

180

220

LCL-

h [m

]

350 400 450 500 550 600 650 700 750CO2 [ppm]

275

280

285

290

295

��

[K]

(b)

0

25

50

�20

20

60

100

140

180

LCL−

h [m

]

350 400 450 500 550 600 650 700 750CO2� [ppm]

275

280

285

290

295

��

[K]

(c)

50

20

60

100

140

180

220

LCL-

h [m

]

Figure 5:Differences between LCL-h (proxy BL-cloud formation) under (a) warmer free troposphericconditions, (b) moister free tropospheric conditions and (c) lower soil moisture content. The LCL-hcontour lines of 0, 25 and 50 m are shown for reference.

23


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