Introduction
Understanding the interaction between vegetation canopies and atmosphere is a
crucial component in the quantification of biosphere–atmosphere exchange of
heat, carbon dioxide, water and trace gas fluxes. It is also important for
the development of numerical weather and climate models where the fluxes in
the canopy surface layer (CSL) and the atmospheric surface layer (ASL) are
parameterized through bulk exchange coefficients of momentum and scalar.
However, idealizations of the forest canopies as horizontally homogeneous
momentum sinks and scalar sources introduces uncertainties in flux
estimations and estimating diffusion coefficients. The presence of
heterogeneities such as roughness transitions, complex topography and
mesoscale circulations are common sources of such uncertainties that give
rise to nonlocal motions and secondary circulations. These secondary
circulations not only occur in forests but are also generic characteristics
of boundary layer flows over natural and man-made landscapes with
discongruity of land use types, surface moisture, temperature, etc.
. Different types of land cover such as
agricultural lands or urban areas can affect local energy balance closure and
the structure of the overlying boundary layer as well as cloud formation and
regional weather . Strong differences in surface
properties and large swaths of such surface patches are known to induce
secondary circulations . Recent works by , and
have suggested that non-closure of the energy balance is
also related to advection and flux divergence due to secondary circulations
. The non-closure of the energy balance refers to
the fact that the available energy Rn-G is often higher than the
turbulent energy H+LE at micrometeorological sites, where Rn is
net radiation, G is soil heat flux, H is sensible heat flux and LE is
latent heat flux. Thus, it is established that studies involving surface
heterogeneities such as a difference in roughness characteristics and albedo
are crucial for the advancements of our understanding of
biosphere–atmosphere interaction since the quasi-universal scaling laws of
turbulent moments and simple parametrizations of exchange coefficients are
disturbed and rendered nonoperational.
Several studies have attempted to study the nature of turbulence across a
roughness transition such as a grassland and a forest canopy by means of
experimental and numerical methods and documented several length scales associated with
the roughness transitions, recirculation zones and the nature of the
turbulent momentum budget. However, all of these studies are concerned with
the flow adjustment in the immediate vicinity of the roughness transition
(edges or gaps). have studied the dynamics of the convective
boundary layer over a well-defined surface heterogeneity – namely Yatir
Forest and the shrubland surrounding it, which are located in the northern
part of the Negev Desert in Israel. Eddy covariance (EC) and Doppler lidar
measurements were conducted by at two sites approximately
6.5 km apart: one in the forest and one in the desert. The forest has a
darker surface and consequently lower albedo (12.5 %) than the desert
(33.7 %). Moreover, the higher surface roughness of the forest results in
higher turbulence intensity, which leads to more efficient heat transfer
above the forest, a phenomenon called canopy convector effect
. The region being very dry, there is very
little latent heat flux (Bowen ratio >10 over the summer), resulting in a
spatial difference in surface buoyancy flux of 220–290 Wm-2
between the desert and the forest. Furthermore, the length scale of surface
heterogeneities (6–10 km) is larger than the minimal length scale needed
for the development of secondary circulations:
Lrau=CRauU/w*≈2–5 km , where U is mean wind speed, w* is the convective velocity
scale and CRau=0.8 is an empirical parameter, so that it is
possible for secondary circulations to develop.
The present work is an attempt to examine this hypothesis of secondary
circulations in more detail. We use eddy covariance and Doppler lidar
measurements at two sites 4.3 km apart over the shrubland and Yatir Forest,
where the shrubland is upwind of the forest in the path of the principal wind
direction (during the summer, there exists a heat-induced low-pressure system
to the east, resulting in the main wind direction from the northwest). We
investigate the individual components of the turbulent kinetic energy budget,
as well as the nature of advection and turbulent transport over the forest
and the desert and determine if there is a relationship between them. Not
many instances were found in the literature where the nature of turbulent
transport was studied across large-scale surface roughness heterogeneities,
except for and . However,
only studied turbulent production and the turbulent velocity fluctuations in
the presence of a complex topography – so the nature of turbulent transport
via secondary circulations was not highlighted. studied
the decay of turbulence over different land surface types. Hence, the
difference in turbulence production and simultaneous transport across
different land use types was not studied, which determines the scope of the
current work.
Method
Theory
The turbulent kinetic energy (TKE) budget is given by without invoking any special
assumption:
∂e∂t+Uj∂e∂xj=δi3gTui′T′‾-ui′uj′‾∂Ui∂xj-∂uj′e‾∂xj-1ρ∂ui′p′‾∂xi-ϵ,
where i and j are the usual tensor indices, which can take the values of 1, 2
and 3 to indicate x, y and z directions, respectively, and δi3
is the Kronecker delta.
e=(1/2)(σu2+σv2+σw2)=(1/2)(u′2‾+v′2‾+w′2‾) is the TKE, U denotes
mean longitudinal velocity; u′, v′ and w′
denote the fluctuations from mean for the longitudinal, transverse and
vertical velocity components; g is acceleration due to gravity; T denotes
mean potential temperature; T′ is the potential temperature
fluctuation; p′ is the dynamic pressure perturbation; ρ is
density of air. The first term on the left-hand side (LHS) denotes storage or
TKE tendency. The second term on the LHS indicates advection of TKE by mean
wind flow. The first term on the right-hand side (RHS) denotes buoyant
production/destruction of TKE. The second term on the RHS denotes
mechanical/shear production of TKE. The third term on RHS denotes turbulent
transport of TKE and can also be called turbulent flux divergence. The fourth
term on RHS denotes transport of TKE by pressure velocity correlation.
ϵ is the dissipation of TKE.
Expanding the equations in terms of x, y and z coordinates, the full
TKE budget can be written as Eq. () as shown in
Appendix . Since it is difficult to
keep track of the full equation due to the large number of terms, it would be
easier to use a simple form of the TKE budget
0=-u′w′‾dUdz+gTw′T′‾-ϵ-Imbalance,
where the “imbalance” is defined in Eq. ().
Note that u′w′‾ and
w′T′‾ denote vertical momentum flux and sensible
heat flux, respectively. Also note that if the term imbalance is set to
zero, one recovers the TKE budget for an idealized surface layer where the
coordinate system is aligned with the mean wind, and a planar, homogeneous
flow with zero subsidence is assumed. Since our objective in the current
problem is to study the effect of heterogeneity, we cannot make these
assumptions. Moreover, we are also constrained by being able to measure only
at two single points in space quite far apart. Single point eddy covariance
measurements cannot compute spatial gradients, and the pressure perturbations
are not measured either. Thus, explicit computations of the imbalance terms
are not possible. Due to the three-dimensional nature of the problem, it is
also difficult to anticipate what degrees of assumptions are sufficient, so
that some of the terms can be ignored safely.
Under these constraints, a strategy is needed to evaluate the TKE budget. The
dominant mechanical production term, the buoyant production/destruction term
and the dissipation term will be evaluated directly from the data. The
residual of the TKE budget will be described as the imbalance as per Eq. () which would contain the effects of advection and
transport terms. The advantage of using this strategy is that since the
original TKE budget equation has to be closed, the errors in computing the
production and dissipation terms can also be assumed to be inside the
imbalance term.
Imbalance=-u′w′‾dUdz+gTw′T′‾-ϵ.
To compute the mechanical production term, we momentarily assume that the TKE
budget is well balanced and Monin–Obukhov similarity theory (MOST)
is valid . This
allows us to write
dUdz=ϕm(ζ)u*κz,
where ϕm is the stability correction function for momentum which varies
with the stability parameter ζ=(z-d)/L and κ=0.4, the von
Kármán constant.
u*=u′w′‾2+v′w′‾2
is the friction velocity, z is the measurement height, and
L=-u*3/(κ(g/T)w′T′‾) is the Obukhov
length; d is zero plane displacement height, taken as 2/3 of canopy height.
The standard MOST scaling relations for ϕm are used, i.e.,
ϕm=0.74+4.7ζ for stable (ζ>0) and ϕm=(1-16ζ)-1/4 for unstable (ζ<0) stratification .
Equation () allows us to compute the mechanical production term in Eq. () as
-u′w′‾dUdz=ϕmu*3(κz).
The buoyancy term can directly be computed from the EC measurements as well.
To compute the dissipation term ϵ, we use the scaling relation of
second-order structure function
Duu=u(x+r)-u(x)2‾ in the inertial subrange
Duu(r)=Cuϵ2/3r2/3,
where Cu≈2 and r is the spatial lag in the
longitudinal direction, which can be computed by multiplying the sampling time
interval with the mean longitudinal velocity, assuming that Taylor's frozen
turbulence hypothesis is valid (r=uΔt). The range of r
where this relation is valid is found to be between 0.2 to 2 m, and ϵ
is found by regression of Eq. (). Note that the
computation of ϵ is independent of any assumptions used to compute
the production terms.
Research site
Map of Yatir Forest in Israel and locations of the measurement stations. Insets:
snapshots of measurement setups. Bottom panel: topography map of Yatir Forest from Google
maps. The blue arrow indicates north.
The measurements were conducted in Yatir Forest and the surrounding shrubland
in Israel between 18 and 30 August 2015 as part of the “Climate feedbacks
and benefits of semi-arid forests” (CliFF) campaign, a joint collaboration
between Karlsruhe Institute of Technology (KIT), Germany, and the Weizmann
Institute, Israel. Figure gives an idea of the locations of
the EC towers. Tower 1 (lat 31.375728, long 35.024262) was located in the
semiarid shrubland 620 m above sea level and tower 2 (lat 31.345315, long
35.052224) was located inside the forest 660 m above sea level. The linear
distance between the two locations was measured to be 4.3 km, and as can be
observed from Fig. , there is a distinct surface heterogeneity
between the two sites. The climate of the area is in between Mediterranean
and semiarid, with a mean annual precipitation of about 285 mm
. Note that the measurement sites reported in this work are
different from those in . The trees in the forest were mostly
Aleppo pine (Pinus halepensis), with an average height of 10 m with
negligible height variation. The surrounding land was sparsely populated by
small shrubs, and in the dry season, when the measurements were conducted,
was mostly free of vegetation. Thus, it is referred to as “desert” for easy
distinction . The measurement height for the forest was 19 m
above ground (9 m above the canopy height). Note that with this height
selection, the measurements were conducted above the roughness sub-layer,
which ends at approximately 2 times the canopy height . A
mast was used over the desert and the measurement height was 9 m until 23
August, after which it was changed to 15 m for the remaining period. In this
zone of the atmospheric surface layer, the longitudinal and crosswise
velocity variances decrease logarithmically with height and the vertical
velocity variance shows independence from height . High-frequency turbulent data were
collected at 20 Hz and 30 min averaging periods were used for both sites.
After conducting quality control of the data following , a
planar fit coordinate rotation is applied to the velocity components since
the data are collected on a sloped ground. The coordinate rotation following
ensures that the cross stream velocity component v is
zero and corrects the tilting of the anemometer with respect to the local
streamlines. Moreover, a different set of coordinate rotation is applied for
the desert data after 23 August.
In addition, two Doppler lidars were used at the two locations which measured
vertical velocities . The Doppler lidars
used were StreamLine systems from HaloPhotonics. They were operated in a
vertical stare mode most of the time (interrupted every half hour for less
than 90 s). Technical specifications and instrument settings of the Doppler
lidars are given in Table 1. The Doppler lidar at tower 1 was not working
from 19 August 2015, 15:00 UTC, until 21 August 2015, 10:30 UTC and very
briefly on the 23 August 2015 around 10:00 UTC due to power cuts.
Instrument specification and settings of the Doppler lidars. From
top to bottom: serial number of the forest and desert lidar, pulse length of
the laser pulse at full width at half maximum, range gate length, pulse
repetition frequency, number of averaged pulses for a backscatter coefficient
profile, and the wavelength of the emitted laser pulse (short wavelength
infrared).
Serial numbers
0114-74 and 0114-75
Pulse length
60 m
Range gate length
18 m
Pulse repetition frequency
15 kHz
Averaged pulses per estimate
15 000
Wavelength of laser light
1.5 µm
Time series of half-hourly averages of mean speed (ms-1),
mean vertical velocity (ms-1), friction velocity (ms-1)
and mean potential temperature (K) for the measurement period. The black line
indicates desert, and the red line indicates forest.
Time series of longitudinal velocity variance (m2s-2),
vertical velocity variance (m2s-2), momentum flux
(m2s-2) and sensible heat flux (Kms-1) for the
measurement period. The black line indicates desert, and the red line
indicates forest.
Results and discussion
Time series of turbulence statistics
Time series of mean speed (ms-1), mean vertical velocity
(W, ms-1) (after applying coordinate
rotation), friction velocity (u*, ms-1) and mean near-surface
air (potential) temperature (T, K) for the measurement period are shown in
Fig. . Figure shows time series of
longitudinal velocity variance (u′u′‾,
m2s-2), vertical velocity variance
(w′w′‾, m2s-2), momentum flux
(u′w′‾, m2s-2) and sensible heat
flux (w′T′‾, Kms-1). The black line
indicates desert, and the red line indicates forest. As noted, the desert is
associated with a higher wind speed because of a lower amount of friction on
the desert surface. The higher vertical velocity over the desert indicates
the presence of stronger updrafts, which would be explained by higher
buoyancy-driven turbulence. The friction velocity (u*) over the forest is
much higher compared to the desert, especially in the daytime, which is
expected because of higher surface roughness over the forest. u*, above
both the forest and the desert, shows a strong diurnal cycle. However, there
seems to be a prominent increase in u* over the desert after 23 August.
This can be attributed to the raising of the tower height. Moreover, the
gentle topography around the desert could result in the strong vertical
updrafts above the desert. Interestingly, the near-surface air temperatures
over both the forest and the desert show a strong diurnal cycle and their
differences are about 5 K on average during daytime and almost zero at
night.
The longitudinal velocity variance u′u′‾ over the
forest and the desert show similar variations over time. The vertical
velocity variance w′w′‾ over the forest is higher
than its desert counterpart; however, after 23 August, the levels of
w′w′‾ over the desert increase as well and become
similar to the forest. This is due to changing the tower height. As the
vertical profiles of w′w′‾ are different between
the desert and the forest (due to roughness length differences), the observed
differences between w′w′‾ are a function of
observation height. At 15 m above the desert and 19 m above the forest
floor, high enough to be in the “constant flux layer”, the vertical
profiles of TKE
(u′u′‾+w′w′‾) converge.
However, when observed at a lower elevation and below the constant flux
layer, the data show clear differences in w′w′‾.
The vertical momentum flux u′w′‾ over the forest
is much higher compared to the desert, which is also expected because of the
higher surface roughness of the forest, making it a much more efficient
momentum sink compared to the desert. Note that the shear transport of
momentum flux is still much more effective over the forest compared to the
desert because of roughness effects even though the mean quantities can be
higher over the desert. The sensible heat flux
w′T′‾ over the forest is also higher, as
discussed before, due to the canopy convector effect.
Time series of mechanical production of TKE (m2s-3),
buoyant production of TKE (m2s-3), full TKE production
(m2s-3), dissipation of TKE (m2s-3) and imbalance
of TKE (m2s-3). The black line indicates desert, and the red
line indicates forest.
Nature of TKE budget
Figure shows the time series of the components of the TKE budget
as discussed in Sect. . The first row shows mechanical production
of TKE (PMech, m2s-3); the second row shows buoyant
production of TKE (PBuoy, m2s-3); the third row
shows full TKE production (PTKE, m2s-3), which is
the sum of mechanical and buoyant TKE production. The fourth row shows
dissipation of TKE (ϵ, m2s-3) and the fifth row shows
an imbalance of TKE (Imb, m2s-3). The black line indicates
desert, and the red line indicates forest. As noted in Fig. , the
production of turbulence is mostly by mechanical or shear forcing because of
the roughness of the forest, whereas mechanical production of TKE over the
desert is very small and does not have a strong diurnal cycle like the
forest, although it increases slightly after 23 August. On the other hand,
TKE production over the desert is mostly carried by buoyancy. Buoyant TKE
production is slightly larger over the forest. The buoyant TKE production
over the desert is also higher after 23 August. Given the moderate
temperature difference between the desert and the forest, the difference in
their corresponding buoyant TKE production is interesting. It also indicates
that mechanical forcing and not buoyancy makes a difference (mechanical
production is higher by approximately 1 order of magnitude than buoyant
production) in the turbulence generation over the desert and the forest. The
diurnal cycle of the TKE dissipation ϵ is interesting as well. The
dissipation of TKE seems to be higher above the forest as well compared to
the desert.
A smaller TKE dissipation is recorded when the measurement location is
further from the ground and above the roughness sub-layer. One strong
argument for observed changes after 23 August being tower-height effects
rather than a change in any large-scale forcing is that changes in the desert
are observed only after the 23 August, while the forest observations maintain
rather consistent dynamics.
The diurnal cycles of the TKE imbalance computed by Eq. ()
are also very interesting. The imbalance over the forest is often positive
over the daytime, while over the desert it is often negative, highlighting
the difference in turbulent transport and advection over the two different
regimes. Also note that the positive imbalance for the forest and negative
imbalance for the desert almost have a phase (anti-)synchronization,
indicting that the turbulence above the forest and the desert are responsive
to one another and that they are part of a coupled system, indicating again
the role of the secondary circulations.
Transport of TKE over the desert and the forest
Figure is used to better understand the nature of
turbulent transport between the desert and the forest. Panel (a) depicts the
TKE imbalance over the desert vs. the net production of TKE over the forest.
As observed, there is a significant correlation (0.5) between them,
indicating that the advection and transport of TKE by flux divergence and
pressure fluctuations reach downstream by means of the secondary circulations
and produce TKE over the forest. On the other hand, the reverse is not true,
as observed in panel (b) of Fig. . There is little
correlation between the imbalance of TKE over the forest and the production
of TKE over the desert (0.14). As observed in panel (c), the production over
the desert is also well correlated with the production over the forest (0.3)
as both the desert and the forest are subject to the same forcing. However,
the TKE production over the desert is not that well correlated with the TKE
imbalance over the desert as seen in panel (d). Thus, while there should be
some cross correlation in panel (a) because of desert production, that is not
the only effect. The nonlocal large-scale motions contribute to the transport
over the desert (without significantly altering TKE production over the
desert) which in turn cause TKE production above the forest because of the
higher mechanical forcing.
Thus, it can be stated that at least in the canopy sub-layer and in the
atmospheric surface layer, the effects of secondary circulations are
transported from over the desert towards the forest following the background
wind direction, and it is not the other way around. It is worth noting here
that the term “secondary circulation” has been used somewhat loosely here
and contain the effects of horizontal transport as well, since partitioning
the imbalance term is not possible within the scope of this campaign. In the
case of transport from the forest towards the desert, it is more likely that
horizontal advection is the main mechanism. The nature of the full extent of
the secondary circulation are a part of a much larger flow pattern and are
not fully captured by the eddy covariance towers, which only capture the
fine-scale turbulence. To reveal the full nature of the secondary
circulations, one can look at lidar observations as shown in
Appendix as well as large
eddy simulations .
(a) TKE imbalance for the desert vs. TKE production for the
forest. (b) TKE imbalance for the forest vs. TKE production for the
desert. (c) TKE production for the desert vs. TKE production for the
forest. (d) TKE production for the desert vs. TKE imbalance for the
desert. Significance: 0.05 level.
Effect of nonlocal motions
Figure shows the time series of the triple moments
w′w′u′‾,
w′w′w′‾ and
w′w′T′‾ in the first three rows. The
vertical velocity skewness term w′w′w′‾
(second row) is of importance as it appears in the transport term of the TKE
budget (Eq. ) and is a measure of non-Gaussian turbulence,
which indicates the presence of nonlocal coherent motions such as sweeps and
ejections. Note that the vertical velocity skewness is often negative above
the canopy, which is consistent with the generic feature of canopy turbulence
. The daytime vertical velocity
skewness over the desert is often positive, indicating again the presence of
nonlocal coherent structures active over the desert. The measure of skewness
increases over the desert after 23 August, which is also due to the height
change. The other two terms w′w′u′‾ and
w′w′T′‾ are also associated with
turbulent transport of momentum and heat as evident from their respective
budget equations .
∂w′u′‾∂t=0=-w′w′‾∂U‾∂z-∂w′w′u′‾∂z+gT‾u′T′‾-1ρu′∂p′∂z‾,
and
∂w′T′‾∂t=0=-w′w′‾∂T‾∂z-∂w′w′T′‾∂z+gT‾T′T′‾-1ρT′∂p′∂z‾.
Top three panels: time series of triple moments w′w′u′‾,
w′w′w′‾ (m3s-3) and
w′w′T′‾ (Km2s-2). Bottom
two panels show the integral timescales of horizontal (Inu) and vertical
velocities (Inw) in seconds.
Moreover, the triple moments have been shown to be directly correlated with
the relative contributions of nonlocal events such as sweeps and ejections
. Note
that momentum transport term w′w′u′‾ is
also opposite in sign for the desert and the forest, and it shows a strong
diurnal cycle. After 23 August, an increase in momentum transport is noted
for the desert. However, the diurnal cycle of the heat transport term
w′w′T′‾ is not as strong as its momentum
counterpart, but it is often found to be larger over the desert compared to
the forest, consistent with the findings from the TKE budget that show heat
is transported from over the desert towards the forest. It is, however,
important to note that the structures are representative of the fine-scale
turbulence and not directly representative of the large-scale circulation
structures spanning the whole boundary layer. The fourth and fifth rows of
Fig. show the time series of the integral timescale of
horizontal (Inu) and vertical (Inw) velocity components in seconds.
Inu and Inw for every half hour time period are computed by integrating
the normalized autocorrelation function of u and w until the first zero
crossing . They can be interpreted as the characteristic
timescale of the most energetic eddies in each direction. As noted in
Fig. , timescales in the horizontal directions are larger
compared to the vertical direction. More interesting is the observation that
the integral timescales for the eddies above the desert are larger than those
above the forest, which also increase after 23 August. This is another
indicator of buoyant production of turbulence, which generates larger eddies
than shear production.
Conclusions
We studied the nature of turbulent transport over a well-defined surface
heterogeneity, comprising a desert and forest in the Yatir semiarid area in
Israel. Eddy covariance and Doppler lidar measurements were conducted for 12
days between 18 and 31 August 2015 over two locations in the forest and the
shrubland (referred to as “desert” because of the almost complete lack of
vegetation during the observation period). Earlier campaigns in this area
focused on energy balance closure and hypothesized that there are secondary
circulations because of surface heterogeneity. The present work was aimed to
study the nature of turbulent transport over the forest and the desert in
more detail to address the following questions:
How does Yatir Forest affect the boundary layer dynamics such as eddy size distribution,
boundary layer height and diurnal variations in turbulent statistics and fluxes compared to the surrounding desert?
Can the existence of secondary circulation be confirmed?
Is there any horizontal energy transport between the forest and the desert and how does it vary with time?
To answer the abovementioned questions, we computed half hour average
turbulent statistics for both the desert and the forest and looked at their
diurnal variations. We also computed individual components of the turbulent
kinetic energy (TKE) budget and argued that the turbulent transport of energy
should be contained in the imbalance of the TKE budget, which consists of the
effects of advection, transport by turbulent flux divergence and pressure
velocity interactions, since we could not compute those terms explicitly.
Moreover, we also computed triple moments, which are associated with nonlocal
motions and coherent structures, and integral timescales, which are
associated with the most energetic eddies. The findings to the questions are
listed below.
The forest is found to be associated with a higher level of turbulent intensity because of
higher roughness although the desert had higher mean speeds and vertical
updrafts, possibly due to the presence of secondary circulations. Gentle
topography around the desert might contribute to the updrafts over the desert
as well. The higher roughness of the desert is also responsible for higher
wind speeds above the desert. There is little air temperature difference
between the desert and the forest, although the mean velocities and
temperature have strong diurnal cycles. Momentum and heat flux are also found
to be stronger above the forest. The presence of the secondary circulation
enhances the turbulent fluxes as well as the turbulent intensity above the
desert.
The role of secondary circulations can be better understood once the components of
the TKE budget are studied. Over the forest, the production of turbulence is
mechanical, while over the desert, TKE production is mostly carried by
buoyancy. The forest is more efficient in dissipating TKE as well. The
imbalance of TKE is taken as the indicator of TKE transport and is found to
vary diurnally almost anti-synchronously over the desert and the forest,
confirming the role of a secondary circulation. The TKE budget is closed
better over the forest compared to the desert. Turbulent triple moments,
which are indicators of nonlocal motions and coherent structures, also show
strong variability over the desert and are opposite in signs also confirming
the role of secondary circulations. The integral timescales are found to be
greater over the desert compared to the forest. This suggests that the
secondary circulations that transport energy are more active over the desert
– however, they cannot produce much turbulence over the desert since they
only rely on buoyancy-driven turbulence as mechanical forcing is missing over
the desert. This is also highlighted by the fact that mean velocities are
higher above the desert while turbulent fluctuations are higher above the
forest.
To elucidate the role of horizontal transport between the desert and the forest, we
studied the correlation between the TKE imbalance over the desert and the TKE
production over the forest. The moderately high correlation suggests that the
secondary circulation is transported from over the desert towards the forest,
enhancing TKE production over the forest, at least in the canopy sub-layer
and the atmospheric surface layer. The low correlation between the TKE
imbalance over the forest and TKE production over the desert confirms the
directionality of this horizontal exchange, which is from the desert towards
the forest and not the other way around.
To summarize, we have examined the existence and role of secondary
circulations that exists because of large-scale surface heterogeneities and
possible due to some topography effects between the desert and the forest by
looking at proxy quantities computed from turbulence measurements. Although
the campaign was conducted at a particular site, the conclusions drawn are
fairly general and can be extended to other scenarios involving surface
heterogeneities, such as urban landscapes and agricultural fields. Future
work will attempt to highlight a more spatially detailed picture of the
turbulent structure under the interesting scenario of secondary circulations
and horizontal energy transport.