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**Earth System Dynamics**
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- About
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- Abstract
- Introduction
- Thermodynamic formulation of the land surface energy balance
- Data sources
- Results
- Discussion
- Conclusions
- Data availability
- Appendix A: Effects of radiative exchange on the limit of a cold heat engine
- Author contributions
- Competing interests
- Special issue statement
- Acknowledgements
- References

ESD | Articles | Volume 9, issue 3

Earth Syst. Dynam., 9, 1127–1140, 2018

https://doi.org/10.5194/esd-9-1127-2018

© Author(s) 2018. This work is distributed under

the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esd-9-1127-2018

© Author(s) 2018. This work is distributed under

the Creative Commons Attribution 4.0 License.

Special issue: Thermodynamics and optimality in the Earth system and its...

**Research article**
21 Sep 2018

**Research article** | 21 Sep 2018

Diurnal land surface energy balance partitioning estimated from the thermodynamic limit of a cold heat engine

- Biospheric Theory and Modelling Group, Max-Planck-Institut für Biogeochemie, Jena, Germany

- Biospheric Theory and Modelling Group, Max-Planck-Institut für Biogeochemie, Jena, Germany

**Correspondence**: Axel Kleidon (akleidon@bgc-jena.mpg.de)

**Correspondence**: Axel Kleidon (akleidon@bgc-jena.mpg.de)

Abstract

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Turbulent fluxes strongly shape the conditions at the land surface, yet they
are typically formulated in terms of semiempirical parameterizations that
make it difficult to derive theoretical estimates of how global change
impacts land surface functioning. Here, we describe these turbulent fluxes as
the result of a thermodynamic process that generates work to sustain
convective motion and thus maintains the turbulent exchange between the land
surface and the atmosphere. We first derive a limit from the second law of
thermodynamics that is equivalent to the Carnot limit but which explicitly
accounts for diurnal heat storage changes in the lower atmosphere. We
call this the limit of a “cold” heat engine and use it together
with the surface energy balance to infer the maximum power that can be
derived from the turbulent fluxes for a given solar radiative forcing. The
surface energy balance partitioning estimated from this thermodynamic limit
requires no empirical parameters and compares very well with the observed
partitioning of absorbed solar radiation into radiative and turbulent heat
fluxes across a range of climates, with correlation coefficients *r*^{2}≥95 %
and slopes near 1. These results suggest that turbulent heat fluxes on
land operate near their thermodynamic limit on how much convection can be
generated from the local radiative forcing. It implies that this type of
approach can be used to derive general estimates of global change that are
solely based on physical principles.

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Kleidon, A. and Renner, M.: Diurnal land surface energy balance partitioning estimated from the thermodynamic limit of a cold heat engine, Earth Syst. Dynam., 9, 1127–1140, https://doi.org/10.5194/esd-9-1127-2018, 2018.

1 Introduction

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The turbulent fluxes of sensible and latent heat play a critical role in the land surface energy balance during the day as these fluxes represent the principal means by which the surface cools and exchanges moisture, carbon dioxide and other compounds with the atmosphere. Due to their inherently complex nature, these fluxes are typically described by semiempirical expressions (Beljaars and Holtslag, 1991; Businger et al., 1971; Louis, 1979). Yet representations of this exchange in land surface and climate models are still associated with a high degree of uncertainty. This uncertainty results, for instance, in biases in evapotranspiration and surface temperatures across different models (Mueller and Seneviratne, 2014), in empirical relationships of land surface exchange outperforming land surface models (Best et al., 2015), and in biases in boundary layer heights (Davy and Esau, 2016). The semiempirical and highly coupled nature of land–atmosphere exchange seems to make it almost impossible to derive simple, physically based estimates of the magnitude of turbulent exchange and how it changes with land cover change or global warming.

An alternative approach to describing surface–atmosphere exchange can be based on thermodynamics (Dhara et al., 2016; Kleidon et al., 2014), an aspect that is rarely considered in the description of surface–atmosphere exchange. In this approach, turbulent exchange is formulated as a thermodynamic process by which turbulent heat fluxes drive a convective heat engine within the atmosphere that does the work to maintain convection and thus the turbulent exchange near the surface. This approach specifically invokes the second law of thermodynamics as an additional constraint on atmospheric dynamics (Lorenz et al., 2001; Ozawa and Ohmura, 1997; Ozawa et al., 2003; Paltridge, 1978). The second law sets a limit on how much work can be derived from the local radiative forcing of the system. The dynamics associated with convection are then essentially captured by the implicit assumption that convection works as hard as it can, so that the use of the thermodynamic limit approximates the emergent convective dynamics. Previous applications of this thermodynamic approach have shown that it can successfully describe the broad climatological variation of surface energy balance partitioning on land and ocean (Dhara et al., 2016; Kleidon et al., 2014), the strength and sensitivity of the hydrologic cycle and surface temperatures to global change (Kleidon and Renner, 2013a, b, 2017; Kleidon et al., 2015), and the dynamics of the Earth system in general (Kleidon, 2016).

Here we extend this approach to the diurnal variation of the surface energy balance on land and compare its estimated partitioning to observations across different climates. As in the previous applications of thermodynamics to land–atmosphere exchange, the starting point is to view turbulent fluxes as the result of a heat engine that is driven by these heat fluxes (Fig. 1). The limit on how much work this heat engine can maximally perform is set by the first and second law of thermodynamics, from which the well-known Carnot limit of a heat engine can be derived (Kleidon, 2016).

When applied to the setting of the diurnal cycle of the land–atmosphere
system, two key aspects need to be considered as these shape the
thermodynamic limit (as illustrated by the two
boxes in Fig. 1b). First, the strong diurnal variation of solar radiation
causes strong changes in heat storage within the system that result in a much
less varying emission of terrestrial radiation to space. In the absence of
such heat storage changes, nighttime temperatures would be much lower than
those found on Earth. In the ideal case that is being considered here, the
strong variation of solar radiation is completely leveled out to yield a
uniform emission of radiation to space, as indicated by the blue line in the
graph at the top of Fig. 1 labeled *R*_{l,out}. While these
heat storage changes predominantly take place below the surface for open
water surfaces such as the ocean and lake systems (Liu et al., 2009), the land–atmosphere system accommodates these changes
mostly in the lower atmosphere (Kleidon and Renner, 2017) because heat
diffusion into the soil is slow (e.g., Oke, 1987). The relevance of this
different way of accommodating heat storage changes over land is that it takes
place within the heat engine that we consider. The heat storage change is
associated with a heating of the engine during the day, which represents an
additional term in the entropy balance of the engine. What we show
here is that the resulting thermodynamic limit is somewhat different to the
common Carnot limit. We refer to this limit as the Carnot limit of a
cold heat engine. Our motivation to refer to this limit as the limit of a
cold heat engine is the behavior of a cold car engine in winter. When the
car engine is still cold just after it has been started, one needs to hit the
gas pedal harder to get the same power. As we will show below, the expression
we derive here shows the same effect, that is, that a heat gain inside the
engine reduces the work output of the engine. We will show that this enhanced
heat flux is consistent with observations, so that this effect of heat
accumulation during the day is an important factor that shapes the magnitude
of turbulent fluxes on land.

The magnitude of the diurnal variation in heat storage is well constrained when assuming that the radiative heating by solar radiation and the emission to space are roughly balanced over the course of day and night. The temporal change in heat storage during the day can then be inferred from the imbalance of radiative fluxes at the top of the atmosphere (Kleidon and Renner, 2017).

The second aspect that shapes the thermodynamic limit is the reduction in surface temperature in the presence of greater turbulent fluxes at the surface (lower panel at the right of Fig. 1). This reduction in surface temperature reduces the temperature difference that is utilized by the heat engine to derive power, thus setting a limit of maximum power of the heat engine (Dhara et al., 2016; Kleidon and Renner, 2013a; Kleidon et al., 2014). (This maximum power limit is very closely related to the proposed principle of maximum entropy production (MEP), as maximum power equals maximum dissipation in steady state, and entropy production is proportional to dissipation. An example of the application of MEP to convection is given by Ozawa and Ohmura, 1997.) We then combine the thermodynamic limit of a cold heat engine with the energy balances of the surface and of the whole surface–atmosphere system and maximize the power output of the heat engine to get a fully constrained description of the system that can, in first approximation, be solved analytically. It yields a description of the turbulent exchange between the land surface and the atmosphere that is fully constrained by thermodynamics and free of empirical turbulence parameterizations.

In the following, we first derive the thermodynamic limit of a cold heat engine, combine it with the energy balances of the system and maximize the power output to estimate surface energy balance partitioning based on the solar forcing of the system. The estimated partitioning is then tested with observations across field sites of contrasting climatological conditions. We then discuss how our thermodynamic approach compares to the common approaches in boundary layer meteorology and consider the utility of our approach for future work as well as potential implications.

2 Thermodynamic formulation of the land surface energy balance

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We consider the land–atmosphere system as a thermodynamic system in a
steady state when averaged over the diurnal cycle. Surface heating by
absorption of solar radiation, *R*_{s}, causes the surface to warm, while the
atmosphere is cooled by the emission of radiation to space, *R*_{l,out}
(Fig. 1). The surface and atmosphere are linked by the net
exchange of terrestrial radiation, *R*_{l,net}, and turbulent heat fluxes,
*J*_{in}, that result from convective motion. We consider this system to be a
locally forced system with no advection. Convective motion within the
boundary layer is seen as the consequence of a heat engine that generates
motion out of the turbulent heat fluxes, where, for simplicity, we do not
distinguish between the effects of the sensible and latent heat flux and the
associated forms of dry and moist convection. The steady-state condition is
used for the radiative forcing of the whole system by requiring that the mean
radiative fluxes taken over the whole day are balanced such that
${R}_{\mathrm{s},\mathrm{avg}}={R}_{\mathrm{l},\mathrm{out}}$ (with *R*_{s,avg} being the average of *R*_{s}).
Furthermore, we assume that the generation of turbulent kinetic energy, or
power *G* (or work per time), and its frictional dissipation, *D*, are in
balance, so that *G*=*D*. In the following, we derive the limit on how much
power can be derived from the forcing of the system directly from the first
and second law of thermodynamics in a general way, so that we do not need to
make the assumption that the atmosphere operates in a Carnot-like cycle. All
variables used in the following are summarized and described in Table 1.

We first derive a thermodynamic limit akin to the Carnot limit from the energy and entropy balances of the heat engine, which specifically includes the change in heat storage within the engine. The first law of thermodynamics applied to this setup is given by

$$\begin{array}{}\text{(1)}& {\displaystyle}{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{e}}}{\mathrm{d}t}}={J}_{\mathrm{in}}+D-{J}_{\mathrm{out}}-G,\end{array}$$

where d*U*_{e}∕d*t* is the change in heat storage within the heat
engine, *J*_{in} represents the addition of heat by the turbulent heat
fluxes from the surface and *J*_{out} is the rate by which the heat engine is
being cooled, which is accomplished by radiative cooling. Note that this
formulation differs from the derivation of the Carnot limit by accounting for
changes in internal energy on the left-hand side and for dissipative heating,
*D*, on the right-hand side as frictional dissipation takes place within the
system. As we consider a steady state with *G*=*D*, note that the
contributions of these terms in Eq. (1) cancel out so that
the equation reduces to d*U*_{e}∕d$t={J}_{\mathrm{in}}-{J}_{\mathrm{out}}$. Also
note that at this point, we neglect the effects of radiative energy transport
from the surface to the atmosphere that would contribute to d*U*_{e}∕d*t* in the
application to the surface–atmosphere system. As it turns out, this
contribution by radiation does not alter the limit, as shown in Appendix A.

The associated entropy budget of the heat engine is given by a change in
entropy associated with the change in heat storage, d*U*_{e}∕d*t*, at an
effective engine temperature *T*_{e}, the entropy input by *J*_{in} at a
temperature *T*_{s}, the entropy export by *J*_{out} at a temperature *T*_{a},
frictional dissipation that is assumed to occur at temperature *T*_{e}, and
possibly some irreversible entropy production *σ*_{irr} within the engine,
i.e. irreversible losses that are not accounted for by the frictional dissipation term,
*D*∕*T*_{e}:

$$\begin{array}{}\text{(2)}& {\displaystyle}{\displaystyle \frac{\mathrm{1}}{{T}_{\mathrm{e}}}}{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{e}}}{\mathrm{d}t}}={\displaystyle \frac{{J}_{\mathrm{in}}}{{T}_{\mathrm{s}}}}+{\displaystyle \frac{D}{{T}_{\mathrm{e}}}}-{\displaystyle \frac{{J}_{\mathrm{out}}}{{T}_{\mathrm{a}}}}+{\mathit{\sigma}}_{\mathrm{irr}}.\end{array}$$

Note that this entropy budget is the entropy budget for thermal entropy, not for radiative entropy. This is an important distinction. A contribution by a radiative flux, e.g., a flux ${R}_{\mathrm{l},\mathrm{out}}/{T}_{\mathrm{a}}$, represents a flux of radiative entropy (and would require an additional factor of 4∕3 as it deals with radiation); i.e., it is entropy reflected in the composition of radiation but not associated with the thermal motion of molecules that describes heat or thermal energy. As we deal with a convective heat engine, we must not include radiative terms as such but only when radiation is absorbed and heats air and water (adds thermal energy) or when the net emission of radiation cools (removes thermal energy). Radiative terms and radiative entropy production are typically much larger in the Earth system than non-radiative contributions (easily by a factor of 100, e.g., Kleidon, 2016). Yet any form of motion is associated with the much smaller but relevant thermal entropy terms.

For the atmospheric temperature, *T*_{a}, we use the radiative temperature
associated with *R*_{l,out} (i.e., we use the Stefan–Boltzmann law,
${R}_{\mathrm{l},\mathrm{out}}=\mathit{\sigma}{T}_{\mathrm{a}}^{\mathrm{4}}$, to infer *T*_{a}, with $\mathit{\sigma}=\mathrm{5.67}\times {\mathrm{10}}^{-\mathrm{8}}$ W m^{−2} K^{−4}
being the Stefan–Boltzmann constant). This is the most
optimistic temperature for the entropy export from the heat engine as it is
the coldest temperature possible to emit radiation at a rate *R*_{l,out} to
space, and it thus represents the highest entropy export from the heat engine
(note that blackbody radiation represents the radiative flux with maximum
entropy). Note also that this temperature is not bound to a particular height
within the atmosphere but is instead inferred from the energy balance
constraint. The effective engine temperature, *T*_{e}, essentially represents
the potential temperature of the lower atmosphere as the temperature
variation within the lower atmosphere is shaped by convection and is thus
approximately adiabatic.

The thermodynamic limit on how much power, *G*, can maximally be derived by
the engine is obtained from the entropy budget using the ideal case in which
*σ*_{irr}=0 (the second law of thermodynamics requires *σ*_{irr}≥0).
This ideal case implies that the only source of entropy production is the
frictional dissipation term, *D*∕*T*_{e} (cf. Eq. 2). Using Eq. (1) to replace *J*_{out} in Eq. (2), we obtain

$$\begin{array}{}\text{(3)}& {\displaystyle}{\displaystyle}G={J}_{\mathrm{in}}\cdot {\displaystyle \frac{{T}_{\mathrm{e}}}{{T}_{\mathrm{s}}}}\cdot {\displaystyle \frac{{T}_{\mathrm{s}}-{T}_{\mathrm{a}}}{{T}_{\mathrm{a}}}}-{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{e}}}{\mathrm{d}t}}\cdot {\displaystyle \frac{{T}_{\mathrm{e}}-{T}_{\mathrm{a}}}{{T}_{\mathrm{a}}}}.\end{array}$$

In this expression, the temperature of the heat engine, *T*_{e}, plays an
important role. In the limiting case of *T*_{e}≈*T*_{a}, this expression
reduces to the common Carnot limit as the effect of the change in heat
content is indistinguishable from the waste heat flux, *J*_{out}, of the heat
engine. As the engine temperature essentially represents the potential
temperature of the lower atmosphere, it is much closer to the surface
temperature, so that the approximation *T*_{e}≈*T*_{s} is better justified.
With this approximation, the thermodynamic limit of power then reduces to

$$\begin{array}{}\text{(4)}& {\displaystyle}{\displaystyle}G\approx \left({J}_{\mathrm{in}}-{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{e}}}{\mathrm{d}t}}\right)\cdot {\displaystyle \frac{{T}_{\mathrm{s}}-{T}_{\mathrm{a}}}{{T}_{\mathrm{a}}}}.\end{array}$$

In the absence of heat storage changes, the term d*U*_{e}∕d*t* vanishes
and yields, again, the common Carnot limit, except that *T*_{a} appears in the
denominator of the Carnot efficiency rather than *T*_{s}, an aspect that has
previously been derived in the context of a “dissipative” heat engine
(Bister and Emanuel, 1998; Renno and Ingersoll, 1996). Note that in the presence of
positive heat storage changes, as is the case during the day, the maximum
power that can be derived from the heat flux *J*_{in} is reduced. That is,
the increase in heat storage within the engine (d*U*_{e}∕d*t*>0) results
in a lower efficiency in converting heat into power (with the efficiency
given by the ratio *G*∕*J*_{in}), consistent with our explanation in the
Introduction of why we refer to this effect as that of a cold heat engine.

We next use the energy balance constraints of the surface and the whole
system to express d*U*_{e}∕d*t* and *T*_{s}−*T*_{a} in terms of the absorption
of solar radiation at the surface, *R*_{s}, and the turbulent heat flux *J*_{in}.
This will allow us to replace these two terms in
Eq. (4), so that the power *G* only depends on *R*_{s}
and *J*_{in}. Note that we refer to the atmospheric heat storage
change, d*U*_{a}∕d*t* in the following rather than the engine heat storage
change, d*U*_{e}∕d*t*. The difference is that when we apply the thermodynamic
limit on the atmosphere, the heat storage is also affected by the net
exchange of longwave radiation, which adds another term to the energy and
entropy budget but which does not go through the engine as a heat flux.
However, the resulting limit remains unaffected, as shown in
Appendix A.

The surface energy balance constrains the relationship between the heat
flux *J*_{in} and the temperature difference that drives the heat engine,
*T*_{s}−*T*_{a}. We express this balance by

$$\begin{array}{}\text{(5)}& {\displaystyle}{\displaystyle}{R}_{\mathrm{s}}-k\left({T}_{\mathrm{s}}-{T}_{\mathrm{a}}\right)-{J}_{\mathrm{in}}-{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{s}}}{\mathrm{d}t}}=\mathrm{0},\end{array}$$

where we linearize the net longwave radiative exchange, ${R}_{\mathrm{l},\mathrm{net}}=k({T}_{\mathrm{s}}-{T}_{\mathrm{a}})$,
between the surface and the atmosphere and where d*U*_{s}∕d*t* describes
heat storage changes below the surface, which is represented by the ground
heat flux. This formulation of the surface energy balance can be used to
express the temperature difference, *T*_{s}−*T*_{a}, as a function of *R*_{s},
*J*_{in}, and heat storage changes below the surface, d*U*_{s}∕d*t*.

The energy balance of the whole system, neglecting heat advection terms, yields a constraint of the form

$$\begin{array}{}\text{(6)}& {\displaystyle}{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{tot}}}{\mathrm{d}t}}={\displaystyle \frac{\mathrm{d}{U}_{\mathrm{a}}}{\mathrm{d}t}}+{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{s}}}{\mathrm{d}t}}={R}_{\mathrm{s}}-{R}_{\mathrm{l},\mathrm{out}}={R}_{\mathrm{s}}-{R}_{\mathrm{s},\mathrm{avg}},\end{array}$$

where d*U*_{tot}∕d*t* is the total change in heat storage within the
surface–atmosphere system. We assume this balance to be in a steady state
when averaged over day and night, so that on average, ${R}_{\mathrm{l},\mathrm{out}}={R}_{\mathrm{s},\mathrm{avg}}$,
where *R*_{s,avg} is the temporal mean of *R*_{s} taken over the
whole day. The energy balance of the whole system provides an expression for
d*U*_{a}∕d*t* as a function of the instantaneous value of absorbed solar
radiation, *R*_{s}, the mean absorption of solar radiation, *R*_{s,avg}, and
the ground heat flux, d*U*_{s}∕d*t*.

The surface energy balance (Eq. 5) can now be used to
express the temperature difference that drives the heat engine, *T*_{s}−*T*_{a},
in the thermodynamic limit given by Eq. (4), while the
energy balance of the whole system (Eq. 6) can be used to
constrain the terms describing the changes in heat storage, d*U*_{a}∕d*t*. As the
power *G* is an increasing function of *J*_{in}, but the temperature
difference declines with greater values of *J*_{in}, the power has a maximum,
which is referred to as the maximum power limit. This limit can be derived
analytically by $\partial G/\partial {J}_{\mathrm{in}}=\mathrm{0}$ and is associated with an
optimum heat flux of the form

$$\begin{array}{}\text{(7)}& {\displaystyle}{\displaystyle}{J}_{\mathrm{opt}}\approx {\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\left({R}_{\mathrm{s}}-{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{s}}}{\mathrm{d}t}}+{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{a}}}{\mathrm{d}t}}\right).\end{array}$$

This expression is consistent with previous work where the optimum heat flux is given by ${J}_{\mathrm{opt}}={R}_{\mathrm{s}}/\mathrm{2}$ in the absence of heat storage changes (Kleidon and Renner, 2013a, b). It is, however, modulated by heat storage changes, and it matters whether these changes take place below the surface or in the lower atmosphere as the two forms of heat storage change enter Eq. (7) with a different sign.

We next consider the two limiting cases. The first limit is when the heat
storage changes take place primarily below the surface, like an open water
surface of a lake. In this case, d*U*_{s}∕d$t\approx \mathrm{d}{U}_{\mathrm{tot}}/$d*t* (and
d*U*_{a}∕d*t*≈0), and the optimum heat flux reduces to

$$\begin{array}{}\text{(8)}& {\displaystyle}{\displaystyle}{J}_{\mathrm{opt}}\approx {\displaystyle \frac{{R}_{\mathrm{s},\mathrm{avg}}}{\mathrm{2}}}.\end{array}$$

The other limiting case is when the heat storage changes take place above the
surface. Then, d*U*_{a}∕d$t\approx \mathrm{d}{U}_{\mathrm{tot}}/$d*t* (with
d*U*_{s}∕d*t*≈0), and the optimum heat flux is

$$\begin{array}{}\text{(9)}& {\displaystyle}{\displaystyle}{J}_{\mathrm{opt}}\approx {R}_{\mathrm{s}}-{\displaystyle \frac{{R}_{\mathrm{s},\mathrm{avg}}}{\mathrm{2}}}.\end{array}$$

This expression implies that the optimum value of the turbulent heat flux
varies directly with the absorbed solar radiation, *R*_{s}, but has a constant
offset given by half of the mean absorption, ${R}_{\mathrm{s},\mathrm{avg}}/\mathrm{2}$. This offset
should be a comparatively small value of about 80–100 W m^{−2}, given a
global mean value of surface absorption of solar radiation of 165 W m^{−2}
(Stephens et al., 2012). Note that the power, however, does not
differ between the two cases and yields the same value of ${G}_{\mathrm{max}}=({R}_{\mathrm{s},\mathrm{avg}}/\mathrm{2})\cdot ({T}_{\mathrm{s}}-{T}_{\mathrm{a}})/{T}_{\mathrm{a}}$.

Hence, the information on absorbed solar radiation (and the ground heat flux to
account for d*U*_{s}∕d*t*) is sufficient to estimate surface energy balance
partitioning from the thermodynamic limit of maximum power.

Evaluating our estimate requires observations of absorbed solar
radiation during the day, *R*_{s}, and the ground heat flux, d*U*_{s}∕d*t*. From
the diurnal course of *R*_{s}, the mean value of *R*_{s,avg} can be calculated,
which in turn yields an estimate for d*U*_{tot}∕d*t*. Taken together with the
ground heat flux, this yields the value of d*U*_{a}∕d*t*, so that all terms in
Eq. (7) can be specified. The resulting estimate
of *J*_{opt} can then be compared to observations of the turbulent heat fluxes or to the available energy, i.e., net radiation reduced by the ground heat flux.

3 Data sources

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We use two types of data sources to test our approach. To test how reasonable
the estimates are for the diurnal heat storage changes in the lower
atmosphere, we first use 6-hourly radiosonde data from the DWD
meteorological observatory Lindenberg in Brandenburg, Germany (data available
at http://weather.uwyo.edu/upperair/sounding.html, last access: 16 April 2018). These observations
allow us to derive an estimate of the diurnal variations in temperature (and
moisture) in the lower atmosphere and thus of d*U*_{a}∕d*t*
(Fig. 2a). We use data from this site because this
observatory provides a long and consistent record of four vertical profiles a
day as well as surface energy balance components, while typically only two
vertical profiles a day are taken during routine radiosonde measurements. We use observations from June for the
years 2006 to 2009 and calculate the moist static energy at each 6 h interval and then take the difference over the time interval to obtain
estimates for changes in atmospheric heat storage. These differences are then
compared to the change in atmospheric heat storage expected from solar
radiation, as described by Eq. (6).

We then use observations of absorbed solar radiation (*R*_{s}) and the ground
heat flux (d*U*_{s}∕d*t*) at six field sites in highly contrasting climatological
settings (listed in Table 2) to calculate the turbulent
heat fluxes from maximum power (Eq. 7). The six sites
include a grassland and a forested site at Lindenberg, Brandenburg, Germany
(Beyrich et al., 2006); three AmeriFlux sites (a tundra site at Anaktuvuk
River, Alaska (Rocha and Shaver, 2011); a grassland site at Southern Great
Plains, Oklahoma (Fischer et al., 2007; Raz-Yaseef et al., 2015); and a tropical
rain forest site at Tapajos National Park, Brazil, (Goulden et al., 2004));
and a site in a planted pine forest at Yatir Forest in Israel
(Rotenberg and Yakir, 2010, 2011). For each site, we use 1 month of observations for a summer period in which solar radiative heating of
the surface is highest and the effects of heat advection are minor; we estimate
turbulent fluxes associated with maximum power (using Eq. 7) and compare these to the observed fluxes.

4 Results

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We first evaluate the extent to which diurnal variations in solar radiation are buffered by heat storage changes in the lower atmosphere. To do so, we use the diagnosed variations of moist static energy from the radiosoundings in Lindenberg, Germany and compare these to the mean variation in absorbed solar radiation at the surface as well as variations in the ground heat flux at the site in Fig. 2b. The comparison shows that the heat storage variations in the lower atmosphere are substantially greater than the ground heat flux so that the diurnal variations in solar radiation are mostly buffered by the lower atmosphere. Although there is considerable variation (as indicated by the blue boxes), mostly due to pressure changes and advective effects, these variations follow the temporal course of what is expected from the variation in absorbed solar radiation (as described by Eq. 6). This confirms our conjecture that the diurnal variations in solar radiation on land are buffered primarily by heat storage changes in the lower atmosphere. This buffering of the diurnal variations over the land surface is rather different to how an open water surface buffers these variations (as also shown by observations; Liu et al., 2009; this is an aspect used previously to explain the difference in climate sensitivity of land and ocean surfaces; Kleidon and Renner, 2017).

The comparison of the estimated surface energy balance partitioning from maximum power to observations at the six sites is shown in Fig. . The correlations are summarized in Table 2 in terms of the correlation coefficient as well as the slope and intercept. During nighttime, there is a mismatch between our approach and observations, which is represented by the intercept shown in Table 2. This mismatch may be explained by the prevalent stable nighttime conditions in which the atmosphere does not act as a heat engine, an aspect that we did not consider in our approach. During daytime, we find very high correlations of above 95 % between the estimated turbulent fluxes from the maximum power limit with observed net radiation (reduced by the ground heat flux), with a very good match of the estimated slopes in the correlation within 15 % of the observed. This high level of agreement is found across the range of climatological settings shown in Fig. .

Also note that the maximum power limit without an explicit consideration of
heat storage changes (i.e., with d*U*_{s}∕d*t*=0 and d*U*_{a}∕d*t*=0 in
Eq. (7), as in Kleidon et al., 2014, and as indicated by
light blue points in Fig. ) estimates turbulent fluxes that also result in a high
correlation but with a magnitude that is too low compared to observations.
This high level of agreement of the maximum power limit with diurnal heat
storage changes suggests that it is an adequate description of surface energy
balance partitioning and land–atmosphere exchange at the diurnal timescale, so that turbulent fluxes appear to operate near their thermodynamic
limit. It further shows that it is critical to account for diurnal variations
in heat storage in the thermodynamic limit to adequately represent the
magnitude of the observed turbulent fluxes.

5 Discussion

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Our approach, of course, only represents a general description of the full dynamics of surface–atmosphere exchange. Notable effects not considered in our approach that could alter the results and potentially modulate the outcome of the maximum power limit include a more detailed representation of radiative transfer, a distinction between the sensible and latent heat fluxes which result in different forms of storage changes in the atmosphere, entrainment effects at the top of the boundary layer, advection and coupling to large-scale atmospheric processes, and a better representation of nighttime processes, particularly regarding the formation of stable conditions at night that prevent convection to occur. These aspects can be explored further in future extensions. Yet even at this highly simplified level, the agreement of the estimated flux partitioning with observations is rather remarkable, indicating that the dominant forcing and the dominant constraints are captured by our approach.

Our results emphasize the importance of considering the constraint imposed by
the second law of thermodynamics on land–atmosphere exchange. While the
complex, turbulent nature of this exchange makes it seem almost impossible to
describe its outcome in simple terms, the generation of turbulent kinetic
energy that drives the diurnal development of the convective boundary layer
is nevertheless constrained by thermodynamics. The very good agreement of our
results with observations suggests that this constraint imposed by
thermodynamics is relevant to this generation, and land–atmosphere exchange
appears to operate near this thermodynamic limit. This is consistent with
previous research that applied thermodynamics and/or heat engine frameworks
to atmospheric motion, for instance approaches using the proposed principle
of maximum entropy production (Lorenz et al., 2001; Ozawa and Ohmura, 1997; Ozawa et al., 2003; Paltridge, 1978) or
applications to hurricanes and atmospheric convection (Emanuel, 1999; Pauluis and Held, 2002a, b). Note that our
maximization of power is almost identical to the maximization of material
entropy production, as we assume a steady state in which power equals
dissipation (*G*=*D*), and entropy production by turbulence is then given by *D*∕*T*,
where *T* is the temperature at which dissipation occurred (with *T*≈*T*_{s}).
Yet our approach differs in that it specifically considered
the effect of heat storage changes in altering the thermodynamic limit and
feedbacks with the surface energy balance that altered the driving
temperature difference of the heat engine. The heat storage changes in the
lower atmosphere result in an additional term in the Carnot limit, and this
can explain why the land–atmosphere system functions quite differently with
its pronounced diurnal variations in turbulent fluxes than the temporally
much more uniform turbulent fluxes over open water surfaces
(Kleidon, 2016; Kleidon and Renner, 2017; Liu et al., 2009). Thermodynamics
combined with these two additional factors then provide sufficient
constraints on the magnitude of turbulent heat fluxes. It would seem that
this could provide valuable information to better parameterize turbulent
fluxes within the Monin–Obukhov similarity theory for unstable conditions,
specifically regarding the stability functions that are used in this approach
(Louis, 1979).

This insight that surface energy balance partitioning is predominantly determined by the local partitioning of the absorbed solar radiation is rather different than the way this exchange is commonly represented in climate models. In these models, surface exchange is parameterized using the aerodynamic bulk approach, in which the aerodynamic drag of the surface and horizontal wind speeds play a dominant role that is modulated by stability functions. Our approach differs in that solar radiation plays the dominant role in surface exchange by the local generation of buoyancy and power to drive convection, rather than wind speed and aerodynamic roughness as what the bulk method would suggest. A recent intercomparison between a number of commonly used land surface models (Best et al., 2015) shows, however, that land surface models using the bulk method generally underestimate the strong correlation of turbulent fluxes with downward solar radiation found in observations. Our approach can resolve this bias and suggests that the bulk method may underestimate the effect of the local forcing by solar radiation on surface–atmosphere exchange.

We think that our approach provides ample opportunities for future
applications and research. First, the simple expression of how
turbulent heat fluxes on land vary during the day, as given by
Eq. (9), provides an easy way to get a first-order
estimate. It could serve as a baseline estimate that is solely based on
physical principles, specifically, the first and second law of
thermodynamics, and does not require tuning. This expression should
nevertheless be further evaluated in a broader range of climatological
conditions and over extended time periods to identify possible shortcomings,
for instance with respect to the simple parameterization of longwave
radiation or regarding the omission of advective effects. For a broader range
of applicability, the approach would need to be extended further to derive an
expression for near-surface air temperature, which would be related to the
changes in atmospheric heat storage (d*U*_{a}∕d*t*), for the aerodynamic
conductance, and for boundary layer development, and the turbulent heat
fluxes should be separated into the fluxes of sensible and latent heat.
It would also be instructive to compare the power associated with the
limit with estimates of the turbulent kinetic energy generation rate from
observations to develop another possibility for testing the maximization approach.

Our approach can then be used to evaluate aspects of global change analytically, such as land cover change or global warming, providing an alternative approach to these topics that complements complex, numerical modeling approaches. More generally, the success of our approach in reproducing observations very well constitutes another example of processes in complex systems appearing to evolve to and operate at their thermodynamic limit (Kleidon, 2016; Kleidon et al., 2010; Martyushev and Seleznev, 2006; Ozawa et al., 2003). This, in turn, encourages the application of thermodynamics to a broader range of questions and topics to understand the evolution and emergent dynamics of complex Earth systems.

6 Conclusions

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We formulated a Carnot limit which accounts for heat storage changes within the atmospheric heat engine and used this limit to estimate the partitioning of the solar radiative forcing into radiative and turbulent cooling at the diurnal timescale. In contrast to common approaches to describe near-surface turbulent heat transfer into the atmosphere, we explicitly consider the thermodynamic constraint imposed by the second law of thermodynamics by treating turbulent heat fluxes and convection as the result of a heat engine. The maximization of the work output of this convective heat engine then yields estimates of turbulent fluxes that compare very well to observations across a range of climates and do not require empirical parameterizations. This demonstrates that our approach represents an adequate, general description of the land surface energy balance that only uses physical concepts and that does not rely on semiempirical turbulence parameterizations.

We conclude that turbulent fluxes over land appear to operate near its thermodynamic limit by which the power of the convective heat engine is maximized. This limit is shaped by the second law of thermodynamics, as in the case of the Carnot limit of a heat engine in classical thermodynamics, but also requires the consideration of two additional factors that relate the heat engine to its environmental setting. The first factor relates to the strong diurnal variation of solar radiation, which results in diurnal heat storage changes. Over land these changes are buffered primarily in the lower atmosphere and these modulate the Carnot limit, resulting in a reduced efficiency and in what we referred to as a cold heat engine. Second, the limit of maximum power of the atmospheric heat engine is shaped by the trade-off in the driving temperature difference between surface and atmosphere, which decreases with greater turbulent heat fluxes. This trade-off results in the maximum power limit and represents a strong coupling between surface conditions and the lower atmosphere.

Overall, our study shows that thermodynamics adds a highly relevant constraint to land–atmosphere coupling. This thermodynamic approach to the surface energy balance and land–atmosphere interactions should help us to better understand the role of the land surface and terrestrial vegetation in the climate system and how they interact with global change.

Data availability

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Data availability.

All data used in this study did not originate from the authors but were obtained from other studies. The sources for the data are provided in Table 2. For reproducing the results of this study, the data is available from the corresponding author upon request.

Appendix A: Effects of radiative exchange on the limit of a cold heat engine

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The derivation of the Carnot limit with heat storage changes in
Sect. 2.1 assumed in the first law that the heat
storage change within the heat engine is entirely caused by the heat flux *J*_{in}.
When applying this approach to turbulent fluxes between the land
surface and the atmosphere, one also needs to consider the net transport of
energy by radiative exchange between the surface and the atmosphere. In the
derivation above, this net exchange is represented by the flux *R*_{l,net}.
This flux contributes to the heat storage change in the lower atmosphere, but
it is not driven by the heat engine. This results in a small inconsistency
when applying the limit of Sect. 2.1 to the lower
atmosphere. In the following, we show that the limit derived in
Sect. 2.1 is still valid. However, whether the lower
atmosphere is opaque to longwave radiative transfer and absorbs *R*_{l,net} or whether it is instead transparent makes a difference in the justification,
which is why we included this derivation here rather than in the main text.

In the following, we assume that the radiative–convective layer of the lower
atmosphere is sufficiently opaque and absorbs the net longwave radiation of
the surface, *R*_{l,net}. Then, the first law described by
Eq. (1) becomes the energy balance of the lower atmosphere
and changes to

$$\begin{array}{}\text{(A1)}& {\displaystyle}{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{a}}}{\mathrm{d}t}}={J}_{\mathrm{in}}+{R}_{\mathrm{l},\mathrm{net}}-{R}_{\mathrm{l},\mathrm{out}}-G+D,\end{array}$$

where *G*=*D* and ${R}_{\mathrm{l},\mathrm{out}}={R}_{\mathrm{s},\mathrm{avg}}$ in steady state.

The second law (Eq. 2) obtains another term related to the
entropy being added by the warming due to the absorption of the net flux of
longwave radiation, *R*_{l,net}. As this warming takes place at the
prevailing physical temperature of the atmosphere (rather than the potential
temperature), its temperature is likely closer to *T*_{a} rather than *T*_{e}
or *T*_{s}. Hence, the entropy budget changes to

$$\begin{array}{}\text{(A2)}& {\displaystyle}{\displaystyle \frac{\mathrm{1}}{{T}_{\mathrm{e}}}}{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{a}}}{\mathrm{d}t}}={\displaystyle \frac{{J}_{\mathrm{in}}}{{T}_{\mathrm{s}}}}+{\displaystyle \frac{D}{{T}_{\mathrm{e}}}}-{\displaystyle \frac{{R}_{\mathrm{l},\mathrm{out}}}{{T}_{\mathrm{a}}}}+{\displaystyle \frac{{R}_{\mathrm{l},\mathrm{net}}}{{T}_{\mathrm{a}}}}+{\mathit{\sigma}}_{\mathrm{irr}}.\end{array}$$

As in Sect. 2.1, we can combine
Eqs. (10) and (11), solve them for *D* (=*G*),
and obtain a limit on the power (*G*) by assuming that the entropy production *σ*_{irr}=0:

$$\begin{array}{}\text{(A3)}& {\displaystyle}{\displaystyle}G={J}_{\mathrm{in}}\cdot {\displaystyle \frac{{T}_{\mathrm{e}}}{{T}_{\mathrm{s}}}}\cdot {\displaystyle \frac{{T}_{\mathrm{s}}-{T}_{\mathrm{a}}}{{T}_{\mathrm{a}}}}-{\displaystyle \frac{\mathrm{d}{U}_{\mathrm{e}}}{\mathrm{d}t}}\cdot {\displaystyle \frac{{T}_{\mathrm{e}}-{T}_{\mathrm{a}}}{{T}_{\mathrm{a}}}}.\end{array}$$

This is the same expression as Eq. (3), so that the effect of net longwave radiative transfer actually cancels out.

In the case in which the lower atmosphere is comparatively transparent for
longwave radiation, the flux *R*_{l,net} passes through the lower
atmosphere without being absorbed. In this case, Eqs. (1)
to (4) remain unaffected.

Author contributions

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Author contributions.

AK and MR jointly developed the idea for this paper. AK performed the theoretical derivation and MR the data analysis. AK led the writing of the manuscript with input from MR.

Competing interests

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Competing interests.

The authors declare that they have no conflict of interest.

Special issue statement

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Special issue statement.

This article is part of the special issue “Thermodynamics and optimality in the Earth system and its subsystems (ESD/HESS inter-journal SI)”. It is not associated with a conference.

Acknowledgements

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Acknowledgements.

We thank two anonymous reviewers for their helpful reviews and Henk de Bruin,
Andreas Chlond, Pierre Gentine and Aljosa Slamersak for constructive
discussions on land–atmosphere exchange. This research contributes to the
“Catchments As Organized Systems (CAOS)” research group (FOR 1598) funded
by the German Science Foundation (DFG). We acknowledge the University of
Wyoming for making the radio sounding data available at
http://weather.uwyo.edu/upperair/sounding.html (last access: 16 April 2018).
Data from Site A were funded by NSF grant 1556772 to the University of Notre Dame. Data from Site B
were supported by the Office of Biological and Environmental Research of the
US Department of Energy under contract no. DE-AC02-05CH11231 as part of the
Atmospheric Radiation Measurement Program, ARM). Data for Site C and D were
provided by the Deutscher Wetterdienst (DWD) –Meteorologisches Observatorium
Lindenberg/Richard-Assmann Observatorium. They were obtained in the context of
the Coordinated Energy and Water Cycle Observation Project (CEOP), which was
initiated as an international effort in 1998 by the World Climate Research
Programme (WCRP) Global Energy and Water Cycle Experiment (GEWEX)
Hydrometeorology Panel (GHP) in support of global climate research interests.
Data for Site E were kindly provided by Eyal Rotenberg and Daniel Yakir. Data
for Site F were provided by the AmeriFlux data server:
http://ameriflux.ornl.gov (last access: 16 April 2018).

The article processing charges for this open-access

publication
were covered by the Max Planck Society.

Edited by: Michel Crucifix

Reviewed by: two anonymous referees

References

Back to toptop
Beljaars, A. C. M. and Holtslag, A. A. M.: Flux Parameterization over Land Surfaces for Atmospheric Models, J. Appl. Meteorol., 30, 327–341, 1991. a

Best, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M., Guo, Z., Haverd, V., van den Hurk, B. J. J., Nearing, G. S., Pak, B., Peters-Lidard, C., Jr, J. A. S., Stevens, L., and Vuichard, N.: The plumbing of land surface models: benchmarking model performance, J. Hydrometeorol., 16, 1425–1442, 2015. a, b

Beyrich, F., Leps, J. P., Mauder, M., Bange, J., Foken, T., Huneke, S., Lohse, H., Luedi, A., Meijninger, W. M. L., Mironov, D., Weisensee, U., and Zittel, P.: Area-averaged surface fluxes over the Litfass region based on eddy-covariance measurements, Bound.-Lay. Meteorol., 121, 33–65, 2006. a, b, c

Bister, M. and Emanuel, K. A.: Dissipative heating and hurricane intensity, Meteorol. Atmos. Phys., 65, 233–240, 1998. a

Businger, J. A., Wyngaard, J. C., Izumi, Y., and Bradley, E. F.: Flux-Profile Relationships in the Atmospheric Surface Layer, J. Atmos. Sci., 28, 181–189, 1971. a

Davy, R. and Esau, I.: Differences in the efficacy of climate forcings explained by variations in atmospheric boundary layer depth, Nat. Commun., 7, 11690, https://doi.org/10.1038/ncomms11690, 2016. a

Dhara, C., Renner, M., and Kleidon, A.: Broad climatological variation of surface energy balance partitioning across land and ocean predicted from the maximum power limit, Geophys. Res. Lett., 43, 7686–7693, 2016. a, b, c

Emanuel, K. A.: Thermodynamic control of hurricane intensity, Nature, 401, 665–669, 1999. a

Fischer, M. L., Billesbach, D. P., Riley, W. J., Berry, J. A., and Torn, M. S.:
Spatiotemporal Variations in Growing Season Exchanges of CO_{2},
H_{2}O, and Sensible Heat in Agricultural Fields of the Southern Great
Plains, Earth Interact., 11, 1–21, 2007. a, b

Goulden, M. L., Miller, S. D., da Rocha, H. R., Menton, M. C., de Freitas, H.
C., Figuera, A. M. S., and de Sousa, C. A. D.: Diel and seasonal patterns of
tropical forest CO_{2} exchange, Ecol. Appl., 14, S42–S54, 2004. a, b

Kleidon, A.: Thermodynamic foundations of the Earth system, Cambridge University Press, Cambridge, 2016. a, b, c, d, e

Kleidon, A. and Renner, M.: Thermodynamic limits of hydrologic cycling within the Earth system: concepts, estimates and implications, Hydrol. Earth Syst. Sci., 17, 2873–2892, https://doi.org/hess-17-2873-2013, 2013a. a, b, c

Kleidon, A. and Renner, M.: A simple explanation for the sensitivity of the hydrologic cycle to climate change, Earth Syst. Dynam., 4, 455–465, https://doi.org/10.5194/esd-4-455-2013, 2013b. a, b

Kleidon, A. and Renner, M.: An explanation for the different climate sensitivities of land and ocean surfaces based on the diurnal cycle, Earth Syst. Dynam., 8, 849–864, https://doi.org/10.5194/esd-8-849-2017, 2017. a, b, c, d, e

Kleidon, A., Malhi, Y., and Cox, P. M.: Maximum entropy production in environmental and ecological systems, Philos. T. Roy. Soc. B, 365, 1297–1302, 2010. a

Kleidon, A., Renner, M., and Porada, P.: Estimates of the climatological land surface energy and water balance derived from maximum convective power, Hydrol. Earth Syst. Sci., 18, 2201–2218, https://doi.org/10.5194/hess-18-2201-2014, 2014. a, b, c, d

Kleidon, A., Kravitz, B., and Renner, M.: The hydrologic sensitivity to global warming and solar geoengineering derived from thermodynamic constraints, Geophys. Res. Lett., 42, 138–144, 2015. a

Liu, H., Zhang, Y., Liu, S., Jiang, H., Sheng, L., and Williams, Q. L.: Eddy covariance measurements of surface energy budget and evaporation in a cool season over southern open water in Mississippi, J. Geophys. Res., 114, D04110, https://doi.org/10.1029/2008JD010891, 2009. a, b, c

Lorenz, R. D., Lunine, J. I., Withers, P. G., and McKay, C. P.: Titan, Mars and Earth: Entropy production by latitudinal heat transport, Geophys. Res. Lett., 28, 415–418, 2001. a, b

Louis, J. F.: A parametric model of vertical eddy fluxes in the atmosphere, Bound.-Lay. Meteorol., 17, 187–202, 1979. a, b

Martyushev, L. M. and Seleznev, V. D.: Maximum entropy production principle in physics, chemistry, and biology, Phys. Rep., 426, 1–45, 2006. a

Mueller, B. and Seneviratne, S. I.: Systematic land climate and evapotranspiration biases in CMIP5 simulations, Geophys. Res. Lett., 41, 128–134, 2014. a

Newey, W. K. and West, K. D.: Automatic Lag Selection in Covariance Matrix Estimation, Rev. Econ. Stud., 61, 631–653, 1994. a

Oke, T. R.: Boundary layer climates, 2nd Edn., Routledge, London, New York, 1987. a

Ozawa, H. and Ohmura, A.: Thermodynamics of a global-mean state of the atmosphere – A state of maximum entropy increase, J. Climate, 10, 441–445, 1997. a, b, c

Ozawa, H., Ohmura, A., Lorenz, R. D., and Pujol, T.: The second law of thermodynamics and the global climate system – A review of the Maximum Entropy Production principle, Rev. Geophys., 41, 1018, https://doi.org/10.1029/2002RG000113, 2003. a, b, c

Paltridge, G. W.: The steady-state format of global climate, Q. J. Roy. Meteorol. Soc., 104, 927–945, 1978. a, b

Pauluis, O. and Held, I. M.: Entropy budget of an atmosphere in radiative convective equilibrium. Part I: Maximum work and frictional dissipation, J. Atmos. Sci., 59, 126–139, 2002a. a

Pauluis, O. and Held, I. M.: Entropy budget of an atmosphere in radiative convective equilibrium. Part II: Latent heat transport and moist processes, J. Atmos. Sci., 59, 140–149, 2002b. a

Raz-Yaseef, N., Billesbach, D. P., Fischer, M. L., Biraud, S. C., Gunter, S. A., Bradford, J. A., and Torn, M. S.: Vulnerability of crops and native grasses to summer drying in the U.S. Southern Great Plains, Agr. Ecosyst. Environ., 213, 209–218, 2015. a, b

Renno, N. O. and Ingersoll, A. P.: Natural convection as a heat engine: A theory for CAPE, J. Atmos. Sci., 53, 572–585, 1996. a

Rocha, A. V. and Shaver, G. R.: Burn severity influences post-fire CO_{2}
exchange in arctic tundra, Ecol. Appl., 21, 477–489, 2011. a, b

Rotenberg, E. and Yakir, D.: Contribution of semi-arid forests to the climate system, Science, 327, 451–454, 2010. a, b

Rotenberg, E. and Yakir, D.: Distinct patterns of changes in surface energy budget associated with forestation in the semiarid region, Global Change Biol., 17, 1536–1548, 2011. a, b

Stephens, G. L., Li, J., Wild, M., Clayson, C. A., Loeb, N., Kato, S., L'Ecuyer, T., Stackhouse, P. W., Lebsock, M., and Andrews, T.: An update on Earth's energy balance in light of the latest global observations, Nat. Geosci., 5,, 691–696, https://doi.org/10.1038/NGEO1580, 2012. a

Zeileis, A.: Econometric Computing with HC and HAC Covariance Matrix Estimators, J. Stat. Softw., 11, 1–17, 2004. a

Short summary

Turbulent fluxes represent an efficient way to transport heat and moisture from the surface into the atmosphere. Due to their inherently highly complex nature, they are commonly described by semiempirical relationships. What we show here is that these fluxes can also be predicted by viewing them as the outcome of a heat engine that operates between the warm surface and the cooler atmosphere and that works at its limit.

Turbulent fluxes represent an efficient way to transport heat and moisture from the surface into...

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