Do roughness changes of tropical deforestation affect surface energy balance partitioning? No, they don’t. That’s what we found when we estimated the effects from first principles.

 

tanguro-brazil_4666

Figure: Areal photo showing the difference between tropical rainforest (lower half) and soybeans (light green) at Tanguro ranch in Brazil. Photo by Paulo Brando (click on the link to see website with the photo and more information on the field site).

Cutting down tropical rainforests and replacing them with soybean fields alters how the land surface functions, and this affects the atmosphere.  Rainforests have a heterogeneous canopy that absorbs sunlight very well and is aerodynamically rough, and they have deep-reaching root systems that allow them to draw water from deep within the soil, especially during the dry season when water input by precipitation is limited.  When trees are cut down and replaced by soybean fields, these physical aspects of the land surface are changed, thus impacting how the absorbed solar energy is partitioned at the surface, and how this energy is transferred into the overlying atmosphere.  Tropical deforestation is one of the many aspects of global change that has been dealt with over the last decades, evaluated with observations and climate models, so what else can add new insights?  And what can these insights be used for?

We recently looked at deforestation in a different way, just published in GRL.  Instead of using semi-empirical formulations of the energy partitioning at the land surface or a climate model, we used our thermodynamic approach to surface energy balance partitioning.  The big problem in estimating the surface energy balance are the turbulent fluxes, as turbulence is inherently a highly complex process with the motion of many whirls of air being involved.  In our approach, we constrain these fluxes by the thermodynamic limit of maximum power.  This additional constraint allows us to treat the surface energy balance partitioning in an analytic way (i.e., with paper and pencil) without the use of semi-empirical turbulence parameterizations that are commonly used in land surface and climate models.  This actually simplifies the formulation of turbulent fluxes and bases them only on physical constraints.

We then used observations from Tanguro Ranch (see Figure above), located in the Brazilian part of Amazonia.  Our institute is involved in maintaining eddy covariance stations over a tropical rainforest and over a nearby soybean field at this ranch. We used absorbed solar radiation and the ground heat flux from these observations as inputs to our thermodynamic approach and predicted the partitioning into net longwave radiation and turbulent fluxes.  We then partitioned the turbulent fluxes further into the sensible and latent heat flux using equilibrium partitioning, which are simple fractions related to the psychrometric constant and the slope of the saturation vapor pressure curve (terms that are used, e.g, by Priestley&Taylor 1972’s equilibrium evaporation, or by Penman 1948, although the approach dates back to the Austrian meteorologist Wilhelm Schmidt, who developed this partitioning back in 1915).

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Figure. Surface energy partitioning of solar radiation (red line) into net longwave radiation and turbulent fluxes (light blue line).  Taken from Fig. 1 of Conte et al. 2019.

We found that our approach worked very well in predicting turbulent fluxes over both sites, rainforest and soybean field (see Figure).  A small offset in the predicted fluxes we could attribute to a change in longwave downwelling radiation due to seasonal changes in atmospheric water vapor content.  Also, the partitioning into sensible and latent heat using the equilibrium partitioning worked very well, except for the soybean site in the dry season where apparently soil water availability severely reduced the latent heat flux.  The magnitude of turbulent fluxes however, as predicted from the maximum power limit, remained the same.

What I found most interesting about this result is that the drastic difference in surface roughness played essentially no role for the turbulent fluxes.  This is not to say that surface roughness does not play a role — it surely affected the vertical wind profile, although we did not evaluate this.  But that we were able to explain surface energy balance partitioning without considering changes in roughness suggests that turbulent fluxes over land, particularly at the diurnal scale, are predominantly driven by surface heating and free convection.  And since this is basically what our thermodynamic approach constrains (the magnitude of free convection), this is likely the reason why our approach worked so well.

What are our next steps?  First, one could use this approach already to estimate impacts of deforestation on evapotranspiration and the water cycle by combining this approach with a soil water balance calculation.  One can also use our approach as a baseline reference for surface energy balance partitioning.  As one of the reviewers pointed out, our approach probably predicts surface energy balance partitioning better than many climate models, something that we want to look into in more detail.  On the development side, we work on linking this approach with estimating surface and air temperatures (see also my previous post here).  And as Hisashi Ozawa, one of the reviewers, pointed out that the thermodynamic formulation is still quite crude as it does not distinguish between dry and moist convection and the dissipative losses by evaporation into unsaturated air.

So there is certainly more to be learned from thermodynamics about land-atmosphere interactions.  Yet, this does not need to rely on incomprehensible complex numerical simulation models, but can be done in a simple, transparent, and analytical way.

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