It has been recently shown for two forests in France (Les Landes and Sologne) that summer cloud cover over the forest is increased relative to its surroundings. This study aims to contribute to the elucidation of the physical mechanisms responsible for this increased cloud cover, focusing on surface flux partitioning. This was done by performing a case study for a heatwave day on which enhanced cloud cover over the forest of Les Landes was observed. Two numerical experiments (large-eddy simulations) with a homogeneous forest cover were performed, one in which the sensible heat flux was increased by approximately 5% of the total available energy and another one in which the same amount of energy was added to the latent heat flux. The addition of energy to the sensible heat flux led to a stronger increase in cloud cover than the same addition to the latent heat flux. The mean relative humidity at the boundary layer top showed only small differences, indicating it was not a sufficient indicator for cloud formation in this case. Important information, which immediately underlines the need for large-eddy simulations, is contained in modifications of the shape of the probability density functions of temperature and humidity. With enhanced sensible heating, the higher peak values of relative humidity contribute to an increased cloud cover. A crucial reason for the differences in cloud cover between the experiments is conjectured to be a decrease in the required amount of energy for air parcels to reach the lifting condensation level, indirectly caused by the boundary layer and near-surface warming associated with the stronger sensible heat flux. As forests in the region do have a higher sensible heat flux than their surroundings, we highlight one potential mechanism for enhanced cloud cover. KEYWORDS cloud formation, flux partitioning, forest cloud cover, large-eddy simulation, sensible heat flux, sensible heating, surface heat fluxes INTRODUCTIONForests are of crucial importance regarding climate change by forming large carbon stores; approximately 800 billion tons of carbon are stored in forest trees and underlying soils (Brown, 1998; as cited by Sohngen and Mendelsohn, 2003). Forests are also known to influence climate by impacting the water and energy balance of the land surface (Beringer et al., 2005;Bonan, 2008;Ellison et al., 2017). A thorough understanding of the interactions between forests and the overlying atmosphere is therefore crucial to make accurate climate predictions in the context of climate change. This knowledge is also crucial to predict the effects of forest clearance or afforestation. Within the broad range of forest-atmosphere interactions, this study will focus on the effect of forests on cloud cover. These effects are still poorly understood , despite the fact that the presence of clouds has a large This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly c...

Vegetation and atmosphere processes are coupled through a myriad of interactions linking plant transpiration, carbon dioxide assimilation, turbulent transport of moisture, heat and atmospheric constituents, aerosol formation, moist convection, and precipitation. Advances in our understanding are hampered by discipline barriers and challenges in understanding the role of small spatiotemporal scales. In this perspective, we propose to study the atmosphere-ecosystem interaction as a continuum by integrating leaf to regional scales (multiscale) and integrating biochemical and physical processes (multiprocesses). The challenges ahead are (1) How do clouds and canopies affect the transferring and in-canopy penetration of radiation, thereby impacting photosynthesis and biogenic chemical transformations? (2) How is the This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Abstract. This paper provides a description of ICLASS 1.0: a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model. This framework can be used to study the atmospheric boundary layer, surface layer or the exchange of gases, moisture, heat and momentum between the land surface and the lower atmosphere. The general aim of the framework is to allow to assimilate various streams of observations (fluxes, mixing ratios at multiple heights, ...) to estimate model parameters, thereby obtaining a physical model that is consistent with a diverse set of observations. The framework allows to retrieve parameters in an objective manner, and enables to estimate information that is difficult to obtain directly by observations, for example free-tropospheric mixing ratios or stomatal conductances. Furthermore it allows to estimate possible biases in observations. Modelling the carbon cycle at ecosystem level is one of the main intended fields of application. The physical model around which the framework is constructed is relatively simple, yet contains the core physics to model the essentials of a well-mixed boundary layer and of land–atmosphere exchange. The model includes an explicit description of the atmospheric surface layer, a region where scalars show relatively large gradients with height. An important challenge is the strong non-linearity of the model, which complicates the estimation of best parameter values. The constructed adjoint of the tangent linear model can be used to mitigate this challenge. The adjoint allows for an analytical gradient of the objective cost function, used for minimisation of this function. An implemented Monte-Carlo way of running ICLASS can further help to handle non-linearity, and provides posterior statistics on the estimated parameters. The paper provides a technical description of the framework, includes a validation of the adjoint code, as well as tests for the full inverse modelling framework and a successful example application for a grassland in the Netherlands.

Abstract. This paper provides a description of ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model. This framework can be used to study the atmospheric boundary layer, surface layer, or the exchange of gases, moisture, heat, and momentum between the land surface and the lower atmosphere. The general aim of the framework is to allow the assimilation of various streams of observations (fluxes, mixing ratios at multiple heights, etc.) to estimate model parameters, thereby obtaining a physical model that is consistent with a diverse set of observations. The framework allows the retrieval of parameters in an objective manner and enables the estimation of information that is difficult to obtain directly by observations, for example, free tropospheric mixing ratios or stomatal conductances. Furthermore, it allows the estimation of possible biases in observations. Modelling the carbon cycle at the ecosystem level is one of the main intended fields of application. The physical model around which the framework is constructed is relatively simple yet contains the core physics to simulate the essentials of a well-mixed boundary layer and of the land–atmosphere exchange. The model includes an explicit description of the atmospheric surface layer, a region where scalars show relatively large gradients with height. An important challenge is the strong non-linearity of the model, which complicates the estimation of the best parameter values. The constructed adjoint of the tangent linear model can be used to mitigate this challenge. The adjoint allows for an analytical gradient of the objective cost function, which is used for minimisation of this function. An implemented Monte Carlo way of running ICLASS can further help to handle non-linearity and provides posterior statistics on the estimated parameters. The paper provides a technical description of the framework, includes a validation of the adjoint code, in addition to tests for the full inverse modelling framework, and a successful example application for a grassland in the Netherlands.

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