Regional atmospheric cooling and wetting effect of permafrost thaw-induced boreal forest loss

In the sporadic permafrost zone of North America, thaw-induced boreal forest loss is leading to permafrost-free wetland expansion. These land cover changes alter landscape-scale surface properties with potentially large, however, still unknown impacts on regional climates. In this study, we combine nested eddy covariance flux tower measurements with satellite remote sensing to characterize the impacts of boreal forest loss on albedo, eco-physiological and aerodynamic surface properties, and turbulent energy fluxes of a lowland boreal forest region in the Northwest Territories, Canada. Planetary boundary layer modelling is used to estimate the potential forest loss impact on regional air temperature and atmospheric moisture. We show that thaw-induced conversion of forests to wetlands increases albedo: and bulk surface conductance for water vapour and decreases aerodynamic surface temperature. At the same time, heat transfer efficiency is reduced. These shifts in land surface properties increase latent at the expense of sensible heat fluxes, thus, drastically reducing Bowen ratios. Due to the lower albedo of forests and their masking effect of highly reflective snow, available energy is lower in wetlands, especially in late winter. Modelling results demonstrate that a conversion of a present-day boreal forest-wetland to a hypothetical homogeneous wetland landscape could induce a near-surface cooling effect on regional air temperatures of up to 3-4 °C in late winter and 1-2 °C in summer. An atmospheric wetting effect in summer is indicated by a maximum increase in water vapour mixing ratios of 2 mmol mol^-1 . At the same time, maximum boundary layer heights are reduced by about a third of the original height. In fall, simulated air temperature and atmospheric moisture between the two scenarios do not differ. Therefore, permafrost thaw-induced boreal forest loss may modify regional precipitation patterns and slow down regional warming trends.

A new approach to predict soil temperature under vegetated surfaces

In this article, the setup and the application of an empirical model, based on Newton’s law of cooling, capable to predict daily mean soil temperature (T_soil) under vegetated surfaces, is described. The only input variable, necessary to run the model, is a time series of daily mean air temperature. The simulator employs 9 empirical parameters, which were estimated by inverse modeling. The model, which primarily addresses forested sites, incorporates the effect of snow cover and soil freezing on soil temperature. The model was applied to several temperate forest sites, managing the split between Central Europe (Austria) and the United States (Harvard Forest, Massachusetts; Hubbard Brook, New Hampshire), aiming to cover a broad range of site characteristics. Investigated stands differ fundamentally in stand composition, elevation, exposition, annual mean temperature, precipitation regime, as well as in the duration of winter snow cover. At last, to explore the limits of the formulation, the simulator was applied to non-forest sites (Illinois), where soil temperature was recorded under short cut grass. The model was parameterized, specifically to site and measurement depth. After calibration of the model, an evaluation was performed, using ~50 % of the available data. In each case, the simulator was capable to deliver a feasible prediction of soil temperature in the validation time interval. To evaluate the practical suitability of the simulator, the minimum amount of soil temperature point measurements, necessary to yield expedient model performance was determined. In the investigated case 13–20 point observations, uniformly distributed within an 11-year timeframe, have been proven sufficient to yield sound model performance (root mean square error <0.9 °C, Nash–Sutcliffe efficiency >0.97). This makes the model suitable for the application on sites, where the information on soil temperature is discontinuous or scarce.

Bidirectional exchange of biogenic volatiles with vegetation: emission sources, reactions, breakdown and deposition

Biogenic volatile organic compound (BVOC) emissions are widely modelled as inputs to atmospheric chemistry simulations. However, BVOC may interact with cellular structures and neighbouring leaves in a complex manner during volatile diffusion from the sites of release to leaf boundary layer and during turbulent transport to the atmospheric boundary layer. Furthermore, recent observations demonstrate that the BVOC emissions are bidirectional, and uptake and deposition of BVOC and their oxidation products are the rule rather than the exception. This review summarizes current knowledge of within-leaf reactions of synthesized volatiles with reactive oxygen species (ROS), uptake, deposition and storage of volatiles, and their oxidation products as driven by adsorption on leaf surface and solubilization and enzymatic detoxification inside leaves. The available evidence indicates that because of the reactions with ROS and enzymatic metabolism, the BVOC gross production rates are much larger than previously thought. The degree to which volatiles react within leaves and can be potentially taken up by vegetation depends upon compound reactivity, physicochemical characteristics, as well as upon their participation in leaf metabolism. We argue that future models should be based upon the concept of bidirectional BVOC exchange and consider modification of BVOC sink/source strengths by within-leaf metabolism and storage.

Constraining ecosystem processes from tower fluxes and atmospheric profiles

The planetary boundary layer (PBL) provides an important link between the scales and processes resolved by global atmospheric sampling/modeling and site-based flux measurements. The PBL is in direct contact with the land surface, both driving and responding to ecosystem processes. Measurements within the PBL (e.g., by radiosondes, aircraft profiles, and flask measurements) have a footprint, and thus an integrating scale, on the order of 1-100 km. We use the coupled atmosphere-biosphere model (CAB) and a Bayesian data assimilation framework to investigate the amount of biosphere process information that can be inferred from PBL measurements. We investigate the information content of PBL measurements in a two-stage study. First, we demonstrate consistency between the coupled model (CAB) and measurements, by comparing the model to eddy covariance flux tower measurements (i.e., water and carbon fluxes) and also PBL scalar profile measurements (i.e., water, carbon dioxide, and temperature) from Canadian boreal forest. Second, we use the CAB model in a set of Bayesian inversions experiments using synthetic data for a single day. In the synthetic experiment, leaf area and respiration were relatively well constrained, whereas surface albedo and plant hydraulic conductance were only moderately constrained. Finally, the abilities of the PBL profiles and the eddy covariance data to constrain the parameters were largely similar and only slightly lower than the combination of both observations.