Tiina Markkanen

Sensitivity of Lagrangian Stochastic footprints to turbulence statistics

This study tests the sensitivity of a Lagrangian Stochastic footprint model to the turbulence statistics describing the flow field, with a focus on the within canopy processes. Representative profiles of the input velocity statistics are taken from a long-term dataset of turbulence measurements within and above a tall spruce canopy. Based on a wavelet tool, which allows a detailed analysis of coherent structures along the vertical profile, we characterize several typical states of coupling and decoupling between surface, canopy and atmosphere. For each coupling regime, three flux footprints using different sources for turbulence statistics are compared: the first based on conditionally-averaged measurements, the second on a simple numerical solution and the third on measurements taken from literature. The effects of profile smoothing and connecting measured canopy data to parametrized atmospheric surface layer profiles are considered. Significant differences between footprints based on modelled and measured profiles were found for exchange regimes with the lower section of the profiles decoupled from the atmospheric surface layer. As such cases are likely to occur for tall canopies with moderate density, our results suggest that the accuracy of Lagrangian Stochastic footprint modelling could be improved by using better turbulence profiles for different exchange regimes.

Seasonal soil moisture and drought occurrence in Europe in CMIP5 projections for the 21st century

Projections for near-surface soil moisture content in Europe for the 21st century were derived from simulations performed with 26 CMIP5 global climate models (GCMs). Two Representative Concentration Pathways, RCP4.5 and RCP8.5, were considered. Unlike in previous research in general, projections were calculated separately for all four calendar seasons. To make the moisture contents simulated by the various GCMs commensurate, the moisture data were normalized by the corresponding local maxima found in the output of each individual GCM. A majority of the GCMs proved to perform satisfactorily in simulating the geographical distribution of recent soil moisture in the warm season, the spatial correlation with an satellite-derived estimate varying between 0.4 and 0.8. In southern Europe, long-term mean soil moisture is projected to decline substantially in all seasons. In summer and autumn, pronounced soil drying also afflicts western and central Europe. In northern Europe, drying mainly occurs in spring, in correspondence with an earlier melt of snow and soil frost. The spatial pattern of drying is qualitatively similar for both RCP scenarios, but weaker in magnitude under RCP4.5. In general, those GCMs that simulate the largest decreases in precipitation and increases in temperature and solar radiation tend to produce the most severe soil drying. Concurrently with the reduction of time-mean soil moisture, episodes with an anomalously low soil moisture, occurring once in 10 years in the recent past simulations, become far more common. In southern Europe by the late 21st century under RCP8.5, such events would be experienced about every second year.

Footprint Evaluation for Flux and Concentration Measurements for an Urban-Like Canopy with Coupled Lagrangian Stochastic and Large-Eddy Simulation Models

A footprint algorithm, based on a Lagrangian stochastic (LS) model embedded into a parallelized large-eddy simulation (LES) model, is used for the evaluation of flux and concentration footprints of passive scalars in flow in and above an urban-like canopy layer of a neutrally stratified

Nitrous oxide emissions from a beech forest floor measured by eddy covariance and soil enclosure techniques

440 \hbox { m}

Effect of chemical degradation on fluxes of reactive compounds - a study with a stochastic Lagrangian transport model

440m deep boundary layer. The urban-like canopy layer is realized by an aligned array of cuboids whose height H is

Comparison of conventional Lagrangian stochastic footprint models against LES driven footprint estimates

40\hbox { m}