For the first time, a model that simulates methane emissions from northern peatlands is incorporated directly into a dynamic global vegetation model. The model, LPJ-WHyMe (LPJ <B>W</B>etland <B>Hy</B>drology and <B>Me</B>thane), was previously modified in order to simulate peatland hydrology, permafrost dynamics and peatland vegetation. LPJ-WHyMe simulates methane emissions using a mechanistic approach, although the use of some empirical relationships and parameters is unavoidable. The model simulates methane production, three pathways of methane transport (diffusion, plant-mediated transport and ebullition) and methane oxidation. A sensitivity test was conducted to identify the most important factors influencing methane emissions, followed by a parameter fitting exercise to find the best combination of parameter values for individual sites and over all sites. A comparison of model results to observations from seven sites resulted in normalised root mean square errors (NRMSE) of 0.40 to 1.15 when using the best site parameter combinations and 0.68 to 1.42 when using the best overall parameter combination

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[1] Northern peatlands and permafrost soils are associated with large carbon stocks. Rising temperatures are likely to affect the carbon balance in high-latitude ecosystems, but to what degree is uncertain. We have enhanced the Lund-Potsdam-Jena (LPJ) dynamic global vegetation model by introducing processes necessary to simulate permafrost dynamics, peatland hydrology, and peatland vegetation. The new version, LPJ-WHy v1.2, was used to study soil temperature, active layer depth, permafrost distribution, and water table position. Modeled soil temperatures agreed well with observations, apart from a Siberian site where the soil is insulated by an extensive shrub layer. Water table positions were generally in the range of observations, with some exceptions. Simulated active layer depth showed a mean absolute error of 44 cm when compared to observations, but the error was reduced to 25 cm when the soil type for seven sites was manually corrected to mirror local conditions. A sensitivity test, in which temperature and precipitation were varied independently, showed that soil temperatures and active layer depths increased more under higher temperatures when precipitation was increased at the same time. The sensitivity experiment suggested persisting wet conditions in peatlands even under temperature increases of up to 9°C as long as annual precipitation is allowed to increase with temperature to the extent indicated by climate model experiments.

[1] Peatlands and permafrost are important components of the carbon cycle in the northern high latitudes. The inclusion of these components into a dynamic global vegetation model required changes to physical land surface routines, the addition of two new peatland-specific plant functional types, incorporation of an inundation stress mechanism, and deceleration of decomposition under inundation. The new model, LPJ-WHy v1.2, was used to simulate net ecosystem production (NEP), net primary production (NPP), heterotrophic respiration (HR), and soil carbon content. Annual peatland NEP matches observations even though the seasonal amplitude is overestimated. This overestimation is caused by excessive NPP values, probably due to the lack of nitrogen or phosphorus limitation in LPJ-WHy. Introduction of permafrost reduces circumpolar (45-90°N) NEP from 1.65 to 0.96 Pg C a À1 and leads to an increase in soil carbon content of almost 40 Pg C; adding peatlands doubles this soil carbon increase. Peatland soil carbon content and hence HR depend on model spin-up duration and are crucial for simulating NEP. These results highlight the need for a regional peatland age map to help determine spin-up times. A sensitivity experiment revealed that under future climate conditions, NPP may rise more rapidly than HR resulting in increases in NEP.

An erratum to this article can be found at http://dx.doi.org/10.1007/s00382-006-0149-

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The lower excited π-electron levels of benzene are calculated by the non-empirical method of antisymmetrized products of molecular orbitals (in LCAO approximation) including configuration interaction. All configurations arising from excitation of one or two electrons from the most stable configuration are considered, and all many-center integrals are retained. The results are in better agreement with experiment and valence-bond calculations than those obtained previously by Craig in a calculation neglecting many-center integrals. Configuration interaction is found to change the order of the 1B1u and 1E2g states but leave unchanged the order of the 3B1u and 3B2u states, in agreement with the assignments 1A1g—3B1u and 1A1g—1E2g for the experimental bands at 3.8 and 6.2 ev.

Abstract-This study investigates overlap priors for variational tracking of the Left Ventricle (LV) in cardiac MagneticResonance (MR) sequences. The method consists of evolving two curves toward the LV endo-and epicardium boundaries. We derive the curve evolution equations by minimizing two functionals each containing an original overlap prior constraint. The latter measures the conformity of the overlap between the nonparametric (kernel-based) intensity distributions within the three target regions-LV cavity, myocardium and background-to a prior learned from a given segmentation of the first frame. The Bhattacharyya coefficient is used as an overlap measure. Different from existing intensity-driven constraints, the proposed priors do not assume implicitly that the overlap between the intensity distributions within different regions has to be minimal. This prevents both the papillary muscles from being included erroneously in the myocardium and the curves from spilling into the background. Although neither geometric training nor preprocessing were used, quantitative evaluation of the similarities between automatic and independent manual segmentations showed that the proposed method yields a competitive score in comparison with existing methods. This allows more flexibility in clinical use because our solution is based only on the current intensity data, and consequently, the results are not bounded to the characteristics, variability, and mathematical description of a finite training set. We also demonstrate experimentally that the overlap measures are approximately constant over a cardiac sequence, which allows to learn the overlap priors from a single frame.