Climate variability as reflected in a regional atmospheric CO2 record

This paper analyses a 15-year long atmospheric CO2 mixing ratio record measured at a mid-continental, low-elevation station (Hegyhatsal, Hungary) to reveal the effect of regional climate variability. While the long-term trend and the temporal fluctuation of the growth rate of CO2 mixing ratio follow the global tendencies to a large extent, the shorter-term variations show special features. We present the distorted seasonal cycle caused by the seasonality in the atmospheric vertical mixing and the tendentious change in its shape, which can be attributed to the gradual warming and to the resulted prolongation of the growing season. The decreasing summer diurnal amplitude and the decreasing seasonal amplitude in the mixing ratio, furthermore the higher than average summer CO2 mixing ratio growth rate in the first period of the measurements (1994-2003) with generally rising temperature and decreasing precipitation are explained as the consequence of the reduced activity of the biosphere in the influence area of the station and that of the reduced biomass under environmental conditions getting increasingly unfavourable. The explanation is supported by the co-located tall tower surface-atmosphere CO2 exchange measurements and by the crop yield statistics of the dominantly agricultural region around the station

Carbon exchange of grass in Hungary

Continuous measurement of net biosphere-atmosphere carbon exchange was performed in western Hungary over a managed semi-natural grassland field using the eddy covariance technique to estimate Net Ecosystem Exchange (NEE). The paper presents the measuring site and instrumentation, as well as the data processing methods applied. The measurements covered the period March 1999 to December 2000 during which, on an annual time scale, the region acted as a net CO2 sink, where NEE was -54 g C m(-2) in 1999 (data for January and February were estimated) and -232 g C m(-2) in 2000 (negative NEE represents CO2 uptake by the vegetation). The remarkable inter-annual difference may be the result of the significant climate difference between 1999 and 2000

Magneto-elastic coupling and competing entropy changes in substituted CoMnSi metamagnets

We use neutron diffraction, magnetometry and low temperature heat capacity to probe giant magneto-elastic coupling in CoMnSi-based antiferromagnets and to establish the origin of the entropy change that occurs at the metamagnetic transition in such compounds. We find a large difference between the electronic density of states of the antiferromagnetic and high magnetisation states. The magnetic field-induced entropy change is composed of this contribution and a significant counteracting lattice component, deduced from the presence of negative magnetostriction. In calculating the electronic entropy change, we note the importance of using an accurate model of the electronic density of states, which here varies rapidly close to the Fermi energy.Comment: 11 pages, 9 figures. Figures 4 and 6 were updated in v2 of this preprint. In v3, figures 1 and 2 have been updated, while Table II and the abstract have been extended. In v4, Table I has updated with relevant neutron diffraction dat

On the ground state energy scaling in quasi-rung-dimerized spin ladders

On the basis of periodic boundary conditions we study perturbatively a large N asymptotics (N is the number of rungs) for the ground state energy density and gas parameter of a spin ladder with slightly destroyed rung-dimerization. Exactly rung-dimerized spin ladder is treated as the reference model. Explicit perturbative formulas are obtained for three special classes of spin ladders.Comment: 4 page

Supporting environmental modelling with Taverna workflows, web services and desktop grid technology

Ecosystem functioning, climate change, and multiple interactions among biogeochemical cycles, climate system, site conditions and land use options are leading-edge topics in recent environmental modelling. Terrestrial ecosystem models are widely used to support carbon sequestration and ecosystem studies under various ecological circumstances. Our team uses the Biome-BGC model (Numerical Terradynamic Simulation Group, University of Montana), and develops an improved model version of it, called Biome-BGC MuSo. Both the original and the improved model estimate the ecosystem scale storage and fluxes of energy, carbon, nitrogen and water, controlled by various physical and biological processes on a daily time-scale. Web services were also developed and integrated with parallel processing desktop grid technology. Taverna workflow management system was used to build up and carry out elaborated workflows like seamless data flow to model simulation, Monte Carlo experiment, model sensitivity analysis, model-data fusion, estimation of ecosystem service indicators or extensive spatial modelling. Straightforward management of complex data analysis tasks, organized into appropriately documented, shared and reusable scientific workflows enables researchers to carry out detailed and scientifically challenging ‘in silico’ experiments and applications that could open new directions in ecosystem research and in a broader sense it supports progress in environmental modelling. The workflow approach built upon these web services allows even the most complicated computations to be initiated without the need of programming skills and deep understanding of model structure and initialization. The developments enable a wider array of scientists to perform ecosystem scale simulations, and to perform analyses not previously possible due to high complexity and computational demand

Sensitivity of simulated soil water content, evapotranspiration, gross primary production and biomass to climate change factors in Euro-Mediterranean grasslands

Grassland models often yield more uncertain outputs than arable crop models due to more complex interactions and the largely undocumented sensitivity of grassland models to environmental factors. The aim of the present study was to assess the impact of single-factor changes in temperature, precipitation, and atmospheric [CO2] on simulated soil water content (SWC), actual evapotranspiration (ET), gross primary production (GPP) and yield biomass, and also to link the sensitivity analysis with experimental results. We employed an unprecedented multi-model framework consisting of seven grassland models at nine sites with different environmental characteristics in Europe and Israel, with two management options at three sites. For warming/cooling and wetting/drying, models showed general consistency in the direction of SWC and ET changes, but less agreement regarding GPP and biomass changes. The simulated responses consistently revealed an overall positive effect of CO2 enrichment on GPP and biomass, while the direction of change differed for SWC and ET. Comparing with single-factor experimental manipulations, SWC simulations slightly underestimated the observed effect of warming, while the overall mean model sensitivity for biomass (+7.5%) closely matched the mean response observed with 1–2 °C warming (+6.6%). The models exhibited lower sensitivity of SWC to wetting or drying compared to the experiments. The overall mean sensitivity of biomass to drying was -4.3%, contrasting with the mean experimental effect size of -9.6%, which proved to be more realistic than the mean wetting effect (+3.2%, against +38.9% in the field trials). The simulated sensitivity of SWC to CO2 enrichment was markedly underestimated, while the biomass response (+12.0%) closely matched the observations (+17.5%). Although the multi-model averaging did not manifestly improve the realism of the simulations, it ensured a realistic response in the direction of change to varying conditions. The results suggest a paradigm shift in grassland modelling meaning that the usual practice of model optimisation/validation needs to be complemented by a sensitivity analysis following the approach presented. The results also highlight the importance of model improvements, especially in terms of soil hydrology representation, a key environmental driver of grassland functioning