| 1. |
- Arneth, Almut, et al.
(författare)
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From biota to chemistry and climate: towards a comprehensive description of trace gas exchange between the biosphere and atmosphere
- 2010
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Ingår i: Biogeosciences. - 1726-4189. ; 7:1, s. 121-149
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Forskningsöversikt (refereegranskat)abstract
- Exchange of non-CO2 trace gases between the land surface and the atmosphere plays an important role in atmospheric chemistry and climate. Recent studies have highlighted its importance for interpretation of glacial-interglacial ice-core records, the simulation of the pre-industrial and present atmosphere, and the potential for large climate-chemistry and climate-aerosol feedbacks in the coming century. However, spatial and temporal variations in trace gas emissions and the magnitude of future feedbacks are a major source of uncertainty in atmospheric chemistry, air quality and climate science. To reduce such uncertainties Dynamic Global Vegetation Models (DGVMs) are currently being expanded to mechanistically represent processes relevant to non-CO2 trace gas exchange between land biota and the atmosphere. In this paper we present a review of important non-CO2 trace gas emissions, the state-of-the-art in DGVM modelling of processes regulating these emissions, identify key uncertainties for global scale model applications, and discuss a methodology for model integration and evaluation.
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| 2. |
- Gharahi Ghehi, N., et al.
(författare)
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N2O and NO emission from the Nyungwe tropical highland rainforest in Rwanda
- 2014
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Ingår i: Geoderma Regional. - 2352-0094. ; 2-3, s. 41-49
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Tidskriftsartikel (refereegranskat)abstract
- Tropical forest soils are a significant source for N2O and NO. Current estimates of N2O and NO emissions are uncertain due to the limited number of fieldmeasurements and model input data. Furthermore, considerable spatial and temporal variability exists due to variation of soil properties, vegetation characteristics and meteorology.We used a process-based model (ForestDNDC-tropica) to estimate N2O and NO emissions from the entire (970 km2) tropical highland forest (Nyungwe) in southwestern Rwanda. Scaling these results to that regional level using legacy soil, meteorological and simulated vegetation data we found in most cases agreement between N2O and NO measurements and model predictions. Limited agreement was found for acid soils with high clay content and reduced metals, indicating that abiotic N2O and NO forming processes in acidic soils might be under-represented in the current ForestDNDC-tropica model. The Nyungwe forest was estimated to emit 439 t N2O-N year−1 (2.8– 5.5 kg N2O-N ha−1 year−1) and 244 t NO-N year−1 (0.8–5.1 kg N ha−1 year−1), corroborating previous studies in tropical forests and highlighting that also tropical highland rainforest soils are a major source of atmospheric N2O and NO. The uncertainty for the N2O and NO emission estimates was 153 and 50 t N2O-N year−1 and 36 and 16 t NO-N year−1 considering uncertainty in model input data and annual variability, respectively. The results showed that soil bulk density and pH were the most influential factors driving spatial variation and model uncertainty. To improve global model-based estimates of N2O and NO emission from tropical forest focus should therefore also be oriented in delivering more detailed soil and vegetation data.
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| 3. |
- van Oijen, M., et al.
(författare)
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A Bayesian framework for model calibration, comparison and analysis : Application to four models for the biogeochemistry of a Norway spruce forest
- 2011
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Ingår i: Agricultural and Forest Meteorology. - : Elsevier BV. - 0168-1923 .- 1873-2240. ; 151:12, s. 1609-1621
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Tidskriftsartikel (refereegranskat)abstract
- Four different parameter-rich process-based models of forest biogeochemistry were analysed in a Bayesian framework consisting of three operations: (1) Model calibration, (2) Model comparison, (3) Analysis of model-data mismatch. Data were available for four output variables common to the models: soil water content and emissions of N(2)O, NO and CO(2). All datasets consisted of time series of daily measurements. Monthly averages and quantiles of the annual frequency distributions of daily emission rates were calculated for comparison with equivalent model outputs. This use of the data at model-appropriate temporal scale, together with the choice of heavy-tailed likelihood functions that accounted for data uncertainty through random and systematic errors, helped prevent asymptotic collapse of the parameter distributions in the calibration. Model behaviour and how it was affected by calibration was analysed by quantifying the normalised RMSE and r(2) for the different output variables, and by decomposition of the MSE into contributions from bias, phase shift and variance error. The simplest model, BASFOR, seemed to underestimate the temporal variance of nitrogenous emissions even after calibration. The model of intermediate complexity. DAYCENT, simulated the time series well but with large phase shift. COUP and MoBiLE-DNDC were able to remove most bias through calibration. The Bayesian framework was shown to be effective in improving the parameterisation of the models, quantifying the uncertainties in parameters and outputs, and evaluating the different models. The analysis showed that there remain patterns in the data - in particular infrequent events of very high nitrogenous emission rate - that are unexplained by any of the selected forest models and that this is unlikely to be due to incorrect model parameterisation.
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| 4. |
- Conde, Lucia, et al.
(författare)
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Genome-wide association study of follicular lymphoma identifies a risk locus at 6p21.32
- 2010
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:8, s. 661-664
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Tidskriftsartikel (refereegranskat)abstract
- To identify susceptibility loci for non-Hodgkin lymphoma subtypes, we conducted a three-stage genome-wide association study. We identified two variants associated with follicular lymphoma at 6p21.32 (rs10484561, combined P = 1.12 x 10(-29) and rs7755224, combined P = 2.00 x 10(-19); r(2) = 1.0), supporting the idea that major histocompatibility complex genetic variation influences follicular lymphoma susceptibility. We also found confirmatory evidence of a previously reported association between chronic lymphocytic leukemia/small lymphocytic lymphoma and rs735665 (combined P = 4.24 x 10(-9)).
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| 5. |
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| 6. |
- Meyer, Astrid, et al.
(författare)
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Integrating mycorrhiza in a complex model system: effects on ecosystem C and N fluxes
- 2012
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Ingår i: European Journal of Forest Research. - : Springer Science and Business Media LLC. - 1612-4669 .- 1612-4677. ; 131:6, s. 1809-1831
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Tidskriftsartikel (refereegranskat)abstract
- During the last decades, ectomycorrhiza has been identified to be of major importance for ecosystem carbon (C) and nitrogen (N) cycling and tree growth. Despite this importance, mycorrhiza has largely been neglected in ecosystem models or regarded only implicitly by a static mycorrhiza term. In order to overcome this limitation, we integrated the dynamic mycorrhiza model MYCOFON (Meyer et al. in Plant Soil 327:493-517, 2010a, Plant Soil 327:519, 2010b) into the ecosystem modelling framework MoBiLE (Modular Biosphere simuLation Environment) and coupled it to available forest growth and development process models. Model testing was done for different beech and spruce forest sites in Germany. Simulation results were compared to a standard model set-up, that is, without explicit consideration of mycorrhiza. Parameters were set in order not to violate previous findings about C partitioning into aboveground and belowground biomasses. Nevertheless, the explicit consideration of mycorrhiza let to considerable differences between sites and deposition scenarios with respect to simulated root biomass, plant nitrogen supply, and gaseous soil C and N emissions. The latter was mainly a result of differences in soil N concentration and dynamics. Our simulation results also show that the C supply to mycorrhizal fungi by plants as well as the importance of mycorrhizal fungi for plant N uptake, that is, the allocation of C and N between plants and fungi, depends on the magnitude of N deposition. This effect is neglected by standard model approaches so far. Therefore, explicit consideration of mycorrhiza in ecosystem models has a high potential to improve model simulations of ecosystem C and N cycling and associated biosphere-hydrosphere-atmosphere exchange processes and consequently simulation of soil CO2 and N trace gas emissions from forest sites.
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