scholarly article | Q13442814 |
P356 | DOI | 10.1111/J.1365-2486.2005.00977.X |
P50 | author | Jens Kattge | Q56556463 |
P2093 | author name string | Wolfgang Knorr | |
P2860 | cites work | Equation of State Calculations by Fast Computing Machines | Q5384234 |
Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model | Q28138410 | ||
A biochemical model of photosynthetic CO2 assimilation in leaves of C 3 species. | Q34389800 | ||
Monte Carlo sampling of solutions to inverse problems | Q55921533 | ||
Global response of terrestrial ecosystem structure and function to CO2and climate change: results from six dynamic global vegetation models | Q56030445 | ||
Modeling temporal and large-scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices | Q57059751 | ||
Carbon balance of the terrestrial biosphere in the Twentieth Century: Analyses of CO2, climate and land use effects with four process-based ecosystem models | Q57189829 | ||
Temperature response of parameters of a biochemically based model of photosynthesis. I. Seasonal changes in mature maritime pine (Pinus pinaster Ait.) | Q57191732 | ||
Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle | Q57242295 | ||
Uncertainties in global terrestrial biosphere modeling: 1. A comprehensive sensitivity analysis with a new photosynthesis and energy balance scheme | Q57242326 | ||
Uncertainties in global terrestrial biosphere modeling, Part II: Global constraints for a process-based vegetation model | Q57242328 | ||
Comparing global models of terrestrial net primary productivity (NPP): overview and key results | Q57242357 | ||
Monte Carlo analysis of inverse problems | Q58071022 | ||
The carbon uptake of a mid latitude pine forest growing on sandy soil | Q58102139 | ||
P433 | issue | 8 | |
P921 | main subject | eddy covariance | Q5336709 |
ecological modeling | Q114110264 | ||
P6104 | maintained by WikiProject | WikiProject Ecology | Q10818384 |
P1104 | number of pages | 19 | |
P304 | page(s) | 1333-1351 | |
P577 | publication date | 2005-08-01 | |
P1433 | published in | Global Change Biology | Q1531580 |
P1476 | title | Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling | |
P478 | volume | 11 |
Q59102059 | A Comparison of Three Gap Filling Techniques for Eddy Covariance Net Carbon Fluxes in Short Vegetation Ecosystems |
Q33989154 | Assimilation of multiple data sets with the ensemble Kalman filter to improve forecasts of forest carbon dynamics |
Q58388256 | Chapter Eighteen Uncertainty and Sensitivity Issues in Process-based Models of Carbon and Nitrogen Cycles in Terrestrial Ecosystems |
Q100395877 | Confronting the Challenge of Modeling Cloud and Precipitation Microphysics |
Q57054759 | Connecting dynamic vegetation models to data - an inverse perspective |
Q57161071 | Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics |
Q51184120 | Estimating annual CO(2) flux for Lutjewad station using three different gap-filling techniques. |
Q31161531 | Evaluation of climate-related carbon turnover processes in global vegetation models for boreal and temperate forests |
Q34983909 | Evidence for strong seasonality in the carbon storage and carbon use efficiency of an Amazonian forest |
Q57161048 | FLUXNET and modelling the global carbon cycle |
Q56755167 | Improving the predictability of global CO2assimilation rates under climate change |
Q33834798 | Integrating models with data in ecology and palaeoecology: advances towards a model-data fusion approach |
Q57261609 | Joint assimilation of eddy covariance flux measurements and FAPAR products over temperate forests within a process-oriented biosphere model |
Q28547533 | Modeled Changes in Potential Grassland Productivity and in Grass-Fed Ruminant Livestock Density in Europe over 1961-2010 |
Q56755178 | OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models |
Q58317307 | Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length |
Q33441547 | Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models |
Q57059662 | Partitioning net ecosystem exchange of CO2: A comparison of a Bayesian/isotope approach to environmental regression methods |
Q35572356 | Predicting ecosystem dynamics at regional scales: an evaluation of a terrestrial biosphere model for the forests of northeastern North America |
Q57210217 | Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction |
Q57182459 | Processes influencing model-data mismatch in drought-stressed, fire-disturbed eddy flux sites |
Q58394866 | Propagating uncertainty through prognostic carbon cycle data assimilation system simulations |
Q30599166 | Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle |
Q58307909 | Regional carbon fluxes from an observationally constrained dynamic ecosystem model: Impacts of disturbance, CO2fertilization, and heterogeneous land cover |
Q33989159 | Relative information contributions of model vs. data to short- and long-term forecasts of forest carbon dynamics |
Q51590780 | Seasonal fluctuations and temperature dependence in photosynthetic parameters and stomatal conductance at the leaf scale of Populus euphratica Oliv. |
Q57182471 | Seasonal variation of photosynthetic model parameters and leaf area index from global Fluxnet eddy covariance data |
Q56755158 | The BETHY/JSBACH Carbon Cycle Data Assimilation System: experiences and challenges |
Q46717075 | The greenhouse gas balance of European grasslands |
Q34013899 | The model-data fusion pitfall: assuming certainty in an uncertain world |
Q57182453 | Uncertainty estimates for 1-h averaged turbulence fluxes of carbon dioxide, latent heat and sensible heat |
Q58238443 | Uncertainty in temperature projections reduced using carbon cycle and climate observations |
Search more.