Interactions between biosphere and atmosphere analysed with the Osnabrück biosphere model

1993 ◽  
pp. 35-51 ◽  
Author(s):  
Gerd Esser ◽  
Helmut Lieth
Keyword(s):  
Biosphere Model

scholarly journals A successful prediction of the record CO 2 rise associated with the 2015/2016 El Niño

2018 ◽  
Vol 373 (1760) ◽  
pp. 20170301 ◽  
Author(s):  
Richard A. Betts ◽  
Chris D. Jones ◽  
Jeff. R. Knight ◽  
Ralph. F. Keeling ◽  
John. J. Kennedy ◽  
...  
Keyword(s):  
El Niño ◽  
El Nino ◽  
Carbon Sink ◽  
Carbon Dioxide Concentration ◽  
Anthropogenic Emissions ◽  
First Year ◽  
Mauna Loa ◽  
Simple Extrapolation ◽  
Biosphere Model ◽  
The Impact

In early 2016, we predicted that the annual rise in carbon dioxide concentration at Mauna Loa would be the largest on record. Our forecast used a statistical relationship between observed and forecast sea surface temperatures in the Niño 3.4 region and the annual CO 2 rise. Here, we provide a formal verification of that forecast. The observed rise of 3.4 ppm relative to 2015 was within the forecast range of 3.15 ± 0.53 ppm, so the prediction was successful. A global terrestrial biosphere model supports the expectation that the El Niño weakened the tropical land carbon sink. We estimate that the El Niño contributed approximately 25% to the record rise in CO 2 , with 75% due to anthropogenic emissions. The 2015/2016 CO 2 rise was greater than that following the previous large El Niño in 1997/1998, because anthropogenic emissions had increased. We had also correctly predicted that 2016 would be the first year with monthly mean CO 2 above 400 ppm all year round. We now estimate that atmospheric CO 2 at Mauna Loa would have remained above 400 ppm all year round in 2016 even if the El Niño had not occurred, contrary to our previous expectations based on a simple extrapolation of previous trends. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

scholarly journals Towards a representation of priming on soil carbon decomposition in the global land biosphere model ORCHIDEE (version 1.9.5.2)

2016 ◽  
Vol 9 (2) ◽  
pp. 841-855 ◽  
Author(s):  
Bertrand Guenet ◽  
Fernando Esteban Moyano ◽  
Philippe Peylin ◽  
Philippe Ciais ◽  
Ivan A Janssens
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Carbon ◽  
Global Scale ◽  
Soil Incubation ◽  
First Order ◽  
Carbon Decomposition ◽  
Litter Manipulation ◽  
Global Land ◽  
Biosphere Model

Abstract. Priming of soil carbon decomposition encompasses different processes through which the decomposition of native (already present) soil organic matter is amplified through the addition of new organic matter, with new inputs typically being more labile than the native soil organic matter. Evidence for priming comes from laboratory and field experiments, but to date there is no estimate of its impact at global scale and under the current anthropogenic perturbation of the carbon cycle. Current soil carbon decomposition models do not include priming mechanisms, thereby introducing uncertainty when extrapolating short-term local observations to ecosystem and regional to global scale. In this study we present a simple conceptual model of decomposition priming, called PRIM, able to reproduce laboratory (incubation) and field (litter manipulation) priming experiments. Parameters for this model were first optimized against data from 20 soil incubation experiments using a Bayesian framework. The optimized parameter values were evaluated against another set of soil incubation data independent from the ones used for calibration and the PRIM model reproduced the soil incubations data better than the original, CENTURY-type soil decomposition model, whose decomposition equations are based only on first-order kinetics. We then compared the PRIM model and the standard first-order decay model incorporated into the global land biosphere model ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems). A test of both models was performed at ecosystem scale using litter manipulation experiments from five sites. Although both versions were equally able to reproduce observed decay rates of litter, only ORCHIDEE–PRIM could simulate the observed priming (R2  =  0.54) in cases where litter was added or removed. This result suggests that a conceptually simple and numerically tractable representation of priming adapted to global models is able to capture the sign and magnitude of the priming of litter and soil organic matter.

scholarly journals Drivers of column-average CO<sub>2</sub> variability at Southern Hemispheric Total Carbon Column Observing Network sites

2014 ◽  
Vol 14 (18) ◽  
pp. 9883-9901 ◽  
Author(s):  
N. M. Deutscher ◽  
V. Sherlock ◽  
S. E. Mikaloff Fletcher ◽  
D. W. T. Griffith ◽  
J. Notholt ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Monsoon Season ◽  
Source Region ◽  
Total Carbon ◽  
Tropical Region ◽  
Data Record ◽  
Tropical Monsoon ◽  
Biosphere Model ◽  
Total Column

Abstract. We investigate factors that drive the variability in total column CO2 at the Total Carbon Column Observing Network sites in the Southern Hemisphere using fluxes tagged by process and by source region from the CarbonTracker analysed product as well as the Simple Biosphere model. We show that the terrestrial biosphere is the largest driver of variability in the Southern Hemisphere column CO2. However, it does not dominate in the same fashion as in the Northern Hemisphere. Local- and hemispheric-scale biomass burning can also play an important role, particularly at the tropical site, Darwin. The magnitude of seasonal variability in the column-average dry-air mole fraction of CO2, XCO2, is also much smaller in the Southern Hemisphere and comparable in magnitude to the annual increase. Comparison of measurements to the model simulations highlights that there is some discrepancy between the two time series, especially in the early part of the Darwin data record. We show that this mismatch is most likely due to erroneously estimated local fluxes in the Australian tropical region, which are associated with enhanced photosynthesis caused by early rainfall during the tropical monsoon season.

scholarly journals Using satellite data to improve the leaf phenology of a global terrestrial biosphere model

2015 ◽  
Vol 12 (23) ◽  
pp. 7185-7208 ◽  
Author(s):  
N. MacBean ◽  
F. Maignan ◽  
P. Peylin ◽  
C. Bacour ◽  
F.-M. Bréon ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Terrestrial Biosphere ◽  
Modis Ndvi ◽  
Growing Season Length ◽  
Median Correlation ◽  
Temperate Deciduous ◽  
Correct Representation ◽  
Biosphere Model ◽  
Deciduous Plant

Abstract. Correct representation of seasonal leaf dynamics is crucial for terrestrial biosphere models (TBMs), but many such models cannot accurately reproduce observations of leaf onset and senescence. Here we optimised the phenology-related parameters of the ORCHIDEE TBM using satellite-derived Normalized Difference Vegetation Index data (MODIS NDVI v5) that are linearly related to the model fAPAR. We found the misfit between the observations and the model decreased after optimisation for all boreal and temperate deciduous plant functional types, primarily due to an earlier onset of leaf senescence. The model bias was only partially reduced for tropical deciduous trees and no improvement was seen for natural C4 grasses. Spatial validation demonstrated the generality of the posterior parameters for use in global simulations, with an increase in global median correlation of 0.56 to 0.67. The simulated global mean annual gross primary productivity (GPP) decreased by ~ 10 PgC yr−1 over the 1990–2010 period due to the substantially shortened growing season length (GSL – by up to 30 days in the Northern Hemisphere), thus reducing the positive bias and improving the seasonal dynamics of ORCHIDEE compared to independent data-based estimates. Finally, the optimisations led to changes in the strength and location of the trends in the simulated vegetation productivity as represented by the GSL and mean annual fraction of absorbed photosynthetically active radiation (fAPAR), suggesting care should be taken when using un-calibrated models in attribution studies. We suggest that the framework presented here can be applied for improving the phenology of all global TBMs.

scholarly journals Biosphere Model Report

2004 ◽  
Author(s):  
D.W. Wu ◽  
A.J. Smith
Keyword(s):  
Biosphere Model

scholarly journals Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model

2009 ◽  
Vol 6 (3) ◽  
pp. 5271-5304 ◽  
Author(s):  
M. Jung ◽  
M. Reichstein ◽  
A. Bondeau
Keyword(s):  
Eddy Covariance ◽  
Learning Algorithm ◽  
Gross Primary Production ◽  
Global Network ◽  
Data Set ◽  
Model Trees ◽  
Ensemble Approach ◽  
Model Tree ◽  
Biosphere Model ◽  
Variance Explained

Abstract. Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble approach using an artificial data set derived from the the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998–2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the model tree ensemble upscaling and associated problems of extrapolation capacity. We show that the model tree ensemble is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of the model tree ensemble over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while the model tree ensemble is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with a model tree ensemble is feasible and able to extract global patterns of carbon flux variability.

Decadal variability of rainfall in the Sahel: results from the coupled GENESIS-IBIS atmosphere-biosphere model

2004 ◽  
Vol 22 (6-7) ◽  
pp. 625-637 ◽  
Author(s):  
G. Wang ◽  
E. A. B. Eltahir ◽  
J. A. Foley ◽  
D. Pollard ◽  
S. Levis
Keyword(s):  
Decadal Variability ◽  
Biosphere Model
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