scholarly journals Atmospheric constraints on gross primary productivity and net ecosystem productivity: Results from a carbon-cycle data assimilation system

2012 ◽  
Vol 26 (1) ◽  
pp. n/a-n/a ◽  
Author(s):  
E. N. Koffi ◽  
P. J. Rayner ◽  
M. Scholze ◽  
C. Beer
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
Gross Primary Productivity ◽  
Net Ecosystem Productivity ◽  
Ecosystem Productivity ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Investigating the usefulness of satellite derived fluorescence data in inferring gross primary productivity within the carbon cycle data assimilation system

2015 ◽  
Vol 12 (1) ◽  
pp. 707-749 ◽  
Author(s):  
E. N. Koffi ◽  
P. J. Rayner ◽  
A. J. Norton ◽  
C. Frankenberg ◽  
M. Scholze
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
Chlorophyll Concentration ◽  
Gross Primary Productivity ◽  
Combined Model ◽  
Leaf Chlorophyll ◽  
Absorbed Photosynthetically Active Radiation ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We investigate the utility of satellite measurements of chlorophyll fluorescence (Fs) in constraining gross primary productivity (GPP). We ingest Fs measurements into the Carbon-Cycle Data Assimilation System (CCDAS) which has been augmented by the fluorescence component of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. CCDAS simulates well the patterns of Fs suggesting the combined model is capable of ingesting these measurements. However simulated Fs is insensitive to the key parameter controlling GPP, the carboxylation capacity (Vcmax). Simulated Fs is sensitive to both the incoming absorbed photosynthetically active radiation (aPAR) and leaf chlorophyll concentration both of which are treated as perfectly known in previous CCDAS versions. Proper use of Fs measurements therefore requires enhancement of CCDAS to include and expose these variables.

scholarly journals Investigating the usefulness of satellite-derived fluorescence data in inferring gross primary productivity within the carbon cycle data assimilation system

2015 ◽  
Vol 12 (13) ◽  
pp. 4067-4084 ◽  
Author(s):  
E. N. Koffi ◽  
P. J. Rayner ◽  
A. J. Norton ◽  
C. Frankenberg ◽  
M. Scholze
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
Carbon Fluxes ◽  
Gross Primary Productivity ◽  
Sensitivity Tests ◽  
Parameter Values ◽  
Data Assimilation System ◽  
Scope Model ◽  
Assimilation System

Abstract. Simulations of carbon fluxes with terrestrial biosphere models still exhibit significant uncertainties, in part due to the uncertainty in model parameter values. With the advent of satellite measurements of solar induced chlorophyll fluorescence (SIF), there exists a novel pathway for constraining simulated carbon fluxes and parameter values. We investigate the utility of SIF in constraining gross primary productivity (GPP). As a first test we assess whether SIF simulations are sensitive to important parameters in a biosphere model. SIF measurements at the wavelength of 755 nm are simulated by the Carbon-Cycle Data Assimilation System (CCDAS) which has been augmented by the fluorescence component of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. Idealized sensitivity tests of the SCOPE model stand-alone indicate strong sensitivity of GPP to the carboxylation capacity (Vcmax) and of SIF to the chlorophyll AB content (Cab) and incoming short wave radiation. Low sensitivity is found for SIF to Vcmax, however the relationship is subtle, with increased sensitivity under high radiation conditions and lower Vcmax ranges. CCDAS simulates well the patterns of satellite-measured SIF suggesting the combined model is capable of ingesting the data. CCDAS supports the idealized sensitivity tests of SCOPE, with SIF exhibiting sensitivity to Cab and incoming radiation, both of which are treated as perfectly known in previous CCDAS versions. These results demonstrate the need for careful consideration of Cab and incoming radiation when interpreting SIF and the limitations of utilizing SIF to constrain Vcmax in the present set-up in the CCDAS system.

scholarly journals Quantifying the constraint of biospheric process parameters by CO<sub>2</sub> concentration and flux measurement networks through a carbon cycle data assimilation system

2013 ◽  
Vol 13 (21) ◽  
pp. 10555-10572 ◽  
Author(s):  
E. N. Koffi ◽  
P. J. Rayner ◽  
M. Scholze ◽  
F. Chevallier ◽  
T. Kaminski
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Primary Productivity ◽  
High Frequency ◽  
Flux Measurement ◽  
Co2 Concentration ◽  
Flux Measurements ◽  
Net Flux ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The sensitivity of the process parameters of the Biosphere Energy Transfer HYdrology (BETHY) model to choices of atmospheric concentration network, high frequency terrestrial fluxes, and the choice of flux measurement network is investigated by using a carbon cycle data assimilation system. We use BETHY-generated fluxes as a proxy of flux measurements. Results show that monthly mean or low-frequency observations of CO2 concentration provide strong constraints on parameters relevant for net flux (NEP) but only weak constraints for parameters controlling gross fluxes. The use of high-frequency CO2 concentration observations, which has led to great refinement of spatial scales in inversions of net flux, adds little to the observing system in the Carbon Cycle Data Assimilation System (CCDAS) case. This unexpected result is explained by the fact that the stations of the CO2 concentration network we use are not well placed to measure such high frequency signals. Indeed, CO2 concentration sensitivities relevant for such high frequency fluxes are found to be largely confined in the vicinity of the corresponding fluxes, and are therefore not well observed by background monitoring stations. In contrast, our results clearly show the potential of flux measurements to better constrain the model parameters relevant for gross primary productivity (GPP) and net primary productivity (NPP). Given uncertainties in the spatial description of ecosystem functions, we recommend a combined observing strategy.

scholarly journals Quantifying the constraint of biospheric process parameters by CO<sub>2</sub> concentration and flux measurement networks through a carbon cycle data assimilation system

2012 ◽  
Vol 12 (9) ◽  
pp. 24131-24172 ◽  
Author(s):  
E. Koffi ◽  
P. Rayner ◽  
M. Scholze ◽  
F. Chevallier ◽  
T. Kaminski
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Process Parameters ◽  
Primary Productivity ◽  
High Frequency ◽  
Flux Measurement ◽  
Co2 Concentration ◽  
Model Parameters ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The sensitivity of the process parameters of the biosphere model BETHY (Biosphere Energy Transfer HYdrology) to choices of atmospheric concentration network, high frequency terrestrial fluxes, and the choice of flux measurement network is investigated by using a carbon cycle data assimilation system. Results show that monthly mean or low-frequency observations of CO2 concentration provide strong constraints on parameters relevant for net flux (NEP) but only weak constraints for parameters controlling gross fluxes. The use of high-frequency CO2 concentration observations, which has allowed a great refinement of spatial scales in direct inversions, adds little to the observing system in this case. This unexpected result is explained by the fact that the stations of the CO2 concentration network we are using are not well placed to measure such high frequency signals. Indeed, CO2 concentration sensitivities relevant for such high frequency fluxes are found to be largely confined in the vicinity of the corresponding fluxes, and are therefore not well observed by background monitoring stations. In contrast, our results clearly show the potential of flux measurements to better constrain the model parameters relevant for gross primary productivity (GPP) and net primary productivity (NPP). Given uncertainties in the spatial description of ecosystem functions we recommend a combined observing strategy.

scholarly journals Constraining a land surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System

2016 ◽  
Author(s):  
G. J. Schürmann ◽  
T. Kaminski ◽  
C. Köstler ◽  
N. Carvalhais ◽  
M. Voßbeck ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Land Surface ◽  
Data Stream ◽  
Atmospheric Co2 ◽  
Surface Model ◽  
Terrestrial Biosphere ◽  
Multiple Observations ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the land surface scheme of the MPI-Earth System Model v1 (JSBACH). The simulated terrestrial biosphere processes (phenology and carbon balance) were constrained by observations of the fraction of photosynthetically active radiation (TIP-FAPAR product) and by observations of atmospheric CO2 at a global set of monitoring stations for the years 2005–2009. The system successfully, and computationally efficiently, improved average foliar area and northern extra-tropical seasonality of foliar area when constrained by TIP-FAPAR. Global net and gross carbon fluxes were improved when constrained by atmospheric CO2, although the system tended to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, therefore overall increasing the appropriateness of the resultant parameter values and biosphere dynamics. Our study thus highlights the role of the TIP-FAPAR product in stabilising the underdetermined atmospheric inversion problem and demonstrates the value of multiple-data stream assimilation for the simulation of terrestrial biosphere dynamics. The constraint on regional gross and net CO2 flux patterns is limited through the parametrisation of the biosphere model. We expect improvement on that aspect through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

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