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Title: A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

Abstract

Here, large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely the Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEEmore » model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr-1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.« less

Authors:
 [1];  [2];  [1];  [1];  [3];  [4];  [1];  [1];  [1];  [1];  [1];  [2]
+ Show Author Affiliations
  1. Lab. des Sciences du Climat et de l'Environnement, Gif-sur-Yvette (France)
  2. Noveltis, Ramonville-Saint-Agne (France)
  3. Lab. des Sciences du Climat et de l'Environnement, Gif-sur-Yvette (France); Univ. of Melbourne, VIC, Melbourne (Australia)
  4. Lab. des Sciences du Climat et de l'Environnement, Gif-sur-Yvette (France); IMASL-CONICET/Univ. Nacional de San Luis, San Luis (Argentina)
Publication Date:
Research Org.:
Oregon State Univ., Corvallis, OR (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1361538
Grant/Contract Number:  
FG02-04ER63917; FG02-04ER63911
Resource Type:
Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online); Journal Volume: 9; Journal Issue: 9; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Peylin, Philippe, Bacour, Cédric, MacBean, Natasha, Leonard, Sébastien, Rayner, Peter, Kuppel, Sylvain, Koffi, Ernest, Kane, Abdou, Maignan, Fabienne, Chevallier, Frédéric, Ciais, Philippe, and Prunet, Pascal. A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle. United States: N. p., 2016. Web. doi:10.5194/gmd-9-3321-2016.
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Peylin, Philippe, Bacour, Cédric, MacBean, Natasha, Leonard, Sébastien, Rayner, Peter, Kuppel, Sylvain, Koffi, Ernest, Kane, Abdou, Maignan, Fabienne, Chevallier, Frédéric, Ciais, Philippe, & Prunet, Pascal. A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle. United States. https://doi.org/10.5194/gmd-9-3321-2016
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Peylin, Philippe, Bacour, Cédric, MacBean, Natasha, Leonard, Sébastien, Rayner, Peter, Kuppel, Sylvain, Koffi, Ernest, Kane, Abdou, Maignan, Fabienne, Chevallier, Frédéric, Ciais, Philippe, and Prunet, Pascal. Tue . "A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle". United States. https://doi.org/10.5194/gmd-9-3321-2016. https://www.osti.gov/servlets/purl/1361538.
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@article{osti_1361538,
title = {A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle},
author = {Peylin, Philippe and Bacour, Cédric and MacBean, Natasha and Leonard, Sébastien and Rayner, Peter and Kuppel, Sylvain and Koffi, Ernest and Kane, Abdou and Maignan, Fabienne and Chevallier, Frédéric and Ciais, Philippe and Prunet, Pascal},
abstractNote = {Here, large uncertainties in land surface models (LSMs) simulations still arise from inaccurate forcing, poor description of land surface heterogeneity (soil and vegetation properties), incorrect model parameter values and incomplete representation of biogeochemical processes. The recent increase in the number and type of carbon cycle-related observations, including both in situ and remote sensing measurements, has opened a new road to optimize model parameters via robust statistical model–data integration techniques, in order to reduce the uncertainties of simulated carbon fluxes and stocks. In this study we present a carbon cycle data assimilation system that assimilates three major data streams, namely the Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) observations of vegetation activity, net ecosystem exchange (NEE) and latent heat (LE) flux measurements at more than 70 sites (FLUXNET), as well as atmospheric CO2 concentrations at 53 surface stations, in order to optimize the main parameters (around 180 parameters in total) of the Organizing Carbon and Hydrology in Dynamics Ecosystems (ORCHIDEE) LSM (version 1.9.5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assimilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i.e., increased amplitude for temperate ecosystems). The assimilation of atmospheric CO2, using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i.e., constraint on large-scale net CO2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2.2 PgC yr-1 (for the 2000–2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more “challenging” implementation of a simultaneous optimization in which all data streams are assimilated together. Most parameters, including the scalar of the initial soil carbon pool size, changed during the optimization with a large error reduction. This work opens new perspectives for better predictions of the land carbon budgets.},
doi = {10.5194/gmd-9-3321-2016},
journal = {Geoscientific Model Development (Online)},
number = 9,
volume = 9,
place = {United States},
year = {Tue Sep 20 00:00:00 EDT 2016},
month = {Tue Sep 20 00:00:00 EDT 2016}
}
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    journal, January 2019

    • Huang, Yuanyuan; Stacy, Mark; Jiang, Jiang
    • Geoscientific Model Development, Vol. 12, Issue 3
    • DOI: 10.5194/gmd-12-1119-2019

    Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)
    journal, January 2018

    • Bastrikov, Vladislav; MacBean, Natasha; Bacour, Cédric
    • Geoscientific Model Development, Vol. 11, Issue 12
    • DOI: 10.5194/gmd-11-4739-2018

    Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion
    journal, February 2020

    • Trotsiuk, Volodymyr; Hartig, Florian; Cailleret, Maxime
    • Global Change Biology, Vol. 26, Issue 4
    • DOI: 10.1111/gcb.15011

    Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination
    journal, October 2018

    Three decades of simulated global terrestrial carbon fluxes from a data assimilation system confronted with different periods of observations
    journal, January 2019

    • Castro-Morales, Karel; Schürmann, Gregor; Köstler, Christoph
    • Biogeosciences, Vol. 16, Issue 15
    • DOI: 10.5194/bg-16-3009-2019

    Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model
    journal, January 2019

    • Norton, Alexander J.; Rayner, Peter J.; Koffi, Ernest N.
    • Biogeosciences, Vol. 16, Issue 15
    • DOI: 10.5194/bg-16-3069-2019

    Constraining estimates of terrestrial carbon uptake: new opportunities using long‐term satellite observations and data assimilation
    journal, August 2019

    • Smith, William K.; Fox, Andrew M.; MacBean, Natasha
    • New Phytologist, Vol. 225, Issue 1
    • DOI: 10.1111/nph.16055

    Consistent assimilation of multiple data streams in a carbon cycle data assimilation system
    journal, January 2016

    • MacBean, Natasha; Peylin, Philippe; Chevallier, Frédéric
    • Geoscientific Model Development, Vol. 9, Issue 10
    • DOI: 10.5194/gmd-9-3569-2016

    Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model
    journal, June 2018

    • Norton, Alexander J.; Rayner, Peter J.; Koffi, Ernest N.
    • Biogeosciences Discussions
    • DOI: 10.5194/bg-2018-270

    Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model
    journal, January 2019

    • Norton, Alexander J.; Rayner, Peter J.; Koffi, Ernest N.
    • Biogeosciences, Vol. 16, Issue 15
    • DOI: 10.5194/bg-16-3069-2019

    Improving Estimates of Gross Primary Productivity by Assimilating Solar‐Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process‐Based SIF Model
    journal, November 2019

    • Bacour, C.; Maignan, F.; MacBean, N.
    • Journal of Geophysical Research: Biogeosciences, Vol. 124, Issue 11
    • DOI: 10.1029/2019jg005040

    Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems
    journal, January 2017

    • Scholze, Marko; Buchwitz, Michael; Dorigo, Wouter
    • Biogeosciences Discussions
    • DOI: 10.5194/bg-2016-557

    Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)
    journal, January 2018

    • Bastrikov, Vladislav; MacBean, Natasha; Bacour, Cédric
    • Geoscientific Model Development, Vol. 11, Issue 12
    • DOI: 10.5194/gmd-11-4739-2018

    Realized ecological forecast through an interactive Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models
    journal, January 2019

    • Huang, Yuanyuan; Stacy, Mark; Jiang, Jiang
    • Geoscientific Model Development, Vol. 12, Issue 3
    • DOI: 10.5194/gmd-12-1119-2019

    Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model
    journal, May 2019

    • Peaucelle, Marc; Bacour, Cédric; Ciais, Philippe
    • Global Ecology and Biogeography, Vol. 28, Issue 9
    • DOI: 10.1111/geb.12937

    Understanding the Land Carbon Cycle with Space Data: Current Status and Prospects
    journal, February 2019

    • Exbrayat, Jean-François; Bloom, A. Anthony; Carvalhais, Nuno
    • Surveys in Geophysics, Vol. 40, Issue 4
    • DOI: 10.1007/s10712-019-09506-2

    Confronting Soil Moisture Dynamics from the ORCHIDEE Land Surface Model With the ESA-CCI Product: Perspectives for Data Assimilation
    journal, November 2018

    • Raoult, Nina; Delorme, Bertrand; Ottlé, Catherine
    • Remote Sensing, Vol. 10, Issue 11
    • DOI: 10.3390/rs10111786

    Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems
    journal, January 2017

    • Scholze, Marko; Buchwitz, Michael; Dorigo, Wouter
    • Biogeosciences Discussions
    • DOI: 10.5194/bg-2016-557

    Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb)
    journal, January 2018

    • Badawy, Bakr; Polavarapu, Saroja; Jones, Dylan B. A.
    • Geoscientific Model Development, Vol. 11, Issue 2
    • DOI: 10.5194/gmd-11-631-2018

    Coupling the Canadian Terrestrial Ecosystem Model (CTEM v. 2.0) to Environment and Climate Change Canada's greenhouse gas forecast model (v.107-glb)
    journal, January 2018

    • Badawy, Bakr; Polavarapu, Saroja; Jones, Dylan B. A.
    • Geoscientific Model Development, Vol. 11, Issue 2
    • DOI: 10.5194/gmd-11-631-2018

    Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH
    journal, January 2019

    • Mäkelä, Jarmo; Knauer, Jürgen; Aurela, Mika
    • Geoscientific Model Development, Vol. 12, Issue 9
    • DOI: 10.5194/gmd-12-4075-2019

    Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data
    journal, January 2018

    Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates
    journal, January 2018

    • Post, Hanna; Hendricks Franssen, Harrie-Jan; Han, Xujun
    • Biogeosciences, Vol. 15, Issue 1
    • DOI: 10.5194/bg-15-187-2018

    Assimilating solar-induced chlorophyll fluorescence into the terrestrial biosphere model BETHY-SCOPE v1.0: model description and information content
    journal, January 2018

    • Norton, Alexander J.; Rayner, Peter J.; Koffi, Ernest N.
    • Geoscientific Model Development, Vol. 11, Issue 4
    • DOI: 10.5194/gmd-11-1517-2018

    Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH
    journal, January 2019

    • Mäkelä, Jarmo; Knauer, Jürgen; Aurela, Mika
    • Geoscientific Model Development, Vol. 12, Issue 9
    • DOI: 10.5194/gmd-12-4075-2019

    Data Assimilation using an Ensemble of Models: A hierarchical approach
    journal, February 2017

    Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems
    journal, January 2017

    Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles
    posted_content, January 2018

    • Wang, Fuxing; Polcher, Jan; Peylin, Philippe
    • Hydrology and Earth System Sciences Discussions
    • DOI: 10.5194/hess-2017-731

    Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles
    journal, January 2018

    • Wang, Fuxing; Polcher, Jan; Peylin, Philippe
    • Hydrology and Earth System Sciences, Vol. 22, Issue 7
    • DOI: 10.5194/hess-22-3863-2018

    Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles
    posted_content, January 2018

    • Wang, Fuxing; Polcher, Jan; Peylin, Philippe
    • Hydrology and Earth System Sciences Discussions
    • DOI: 10.5194/hess-2017-731
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