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

Abstract. 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 Meteorologie 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.

[1]  Andrew J. Watson,et al.  Corrigendum to "Climatological mean and decadal change in surface ocean pCO2, and net sea-air CO2 flux over the global oceans" [Deep Sea Res. II 56 (2009) 554-577] , 2009 .

[2]  Fabienne Maignan,et al.  CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements , 2010 .

[3]  Tim E. Jupp,et al.  Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0 , 2016 .

[4]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[5]  M. Tiedtke.  A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models , 1989 .

[6]  G. Collatz,et al.  Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants , 1992 .

[7]  N. Gobron,et al.  Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana , 2012 .

[8]  Dennis D. Baldocchi,et al.  Estimating parameters in a land‐surface model by applying nonlinear inversion to eddy covariance flux measurements from eight FLUXNET sites , 2007 .

[9]  M. Scholze,et al.  Atmospheric constraints on gross primary productivity and net ecosystem productivity: Results from a carbon‐cycle data assimilation system , 2012 .

[10]  P. Ciais,et al.  Using satellite data to improve the leaf phenology of a global terrestrial biosphere model , 2015 .

[11]  Philippe Ciais,et al.  Ecosystem model optimization using in situ flux observations: Benefit of Monte Carlo versus variational schemes and analyses of the year-to-year model performances , 2013 .

[12]  R. Houghton.  Revised estimates of the annual net flux of carbon to the atmosphere from changes in land use and land management 1850 – 2000 , 2003 .

[13]  Martin Wahlen,et al.  Interannual variability in the oxygen isotopes of atmospheric CO2 driven by El Niño , 2011, Nature.

[14]  Albert Tarantola,et al.  Inverse problem theory - and methods for model parameter estimation , 2004 .

[15]  S. Running,et al.  Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active , 1998 .

[16]  Eric A Davidson,et al.  Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle. , 2013, Ecological applications : a publication of the Ecological Society of America.

[17]  P. Delecluse,et al.  OPA 8.1 Ocean General Circulation Model reference manual , 1998 .

[18]  J. Greet,et al.  Trends in global CO2 emissions: 2012 report , 2012 .

[19]  Fabienne Maignan,et al.  Constraining a global ecosystem model with multi-site eddy-covariance data , 2012 .

[20]  Atul K. Jain,et al.  Global Carbon Budget 2018 , 2014, Earth System Science Data.

[21]  Shamil Maksyutov,et al.  Optimization of a prognostic biosphere model for terrestrial biomass and atmospheric CO2 variability , 2014 .

[22]  Natasha MacBean,et al.  Consistent assimilation of multiple data streams in a carbon cycle data assimilation system , 2016 .

[23]  Arief Wijaya,et al.  An integrated pan‐tropical biomass map using multiple reference datasets , 2016, Global change biology.

[24]  S. Bony,et al.  The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection , 2006 .

[25]  G. Kiely,et al.  Model-data fusion across ecosystems : From multisite optimizations to global simulations , 2014 .

[26]  W. Parton,et al.  Dynamics of C, N, P and S in grassland soils: a model , 1988 .

[27]  Inez Y. Fung,et al.  Application of Advanced Very High Resolution Radiometer vegetation index to study atmosphere‐biosphere exchange of CO2 , 1987 .

[28]  Vincent R. Gray.  Climate Change 2007: The Physical Science Basis Summary for Policymakers , 2007 .

[29]  Nicolas Gruber,et al.  The Oceanic Sink for Anthropogenic CO2 , 2004, Science.

[30]  I. Prentice,et al.  Reliable, robust and realistic: the three R's of next-generation land-surface modelling , 2014 .

[31]  E. Davidson,et al.  Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints , 2010, Oecologia.

[32]  A. Arneth,et al.  Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .

[33]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[34]  Philippe Bousquet,et al.  Daily CO2 flux estimates over Europe from continuous atmospheric measurements: 1, inverse methodology , 2005 .

[35]  W. Knorr,et al.  Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling , 2005 .

[36]  P. Ciais,et al.  On the role of atmospheric chemistry in the global CO2 budget , 2005 .

[37]  E. Dufrene,et al.  Joint assimilation of eddy covariance flux measurements and FAPAR products over temperate forests within a process‐oriented biosphere model , 2015 .

[38]  Philippe Ciais,et al.  Anthropogenic perturbation of the carbon fluxes from land to ocean , 2013 .

[39]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[40]  N. Coops,et al.  Intercomparison of fraction of absorbed photosynthetically active radiation products derived from satellite data over Europe , 2014 .

[41]  S. Higgins,et al.  TRY – a global database of plant traits , 2011, Global Change Biology.

[42]  I. C. Prentice,et al.  A dynamic global vegetation model for studies of the coupled atmosphere‐biosphere system , 2005 .

[43]  Wolfgang Knorr,et al.  Impact of drought stress and other factors on seasonal land biosphere CO2 exchange studied through an atmospheric tracer transport model , 1995 .

[44]  R. Giering,et al.  Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS) , 2005 .

[45]  M. Lomas,et al.  Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends , 2013, Global change biology.

[46]  Atul K. Jain,et al.  Global Carbon Budget 2018 , 2014, Earth System Science Data.

[47]  B. Law,et al.  An improved analysis of forest carbon dynamics using data assimilation , 2005 .

[48]  Philippe Bousquet,et al.  Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data , 2005 .

[49]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[50]  W. Post,et al.  Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length , 2011 .

[51]  K. Davis,et al.  Uncertainty in model parameters and regional carbon fluxes: A model-data fusion approach , 2014 .

[52]  R. Wanninkhof.  Relationship between wind speed and gas exchange over the ocean , 1992 .

[53]  B O B B,et al.  Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations , 2005 .

[54]  Ranga B. Myneni,et al.  Recent trends and drivers of regional sources and sinks of carbon dioxide , 2015 .

[55]  Liang Feng,et al.  The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times , 2016, Proceedings of the National Academy of Sciences.

[56]  Christopher Justice,et al.  Towards a Generalized Approach for Correction of the BRDF Effect in MODIS Directional Reflectances , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[57]  R. Monson,et al.  Joint data assimilation of satellite reflectance and net ecosystem exchange data constrains ecosystem carbon fluxes at a high-elevation subalpine forest , 2014 .

[58]  Nuno Carvalhais,et al.  Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0 , 2016 .

[59]  P. Tans,et al.  Atmospheric carbon dioxide at Mauna Loa Observatory: 2. Analysis of the NOAA GMCC data, 1974–1985 , 1989 .

[60]  Soroosh Sorooshian,et al.  Multi-objective global optimization for hydrologic models , 1998 .

[61]  P. Ciais,et al.  The potential benefit of using forest biomass data in addition to carbon and water flux measurements to constrain ecosystem model parameters: Case studies at two temperate forest sites , 2017 .

[62]  Urs Wegmüller,et al.  Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR , 2015 .

[63]  R. Giering,et al.  Consistent assimilation of MERIS FAPAR and atmospheric CO2 into a terrestrial vegetation model and interactive mission benefit analysis , 2011 .

[64]  Frédéric Hourdin,et al.  The Use of Finite-Volume Methods for Atmospheric Advection of Trace Species. Part I: Test of Various Formulations in a General Circulation Model , 1999 .

[65]  Georg Wohlfahrt,et al.  Assessing parameter variability in a photosynthesis model within and between plant functional types using global Fluxnet eddy covariance data , 2011 .

[66]  E. Davidson,et al.  Using model‐data fusion to interpret past trends, and quantify uncertainties in future projections, of terrestrial ecosystem carbon cycling , 2012 .

[67]  T. Vesala,et al.  Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation , 2006 .

[68]  Jean-Francois Lamarque,et al.  Interactive chemistry in the Laboratoire de Météorologie Dynamique general circulation model: Description and background tropospheric chemistry evaluation: INTERACTIVE CHEMISTRY IN LMDZ , 2004 .

[69]  S. Bony,et al.  Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5 , 2013, Climate Dynamics.

[70]  J. Shutler,et al.  Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM) , 2015 .

[71]  Thomas Kaminski,et al.  Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle , 2002 .

[72]  P. Ciais,et al.  Two decades of OH variability as inferred by an inversion of atmospheric transport and chemistry of methyl chloroform , 2005 .

[73]  P. Alton,et al.  From site-level to global simulation: Reconciling carbon, water and energy fluxes over different spatial scales using a process-based ecophysiological land-surface model , 2013 .

[74]  R. Giering,et al.  The BETHY/JSBACH Carbon Cycle Data Assimilation System: experiences and challenges , 2013 .

[75]  Hans Pretzsch,et al.  A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes , 2014 .

[76]  Markus Reichstein,et al.  Influences of observation errors in eddy flux data on inverse model parameter estimation , 2008 .

[77]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[78]  Andrew J. Watson,et al.  Corrigendum to Climatological mean and decadal change in surface ocean pCO2, and net sea―air CO2 flux over the global oceans , 2009 .

[79]  A. Perrier,et al.  SECHIBA : a new set of parameterizations of the hydrologic exchanges at the land-atmosphere interface within the LMD atmospheric general circulation model , 1993 .

[80]  R. Weiss.  Carbon dioxide in water and seawater: the solubility of a non-ideal gas , 1974 .

[81]  R. Monson,et al.  Estimating transpiration and the sensitivity of carbon uptake to water availability in a subalpine forest using a simple ecosystem process model informed by measured net CO2 and H2O fluxes , 2008 .

[82]  Peter A. Coppin,et al.  Parameter estimation in surface exchange models using nonlinear inversion: how many parameters can we estimate and which measurements are most useful? , 2001 .

[83]  Rik,et al.  Relationship Between Wind Speed and Gas Exchange , 2013 .

[84]  Representing subgrid‐scale edaphic heterogeneity in a large‐scale ecosystem model: A case study in the circumpolar boreal regions , 2008 .

[85]  Philippe Ciais,et al.  Evaluation of SF6, C2Cl4, and CO to approximate fossil fuel CO2 in the Northern Hemisphere using a chemistry transport model , 2006 .

[86]  M. Castro,et al.  Sensitivity of the Continental Hydrological Cycle to the Spatial Resolution over the Iberian Peninsula , 2004 .

[87]  J. Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..

[88]  Philippe Peylin,et al.  Quantifying the model structural error in carbon cycle data assimilation systems , 2012 .

[89]  Peter J. Rayner,et al.  The current state of carbon-cycle data assimilation , 2010 .

[90]  Fabienne Maignan,et al.  Evaluation of a Global Vegetation Model using time series of satellite vegetation indices , 2011 .

[91]  K. Davis,et al.  Causes of interannual variability in ecosystem–atmosphere CO2 exchange in a northern Wisconsin forest using a Bayesian model calibration , 2008 .