Estimation of global CO2 fluxes at regional scale using the maximum likelihood ensemble filter

[1] We use an ensemble-based data assimilation method, known as the maximum likelihood ensemble filter (MLEF), which has been coupled with a global atmospheric transport model to estimate slowly varying biases of carbon surface fluxes. Carbon fluxes for this test consist of hourly gross primary production and ecosystem, respiration over land, and air-sea gas exchange. Persistent multiplicative biases intended to represent incorrectly simulated biogeochemical or land-management processes such as stand age, soil fertility, or coarse woody debris were estimated for 1 year at 10° longitude by 6° latitude spatial resolution and with an 8-week time window. We tested the model using a pseudodata experiment with an existing observation network that includes flasks, aircraft profiles, and continuous measurements. Because of the underconstrained nature of the problem, strong covariance smoothing was applied in the first data assimilation cycle, and localization schemes have been introduced. Error covariance was propagated in subsequent cycles. The coupled model satisfactorily recovered the land biases in densely observed areas. Ocean biases, however, were poorly constrained by the atmospheric observations. Unlike in batch mode inversions, the MLEF has a capability of assimilating large observation vectors and hence is suitable for assimilating hourly continuous observations and satellite observations in the future. Uncertainty was reduced further in our pseudodata experiment than by previous batch methods because of the ability to assimilate a large observation vector. Propagation of spatial covariance and dynamic localization avoid the need for prescribed spatial patterns of error covariance centered at observation sites as in previous grid-scale methods.

[1]  Clive D Rodgers,et al.  Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .

[2]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[3]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[4]  Sander Houweling,et al.  CO 2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport , 2003 .

[5]  J. Randerson,et al.  An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker , 2007, Proceedings of the National Academy of Sciences.

[6]  G. J. Collatz,et al.  Simulations of terrestrial carbon metabolism and atmospheric CO2 in a general circulation model. Part 1: Surface carbon fluxes , 1996 .

[7]  P. Tans,et al.  Spatial and temporal resolution of carbon flux estimates for 1983–2002 , 2007 .

[8]  Thomas Kaminski,et al.  A coarse grid three-dimensional global inverse model of the atmospheric transport 1. Adjoint model and Jacobian matrix , 1999 .

[9]  S. Pawson,et al.  Global CO 2 transport simulations using meteorological data from the NASA data assimilation system , 2004 .

[10]  A. Denning,et al.  Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF) , 2007 .

[11]  A. Scott Denning,et al.  Simulations of terrestrial carbon metabolism and atmospheric CO2 in a general circulation model: Part 1: Surface carbon fluxes , 1996 .

[12]  Dusanka Zupanski,et al.  An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations , 2005 .

[13]  Eve Gruntfest,et al.  The Flash Flood Laboratory at Colorado State University’s Cooperative Institute for Research in The Atmosphere , 2001 .

[14]  A. Tarantola.  Inverse problem theory : methods for data fitting and model parameter estimation , 1987 .

[15]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .

[16]  Scott C. Doney,et al.  Variational data assimilation for atmospheric CO2 , 2006 .

[17]  Steven Pawson,et al.  Global CO2 transport simulations using meteorological data from the NASA data assimilation system , 2004 .

[18]  P. Tans,et al.  A geostatistical approach to surface flux estimation of atmospheric trace gases , 2004 .

[19]  C. Sweeney,et al.  On the global distribution, seasonality, and budget of atmospheric carbonyl sulfide (COS) and some similarities to CO2 , 2007 .

[20]  C. Sweeney,et al.  Global sea-air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects , 2002 .

[21]  P. Houtekamer,et al.  A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation , 2001 .

[22]  Taro Takahashi,et al.  Towards robust regional estimates of CO2 sources and sinks using atmospheric transport models , 2002, Nature.

[23]  Thomas Kaminski,et al.  On aggregation errors in atmospheric transport inversions , 2001 .

[24]  Shian‐Jiann Lin,et al.  Multidimensional Flux-Form Semi-Lagrangian Transport Schemes , 1996 .

[25]  S. Cohn,et al.  An Introduction to Estimation Theory , 1997 .

[26]  Florence Rabier,et al.  Channel selection methods for Infrared Atmospheric Sounding Interferometer radiances , 2002 .

[27]  A. Scott Denning,et al.  Effect of climate on interannual variability of terrestrial CO2 fluxes , 2002 .

[28]  Kenneth L. Denman Canada.  Couplings between changes in the climate system and biogeochemistry , 2008 .

[29]  D. Hauglustaine,et al.  Naturally driven variability in the global secondary organic aerosol over a decade , 2005 .

[30]  A. Scott Denning,et al.  Simulated and observed fluxes of sensible and latent heat and CO2 at the WLEF‐TV tower using SiB2.5 , 2003 .

[31]  J. Whitaker,et al.  Ensemble Data Assimilation without Perturbed Observations , 2002 .

[32]  Dusanka Zupanski,et al.  Applications of information theory in ensemble data assimilation , 2007 .

[33]  M. Zupanski.  Maximum Likelihood Ensemble Filter: Theoretical Aspects , 2005 .

[34]  H.-L. Huang,et al.  Estimating effective data density in a satellite retrieval or an objective analysis , 1993 .

[35]  Wouter Peters,et al.  An improved Kalman Smoother for atmospheric inversions , 2005 .

[36]  David Crisp,et al.  The orbiting carbon observatory mission , 2005 .

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

[38]  M. Fisher.  Estimation of Entropy Reduction and Degrees of Freedom for Signal for Large Variational Analysis Systems , 2003 .

[39]  Stephen E. Cohn,et al.  An Introduction to Estimation Theory (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice) , 1997 .

[40]  Kevin R. Gurney,et al.  On error estimation in atmospheric CO2 inversions , 2002 .

[41]  Steven J. Fletcher,et al.  A data assimilation method for log‐normally distributed observational errors , 2006 .

[42]  Dusanka Zupanski,et al.  Model Error Estimation Employing an Ensemble Data Assimilation Approach , 2006 .

[43]  Thomas Kaminski,et al.  A coarse grid three-dimensional global inverse model of the atmospheric transport. 2. Inversion of the transport of CO2 in the 1980s , 1999 .

[44]  Philippe Ciais,et al.  Transcom 3 inversion intercomparison: Model mean results for the estimation of seasonal carbon sources and sinks , 2004, Global Biogeochemical Cycles.