Estimation of regional surface CO2 fluxes with GOSAT observations using two inverse modeling approaches

Inverse estimation of surface C02 fluxes is performed with atmospheric transport model using ground-based and GOSAT observations. The NIES-retrieved C02 column mixing (Xc02) and column averaging kernel are provided by GOSAT Level 2 product v. 2.0 and PPDF-DOAS method. Monthly mean C02 fluxes for 64 regions are estimated together with a global mean offset between GOSAT data and ground-based data. We used the fixed-lag Kalman filter to infer monthly fluxes for 42 sub-continental terrestrial regions and 22 oceanic basins. We estimate fluxes and compare results obtained by two inverse modeling approaches. In basic approach adopted in GOSAT Level4 product v. 2.01, we use aggregation of the GOSAT observations into monthly mean over 5x5 degree grids, fluxes are estimated independently for each region, and NIES atmospheric transport model is used for forward simulation. In the alternative method, the model-observation misfit is estimated for each observation separately and fluxes are spatially correlated using EOF analysis of the simulated flux variability similar to geostatistical approach, while transport simulation is enhanced by coupling with a Lagrangian transport model Flexpart. Both methods use using the same set of prior fluxes and region maps. Daily net ecosystem exchange (NEE) is predicted by the Vegetation Integrative Simulator for Trace gases (VISIT) optimized to match seasonal cycle of the atmospheric C02 . Monthly ocean-atmosphere C02 fluxes are produced with an ocean pC02 data assimilation system. Biomass burning fluxes were provided by the Global Fire Emissions Database (GFED); and monthly fossil fuel C02 emissions are estimated with ODIAC inventory. The results of analyzing one year of the GOSAT data suggest that when both GOSAT and ground-based data are used together, fluxes in tropical and other remote regions with lower associated uncertainties are obtained than in the analysis using only ground-based data. With version 2.0 of L2 Xc02 the fluxes appear reasonable for many regions and seasons, however there is a need for improving the L2 bias correction, data filtering and the inverse modeling method to reduce estimated flux anomalies visible in some areas. We also observe that application of spatial flux correlations with EOF based approach reduces flux anomalies.

[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]  Justus Notholt,et al.  Simulations of column-averaged CO 2 and CH 4 using the NIES TM with a hybrid sigma-isentropic (σ-θ) vertical coordinate , 2012 .

[3]  Tatsuya Yokota,et al.  On the accuracy of the CO2 surface fluxes to be estimated from the GOSAT observations , 2009 .

[4]  Tatsuya Yokota,et al.  Retrieval algorithm for CO 2 and CH 4 column abundances from short-wavelength infrared spectral observations by the Greenhouse gases observing satellite , 2010 .

[5]  Shamil Maksyutov,et al.  TransCom satellite intercomparison experiment: Construction of a bias corrected atmospheric CO2 climatology , 2011 .

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

[8]  Yan Xue,et al.  EVALUATION OF THE GLOBAL OCEAN DATA ASSIMILATION SYSTEM AT NCEP: THE PACIFIC OCEAN , 2003 .

[9]  Hartmut Boesch,et al.  Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space: Validation of PPDF-based CO_2 retrievals from GOSAT , 2012 .

[10]  P. Rayner,et al.  The utility of remotely sensed CO2 concentration data in surface source inversions , 2001 .

[11]  Tatsuya Yokota,et al.  Preliminary validation of column-averaged volume mixing ratios of carbon dioxide and methane retrieved from GOSAT short-wavelength infrared spectra , 2010 .

[12]  Shamil Maksyutov,et al.  Technical Note: A novel approach to estimation of time-variable surface sources and sinks of carbon dioxide using empirical orthogonal functions and the Kalman filter , 2011 .

[13]  I. Fung,et al.  Observational Contrains on the Global Atmospheric Co2 Budget , 1990, Science.

[14]  Jorge L. Sarmiento,et al.  Optimal sampling of the atmosphere for purpose of inverse modeling: A model study , 2000 .

[15]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

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

[17]  Robert A. Houze,et al.  A Technique for Computing Vertical Transports by Precipitating Cumuli , 1973 .

[18]  Shamil Maksyutov,et al.  A very high-resolution (1 km×1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights , 2011 .

[19]  J. Randerson,et al.  Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009) , 2010 .

[20]  Ian G. Enting,et al.  A synthesis inversion of the concentration and δ 13 C of atmospheric CO 2 , 1995 .

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

[22]  P. Sutton,et al.  Radiance Calibration of DMSP-OLS Low-Light Imaging Data of Human Settlements , 1999 .

[23]  Rebecca Castano,et al.  The ACOS CO 2 retrieval algorithm – Part 1: Description and validation against synthetic observations , 2011 .

[24]  J. Randerson,et al.  Assessing variability and long-term trends in burned area by merging multiple satellite fire products , 2009 .

[25]  S. Kobayashi,et al.  The JRA-25 Reanalysis , 2007 .

[26]  Steffen Fritz,et al.  A Global Forest Growing Stock, Biomass and Carbon Map Based on FAO Statistics , 2008 .

[27]  W. Parton,et al.  TERRESTRIAL NPP: TOWARD A CONSISTENT DATA SET FORGLOBAL MODEL EVALUATION , 1999 .

[28]  Martyn P. Chipperfield,et al.  Off-line algorithm for calculation of vertical tracer transport in the troposphere due to deep convection , 2012 .

[29]  Yuyu Zhou,et al.  High resolution fossil fuel combustion CO2 emission fluxes for the United States. , 2009, Environmental science & technology.

[30]  Shamil Maksyutov,et al.  Design and Validation of an Offline Oceanic Tracer Transport Model for a Carbon Cycle Study , 2008, Journal of Climate.

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

[32]  Makoto Saito,et al.  Regional CO2 flux estimates for 2009–2010 based on GOSAT and ground-based CO2 observations , 2012 .

[33]  Shamil Maksyutov,et al.  Role of biomass burning and climate anomalies for land‐atmosphere carbon fluxes based on inverse modeling of atmospheric CO2 , 2005, Global Biogeochemical Cycles.

[34]  Makoto Saito,et al.  On the Benefit of GOSAT Observations to the Estimation of Regional CO2 Fluxes , 2011 .

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

[36]  Tatsuya Yokota,et al.  Role of simulated GOSAT total column CO2 observations in surface CO2 flux uncertainty reduction , 2009 .

[37]  Galen A. McKinley,et al.  Mechanisms of air‐sea CO2 flux variability in the equatorial Pacific and the North Atlantic , 2004 .

[38]  Akihiko Ito,et al.  Changing ecophysiological processes and carbon budget in East Asian ecosystems under near-future changes in climate: implications for long-term monitoring from a process-based model , 2010, Journal of Plant Research.

[39]  Corinne Le Quéré,et al.  Regional changes in carbon dioxide fluxes of land and oceans since 1980. , 2000, Science.

[40]  G. Marland,et al.  Monthly, global emissions of carbon dioxide from fossil fuel consumption , 2011 .

[41]  Shamil Maksyutov,et al.  Simulation and assimilation of global ocean pCO2 and air–sea CO2 fluxes using ship observations of surface ocean pCO2 in a simplified biogeochemical offline model , 2010, Tellus B: Chemical and Physical Meteorology.

[42]  Shamil Maksyutov,et al.  NIES/FRCGC Global Atmospheric Tracer Transport Model: Description, Validation, and Surface Sources and Sinks Inversion , 2008 .

[43]  Makoto Saito,et al.  Evaluation of Biases in JRA-25/JCDAS Precipitation and Their Impact on the Global Terrestrial Carbon Balance , 2011 .

[44]  Philippe Ciais,et al.  Weak Northern and Strong Tropical Land Carbon Uptake from Vertical Profiles of Atmospheric CO2 , 2007, Science.

[45]  Masakatsu Nakajima,et al.  Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring. , 2009, Applied optics.

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

[47]  Kevin R. Gurney,et al.  TransCom 3 CO2 inversion intercomparison: 2. Sensitivity of annual mean results to data choices , 2003 .

[48]  A. Kozyr,et al.  Global Ocean Surface Water Partial Pressure of CO2 Database: Measurements Performed During 1968-2007 (Version 2007) , 2008 .

[49]  Ian G. Enting,et al.  Reconstructing the recent carbon cycle from atmospheric CO2, δ13C and O2/N2 observations* , 1999 .

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

[51]  Shamil Maksyutov,et al.  Mass-conserving tracer transport modelling on a reduced latitude-longitude grid with NIES-TM , 2011 .

[52]  D. Roy,et al.  An active-fire based burned area mapping algorithm for the MODIS sensor , 2009 .