Quantifying the benefit of A-SCOPE data for reducing uncertainties in terrestrial carbon fluxes in CCDAS

ESA’s Earth Explorer candidate mission A-SCOPE aims at observing CO2 from space with an active LIDAR instrument. This study employs quantitative network design techniques to investigate the benefit of A-SCOPE observations in a Carbon Cycle Data Assimilation System. The system links the observations to the terrestrial vegetation model BETHY via the fine resolution version of the atmospheric transport model TM3. In the modelling process chain the observations are used to reduce uncertainties in the values of BETHY’s process parameters, and then the uncertainty in the process parameters is mapped forward to uncertainties in both in long-term net carbon flux and net primary productivity over three regions. A-SCOPE yields considerably better reductions in posterior uncertainties than the ground-based GLOBALVIEW station network. This is true for assimilating monthly mean values and instantaneous values, and it is true for two potential vertical weighting functions. The strength of the constraint through A-SCOPE observations is high over the range of observational uncertainties.

[1]  Martin Heimann,et al.  The global atmospheric tracer model TM3 , 1995 .

[2]  C. D. Keeling,et al.  Atmospheric CO 2 records from sites in the SIO air sampling network , 1994 .

[3]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): overview and key results , 1999 .

[4]  Ian G. Enting,et al.  Optimizing the CO2 observing network for constraining sources and sinks , 1996 .

[5]  Wolfgang Knorr,et al.  Annual and interannual CO2 exchanges of the terrestrial biosphere: process-based simulations and uncertainties , 2000 .

[6]  Sarah L. Dance,et al.  Estimating surface CO 2 fluxes from space-borne CO 2 dry air mole fraction observations using an ensemble Kalman Filter , 2008 .

[7]  Wolfgang Knorr,et al.  Uncertainties in global terrestrial biosphere modeling: 1. A comprehensive sensitivity analysis with a new photosynthesis and energy balance scheme , 2001 .

[8]  Thomas Kaminski,et al.  Recipes for adjoint code construction , 1998, TOMS.

[9]  P. Ciais,et al.  Benchmarking coupled climate‐carbon models against long‐term atmospheric CO2 measurements , 2010 .

[10]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[11]  Ian G. Enting,et al.  Inverse problems in atmospheric constituent transport , 2002 .

[12]  R. Valentini,et al.  The Continental-Scale Greenhouse Gas Balance of Europe , 2010 .

[13]  Thomas Kaminski,et al.  Propagating uncertainty through prognostic carbon cycle data assimilation system simulations , 2007 .

[14]  Sander Houweling,et al.  Inverse modeling of CO2 sources and sinks using satellite data: a synthetic inter-comparison of measurement techniques and their performance as a function of space and time , 2003 .

[15]  Wolfgang Knorr.  Satellitengestützte Fernerkundung und Modellierung des globalen CO2-Austauschs der Landvegetation , 1997 .

[16]  E. Tipping.  Inverse Problems in Atmospheric Constituent Transport , 2005 .

[17]  Jörg Kaduk,et al.  Comparing global models of terrestrial net primary productivity (NPP): comparison of NPP to climate and the Normalized Difference Vegetation Index (NDVI) , 1999 .

[18]  François-Marie Bréon,et al.  Contribution of the Orbiting Carbon Observatory to the estimation of CO2 sources and sinks: Theoretical study in a variational data assimilation framework , 2007 .

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

[20]  F. Woodward,et al.  Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models , 2001 .

[21]  Yasuhiro Sasano,et al.  An evaluation of CO2 observations with Solar Occultation FTS for Inclined-Orbit Satellite sensor for surface source inversion , 2003 .

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

[23]  Thomas Kaminski,et al.  Assimilation and Network Design , 2008 .

[24]  Michael J. Prather,et al.  CO2 source inversions using satellite observations of the upper troposphere , 2001 .

[25]  Scott C. Doney,et al.  Carbon source/sink information provided by column CO 2 measurements from the Orbiting Carbon Observatory , 2008 .

[26]  J. Randerson,et al.  Systematic assessment of terrestrial biogeochemistry in coupled climate–carbon models , 2009 .

[27]  M. Heimann,et al.  Inverse modelling approaches to infer surface trace gas fluxes from observed atmospheric mixing ratios , 1999 .

[28]  T Kaminski,et al.  Inverse modeling of atmospheric carbon dioxide fluxes. , 2001, Science.

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

[30]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): global pattern and differentiation by major biomes , 1999 .

[31]  Thomas Kaminski,et al.  An Example of an Automatic Differentiation-Based Modelling System , 2003, ICCSA.

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

[33]  S. Dance,et al.  Estimating surface CO2 fluxes from space-borne CO2 dry air mole fraction observations using an ensemble Kalman Filter , 2009 .

[34]  David Crisp,et al.  Precision requirements for space-based XCO2 data , 2007 .

[35]  R. Schnur,et al.  Climate-carbon cycle feedback analysis: Results from the C , 2006 .

[36]  S. Houweling,et al.  Space-borne remote sensing of CO2, CH4, and N2O by integrated path differential absorption lidar: a sensitivity analysis , 2008 .

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

[38]  M. Scholze.  Model studies on the response of the terrestrial carbon cycle to climate change and variability , 2003 .

[39]  Rachel M. Law,et al.  Global observations of the carbon budget 3. Initial assessment of the impact of satellite orbit, scan geometry, and cloud on measuring CO2 from space , 2002 .