S. Maksyutov, P.K. Patra and M. Ishizawa Jena; 13 May 2003 TDI experiment with NIES model and interannually varying NCEP winds

Objectives Time-dependent inversion Study effect of meteorological fields on inversion Analyse climate impact on CO2 concentration anomaly Link CO2 flux variability to ecosystem model simulations

Experiments – Three transport options ECMWF analysis for 1997 (cyclostationary) NCEP reanalysis for 1997 (followed by 98,99) NCEP reanalysis winds (interannually varying) Other Issues: Other Issues: Fossil fuel emission trends: (Marland et al., 2002) and is extrapolated, spatial patterns for 90 and 95 are used

Preprocessing NCEP winds NCEP reanalysis (Period: 1988 to 2001), pressure level data at 2.5 deg resolution. Vertical winds are up to 100 mb originally. Diagnostic vertical wind above 100 mb. Vertical motion along isentropic trajectories is assumed. w=(U*dTp/dx+V*dTp/dy)/(dTp/dp) w=(U*dTp/dx+V*dTp/dy)/(dTp/dp) This simplified approach fails at the poles. Polar values are smoothed from vicinity (second row from the pole).

TDI Setup Basically the same as T3 L2 source code (Ft Collins meeting by K. Gurney et al) Originally developed at CSIRO Changes are made to ingest Green’s function matrices using multiple-year meteorology Period of source estimation: 1988 – 2001 (after 3 years spin up time)

CO 2 Observation data GLOBALVIEW (August 2002 release) Maximum number of stations: 189 Data period: Jan 1979 to Jan 2002 (2001 depleted) Bare minimum modifications to R. Law’s program

Transport model re-configuration for Earth Simulator Parallelisation idea: array decomposition at tracer dimension (no reaction between tracers), rather than latitude bands etc. Total number of pulses are: (22*12+4) per year; 14 years processed (88-01) Single run uses 72 NEC-SX processors –each running 22 pulses (9 nodes on Earth Simulator) –simulating 6 years of monthly-pulses at once Asynchronous meteorology (each process is allowed to run its own time); i.e., no communication between processes

Other computer system issues Virtual file system: –files are copied to each process’s virtual disk space at the job preparation stage –then disposed after run to disk or tape –process-specific input, output file extensions are added (style like.000,.001) Job script limitations (limited to 256 explicit file declarations): –had to reduce the number of files used –we put the meteorological data for one month in one file

Influence of Atmospheric transport on CO2 data inversion Number of observations stations: 69 (55% real data in )

Inversion results: fitting to the data

Annual Mean Fluxes -reasonably good agreement Case 1: NCEP-int closer to NCEP-97 than ECMWF-97 (constant offset: L-04, L-06, O-09) Case 2: NCEP-97 and ECMWF-97 are different from NCEP- int (L-05, L-01) emission – distributed evenly between tropical land areas

Seasonal Cycles As it comes out of the inversion model calculation! Well produced for the well constrained regions

Monthly Flux anomaly Noisy! Ln-01 & Ln-07 well correlated 1998 emission

Comparison with T3L3 base case (L2 by David Baker) Number of observations stations: 76

Annual Mean Fluxes Trouble with well constrained regions! Something is still missing?

Average Seasonal Cycles Matches fairly well Within ~20%? (haven’t done that precisely) Reasons??

Monthly Flux anomaly Too much variation Still compares quite well The 1998 emission peak gone missing

Derived Flux and ENSO Index

Future Outlook High Resolution Inversion (53 regions) Testing of different ecosystem model Use ecosystem model output at high time resolution (daily? May be…)

Conclusions Three types of transport fields are used in interannual inversion which show good agreement Some climate impact on CO2 emission can be studied; e.g 1998 Indonesian fire Comparison with T3L2 tending to match We wish to contribute to T3L3 by using a high resolution inverse model and/or with a different ecosystem model results