Study of interannual variability in CO 2 fluxes using inverse modelling

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1 Study of interannual variability in CO 2 fluxes using inverse modelling Prabir K. Patra, Shamil Maksyutov, Misa Ishizawa, and Takakiyo Nakazawa Greenhouse Gases Modelling Group (D4) Acknowledgment: Gen Inoue (NIES), Taro Takahashi (LDEO), Hajime Akimoto FRSGC Annual Symposium, Yokohama, 24 March 24

2 Tracer Transport: Basic Principles The transport equations is: q t k where, q k is the tracer concentration with index k, S is the source function, V (σ) denote the horizontal (vertical) components of winds, F k represents the PBL flux or convective transport. We have used: + V σ q k k q + & σ σ F σ the NCEP/NCAR reanalysis data for pressure level fields monthly PBL heights are cyclostationary (from NASA - DAO) global distribution of yearly or monthly sources (cyclostat.) = k + S k

3 Background CO2 fluxes: a justification for high resolution inverse model The fossil fuel emission do not have seasonality. Oceanic sources and sinks are weaker compared to the land and less heterogeneous.

4 Transport Model Simulations: Combined (FOS, NEP, OCN) signals of CO2 at various layers of the atmosphere (left panels) and the estimated RSDs (right panels). Patra et al., J. Geophys. Res., 23

5 Inverse Model: Basic Equations The problem of surface source (S) inversion is mathematically the inversion of the forward problem: D =G.S, where the G a linear operator representing atmospheric transport (no chemistry). The results are CO2 fluxes with uncertainty: T T S = S + G C G+ C G C D GS Estimated Flux A Priori Flux ( ) ( ); D S D C = ( G C G+ C ) T S D S Estimated Flux Cov. A Priori Flux Cov. Atmospheric CO2 Data

6 Development of 64-Regions Inverse Model Patra et al., Global Biogeochem. Cycles, submitted

7 Inversion results: fitting to the data Testing time dependent inversion: an interannual case NWR (4N) 37 SEY (4S) MLO (19N) 365 SPO (9S) 365 Prior Data Predicted (ECMWF 97) Predicted (NCEP 97) Predicted (NCEP Real) NCEP 64 reg

8 Averages of CO2 Fluxes for 199s Estimates This Work IPCC 21 Gurney et al., 24 Bousquet et al, 2 Rodenbeck et al., 23 Land {.14*} -1.4 (.7) (.73) -1.4 (.8) -1.2 (.4) Ocean {.18*} -1.7 (.5) (.76) -1.8 (.6) -1.7 (.4) Global * Spread based on sensitivity tests Patra et al., 24a, Global Biogeochem. Cycles, submitted

9 Land and Ocean Flux - sensitivity Patra et al., 24a, Global Biogeochem. Cycles, submitted CO 2 Flux Anomaly (Pg C yr ) CO 2 Flux Anomaly (Pg C yr ) Control Run (87 Sta.) C_S (ocean) * 2 C_S (all) * 2 C_D (all) * 2 C_S * 2; C_D * 2 Depence of Inverse Model Flux Anomaly on Prior Uncertainties Control Run (87 sta.) Test CR (19 Sta.) Test PB (67 Sta.) Test Pp (1 Sta.) A. Land C. Land Year B. Ocean Dependence of Inverse Model Flux Anomalies on Observation Network Selection D. Ocean Year

10 Anomalies in Land and Ocean CO 2 Fluxes 1 1 CO 2 Flux Anomaly (Pg C yr ) 5 Pinatubo Eruption A. Land 5 The shaded region in background shows multi variate ENSO Index B. Ocean Calendar Year Calendar Year

11 Flux anom. (Pg-C per Year) ) C Dore et al. (23) This Work (NP S) This Work (NP N).2.2 Bousquet et al. (2) Feely et al. (1999) This Work (EP + WP) Year Patra et al., 24a, Global Biogeochem. Cycles, Submitted A C Equatorial Pacific Comparison of oceanic flux anomaly: observations and models North Pacific

12 Regional Land Fluxes CO 2 Flux Anom. Air Temp. Anom A. Tropical Asia B. Tropical South America C. Boreal Asia Aggr S W S E N W N E Rainfall Anom Calendar Year Patra et al., 24b, Global Biogeochem. Cycles, Submitted

13 Comparison of land flux anomalies: Observations /estimations, and Biome-BGC ecosystem model fluxes Patra et al., 24b CO 2 Flux Anom. (Pg C yr ) / Fire Anom (count/1) 2 A. Tropical Asia TDI (this study) Page et al. Biome BGC 1 Fire Count Anom B. Tropical S. America C. Boreal Asia Kasischke et al Calendar Year

14 Indonesia ablaze, These widespread fires released massive amounts of carbon into the atmosphere WEISSERT and BERNASCONI, Nature 428, (11 March 24)

15 Capturing the time evolution of fires

16 CO2 Flux Anomaly With MEI ENSO Index Correlation Analysis CO2 Flux Anomaly With IOD Index

17 Conclusions 1. We have derived CO 2 fluxes from 42 land and 22 ocean regions. 2. The inverse method fairly successfully captures the flux variability due to climate variation. 3. The highest influence of weather/climate is observed over the tropical lands. 4. Major modes of CO 2 flux variability are connected to ENSO/IOD, Biomass burning (indirect climate forcing?). 5. A better measurement system is required to understand the global carbon cycle better.

18 Extras

19 Comparison of Ocean Flux Anomalies 3 2 This Work (Control) OGCM (LeQuere et al.) IPSL (Bousquet et al.) CSIRO (Rayner et al.) SCRIPS (Keeling et al.) MPI (Rodenbeck et al.) CO 2 Flux Anomaly (Pg C yr ) 1 2 ENSO Index is shown as shaded background Year

20 Flux Anom./PC vs. Met./Clim. Index Region/PC ENSO IOD Rain* Temp. Temp. N. A Trop. S. A Temp. Asia Trop. Africa South Africa Trop. Asia Australia PC PC PC In Tropics: CO2 flux & Temp: +ve CO2 flux & Rain : -ve * CO2 flux anomaly lags 3-month the rainfall anomaly.

21 EOF Distribution of Flux Anomaly

Interannual and decadal changes in the sea-air CO 2 flux from atmospheric CO 2 inverse modeling

GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 19,, doi:10.1029/2004gb002257, 2005 Interannual and decadal changes in the sea-air CO 2 flux from atmospheric CO 2 inverse modeling Prabir K. Patra, Shamil Maksyutov,