1. GERFS1 Top-down approach to estimation of the regional carbon budget in West Siberia S. Maksyutov (1) T. Machida, K. Shimoyama, N.Kadygrov, A. Itoh (1) P. Patra (2) M. Arshinov, O. Krasnov, B. Belan (3), N. Fedoseev (4) (1) CGER/NIES, Tsukuba, Japan (2) FRCGC/JAMSTEC, Yokohama, Japan (3)IAO, Tomsk, Russia (4)Permafrost Research Institute, Yakutsk, Russia Inverse modeling of regional CO2 fluxes using tower network in West Siberia Transcom workshop, Purdue Univ., Apr 23-27, 2007

2. GERFS1 Method: Inverse model of the atmospheric CO2 transport is applied to constrain surface CO2 fluxes by the observed patterns of the atmospheric CO2 (with seasonal cycles) Components: -Bottom-up estimate of C fluxes (long term), based on the terrestrial ecosystem NPP, respiration, biomass, biomass change. 1.Forward models: terrestrial ecosystem flux model (hourly to seasonal scale): coupled to atmospheric transport model. 2.Inverse model of atmospheric transport, finding optimal corrections to the surface fluxes Top-down approach to estimation of the regional carbon budget in West Siberia

3. GERFS1 Q. (Back in 2002) Why do inverse modeling of carbon fluxes in West Siberia? -Established collaboration programs on atmospheric and ground based observations -Advantage for atmospheric transport modeling: simplified meteorology, flat terrain. -Scientifically interesting region: strong impact by climate change. Top-down approach to estimation of the regional carbon budget in West Siberia

4. GERFS1 Integrated vegetation map (1x1 km) based on GLC2000, forest inventory MODIS VCF vegetation continuous fields (500m) wetland typology map

5. GERFS1 Bottom-up estimate of C fluxes (long term), based on the terrestrial ecosystem NPP, respiration, biomass, biomass change. Inventory of the carbon content and its long-term changes using field observations and forest survey data. -Forest inventory: provides observations of the wood stock (carbon stock) and annual change in forest area by category (felling, fire, etc) -Soil carbon stock inventory (by agricultural soil profile observations) -Wetlands (25% of the area, accumulators of peat deposits: need to know the area, and area fraction by landscape elements Top-down approach to estimation of the regional carbon budget in West Siberia

6. GERFS1 Empirical modeling of the forest carbon stock inventory and dynamics Forest state account (FSA): provides observations of the wood stock and annual change in forest area by category (felling, fire, etc), Frequency: reporting 1 year, full reporting 5 years, unit survey about 15 years Top-down approach to estimation of the regional carbon budget in West Siberia FSA: regions and enterprises FSA data for each unit: wood stock, area - by species, age class Input to empirical dynamic model Cedar (pinus sibirica) NPP by age and productivity class. Based on yield tables.

7. GERFS1 Top-down approach to estimation of the regional carbon budget in West Siberia 1. Comparison of the soil carbon surveys in recent 30 years indicates soil carbon loss to erosion. (Titlyanova et al) Problem: While forests and wetlands provide carbon sink, the agricultural lands show some loss of carbon 2. Long term observations of the soil carbon after conversion to arable lands show stabilization of carbon content after ~50years. (Barsukov et al) Siberian soil carbon profile database (Titlyanova et al) soil map

8. Surgut 1993 - Novosibirsk Yakutsk GERFS1 Top-down approach to estimation of the regional carbon budget in West Siberia Airborne observations: air sampling and analysis

9. GERFS1 Top-down approach to estimation of the regional carbon budget in West Siberia OBSERVATIONS Igrim(IGR) （ 63 o 12 ’ N, 64 o 24 ’ E ） 47m, 24m Demyanskoe(DEM) （ 59 o 47 ’ N, 70 o 52 ’ E ） 63m, 45m Parabel(PRB) （ 58 o 15 ’ N, 82 o 24 ’ E ） 67m, 35m Berezorechka (BRZ) （ 56 o 09 ’ N, 84 o 20 ’ E ） 80m, 40m, 20m, 5m Yakutsk(YAK) （ 62 o 50 ’ N, 129 o 21 ’ E ） 70m, 11m Noyabrsk(NOY) （ 63 o 26 ’ N, 76 o 46 ’ E ） 43m, 21m planned Noyabrsk Igrim Beloretsk Demyanskoe Azovo Parabel Berezorechka Savuushka Zotino Yakutsk Igrim(IGR) （ 63 o 12 ’ N, 64 o 24 ’ E ） 47m, 24m Demyanskoe(DEM) （ 59 o 47 ’ N, 70 o 52 ’ E ） 63m, 45m Parabel(PRB) （ 58 o 15 ’ N, 82 o 24 ’ E ） 67m, 35m Berezorechka (BRZ) （ 56 o 09 ’ N, 84 o 20 ’ E ） 80m, 40m, 20m, 5m Yakutsk(YAK) （ 62 o 50 ’ N, 129 o 21 ’ E ） 70m, 11m Noyabrsk(NOY) （ 63 o 26 ’ N, 76 o 46 ’ E ） 43m, 21m Working Noyabrsk Igrim Vaganovo Demyanskoe Azovo Parabel Berezorechka Savuushka Zotino Yakutsk

10. Observations: hourly data, -> selected afternoon data and fitted with Globalview type fits GERFS1 Top-down approach to estimation of the regional carbon budget in West Siberia

11. Inverse modeling: monthly fluxes from 66 regions 64 region map 66 region map (color map – by region number) Inverse model used in this study is documented in: Patra, P.K., M. Ishizawa, S. Maksyutov, T. Nakazawa, and G. Inoue, (2005) Global Biogeochem. Cycles, 19, GB3005, doi:10.1029/2004GB002258.

12. Seasonal variation of CO2 Flux with 66 region inversion and 1 year (2005) of tower data W.Siberia South (top), North (bottom) Central.Siberia South (bottom), North (top)

13. Seasonal variation of CO2 Flux with 66 region inversion and 1 year (2005) of tower data W.Siberia South (top), North (bottom) Central.Siberia South (bottom), North (top)

14. Seasonal variation of CO2 Flux with 66 region inversion and WITHOUT 1 year (2005) of tower data W.Siberia South (top), North (bottom) Central.Siberia South (bottom), North (top)

15. Seasonal variation of CO2 Flux, Comparison with another biospheric model (Simcycle) W.Siberia South (bottom), North (top) Central.Siberia South (bottom), North (top)

16. GERFS1 Top-down approach to estimation of the regional carbon budget in West Siberia Summary and conclusions: Tower observations in 2002-2006 were fitted using data filtering procedure (Nakazawa et al) at 5 W.S. sites to produce Globalview type fits. Only daytime observations were used Seasonal flux variability in well constrained regions shows same amplitude as CASA (prior) but earlier summer drawdown. Seasonal variability in under-constrained ones is not that reasonable. The inverted fluxes amplitude and seasonality look similar to those simulated by Simcycle – prognostic model, which may suggest that: 1)the inverse model fluxes are reasonable (biologically) 2)regional scale inverse model might be used as a tool to observe(!) regional scale seasonality of natural terrestrial carbon cycle