Research on carbon fluxes and carbon cycle

Shamil Maksyutov (NIES)

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Contents

Use of GOSAT data in carbon cycle research and newchallenges for research community Bottom-up inventories of the carbon dioxide/methane fluxes
 Modeling of the terrestrial biosphere fluxes, including seasonaland diurnal change, forest fire/biomass burning Accurate atmospheric transport modeling Improvements in inverse modeling and data assimilation
 Applications to understanding relationships between climatechange and carbon cycle trends in the Earth system scale.

2What will change with GOSAT data made available: observational coverage

Currect ground based observations of CO2:~40 continuous sites (hourly data)~100 flask samples per week
~10 aircraft profiles per day
Map by WDCGG at JMA, TokyoGOSAT orbit and XCO2 (with global offset
subtracted), ~1000 cloud-freeobservations per day

GOSAT data challenge: 10-20 times increase of the available data, demanding
use of new, more accurate models, inverse techniques
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Long standing problems: flux estimation criticallydepends on emission inventories, surface flux models
Two most successful models of natural CO2 fluxes
Terrestrial biosphere, CASA model

Ocean (Takahashi et all, 2002, 2008)

CO2 fluxes in July, gC/m2/day
Goal: to improve seasonal cycle, add
Goal: to make interannually varying fluxes,daily, diurnal variations.
improve southern ocean flux seasonality

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Anthropogenic fluxes: fossil fuel consumption

Annual fossil fuel use, Adres, CDIAC

CO2 fluxes in 1998 gC/m2/day, 1x1 deg grid
Goal: make seasonally and diurnallyvarying global emissions approaching
GOSAT pixel resolution (10x10 km)

High resolution emissions, Vulcan project

log10CO2 fluxes in 2002,tC per 10x10 km, annual
Goal: to make interannually varying fluxes,
global coverage

5Inverse modeling: from monthly fluxes at regional scale to high resolution grid

Aggregated fluxes with 64 region inversion
Patra et al 2005

CO2 fluxes in July 1996, gC/m2/day

Surface flux corrections (Chevallier et al 2005)

CO2 fluxes in Aug 1990, gC/m2/year

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Inverse modeling: Transcom model intercomparison of flux inversions gives
better understanding of the model biases and errors

Multimodel fluxes

Independent validation with vertical CO2 profiles(Stephens et al, Science, 2007)

Observations (grey) suggest
which inverse model is right.
Mean CO2 fluxes estimated with 13 models Multi-model estimates of fluxes withof atmospheric transport and inversion,
several alternative approaches
using surface observations,demonstrated to be robust
Transcom project, Baker et al, GBC, 2006
Units Gt/Year7

Summary
Expected advances in carbon cycle research with available GOSAT
data• Improved understanding of carbon cycle and its variability atregional scale, its sensitivity to climate variability and climatechange.
• Robust estimates of the regional flux distributions based on multimodel intercomparison results
Expected improvement in flux estimation methods, which contributeto and provide feedback/validation to operational algorithms for
CO2 flux estimates.
• Bottom-up flux inventories.
• Modeling of the terrestrial biosphere fluxes• Accurate atmospheric transport modeling• Improvements in inverse modeling and data assimilation
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