CO2 budget for Amazon Basin using Inverse modeling

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Tuesday, 4 February 2014
Hall C3 (The Georgia World Congress Center )
Monica D'Amelio, University of Sao Paulo, Sao Paulo, Brazil; and L. V. Gatti, J. Miller, H. R. Rocha, E. R. Gutierrez, D. S. Alvim, D. S., and M. Gloor

Handout (2.9 MB)

Great efforts on researches and international partnerships have been expended to estimate the carbon budget on the Amazon Basin, this basin stores about 150-200 PgC, and any disturbance could promote an important global impact. Measurements of carbon flux and carbon concentration to estimates carbon flux have been performed in order to answers questions as what the role of Amazon is: source or sink of carbon? How Amazon Basin answers to climate change? This study promote the knowledge about the biosphere carbon fluxes and pools, it also contributes to simulations of potential anthropogenic influence on global climate.

The Amazon forest budget has been estimated through measurements in small spatial or temporal scales, then extrapolated to the whole basin. Recently applied forward model, a quasi-Lagrangian air-column budget technique, on vertical profiles CO2 mole fraction to calculate carbon flux relative to four Amazon basin sites. However, atmospheric inverse modeling allows one to extract information from atmospheric CO2 observation networks to either directly constrain carbon budget estimates on various scales or provide independent reference data to evaluate and optimize biosphere carbon flux models, so the variability captured in the time series integrates the influence of biosphere activity on atmospheric composition over large areas.

The inverse modeling approaches on temporal resolution have successfully demonstrated the potential of this technique to assess overall carbon budgets and their distribution among sub regions including temporal dynamics and uncertainties.

The Bayesian synthesis inversion technique has been commonly employed to estimate carbon. For this method, the function formulated has two terms: one involving the observations and one involving a prior estimate of the fluxes. To permit estimation of fluxes on the scales of interest, the prior flux is needed, because of the sparse of observational network. More specifically, the problem tends to be under determined in regions where observations are sparse, and possibly over determined in regions where there are many observations, depending on the spatial scale of the fluxes to be estimated. Most studies employing the Bayesian synthesis inversion technique have been executed in what has been referred to as “batch” mode wherein fluxes for all source regions are estimated at all times simultaneously using all of the observations.

To date, some studies above North America and Europe, using a Lagrangian inverse model approach have been applied. However, until now, there have been no studies using this technique for the Amazon Basin.  Here, we apply a Lagrangian inverse modeling framework to CO2 vertical profile measurements from four sites above the Brazilian Amazon in 2010 in order to enhance our understanding of the carbon cycle in Amazon Basin.  The CO2 vertical data were sampled at 4 sites: Alta Floresta, Mato Grosso State (ALF), Rio Branco, Acre State (RBA), Tapajós National Forest, Pará State (SAN), and Tabatinga, Amazonas State (TAB).   The Lagrangian model FLEXPART was used to calculate surface flux sensitivities (i.e. “footprints”) for each air sample, and these were used in a regional inverse modeling framework to determine fluxes using a batch inversion approach.

The mean CO2 flux calculated for Amazon Basin using the method described above was 0.09 +/- 1.2 mmolCO2.m-2.s-1 for 2010. The NEE presented more contribution on total flux during the year. The anthropogenic flux, mainly due biomass burning, had more influence during two months of dry season. The biologic flux had average of sink of 0.3 +/- 1.3 mmolCO2.m-2.s-1, while the anthropogenic source of 0.0 +/-1.5 mmolCO2.m-2.s-1. The budget accumulated for 2010 presented a sink of 0.3 tonC.ha-1.condition of null.