Investigating drought impacts on Amazon Basin carbon fluxes through regional inverse modeling of CO2 from aircraft vertical profiles

Tuesday, 16 December 2014: 12:05 PM
Caroline B Alden1, John B Miller2, Luciana Gatti3, Manuel Gloor4, Kaiyu Guan1, Noah S Diffenbaugh1, Kirk W Thoning5 and Yoichi Paolo Shiga6, (1)Stanford University, Stanford, CA, United States, (2)NOAA/ESRL, Boulder, CO, United States, (3)IPEN Nuclear Energy Research Institute, Sao Paulo, Brazil, (4)University of Leeds, Geography Department, Leeds, United Kingdom, (5)NOAA/ESRL GMD, Boulder, CO, United States, (6)Stanford University, Civil and Environmental Engineering, Stanford, CA, United States
The large amount of carbon stored as biomass in the Amazon Basin makes this area a critically important component of the global carbon cycle. Its importance is compounded by the vulnerability of these carbon stocks to drought, fire and other disturbances. It is not well known how current and future climate changes will impact Amazon carbon stores and fluxes, however, and field-level observations are difficult to scale up due to forest heterogeneity and the immense size of the Basin. Recent efforts to systematically and regularly sample atmospheric CO2 profiles by light aircraft have resulted in an unprecedented view of the integrated signal of net CO2 fluxes in the Amazon. In particular, measurements made over the period of 2010-2013 offer insights into Amazon forest carbon cycling in both dry and wet years. These observations have shown, for example, that during the 2010 drought, the Amazon was less of a sink for atmospheric CO2 than during 2011 (a wetter year). In order to investigate the geographic and temporal patterns of changing sink strengths in response to drought stress, we have developed a regional inversion for fluxes of CO2 in the Amazon Basin for 2010-2013 using these same data. Measurements of CO in the same air samples allow us to perform a second inversion to optimize estimates of biomass burning fluxes. As part of the inversion framework, we have developed novel approaches for prior estimation of and optimization of boundary or background conditions. We use two Lagrangian Transport Models (FLEXPART/GFS and HYSPLIT/GDAS) to estimate the potential influence of convection parameterization on the inversion results. We will show the results of these inversions as well as comparison of our results with satellite observations of solar induced chlorophyll fluorescence (a proxy for gross primary productivity and water stress) to investigate hot-spots of drought impact on Amazon net CO2 fluxes for 2010-2013.