Net carbon exchanges in the Amazon conciliating satellite data, ground data, and atmospheric profile in a decade of observation using bottom-up approach

Abstract

Carbon emissions from fires (C-fire) account roughly for one-tenth of the global annual C emissions, wherein Amazon has a great role due to the rapid land-use change (LUC) in the last decades. Recent researches have shown an increase of 11% global fires emissions by 1997-2016. South America and especially the Brazilian Amazon have also experienced an increase in forest fires, which has been significant following the years of the El Niño and/or La Niña events (Werf et al. 2017; Aragão et al. 2018). In these years, fire emission from tropical forest may surpass the emissions from grassland and savannas, becoming a two-way trouble for the ecosystem. First, these forests are not adapted to fire; therefore we do not know how much carbon is released to the atmosphere after burn neither what are the impact of subsequent tree mortality on residence times of C flux. Second, we still do not know how long it takes the forest to recover after a fire in terms of carbon stocks. Therefore, we will use the CARbon Data Model framework (CARDAMOM) carbon assimilation model to improve the understanding of net carbon balance in post-fires degradation areas using satellite data, ground observations, literature, and atmospheric profiles of CO2 in a bottom-up approach. The main objective of this BEPE is to run the CARDAMOM model incorporating realistic parameters of burned area, carbon exchange, forest restoration, and other fire-related processes throughout the Amazon to adjust them to the measured annual CO2 profiles since 2010. Once it has established good model performance, the candidate will be able to run scenarios, e.g., of precipitation reduction and temperature increase, on the model data in order to understand the dynamics of carbon budget due to climate change in the Amazon. The CARDAMOM has been developed at the University of Edinburgh by Dr. Mathew Williams and his collaborators; therefore it will be necessary spend, with this proposal, a year to learn and apply the model in partnership with his group. We hope that this internship collaborate not only to pursue the candidate's objective, but also to better understand the role of forest fires over the Amazon in the global carbon cycle. (AU)