Cooperative Institute for Research in the Atmosphere

Untangling the Amazon Carbon Cycle with Satellite Data

In this talk, we will present a ‘farm to table’ description of how satellite data is being used to inform carbon cycle processes in Amazonia, which is both poorly sampled at the surface and critical to the global carbon cycle. We will describe how results from statistical ‘top down’ inversion models are used to confront process-based ‘bottom-up’ models of ecophysiological mechanisms to gain fuller understanding. Inversion models assimilate column CO2 from greenhouse gas satellites which, when coupled with aircraft data and process models, allows a description and understanding of regional biophysics that was not previously possible.

The Amazon is the largest tropical evergreen forest on the planet and has a significant influence on the global carbon cycle. This influence translates into variability in the airborne fraction of CO2 in response to anthropogenic sources and interannual variability (IAV) forced by modes of climate variability such as ENSO and Tropical North Atlantic indices. This variability will be realized as anomalies around the mean annual cycle of carbon flux for the region. Consensus on this mean flux, in terms of timing, amplitude, and biophysical determinants is elusive, forming an obstruction to our predictive capability.

Seasonality is defined by wet and dry seasons, and anomalies in timing, intensity and duration determine IAV. A generation ago, we believed that Amazonian carbon flux was directly coupled to precipitation, with carbon uptake during the wet season and efflux during seasonal drought. Findings from (a few) surface observations flipped the paradigm, with the result that most bottom-up regional estimates now simulate uptake during the dry season and efflux during seasonal rains. Using satellite-based and aircraft data, we now have strong evidence that carbon flux sign is indirectly tied to precipitation seasonality, the amplitude of the seasonal net flux is much smaller, and the determining processes are completely different than previously thought.