Decomposing terrestrial carbon flux anomalies after El Niño: process-based predictability of land carbon sinks and sources

Anthropogenic fossil fuel emissions are increasing, and about a half of these emissions is absorbed by land and ocean. The CO2 fraction remaining in the atmosphere, the airborne fraction, is varying from year to year. Most of this variability can be explained by the land-atmosphere carbon fluxes. This variability is strongly affected by the El Niño – Southern Oscillation (ENSO); however, it is difficult to determine the cause of the flux anomalies due to the complex interactions between the climatic effects of the ENSO cycle. Here, we use MPI Earth System Model, MPI-ESM, to study the mechanisms of post El Niño carbon fluxes and assess their predictability. 10-member ensemble simulations with small perturbations are initialized at six El Niño events of a 1000-year control run. After removing the long-term mean from the ensemble simulations, a density-based clustering algorithm is applied to the carbon fluxes due to primary productivity, respiration and fires. This allows to identify and delimit the individual hotspots of ENSO-related carbon flux anomalies that contribute most to the atmospheric CO2 change.
We found that the main carbon sources are due to a reduction of primary production in the tropics, while the carbon sinks are due to reduced respiration or increased primary production in the extratropics. The potential predictability of the carbon fluxes from these clusters was assessed by using the perfect model approach. In accordance with this method, the predictive horizon is estimated as the time, when the variability within the ensemble members exceeds the long-term variability. As climate change will likely modify the frequency of El Niño events, this decomposition of the ENSO carbon flux anomalies could be used to improve our understanding of the future trends of land carbon sinks.