Preprint
Oct 2026
Forecasting carbon-risk hotspots ahead of the extreme 2026 El Ni\~no
A potentially super El Ni\~no is forecasted in 2026, raising the need to anticipate land carbon-cycle risks before its impacts unfold. We develop a prospective early-warning framework that uses dynamic global vegetation models driven by an ensemble of ECMWF seasonal climate forecasts and machine-learning burned area pr...
Xiao-Meng Du, Run Jiang, P. Ciais et al.
· 0 citations