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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

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