Showing physics.ao-phShow all
3 papers · 1 filter
physics.ao-ph2026
Machine learning emulators of dynamic global vegetation models for fast gridded carbon budget predictions
Piyu Ke, Xiaofan Gui, Stephen Sitch +5
The land carbon sink explains most of the variability of the atmospheric CO2 growth rate, but the Global Carbon Budget estimates it with dynamic global vegetation models (DGVMs) ru…
physics.ao-ph2026
Forecasting carbon-risk hotspots ahead of the extreme 2026 El Niño
Xiaomeng Du, Runda Jiang, Philippe Ciais +14
A potentially super El Niño is forecasted in 2026, raising the need to anticipate land carbon-cycle risks before its impacts unfold. We develop a prospective early-warning framewor…
physics.ao-ph2026
Low latency global carbon budget reveals strong land sink recovery in 2025
Philippe Ciais, Piyu Ke, Xiangjun Tian +32
The atmospheric CO2 growth rate fell sharply in 2025, from a record 3.76 0.09 ppm yr-1 in 2024 to 2.06 0.09 ppm yr-1 (NOAA marine boundary layer observations), below th…