A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1

摘要: In order to investigate Last Glacial Maximum and future climate, we “precalibrate” the intermediate complexity model GENIE-1 by applying a rejection sampling approach deterministic emulations of model. We develop ~1,000 parameter sets which reproduce main features modern but not precise observations. This allows wide range large-scale feedback response strengths generally encompass GCM behaviour. build emulator climate sensitivity quantify contributions atmospheric (±0.93°C, 1σ) vegetation (±0.32°C), ocean (±0.24°C) sea–ice (±0.14°C) parameterisations total uncertainty. then perform an LGM-constrained Bayesian calibration, incorporating data-driven priors formally accounting for structural error. estimate as likely (66% confidence) lie in 2.6–4.4°C, with peak probability at 3.6°C. LGM cooling 5.3–7.5°C, 6.2°C. addition estimates global temperature change, apply our ensembles derive 2xCO2 distributions land carbon storage, Atlantic overturning coverage. Notably, under calculate 37% that equilibrium terrestrial storage is reduced from values, so sink has become net source CO2.