Anthropogenic methane (CH4) currently contributes more than 0.6 ∘C to global warming, and CH4 mitigation is a powerful option to limit near-term warming. Assessments of greenhouse gas (GHG) mitigation rely on Integrated Assessment Model scenarios with typically non-linear emission trajectories (due to economic optimization) and similar mitigation ambition for CO2 and CH4. However, climate targets are often linear and focused primarily on CO2. Here, we present a complementary scenario generation approach to systematically map peak warming resulting from two political choices: the net zero emissions year for CO2 or GHG after linear reductions, and the simultaneous change in CH4 emissions. We show that without CH4 mitigation, peak warming exceeds 1.7 ∘C (50% likelihood), and the likelihood of keeping warming below 2 ∘C drops below 50% with net zero CO2 emissions after 2050. Irrespective of CH4 mitigation stringency, limiting warming to 1.5 ∘C is no longer plausible. An additional sustained linear 10% reduction in CH4 emissions ( ~ 35 MtCH4/year) until 2050 increases the 2 ∘C-compatible remaining carbon budget by ~ 165 GtCO2. These results emphasize the benefit of near-term CH4 mitigation. Systematically mapping peak warming across two main choices for climate policy – when carbon dioxide reaches net zero emissions and how fast methane emissions are cut – shows that near-term methane mitigation is essential for keeping warming below 2 °C, as suggested by climate model experiments under mitigation scenarios.
Konstantin Weber, R. Knutti, Lena Brun et al.· Communications Earth & Envir...· 0 citations
Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range forecasting, at far lower inference cost. Many deployed MLWMs are deterministic, producing a single forecast with no estimate of its own uncertainty, whereas a growing family of trained-probabilistic models generate calibrated ensembles directly, at the price of a dedicated training run. We ask instead how much uncertainty can be extracted from a deterministic checkpoint that already exists, without retraining it. Where physical ensembles represent model uncertainty by stochastically perturbing parametrisation tendencies, we perturb the network's raw weight tensors at inference time, a scheme we call stochastically perturbed weights (SPW). We also ask whether it works, where and on which scales to inject the noise, and where it fails. A three-phase ablation across four deterministic backbones, Aurora, GraphCast, SFNO, and AIFS, selects one production baseline per model, benchmarked against the trained-probabilistic AIFS-ENS, FourCastNet 3 and Atlas as well as the operational ECMWF ensemble (IFS-ENS) over 112 initialisation times. At a 240 h (10-day) lead time the SPW ensembles reach continuous ranked probability skill scores (CRPSS) between 0.04 and 0.13 below the best trained-probabilistic baseline, at zero marginal training cost. No injection site works across models: the productive tensor group is architecture-specific, so SPW is at present a tuning procedure rather than a plug-and-play recipe. Its main failure mode is a coherent whole-field offset that overdisperses the domain mean, and restricting the noise to coarse scales or perturbing the initial conditions each repair part of it.
Simon Adamov, O. Fuhrer, R. Knutti et al.· 0 citations
Earth's feedback parameter measures how the Earth system responds to forcing and is inversely proportional to climate sensitivity. Sea-surface temperature (SST) patterns can modulate the value of the feedback parameter. Differences between observed and simulated SSTs have raised the question how the observed SSTs evolution impacts the global feedback. The standard method for estimating this effect uses observed SSTs prescribed to an atmospheric model with fixed pre-industrial atmospheric forcing. This method makes two assumptions: first, that the observed SSTs capture all relevant effects from the forcing, so that prescribing a time-varying forcing is unnecessary; second, that the temporal variations in the feedback parameter are driven by the evolving SST pattern and can be estimated via moving-window regressions. We test these assumptions by running controlled experiments in which SSTs from fully-coupled historical simulations are prescribed to an atmospheric model. We find that the prescribed-SST experiments fail to capture the coupled feedback evolution. This is explained by two effects: First, the absence of prescribed atmospheric forcing, and second, statistical noise arising from the computation of moving-window regressions. We find no evidence of any significant relationship between evolving SST patterns and changes in the feedback time series in a 4000-year pre-industrial control simulation. Any trends in the feedback parameter detected on timescales shorter than ~100 years are indistinguishable from statistical noise, making their attribution to the evolving SST pattern extremely difficult. Our results imply that prescribed SST simulations offer limited potential for inferring temporal changes in Earth's parameter over the observational period.
G. Gyuleva, R. Knutti, R. Noyelle et al.· 0 citations
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