SearcharxivSearch

arXiv · 2209.01911

Depletion of fossil fuel reserves and projections of CO$_2$ concentration in the Earth atmosphere

Abstract

The paper has been suggested by two observations: 1) the atmospheric CO$_2$ growth rate is smaller than that ascribed to the emission of fossil fuels combustion, 2) the fossil fuel reserves are finite. The first observation has lead the way to a simple kinetic mode, based on the balance of 1) land/ocean CO$_2$ absorption and 2) CO$_2$ anthropogenic emission limited solely by depletion of the present day fossil-fuel reserves, in a business-as-usual scenario. The second observation has suggested to extrapolate past CO$_2$ emissions by fossil fuel combustion in the future years up to 2200 CE, by constraining emissions to the physical limits of reserves availability. The Meixner curve (hyperbolic secant distribution) has been used to model the pathway of resource exploitation for the three main classes of fossil fuels, crude oil, natural gas and coal. The kinetic model, driven by the extrapolated emissions, has been employed to project the CO$_2$ atmospheric concentration due to fossil fuel combustion close to the zero-reserve epoch. The result is just the output of simple models tuned on well-known experimental data. Error analysis of literature data provides the method robustness and the relevant uncertainty band. Contribution of other greenhouse gases like methane and nitrous oxide has been neglected, since their emissions cannot be projected with the paper methodology (they do not derive from fossil reserves). Notwithstanding this limitation, paper results clearly demonstrate that some of the IPCC projections of the CO$_2$ concentration are largely overestimated if compared to the physical limits of fossil fuel exploitation.

Explore related subjects

Keep this discovery

BibTeXRIS

Daniele Mazza, Enrico Canuto. 2022-09-05. Depletion of fossil fuel reserves and projections of CO$_2$ concentration in the Earth atmosphere. https://doi.org/10.1007/s10666-024-09985-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph