SearcharxivSearch

arXiv · 1502.00805

Factors controlling the time-delay between peak CO2 emissions and concentrations

Abstract

Carbon-dioxide (CO2) is the main contributor to anthropogenic global warming, and the timing of its peak concentration in the atmosphere is likely to govern the timing of maximum radiative forcing. It is well-known that dynamics of atmospheric CO2 is governed by multiple time-constants, and here we approximate the solutions to a linear model of atmospheric CO2 dynamics with four time-constants to identify factors governing the time-delay between peaks in CO2 emissions and concentrations, and therefore the timing of the concentration peak. The main factor affecting this time-delay is the ratio of the rate of change of emissions during its increasing and decreasing phases. If this ratio is large in magnitude then the time-delay between peak emissions and concentrations is large. Therefore it is important to limit the magnitude of this ratio through mitigation, in order to achieve an early peak in CO2 concentrations. This can be achieved with an early global emissions peak, combined with rapid decarbonization of economic activity, because the delay between peak emissions and concentrations is affected by the time-scale with which decarbonization occurs. Of course, for limiting the magnitude of peak concentrations it is also important to limit the magnitude of emissions throughout its trajectory, but that aspect has been studied elsewhere and is not examined here. The carbon cycle parameters affecting the timing of the concentration peak are primarily the long multi-century time-constant of atmospheric CO2, and the ratio of contributions to the impulse response function of atmospheric CO2 from the infinite time-constant and the long time-constant respectively. Reducing uncertainties in these parameters can reduce uncertainty in forecasts of the radiative forcing peak.

Explore related subjects

Keep this discovery

BibTeXRIS

Ashwin K. Seshadri. 2015-02-03. Factors controlling the time-delay between peak CO2 emissions and concentrations. https://doi.org/10.1016/j.gloplacha.2016.09.004

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