SearcharxivSearch

arXiv · 2512.05305

The Great Oxidation Event (GOE): Biogeochemical Feedback and Tipping Points

Abstract

Approximately 1.4 Ga after life first appeared, atmospheric oxygen suddenly jumped by more than an order of magnitude over a 20-50 Ma period. The contrast between these two timescales does not seem to be due to any sudden, large amplitude change in external forcing. However, it could be due to processes intrinsic to the geobiological system itself, namely, positive feedback between atmospheric oxygen and photosynthetic bacteria: More oxygen leads to more photosynthesis, which leads to more oxygen, and so on. Already-published feedbacks include buildup of an ozone shield and nutrient production by oxidative weathering. The feedback proposed here is the 15-fold greater efficiency of aerobic vs anaerobic respiration and the tight coupling of respiration and photosynthesis inside the cell. As in the climate system, feedback leads to tipping points, where a rapid, large amplitude change in the state of the system occurs. For the geobiological system, the GOE is the tipping point, and the long buildup before the GOE is the gradual oxidation of the crust and ocean, due either to burial of organic matter, oxidation of volcanic gases, or escape of hydrogen to space. The feedback hypothesis is a framework for interpreting observations leading to the GOE.

Explore related subjects

Keep this discovery

BibTeXRIS

Andrew P. Ingersoll. 2025-12-04. The Great Oxidation Event (GOE): Biogeochemical Feedback and Tipping Points. https://arxiv.org/abs/2512.05305

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