SearcharxivSearch

arXiv · 2407.08363

Alternative climatic steady states near the Permian-Triassic Boundary

Abstract

Due to spatial scarcity and uncertainties in sediment data, initial and boundary conditions in deep-time climate simulations are not well constrained. On the other hand, the climate is a nonlinear system with a multitude of feedback mechanisms that compete and balance differently depending on the initial and boundary conditions. This opens up the possibility to obtain multiple steady states under the same forcing in numerical experiments. Here, we use the MIT general circulation model with a coupled atmosphere-ocean-thermodynamic sea ice-land configuration to explore the existence of such alternative steady states around the Permian-Triassic Boundary (PTB). We construct the corresponding bifurcation diagram, taking into account processes on a timescale of thousands of years, in order to identify the stability range of the steady states and tipping points as the atmospheric CO$_2$ content is varied. We find three alternative steady states with a difference in global mean surface air temperature of about 10 $^\circ$C. We also examine how these climatic steady states are modified when feedbacks operating on comparable or longer time scales are included, in particular vegetation dynamics and air-sea carbon exchanges. Our findings on multistability provide a useful framework for explaining the climatic variations observed in the Early Triassic geological record, as well as some discrepancies between numerical simulations in the literature and geological data at PTB and its aftermath.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Charline Ragon, Christian Vérard, Jérôme Kasparian, Maura Brunetti. 2024-07-11. Alternative climatic steady states near the Permian-Triassic Boundary. https://doi.org/10.1038/s41598-024-76432-8

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