SearcharxivSearch

arXiv · 2102.01745

The climate system and the second law of thermodynamics

Abstract

The second law of thermodynamics implies a relationship between the net entropy export by the Earth and its internal irreversible entropy production. The application of this constraint for the purpose of understanding Earth's climate is reviewed. Both radiative processes and material processes are responsible for irreversible entropy production in the climate system. Focusing on material processes, an entropy budget for the climate system is derived which accounts for the multi-phase nature of the hydrological cycle. The entropy budget facilitates a heat-engine perspective of atmospheric circulations that has been used to propose theories for convective updraft velocities, tropical cyclone intensity, and the atmospheric meridional heat transport. Such theories can only be successful, however, if they properly account for the irreversible entropy production associated with water in all its phases in the atmosphere. Irreversibility associated with such moist processes is particularly important in the context of global climate change, for which the concentration of water vapor in the atmosphere is expected to increase, and recent developments toward understanding the response of the atmospheric heat engine to climate change are discussed. Finally, the application of variational approaches to the climate and geophysical flows is briefly reviewed, including the use of equilibrium statistical mechanics to predict behavior of long-lived coherent structures, and the controversial maximum entropy production principle.

Explore related subjects

Keep this discovery

BibTeXRIS

Martin S. Singh, Morgan E O'Neill. 2021-02-02. The climate system and the second law of thermodynamics. https://doi.org/10.1103/revmodphys.94.015001

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