SearcharxivSearch

arXiv · 2111.06706

The global distribution of natural tritium in precipitation simulated with an Atmospheric General Circulation Model and comparison with observations

Abstract

The description of the hydrological cycle in Atmospheric General Circulation Models (GCMs) can be validated using water isotopes as tracers. Many GCMs now simulate the movement of the stable isotopes of water, but here we present the first GCM simulations modelling the content of natural tritium in water. These simulations were obtained using a version of the LMDZ General Circulation Model enhanced by water isotopes diagnostics, LMDZ-iso. To avoid tritium generated by nuclear bomb testing, the simulations have been evaluated against a compilation of published tritium datasets dating from before 1950, or measured recently. LMDZ-iso correctly captures the observed tritium enrichment in precipitation as oceanic air moves inland (the so-called continental effect) and the observed north-south variations due to the latitudinal dependency of the cosmogenic tritium production rate. The seasonal variability, linked to the stratospheric intrusions of air masses with higher tritium content into the troposphere, is correctly reproduced for Antarctica with a maximum in winter. LMDZ-iso reproduces the spring maximum of tritium over Europe, but underestimates it and produces a peak in winter that is not apparent in the data. This implementation of tritium in a GCM promises to provide a better constraint on: (1) the intrusions and transport of air masses from the stratosphere and (2) the dynamics of the modelled water cycle. The method complements the existing approach of using stable water isotopes.

Explore related subjects

Keep this discovery

BibTeXRIS

Alexandre Cauquoin, Philippe Jean-Baptiste, Camille Risi, Élise Fourré, Barbara Stenni, Amaelle Landais. 2021-11-12. The global distribution of natural tritium in precipitation simulated with an Atmospheric General Circulation Model and comparison with observations. https://doi.org/10.1016/j.epsl.2015.06.043

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