SearcharxivSearch

arXiv · 2101.06058

Eddy induced trapping and homogenization of freshwater in the Bay of Bengal

Abstract

Freshwater from rivers influences Indian summer monsoon rainfall and regional tropical cyclones by shallowing the upper layer and warming the subsurface ocean in the Bay of Bengal. Here, we use in situ and satellite data with reanalysis products to showcase how river water can experience a significant increase in salinity on subseasonal timescales. This involves the trapping and homogenization of freshwater by a cyclonic eddy in the Bay. Specifically, in October 2015, river water is shown to enter a particularly long-lived eddy along with its attracting manifolds within a period of two weeks. The eddy itself is quite unique in that it lasted for 16 months in the Bay where average lifespans are of the order of 2-3 months. This low salinity water results in the formation of a highly stratified surface layer. In fact, when freshest, the eddy has the highest sea-level anomalies, spins fastest, and supports strong lateral gradients in salinity. Subsequently, observations reveal progressive homogenization of salinity and relaxation of sea-level anomalies and salinity gradients within a month. In particular, salty water spirals in, and freshwater is pulled out across the eddy boundary. Lagrangian experiments elucidate this process, whereby horizontal chaotic mixing provides a mechanism for the rapid increase in surface salinity on the order of timescale of a month. This pathway is distinct from vertical mixing and likely to be important in the eddy-rich Bay of Bengal.

Explore related subjects

Keep this discovery

BibTeXRIS

Nihar Paul, Jai Sukhatme, Debasis Sengupta, Bishakdatta Gayen. 2021-01-15. Eddy induced trapping and homogenization of freshwater in the Bay of Bengal. https://doi.org/10.1029/2021jc017180

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