Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2505.17199

A statistical study of sea ice thickness and coverage in the Canadian Arctic

Arya Kimiaghalam
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Arctic sea ice cover has significantly declined over the recent decades. The debate on whether this decline is caused by anthropogenic activity or internal cycles is still ongoing. However, despite this uncertainty, some physical factors reinforce this declining trend, one of which is sea ice thickness. The thinning of Arctic sea ice facilitates the melting of sea ice by reducing the heat capacity of the ice volume. The progression of this thinning can potentially accelerate sea ice loss. In this work, we attempt to understand the broad relationship of sea ice cover levels and average sea ice thickness in the Arctic. First, we attempt to understand whether the trend in the Arctic sea ice thickness is statistically significant over multi-year and inter-year seasonal scales, by using mostly non-parametric trend analysis tools. We subsequently study how sea ice thickness, as well as its momentum and fluctuations, are statistically correlated to those of sea ice cover in the Arctic. For this task, we use publicly available Arctic sea ice cover and thickness data from 1979 to 2021, provided by the Pan-Arctic Ice Ocean Modelling and Assimilation System (PIOMAS) and the National Snow and Ice Data Center (NSIDC).

Explore related subjects

physics.ao-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Arya Kimiaghalam. 2025-05-22. A statistical study of sea ice thickness and coverage in the Canadian Arctic. https://arxiv.org/abs/2505.17199

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

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↗
arXiv2026

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↗