SearcharxivSearch

arXiv · 2503.01117

Gulf Stream contribution to recent North Pacific warming

Abstract

Recent unprecedented ocean warming has produced coherent sea surface temperature (SST) anomalies across the Northern Hemisphere extratropics. While the tropical Pacific is a natural source of North Pacific variability, the influence of the midlatitude North Atlantic has remained poorly understood. Here we show that Gulf Stream SST variability remotely modulates Kuroshio variability, explaining 13% of internal Kuroshio SST variance in climate model simulations, with no significant reverse influence. Positive Gulf Stream SST anomalies excite a Northern Annular Mode (NAM)-like atmospheric circulation response, weakening the Aleutian Low over the North Pacific. The resulting northward shift of the Kuroshio Extension enhances northward warm-water transport and favors positive SST anomalies in the western midlatitude North Pacific. We also show that a high-resolution ocean model is required to properly capture this SST-NAM coupling, suggesting the importance of representations of western boundary currents and associated SST fronts for this interbasin pathway. These findings reveal the Gulf Stream-Kuroshio linkage through which North Atlantic variability has measurably contributed to the recent exceptional North Pacific warming, implying a previously underrecognized source of decadal climate predictability.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yoko Yamagami, Hiroaki Tatebe, Tsubasa Kohyama, Shoichiro Kido, Satoru Okajima, Yoshiki Komuro. 2025-03-03. Gulf Stream contribution to recent North Pacific warming. https://arxiv.org/abs/2503.01117

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