SearcharxivSearch

arXiv · 2304.07436

The Rapid Rise of Severe Marine Heat Wave Systems

Abstract

We introduce a new methodology to study marine heat waves, extreme events in the sea surface temperature (SST) of the global ocean. Motivated by previously large and impactful marine heat waves and by theoretical expectation that the dominant heating processes coherently affect large regions of the ocean, we introduce a methodology from computer vision to construct marine heat wave systems (MWHSs) -- the collation of SST extrema in dimensions of area and time. We identify 649,475 MHWSs in the 37 year period (1983-2019) of daily SST records and find that the duration t_dur (days), maximum area A_max (km$^2$), and total ``volume'' N_vox (days km$^2$) for the majority of MHWSs are well-described by power-law distributions: t_dur^(-3), A_max^(-2) and N_vox^(-2). These characteristics confirm SST extrema exhibit strong spatial coherence that define the formation and evolution of marine heat waves. Furthermore, the most severe MHWSs deviate from these power-laws and are the dominant manifestation of marine heat waves: extrema in ocean heating are driven by the ~200 systems with largest area and duration. We further demonstrate that the previously purported rise in the incidence of marine heat wave events over the past decade is only significant in these severe systems. A change point analysis reveals a rapid increase in days under a severe MHW in most regions of the global ocean over the period of 2000-2005. Understanding the origin and impacts of marine heat waves in the current and future ocean, therefore, should focus on the production and evolution of the largest-scale and longest-duration heating phenomena.

Explore related subjects

Keep this discovery

BibTeXRIS

J. Xavier Prochaska, Claudie Beaulieu, Katerina Giamalaki. 2023-04-15. The Rapid Rise of Severe Marine Heat Wave Systems. https://doi.org/10.1088/2752-5295%2Faccd0e

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