SearcharxivSearch

arXiv · 2605.00621

The role of the oceans for subseasonal prediction: insights from eddy-permitting and eddy-rich coupled forecast systems

Abstract

The oceans play a fundamental role in Earth's climate system, redistributing heat and influencing global and regional climate variability and predictability across weather and climate timescales. The benefits of ocean-atmosphere coupling for initialised predictions depend on the balance between improvements associated with more realistic air-sea interactions and dynamics, and degradations arising from the development of systematic biases at the coupling interface. Here, we draw on recent developments in modelling and data assimilation at ECMWF to revisit the role of ocean-atmosphere coupling in subseasonal predictions. In particular, we evaluate the impact of ocean-atmosphere coupling in 46-day reforecasts produced with the ECMWF Integrated Forecasting System (IFS) and explore the potential for improvements through increased horizontal resolution and a better representation of the ocean mesoscale. We find that ocean-atmosphere coupling significantly enhances ensemble forecast skill in the tropics, with positive effects increasing at longer lead times. In particular, Madden-Julian Oscillation (MJO) forecasts are substantially improved, with forecast skill extended by approximately 5 days compared to the uncoupled configuration. In contrast, ocean-atmosphere coupling has a more limited impact on the extratropical atmosphere at subseasonal timescales, with marginal impacts on the predictability of major tropospheric and stratospheric circulation indices. Finally, we present selected results from an experimental eddy-rich coupled configuration of the IFS, with a horizontal ocean resolution of approximately 8 km. We find that a better-resolved representation of the ocean mesoscale has a limited impact on atmospheric forecasts at subseasonal lead times, which suggests that many of the known deficiencies of the eddy-permitting reference configuration are mitigated by accurate initialisation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Christopher David Roberts, Sarah Keeley, Kristian Mogensen, Charles Pelletier, Hao Zuo. 2026-05-01. The role of the oceans for subseasonal prediction: insights from eddy-permitting and eddy-rich coupled forecast systems. https://arxiv.org/abs/2605.00621

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