As global temperatures climb, the dynamic interactions between oceans and coastal cities are drawing intense scrutiny. A groundbreaking study published in Nature Climate Change reveals that rising sea surface temperatures (SST) are significantly weakening the sea–land breeze (SLB) phenomenon, a crucial natural cooling mechanism for coastal megacities. This insight not only advances our understanding of urban climate resilience but also underscores the complex challenges that ocean warming imposes on expanding metropolitan regions at the ocean’s edge.
The sea–land breeze cycle, driven primarily by thermal contrasts between land and adjacent ocean bodies, plays a pivotal role in regulating local climates in coastal zones. However, conventional models that rely on coarse, low-resolution SST data have long struggled to capture the nuanced thermal gradients essential to accurate SLB simulation, particularly in nearshore areas. The current research overcomes these limitations by integrating high-resolution, kilometer-scale SST data derived from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs. This methodological leap enables thermal gradient assessments with sub-0.1 °C precision.
Notably, the research team chose the Beijing Climate Center’s BCC-CSM2-MR model from CMIP6 due to its superior fidelity in simulating monthly SST variations when benchmarked against multiple reanalysis datasets and satellite assimilations. This robust SST data was dynamically incorporated into the Weather Research and Forecasting (WRF) mesoscale model, replacing the traditional static SST boundary conditions that often degrade atmospheric coupling accuracy. By doing so, the study traces diurnal thermal signatures along marine domains adjacent to 18 global coastal megacities.
The refined modelling approach employed geographic coordinate transformations and sophisticated spatiotemporal matching algorithms, ensuring accurate spatial alignment of SST data with WRF’s marine grid boundaries. The researchers dynamically imposed daily and diurnal SST fields for both historical and projected future periods under SSP245 and SSP585 emission scenarios. This forward-looking analysis allowed an unprecedented examination of how ongoing ocean warming trends might reshape diurnal SLB cycles and their cooling efficacy in the decades ahead.
Remote sensing advances and multisource data assimilation techniques further fortified the SST dataset validation process. Cross-comparisons revealed strong concordance between BCC-CSM2-MR simulations and reanalysis products, as well as with ultrahigh-resolution satellite-derived SST observations from the Group for High-Resolution SST (GHRSST) initiative. Despite high correlations, residual biases in SLB-day intensity persisted, hinting at lingering uncertainties in nearshore SST characterizations and the intricate representation of coastal wind dynamics, especially for cities with complex shoreline geometries.
Employing WRF version 4.2.2, the study conducted meticulously controlled sensitivity experiments to isolate the influence of SST anomalies on SLB day frequency and intensity. The simulation framework maintained fixed parameters for land use, anthropogenic heat emissions, and surface roughness, while leveraging identical meteorological inputs for consistent baseline comparisons. Spin-up intervals of 22 days optimized initial condition stabilization, enhancing the reliability of year-long (365-day) simulations across two nested domains at 27 km and 9 km spatial resolutions.
The selection of megacities for analysis was guided by rigorous criteria encompassing urban magnitude, geographic distribution, climate zone representation, and coastal positioning. Leveraging night-time light intensity data helped identify urban hotspots within SLB-dominant climatic zones to ensure the representativeness of the study’s spatial scope. This enabled a comprehensive examination of interactions between climatological factors and urban meteorology across diverse coastal contexts globally.
A standardized definition for identifying SLB days was adopted to ensure consistency in evaluating model outputs. Essential criteria required the concurrent occurrence of both sea and land breezes within a 24-hour window, exhibiting distinct transition phases. Wind direction and speed thresholds, along with relative marine-to-urban temperature distinctions, were rigorously applied, along with exclusion of days featuring wind speeds exceeding 10 m/s that could overwhelm mesoscale breeze dynamics. Such methodological stringency ensures that quantified SLB days reflect robust atmospheric coupling rather than extraneous synoptic influences.
To unravel complex SST–SLB frequency relationships, the investigators applied an unsupervised k-means clustering analysis. This machine-learning technique enabled the classification of coastal zones into three clusters based on the joint statistical distribution of SST anomalies and SLB day frequency. Employing optimization criteria such as minimum sum-of-squared-errors and silhouette coefficients, the study rigorously evaluated cluster validity. Monte Carlo resampling affirmed cluster stability, granting confidence to the interpretation of thermodynamic and meteorological regimes underpinning differential coastal responses.
Complementing the physical process modelling, a novel spatiotemporal statistical downscaling methodology was developed to dynamically generate WRF-compatible atmospheric initial and boundary conditions from CMIP6 global model outputs. This technique preserves the integrity of large-scale atmospheric forcing while enriching local-scale variability, a critical step for capturing heterogeneous urban–ocean interactions. The method involves careful coordinate transformations, temporal-nearest-neighbor matching, bilinear spatial interpolation, and log-pressure remapping for vertical consistency.
Collectively, the results shed light on a concerning climatological feedback: as oceans warm, the traditionally strong sea–land thermal gradient weakens, diminishing the SLB circulation that normally convects heat away from urban coastal areas during daytime and moderates nocturnal temperatures via land breezes. This phenomenon could exacerbate heat stress in megacities reliant on natural breeze-mediated cooling, intensifying heatwave impacts and air quality challenges amid rapid urban expansion.
Importantly, the study demonstrates that incorporating temporally varying, high-resolution SST data into mesoscale weather models fundamentally alters simulated SLB patterns versus conventional static SST treatments. This highlights the critical necessity of advancing ocean-atmosphere coupling parameterizations within regional climate modelling frameworks to better anticipate the localized consequences of global ocean warming.
These findings also invoke pressing implications for urban planning and adaptation strategies in coastal megacities worldwide. Climate-resilient infrastructure designs must now account for anticipated reductions in SLB intensity, potentially necessitating engineered ventilation corridors and green infrastructure to compensate for diminished natural cooling. Policymakers and urban climatologists alike must integrate these nuanced oceanic influences into heat mitigation frameworks.
Furthermore, the corroborated superiority of BCC-CSM2-MR among CMIP6 models in replicating coastal SST variability spotlights the importance of continuous model evaluation and refinement against multi-modal observational benchmarks. The authors advocate for future work leveraging emerging ultrahigh-resolution SST products and refined coastal wind representations to further constrain uncertainties and enhance projections of coastal urban climate dynamics under various emission trajectories.
In sum, this pioneering integration of advanced SST datasets into dynamic mesoscale modelling elucidates a critical axis of climate vulnerability for coastal megacities—one where ocean warming inexorably undermines a core local breeze mechanism, with profound implications for urban habitability and climate adaptation worldwide. The study’s sophisticated technical workflow, spanning high-resolution SST assimilation, rigorous wind and temperature criteria for SLB identification, and machine-learning classification, represents a major leap forward in coastal climate research and modelling fidelity.
As the global scientific community grapples with unprecedented climate change impacts, such innovative cross-disciplinary efforts are essential for producing actionable insights tailored to the unique challenges and opportunities of metropolitan coastlines. With improved modelling capabilities, cities can better anticipate and prepare for the complex interplay of rising oceans and evolving atmospheric dynamics shaping their future climates.
Subject of Research: The weakening effect of ocean warming on sea–land breeze dynamics in coastal megacities.
Article Title: Ocean warming weakens the sea–land breeze in coastal megacities.
Article References:
Xiao, Y., Liu, Y., Nie, Y. et al. Ocean warming weakens the sea–land breeze in coastal megacities. Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02618-9
