Recently, researchers from the Institute of Marine Science and Technology at Shandong University published a research paper titled "Global mapping and evolution of persistent fronts in Large Marine Ecosystems over the past 40 years" in Nature Communications. This study is based on a newly developed oceanfront detection algorithm proposed by Dr Yu Haiqing's Lab, which conducted a comprehensive survey of persistent (quasi-stationary) fronts surrounding global Large Marine Ecosystemsandproduceda detailed atlas of these persistent fronts. Furthermore, this study found that in boundary currents, upwelling systems, and Arctic waters, the occurrence and intensity of persistent fronts are rapidly increasing, while this trend is not the most advanced eddy-resolving CMIP6 climate model analysis dataset.

Ocean fronts are highly regarded dynamic processes in the ocean, defined as regions with narrow, high-gradient areas compared to the surrounding waters. Fronts can significantly impact local ocean dynamics and air-sea interactions, serving as pathways for transporting heat, carbon, oxygen, and other important climate-related gases to the deep ocean. The convergence effect caused by fronts aggregates phytoplankton and zooplankton, eventually forming biodiversity hotspots that attract fish schools and human fishing activities. Fronts play vital roles in air-sea interactions, marine ecology, fisheries, military operations, biogeochemistry, and pollutant distribution. In recent decades, global climate change has been altering global ocean circulation and dynamic processes, but how ocean fronts have changed in the context of global warming remains unknown. Identifying ocean fronts and their evolution under climate change is crucial for understanding and predicting climate, ocean circulation, and ecosystems.

Fig.1 Distribution of Persistent Fronts in Global Large Marine Ecosystems

Dr Yu Haiqing's Lab previously designed an algorithm using mathematical morphology operators combined with inverse distance weighting, addressing the limitations of previous algorithms in identifying coastal fronts, maintaining continuity, and avoiding duplicate identification (Xing et al., 2023). Utilizing this algorithm and satellite-observed high-resolution daily global SST from 1982 to 2021, this paper proposed a framework for identifying persistent fronts and conducted a detailed survey and naming of persistent fronts in global Large Marine Ecosystems. For the first time, this paper mapped out a detailed distribution atlas of global persistent fronts (Fig.1). This data is publicly available for download at https://doi.org/10.5281/zenodo.10968361.

Fig. 2 Long-term Changes of Persistent Fronts in Global Large Marine Ecosystems

Fig. 3 Comparison of Long-term Changes in Persistent Fronts Observed by Satellites with Eddy-Resolving Climate Models and Reanalysis Data

The study further found that the occurrence and intensity of global persistent fronts have shown a significant upward trend overall. Persistent fronts around boundary currents, upwelling systems, and the Arctic region exhibited rapid intensification, with trends increasing by 3-4%, 6-7%, and over 10% per decade, respectively, while tropical regions remained stable or slightly decreased (Fig.2). The significant intensification of persistent fronts is closely related to the strengthening of boundary currents and upwelling systems, as well as the melting of Arctic sea ice under global climate change. The study also used multiple eddy-resolving CMIP6 climate models and reanalysis datasets to conduct similar identification research. However, these models, even those incorporating satellite data assimilation, did not replicate the observed signal of persistent front intensification seen in satellite observations (Fig.3). This finding suggests that the ecosystem services provided by fronts, such as fisheries, biodiversity, and carbon sequestration, may be redistributed under climate change. This provides essential foundational information for understanding frontal dynamics and their bio-physical interactions and offers a practical baseline for improving high-resolution Earth system models.

This work was carried out by Dr. Yu Haiqing’s Lab at the Institute of Marine Science and Technology, Shandong University. Dr.Yu Haiqing's Lab has long been dedicated to research in physical oceanography, marine ecological dynamics, operational oceanography, artificial intelligence oceanography, etc. The first author of this paper is PhD student Xing Qinwang , and the corresponding author is Dr Yu Haiqing.