Paper Push: 2026-06-15

每日论文推送：BGC-Argo、海色/海洋光学、海洋热浪与碳泵Daily Paper Push: BGC-Argo, ocean colour/ocean optics, marine heatwaves and carbon pump

本期由 GitHub Actions 自动检索生成：Nature/Science 系列优先，其次是用户指定重点期刊，再补充重点关注团队的新论文，最后纳入其他相关期刊；历史去重后保留 2 篇，不超过每日 50 篇上限。 This issue was generated automatically by GitHub Actions: Nature and Science series first, then the user-defined priority journals, then new papers from the focused team, followed by other relevant journals as topical supplements. After deduplication, 2 papers remain, below the daily limit of 50.

Download Word summary

无 mechanism sketch 图。今天的意大利语卡片： No mechanism sketch figure today. Daily Italian card:

每日一句意大利语Daily Italian

Trasumanar significar per verba non si poria.

Dante, Commedia, Paradiso I, 70-71; Italian original from Kalliope

这句说“超越人的状态，不能完全用语言说明”。它表达经验超过普通语言边界时的困难。

The line says that going beyond the human condition cannot fully be expressed in words. It points to experience at the edge of language.

趋势总结Trend Summary

本期重点关注 BGC-Argo、海色遥感/海洋光学、海洋热浪、浮游植物垂向结构和碳泵过程。筛选逻辑不再只限于重点期刊；当高影响力期刊当天新增较少时，会额外检索重点关注团队作者的新论文，并用海洋、海色/光学和碳循环关键词过滤，再从其他相关期刊补充候选论文。

This issue focuses on BGC-Argo, ocean-colour remote sensing, ocean optics, marine heatwaves, vertical phytoplankton structure and carbon-pump processes. The selection is no longer limited to priority journals; when few high-impact papers are newly available, the workflow also checks focused-team authors and filters those papers with ocean, ocean-colour/optics, and carbon-cycle keywords before adding other relevant journals as supplements.

重点期刊：按影响力和相关性排序Key journals: ordered by impact and relevance

1. Observed Changes in the Kuroshio Extension and Gulf Stream Based on Surface Drifter and OSCAR

作者Authors: Wenhao Gong; Yuhang Zheng; Minyang Wang; Wei Wu; Yuhong Zhang; Zesheng Chen; Yan Du
发表月份Publication month: 2026-06 2026-06
Journal of Geophysical Research: Oceans · DOI: 10.1029/2026jc024135

关键词Tags: vertical structure vertical structure

摘要：评估海洋环流变化的主要挑战仍然是缺乏现场海流观测。通过将漂流者获得的数据与卫星获得的表面电流相结合，这项研究首次提供了 1998 年至 2023 年黑潮延伸带 (KE) 和湾流 (GS) 表面电流变化的直接观测特征。KE 和 GS 系统表现出一致的年代际变化，信号幅度超过了这一时期的长期趋势。分析表明，北大西洋中纬度（NAM）海面温度（SST）变化可能通过相关的大气遥相关调节北太平洋副热带环流上空的风应力旋度场，从而影响KE电流强度的强度。相比之下，GS 变化主要由垂直积分温度的变化直接驱动，并且几乎与当地海温变化同步。除了强度变化之外，KE 和 GS 都经历了显着的风驱动极向迁移，其中 GS 还经历了明显的陆上位移。在年代际速度增强期间（2011-2023），主流轴表现出更明显的向极移。这些发现加强了我们对近期地表流变化的理解，并为模拟海洋环流对气候变化的响应提供了观测约束。

Abstract: A primary challenge in assessing ocean circulation changes remains the scarcity of in situ current observations. By integrating drifter‐derived data with satellite‐derived surface currents, this study provides the first direct observational characterization of surface current variability of the Kuroshio Extension (KE) and Gulf Stream (GS) from 1998 to 2023. The KE and the GS systems exhibit consistent decadal variability, with the signal amplitude surpassing their long‐term trends during this period. The analysis suggests that North Atlantic Mid‐latitude (NAM) sea surface temperature (SST) variations may, through associated atmospheric teleconnections, modulate wind stress curl field over the North Pacific subtropical gyre, and subsequently influence the intensity of the KE current strength. In contrast, GS variations are primarily driven directly by changes in the vertically integrated temperature and are nearly synchronous with local SST changes. In addition to intensity variations, both the KE and GS have undergone significant wind‐driven poleward migrations, with the GS additionally experiencing a distinct onshore displacement. The main current axes exhibit more pronounced poleward shifts during periods of decadal velocity intensification (2011–2023). These findings strengthen our understanding of recent changes in surface currents and provide observational constraints for modeling the response of ocean circulation to climate change.

Open paper

其他相关期刊：按主题相关性补充Other relevant journals: topical supplements

2. The marine microbiome can accurately predict its chemical and biological environment

作者Authors: Emma Bell; Karin Garefelt; Krzysztof T. Jurdzinski; Luis F. Delgado; Fanny Lindrooth; Bengt Karlson; Anders F. Andersson
发表月份Publication month: 2026-06 2026-06
Communications Earth & Environment · DOI: 10.1038/s43247-026-03715-5

关键词Tags: phytoplankton; microbial carbon phytoplankton; microbial carbon

摘要：微生物群落对环境中的物理化学变化做出反应，使微生物组成为生态系统状态的敏感指标。因此，监测水生微生物组对于了解生态系统健康和对变化的反应至关重要。虽然传统监测依赖于显微镜，但利用高通量测序技术进步的基于 DNA 的方法越来越多地被采用。在这里，我们评估使用元条形码来预测波罗的海时空梯度非生物和生物参数的潜力。该数据集包含 397 个海水样本，将原核和真核（16S 和 18S rRNA 基因）元条形码数据与环境测量值和浮游生物显微镜计数相结合。基于元条形码数据的随机森林模型准确预测了多个物理化学参数，其性能与其他两种机器学习方法（XGBoost 和 TabPFN）相当。使用 16S rRNA 基因数据的模型比使用 18S rRNA 基因数据的模型表现更好，扩增子序列变异水平产生最准确的结果。元条形码在预测非生物因素方面也超过了浮游生物显微镜，并有效预测了 ≤1 L 水中浮游植物和浮游动物属的存在。在独立数据集上训练的模型准确地预测了几个物理化学参数，尽管其他模型的性能有所下降，凸显了可转移性方面的挑战。最后，基于元条形码的预测与已建立的环境状况富营养化指标密切匹配，证明了基于微生物组的方法在海洋生态系统监测和管理方面的实用性。

Abstract: Microbial communities respond to physicochemical changes in the environment, making the microbiome a sensitive indicator of ecosystem status. Monitoring aquatic microbiomes is therefore essential for understanding ecosystem health and responses to change. While traditional monitoring relies on microscopy, DNA-based approaches that leverage advances in high-throughput sequencing are increasingly incorporated. Here, we evaluate the potential of using metabarcoding to predict abiotic and biotic parameters across spatiotemporal gradients of the Baltic Sea. The dataset comprises 397 seawater samples integrating prokaryotic and eukaryotic (16S and 18S rRNA gene) metabarcoding data with environmental measurements and plankton microscopy counts. Random Forest models based on metabarcoding data accurately predicted multiple physicochemical parameters and performed comparably to two other machine learning methods, XGBoost and TabPFN. Models using 16S rRNA gene data performed better than those using 18S rRNA gene data, with amplicon sequence variant-level yielding the most accurate results. Metabarcoding also exceeded plankton microscopy in predicting abiotic factors and effectively predicted the presence of phytoplankton and zooplankton genera from ≤1 L of water. Models trained on independent datasets accurately predicted several physicochemical parameters, though performance decreased for others highlighting challenges in transferability. Finally, metabarcoding-based predictions closely matched established eutrophication indicators of environmental status, demonstrating the utility of microbiome-based approaches for marine ecosystem monitoring and management.