Understanding the behavior of western boundary current systems is crucial for predictions of biogeochemical cycles, fisheries, and basin-scale climate modes over the midlatitude oceans. Studies indicate that anthropogenic climate change induces structural changes in the Kuroshio Extension (KE) system, including a northward migration of its oceanic jet. However, changes in the KE temporal variability remain unclear. Using large ensembles of a global coupled climate model, we show that in response to increasing greenhouse gases, the time scale of KE sea surface height (SSH) shifts from interannual scales toward decadal and longer scales. We attribute this increased low-frequency KE variability to enhanced mid-latitude oceanic Rossby wave activity induced by regional and remote atmospheric forcing, due to a poleward shift of midlatitude surface westerly with climatology and an increase in the tropical precipitation activity, which lead to stronger atmospheric teleconnections from El Niño to the midlatitude Pacific and the KE region. Greenhouse warming leads to both a positive (elongated) KE state that restricts ocean perturbations (e.g., eddy activity) and stronger wind-driven KE fluctuations, which enhances the contributions of decadal KE modulations relative to short-time scale intrinsic oceanic KE variations. Our spectral analyses suggest that anthropogenic forcing may alter the future predictability of the KE system.

了解西部边界流系统的行为对于预测中纬度海洋的生物地球化学循环、渔业和盆地尺度气候模式至关重要。研究表明，人为气候变化引起了黑潮延伸（KE）系统的结构变化，包括其海洋急流向北迁移。然而，KE 时间变异性的变化仍不清楚。使用全球耦合气候模型的大型集合，我们表明，随着温室气体的增加，KE 海面高度 (SSH) 的时间尺度从年际尺度向年代际尺度和更长尺度转变。我们将这种增加的低频 KE 变异性归因于区域和远程大气强迫引起的中纬度海洋罗斯贝波活动增强，由于中纬度表面随着气候向西西移和热带降水活动的增加，导致从厄尔尼诺到中纬度太平洋和KE地区的大气遥相关更强。温室变暖导致限制海洋扰动（例如，涡流活动）的正（拉长）KE 状态和更强的风驱动 KE 波动，这增强了十年 KE 调制相对于短时间尺度内在海洋 KE 变化的贡献。我们的光谱分析表明，人为强迫可能会改变 KE 系统未来的可预测性。温室变暖导致限制海洋扰动（例如，涡流活动）的正（拉长）KE 状态和更强的风驱动 KE 波动，这增强了十年 KE 调制相对于短时间尺度内在海洋 KE 变化的贡献。我们的光谱分析表明，人为强迫可能会改变 KE 系统未来的可预测性。温室变暖导致限制海洋扰动（例如，涡流活动）的正（拉长）KE 状态和更强的风驱动 KE 波动，这增强了十年 KE 调制相对于短时间尺度内在海洋 KE 变化的贡献。我们的光谱分析表明，人为强迫可能会改变 KE 系统未来的可预测性。