一个基于人工智能的框架,用于模拟印度的植物光合作用异常,与热带海洋变化相对应
Roma Varghese1,2, Swadhin Behera3, Mukunda Dev Behera2
1Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India.
Global change biology
|December 6, 2025
概括
这项研究将印度植被光合作用与太平洋温度联系起来,使用人工智能预测植被压力. 了解这些气候远程连接可以改善对粮食安全的早期预警.
科学领域:
- 地球系统科学 地球系统科学
- 气候科学 气候科学
- 遥感 遥感 遥感 遥感
背景情况:
- 由海洋扰动 (如ENSO) 驱动的年度间气候变化,对陆地碳循环和全球粮食生产产生重大影响.
- 陆地植被对大规模气候模式的反应对于预测食物异常和了解碳循环动态至关重要.
研究的目的:
- 用太阳诱导的叶绿素光 (SIF) 异常来研究印度植物光合作用的时间变异性.
- 评估印度植被活动对热带太平洋海面温度 (SST) 变化的反应.
- 开发一种人工智能驱动的模型,以基于SST远程连接来预测植被光合作用变异性.
主要方法:
- 利用太阳引起的叶绿素光 (SIF) 异常来评估印度的植被光合作用变异性.
- 在6月至8月 (JJA) 期间 (2001-2020) 分析了印度SIF异常和尼诺3SST异常之间的相关性.
- 采用了具有多层感知子 (MLP) 架构的非线性框架,集成热带SST和当地水气候数据,以建模SIF异常. 纳入数据增强与季节性滞后和模型改进的整体方法.
主要成果:
- 在JJA期间,在印度SIF异常和尼诺3SST异常之间发现了一个强大的反向关系 (R = -0.68).
- 开发的MLP模型,特别是滞后1-3的整体 (Ensemble JJA,1-3),有效地捕获了SIF变化 (R2 = 0.9).
- 可解释的人工智能确定尼诺3和印度洋西部SST异常是植物光合作用变异的重要贡献者 (~45%).
结论:
- 海洋SST异常,特别是来自热带太平洋,对印度植被光合作用产生重大影响.
- 支持人工智能的非线性建模方法成功捕获了复杂的SST-植被远程连接.
- 这项研究提高了对印度植被压力的早期警告的能力,这对气候适应和粮食安全至关重要.
关键词:
厄尔尼诺-南部振荡人工智能的人工智能是人工智能.气候变化,气候的变化.粮食安全 粮食安全土地上的光合作用.海洋与植被的相互作用海洋表面温度 海洋表面温度太阳引起的叶绿素光.地上的二氧化碳动态.热带印度洋热带印度洋的变化.更多相关视频
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