通过使用机器学习,改进了印度洋的长期高分辨率表面pCO2数据产品
Prasanna Kanti Ghoshal1,2, A P Joshi1, Kunal Chakraborty3
1Indian National Centre for Ocean Information Services, Ministry of Earth Sciences, Hyderabad, India.
Scientific data
|April 5, 2025
概括
机器学习通过纠正模型模拟来改善表面海洋二氧化碳 (pCO2) 部分压力估计. 这增强了对海洋碳循环和气候变化影响的理解.
科学领域:
- 海洋学 海洋学 海洋学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 精确的表面海洋二氧化碳 (pCO2) 部分压力估计对于了解海洋在全球碳循环中的作用及其对气候变化的反应至关重要.
- 高分辨率模型模拟经常显示与观测数据的偏差,需要纠正可靠的气候研究.
研究的目的:
- 通过机器学习提高表面海洋pCO2模型模拟的准确性.
- 为1980-2019年期间开发一个改进的表面pCO2数据产品.
主要方法:
- 采用机器学习算法 (XGBoost) 来纠正模型模拟 (pCO2model) 和观测 (pCO2obs) 之间的偏差.
- 训练模型生成时空偏差并将其添加回原始模型模拟中.
- 将改进的数据产品与停泊观测和其他网格数据集进行比较 (SOCAT,CMEMS-LSCE-FFNN,OceanSODA).
主要成果:
- 与现有数据集相比,机器学习方法在RMSE中提高了表面pCO2数据产品准确度约40%±3.31%.
- 添加气候偏差带来了比添加年间偏差更大的改善.
- 在模拟模型的表面pCO2输出中显著提高.
结论:
- 机器学习算法有效地纠正和改进了高分辨率的表面海洋pCO2模型模拟.
- 开发的数据产品提供了更准确的海洋表面pCO2的表示,这对气候变化研究至关重要.
- 气候学纠正在提高表面海洋pCO2的模型准确性方面特别有效.
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