全球海洋表面的时空重建pCO2基于优化的随机森林
Huisheng Wu1, Lejie Wang1, Xiaochun Ling2
1College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, Shandong 266580, China.
The Science of the total environment
|December 13, 2023
概括
研究人员开发了一种改进的优化随机森林方法,以准确地绘制全球海洋表面的二氧化碳 (CO2) 水平. 这种整体方法提高了对海洋碳预算和酸化影响的理解.
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:
- 海洋表面二氧化碳 (pCO2) 的部分压力对于碳预算计算和评估海洋酸化至关重要.
- 当前的研究经常将全球海洋分为区域,这可能会限制整体的理解.
研究的目的:
- 开发一种整体方法来重建全球海洋表面pCO2,避免区域偏差.
- 评估12个预测因素与全球海洋表面pCO2的相关性.
- 为了比较改进的优化随机森林 (ORF) 方法与其他机器学习技术的性能.
主要方法:
- 利用了来自全球表面pCO2 (LDEO) 数据库,NASA海洋彩色卫星数据和哥白尼号海洋再分析数据的数据.
- 采用了改进的优化随机森林 (ORF) 方法来进行全球pCO2重建.
- 使用独立的观测和时间序列数据验证了ORF模型.
主要成果:
- 在全球海洋表面pCO2建模中,ORF方法表现出卓越的准确性 (MAE:6.27μatm,RMSE:15.34μatm,R2:0.92).
- 2010-2019年全球海洋表面pCO2分布图的高分辨率 (0.25° × 0.25°) 已成功重建.
- 确定了pCO2与叶绿素,盐度,温度和营养度等因素之间的相关性.
结论:
- 开发的ORF方法提供了一个高度准确和整体的方法来重建全球海洋表面pCO2.
- 重建的pCO2地图对于推进全球海洋碳循环研究和碳评估具有重要意义.
- 这项研究强调了整合各种数据集和先进的机器学习对大规模海洋研究的重要性.
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