长期 (2000-2020) 全球0.05°连续大气二氧化碳映射,结合OCO-2观测和模型模拟
Xiaobin Guan1, Zhihao Sun1, Dong Chu2
1School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China.
The Science of the total environment
|November 4, 2024
概括
使用机器学习创建了一个新的全球二氧化碳数据集 (GCXCO2),为气候研究提供无覆盖和高精度. 这一数据集显示了大气二氧化碳度在2000年至2020年间的上升趋势.
科学领域:
- 地球科学 地球科学 地球科学
- 大气科学 大气科学
- 遥感 遥感 遥感 遥感
背景情况:
- 准确的,长期的全球大气二氧化碳监测对于了解气候变化和碳循环动态至关重要.
- 现有的数据集往往缺乏全球无覆盖或足够的空间分辨率.
- 遥感为全球持续的二氧化碳观测提供了一个有希望的途径.
研究的目的:
- 开发一个高分辨率,连续的全球 CO2 (XCO2) 列平均数据集,从2000年到2020年.
- 在空间覆盖,分辨率和精度方面改进现有的二氧化碳产品.
- 为气候变化研究和碳平衡研究提供一个有价值的工具.
主要方法:
- 使用堆叠机器学习方法重建了一个8天的全球XCO2产品 (GCXCO2).
- 整合陆地/海洋遥感数据与模型模拟.
- 制定动态规范化策略,以应对时间变化,并确保模型的时间扩展.
主要成果:
- GCXCO2产品具有0.05°空间分辨率的无全球覆盖,性能优于CarbonTracker和CAMS模拟.
- 通过10倍交叉验证进行验证,得到R2=0.974和RMSE=0.551ppm.
- 该数据集在没有卫星观测的地区和时期准确预测了XCO2 (R2 = 0.958和R2 = 0.886).
- 与TCCON地面站的比较证实了GCXCO2在模型数据上的卓越准确性和分辨率.
- 在2000-2020年间,全球XCO2上升趋势为~2.105ppm/年,北半球和南半球季节性模式明显.
结论:
- GCXCO2数据集提供了一个可靠的,高精度的,长期记录全球XCO2.
- 它是促进对气候变化和碳平衡的理解的重要工具.
- 该数据集有助于检测二氧化碳度异常,并支持气候建模工作.
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