在CMAQ中建模的美国背景和人为臭氧的源特定偏差校正
T Nash Skipper1, Christian Hogrefe2, Barron H Henderson2
1School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Geoscientific model development
|January 29, 2025
概括
这项研究通过使用数据融合方法纠正化学运输模型偏差来完善美国背景臭氧 (O3) 的估计. 它揭示了美国人为臭氧的高估值和平流层臭氧影响的低估值.
科学领域:
- 大气化学 大气化学
- 环境科学 环境科学
- 气候建模气候模型
背景情况:
- 美国 (US) 背景臭氧 (O3) 的估计依赖于化学运输模型 (CTM),这些模型表现出可变性.
- 准确的背景臭氧评估对于了解空气质量和政策影响至关重要.
研究的目的:
- 开发一个测量模型数据融合方法,以估计和纠正美国背景臭氧CTM偏差.
- 量化美国人为臭氧和各种背景臭氧来源的偏差,包括自然排放,国际运输和平流层臭氧流入.
主要方法:
- 采用数据融合技术,根据观测数据调整模拟的臭氧成分.
- 偏差校正因子在空间和时间上应用于人为和背景臭氧模拟.
- 使用平流层臭氧标记器来评估与平流层臭氧入侵相关的偏差.
主要成果:
- 在美国东部确定了模拟的美国人为臭氧的积极偏差,随着模拟模型的粗分辨率的增加而增加.
- 人为臭氧偏差的大小估计为2-11 ppb (11-49%),取决于模型分辨率.
- 在模拟的平流层臭氧表面影响中发现了负偏差,特别是在春季的美国西部 (-3.5ppb或-25%).
结论:
- 数据融合方法有效地纠正了CTM偏差,改善了对美国背景臭氧成分的估计.
- 了解和纠正模型偏差对于准确的臭氧评估和有效的空气质量管理至关重要.
- 这种方法可以扩展到包括多个模型,进一步减少背景臭氧估计的不确定性.
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