气候变化领域集成服务门户(CCPortal): Correcting ozone biases in a global chemistry-climate model: implications for future ozone

CCPortal

DOI	10.5194/acp-22-12543-2022
	Correcting ozone biases in a global chemistry-climate model: implications for future ozone
	Liu, Zhenze; Doherty, Ruth M.; Wild, Oliver; O'Connor, Fiona M.; Turnock, Steven T.
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	12543
结束页码	12557
卷号	22 期号:18 页码:15
英文摘要	Weaknesses in process representation in chemistry-climate models lead to biases in simulating surface ozone and to uncertainty in projections of future ozone change. We here develop a deep learning model to demonstrate the feasibility of ozone bias correction in a global chemistry-climate model. We apply this approach to identify the key factors causing ozone biases and to correct projections of future surface ozone. Temperature and the related geographic variables latitude and month show the strongest relationship with ozone biases. This indicates that ozone biases are sensitive to temperature and suggests weaknesses in representation of temperature-sensitive physical or chemical processes. Photolysis rates are also an important factor, highlighting the sensitivity of biases to simulated cloud cover and insolation. Atmospheric chemical species such as the hydroxyl radical, nitric acid and peroxyacyl nitrate show strong positive relationships with ozone biases on a regional scale. These relationships reveal the conditions under which ozone biases occur, although they reflect association rather than direct causation. We correct model projections of future ozone under different climate and emission scenarios following the shared socio-economic pathways. We find that changes in seasonal ozone mixing ratios from the present day to the future are generally smaller than those simulated without bias correction, especially in high-emission regions. This suggests that the ozone sensitivity to changing emissions and climate may be overestimated with chemistry-climate models. Given the uncertainty in simulating future ozone, we show that deep learning approaches can provide improved assessment of the impacts of climate and emission changes on future air quality, along with valuable information to guide future model development.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000859019300001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273038
作者单位	University of Edinburgh; Lancaster University; Met Office - UK; Hadley Centre; Met Office - UK; University of Leeds
推荐引用方式 GB/T 7714	Liu, Zhenze,Doherty, Ruth M.,Wild, Oliver,et al. Correcting ozone biases in a global chemistry-climate model: implications for future ozone[J],2022,22(18):15.
APA	Liu, Zhenze,Doherty, Ruth M.,Wild, Oliver,O'Connor, Fiona M.,&Turnock, Steven T..(2022).Correcting ozone biases in a global chemistry-climate model: implications for future ozone.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(18),15.
MLA	Liu, Zhenze,et al."Correcting ozone biases in a global chemistry-climate model: implications for future ozone".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.18(2022):15.