干旱区生态环境知识资源中心(Arid): Projection of Low Cloud Variation Through Robust Meteorological Linkage and Its Comparison With CMIP6 Models at the SACOL Site

Arid

DOI	10.1029/2023JD040668
	Projection of Low Cloud Variation Through Robust Meteorological Linkage and Its Comparison With CMIP6 Models at the SACOL Site
	Li, Yize; Ge, Jinming; Du, Jiajing; Peng, Nan; Su, Jing; Hu, Xiaoyu; Zhang, Chi; Mu, Qingyu; Li, Qinghao
通讯作者	Ge, JM
来源期刊	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
ISSN	2169-897X
EISSN	2169-8996
出版年	2024
卷号	129 期号:16
英文摘要	Low clouds significantly influence Earth's energy budget by reflecting solar radiation. Consequently, inadequate representation of these clouds in models introduces the largest uncertainty in predicting future climate change. This study investigates low cloud cover (LCC) variation using 6 years (2014-2019) of high-precision ground-based Ka-band Zenith Radar (KAZR) observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL). We analyze the relationship between observed low cloud properties and four large-scale meteorological factors: 700 hPa relative humidity, estimated inversion strength, low-level wind shear, and 700 hPa vertical velocity. These factors are identified as key parameters influencing low cloud evolution over this semi-arid region. We utilize principal component analysis to integrate these parameters into a single meteorological predictor (PC1) and establish a robust linkage between meteorological conditions and low cloud properties. By comparing LCC fluctuations derived from the meteorological factors with those directly simulated by models over the same period, we assess the projected LCC trends under various carbon emission scenarios. Contrary to the declining LCC projected by CMIP6 models outcomes, the LCC form PC1 shows a rising tendency by 2100 under global warming. This discrepancy implies that CMIP6 models may exaggerate the extent of future warming at the SACOL site. Our approach can be applied to a broader global distribution of low clouds to examine the differences between low cloud variations constrained by meteorological fields and those from direct model simulations. This will enhance our understanding of low cloud feedback on future climate change. Low clouds of mid-latitude continental are an important source of uncertainty in equilibrium climate sensitivity estimation. This study examines low clouds change at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) in Northwest China by using the ground-based Ka-band Zenith Radar observations and satellite data from 2014 through 2019. We found obvious seasonal variations for low clouds at the SACOL site, with fewer but thicker clouds in summer and more frequent but thinner clouds in winter. We identify four meteorological factors strongly correlated with low cloud properties. Using principal component analysis, we condense these factors into the leading principal component (PC1), which can represent much better conditions for low cloud formation. Specifically, PC1 projects a slight increase of low cloud cover under high carbon emission scenario, which is contrast to the decrease trend from CMIP6 internal model parameterizations results. We expect to use PCA method for a better understanding of low cloud trends and their feedback effects on future global warming over a wider region of the globe. Distinct seasonal variation of low clouds is revealed by 6-year of continuous Ka-band Zenith Radar (KAZR) observations at the SACOL site A robust linkage between low cloud properties and multi meteorological fields is established through principal component analysis (PCA) Contrary to the decrease in projected low cloud cover by CMIP6 models, PCA indicates an upward trend by 2100 at the SACOL site
英文关键词	low cloud large scale meteorology principal component analysis SACOL KAZR
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001297374900001
WOS关键词	MIDLATITUDE CONTINENTAL CLOUDS ; LARGE-SCALE METEOROLOGY ; SGP CENTRAL FACILITY ; LOW-LEVEL CLOUD ; CLIMATE ; SURFACE ; FEEDBACK ; COVER ; SIMULATIONS ; CUMULUS
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404525
推荐引用方式 GB/T 7714	Li, Yize,Ge, Jinming,Du, Jiajing,et al. Projection of Low Cloud Variation Through Robust Meteorological Linkage and Its Comparison With CMIP6 Models at the SACOL Site[J],2024,129(16).
APA	Li, Yize.,Ge, Jinming.,Du, Jiajing.,Peng, Nan.,Su, Jing.,...&Li, Qinghao.(2024).Projection of Low Cloud Variation Through Robust Meteorological Linkage and Its Comparison With CMIP6 Models at the SACOL Site.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,129(16).
MLA	Li, Yize,et al."Projection of Low Cloud Variation Through Robust Meteorological Linkage and Its Comparison With CMIP6 Models at the SACOL Site".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 129.16(2024).