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- Title
Projection of Low Cloud Variation Through Robust Meteorological Linkage and Its Comparison With CMIP6 Models at the SACOL Site.
- Authors
Li, Yize; Ge, Jinming; Du, Jiajing; Peng, Nan; Su, Jing; Hu, Xiaoyu; Zhang, Chi; Mu, Qingyu; Li, Qinghao
- Abstract
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. Plain Language Summary: 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. Key Points: Distinct seasonal variation of low clouds is revealed by 6‐year of continuous Ka‐band Zenith Radar (KAZR) observations at the SACOL siteA 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
- Subjects
CLIMATE feedbacks; PRINCIPAL components analysis; CLOUDINESS; LOWS (Meteorology); WIND shear
- Publication
Journal of Geophysical Research. Atmospheres, 2024, Vol 129, Issue 16, p1
- ISSN
2169-897X
- Publication type
Article
- DOI
10.1029/2023JD040668