著者
LONG Jingchao WANG Yuqing ZHANG Suping
出版者
Meteorological Society of Japan
雑誌
気象集誌. 第2輯 (ISSN:00261165)
巻号頁・発行日
(Released:2018-01-15)

The cloud variability and regime transition from-stratocumulus-to-cumulus across the sea surface temperature front in the Kuroshio region over the East China Sea are important regional climate features and may affect the earth’s energy balance. However, because of large uncertainties among available cloud products, it is unclear which cloud datasets are more reliable for use in studying the regional cloud features and to validate cloud simulations in the region by climate models. In this study, the monthly low cloud amount (LCA) and total cloud amount (TCA) datasets in the region from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), Moderate-resolution Imaging Spectroradiometer (MODIS) and International Comprehensive Ocean-Atmosphere Data Set (ICOADS) are validated against the combined product of CloudSat+CALIPSO (CC) in terms of the consistency and discrepancy in the climatologically mean, seasonal cycle, and interannual variation. The results show that LCA and TCA derived from MODIS and CALIPSO present relatively high consistency with CC data in the climatological annual mean and show similar behavior in seasonal cycle. The consistency in LCA between the three datasets and the CC is generally good in cold seasons (winter, spring and fall) but poor in summer. MODIS shows the best agreement with CC in fall with the correlation coefficient of 0.77 at the confidence level over 99%. CALIPSO and MODIS can provide competitive description of TCA in all seasons while ICOADS is good in terms of the climatological seasonal mean of TCA in winter only. Moreover, the interannual variation of LCA and TCA from all datasets is highly correlated with that from CC in both winter and spring with the Matching Score ranging between 2/3 and 1. Further analysis with long-term data suggests that both LCA and TCA from ICOADS and MODIS can be good references for the studies of cloud interannual variability in the region.

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JMSJ New paper published: Long, J., Y. Wang, and S. Zhang, 2018: Intercomparison of cloud amount datasets in the Ku… https://t.co/ybniqCydIp
JMSJ New paper: Long, J., Y. Wang, and S. Zhang, 2018: Intercomparison of cloud amount datasets in the Kuroshio reg… https://t.co/iZD1EENIEY

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