Comparison of the Spatial and Temporal Variability of Cloud Amounts over China Derived from Different Satellite Datasets

Abstract

Various cloud cover products have been developed over the past few decades, but their uncertainties have not been sufficiently assessed, especially at a regional scale, which is vital for the application of satellite products to climate studies. In this study, we compare the spatial–temporal variability of the cloud amount over China from the 11 datasets provided by the Global Energy and Water Cycle Experiment (GEWEX) cloud assessment project at a horizontal resolution of 1° × 1° from the 1980s to 2000s, using the site data as a reference. The differences among these datasets are quantified in terms of the standard deviations and the correlation coefficients between different datasets. Most of the datasets show a similar spatial distribution of total cloud amounts (TCAs), but their magnitudes differ. The standard deviations of the annual, winter, and summer mean TCA are approximately 9–18% for the regional mean TCAs over the four typical regions of China, including the northwestern region (NW), northeastern region (NE), Tibetan Plateau region (TP), and southern China region (SC), with the largest standard deviations of 13–18% in the TP. By analyzing the factors that influence the satellite inversion data, such as the observation instrument, inversion algorithm, and observation time, we found that the difference caused by the observation instrument or algorithm is greater than the effect of the observation time, and the satellite cloud datasets with better recognition capability for cloud types show lower uncertainties when compared with the station observation. In terms of seasonal cycle, except HIRS and MODIS-ST, most satellite datasets can reproduce the observed seasonal cycle with the largest TCA in summer and the smallest TCA in autumn and winter. For the interannual variation, ISCCP-D1, MODIS-CE, and MODIS-ST are most consistent with the site data for the annual mean TCA, and two of the remaining datasets (PATMOSX and TOVSB) show more consistent temporal variations with the site observation in summer than in winter, especially over NW and NE regions. In general, MODIS-CE shows the best performance in reproducing the spatial pattern and interannual variation of TCA amongst the 11 satellite datasets, and PATMOSX, MODIS-ST, CALIPSO-GOCCP, and CALIPSO-ST also show relatively good performance.