Analysis of Daytime Cloud Fraction Spatiotemporal Variation over the Arctic from 2000 to 2019 from Multiple Satellite ProductsSource: Journal of Climate:;2022:;volume( 035 ):;issue: 023::page 3995DOI: 10.1175/JCLI-D-22-0007.1Publisher: American Meteorological Society
Abstract: Insufficient understanding of complex Arctic cloud properties introduced large errors in estimating radiant energy balance parameters at the regional and global scales. Comprehensive and reliable cloud information is necessary for improving the accuracy of flux inversion. This study evaluated daytime cloud fraction (CF) uncertainties from 16 available satellite products and estimated the spatiotemporal distributions of Arctic daytime CF during 2000–19. Our results show that the differences among multiple products had significant temporal and spatial heterogeneities. Temporally, the maximum and minimum interproduct discrepancies occurred in April and the summer months, respectively. Spatially, the largest uncertainties were seen over Greenland. Substantial inconsistency also occurred on the central and Pacific sides of the Arctic Ocean. The active satellite product tended to capture more clouds in these two regions. We found that the inconsistencies caused by sensor differences were smaller than those caused by algorithm differences; that is, for MODIS based CF products, the inconsistencies caused by different sensors and different algorithms are ±2% and ±5%, while for AVHRR-based products, these inconsistencies are ±6% and ±15%, respectively. The annual average daytime CF in sunlit months was 70.9% ± 2.93% and increased over the Arctic during study periods. These upward trends might cool the Arctic by approximately 0.05–0.5 W m
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| contributor author | Xinyan Liu | |
| contributor author | Tao He | |
| contributor author | Lin Sun | |
| contributor author | Xiongxin Xiao | |
| contributor author | Shunlin Liang | |
| contributor author | Siwei Li | |
| date accessioned | 2023-04-12T18:43:01Z | |
| date available | 2023-04-12T18:43:01Z | |
| date copyright | 2022/11/11 | |
| date issued | 2022 | |
| identifier other | JCLI-D-22-0007.1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4290120 | |
| description abstract | Insufficient understanding of complex Arctic cloud properties introduced large errors in estimating radiant energy balance parameters at the regional and global scales. Comprehensive and reliable cloud information is necessary for improving the accuracy of flux inversion. This study evaluated daytime cloud fraction (CF) uncertainties from 16 available satellite products and estimated the spatiotemporal distributions of Arctic daytime CF during 2000–19. Our results show that the differences among multiple products had significant temporal and spatial heterogeneities. Temporally, the maximum and minimum interproduct discrepancies occurred in April and the summer months, respectively. Spatially, the largest uncertainties were seen over Greenland. Substantial inconsistency also occurred on the central and Pacific sides of the Arctic Ocean. The active satellite product tended to capture more clouds in these two regions. We found that the inconsistencies caused by sensor differences were smaller than those caused by algorithm differences; that is, for MODIS based CF products, the inconsistencies caused by different sensors and different algorithms are ±2% and ±5%, while for AVHRR-based products, these inconsistencies are ±6% and ±15%, respectively. The annual average daytime CF in sunlit months was 70.9% ± 2.93% and increased over the Arctic during study periods. These upward trends might cool the Arctic by approximately 0.05–0.5 W m | |
| publisher | American Meteorological Society | |
| title | Analysis of Daytime Cloud Fraction Spatiotemporal Variation over the Arctic from 2000 to 2019 from Multiple Satellite Products | |
| type | Journal Paper | |
| journal volume | 35 | |
| journal issue | 23 | |
| journal title | Journal of Climate | |
| identifier doi | 10.1175/JCLI-D-22-0007.1 | |
| journal fristpage | 3995 | |
| journal lastpage | 4023 | |
| page | 3995–4023 | |
| tree | Journal of Climate:;2022:;volume( 035 ):;issue: 023 | |
| contenttype | Fulltext |