Your browser doesn't support javascript.
loading
Significant underestimation of radiative forcing by aerosol-cloud interactions derived from satellite-based methods.
Jia, Hailing; Ma, Xiaoyan; Yu, Fangqun; Quaas, Johannes.
Affiliation
  • Jia H; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, and Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, China.
  • Ma X; Atmospheric Sciences Research Center, University at Albany, Albany, NY, USA.
  • Yu F; Institute for Meteorology, Universität Leipzig, Leipzig, Germany.
  • Quaas J; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, and Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, China. xma@nuist.edu.
Nat Commun ; 12(1): 3649, 2021 Jun 15.
Article in En | MEDLINE | ID: mdl-34131118
Satellite-based estimates of radiative forcing by aerosol-cloud interactions (RFaci) are consistently smaller than those from global models, hampering accurate projections of future climate change. Here we show that the discrepancy can be substantially reduced by correcting sampling biases induced by inherent limitations of satellite measurements, which tend to artificially discard the clouds with high cloud fraction. Those missed clouds exert a stronger cooling effect, and are more sensitive to aerosol perturbations. By accounting for the sampling biases, the magnitude of RFaci (from -0.38 to -0.59 W m-2) increases by 55 % globally (133 % over land and 33 % over ocean). Notably, the RFaci further increases to -1.09 W m-2 when switching total aerosol optical depth (AOD) to fine-mode AOD that is a better proxy for CCN than AOD. In contrast to previous weak satellite-based RFaci, the improved one substantially increases (especially over land), resolving a major difference with models.

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Nat Commun Journal subject: BIOLOGIA / CIENCIA Year: 2021 Document type: Article Affiliation country: China Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Nat Commun Journal subject: BIOLOGIA / CIENCIA Year: 2021 Document type: Article Affiliation country: China Country of publication: United kingdom