Seasonal prediction of Indian wintertime aerosol pollution using the ocean memory effect
Gao, M.; Sherman, P.; Song, S.; Yu, Y.; Wu, Z.; McElroy, M.B. (2019). Seasonal prediction of Indian wintertime aerosol pollution using the ocean memory effect. Science Advances 5(7): eaav4157. https://dx.doi.org/10.1126/sciadv.aav4157
In: Science Advances. AAAS: New York. ISSN 2375-2548; e-ISSN 2375-2548, meer
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| Auteurs | | Top |
- Gao, M.
- Sherman, P.
- Song, S.
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- Yu, Y.
- Wu, Z.
- McElroy, M.B.
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| Abstract |
As China makes every effort to control air pollution, India emerges as the world’s most polluted country, receiving worldwide attention with frequent winter (boreal) haze extremes. In this study, we found that the interannual variability of wintertime aerosol pollution over northern India is regulated mainly by a combination of El Niño and the Antarctic Oscillation (AAO). Both El Niño sea surface temperature (SST) anomalies and AAO-induced Indian Ocean Meridional Dipole SST anomalies can persist from autumn to winter, offering prospects for a prewinter forecast of wintertime aerosol pollution over northern India. We constructed a multivariable regression model incorporating El Niño and AAO indices for autumn to predict wintertime AOD. The prediction exhibits a high degree of consistency with observation, with a correlation coefficient of 0.78 (P < 0.01). This statistical model could allow the Indian government to forecast aerosol pollution conditions in winter and accordingly improve plans for pollution control. |
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