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The seasonal cycle of ice-nucleating particles linked to the abundance of biogenic aerosol in boreal forests.
- Source :
- Atmospheric Chemistry & Physics Discussions; 07/31/2020, p1-18, 18p
- Publication Year :
- 2020
-
Abstract
- Ice-nucleating particles (INPs) trigger the formation of cloud ice crystals in the atmosphere. Therefore, they strongly influence cloud microphysical and optical properties, as well as precipitation and the life cycle of clouds. Improving weather forecasting and climate projection requires an appropriate formulation of atmospheric INP concentrations. This remains challenging, as the global INP distribution and variability depend on a variety of aerosol types and sources, and neither their short-term variability nor their long-term seasonal cycles are well covered by continuous measurements. Here, we provide the first year-long set of observations with a pronounced INP seasonal cycle in a boreal forest environment. Besides the observed seasonal cycle in INP concentrations with a minimum in wintertime and maxima in early and late summer, we also provide indications for a seasonal variation in the prevalent INP type. We show that the seasonal dependency of INP concentrations and prevalent INP types is most likely driven by the abundance of biogenic aerosol. As current parameterizations do not reproduce this variability, we suggest a new parameterization approach, which considers the seasonal variation of INP concentrations. For this, we use the ambient air temperature as a proxy for the season which affects the source strength of biogenic emissions and by that the INP abundance over the boreal forest areas. Furthermore, we provide new INP parameterizations based on the Ice Nucleation Active Surface Site (INAS) approach, which specifically describes the ice nucleation activity of boreal aerosols particles prevalent in different seasons. Our results characterize the boreal forest as an important but variable INP source and provide new perspectives to describe these new findings in atmospheric models. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16807367
- Database :
- Complementary Index
- Journal :
- Atmospheric Chemistry & Physics Discussions
- Publication Type :
- Academic Journal
- Accession number :
- 144879875
- Full Text :
- https://doi.org/10.5194/acp-2020-683