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ZENODO
Dataset . 2019
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ZENODO
Dataset . 2019
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ZENODO
Dataset . 2019
License: CC BY
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Model sensitivity runs for Southern Ocean studies

Authors: Varma, Vidya;

Model sensitivity runs for Southern Ocean studies

Abstract

The present generation of global climate models is characterized by insufficient reflection of short-wave radiation over the Southern Ocean due to misrepresentation of clouds. This is a significant concern as it leads to excessive heating of the ocean surface, sea surface temperature biases, and subsequent problems with atmospheric dynamics. Using the recent version of the Met Office's Unified Model, we show that modifying the cloud micro-physics scheme with a more realistic value for the shape parameter of atmospheric ice-crystals suggested by theory and observations, benefits the simulation of short-wave radiation. In the control model, for calculating the growth rate through deposition process, it is assumed that all ice particles are spherical in shape. By approximating the value for plates or aggregates of ice crystals, along with modified ice nucleation temperatures, we show that there is a reduction in the annual-mean short-wave cloud radiative effect over the Southern Ocean.

Keywords

southern ocean, short-wave radiation bias, cloud micro-physics, ice crystal shape

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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