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Evaluation of sea salt aerosols in climate systems: global climate modeling and observation-based analyses*
Author(s) -
Yi-Chun Chen,
Jui Lin F. Li,
Wei Liang Lee,
David J. Diner,
M. J. Garay,
Jonathan H. Jiang,
Yi Hui Wang,
Jingshan Yu,
О. В. Калашникова
Publication year - 2020
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/ab751c
Subject(s) - environmental science , climatology , precipitation , sea ice , climate model , radiative transfer , coupled model intercomparison project , atmospheric sciences , sea surface temperature , climate change , meteorology , geology , oceanography , geography , physics , quantum mechanics
Sea salt aerosols (SSA), one of the most abundant aerosol species over the global oceans, play important roles for Earth’s climate. State-of-the-art SSA parameterizations in global climate models (GCMs) are typically modeled using near-surface wind speed, sea surface temperature (SST), and precipitation. However, these have non-trivial biases in CMIP3 and CMIP5 GCMs over the tropical Pacific Ocean that can contribute to biases in the simulated SSA. This study investigates the impacts of falling ice radiative effects on the biases of the aforementioned modeled parameters and the resulting modeled SSA biases. We compare the CMIP5 modeled SSA against satellite observations from MISR and MODIS using a pair of sensitivity experiments with falling ice radiative effects on and off in the CESM1-CAM5 model. The results show that when falling ice radiative effects are not taken into account, models have weaker surface wind speeds, warmer SSTs, excessive precipitation, and diluted sea surface salinity (SSS) over the Pacific trade-wind regions, leading to underestimated SSA. In the tropical Pacific Ocean, the inclusion of falling ice radiative effects leads to improvements in the modeled near-surface wind speeds, SSTs, and precipitation through cloud-precipitation-radiation-circulation coupling, which results in more representative patterns of SSA and reduces the SSA biases by ∼10% to 15% relative to the satellite observations. Models including falling ice radiative effects in CMIP5 produce smaller biases in SSA than those without falling ice radiative effects. We suggest that one of the causes of these biases is likely the failure to account for falling ice radiative effects, and these biases in turn affect the direct and indirect effects of SSA in the GCMs.

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