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- C31D-0960: Dust, Ice, and Arctic Clouds: Evaluating dust-based ice-nucleating particle parameterizations to improve mixed-phase cloud modeling
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Board 0960‚ Hall EFG (Poster Hall)NOLA CC
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Katie Chan, University of California Los Angeles (First Author, Presenting Author)
Flor Maciel, University of California Los Angeles
Kathryn Moore, University of Maryland College Park
Kevin Barry, Colorado State University
Jasper Kok, University of California Los Angeles
The Arctic is warming faster than any other region, and clouds play an important role in this trend. Arctic clouds, which contain both ice and supercooled liquid water, affect how much sunlight is reflected or trapped, influencing the region’s temperature. Ice-nucleating particles, such as mineral dust, help form ice crystals in these clouds and strongly impact their behavior. Dust reaching the Arctic comes from distant sources as well as local emissions. Many climate models do not fully capture how dust varies by season or how it interacts with clouds, leading to uncertainty in Arctic climate predictions. In this study, we evaluate two common methods for estimating dust ice-nucleating particle concentrations using a global dust dataset and real-world measurements collected during the year-long MOSAiC expedition in the central Arctic. Our findings show that one method (Ullrich et al., 2017) generally predicts higher dust ice-nucleating particle concentrations and aligns more closely with observed data than the other (DeMott et al., 2015), especially during fall and winter. As Arctic warming is expected to increase local dust emissions, improving how models represent dust-driven ice formation is critical for more accurate simulations of Arctic clouds and climate.
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