2022
DOI: 10.1029/2022gl099821
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Impact of Sea Surface Temperature Variability at Different Ocean Basins on Dust Activities in the Gobi Desert and North China

Abstract: The frequency and intensity of dust activities are closely related to the meteorological condition. The higher temperature results in more substantial soil water evaporation conducive to the dryer and looser soil, thus favorable for generating floating sands (Zhang and Wang, 2001). On the synoptic scale, frequent cyclones and strong gusts are the main drivers of spring dust activities in North China (Lu et al., 2003;Qiu et al., 2001). These local meteorological conditions are also modulated by the teleconnecti… Show more

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Cited by 10 publications
(9 citation statements)
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References 53 publications
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“…(b) inLiu et al (2022) highlighted the presence of multidecadal oscillating variability in dust frequency over Gobi Desert, aligning with the findings of our study. Trends beyond 2015 remain uncertain due to the relatively short duration of available time, both in Liu et al (2022) and our study.…”
supporting
confidence: 92%
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“…(b) inLiu et al (2022) highlighted the presence of multidecadal oscillating variability in dust frequency over Gobi Desert, aligning with the findings of our study. Trends beyond 2015 remain uncertain due to the relatively short duration of available time, both in Liu et al (2022) and our study.…”
supporting
confidence: 92%
“…Nevertheless, our study indicates that NASST exerts the most substantial influence on dust occurrences in Gobi Desert. If we employ the same NAO region as Liu et al (2022), tropical Atlantic SST generally exhibits the most significant impacts, followed by NASST, aligning with the findings of Liu et al (2022). Li et al (2020) and DeAngelis et al (2023) connected NASST and/or tropical SST anomaly to heat and drought events over Northeastern China and Siberia via Rossby wave trains and teleconnections related to anomalous tropical heating.…”
Section: Decadal Change In Dust Frequency: Possible Linkage With Ocea...supporting
confidence: 77%
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“…Therefore, we construct a statistical model for simulating FWI in these experiments using the random forest regression (RFR) method, similar to Liu et al. (2022, 2023). Finally, the input variables comprise T2M, SLP, TP, and WND10 during the boreal spring, while the output is FWI, representing fire weather potential.…”
Section: Methodsmentioning
confidence: 99%
“…The above-mentioned experiments, referred to as OBE, are in line with the methodologies in earlier research. 40,41 The details of the experiment setup and the calculation of the contribution of sea ice to FWI in 2019 are provided in the Supporting Information.…”
Section: Ocean Basin Experimentsmentioning
confidence: 99%