2020
DOI: 10.1029/2019jd030675
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Enhanced Dust Influx to South Atlantic Sector of Antarctica During the Late‐20th Century: Causes and Contribution to Radiative Forcing

Abstract: Atmospheric dust influences global climate and ocean biogeochemistry. Here we present a high-resolution ice core dust record (1905 from coastal Dronning Maud Land (71°20′S, 11°35′ E), East Antarctica, to understand dust flux variability, its causes, and potential contribution to radiative forcing during the 20th century in the South Atlantic sector of East Antarctica (SASA). The dust flux profile (sum of 1-25 μm size fractions) reveals three stepwise increase during 1905 -1929 , 1930-1979 , and 1980 with an a… Show more

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Cited by 17 publications
(8 citation statements)
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“…We furthermore do not use recently analyzed dust cycle results from CMIP5 models (Pu and Ginoux, 2018;Wu et al, 2020) because less than half of CMIP5 models with prognostic dust cycles reported total deposition fluxes, which are needed for the analyses against measurements (see previous section). In addition, many CMIP5 models did not include a prognostic dust cycle and instead read in pre-calculated dust emissions (Lamarque et al, 2010). But note that CMIP5 model errors against measurements are similar to those for AeroCom models and those for our model ensemble (e.g., compare Figs.…”
Section: Comparison Of Inverse Model Results Againstsupporting
confidence: 52%
“…We furthermore do not use recently analyzed dust cycle results from CMIP5 models (Pu and Ginoux, 2018;Wu et al, 2020) because less than half of CMIP5 models with prognostic dust cycles reported total deposition fluxes, which are needed for the analyses against measurements (see previous section). In addition, many CMIP5 models did not include a prognostic dust cycle and instead read in pre-calculated dust emissions (Lamarque et al, 2010). But note that CMIP5 model errors against measurements are similar to those for AeroCom models and those for our model ensemble (e.g., compare Figs.…”
Section: Comparison Of Inverse Model Results Againstsupporting
confidence: 52%
“…The DML coast is characterised by distinct topographic features like ice rises having their specific local flow, climate regime and surface mass-balance variability (Lenaerts and others, 2014; Drews and others, 2015; Matsuoka and others, 2015; Goel and others, 2017; Rignot and others, 2019; Pratap and others, 2021). The coastal DML also offers opportunities for reconstructing annually resolved decadal to multi-decadal variability in temperature, westerly winds, sea ice and their interactions with various regional climatic modes (Naik and others, 2010; Philippe and others, 2016; Goel and others, 2020; Laluraj and others, 2020; Thamban and others, 2020; Ejaz and others, 2021; Pratap and others, 2021).
Fig.
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Section: Methodsmentioning
confidence: 99%
“…The inverse model results characterize the dust cycle for the years 2004-2008, whereas the concentration data were taken for different dates in the period 1981-2000 (Prospero et al, 1989;Arimoto et al, 1995) and the deposition flux measurements were taken one to several decades earlier (Edwards et al, 2006;McConnell et al, 2007). This mismatch in time periods could cause modeled deposition fluxes to exceed measured fluxes as several studies have 780 reported increases in dust emissions from South America and in dust deposition at Antarctica over the past century or so (McConnell et al, 2007;Gasso and Torres, 2019;Laluraj et al, 2020). Furthermore, there is substantial interannual variability in dust concentration that could affect the mismatch in time between models and measurements, especially for less dusty regions such as in the SH (Smith et al, 2017).…”
Section: Performance Of Inverse Model Results Against Independent Meamentioning
confidence: 99%