Atmospheric dust variability from Arabia and China over the last 500,000 years

TitleAtmospheric dust variability from Arabia and China over the last 500,000 years
Publication TypeJournal Article
Year of Publication2011
AuthorsRoberts, AP, Rohling, EJ, Grant, KM, Larrasoaña, JC, Liu, Q
JournalQuaternary Science Reviews
ISBN Number0277-3791
KeywordsAeolian dust, Climate, Glacial, Interglacial, Radiative perturbation

Atmospheric mineral dust aerosols affect Earth’s radiative balance and are an important climate forcing and feedback mechanism. Dust is argued to have played an important role in past natural climate changes through glacial cycles, yet temporal and spatial dust variability remain poorly constrained, with scientific understanding of uncertainties associated with radiative perturbations due to mineral dust classified as “very low”. To advance understanding of the dust cycle, we present a high-resolution dust record from the Red Sea, sourced principally from Arabia, with a precise chronology relative to global sea level/ice volume variability. Our record correlates well with a high-resolution Asian dust record from the Chinese Loess Plateau. Importing our age model from the Red Sea to the Chinese Loess Plateau provides a first detailed millennial-scale age model for the Chinese loess, which has been notoriously difficult to date at this resolution and provides a basis for inter-regional correlation of Chinese dust records. We observe a high baseline of dust emissions from Arabia and China, even through interglacials, with strong superimposed millennial-scale variability. Conversely, the distal EPICA Dome C Antarctic ice core record, which is widely used to calculate the radiative impact of dust variations, appears biased to sharply delineated glacial/interglacial contrasts. Calculations based on this Antarctic dust record will therefore overestimate the radiative contrast of atmospheric dust loadings on glacial/interglacial timescales. Additional differences between Arabian/Asian and circum-Saharan records reveal that climate models could be improved by avoiding ‘global mean’ dust considerations and instead including large-scale regions with different dust source variability.