Editing IPCC:AR6/SRCCL/Chapter-2 (section)

=== 2.4.1 Mineral dust ===

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One of the most abundant atmospheric aerosols emitted into the atmosphere is mineral dust, a ‘natural’ aerosol that is produced by wind strong enough to initiate the emissions process of sandblasting. Mineral dust is preferentially emitted from dry and unvegetated soils in topographic depressions where deep layers of alluvium have been accumulated (Prospero et al. 2002 <sup>[[#fn:r807|807]]</sup> ). Dust is also emitted from disturbed soils by human activities, with a 25% contribution to global emissions based on a satellite-based estimate (Ginoux et al. 2012 <sup>[[#fn:r808|808]]</sup> ).

Dust is then transported over long distances across continents and oceans. The dust cycle, which consists of mineral dust emission, transport, deposition and stabilisation, has multiple interactions with many climate processes and biogeochemical cycles.

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==== 2.4.1.1 Mineral dust as a short-lived climate forcer from land ====

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Depending on the dust mineralogy, mixing state and size, dust particles can absorb or scatter shortwave and longwave radiation. Dust particles serve as cloud condensation nuclei and ice nuclei. They can influence the microphysical properties of clouds, their lifetime and precipitation rate (Kok et al. 2018 <sup>[[#fn:r809|809]]</sup> ). New and improved understanding of processes controlling emissions and transport of dust, its regional patterns and variability, as well as its chemical composition, has been developed since AR5.

While satellites remain the primary source of information to locate dust sources and atmospheric burden, in-situ data remains critical to constrain optical and mineralogical properties of the dust (DiBiagio et al. 2017 <sup>[[#fn:r810|810]]</sup> ; Rocha-Lima et al. 2018 <sup>[[#fn:r811|811]]</sup> ). Dust particles are composed of minerals, including iron oxides which strongly absorb shortwave radiation and provide nutrients for marine ecosystems. Another mineral such as feldspar is an efficient ice nuclei (Harrison et al. 2016 <sup>[[#fn:r812|812]]</sup> ). Dust mineralogy varies depending on the native soils, so global databases were developed to characterise the mineralogical composition of soils for use in weather and climate models (Journet et al. 2014 <sup>[[#fn:r813|813]]</sup> ; Perlwitz et al. 2015 <sup>[[#fn:r814|814]]</sup> ). New field campaigns, as well as new analyses of observations from prior campaigns, have produced insights into the role of dust in western Africa in climate system, such as long-ranged transport of dust across the Atlantic (Groß et al. 2015 <sup>[[#fn:r815|815]]</sup> ) and the characterisation of aerosol particles and their ability to act as ice and cloud condensation nuclei (Price et al. 2018 <sup>[[#fn:r816|816]]</sup> ). Size distribution at emission is another key parameter controlling dust interactions with radiation. Most models now use the parametrisation of Kok (2011) <sup>[[#fn:r817|817]]</sup> based on the theory of brittle material. It was shown that most models underestimate the size of the global dust cycle (Kok 2011 <sup>[[#fn:r818|818]]</sup> ).

Characterisation of spatial and temporal distribution of dust emissions is essential for weather prediction and climate projections ( ''high confidence'' ). Although there is a growing confidence in characterising the seasonality and peak of dust emissions (i.e., spring–summer (Wang et al. 2015 <sup>[[#fn:r819|819]]</sup> )) and how the meteorological and soil conditions control dust sources, an understanding of long-term future dust dynamics, inter-annual dust variability and how they will affect future climate still requires substantial work. Dust is also important at high latitude, where it has an impact on snow-covered surface albedo and weather (Bullard et al. 2016 <sup>[[#fn:r820|820]]</sup> ).

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==== 2.4.1.2 Effects of past climate change on dust emissions and feedbacks ====

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A limited number of model-based studies found that dust emissions have increased significantly since the late 19th century: by 25% from the preindustrial period to the present day (e.g., from 729 Tg yr <sup>–1</sup> to 912 Tg yr <sup>–1</sup> ) with about 50% of the increase driven by climate change and about 40% driven by land use cover change, such as conversion of natural land to agriculture ( ''low confidence'' ) (Stanelle et al. 2014 <sup>[[#fn:r821|821]]</sup> ). These changes resulted in a clear sky radiative forcing at the top of the atmosphere of –0.14 W m <sup>–2</sup> (Stanelle et al. 2014 <sup>[[#fn:r822|822]]</sup> ). The authors found that, in North Africa, most dust is of natural origin, with a recent 15% increase in dust emissions attributed to climate change. In North America two-thirds of dust emissions take place on agricultural lands and both climate change and land-use change jointly drive the increase; between the pre-industrial period and the present day, the overall effect of changes in dust was –0.14 W m <sup>–2</sup> cooling of clear sky net radiative forcing on top of the atmosphere, with –0.05 W m <sup>–2</sup> from land use and –0.083 W m <sup>–2</sup> from changes in climate.

The comparison of observations for vertically integrated mass of atmospheric dust per unit area (i.e., dust mass path (DMP)) obtained from the remotely sensed data and the DMP from CMIP5 models reveal that the model-simulate range of DMP was much lower than the estimates (Evan et al. 2014 <sup>[[#fn:r823|823]]</sup> ). ESMs typically do not reproduce inter- annual and longer timescales variability seen in observations (Evan et al. 2016 <sup>[[#fn:r824|824]]</sup> ). Analyses of the CMIP5 models (Evan 2018 <sup>[[#fn:r825|825]]</sup> ; Evan et al. 2014 <sup>[[#fn:r826|826]]</sup> ) reveal that all climate models systematically underestimate dust emissions, the amount of dust in the atmosphere and its inter- annual variability ( ''medium confidence'' ).

One commonly suggested reason for the lack of dust variability in climate models is the models’ inability to simulate the effects of land surface changes on dust emission (Stanelle et al. 2014 <sup>[[#fn:r827|827]]</sup> ). Models that account for changes in land surface show more agreement with the satellite observations both in terms of aerosol optical depth and DMP (Kok et al. 2014 <sup>[[#fn:r828|828]]</sup> ). New prognostic dust emissions models are now able to account for both changes in surface winds and vegetation characteristics (e.g., leaf area index and stem area index) and soil water, ice and snow cover (Evans et al. 2016 <sup>[[#fn:r829|829]]</sup> ). As a result, new modelling studies (e.g., Evans et al. 2016 <sup>[[#fn:r830|830]]</sup> ) indicate that, in regions where soil and vegetation respond strongly to ENSO events, such as in Australia, inclusion of dynamic vegetation characteristics into dust emission parameterisations improves comparisons between the modelled and observed relationship with long-term climate variability (e.g., ENSO) and dust levels (Evans et al. 2016 <sup>[[#fn:r831|831]]</sup> ). Thus, there has been progress in incorporating the effects of vegetation, soil moisture, surface wind and vegetation on dust emission source functions, but the number of studies demonstrating such improvement remains small ( ''limited evidence, medium agreement'' ).

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==== 2.4.1.3 Future changes of dust emissions ====

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There is no agreement about the direction of future changes in dust emissions. Atmospheric dust loading is projected to increase over the southern edge of the Sahara in association with surface wind and precipitation changes (Pu and Ginoux, 2018 <sup>[[#fn:r832|832]]</sup> ), while Evan et al. (2016) <sup>[[#fn:r833|833]]</sup> project a decline in African dust emissions. Dust optical depth (DOD) is also projected to increase over the central Arabian peninsula in all seasons, and to decrease over northern China from March-April-May to September-October-November (Pu and Ginoux 2018 <sup>[[#fn:r834|834]]</sup> ). Climate models project rising drought risks over the south-western and central US in the 21st century. The projected drier regions largely overlay the major dust sources in the US. However, whether dust activity in the US will increase in the future is not clear, due to the large uncertainty in dust modelling (Pu and Ginoux 2017 <sup>[[#fn:r835|835]]</sup> ). Future trends of dust emissions will depend on changes in precipitation patterns and atmospheric circulation ( ''limited evidence, high agreement'' ). However, implication of changes in human activities, including mitigation (e.g., bioenergy production) and adaption (e.g., irrigation) are not characterised in the current literature.

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