<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing with OASIS Tables v3.0 20080202//EN" "journalpub-oasis3.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:oasis="http://docs.oasis-open.org/ns/oasis-exchange/table" dtd-version="3.0"><?xmltex \makeatother\@nolinetrue\makeatletter?>
  <front>
    <journal-meta><journal-id journal-id-type="publisher">ACP</journal-id><journal-title-group>
    <journal-title>Atmospheric Chemistry and Physics</journal-title>
    <abbrev-journal-title abbrev-type="publisher">ACP</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Atmos. Chem. Phys.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1680-7324</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/acp-17-13731-2017</article-id><title-group><article-title>Direct radiative effects of dust aerosols emitted from the Tibetan Plateau
on the East Asian summer monsoon – a regional climate model simulation</article-title>
      </title-group><?xmltex \runningtitle{Direct radiative effects of dust aerosols}?><?xmltex \runningauthor{H. Sun et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Sun</surname><given-names>Hui</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-2937-1687</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Liu</surname><given-names>Xiaodong</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-0355-5610</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3 aff4">
          <name><surname>Pan</surname><given-names>Zaitao</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences,
Xi'an, 710061, China</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing,
100101, China</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Department of Earth and Atmospheric Sciences, Saint Louis University,
St. Louis, Missouri, MO 63108, USA</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Key Laboratory of Meteorological Disaster, Ministry of Education,
Nanjing University of Information Science and Technology, Nanjing, Jiangsu,
China</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">H. Sun (sunhui@ieecas.cn)</corresp></author-notes><pub-date><day>17</day><month>November</month><year>2017</year></pub-date>
      
      <volume>17</volume>
      <issue>22</issue>
      <fpage>13731</fpage><lpage>13745</lpage>
      <history>
        <date date-type="received"><day>21</day><month>January</month><year>2017</year></date>
           <date date-type="rev-request"><day>28</day><month>February</month><year>2017</year></date>
           <date date-type="rev-recd"><day>17</day><month>September</month><year>2017</year></date>
           <date date-type="accepted"><day>9</day><month>October</month><year>2017</year></date>
      </history>
      <permissions>
        
        
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/3.0/">https://creativecommons.org/licenses/by/3.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://acp.copernicus.org/articles/.html">This article is available from https://acp.copernicus.org/articles/.html</self-uri><self-uri xlink:href="https://acp.copernicus.org/articles/.pdf">The full text article is available as a PDF file from https://acp.copernicus.org/articles/.pdf</self-uri>
      <abstract>
    <p id="d1e118">While dust aerosols emitted from major Asian sources such
as Taklimakan and Gobi deserts have been shown to have strong effect on
Asian monsoon and climate, the role of dust emitted from Tibetan Plateau
(TP) itself, where aerosols can directly interact with the TP “heat pump”
because of their physical proximity both in location and elevation, has not
been examined. This study uses the dust-coupled RegCM4.1 regional climate
model (RCM) to simulate the spatiotemporal distribution of dust aerosols
originating within the TP and their radiative effects on the East Asian
summer monsoon (EASM) during both heavy and light dust years. Two 20-year
simulations with and without the dust emission from TP showed that direct
radiative cooling in the mid-troposphere induced by the TP locally produced
dust aerosols resulted in an overall anticyclonic circulation anomaly in the
low troposphere centered over the TP region. The northeasterly anomaly in
the EASM region reduces its strength considerably. The simulations found a
significant negative correlation between the TP column dust load produced by
local emissions and the corresponding anomaly in the EASM index
(<inline-formula><mml:math id="M1" display="inline"><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.46</mml:mn></mml:mrow></mml:math></inline-formula>). The locally generated TP dust can cause surface cooling far
downstream in Bohai Gulf and the China–North Korea border area through
stationary Rossby wave propagation. Although dust from within TP (mainly
Qaidam Basin) is a relatively small portion of total Asian aerosols, its
impacts on Asian monsoon and climate seems disproportionately large, likely
owning to its higher elevation within TP itself.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

      <?xmltex \hack{\newpage}?>
<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p id="d1e144">Dust is one of the most important components of atmospheric aerosols. The
main source of atmospheric dust is wind erosion in arid and semi-arid
regions; it is estimated that the global atmospheric dust emission may be as
high as 200–5000 Mt yr<inline-formula><mml:math id="M2" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> (Goudie, 1983). Because of the large amount
in the atmosphere, dust effects on the environment and the climate system
have attracted much attention. The inhalation of dust aerosols can harm both
human and animal health; it can also affect visibility and thus potentially
increase the number of traffic accidents (Park and Kim, 2005). Dust aerosols
also are important drivers of the global climate because of their direct
radiative effects on the Earth–atmosphere radiation balance and temperature
(Tegen and Lacis, 1996; Miller et al., 2004). They can alter the atmospheric
hydrological cycle by acting as cloud condensation nuclei and thus can
modulate both the regional and global precipitation (Rosenfeld et al., 2001).
Satellite observations have shown that dust originating from the Taklimakan
Desert can travel around the globe within 2 weeks and alter the interaction
between the atmospheric CO<inline-formula><mml:math id="M3" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> and the global climate by providing
nutrients to and interacting with the marine ecosystem (Uno et al., 2009).</p>
      <p id="d1e168">East Asia is an important source region for dust (Zhang et al., 1996) and is
home to more than half of the world's population. The lives of people in East
Asia are deeply affected by the East Asian summer monsoon (EASM) and the
relationship between dust aerosols and the EASM is of great interest to the
scientific community. Simulations have shown that dust aerosols not only
weaken the EASM (Sun et al., 2012; Guo and Yin, 2015), but can also reduce
the atmospheric heat source over the Tibetan Plateau (TP) and delay the onset
of the EASM (Sun and Liu, 2016). Aerosols, including dust aerosols, have been
shown to affect the intensity of the EASM (Li et al., 2016) and variations in
the EASM can modulate the spatiotemporal distribution of dust aerosols in
East Asia. A recent modeling study by Lou et al. (2016) indicated that there
was a negative correlation between the spring dust loading in eastern China
and the East Asian monsoon.</p>
      <p id="d1e171">Dust aerosols in East Asia are mainly derived from arid and semi-arid areas,
including the Taklimakan and Gobi deserts. However, some studies have
indicated that the TP itself may also be an important source region for dust
(Zhang et al., 1996; Fang et al., 1999) and that the region is more conducive
to the atmospheric transportation of dust due to its high altitude and can
interact directly with the TP thermal pump (Wu et al., 2012). However, the
source and spatiotemporal distribution of dust aerosols over the TP have not
been established yet. At present, there are three viewpoints about the source
of dust aerosols over the TP. First, an investigation by Fang et al. (1995,
1999) showed that there exists <inline-formula><mml:math id="M4" display="inline"><mml:mrow><mml:mn mathvariant="normal">2047.41</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mn mathvariant="normal">4</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> ha of desert within the TP,
suggesting that the TP may be a potential source for dust. A numerical
simulation by Chen et al. (2013) showed that dust aerosols were produced by
local emissions over the TP in spring and winter. Second, satellite
observations have shown that the aerosols over the TP are dominated by dust
in spring and summer and that the dust aerosols were probably derived from
the Taklimakan and Gurbantunggut deserts to the north of the TP (Huang et
al., 2007; Jia et al., 2015). Third, some studies have indicated that the
dust emitted from the south of the TP, such as from the Great Indian Desert,
can also be transported over the Himalaya (Lau et al., 2006, 2010).</p>
      <p id="d1e189">As a massive, elevated heat source, the TP can directly heat the upper
troposphere. The heating anomaly over the TP has a great impact on the EASM
(Yanai et al, 2006; Duan et al., 2012). Studies have shown that dust
aerosols over the TP can alter the local atmospheric radiation balance,
affecting both the heat source over the TP and the Asian monsoon (Lau et
al., 2006, 2010; Chen et al., 2013; Sun et al., 2016). However, most
previous simulation studies have focused on dust aerosols originating from
the Taklimakan and Gobi deserts (Zhao et al., 2006; Wang et al., 2008; Huang
et al., 2009; Sun et al., 2012), and there have been few investigations of
the impact of dust aerosols emitted by the TP on the East Asian climate. The
work reported here used the RegCM4.1 model to simulate climatic effects of
distribution of dust aerosols surrounding the TP by performing numerical
experiments with and without the emission of dust over the TP.</p>
</sec>
<sec id="Ch1.S2">
  <title>Numerical model and experiment design</title>
<sec id="Ch1.S2.SS1">
  <title>RegCM4.1 model</title>
      <p id="d1e203">We used the RegCM4.1 model (Regional Climate Model version 4.1), which is
developed and supported by the National Center for Atmospheric Research
(NCAR) and the International Center for Theoretical Physics. The model has
been widely used for more than 20 years in studies of regional climatic and
environmental change, especially in the simulation of the effect of aerosols
on climate (Qian et al., 2003; Solmon et al., 2008; Zhang et al., 2009;
Zanis et al., 2012; Ji et al., 2011, 2015; Das et al., 2015a, 2016; Mbienda
et al., 2017).</p>
      <p id="d1e206">The dynamic framework of RegCM4.1 core is based on the hydrostatic core of
the mesoscale model MM5 (Grell et al., 1994). The radiation scheme in
RegCM4.1 is the CCM3 radiation transfer process (Kiehl et al., 1996).
RegCM4.1 has two land surface process schemes: (1) the biosphere atmosphere
transfer scheme (BATS1e; Dickinson et al., 1993) and (2) the Common Land
Surface process module (CLM3.5; Oleson et al., 2008). The dust cycle can
only be diagnosed when BATS1e is used. The planetary boundary layer
parameterization in RegCM4.1 follows the scheme of Holtslag et al. (1990)
and there are three cumulus convection parameterization schemes, including
Grell (Grell et al., 1993), Kuo (Anthes, 1977) and MIT-Emanuel (Emanuel,
1991). The dust module coupled in RegCM4.1 is based on the dust emission
model (DPM) of Marticorena et al. (1995) and Alfaro and Gomes (2001). It
considers dust emission, dry/wet deposition and the diagnosis of the optical
and radiation characteristics of dust (including long- and shortwave
radiation; Zakey et al., 2006; Zhang et al., 2009).</p>
      <p id="d1e209">The dust-coupled module has been described in detail in previous articles
(Zakey et al., 2006; Zhang et al., 2009), so only a brief introduction is
given here. There are four steps in dust parameterization. First, each model
grid cell is classified as either desert or non-desert according to its soil
properties (such as texture, soil type, particle size and composition) based
on the United States Department of Agriculture textural classification.
Second, dust emission is assumed to be a function of friction velocity
(<inline-formula><mml:math id="M5" display="inline"><mml:mrow><mml:msup><mml:mi>u</mml:mi><mml:mo>*</mml:mo></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>; dust aerosols are lifted off the ground once <inline-formula><mml:math id="M6" display="inline"><mml:mrow><mml:msup><mml:mi>u</mml:mi><mml:mo>*</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula>
exceeds a threshold value (<inline-formula><mml:math id="M7" display="inline"><mml:mrow><mml:msubsup><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mo>*</mml:mo></mml:msubsup><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>.
            <disp-formula id="Ch1.E1" content-type="numbered"><mml:math id="M8" display="block"><mml:mrow><mml:msubsup><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mo>*</mml:mo></mml:msubsup><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:msubsup><mml:mi>u</mml:mi><mml:mtext>ts</mml:mtext><mml:mo>*</mml:mo></mml:msubsup><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo><mml:mo>⋅</mml:mo><mml:msub><mml:mi>f</mml:mi><mml:mtext>eff</mml:mtext></mml:msub><mml:mo>⋅</mml:mo><mml:msub><mml:mi>f</mml:mi><mml:mtext>w</mml:mtext></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where <inline-formula><mml:math id="M9" display="inline"><mml:mrow><mml:msubsup><mml:mi>u</mml:mi><mml:mtext>ts</mml:mtext><mml:mo>*</mml:mo></mml:msubsup><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> depends on soil particle size
(<inline-formula><mml:math id="M10" display="inline"><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>, and <inline-formula><mml:math id="M11" display="inline"><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mtext>eff</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M12" display="inline"><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mtext>w</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> are the correction terms
for non-erodible surface roughness elements (Marticorena and Bergametti,
1995) and soil moisture content (Fécan et al., 1999),
respectively.<?xmltex \hack{\newpage}?></p>
      <p id="d1e376">Third, the horizontal mass flux is treated as a function of the frictional
velocity and is given by

                <disp-formula specific-use="align" content-type="numbered"><mml:math id="M13" display="block"><mml:mtable displaystyle="true"><mml:mlabeledtr id="Ch1.E2"><mml:mtd/><mml:mtd><mml:mstyle displaystyle="true" class="stylechange"/></mml:mtd><mml:mtd><mml:mrow><mml:mstyle class="stylechange" displaystyle="true"/><mml:mtext>d</mml:mtext><mml:msub><mml:mi>H</mml:mi><mml:mi>F</mml:mi></mml:msub><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo><mml:mo>=</mml:mo></mml:mrow></mml:mtd></mml:mlabeledtr><mml:mtr><mml:mtd><mml:mstyle displaystyle="true" class="stylechange"/></mml:mtd><mml:mtd><mml:mrow><mml:mstyle displaystyle="true" class="stylechange"/><mml:mi>E</mml:mi><mml:mo>⋅</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msub><mml:mi mathvariant="italic">ρ</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow><mml:mi>g</mml:mi></mml:mfrac></mml:mstyle><mml:mo>⋅</mml:mo><mml:msup><mml:mi>u</mml:mi><mml:mrow><mml:mo>*</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup><mml:mo>⋅</mml:mo><mml:mo>(</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>+</mml:mo><mml:mi>R</mml:mi><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo><mml:mo>)</mml:mo><mml:mo>⋅</mml:mo><mml:mo>(</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>-</mml:mo><mml:msup><mml:mi>R</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo><mml:mo>)</mml:mo><mml:mo>⋅</mml:mo><mml:mtext>d</mml:mtext><mml:msub><mml:mi>S</mml:mi><mml:mtext>rel</mml:mtext></mml:msub><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>

            where <inline-formula><mml:math id="M14" display="inline"><mml:mi>E</mml:mi></mml:math></inline-formula>, <inline-formula><mml:math id="M15" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">ρ</mml:mi><mml:mi>a</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M16" display="inline"><mml:mi>g</mml:mi></mml:math></inline-formula> are the ratio of the erodible to total surface
areas, the surface air density and the gravitational acceleration,
respectively. <inline-formula><mml:math id="M17" display="inline"><mml:mrow><mml:mi>R</mml:mi><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is the ratio of the threshold frictional
velocity in Eq. (1) to the frictional velocity <inline-formula><mml:math id="M18" display="inline"><mml:mrow><mml:msup><mml:mi>u</mml:mi><mml:mo>*</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula> calculated within each
grid cell from model prognostic surface wind and surface roughness height.
<inline-formula><mml:math id="M19" display="inline"><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mi>S</mml:mi><mml:mtext>rel</mml:mtext></mml:msub><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is the relative surface area of a soil
aggregate of diameter <inline-formula><mml:math id="M20" display="inline"><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> to the total surface area of soil
aggregates. The vertical flux corresponding to each emission mode is
calculated by
            <disp-formula id="Ch1.E3" content-type="numbered"><mml:math id="M21" display="block"><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mrow><mml:mtext>dust</mml:mtext><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mfenced open="(" close=")"><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub></mml:mfenced><mml:mo>=</mml:mo><mml:mfenced close=")" open="("><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mi mathvariant="italic">π</mml:mi><mml:mn mathvariant="normal">6</mml:mn></mml:mfrac></mml:mstyle></mml:mfenced><mml:mo>⋅</mml:mo><mml:msub><mml:mi mathvariant="italic">ρ</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>⋅</mml:mo><mml:msubsup><mml:mi>D</mml:mi><mml:mi>i</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msubsup><mml:mo>⋅</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where <inline-formula><mml:math id="M22" display="inline"><mml:mrow><mml:msub><mml:mi mathvariant="italic">ρ</mml:mi><mml:mtext>p</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> is the aggregate density (2.65 g cm<inline-formula><mml:math id="M23" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>,
<inline-formula><mml:math id="M24" display="inline"><mml:mrow><mml:msub><mml:mi>D</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is the median diameter and <inline-formula><mml:math id="M25" display="inline"><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is a function of
the kinetic energy flux.</p>
      <p id="d1e700">The dust particles are divided into four size bins (or modes): fine
(0.01–1.0 <inline-formula><mml:math id="M26" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m), accumulation (1.0–2.5 <inline-formula><mml:math id="M27" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m), coarse
(2.5–5 <inline-formula><mml:math id="M28" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m) and giant (5.0–20.0 <inline-formula><mml:math id="M29" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m). The dust transport,
deposition and removal processes are given by Qian et al. (2001) and Qian and
Giorgi (1999):
            <disp-formula id="Ch1.E4" content-type="numbered"><mml:math id="M30" display="block"><mml:mrow><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mo>∂</mml:mo><mml:msup><mml:mi mathvariant="italic">χ</mml:mi><mml:mi>i</mml:mi></mml:msup></mml:mrow><mml:mrow><mml:mo>∂</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mover accent="true"><mml:mi>V</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mo>⋅</mml:mo><mml:mi mathvariant="normal">∇</mml:mi><mml:msup><mml:mi mathvariant="italic">χ</mml:mi><mml:mi>i</mml:mi></mml:msup><mml:mo>+</mml:mo><mml:msubsup><mml:mi>F</mml:mi><mml:mtext>H</mml:mtext><mml:mi>i</mml:mi></mml:msubsup><mml:mo>+</mml:mo><mml:msubsup><mml:mi>F</mml:mi><mml:mtext>V</mml:mtext><mml:mi>i</mml:mi></mml:msubsup><mml:mo>+</mml:mo><mml:msubsup><mml:mi>F</mml:mi><mml:mtext>C</mml:mtext><mml:mi>i</mml:mi></mml:msubsup><mml:mo>+</mml:mo><mml:msup><mml:mi>S</mml:mi><mml:mi>i</mml:mi></mml:msup><mml:mo>-</mml:mo><mml:msubsup><mml:mi>R</mml:mi><mml:mtext>Wls</mml:mtext><mml:mi>i</mml:mi></mml:msubsup><mml:mo>-</mml:mo><mml:msubsup><mml:mi>R</mml:mi><mml:mtext>Wc</mml:mtext><mml:mi>i</mml:mi></mml:msubsup><mml:mo>-</mml:mo><mml:msubsup><mml:mi>D</mml:mi><mml:mtext>d</mml:mtext><mml:mi>i</mml:mi></mml:msubsup><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where <inline-formula><mml:math id="M31" display="inline"><mml:mi mathvariant="italic">χ</mml:mi></mml:math></inline-formula> is the dust mixing ratio, <inline-formula><mml:math id="M32" display="inline"><mml:mover accent="true"><mml:mi>V</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover></mml:math></inline-formula> is
vector wind and <inline-formula><mml:math id="M33" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mover accent="true"><mml:mi>V</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mo>⋅</mml:mo><mml:mi mathvariant="normal">∇</mml:mi><mml:msup><mml:mi mathvariant="italic">χ</mml:mi><mml:mi>i</mml:mi></mml:msup></mml:mrow></mml:math></inline-formula> is the advection,
<inline-formula><mml:math id="M34" display="inline"><mml:mrow><mml:msubsup><mml:mi>F</mml:mi><mml:mtext>H</mml:mtext><mml:mi>i</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> is the horizontal turbulent diffusion, <inline-formula><mml:math id="M35" display="inline"><mml:mrow><mml:msubsup><mml:mi>F</mml:mi><mml:mtext>V</mml:mtext><mml:mi>i</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> is the
vertical turbulent diffusion and <inline-formula><mml:math id="M36" display="inline"><mml:mrow><mml:msubsup><mml:mi>F</mml:mi><mml:mtext>C</mml:mtext><mml:mi>i</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> is the convective transport.
<inline-formula><mml:math id="M37" display="inline"><mml:mrow><mml:msubsup><mml:mi>R</mml:mi><mml:mtext>Wls</mml:mtext><mml:mi>i</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M38" display="inline"><mml:mrow><mml:msubsup><mml:mi>R</mml:mi><mml:mtext>Wc</mml:mtext><mml:mi>i</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> are the wet removal terms, represented by
large-scale and convective precipitation. <inline-formula><mml:math id="M39" display="inline"><mml:mrow><mml:msubsup><mml:mi>D</mml:mi><mml:mtext>d</mml:mtext><mml:mi>i</mml:mi></mml:msubsup></mml:mrow></mml:math></inline-formula> is the dry
deposition, represented by assuming fixed depositional velocities over both
land and water.</p>
      <p id="d1e953">The dust SW radiation is calculated using an asymmetry factor, single-scattering albedo (SSA) and mass extinction coefficient based on Mie theory.
Radiative flux calculation use the <inline-formula><mml:math id="M40" display="inline"><mml:mi mathvariant="italic">δ</mml:mi></mml:math></inline-formula>-Eddington approximate, and the
optical spectrum is within 0.2–4.5 <inline-formula><mml:math id="M41" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m and is divided into 18
wavelength bands. One is in the visible band. Seven are in the ultraviolet
band between 0.2 and 0.35 <inline-formula><mml:math id="M42" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m, and the rest are in the infrared band.
Refractive index of dust for the SW window is from the Optical Properties of
Aerosols and Clouds (OPAC) database (Hess et al., 1998). The dust SSA of the
four bins is considered to be 0.95 (0.01–1.0 <inline-formula><mml:math id="M43" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m), 0.89
(1.0–2.5 <inline-formula><mml:math id="M44" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m), 0.80 (2.5–5.0 <inline-formula><mml:math id="M45" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m) and 0.7
(5.0–20.0 <inline-formula><mml:math id="M46" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m), respectively. The corresponding extinction
efficiencies are 2.45, 0.85, 0.38 and 0.17, and asymmetry parameters are 0.64,
0.76, 0.81 and 0.87, respectively. In the LW domain, dust effects on
emissivity (and hence absorptivity) use the parameterization of Kiehl et
al. (1996).
            <disp-formula id="Ch1.E5" content-type="numbered"><mml:math id="M47" display="block"><mml:mrow><mml:msub><mml:mi mathvariant="italic">ε</mml:mi><mml:mtext>LW</mml:mtext></mml:msub><mml:mo>(</mml:mo><mml:mi>z</mml:mi><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn><mml:mo>-</mml:mo><mml:msup><mml:mi mathvariant="normal">ℓ</mml:mi><mml:mrow><mml:mo>-</mml:mo><mml:mi>D</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mtext>lwabs</mml:mtext></mml:msub><mml:mo>⋅</mml:mo><mml:mi>b</mml:mi><mml:mo>(</mml:mo><mml:mi>z</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:msup><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where <inline-formula><mml:math id="M48" display="inline"><mml:mrow><mml:mi>D</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1.66</mml:mn></mml:mrow></mml:math></inline-formula> is the diffusivity factor, <inline-formula><mml:math id="M49" display="inline"><mml:mrow><mml:mi>b</mml:mi><mml:mo>(</mml:mo><mml:mi>z</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is the dust burden
(g m<inline-formula><mml:math id="M50" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> of a given layer and <inline-formula><mml:math id="M51" display="inline"><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mtext>lwabs</mml:mtext></mml:msub></mml:mrow></mml:math></inline-formula> (m<inline-formula><mml:math id="M52" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> g<inline-formula><mml:math id="M53" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> is the mass absorption coefficient calculated based on
the Mie theory for each size bin of the relevant LW spectral windows using
the LW refractive indices consistent with Wang et al. (2006).</p>
</sec>
<sec id="Ch1.S2.SS2">
  <title>Experimental design and observational data</title>
      <p id="d1e1139">Two numerical experiments were designed; both integrate for 20 years
(excluding first 2 years of spin-up) using the dust-coupled RegCM4.1. The
first experiment was a control experiment (CON) that used the default land
use types from the Global Land Cover Characterization data set (Loveland et
al., 2000), meaning that dust-emitting sources both within and outside the
TP are present. The second experiment was a sensitivity experiment (SEN),
where we turned off the dust emission in the northern and northeastern TP
(the deserts inside the black outline of the TP contour in Fig. 1b). To
eliminate the dust emission in the TP, we set <inline-formula><mml:math id="M54" display="inline"><mml:mrow><mml:msubsup><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mo>*</mml:mo></mml:msubsup><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> in
Eq. (2) and <inline-formula><mml:math id="M55" display="inline"><mml:mrow><mml:msub><mml:mi>F</mml:mi><mml:mrow><mml:mtext>dust</mml:mtext><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>(</mml:mo><mml:msub><mml:mi>D</mml:mi><mml:mtext>p</mml:mtext></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> in Eq. (3) to zero over the TP.
All the other conditions in the sensitivity experiment were the same as in
the control experiment. In order to isolate the effect of dust aerosols,
only dust aerosols are included in our simulations, without considering
other aerosols (such as anthropogenic or marine aerosols).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><caption><p id="d1e1191"><bold>(a)</bold> Model domain and topography (units: km) and
<bold>(b)</bold> dust source regions (yellow area) over the Tibetan Plateau and
surrounding areas. Rectangles in <bold>(a)</bold> indicate Tibetan Plateau (A;
27–39<inline-formula><mml:math id="M56" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, 80–105<inline-formula><mml:math id="M57" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E), north EASM region (B;
34–42<inline-formula><mml:math id="M58" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, 105–120<inline-formula><mml:math id="M59" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E) and south EASM region (C;
22–30<inline-formula><mml:math id="M60" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E, 105–120<inline-formula><mml:math id="M61" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N).</p></caption>
          <?xmltex \igopts{width=455.244094pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f01.png"/>

        </fig>

      <p id="d1e1263">The initial and boundary conditions were taken from the NCAR/NCEP re-analysis
data set (Kalnay et al., 1996). The sea surface temperature used the National
Oceanic and Atmospheric Administration sea surface temperature data set
(Reynolds et al., 2002). The topography of the TP is very complex,
necessitating high spatial resolution (<inline-formula><mml:math id="M62" display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> 60 km) to resolve localized
precipitation (Gao et al., 2006). The horizontal resolution in RegCM4.1 runs
was therefore set to 40 km. The simulation domain is shown in Fig. 1a and
the model domain center was at 32<inline-formula><mml:math id="M63" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N,
105<inline-formula><mml:math id="M64" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E with 240 grid
cells in the west–east direction and 160 grid cells in the north–south
direction. The model was run in the standard configuration of 18 vertical
<inline-formula><mml:math id="M65" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> layers with the model top at 10 hPa. The integration duration for
both experiments was from 1 January 1988 to 31 December 2009. The first 2
years were treated as the model spin-up time, and only the results from the
last 20 years were analyzed.</p>
      <p id="d1e1298">Five main types of observations were used to evaluate the simulated results
of CON:
<list list-type="order"><list-item>
      <p id="d1e1303">The monthly mean surface air temperature and precipitation, with
a high resolution of <inline-formula><mml:math id="M66" display="inline"><mml:mrow><mml:msup><mml:mn mathvariant="normal">0.5</mml:mn><mml:mo>∘</mml:mo></mml:msup><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">0.5</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula>, provided by the
Climate Research Unit (CRU) of the University of East Anglia (Mitchell and
Jones, 2005), which was used to evaluate the simulated surface temperature
and precipitation in CON.</p></list-item><list-item>
      <p id="d1e1325">The NCEP–DOE re-analysis wind field
(<inline-formula><mml:math id="M67" display="inline"><mml:mrow><mml:msup><mml:mn mathvariant="normal">2.5</mml:mn><mml:mo>∘</mml:mo></mml:msup><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">2.5</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula>) at 850 hPa, which was used to compare
the simulated atmospheric circulation.</p></list-item><list-item>
      <p id="d1e1347">Level-3 monthly mean aerosol optical depth (AOD) data from
2000 to 2009 obtained from the Multiangle Imaging Spectroradiometer (MISR)
onboard NASA's Earth Observation System Terra satellite
(<uri>http://www-misr.jpl.nasa.gov/</uri>). Since MODIS AOD has a large portion of
missing data in northwest China, MISR was used to evaluate the simulated
dust AOD in CON. The effectiveness of the MISR data was investigated by
Martonchik et al. (1998, 2004) and Bibi et al. (2015).</p></list-item><list-item>
      <p id="d1e1354">Level-3 monthly
mean pure dust AOD data under cloud-free scenes (<inline-formula><mml:math id="M68" display="inline"><mml:mrow><mml:msup><mml:mn mathvariant="normal">2</mml:mn><mml:mo>∘</mml:mo></mml:msup><mml:mo>×</mml:mo><mml:msup><mml:mn mathvariant="normal">5</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math></inline-formula>) from 2007 to 2009 obtained from Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observations (CALIPSO; Winker et al., 2013), which was
also used to evaluated the simulated dust AOD in CON. The most recent version
of the L3 product included averaging of individual types of aerosols (Liu et
al., 2008; Amiridis et al., 2013; Marinou et al., 2017).</p></list-item><list-item>
      <p id="d1e1376">The AOD observed
in situ by the Aerosol Robotic Network (AERONET), which was used to evaluate the
simulated dust seasonal and interannual variation in CON.</p></list-item></list></p>
</sec>
</sec>
<sec id="Ch1.S3">
  <title>Results of simulations</title>
      <p id="d1e1386">In this section we will first evaluate the CON simulation using the observed
data described in the previous section. Then the results from CON and SEN
experiments will be compared to determine the roles of dust aerosols
generated from the TP play in the thermodynamic fields and circulations
including the EASM.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><caption><p id="d1e1391">Spatial distribution of <bold>(a, b)</bold> summer surface air
temperature (<inline-formula><mml:math id="M69" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C) and <bold>(c, d)</bold> summer precipitation
(mm day<inline-formula><mml:math id="M70" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> simulated in the control experiment <bold>(a, c, e)</bold> and
the CRU observations <bold>(b, d, f)</bold> for 1990–2009. The bottom two panels
are wind vectors at 850 hPa simulated in <bold>(e)</bold> the control experiment
and <bold>(f)</bold> the NCEP–DOE re-analysis during the summer monsoon season
(June–August) averaged for 1990–2009.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f02.png"/>

      </fig>

<sec id="Ch1.S3.SS1">
  <title>Validation</title>
<sec id="Ch1.S3.SS1.SSS1">
  <title>Basic model climatology</title>
      <p id="d1e1453">The simulated climatology can influence the distribution of dust aerosols and
their climatic effects, so CON was used to analyze the surface temperature,
precipitation and atmospheric circulation at 850 hPa. The CON-simulated and
CRU-observed 20-year average summer surface temperatures in East Asia are
presented, respectively, in Fig. 2a and b. The CRU-observed temperature is
<inline-formula><mml:math id="M71" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 25 <inline-formula><mml:math id="M72" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in southern China, NW China and northern India, and it is
<inline-formula><mml:math id="M73" display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> 7 <inline-formula><mml:math id="M74" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C over the TP. The observed north–south gradient and
location of the maximum and minimum centers were captured well. The model
captured the major distribution patterns of precipitation, including the
reasonable SE–NW gradient and the maximum centers in southern China, the
Himalaya and Indian Peninsula, with a 2–4 mm day<inline-formula><mml:math id="M75" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> negative bias in
the Korean Peninsula and southern Japan and a 2–4 mm day<inline-formula><mml:math id="M76" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> positive bias
in the Tianshan (Fig. 2c, d). These simulated deviations are likely
related to the cumulus convective scheme in the model (Zhang et al., 2008;
Wang and Yu, 2011). RegCM4.1 captured the major characteristics of the
circulations in East Asia, where southwesterlies dominate to the south side
of the TP, and the location of the Indian Low is consistent with the
NCEP–DOE observations (Fig. 2e, f).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3"><caption><p id="d1e1515">Spatial distribution of the dust AOD simulated by the control
experiment <bold>(a, c, e, g)</bold> and the total AOD observed
by MISR at 550 nm <bold>(b, d, f, h)</bold> averaged in <bold>(a, b)</bold> spring,
<bold>(c, d)</bold> summer, <bold>(e, f)</bold> autumn and <bold>(g, h)</bold> winter
during the time period 2000–2009.</p></caption>
            <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f03.png"/>

          </fig>

</sec>
<sec id="Ch1.S3.SS1.SSS2">
  <title>Simulated and MISR-observed dust AOD comparison</title>
      <p id="d1e1549">Satellite and in situ observations include all types of aerosols, such as black
aerosols, SO<inline-formula><mml:math id="M77" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> and organic carbon; observed data for dust AOD alone are
scarce. Therefore we used the MISR AOD data, as in most previous studies
(e.g., Zakey et al., 2006; Zhang et al., 2009), to evaluate the
spatiotemporal distribution of the dust AOD simulated by the model. Both the
simulation and observations showed that the dust AOD over the TP and its
surrounding areas was higher in spring and summer (Fig. 3a and c) and
lower in autumn and winter (Fig. 3e and g). There were three maximum
centers (<inline-formula><mml:math id="M78" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 0.6) of dust AOD in spring and summer, located in the
Taklimakan, Gobi and Great Indian deserts,
respectively. The dust AOD over the Qaidam Basin in the NE of the TP was
also <inline-formula><mml:math id="M79" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 0.5 and the dust AOD over the northern TP, adjacent to the
southern Taklimakan Desert, was between 0.3 and 0.5. The simulated dust AOD
in these regions was reduced in autumn and winter (Fig. 3e and g). The
MISR-observed AOD was largely consistent with the model results for the
Taklimakan and Gobi deserts and the Qaidam Basin, but was relatively
low in the Great Indian Desert in summer. The large value of the MISR AOD in
the Sichuan Basin to the east of the TP was due to industrial emissions,
which were not incorporated into our model simulation.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4"><caption><p id="d1e1577">Comparison between the simulated variation of the monthly mean dust
AOD in the control experiment and the AERONET-observed variation of the
monthly mean aerosol AOD (500 nm) at Dalanzadgad from 1997 to 2006
(<inline-formula><mml:math id="M80" display="inline"><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">0.66</mml:mn></mml:mrow></mml:math></inline-formula>).</p></caption>
            <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f04.png"/>

          </fig>

</sec>
<sec id="Ch1.S3.SS1.SSS3">
  <title>Simulated and AERONET-observed dust AOD comparison</title>
      <p id="d1e1604">Figure 4 compares the in situ observed monthly mean AOD from AERONET and that
simulated by RegCM4.1 at Dalanzadgad (43.6<inline-formula><mml:math id="M81" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, 104.4<inline-formula><mml:math id="M82" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E).
This is the only available AERONET site in the vicinity of the dust
sources with continuous records for <inline-formula><mml:math id="M83" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 10 years. The model
captured the seasonal and interannual variations of AOD well, including the
year with extremely high levels of dust. Observations over the TP are scarce
and we could only find a site with continuous aerosol records from AERONET
at Nam Co (30.77<inline-formula><mml:math id="M84" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, 90.96<inline-formula><mml:math id="M85" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E). The seasonal variation
of AOD at this site is well captured. Both the simulation and the
observations showed that the dust AOD increases in spring at Nam Co (Fig. 5).</p>
</sec>
<sec id="Ch1.S3.SS1.SSS4">
  <title>Simulated and CALIPSO-observed dust AOD comparison</title>
      <p id="d1e1656">While MISR and AERONET data contain all types of aerosols including those
anthropogenic ones, the CALIPSO observation is solely devoted to dust aerosols.
Figure 6 shows that the simulated seasonal variation, center positions and
magnitude of dust AOD are very consistent with those observed by CALIPSO.
Both simulations and observations not only showed that dust AOD increased in
spring and summer and decreased in autumn and winter, but they also captured
three maximum centers of dust AOD in Taklimakan and Great Indian deserts and
Qaidam Basin located in the northern TP in spring. The simulated center
values were still high in summer.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5"><caption><p id="d1e1661">Comparison between the AERONET-observed monthly mean aerosol AOD
(500 nm) at Nam Co and simulated by the control experiment at the grid near
Nam Co in <bold>(a)</bold> 2007 and <bold>(b)</bold> 2009.</p></caption>
            <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f05.png"/>

          </fig>

</sec>
</sec>
<sec id="Ch1.S3.SS2">
  <title>Relationship between the EASM and dust loading over the TP</title>
      <p id="d1e1683">To study the relationship between dust aerosols and the EASM, we used the
average summer meridional wind at 850 hPa over eastern China
(20–45<inline-formula><mml:math id="M86" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, 105–122.5<inline-formula><mml:math id="M87" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E) as an EASM index, following Xie
et al. (2016). This index measures the intensity of the southerly wind to the
east of the TP in the lower troposphere over East Asia. It has been widely
used to examine both modern and paleo-changes in the East Asian monsoon (Wang
et al., 2008; Jiang and Lang, 2010). We found that the simulated difference
in the EASM index (CON–SEN) and the difference in the model-simulated column
dust load averaged over the TP are highly anticorrelated with a correlation
coefficient <inline-formula><mml:math id="M88" display="inline"><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.46</mml:mn></mml:mrow></mml:math></inline-formula> (Fig. 7). The dust aerosol increases and decreases over
the TP as the index weakens and enhances, respectively. Lou et al. (2016)
also demonstrated a clear negative correlation between the EASM and the dust
concentration over eastern China in spring. Based on the variation in the
column dust load shown in Fig. 7, we chose 1994 and 2009 as heavy dust years
and 2003 and 2007 as light dust years and then contrasted the dust
distribution over the TP and its effects on the summer climate in the
heavy/light dust years.<?xmltex \hack{\newpage}?></p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6"><caption><p id="d1e1721">Spatial distribution of the dust AOD simulated by the control
experiment <bold>(a, c, e, g)</bold> and the corresponding observed by CALIPSO
<bold>(b, d, f, h)</bold> averaged in <bold>(a, b)</bold> spring,
<bold>(c, d)</bold> summer, <bold>(e, f)</bold> autumn and <bold>(g, h)</bold> winter
during the time period 2007–2009.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f06.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS3">
  <title>Dust aerosol distribution in heavy/light dust years</title>
      <p id="d1e1756">In the heavy dust years, the difference in the column dust load over the TP
was greater than that in the light dust years, as expected. Two centers of
maximum column dust load existed over the TP in the heavy dust years
(Fig. 8a). One was located in the Qaidam Basin and the other was in the NW of
the TP. The maximum values at both centers were <inline-formula><mml:math id="M89" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 70 mg m<inline-formula><mml:math id="M90" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>.
However, the difference in the column dust load over the NW of the TP in the
light dust years was much lower than in the heavy dust years and the average
value was
<inline-formula><mml:math id="M91" display="inline"><mml:mo>&lt;</mml:mo></mml:math></inline-formula> 25 mg m<inline-formula><mml:math id="M92" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>. From the vertical profiles of the dust load (Fig. 8c
and d), we can see that the dust mixing ratio was higher in heavy dust years
in the western TP with an average value <inline-formula><mml:math id="M93" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 5 <inline-formula><mml:math id="M94" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>g kg<inline-formula><mml:math id="M95" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>. The
mixing ratio was lower in the western TP in the light dust years.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7" specific-use="star"><caption><p id="d1e1826">Difference of CON minus SEN in the normalized regional mean dust
column load averaged over the Tibetan Plateau (27–39<inline-formula><mml:math id="M96" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N,
80–105<inline-formula><mml:math id="M97" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E) and in the EASM index for summer during the period
1990–2009 (<inline-formula><mml:math id="M98" display="inline"><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.46</mml:mn></mml:mrow></mml:math></inline-formula>).</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f07.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F8" specific-use="star"><caption><p id="d1e1869"><bold>(a, b)</bold> Simulated differences (CON minus SEN) in the
horizontal distribution of the column dust load (mg m<inline-formula><mml:math id="M99" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> and
<bold>(c, d)</bold> the longitude–height cross section (averaged over
32–36<inline-formula><mml:math id="M100" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N) of the dust mixing ratio (<inline-formula><mml:math id="M101" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>g kg<inline-formula><mml:math id="M102" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> for
summer in heavy <bold>(a, c)</bold> and light dust years <bold>(b, d)</bold>.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f08.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F9" specific-use="star"><caption><p id="d1e1939">Longitudinal cross section of the differences between CON and SEN
averaged over 32–36<inline-formula><mml:math id="M103" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N in summer. <bold>(a, b)</bold> Net radiative
cooling rate (shortwave heating rate plus the longwave cooling rate,
<inline-formula><mml:math id="M104" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C day<inline-formula><mml:math id="M105" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> in heavy and light dust years, respectively.
Panels <bold>(c)</bold> and <bold>(d)</bold> are similar to <bold>(a)</bold> and <bold>(b)</bold> but for atmospheric
temperature (<inline-formula><mml:math id="M106" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C).</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f09.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F10" specific-use="star"><caption><p id="d1e2008">Simulated difference in summer surface air temperature between CON
and SEN in <bold>(a)</bold> heavy and <bold>(b)</bold> light dust years.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f10.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F11" specific-use="star"><caption><p id="d1e2025">Simulated difference in atmospheric circulation at 850 hPa (vector,
m s<inline-formula><mml:math id="M107" display="inline"><mml:mrow><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> and geopotential height at 600 hPa (shaded, m) in summer
between CON and SEN in <bold>(a)</bold> heavy and <bold>(b)</bold> light dust years.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f11.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F12" specific-use="star"><caption><p id="d1e2057">Simulated difference in summer precipitation between CON and SEN in
<bold>(a)</bold> heavy and <bold>(b)</bold> light dust years.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f12.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS4">
  <title>East Asian climate anomalies in heavy/light dust years induced by dust
aerosols</title>
<sec id="Ch1.S3.SS4.SSS1">
  <title>Temperature anomaly</title>
      <p id="d1e2083">Both the atmospheric heating rate and the atmospheric temperature over the
TP decreased in heavy/light dust years (Fig. 9). During the heavy dust
years, the dust aerosol resulted in cooling anomaly centers in the lower
troposphere (600–400 hPa) over TP core, with a cooling of <inline-formula><mml:math id="M108" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 0.5 K day<inline-formula><mml:math id="M109" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>
due to the large dust load. These cooling anomalies resulted in
a low temperature center at 500 hPa over the TP, with its average value
reduced by <inline-formula><mml:math id="M110" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 0.8 <inline-formula><mml:math id="M111" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C. The dust aerosol load over the TP
in the light dust years was much less than in the heavy dust years (Fig. 8a
and c), so the cooling effect in the light dust years was weaker than in
the heavy dust years.</p>
      <p id="d1e2121">The surface temperature over the TP decreased in both the heavy and light
dust years (Fig. 10). In the heavy dust years, the surface temperature
decreased by 0.6 <inline-formula><mml:math id="M112" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C over the TP and consequently the sea–land
thermal contrast was reduced. It is worth noting that the effects of TP
aerosols on surface temperature were not limited to local or surrounding
regions. In fact, the largest impact was in NE China, more than 2000 km away (Fig. 10a). The remote cooling is likely contributable to
a cold air advection stemming from the upstream TP aerosols to be discussed
in the next subsection (Fig. 11). Similar phenomena were reported earlier
in Europe for anthropogenic aerosols (Zanis, 2009; Zanis et al., 2012) and
in South Asia for natural aerosols (Das et al., 2015a).</p>
</sec>
<sec id="Ch1.S3.SS4.SSS2">
  <title>Circulation</title>
      <p id="d1e2139">The overall effects of TP aerosols cool the troposphere surrounding the TP
(Fig. 10a), and thus the land–sea thermal contrast was reduced by the dust
aerosols over the TP. The atmospheric circulation anomaly induced by the
dust aerosols emitted over the TP in heavy dust years shows an overall
gigantic anticyclonic circulation centered over the TP with a positive
anomaly (<inline-formula><mml:math id="M113" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 10 m) at geopotential height (Fig. 11a). The
northeasterlies that run against the southwesterly monsoon are especially
strong over the EASM region, which indicates that the EASM was weakened
greatly. The anomaly still existed in the light dust years, but its
intensity was much weaker than in the heavy dust years (Fig. 11b).</p>
</sec>
<sec id="Ch1.S3.SS4.SSS3">
  <title>Precipitation</title>
      <p id="d1e2155">Figure 12 shows the simulated change in summer precipitation in East Asia
induced by dust emitted over the TP in heavy and light dust years. The
precipitation decreased in both the southern and the northern monsoon regions
in summer in the heavy dust years as a result of weakening of the EASM
(Fig. 11), and the reduction in the southern monsoon region is greater than
that in the northern monsoon region. The dust aerosols also reduced
precipitation in the two monsoon regions in the light dust years. This
simulated suppressive effects of the dust aerosols were consistent with
previously reported modeling results (Sun et al., 2012; Guo et al., 2015). In
addition, precipitation in the heavy dust years reduced more than that in the
light dust years in the TP, which may be due to the enhancement of descending
motion induced by the strong cooling effects of dust aerosol over the
TP.<?xmltex \hack{\newpage}?></p>
</sec>
</sec>
</sec>
<sec id="Ch1.S4">
  <title>Discussion</title>
      <p id="d1e2168">Previous research has shown that dust emitted from Asian deserts can weaken
the EASM (Sun et al., 2012; Guo et al., 2015; Li et al., 2016), although the
details of weakening mechanisms are still unclear. It has been suggested by
some authors that the weakening of the EASM is a result of the reduction in
the thermal contrast between the land and the sea induced by dust aerosols
(Guo and Yin, 2015; Li et al., 2016). However, the modeling result of Sun et
al. (2012) showed that the EASM is reduced by the large-scale atmospheric
circulation disturbances (cyclone–anticyclone–cyclone Rossby wave train)
generated by the radiative cooling of dust aerosols. In the work reported
here, we considered the effects of dust aerosols emitted only within the TP
itself on regional climate and found that they can also reduce the EASM
significantly by weakening the heat source (pump) over the TP and thus
reduce the land–sea thermal contrast. The locally generated TP dust can
cause surface cooling far downstream in Bohai Gulf and the China–North Korea
border area through stationary Rossby wave propagation. Our sensitivity
simulations showed there was a negative correlation between the EASM and
dust aerosols emitted from the TP locally.</p>
      <p id="d1e2171">The spring dust aerosols from the TP have a close relationship with EASM.
Although the cause–effect relationship is not immediately clear, the
following processes are proposed as a possible mechanism
based on the results in our simulation (Fig. 13). Firstly, increasing
(decreasing) in dust aerosol over the TP in the heavy (light) dust years in
spring can weaken (enhance) the TP heat source and thus reduce (increase)
precipitation over the TP. Reduction (increase) in precipitation over the TP
can also further enhance (diminish) dust emission over the TP (labeled 1 in
Fig. 13). Secondly, the weakened (enhanced) TP heat source can persist from
spring to summer and shrink (expand) the land–sea thermal contrast and thus
weaken (enhance) the EASM. Therefore, the change of dust over the TP is anti-correlated with the variation of EASM circulation intensity (labeled 2).
Thirdly, weakened (enhanced) monsoon circulation can reduce (increase)
precipitation in East Asia (labeled 3). As a result, the precipitation
variation of the TP presents a positive correlation with that of EASM.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F13"><caption><p id="d1e2176">Schematic diagram showing the relationship of dust aerosols emitted from
the TP in spring with EASM precipitation. See text for details.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f13.png"/>

      </fig>

      <p id="d1e2185">It is worth noting that Sun and Liu (2016) demonstrated that dust emitted
from Taklimakan and Gobi deserts weakens the Asian monsoon through
large-scale atmospheric circulations by 2 m s<inline-formula><mml:math id="M114" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> of wind at 700 hPa.
This magnitude of reduction in the wind seems small compared to the values in
the present study even though the emission source extent in the previous
study is larger. We think both high-altitude source like the TP and
low-altitude sources such as Taklimakan and Gobi deserts can weaken the EASM,
but the mechanism could differ. The dust emitted from low-altitude source
(mainly Taklimakan and Gobi deserts) reduces the EASM mainly by the
large-scale atmospheric disturbances (Rossby wave train), while the dust emitted
from high-altitude source weakens the EASM by the reduction in the TP heating
and in thermal contrast in the middle troposphere between the land and sea.
The column dust load induced by local emissions from the TP in heavy dust
years accounted for 20 %, (CON <inline-formula><mml:math id="M115" display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula> SEN) <inline-formula><mml:math id="M116" display="inline"><mml:mo>/</mml:mo></mml:math></inline-formula> CON, of the total loading
over the TP; its impacts on Asian monsoon and climate seems more important
than the low-altitude sources such as Taklimakan and Gobi deserts in East
Asia. This disproportionately large impact from TP locally emitted dust is
likely due to its higher elevation within TP itself so that the dust-induced
cooling can more effectively weaken the TP from acting as a heat pump for the
Asian monsoon. Further studies on this are certainly warranted.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F14"><caption><p id="d1e2217">Predicted SSA in summer (CON experiment).</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/17/13731/2017/acp-17-13731-2017-f14.png"/>

      </fig>

      <p id="d1e2226">One interesting finding of this study is the net negative radiative heating
rate in the lower troposphere over the TP (Fig. 9). Dust direct radiative
effects on the atmosphere have been reported to be predominantly positive
(warming) over land areas in most previous research (Saeed et al., 2014;
Osborne et al., 2011; Zhang et al., 2013; Banks et al., 2014; Chen et al.,
2013, 2017). However, the research of Wang et al. (2011) reported strong
cooling of dust aerosol in East Asian deserts, and their research
demonstrated that dust storms with the same intensity over the East Asia
deserts and nearby regions may have different or even opposite direct
radiative effects on the Earth–atmosphere system, including its thermodynamic
and dynamic structures, depending on season
and time (of day) of dust storm occurrence. Here we offer following possible
explanations for the lower tropospheric cooling:
<list list-type="custom"><list-item><label>1.</label>
      <p id="d1e2231">Magnitudes and even signs of dust aerosol direct radiative forcing in
solar spectrum on the atmosphere are largely determined by the aerosol
single-scattering albedo (SSA) and to a lesser degree by the albedo of the
underlying surface. The SSA of dust aerosols is determined by size
distribution, morphology and complex refractive index (Moosmüller et
al., 2009). The chemical composition of dust also effects SSA values. For
example, the SSA of fine mineral dust particles is determined by iron
concentration (Moosmüller et al., 2012). Depending on their sources, the
SSA of the dust aerosols can be quite different. Figure 14 shows the spatial
distribution of SSA as calculated in the RegCM4.1. The SSA values are
<inline-formula><mml:math id="M117" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 0.9 over the TP, considerably larger than those over Taklimakan, Gobi and Great Indian deserts, where SSA is about 0.7.
Past studies have shown that the radiative roles of dust aerosol plays is
largely dependent on SSA. A 5 % change in its value can significantly
alter the magnitudes or even sign of SW radiative forcing (Hatzianastassiou
et al., 2004; Solmon et al., 2008; Das et al., 2015b; Papadimas et al.,
2012). Over the TP with SSA <inline-formula><mml:math id="M118" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 0.9, only less than 10 % of
extinct solar radiation is absorbed by the dust aerosol, compared to
20–30% elsewhere, meaning that the TP dust aerosol is only one-third to
one-half the efficiency of those over surrounding sources.</p></list-item></list></p>
      <p id="d1e2248">Why is SSA of the TP dust aerosol smaller than dust elsewhere? For a given
wavelength, SSA depends on dust particle size, among other factors. The dust
particle size spectrum varies among different sources. Those fine dust
particles (0.01–1.0 <inline-formula><mml:math id="M119" display="inline"><mml:mi mathvariant="normal">µ</mml:mi></mml:math></inline-formula>m) over the large part of TP contribute as
much as 70 % of total dust particles mass compared to 50–60 % in
Taklimakan Desert and some other areas (figure not shown).
<list list-type="custom"><list-item><label>2.</label>
      <p id="d1e2260">Unlike SW forcing that occurs only in the daylight hours, the LW cooling
persists day and night. In addition, the TP dust tends to exist during night
because of the nocturnal convergence driven by diurnal cycle of
thermodynamics over the TP (Liu et al., 2009). This would further minimize
the already weakened SW absorption (Fig. S1). The combination of reduced SW
heating and maintaining of LW cooling resulted in a net negative radiative
heating rate in the lowest 200–300 hPa of troposphere shown in Fig. 9.</p></list-item><list-item><label>3.</label>
      <p id="d1e2264">Because of the off-noon emission of dust in the TP, the zenith angle
would be larger than near noontime. The investigation of Quijano et al. (2000)
showed that dust direct radiative effects can become negative under
specific situations like a large zenith angle. In fact many previous
studies showed local noon instantaneous irradiance when zenith angle is
smaller. This potentially relatively larger zenith angle may also contribute
to the net negative radiative heating rate. In addition, many dust
studies are during dust storm events, most notably in daytime at the synoptic scale
(<inline-formula><mml:math id="M120" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> days), while our study is at the climate scale (<inline-formula><mml:math id="M121" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> years). The feedback
processes are more complex as the timescale grows larger. For
example, the heating rate in Fig. 9c and d that looks more extensive than
the net radiative flux reflects the contribution to the cooling from other
processes.</p></list-item></list></p>
      <p id="d1e2281">Although Fig. 9 shows a negative heating rate over the TP within the lowest
200–300 hPa of the atmosphere locally, the absorbed SW by atmospheric
column measured by the flux difference between the top of the atmosphere (TOA) and surface, as normally
done in the literature, is still positive outside TP over major source
regions (Fig. S2b minus Fig. S2a). Thus strictly speaking, by conventional
definitions, the absorbed SW radiation is still mostly positive over the
whole domain, even though the TP dust results in a cooling effect in the
lower troposphere locally (Fig. S2e, f).</p>
      <p id="d1e2284">Finally it is noteworthy that, given the large variability of dust SSA among
different sources in Asia, it is possible that the magnitudes or even signs
of dust direct net radiative forcing on the atmosphere could vary among case
studies and climate simulations over different continents. For example, the
LW forcing of dust at Zhangye, China, was found to be about a factor of 2
larger than that over Saharan measured at Sal Island, Cabo Verde, owing to
differences in the dust absorptive properties (Hansell et al., 2012).</p>
      <p id="d1e2287">It is very beneficial to study the impact of aerosols on climate using a regional climate model
(RCM) instead of a coarse-resolution global climate model (GCM). However, limited-area RCMs naturally cannot fully account for external forcing remote from the
domain of interest although the lateral boundary conditions allow
large-scale features to propagate into the domain. Our domain size
(9600 <inline-formula><mml:math id="M122" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 640 km) is reasonably large enough so that the weather and
climate systems can have adequate spatial extent to develop within the
domain, as attested by reasonable validation of wind pattern, temperature
field and precipitation (Sect. 3.1). Caution should be exercised,
however, as results from regional simulations could be somewhat
domain-size dependent quantitatively although main results should not be
affected. It is worth mentioning that the model's internal
variability could influence the results; thus, we compared the standard
deviation of summer surface temperature and precipitation in CON with the
signal induced by the dust effects (CON minus SEN) during the heavy dust
years. The signal induced by the dust is much greater than the standard
deviations (figures not shown). Therefore, the dust effect reported in our
simulation is significant in the heavy dust years.</p>
      <p id="d1e2298">Only direct radiative effects of dust were included in our model and future
studies should include both direct and indirect effects. The semi-direct
effects (Hansen et al., 1997) are included in this study as part of
atmospheric feedback, but are not explicitly discussed here since they would
be better discussed along with the indirect effects as both involve clouds.
The simulated effects of dust aerosols on climate were highly sensitive to
the physical characteristics of the dust aerosols, such as the SSA (Huang et
al., 2014; Colarco et al., 2014; Das et al., 2015b). Therefore our results
also need to be validated by sensitivity experiments using aerosols with
different properties. A recent study by Tsikerdekis et al. (2017)
demonstrated that simulated dust load and induced radiation change are
sensitive to the dust particle size division in the model, so further
sensitivity experiments using more dust size bins would be worthwhile. In
addition, many other factors can also affect the EASM, including the El
Niño–Southern Oscillation (Zhao et al., 2012; Liu et al., 2015), the
North Atlantic Oscillation (Wu et al., 2009) and heat sources over the TP
(Yanai et al., 2006; Duan et al., 2012). A recent numerical simulation by
Wang et al. (2017) showed that aerosol emissions from outside East Asian
play an important part in weakening the circulation of the EASM.</p>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <title>Conclusions</title>
      <p id="d1e2307">We conducted two numerical experiments to quantify the effects of dust
aerosols emitted over the TP on the EASM in heavy/light dust years using a
high-resolution RCM. Satellite and in situ observations were
used to evaluate the simulated spatial distribution of dust aerosols and
their seasonal and interannual variations. We analyzed the change in dust
aerosols induced by emissions over the TP and their radiative effects on the
EASM and summer precipitation in heavy/light dust years.</p>
      <p id="d1e2310">The spatiotemporal distribution of the dust AOD and their seasonal and
interannual variation were captured well by the RegCM4.1 model compared with
the MISR AOD and in situ observations from AERONET. Both the simulated and observed
AOD were higher in spring/summer and lower in autumn/winter. The simulated
dust AOD was higher in the Taklimakan, Gobi and Great
Indian deserts, with peak values of <inline-formula><mml:math id="M123" display="inline"><mml:mo>&gt;</mml:mo></mml:math></inline-formula> 0.6. The simulated dust AOD in
the Qaidam Basin and the northern TP was also higher. The seasonal
variation in the dust AOD at Nam Co was captured well by RegCM4.1 compared
to the observed aerosol AOD.</p>
      <p id="d1e2320">Comparative analyses of the two simulations indicated that the dust aerosols
generated over the TP had a profound influence on the EASM. The differences
in the EASM index and column dust load between CON and SEN experiments are
negatively correlated (<inline-formula><mml:math id="M124" display="inline"><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.46</mml:mn></mml:mrow></mml:math></inline-formula>). The index also weakened (strengthened) as
the combined imported–local dust aerosol increased (decreased) over the TP.
The net atmospheric heating rate was negative over the TP in heavy dust
years as a result of the radiative cooling effects of the dust aerosols,
leading to a 0.6 <inline-formula><mml:math id="M125" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C cooling in the surface and atmospheric
temperatures. The land–sea thermal contrast and EASM were therefore both
weakened, causing a 27 % reduction in precipitation in the southern
monsoon region. The dust load over the TP in the light dust years was much
less than in the heavy dust years, implying large interannual variability.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability">

      <p id="d1e2350">Model outputs are stored in the Galactic GT8000 super-blade
computing system at the Institute of Earth Environment, Chinese Academy of
Sciences, and they are available upon request from Hui Sun
(sunhui@ieecas.cn). The public data, including CRU, NCEP-DOE, MISR, CALIPSO
and AERONET data used in this study, can be obtained from
<uri>https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.23/cruts.1506241137.v3.23/</uri>,
<uri>https://www.esrl.noaa.gov/</uri>, <uri>http://www-misr.jpl.nasa.gov/</uri>,
<uri>https://eosweb.larc.nasa.gov/</uri> and <uri>https://aeronet.gsfc.nasa.gov/</uri>,
respectively.</p>
  </notes><notes notes-type="competinginterests">

      <p id="d1e2371">The authors declare that they have no conflict of
interest.</p>
  </notes><?xmltex \hack{\newpage}?><app-group>
        <supplementary-material position="anchor"><p id="d1e2375"><bold>The Supplement related to this article is available online at <inline-supplementary-material xlink:href="https://doi.org/10.5194/acp-17-13731-2017-supplement" xlink:title="pdf">https://doi.org/10.5194/acp-17-13731-2017-supplement</inline-supplementary-material>.</bold></p></supplementary-material>
        </app-group><ack><title>Acknowledgements</title><p id="d1e2381">The authors thank the two anonymous reviewers for valuable comments and
suggestions. This research was jointly supported by the National Key
Research and Development Program of China (2016YFA0601904) and the National
Natural Science Foundation of China (41405093, 41572150 and 41475085).<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>
Edited by: Nikos Hatzianastassiou<?xmltex \hack{\newline}?>
Reviewed by: two anonymous referees</p></ack><ref-list>
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    <!--<article-title-html>Direct radiative effects of dust aerosols emitted from the Tibetan Plateau on the East Asian summer monsoon – a regional climate model simulation</article-title-html>
<abstract-html><p class="p">While dust aerosols emitted from major Asian sources such
as Taklimakan and Gobi deserts have been shown to have strong effect on
Asian monsoon and climate, the role of dust emitted from Tibetan Plateau
(TP) itself, where aerosols can directly interact with the TP <q>heat pump</q>
because of their physical proximity both in location and elevation, has not
been examined. This study uses the dust-coupled RegCM4.1 regional climate
model (RCM) to simulate the spatiotemporal distribution of dust aerosols
originating within the TP and their radiative effects on the East Asian
summer monsoon (EASM) during both heavy and light dust years. Two 20-year
simulations with and without the dust emission from TP showed that direct
radiative cooling in the mid-troposphere induced by the TP locally produced
dust aerosols resulted in an overall anticyclonic circulation anomaly in the
low troposphere centered over the TP region. The northeasterly anomaly in
the EASM region reduces its strength considerably. The simulations found a
significant negative correlation between the TP column dust load produced by
local emissions and the corresponding anomaly in the EASM index
(<i>r</i> = −0.46). The locally generated TP dust can cause surface cooling far
downstream in Bohai Gulf and the China–North Korea border area through
stationary Rossby wave propagation. Although dust from within TP (mainly
Qaidam Basin) is a relatively small portion of total Asian aerosols, its
impacts on Asian monsoon and climate seems disproportionately large, likely
owning to its higher elevation within TP itself.</p></abstract-html>
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