Section 3: Dust Source Regions & Section 4: Dust Climatology
Subsection 1: Identifying Source Regions
Page 1: Dust-Prone Regions from Land Cover Types
Page 2: Source Regions from Satellite Data
Page 3: Examples
Page 4: Dust Source Information from NRL
Page 5: Tracking Dust Plume Speed & Direction
Page 6: Dust Source Information From Models
Subsection 2: Role of Soil Type, Topography, & Climate
Page 1: Soils
Page 2: Topography
Page 3: Climate
Page 4: Dust Source Climatology
Page 5: Interannual Variations in Dust Source Regions
SUBSECTION 1: IDENTIFYING SOURCE REGIONS
Page 1: Dust-Prone Regions from Land Cover Types
Dust storms can only form if there’s an appropriate source region. In this section, we will look at where
these regions are located and the key factors that make them prone to dust storms. These include soil
type, topography, and climate.
Eight land covers are thought to produce dust: low sparse grassland in Mongolia; bare desert
equatorward of 60° latitude; sand desert; semi-desert shrubs equatorward of 60° latitude; semi-desert
sage; polar and alpine desert; salt playas/sabkhas; and sparse dunes and ridges.
When these land cover types are combined with wetness values, we get a bulk measure of erodibility.
The figures show how the world's deserts dominate the resulting pattern.
Page 2: Source Regions from Satellite Data
Identifying dust-prone regions based on land cover characteristics can be refined by incorporating
satellite data. [Olga: We go on to talk about satellite retrievals but are they the only type of satellite
products used for identifying source regions? What about DEP? If there’s more than just retrievals, list
them and then discuss satellite retrievals [followed by DEP…]. Also, we should have a little section on
models that identify dust source regions.
Satellite retrievals make it possible to monitor the total dust loading over regions where other types of
observations are scarce or non-existent. Olga: Very briefly define satellite retrievals, noting the
instruments involved and mentioning our examples (TOMS and TOMS AI, MODIS Deep Blue, and
the Multiangle Imaging Spectroradiometer (MISR) aerosol retrieval capability). Add some non-US
examples if appropriate.
HERE’S THE REST OF THE INFO ON RETRIEVALS: Total Ozone Mapping Spectrometer
(TOMS) UV absorbing aerosol products, particularly the Aerosol Index (TOMS AI), are used for
mapping dust sources [Prospero et al., 2002] and monitoring dust inter-annual variability
[Chiapello et al., 2005].
TOMS/OMI retrievals are supplemented and enhanced by other satellite-based techniques, such
as MODIS “Deep Blue” retrievals, which study total dust loading over deserts, and the Multiangle
Imaging Spectroradiometer (MISR) aerosol retrieval capability, which is sensitive to near-surface
aerosols and surface properties.
Check against original: The Multiangle Imaging Spectroradiometer (MISR) aerosol
retrieval capability over bright surfaces—and its sensitivity to near-surface aerosols and
surface properties—supplements and enhances TOMS/OMI and MODIS retrievals over
deserts [Martonchik et al., 2004].
Before we look at examples of satellite retrievals, note that UV remote sensing is not able to characterize
all of the large areas affected by dust around the world. For example, the complex topography of Northern
China and the presence of air pollution, especially UV-absorbing carbonaceous aerosols, limit the
application of UV retrieval techniques over much of Asia. [Olga: Does this note apply to all satellite dust
detection or only one type?]
Page 3: Examples
This TOMS plot bases the dust productivity of the earth surface on the observed frequency of high
aerosol values and results in a much more refined view of global dust source regions than the land cover
type database seen previously. Clearly, the majority of the world’s dust storms arise in relatively few
areas, in particular, the Sahara, Middle East, Southwest Asia, China, Mongolia, and Southwestern North
America. Using TOMS AI to identify dust source regions, Prospero et. al., 2001 hypothesized that dust
sources are associated with topographical lows and depressions. [is the last sentence worth keeping?]
[I’m not sure we need this paragraph, especially if there’s not an accompanying graphic that
shows interesting information.] Koven and Fang, 2008 examined surface characteristics from the highresolution (90 m at the equator) global Shuttle Radar Topography Mission digital elevation model and
aerosol optical depth (AOD) measurements from the MISR instrument to find landscape-scale
characteristics common to dust producing regions.
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A remarkable combination of geography and climate makes the Bodélé depression in northern Chad, the
world's dustiest spot. Downward mixing of persistent nocturnal low-level jet winds in the early to midmorning generates strong dust outbreaks observed by many satellite instruments. The MISR instrument
on NASA’s Terra satellite passes over the Bodélé around 10:30 AM local time, near the time of peak
activity. The plot shows the global 10-year mean MISR aerosol optical depth (AOD) of all aerosols,
including dust, pollution, and smoke. Notice how the Saharan dust sources, particularly the Bodélé
depression, dominate AOD. Other important dust source regions are evident in the Taklamakan Desert in
western China and the deserts of the Middle East. Mixed dust pollution aerosols are evident in South
Asia.
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Many great deserts and much semi-arid land lie in East Asia, where dust outbreaks are very common and
especially severe in spring. Asian dust storms have been documented for thousands of years. Since dust
storm frequency and severity affect soil moisture content, air and surface temperatures, rainfall, and
downwind air quality, these records have significance for climate and are being used to determine past
and current climate change. The figure shows MODIS Deep Blue monthly-mean AOD for April 2001, the
strongest period of dust activity in Asia from 2000 to 2010. Notice how the Taklamakan desert stands out
in the satellite observations.
Page 4: Dust Source Information from NRL
The U.S. Naval Research Lab (NRL) identifies dust emission areas in Southwest Asia using its satellitederived Dust Enhancement Product (DEP). DEP’s 1-km resolution allows for the identification of individual
plume heads that often measure 10 km or less across.
The MODIS true color image and NRL DEP image show southern Afghanistan, northwestern Pakistan,
and eastern Iran on 20 August 2003. By comparing the images, we see the benefit of DEP for identifying
small dust plumes. The one in the white rectangle is barely visible in the true color image while it is readily
apparent in the dust enhancement product in shades of pink.
NEXT PART OF PAGE [do we need this here? If it’s covered in the satellite section, remove!]
Left: MODIS Terra (DEP) 0820 UTC 15 Nov 2009
[ADD BOXES TO EACH AREA!!!!]
Right: MODIS Aqua (DEP) 1130 UTC 15 Nov 2009
DEP products derived from two MODIS instruments on Terra (morning overpass) and Aqua (afternoon
overpass) satellites let us track the temporal evolution of dust plumes and see the constraints of model
forecasts.
[MARIANNE ONLY: Make the point about individual point sources merging to form a larger plume – see
question in other module]
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NRL maintains a high-resolution (1-km) Dust Source Database (DSD) based on the individual point
sources identified in its dust enhancement products.
Explain the relation between this plot and the DSD!!!
Here we see 1-km dust sources plotted in red for the 10°X10° tile covering Iraq. Each red area identifies
land that has eroded and produced a dust plume.
This plot shows the NRL 1-km dust sources averaged on an 18-km grid where the grid erodible fraction
varies from 0 (non-erodible or non-dust producing) to 1.0 (completely erodible and dust producing). Note
the many dust-prone areas in eastern portions of the Arabian Peninsula and the spotty source regions in
Iran and Afghanistan.
NEXT PART OF PAGE Olga: Are these DEP images? If we use these images, describe the model
overlays and graphs. What are they showing?
This example shows a dust outbreak in Afghanistan on 15 November 2009.
Page 5: Tracking Dust Plume Speed & Direction
Olga:
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Does the following info belong here?
What are we using it to teach about source regions?
Is the data readily available to forecasters around the world?
Where are the graphs from? I need to know who owns them so I can get copyright permission to
use them.
The multi-angle viewing geometry of the MISR instrument [should we say which satellite it’s on?] lets us
track the wind [need ‘wind’?] speed and direction of dust plumes and measure the top of their heights.
This MISR plot in southern Afghanistan on 15 November 2009 demonstrates strong differences between
dust plume dynamics in different dust sources. [Consider asking a question about the data]
Page 6: Dust Source Information from Models
Dust models and models tuned for dust [is that OK?] also provide useful information about dust source
regions. Here we see dust source areas identified by the U.S. Air Force Weather Dust Transport
Application (DTA) model. The oranges and reds indicate strong dust source areas.
SUBSECTION 2: SOIL, TOPOGRAPHY, & CLIMATE
Page 1: Soils
Having identified the world’s major dust source regions, let’s consider the types of soils that actually
generate dust storms. This may seem counterintuitive, but even in bare desert, silt- and clay-rich finegrained soils are responsible for most dust storms, not sandy areas. These fine-grained soils are found in
areas with dry lake beds and river flood plain deposits.
The minerals found in arid soils are transported during dust storms and can impact:
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Climate (for example, atmospheric radiation and cloud generation)
Environment (for example, the nutrients in the marine environment)
Health (for example, asthma, heart problems, and meningitis outbreaks)
See Nickovic et al., 2010 - see http://www.seevccc.rs/?p=623]
CHECK AGAINST ORIGINAL: Fraction of different minerals in dust aerosols generated by storms
are very much dependent on mineral composition of arid soils. The information on source
composition is important for constraining atmospheric dust models and predicting dust environmental
effects.
These global datasets show the mineral content of clay and silt. Notice [add things for them to notice
and/or mention a few environmental impacts of the different minerals!!!!!!]
[ORIGINAL: These global datasets show the effective mineral content of clay and silt populations
masked with referential sources. GMINER30]
The Mineral Content of Clay Marianne: Deal with CR]
The Mineral Content of Silt
Page 2: Topography
Soil type is not the only important factor in determining which areas are more prone to dust storms than
others; topography and climate play a role as well. We’ll explore them using the Middle East as an
example.
The map shows areas in the Middle East rich in silt and clay soils. Those in Iran and Iraq are responsible
for most dust storms in the region [OR: in the northern Persian Gulf??], as this second map corroborates.
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The low-lying regions of the eastern Arabian Peninsula, southern Syria, and western Iraq are particularly
prone to dust storm generation because prevailing west/northwesterly winds are unimpeded by higher
terrain.
Page 3: Climate
koppen_dry.jpg [modified from intro to climo]
The climate in these areas also makes them susceptible to dust storm generation. By climate, we mean
their precipitation patterns, prevailing wind direction and speed, and normal location of low- and highpressure centers.
annual_cond_prod.gif
Page 4: Dust Source Climatology
If you’re forecasting for a new region, you can build a dust storm climatology from archived satellite
imagery to establish the most prevalent source areas. This is similar to the TOMS Aerosol Index
climatology discussed earlier except that it can be much more precise.
For example, this sequence of images reveals that the same light-colored areas in western Afghanistan
repeatedly serve as the source for dust storms.
Once you know the color characteristics of source areas in a given region, you should look for other
potential areas with a similar appearance.
Page 5: Interannual Variations in Dust Source Regions
Periods of extended drought dry out lakes, wetlands, and otherwise productive agricultural land, often
resulting in new and expanded dust sources. The opposite occurs with wet winters, when numerous
storms, heavy rains, and/or above-average snowfall can flood lakes, rivers, and streams and shut off
active dust sources.
For example, Southwest Asia experienced an extended drought from 1998 to 2005. Then in 2005, heavy
rain and melting snow led to numerous floods in southern Afghanistan.
This MODIS true color image shows the Sistan Basin, one of the world’s driest basins, as it was on 21
February 2005 before it experienced heavy rains and snow melt.
The false color image from 7 March 2005 shows how much the basin changed. The dark blue indicates
clear, deep water, the light blue mud-laden water.
The oval in this MODIS true color image from 12 October 2005 shows Lake Saberi. Notice that it is still
filled with muddy, brown water after the long, hot summer.
When the Hamoun Lakes and wetlands are filled with water, the production of dust plumes and storms
decreases.
These NRL DEP images of Pakistan and Afghanistan on 2 May 2003 and 12 October 2005 demonstrate
the difference between a drought-ravaged basin and one that has experienced a wet period.
SECTION: CLIMATOLOGY
Page 1: Dust Storm Seasonality and Frequency
Climatologies tell us what happened in the past, which helps forecasters anticipate future events and
improve their forecasting. Climatology provides several types of data that help with forecasting the
location, seasonality, frequency, and severity of dust storms.
We’ve seen how data from the TOMS Aerosol Index helps us map dust source regions. That same data
can help us determine seasonal variations in dust storms.
This animation shows the seasonal variability of dust storms in the dust belt that stretches from western
Africa up through the Taklamakan Desert in central Asia. Note the strong seasonal dependence of dust
storm frequency. For example, dust storms in this desert show a pronounced peak in May while the
maximum values for West African dust storms shift northward from winter to summer.
Page 2: Dust Storm Frequency and Severity
Climatologies compiled by the Air Force Weather Agency Metsat Applications Branch show the monthly
frequency of dust storms. Note how the number of storms in the Gobi Desert spikes in March and April
and tapers off from May through July.
When we categorize the dust storms by visibility, the picture becomes clearer. Not only is the highest
frequency in the early spring, but the majority of severe dust storms occurs in March and April, more than
the rest of the year combined.
This graph of dust storm climatology for Iraq reveals some important information. Dust storms tend to be
most frequent in the summer, although severe storms can occur from spring through autumn.
Page 3: Dust Storm Frequency and Precipitation
If you are forecasting in a region and don't have access to information on the frequency of dust storms,
you may be able to infer a climatology by examining other climatologic data such as the frequency of dust
events vs. annual precipitation rates (PR).
Obviously, drier, hotter conditions favor more dust storms. Here we see a minimum for precipitation
events and a maximum for temperature in central Iraq through the summer months, the dustiest time of
the year.