1. Black Sunday
On the 14th day of April of 1935,
There struck the worst of dust storms
that ever filled the sky.
You could see that dust storm comin', the
cloud looked deathlike black,
………..
From Oklahoma City to the Arizona line,
Dakota and Nebraska to the lazy Rio
Grande,
It fell across our city like a curtain of
black rolled down,
We thought it was our judgement, we
thought it was our doom.
http://en.wikipedia.org/wiki/Dust_Bowl
http://altereddimensions.net/20
12/the-dust-bowl-black-sunday

2. Improving dust emission characterization in
climate models
MODIS image on 03/19/2012
(Courtesy of NASA)
MODIS image on 03/18/2012 (Courtesy of NASA)
- Sagar Parajuli
11/21/2013

3. 3
Outline
1. Introduction
2. Proposed research
3. Methods
4. Preliminary results
5. Expected results and significance
6. Broader Impact
7. References

4. 4
Outline
1. Introduction
2. Proposed research
3. Methods
4. Preliminary results
5. Expected results and significance
6. Broader Impact
7. Research timeline
8. References

5. 5
Introduction
1Sokolik
and Toon 1996
1. Introduction
2Rosenfeld
et al., 2001
3Kellogg
and Griffin 2006
1.1 Dust modeling
4Koren,
et al., 2006

6. 6
Introduction
•
Dust affects the earth radiation budget 1
by scattering and absorbing shortwave
and longwave radiation.
•
Dust modifies cloud microphysical properties by forming Ice Nuclei and
Cloud Condensation Nuclei 2.
•
Dust storms affect daily life activities and hinders air/ground traffic
operation.
•
Dust storms degrade air quality and transmit human/plant diseases 3.
•
Mineral dust acts as a fertilizer (P, Fe) for terrestrial and marine
ecosystem 4.
•
Dust deposition affects efficiency of solar panels.
1Sokolik
and Toon 1996
1. Introduction
2Rosenfeld
et al., 2001
1.1 Key definitions
3Kellogg
and Griffin 2006
1.2 Dust modeling
4Koren,
et al., 2006

7. Key definitions
7
•
1. Introduction
1.1 Key definitions
1.2 Dust modeling

8. 8
Dust Emission modeling
•
Dust emission is initiated when the wind speed exceeds a
dynamic threshold known as threshold friction speed. 1
•
Major inputs required by the dust models are surface wind
speed, soil types, soil moisture, roughness length, bare soil
fraction etc.
•
Dust modeling is challenging because dust emission is affected
both by geomorphic processes and atmospheric phenomena.
1Bagnold
1. Introduction
1941
1.1 Key definitions
1.1 Dust modeling

9. 9
Dust emission modeling
F = Vertical dust mass flux
DEAD 1 (Saltation model)
GOCART 2 (non-saltation model)
1
Dust Entrainment and Deposition Model (Zender et al. 2003)
2 Global Ozone Chemistry Aerosol Radiation and Transport (Ginoux et al. 2001)
1. Introduction
1.1 Dust modeling

10. 10
Outline
1. Introduction
2. Proposed research
3. Methods
4. Preliminary results
5. Expected results and significance
6. Broader Impact
7. Research timeline
8. References

11. 11
Problem statement
1.
There is the general unavailability of accurate and high
resolution surface input data (mainly clay content,
bare-soil fraction and soil moisture).
% clay content map1 used in CLM2
1Post
and Zobler 2000
2Community land model
2. Proposed Research
2.1 Problem Statement
2.2 Research Questions
2.3 Hypotheses

12. 12
Problem Statement
2.
Static erodibility factor is used to constrain the simulated dust
emission, but the factors determining the erodibility are not
well understood.
ERA-Interim wind
NCEP wind
= 13596
= 2881
9643
1603
Dust
emission
simulated by CLM
under
different
reanalysis
wind
forcing (2003).
Annual
dust (Tg)
2. Proposed Research
2.1 Problem Statement
1Ginoux
et al. 2001
2.2 Research Questions
2.3 Hypotheses

13. 13
Problem Statement
3.
Bulk parameterizations derived from controlled wind tunnel
experiments are used for calculating saltating flux and vertical
dust mass flux, which do not adequately represent the range of
soil types, geomorphology and environment found in dust
sources regions.
(Marticorena and Bergamatti 1995)
2. Proposed Research
2.1 Problem Statement
(Gillette 1978)
2.2 Research Questions
2.3 Hypotheses

14. 14
Research Questions
1.
What details of geomorphology are important for
characterizing the dust sources and how can these be best
represented in the dust models?
2.
What factors determine the soil erodibility and how can
we better quantify this parameter to represent the spatial
and temporal dynamics of dust sources?
3.
Why different parameterizations derived from wind tunnel
experiments yield varying vertical dust mass flux and how
can these be adapted to represent the wind-dust
relationship for different geomorphic types?
2. Proposed Research
2.1 Problem Statement
2.2 Research Questions
2.3 Hypotheses

15. 15
Hypotheses
1.
Threshold friction speed depends upon geomorphic types.
Thus, representing the heterogeneity of landforms in sub-grid
scale by using a geomorphic map can improve the vertical
dust mass flux.
2.
Erodibility has both spatial and temporal variability. The
correlation between observed wind speed and aerosol optical
depth (AOD) can be used as a proxy for representing the
dynamics of erodibility.
2. Proposed Research
2.1 Problem Statement
2.2 Research Questions
2.3 Hypotheses

16. 16
Hypotheses
3.
Difference in vertical mass flux observed in different wind
tunnel experiments is because of the variation in sampling
techniques. Therefore, location-independent dust
measurement can provide more accurate relationship
between wind and dust. These parameterizations can be
adapted to different geomorphic types by constraining
against wind-dust relationship near geomorphic types.
2. Proposed Research
2.1 Problem Statement
2.2 Research Questions
2.3 Hypotheses

17. 17
Outline
1. Introduction
2. Proposed research
3. Methods
4. Preliminary results
5. Expected results and significance
6. Broader Impact
7. Research timeline
8. References

18. The Study Area
•
The Middle East and
North Africa (MENA),
commonly known as the
dust belt, as it contains
more than 50% of global
mineral dust sources 1.
•
To be expanded to
global scale ultimately.
Mean aerosol optical depth (AOD)2
Bodélé, Chad
1Shao
2http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=aerosol_daily
2008
3. Methods 3.1 Study area
3.2 Datasets 3.3 Geomorphic map 3.4 Erodibility 3.5 Paramaterization

19. 19
Datasets
Datasets
Hi-resolution images of Google
Earth Pro and EsriBasemap
MODIS Aqua level 3 aerosol
product (MYD08_D3)
MODIS Terra level 3 aerosol
product (MOD08_D3)
MODIS Aqua level 2 aerosol
product (MYD04_L2)
MODIS Terra level 2 aerosol
product (MOD04_L2)
AERONET aerosol data products
Resolution
Geomorphic
features
Aerosol
properties
Station
(15 min)
ERA-Interim reanalysis wind
NCEP reanalysis 1 wind
3. Methods
3.1 Study area
Wind speed
3.2 Datasets 3.3 Geomorphic map 3.4 Erodibility 3.5 Paramaterization

20. Geomorphic mapping
Visually examine high resolution
images and identify the type of
landforms.
Create polygons of unique
landforms. Verify against
secondary data and maps.
Group the polygons into several
geomorphic types and create
gridded geomorphic map.
3. Methods 3.1 Study area
3.2 Datasets 3.3 Geomorphic map
3.4 Erodibility 3.5 Paramaterization

21. 21
Quantification of erodibility
Identify the accurate surface wind and AOD data
either from ground based observations or from
satellite data.
Calculate correlation between surface wind and
AOD near known geomorphic types.
Quantify the mean erodibility of a geomorphic
type by removing the effect of the environment.
3. Methods
3.1 Study area 3.2 Datasets 3.3 Geomorphic map
3.4 Erodibility 3.5 Paramaterization

22. Parameterization improvement
Measure the vertical dust mass
emitted directly from the substrate
bed in the wind tunnel.
Establish wind-dust relationship.
Tune with observed wind-dust
relationship near geomorphic types.
3. Methods 3.1 Study area 3.2 Datasets 3.3 Geomorphic map 3.4 Erodibility
3.5 Paramaterization

23. 23
Outline
1. Introduction
2. Proposed research
3. Methods
4. Preliminary results
5. Expected results and significance
6. Broader Impact
7. Research timeline
8. References
4. Preliminary Results
4.1 Geomorphic map
4.2 Erodibility

24. 24
Geomorphic mapping
4. Preliminary Results
4.1 Geomorphic map
4.2 Erodibility

25. Erodibility
Low wind but high AOD:
Pollution?
Transported dust?
Strong saltation?
AOD Vs. ERA-Interim wind at Bodélé
5
AOD = 0.047*WIND2 - 0.3*WIND + 1.2
R2 = 0.48
4
Deep Blue AOD550nm
Deep Blue AOD550nm
5
3
2
1
0
0
2
4
6
8
10
1000 hPa ERA Wind (ms-1)
4. Preliminary Results
12
4.1 Geomorphic map
14
High wind but low AOD:
Supply-limited case?
High soil moisture?
Absence of saltation?
25
AOD Vs. NCEP wind at Bodélé
AOD = 0.032*WIND2 - 0.095*WIND + 1
R2 = 0.19
4
3
2
1
0
0
2
4
6
8
10
12
-1
1000 hPa NCEP Wind (ms )
4.2 Erodibility
14

26. 26
Erodibility
Developed erodibility map
1Ginoux
et al. 2001.
4. Preliminary Results
Topographic erodibility1
Proposed method emphasizes agricultural dust sources and
eliminates fictitious sources involving long-range transport,
pollution and biomass burning.
4.1 Geomorphic map
4.2 Erodibility

27. 27
Erodibility
•
Erodibility is highly dynamic
spatially and temporally.
•
Dust source intensity and
distribution is maximum in
the winter and spring.
•
Dust sources less active during
summer and fall (effect of west
African monsoon).
Correlation between annual
cycles of ERA-Interim wind
and MODIS AOD (2003-2012).
4. Preliminary Results
4.1 Geomorphic map
4.2 Erodibility

28. Erodibility of geomorphic types
Geomorphic
types
Bedrock,
with sediments
Sand deposit
Sand deposit, on
bedrock
Sand deposits,
stabilized
Land Use
(Agriculture)
Fluvial system
28
Derived
location
24N, 8E
Jan
Feb Mar Apr May Jun July Aug Sep Oct Nov Dec
0.26
0.15 0.12 0.10 0.10
24.4N, 13E
18.4N, 26E
0.22
0.24
0.1 0.11 0.08 0.21 0.01 0
0 0.14 0.19 0.09 0.14
0.24 0.24 0.24 0.30 0.25 0.12 0.15 0.13 0.33 0.15 0.10
25.4N, 49E
0.22
0.33 0.25 0.20 0.22 0.11 0.36 0.04
17.3N, 2W
0.51
0.35 0.17 0.01 0.2 0.02 0.25 0.16 0.04 0.03 0.04 0.28
21.4N, 15W
0.42
Stony surface
19.4N, 57E
0
Playa/Sabkha
16.4N, 17E
0.71
0
0.1
0
0.12
0.07 0.12
0
0
0
0
0
0
0
0
0
0
0
0.19 0.06 0.22
0.1 0.10 0.17
0.12 0.13 0.34
0.21 0.11 0.23 0.11 0.08 0.08 0.12
0.67 0.54 0.42 0.34 0.28 0.30 0.17 0.28 0.32 0.60 0.65
Erodibility is highest to lowest for playa, land use, fluvial system,
sand Deposits: stabilized, sand deposits: on bedrock, sand deposits:
with sediments and sand deposits in order.
4. Preliminary Results
4.1 Geomorphic map
4.2 Erodibility

29. 29
Expected results and significance
•
The proposed work will more accurately map the intensity and
distribution of mineral dust sources around the world.
•
Inclusion of the proposed changes in the dust model should
improve the simulated vertical dust mass flux that should
better match with the remote sensing and ground-based
observations.
•
Improved dust source characterization will ultimately help to
reduce the large uncertainty in the sign and magnitude of
radiative forcing of dust aerosols that exists presently (IPCC,
2007).
5. Expected results and significance
6. Broader Impact
7. References

30. 30
Broader Impact
•
Since dust storms are common phenomena in many parts of the
world, this work is directly relevant to the public health and safety.
•
Proposed changes will be implemented in the community land
model (CLM), which enhances community participation in
scientific research.
•
Proposed work will also aid in developing dust storm monitoring
and forecasting tools.
•
Proposed work is an interdisciplinary research and seeks
collaboration among:
5. Expected results and significance 6. Broader Impact
7. References

31. 31
References
Bullard, J. E., Harrison, S. P., Baddock, M. C., Drake, N., Gill, T. E., McTainsh, G., & Sun, Y. (2011).
Preferential dust sources: a geomorphological classification designed for use in global dustcycle models. Journal of Geophysical Research, 116(F4), F04034.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., … Vitart, F. (2011).
The ERA-Interim reanalysis: configuration and performance of the data assimilation system.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., & Lin, S. J. (2001). Sources and
distributions of dust aerosols simulated with the GOCART model. Journal of Geophysical Research,
106(D17), 20255–20.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., … others. (1996). The
NCEP/NCAR 40-year reanalysis project. Bulletin of the American meteorological Society, 77(3),
437–471.
Oleson, K. W., Lawrence, D. M., Gordon, B., Flanner, M. G., Kluzek, E., Peter, J., … others. (2010).
Technical description of version 4.0 of the Community Land Model (CLM). Retrieved from
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.7769
Prospero, J. M., Ginoux, P., Torres, O., Nicholson, S. E., & Gill, T. E. (2002). Environmental
characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total
Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev. Geophys, 40(1), 1002.
Zender, C. S., Bian, H., & Newman, D. (2003). Mineral Dust Entrainment and Deposition (DEAD)
model: Description and 1990s dust climatology. J. Geophys. Res, 108(D14), 4416.
5. Expected results and significance
6. Broader Impact
7. References

32. 32
Thank you!