dust emission modeling
DESCRIPTION
Dust modeling using community land model.TRANSCRIPT
Evaluation of CLM4.0 in simulating natural dust storms
- Sagar Prasad Parajuli December 03, 2012
Dust storm approaching Stratford, Texas on April 18, 1935
http://www.photolib.noaa.gov/htmls/theb1365.htm
Dust storms
• Hazardous air quality
• Net cooling effect on climate by reflecting/scattering1
• Transport of human diseases, plant nutrition, and crop diseases2
• Corrosion of historical buildings and monuments3
• Affect solar cell efficiency
1(Sokolik and Toon 1996). 2(Kellogg & Griffin, 2006) 3(Varotsos et al. 2009)
Hypotheses: CLM4.0 can effectively capture
natural dust storms
Research Questions:
• Can CLM4.0 simulate natural dust storms?
• How well CLM4.0 simulated result compare with ground-based and remote-sensing
measurements?
Dust emission
(Zender, Bian, & Newman, 2003)
(Source: NASA)
Study Area AERONET station
Data sets and period
Dataset Temporal res. Spatial res.
CLM output Daily and monthly (2003)
0.9ο ×1.25ο
AERONET AOT at 500nm
15min (2003) Station
Level 3 MODIS AOD at 550nm
Daily and monthly
1ο ×1ο
Methodology
Compare
Seasonality, spatial extent, daily and monthly variation
Process Data
AERONET AOT: 2003 daily mean extract daily mean MODIS AOD
Simulate
1. Extract CLM4.0 output for one pixel 2. Calculate global emission
Results
Temporal variations (2003)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
0.5
1
1.5
AO
T a
t 50
0nm
Mean daily AERONET AOT at 500nm
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0
2
4
6
8
10x 10
4
Du
st f
lux
(to
ns/
da
y)
CLM4 simulated mean daily dust flux
Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec1
2
3
4
5
WS
at
10
m
Mean daily WS at 10m
Mean monthly comparison
J F M A M J J A S O N D-1
0
1
2
3
4
5x 10
4
Mea
n m
onth
ly d
ust f
lux
(tons
/mon
th)
Variation of mean monthly dust flux in 2003 (error bar = 1SD)
J F M A M J J A S O N D-0.2
0
0.2
0.4
0.6
0.8
1
1.2
AO
T at
500
nm
Variation of mean monthly AOT at 500 nm in 2003 (error bar = 1SD)
Scatter plots of mass concentration (g/m2)
0 1 2 3 4 5 6 70
0.5
1
1.5
CLM4.0 simulated
AE
RO
NE
T e
stim
ated
Scatter plot of daily mean mass concentration for 2003 (g/m2)
Underestimate
Overestimate
Well prediction
Wind speed and dust emission
1 1.5 2 2.5 3 3.5 4 4.5 5-2
0
2
4
6
8
10
12x 10
4
Daily mean wind speed at 10m
Mea
n d
ust
flu
x (
ton
s/d
ay
)
Correlation between wind speed (10m) and simulated dust emission
y = 1.4e+003*x3 - 2.6e+003*x
2 - 2.3e+003*x + 4.2e+003
F = α Un
Global dust simulation
Comparison of global simulation Description This work
(Untuned)
(Mahowald et al.,
2006)
DEAD (Zender
et al., 2003)
GOCART(Ginoux et
al., 2001)
Annual emission 18448.7 4483 1490 1814
Annual deposition 2022.16 4493 (2990 + 1503 =
wet + dry)
- 1841
Model used CLM4.0 with
atmospheric forcing
data
CCSM with CAM,
CLM3 and SOM (slab
ocean model)
DEAD
GOCART
Particle size bin (µm) 0.1-10 0.1-10 (largest mode
= 5-10 (67%)
0.1-10 (largest
mode = 5-10
(38%)
0.1-6 (largest mode –
3-6 (33%)
Simulation period 2003 1983-2000 1990-1999 1987-1990 and 1996
Case study
• Maximum AOT measured at Solar Village, Saudi Arabia
• Maximum AOT = 2.21
• June 25, 2003
Saudi Arabia
dust storm
MSG Channels IR8.7 - IR10.8, 25 June 2003, 10:00 UTC
http://oiswww.eumetsat.org/WEBOPS/msg_interpretation/PowerPoints/Atmospheric/Dust_storm.ppt
Sudan Dust
storm
MODIS Aqua image
http://visibleearth.nasa.gov/view.php?id=66891
AOT at Solar Village
7:32 7:43 7:54 8:07 8:59 9:42 10:12 11:05 11:4215:05 7:12 7:270
0.5
1
1.5
2
2.5
Local Time
AO
T a
t 5
00
nm
Evolution of duststorm on (6/25-26/2003)
Wind speed and pressure
CLM Simulated dust flux MODIS AOT
Coupled (CLM-CAM) run
• Erodibility is not accounted in the offline mode
• Tuning factor is not introduced in offline mode
• Dust emission is the maximum possible from a grid
MODIS Monthly AOT Simulated monthly AOT
Conclusion
• Wind speed is the dominant factor affecting CLM dust output compared to soil moisture, soil types etc.
• Prediction of dust storms by CLM is limited by the accuracy of wind speed (both magnitude and resolution)
• Offline simulation of CLM is not enough to completely describe dust emission, transport and deposition
Future works
• Run coupled CLM-CAM simulation for longer period and compare daily AOT with observations
• Evaluate the accuracy of wind speed
• Establish wind speed – dust emission relationship in larger scale using remote sensing data (aerosol optical thickness) and reanalysis data (wind speed)
References
• Bagnold, R. A. (1941). The physics of wind blown sand and desert dunes. Methuen, London, 265.
• 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.
• Holben, B. N., Eck, T. F., Slutsker, I., Tanre, D., Buis, J. P., Setzer, A., Vermote, E., et al. (1998). AERONET—A federated instrument network and data archive for aerosol characterization. Remote sensing of environment, 66(1), 1–16.
• Kellogg, C. A., & Griffin, D. W. (2006). Aerobiology and the global transport of desert dust. Trends in ecology & evolution, 21(11), 638–644.
• Koren, I., Kaufman, Y. J., Washington, R., Todd, M. C., Rudich, Y., Martins, J. V., & Rosenfeld, D. (2006). The Bodélé depression: a single spot in the Sahara that provides most of the mineral dust to the Amazon forest. Environmental Research Letters, 1(1), 014005.
• Marticorena, B., & Bergametti, G. (1995). Modeling the atmospheric dust cycle: 1. Design of a soil-derived dust emission scheme. JOURNAL OF GEOPHYSICAL RESEARCH-ALL SERIES-, 100, 16–16.
• Miller, R. L., & Tegen, I. (1998). Climate response to soil dust aerosols. Journal of Climate, 11(12), 3247.
• Oleson, K. W., Lawrence, D. M., Gordon, B., Flanner, M. G., Kluzek, E., Peter, J., Levis, S., et al. (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
• Post, W., & Zobler, L. (2000). Global Soil Types, 0.5-Degree Grid (Modified Zobler). Oak Ridge National Laboratory Distributed Active Archive Center. Retrieved from http://www.daac.ornl.gov
• Qian, T., Dai, A., Trenberth, K. E., & Oleson, K. W. (2006). Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluations. Journal of Hydrometeorology, 7(5), 953–975.
• Sokolik, I. N., & Toon, O. B. (1996). Direct radiative forcing by anthropogenic airborne mineral aerosols. Nature, 381(6584), 681–683.
• Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., et al. (2007). The physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change, 235–337.
• Vertenstein, M., Craig, T., Middleton, A., Feddema, D., & Fischer, C. (2012). CESM1. 0.4 User’s Guide. National Center for Atmospheric Research, Boulder, CO [online]. Available from: http://www. cesm. ucar. edu/models/cesm1. 0/cesm/cesm_doc_1_0_4/book1. html [Accessed 4 May 2012]. Retrieved from http://www.cesm.ucar.edu/models/cesm1.0/cesm/cesm_doc_1_0_4/ug.pdf
• 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.
Questions?