Dust emission modeling

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?