Improving dust emission scheme in climate models

- Sagar ParajuliMODIS image on 03/19/2012 (Origin: Afghanistan/Pakistan)

ScatteringAbsorption

Wind

Creep

Dry/Wet deposition

Dust-cloud interactionCCN/IN

Biogeochemical processes

Longwave back radiation

Figure 1. Mean MODIS aerosol optical thickness ( 2003-2012) indicating average level of dust concentration in the study area (Middle East and North Africa). The diameter of circle is proportional to the population of the city.

Existing Dust scheme in CLM: (DEAD1) 1Dust Entrainment and Deposition Model

(1Zender, Bian, & Newman, 2003)

CLM dust simulation evaluation

Dataset Temporal res. (year:

2003)

Spatial res.

CLM simulation(atmospheric

forcing: Qian et al. 2006)

Daily 0.9ο ×1.25ο

AERONET AOT at 500nm

15 min Station (Solar Village in

Saudi Arabia)

Level 3 MODIS AOD at 550 nm

Daily and monthly

1ο × 1ο

CLM Simulated dust flux

MODIS AOT

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

0n

m

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

Key issues• Current CLM gives the maximum possible dust emission from

bare surface• Threshold friction speed is key in controlling dust flux which is a

function of mainly soil moisture and percentage clay content • Soil moisture variation in dust source region being very low,

percentage clay content mainly modifies threshold friction speed

Given the unavailability of accurate percentage clay content map, the only way to improve dust emission is:

Either use different parameterization for different geomorphological surfaces

Or use erodibility map in constraining the model estimate

Animation: Mean monthly AOD in the study area for 2012

Evaluation of the wind data

Description

Spatial res.

Temporal Res.

Data range

References

MODIS Deep Blue AOT at 550 nm

1° × 1°

Daily (01:30 PM local time)

2003-2012

(Hsu et al. 2006)

ERA-Interim Wind Speed

1.5° × 1.5°

6 hourly 2003-2012

(Kalnay et al. 1996)

NCEP/NCAR Reanalysis Wind Speed

2.5° × 2.5°

6 hourly 2003-2012

Uppala et al. 2005)

Wind speed and AOT at Bodele

0 2 4 6 8 10 120

1

2

3

4

5

6

1000 hPa NCEP Wind Speed (m/s)

Dee

p B

lue

AO

T a

t 550

nm

y = 0.026*x2 - 0.054*x + 0.91R-square = 0.19

0 2 4 6 8 10 12 140

1

2

3

4

5

6

1000 hPa ERA-Interim Wind Speed (m/s)

Dee

p B

lue

AO

T a

t 550

nm

y = 0.043*x2 - 0.25*x + 1.1R-square = 0.48

0 2 4 6 8 100

2

4

6

8

ERA 10m wind at Mezaira (m/s)

Mez

aira

sta

tion

10m

win

d (m

/s)

R - Square = 0.29

SDstn

/SDera

= 0.84

0 0.5 1 1.50

0.2

0.4

0.6

0.8

1

1.2

1.4

Deep Blue AOD at 550nm

AE

RO

NE

T A

OT

at 5

50nm

SDaeronet

/SDmodis

= 0.98

R-square = 0.59

Comparison with ground-based observations

Daily data at 01:30 PM (2003)

6 hourly data (2010)

Improving the model: use of Erodibility Map

Proposed erodibility map

(correlation map between Deep Blue AOT and 10m wind (data: 365 observations of 2012)

Expected improvements:

•Vegetated area with mountainous topography •Agricultural areas

Topographic erodibility map(Ginoux et al. 2001)

Use of dust source map

• Proposed dust source map can be used to mask non-erodible areas (represented by insignificant correlation in the map)

• Since residence time of dust is relatively longer than the wind persistence time, this method eliminates the false identification of dust sources associated with transported dust

• Monthly erodibility map can be used to account for the dependence of threshold friction speed on vegetation and seasonality

Proposed geomorphological map

(Bullard et al. 2011)

Future works

• Implement the developed erodibility in CLM and evaluate the resulting emission

• Use ERA-Interim wind for forcing CLM;

• Look for better percentage clay content map

• Develop geomorphological map from google earth image using image classification algorithm (e.g. maximum likelihood method)

• Integrate geomorphological map into CLM

• Develop dust storm forecasting tool using combination of model and satellite data

Thank you!

Existing Model: (DEAD1) Dust Entrainment and Deposition Model

T is a global factor to compensate model’s horizontal and temporal resolution sensitivity = 5 × 10-4

S = 1 (source erodibility factor)

(1Zender, Bian, & Newman, 2003)

DEAD cont..

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DEAD cont.…

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•

•

•

•

•

•

•

•

DEAD cont.…

http://ldas.gsfc.nasa.gov/gldas/GLDASsoils.php (avilable at .25 and 1 degree)

0 30 60 90 120 150 180 210 240 270 300 330 3600.04

0.05

0.06

0.07

0.08

Julian Days

Surf

ace

SM (

gm/c

m3)

Soil moisture variation in dust source region

Soil texture used in GLDAS2/Noah• http://disc.sci.gsfc.nasa.gov/hydrology/data-

holdings

• Unlike in visible bands, UV surface reflectivity is low and is not affected by albedo

• Non-absorbing aerosols(e.g., sulfate aerosols and sea-salt particles) yield negative AI values. UV-absorbing aerosols (e.g., dust and smoke) yield positive AI values. Clouds yield near-zero values.