Improving dust characterization in climate models

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://altereddimensions.net/2012/the-dust-bowl-black-sunday

http://en.wikipedia.org/wiki/Dust_Bowl

Improving dust emission characterization in climate models

- Sagar ParajuliMODIS image on 03/18/2012 (Courtesy of NASA)

11/21/2013

MODIS image on 03/19/2012(Courtesy of NASA)

Outline

1. Introduction

2. Proposed research

3. Methods

4. Preliminary results

5. Expected results and significance

6. Broader Impact

7. References

3

Outline

1. Introduction


3. Methods



6. Broader Impact

7. Research timeline

8. References

4

5

Introduction

1. Introduction 1.1 Dust modeling

1Sokolik and Toon 1996 2Rosenfeld et al., 2001 3Kellogg and Griffin 2006 4Koren, et al., 2006

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.

1. Introduction 1.1 Key definitions

6

1Sokolik and Toon 1996 2Rosenfeld et al., 2001 3Kellogg and Griffin 2006 4Koren, et al., 2006

1.2 Dust modeling

Key definitions• Aerosol Optical Depth (AOD) represents the fraction of radiation

attenuated by dust, 0 for low dust, 4 for high dust.

• Erodibility is defined as the soil erosion efficiency under a given meteorological forcing.

• Geomorphic types are different landform types (e.g. sand deposits, fluvial system, agricultural areas etc.)

• Reanalysis data are the gridded optimal datasets obtained by

blending observations and

model outputs.

7

1. Introduction 1.1 Key definitions 1.2 Dust modeling

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.

8


1Bagnold 1941

1.1 Key definitions

Dust emission modeling9

F = Vertical dust mass fluxDEAD 1 (Saltation model) GOCART 2 (non-saltation model) = Where,T is a tuning factor, is erodibility is grid cell bare-soil fraction, = , (1-, is threshold friction speed, is friction velocity, is a saltation constant = 2.1.

= Where, is a dimensional factor, is erodibility, is the fraction of clay or silt in the soil, is the wind speed at 10m height, is threshold friction speed.

1 Dust Entrainment and Deposition Model (Zender et al. 2003)2 Global Ozone Chemistry Aerosol Radiation and Transport (Ginoux et al. 2001)


Outline

1. Introduction


3. Methods



6. Broader Impact


8. References

10

Problem statement1. There is the general unavailability of accurate and high

resolution surface input data (mainly clay content, bare-soil fraction and soil moisture).

2.1 Problem Statement 2.3 Hypotheses2.2 Research Questions2. Proposed Research

11

1Post and Zobler 2000 2Community land model

% clay content map1 used in CLM2

Problem Statement2. Static erodibility factor is used to constrain the simulated dust

emission, but the factors determining the erodibility are not well understood.

12


Dust emission simulated by CLM under different reanalysis wind forcing (2003).

= 13596 9643 = 2881 1603

𝜇𝑔𝑚−2𝑠−1

NCEP windERA-Interim wind

1Ginoux et al. 2001

{ 𝑤/𝑜𝑒𝑟𝑜𝑑𝑖𝑏𝑖𝑙𝑖𝑡𝑦h𝑤𝑖𝑡 𝑒𝑟𝑜𝑑𝑖𝑏𝑖𝑙𝑖𝑡𝑦 1

Annual dust (Tg)

Problem Statement3. 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.

13


(Marticorena and Bergamatti 1995) (Gillette 1978)

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?


14

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.


15

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.

16

Hypotheses


Outline

1. Introduction


3. Methods



6. Broader Impact


8. References

17

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.

3. Methods 3.5 Paramaterization3.3 Geomorphic map 3.4 Erodibility3.1 Study area 3.2 Datasets

1Shao 2008 2http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=aerosol_daily

Mean aerosol optical depth (AOD)2

Bodélé, Chad

http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=aerosol_daily






Datasets


19

Datasets ResolutionHi-resolution images of Google Earth Pro and Esri Basemap

1 to 15 m

MODIS Aqua level 3 aerosol product (MYD08_D3)

Daily (01:30 PM)

MODIS Terra level 3 aerosol product (MOD08_D3)

Daily (10:30 AM)

MODIS Aqua level 2 aerosol product (MYD04_L2)

Daily (01:30 PM)

MODIS Terra level 2 aerosol product (MOD04_L2)

Daily (10:30 AM)

AERONET aerosol data products

Station(15 min)

ERA-Interim reanalysis wind 6 hourly

NCEP reanalysis 1 wind 6 hourly

Geomorphic features

Aerosol properties

Wind speed

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.

Quantification of erodibility


21

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.

Parameterization improvement


Establish wind-dust relationship.

Tune with observed wind-dust relationship near geomorphic types.

Measure the vertical dust mass emitted directly from the substrate

bed in the wind tunnel.

Outline

1. Introduction


3. Methods



6. Broader Impact


8. References

23

4. Preliminary Results 4.2 Erodibility4.1 Geomorphic map

Geomorphic mapping


24

Erodibility

0 2 4 6 8 10 12 140

1

2

3

4

5

Dee

p B

lue

AO

D5

50

nm

1000 hPa ERA Wind (ms-1)

AOD = 0.047*WIND2 - 0.3*WIND + 1.2R2 = 0.48

0 2 4 6 8 10 12 140

1

2

3

4

5

Dee

p B

lue

AO

D5

50

nm

1000 hPa NCEP Wind (ms-1)

AOD = 0.032*WIND2 - 0.095*WIND + 1R2 = 0.19


25Low wind but high AOD: Pollution? Transported dust? Strong saltation?

High wind but low AOD: Supply-limited case? High soil moisture?Absence of saltation?

AOD Vs. ERA-Interim wind at Bodélé

AOD Vs. NCEP wind at Bodélé


1Ginoux et al. 2001.

26

Erodibility

Developed erodibility map Topographic erodibility1

Proposed method emphasizes agricultural dust sources and eliminates fictitious sources involving long-range transport, pollution and biomass burning.


Correlation between annual cycles of ERA-Interim wind and MODIS AOD (2003-2012).

• 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).

27

Erodibility

Geomorphic types

Derived location

Jan Feb Mar Apr May Jun July Aug Sep Oct Nov Dec

Bedrock, with sediments

24N, 8E 0.26 0.15 0.12 0.10 0.10 0 0 0 0 0.19 0.06 0.22

Sand deposit 24.4N, 13E 0.22 0.1 0.11 0.08 0.21 0.01 0 0 0.14 0.19 0.09 0.14Sand deposit, on bedrock

18.4N, 26E 0.24 0.24 0.24 0.24 0.30 0.25 0.12 0.15 0.13 0.33 0.15 0.10

Sand deposits, stabilized

25.4N, 49E 0.22 0.33 0.25 0.20 0.22 0.11 0.36 0.04 0 0.1 0.10 0.17

Land Use (Agriculture)

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

Fluvial system 21.4N, 15W 0.42 0 0.1 0 0.12 0 0 0 0 0.12 0.13 0.34

Stony surface 19.4N, 57E 0 0.07 0.12 0 0 0.21 0.11 0.23 0.11 0.08 0.08 0.12

Playa/Sabkha 16.4N, 17E 0.71 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.

28Erodibility of geomorphic types

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).

29

5. Expected results and significance 6. Broader Impact 7. References

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:

30


ReferencesBullard, 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 dust-cycle 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.

31


http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.7769

Thank you!

32

Technology

Improving dust characterization in climate models