30 years of african dust from emission to deposition
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30 years of African dust: From emission to deposition. David A. Ridley, Colette L. Heald. Using GEOS- Chem and MERRA to determine the causes of variability and trends. Toward resolution-independent dust emissions in global models: Impacts on the seasonal and spatial distribution of dust

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30 years of african dust from emission to deposition

30 years of African dust: From emission to deposition

David A. Ridley, Colette L. Heald

Using GEOS-Chem and MERRA to determine the causes of variability and trends

Toward resolution-independent dust emissions in global models: Impacts on the seasonal and spatial distribution of dust

D. A. Ridley, C. L. Heald, J. R. Pierce and M. J. Evans

Geophysical Research Letters, Vol 40, 1-5, doi:10.1002/grl.50409, 2013

Acknowledgements: R. Lucchesi, M. Karki (NASA GMAO), J. Marshamand M. Hobby (U. of Leeds)

This work is supported by internal funding at MIT

motivation
Motivation

Annual Dust AOD (2012)

  • African dust accounts for half of global emissions (Huneeus et al., 2011)
  • Wintertime dust emission and transport highly variable (Propero et al., 2013; Doherty et al., 2012; Ben-Ami et al., 2012)
  • Land-use changes often cited as possible cause of change in dust emission over recent decades (Chiapello et al., 2005; Evan et al., 2011; Mulitza et al., 2010)
two questions
Two Questions:
  • Can GEOS-Chem capture the emission and long-range transport of African dust over decadal timescales?
  • What drives the inter-annual variability in winter African dust emissions?

Are land-use changes important??

modelling dust e mission
Modelling Dust Emission
  • Solving the resolution issue…
  • Dust emission proportional to 10m-wind cubed
  • Wind threshold must be exceeded for emission
  • DEAD dust scheme (Zender et al., 2003)
  • TOMS AI dust source map (Ginouxet al., 2001, Prospero et al., 2002)

Wind Speed

Erodible Area

Clay Fraction

  • Weibull PDF used to represent sub-grid winds in GEOS-Chem

4 ° x 5°

2° x 2.5°

March 2012

Soil Moisture

  • Reduction in resolution-dependence of emissions

Vegetation

Ridley et al. (2013)

winter djfm dust aod
Winter (DJFM) Dust AOD
  • Comparison with observations (AERONET, MODIS, MISR & surface concentration at Barbados) shows seasonality is captured.

(Ridley et al., 2012)

  • Daily comparison (2000-2008) with AERONET and MODIS yields good spatial and temporal correlation in winter.
  • Most events captured, however model underestimates peak AOD

(R = 0.60 – 0.80 at key AERONET sites)

MODIS

GEOS-Chem

(MODIS dust AOD derived using Eq. A3 from Kaufman et al., 2005)

inter annual variability downwind
Inter-annual variability downwind
  • Using 26 years of dust AOD (DAOD) derived from AVHRR & MODIS satellite data

(Evan & Mukhopadhyay, 2010)

  • Significant decrease in dustiness of 11-20% per decade in both observations and model
is land use change important
Is Land-Use Change Important?
  • Using a relaxed source map modulated by surface bareness from AVHRR NDVI (Koven et al., 2006; Kim et al., 2013)

Sahel

  • A greening of the Sahel over the past 30 years observed from AVHRR
  • Varying vegetation alters emissions by only 1-5%
  • Accounting for dynamic vegetation may be more important in other regions
what causes the variability
What causes the variability?
  • North Atlantic Oscillation (NAO) shown to correlate with Atlantic dust AOD and concentration (Moulin et al., 1997; Ginoux et al., 2004)
  • GEOS-Chem coastal Africa dust AOD correlates with NAO (R=0.63)
  • Large scale changes in source wind account for a 30% of the variance in AOD downwind
two answers
Two Answers:
  • GEOS-Chem captures the trend and variability in wintertime AOD downwind
  • Land use changes do not seem to drive changes in dust (dust variability is primarily controlled by meteorology)
what about summer
What about summer?

AOD

Winter

>0.75

0.25-0.75

<0.25

Days

AERONET

Model

AERONET

AERONET

Model

Model

AOD

Days

Summer

>0.75

0.25-0.75

<0.25

AERONET

Model

AERONET

AERONET

Model

Model

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