What drives the observed variability and decadal trends in North African dust export? David A. Ridley, Colette L. Heald Dept. Civil & Environmental Engineering, MIT Thanks to: Joe Prospero, Charlie Koven, Kerstin Schepanski, Sophie Cowie, John Marsham and Owen Doherty. This work is supported by the Charles E. Reed Faculty Initiative Fund

African dust aerosol African dust accounts for half of global emissions Health and ecosystem impacts Radiative impacts, especially over the Atlantic Sahel Precip Index no longer a good predictor of trans-Atlantic dust Annual Dust AOD (2012) Ridley et al. (2013) Prospero et al. (2003) Summer dust conc Previous year’s Precip. Index Huneeus et al. (2011) Evan et al. (2006, 2010, 2011) “Since the early 1990s there have been large year-to-year changes in Sahel rainfall but there is no consistent relationship to dust on Barbados or between dust and common climate indices.” J. Prospero, International Workshop on African Dust, Puerto Rico, 2011 Prospero et al. (1999), Griffin et al. (2001), Swap et al. (1992)

DEAD dust scheme (Zender et al., 2003) Topographical source map (Koven et al., 2008) Vegetation modulation (Kim et al., 2013) Sub-grid wind parameterization (Ridley et al., 2013) Modelling Dust Emission Wind Speed Soil texture Soil Moisture Vegetation Ginoux et al., 2001 Koven et al., 2008 No ‘bare’ pixels  no emission If vegetation cover > 0.3  no emission Based on AVHRR NDVI Bareness fraction, GEOS-Chem chemical transport model MERRA reanalysis meteorology

Seasonality and variability in dust AOD and concentration well captured Evaluation of the dust simulation Surface concentration at Barbados Observations GEOS-Chem 5-95% 25-75% mean Daily AOD comparison over Africa using MODIS Deep Blue ( ) Daily AOD comparison with 5 years of AERONET data at 8 sites WINTER SUMMER

Using 27 years of dust AOD (DAOD) derived from AVHRR & MODIS satellite data (Evan & Mukhopadhyay, 2010) Variability and trends in dust AOD (DAOD)

Model reproduces seasonal trends observed in satellite DAOD and Barbados concentration Variability and trends in dust AOD (DAOD) OBSERVATIONS MODEL per decade per decade per decade per decade per decade SUMMER Barbados -3µg/m3 per decade -5µg/m3 per decade SUMMER

Radiative impacts of dust trends Warming trend over the mid-Atlantic of 0.52Wm -2 at surface and 0.24Wm -2 at TOA Comparable direct radiative effect to the regional CO 2 forcing since 1750 Surface 1982 – 2008 annual trend Surface W/m 2 /decade TOA W/m 2 /decade GEOS-Chem model coupled with the RRTMG model (Heald et al., submitted)

SURFACE WINDS CONSTANT Vegetation changes do not directly contribute to the trend Precipitation and transport account for <10% close to source and 50% downwind Surface winds account for most of trend close to source and 50% downwind Wind stilling at dust source regions accounts for the majority of the trend in DAOD What causes the trends in dust AOD METEOROLOGY CONSTANT VEGETATION CONSTANT Surface winds drive the trend

Stilling of surface winds doesn’t coincide with location of the greening ( minus ) Winter Summer Can we rule out vegetation as a driver of trends? m/s

Reduced dust emission Weaker surface winds Northward shift of tropical rainfall Greening of Sahel Weakening of large-scale winds [Cowie, 2013] [Charney, 1975; Levis, 2004] ? Warmer N. Atlantic SST [Delworth, 2007; Hwang, 2013; Dunstone, 2013; Friedman et al., 2013] Reduced N. Atlantic aerosol ? Northward ITCZ shift [Doherty 2012, Fontaine, 2011] Anthropogenic aerosol connection with ‘natural’ dust aerosol in the Tropics? Reduced inter-hemispheric SST gradient [Booth, 2012] Reduced dust emission Weaker surface winds Large-scale climate-aerosol connections Greening of Sahel ? Correlation between CMIP5 & reanalysis wind trends

Thank you

How realistic are MERRA wind trends? Significant trends in wind stilling across Africa and mid-Atlantic Similar trends observed in ERA- Interim and NCEP reanalyses

Cause of inter-annual variability Surface winds are the main cause of variability off the coast of Africa Transport and precipitation contribute up to two-thirds downwind Vegetation has a negligible impact AMJJAS DJFM

GEOS-Chem dust scheme Increase of emissions from Bodele relative to West Africa Small improvement relative to AERONET coarse AOD (r=0.73 and r=0.61, for winter and summer) Increase in spatial correlation with MODIS coarse AOD (r=0.80 and r=0.75, for winter and summer

Evaluation of the dust simulation March 2012 Sub-grid wind PDF brings a reduction in the resolution dependence of emissions and better spatial agreement. 4x5 2x2.5