Climate response to dust N. Mahowald, M. Yoshioka, D. Muhs, W. Collins, A. Conley, C. Zender, D. Fillmore, D. Coleman, P. Rasch

Climate response to dust

• N. Mahowald, M. Yoshioka, D. Muhs, W. Collins, A. Conley, C. Zender, D. Fillmore, D. Coleman, P. Rasch

Desert dust/mineral aerosols

• Soil particles suspended in area

• Source:

• Unvegetated, dry soils with strong winds

• Removal

• Dry deposition, especially gravitation settling
• Wet deposition, during precipitation

• Model the sources, transport and deposition processes in 3-dimensional model (offline transport model: MATCH/NCEP or NCAR Community Atmospheric Model (CAM3) from the Community Climate System Model (CCSM3))

• Papers available at www.cgd.ucar.edu/tss/staff/mahowald

Sensitivity study (Yoshioka et al., in press)

• Using CAM3 and slab ocean model

• AMIP runs (SST impacts)

• Vegetation changes (force model to change similar to estimated changes 1960s to 1990s)

• Green house gas changes (2x co2 SOM runs)

• Dust changes (with and without dust direct radiative forcing)

• Only include direct radiative effects (ignoring CCN or IN interactions, which may be important)
• Can’t get dust signal with amip and vegetation changes—need to force model to capture dust change at barbados
• Model error
• Land use source of dust
• Vegetation change source of dust

Climate response to dust under different climates

How robust is this response?

• Physical parameterizations or physical biases will impact our simulation of dust (or x variable we are interested in).

• How does this impact our precipitation sensitivity?

• Shift in precip due to dust radiative forcing is not sensitive to climate in our model (Mahowald et al., 2006)

• Response is sensitive to single scattering Albedo (Miller et al., 2004; other papers)

• Radiative properties of dust are not well established
• Dust absorbs and scatters in long AND short wave
• NOT spherical particles!

Smaller scale interactions

• Dust and easterly waves

• 20-40% of dust is generated and transported associated with easterly waves (Jones et al., 2003; using NCEP and NCEP/MATCH)
• Easterly waves maybe enhanced by dust (Jones et al., 2004 NCEP/MATCH; Jones et al in prep (CAM3 T85)

• Dust and hurricanes

• Dust cools surface and suppresses precip in our model, some observation studies….(Yoshioka et al., in press, Wong and Dessler, Evans et al., in prep)…

Summary/conclusions

• In this set of model simulations:

• SSTs are responsible for 50% of the Sahel signal (pretty robust across models)

• Vegetation NS (different models show different results)

• Dust responsible for up to 30% of Sahel drought signal in this model (consistent with one existing study? Need more models!)

• Dust could be ‘natural’ or anthropogenic (Mahowald et al., 2002; Prospero and Lamb, 2003; Mahowalld and Luo, 2003; Tegen et al., 2004; Mahowald et al., 2004)

• GHG in this model lead to higher precip—not robust result

• Dust potentially an important feedback factor that should be better explored.

• Not discussed here at length, but should not be ignored:

• Anthropogenic changes in ‘natural’ aerosol are potentially large and should not be ignored

• from direct perturbation of land (land use), climate change or carbon dioxide fertilization of plants

• our estimates: (Mahowald et al., 2006; Mahowald and Luo, 2003)

• PreindustrialI to present (-0.1 to 0.30C)
• Present to future (doubled CO2) is about ~+0.06C

• Dust changes could also be driving changes in ocean biogeochemistry and carbon dioxide fluxes (Mahowald et al., 2006; Moore et al., 2006)

Dust response to climate

Compare model changes to obs

• Can’t distinguish preindustrial from current (Mahowald and Luo, 2003; Mahowald et al. 2006.

• Compare against all available data in current climate. Dust deposition records for last glacial maximum.