Climate response to dust

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Climate response to dust

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

Funded by NSF, NCAR
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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/mah
  owald

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U. Miami data, Mbouru et al., visibility data
We know that dust responds to climate on regional
to global on short to long time scales by a
factor of 4 to 100. Dust is often used a proxy
for climate change in the paleo record. How does
this change in dust impact climate?
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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)
• Cant get dust signal with amip and vegetation
  changesneed to force model to capture dust
  change at barbados
• Model error
• Land use source of dust
• Vegetation change source of dust

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CAM3/SOM Dust radiative feedback impacts on
precip Not including long wave in CCSM3 enhances
double ITCZ (not shown)
Impacts of dust onto climate/precipitation

• Impacts on Sahel precip.
• SSTs 50 of observed precip change
• Vegetation changes Not significant
• GHG has wrong signal (increases precip in Sahel)
• Model cant capture dust changes observed, but
  observed dust changes (when forced onto model)
  cause 30 change in observed precip in Sahel
• Dust could be important feedback on Sahel precip

Yoshioka et al., in press
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Yoshioka et al., in press
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Climate response to dust under different climates
Mahowald et al., 2006
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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!

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Miller et al., 2004 Precip is sensitive to
single scattering albedo If single scattering
albedo is larger than their base case (as in our
case), see consistent shifts, maybe. New version
of GISS model has higher single scattering albedo
than used in Miller et al., 2004. , due to
updates in optical properties.
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• How sensitive is the response of desert regions
  to climate model used?
• Use PCMDI models (17)
• Use anomalies from current climate to force
  BIOME4 vegetation model (Kaplan et al., 2003).
  (Asynchronous coupling, desert vegetation reaches
  equilibrium quickly)
• Calculate estimated desert area for future
  climate
• (use co2 fertilization and no fertilization).

• Lots of spread!
• CCSM is somewhat extreme in wetting Sahel in
  future
• (Regional model have different response?)
• Want to do this interactively in model (Andrea
  Sealy in working on dust/AOVM modeling now within
  CCSM)

Latitude
Latitude
Latitude
Latitude
Latitude
Latitude
Mahowald et all, in prep
averages over -40 to 20E vs. latitude
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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)

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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 precipnot
  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)

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Dust response to climate

• Assume only natural sources of dust (cant
  eliminate 0-50 potential contribution from land
  or water use (Mahowald et al., 2002 2004
  Mahowald and Dufresne, 2003 Mahowald and Luo,
  2003), but ignore for now).
• Assume climate (precip, Ts, cloudiness) and
  carbon dioxide fertilization of plants important.
  (Smith et al., 2000 Moore et al., in press
  suggests carbon dioxide fertilization reasonable
  with 2x co2).
• Vegetation response most important in this model
  (similar to Mahowald et al., 1999 dissimilar to
  Werner et al.,2002). No dynamic veg
  (asynchronous coupling with BIOME3).

Mahowald et al., 2006
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Compare model changes to obs

• Cant 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.

• For LGM Use geological record to infer
  glaciogenic sources and best match terrestrial
  sediment record

Current SOMB
SOMBLGMC
SOMBLGMT
Mahowald et al., 2006
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Linearity in response in RF (surface or top of
atmosphere) or global surface temperature or
global precipitation in this model
Squares TOA, triangles SFC
True in other models? Compare to sensitivity
experiments in the single scattering albedy done
in GISS model (values courtesy of R. Miller
Miller and Tegen, 1998 Miller et al., 2004)