PowerPointPrsentation

1
Direct radiative forcing of aerosol

• Model simulation A. Rinke, K. Dethloff, M.
  Fortmann
• Thermal IR forcing - FTIR J. Notholt, C. Rathke,
  (C. Ritter)
• Challenges for remote sensing retrieval A.
  Kirsche, C. Böckmann, (C. Ritter)

2

• A modeling study with the regional climate
  model HIRHAM
• Specification of aerosol from Global Aerosol Data
  Set (GADS)
• 2) Input from GADS into climate model
• for each grid point in each vertical level
  aerosol mass mixing ratio
• (0.5 º, 19 vertical)
• optical aerosol properties for short- and
  longwave spectral intervals
• f(RH)
• aerosol was distributed homogeneously between
  300 2700m altitude,
• no transport
• Climate model run with and without aerosol ?
  aerosol radiative forcing
• months March (1989 1995)

3
Global Aerosol Data Set (GADS) Koepke et al.,
1997
? Arctic Haze WASO, SOOT, SSAM
Properties taken from ASTAR 2000 case (local), so
overestimation of aerosol effect
4
Direct climatic effect of Arctic aerosols in
climate model HIRHAM via specified aerosol from
GADS
u(x,y,z) v(x,y,z) ps(x,y) T(x,y,z)
q(x,y,z) qw(x,y,z) a(x,y) µ(x,y)
Effective aerosol distribution as function of
(x,y,z)
Direct aerosol forcing in the vertical column
Additional diabatic heating source Qadd Qsolar
QIR
Aerosol Radiation - Circulation - Feedback
Dynamical changes ?u(x,y,z) ?v (x,y,z)
?ps(x,y)
?T(x,y,z) ?q(x,y,z) ?qw(x,y,z)
New effective aerosol distribution due to 8
humidity classes in the aerosol block
5
Direct effect of Arctic Haze
Aerosol run minus Control run, March ensemble
2m temperature change
Height-latitude temperature change
Temperature profiles at selected points
5 4 3 2 1 0
W1
x W2
Height km
x W1
x C1
x C2
Height km
C2
W2
C1
C
65 70 75 80
85
-3 -2 -1 0 1 2 3
Geographical latitude
Temperature change C
1990
C
?Fsrfc 5 to 3 W/m2 1d radiative model
studies ?Fsrfc-0.2 to -6 W/m2
Fortmann, 2004
6
Directindirect effect of Arctic Haze
(Aerosol run minus Control run) directindirect
(Aerosol run minus Control run) direct
March 1990
2m temperature change
K
Sea level pressure change
hPa
Rinke et al., 2004
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Conclusion modeling

• Critical parameters are
• Surface albedo, rel. humidity, aerosol height
  (especially in comparison to clouds)
  (indirect liquid water)
• But aerosol properties were prescribed here
  so no direct statement on sensitivity of aerosol
  properties (single scat. albedo) according to
  GADS,
• however chemical composition, concentration
  and size distribution of aerosol did show strong
  influence on results (surface temperature)
• aerosol has the potential to modify global-scale
  circulation via affected teleconnection patterns

8
FTIR
Rathke, Fischer 2000
Note deviation is grey
12.5µ
8.0µ
9
Height, temperature and opt. depth of aerosol
required
Easier radiance flux Flux (aerosol)
- flux (clear)
significant
10
AOD from spectrum of radiance residuals
Note similar spectral shape
For TOA Assumption purely absorbing (!)
11
Radiosonde launch 11UT (RS82)
11. Mar cold and wet diamond dust possible For
30. Oct, 17. Nov ?T of 1.5 C needed for
saturation
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Conclusion FTIR observation

• Observational facts
• grey excess radiance was found for some days
  where back trajectories suggest pollution
• diamond dust unlikely for 30 Oct, 17 Nov.
• So IR forcing by small (0.2µm) Arctic aerosol?
• Consider complex index of refraction at 10µm
  for sulfate, water-soluble, sea-salt and soot
  (much) higher than for visible light!
  (Atmospheric Aerosols)
• example
• ? \ specimen sulfate water-solu.
  soot oceanic
• 0.5 µ 1.431e-8i 1.535e-3i
  1.750.45i 1.3826.14e-9i
• 10µ 1.894.55e-1i 1.829e-2i
  2.210.72i 1.314.06e-2i
• Mie calculation (spheres 0.2µm, sulfate) vis
  no absorption, ?1
• 
  IR almost no scat. ?0
• so ?, n, phase function are all (?)

14
Scattering properties by remote sensing?

• Have seen single scattering very important,
  depend on index of refraction.
• Multi wavelengths Raman lidars can principally
  calculate / estimate size distribution
  refractive index (n) gt scattering
  characteristics.
• One difficulty estimation of n

forward problem
d data v coefficients of volume distribution
function M matrix of scattering efficiencies (?,
k ), depend on n
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algorithm
to solve Fredholm Integ. Eq. of first kind
integral operator
so
17

• vd shall be element of a finite dimen. subspace
  of L2(r_min, r_max) so

so
T
let d (data) be d
T
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KARL specs
20
Direct effect of Arctic Haze
Aerosol run minus Control run, March ensemble
2m temperature change (mean)
Height-latitude temperature change
Temperature profiles at selected points
5 4 3 2 1 0
W1
x W2
Height km
x W1
x C1
x C2
Height km
C2
W2
C1
C
65 70 75 80
85
-3 -2 -1 0 1 2 3
Geographical latitude
Temperature change C
1990
C
?Fsrfc 5 to 3 W/m2 1d radiative model
studies ?Fsrfc-0.2 to -6 W/m2
Fortmann, 2004