© Imperial College LondonPage 1 Estimating the radiative effect of mineral dust over ocean The...

© Imperial College London

Estimating the radiative effect of mineral dust over ocean

• The radiance to flux problem• Dust retrieval quality• Applying an aerosol correction:

(i) RT code issues; (ii) sensitivity to aerosol representation• Impact on GERB fluxes and SW dust forcing efficiency

H.Brindley, GIST25, 23-25 Oct 2006


• SW Radiance to Flux conversion:

F(s) = L(s,v,r) / R(s,v,r)

• R(s,v,r) obtained from CERES TRMM ADMs according to RMIB scene ID

BUT, no explicit aerosol treatment in edition 1 release

• Possible solutions:

(i) use CERES approach - F = LOBS / (Rclr x (Rth(LOBS)/Rth(LADM))

(ii) use ‘aerosol contaminated’ GERB radiances to test theoretical predictions

(iii) build empirical GERB aerosol models

Using clear ADM: TOA flux underestimated: aerosol forcing/efficiency reduced

Using clear ADM: TOA flux overestimated: aerosol forcing/efficiency inflated


AOD ‘sanity check’ (I):

GERB ARCH (SEVIRI) vs AERONET

BAHRAIN

FORTH CRETE

DAKAR

GRANADABLIDA


AOD ‘sanity check’ (II): GERB ARCH (SEVIRI) vs MODIS (Terra)

MODIS SEVIRI (GERB ARCH)


Dust forcing efficiency: GERB Edition I product (AERONET points)

For a given geometry use linear interpolation in L to find FTH(LOBS) and FTH(LADM).

Then:

R(LOBS) = LOBS/ FTH(LOBS)

and

R(LADM) = LADM/ FTH(LADM)


Sensitivity of correction to RT code

Assuming L varies linearly with , can also infer a ‘broad-band’ optical depth

Applying correction to all scenes


F = L / (Rclr x (Rth(LOBS)/Rth(LADM))R = L / F

Black: s:65º, v:25º, r:140º

Blue: s:20º,v:65º, r:170º

Red: s:55º, v:45º, r:110º

LOBSLADM


RT codes recap

• MODTRAN simulations tend to underestimate L and F as a function of compared to identical 6S and SDBART calculations. Results in a reduced dR/dL, higher MODTRAN corrected fluxes under low to moderate aerosol amounts and a flattening off of MODTRAN inferred fluxes under large aerosol loadings

• The 6S and SBDART simulations also show reasonable agreement in terms of ‘retrieved’ (using BB radiances) versus the GERB aerosol product (from 0.63 m SEVIRI radiances). Suggests they capture the true behaviour well and could be used to correct scenes with ‘heavy’ aerosol contamination using the CERES approach

• Results presented in the remainder of this presentation use 6S simulations


Sensitivity to aerosol representation

0.63 m


F = LOBS / (Rclr x (Rth(LOBS)/Rth(LADM))

Rclr/Rth(LADM)

(Hess Tropical Maritime)

Rclr/Rth(LADM)

(Dubovik non-spherical dust)

s = 45°


2 (4.5 %)

3 (9.2 %)

4 (3.3 %)

169 (23.1 %)

170 (43.2 %)

171 (7.1 %)

Other (9.6 %)

2 (1.9 %)

3 (20.7 %)

4 (15.5 %)

169 (19.8 %)

170 (23.4 %)

171 (4.7 %)

Other (14.0 %)

2 (7.1 %)

3 (20.0 %)

4 (10.3 %)

169 (18.6 %)

170 (26.2 %)

171 (3.2 %)

Other (14.6 %)

2 (4.3 %)

3 (13.1 %)

4 (6.8 %)

168 (21.8 %)

170 (36.7 %)

171 (6.1 %)

Other (11.2 %)

April: 1173177 May: 729547

June: 3586795 All: 5489519

‘Dusty’ ocean points through April-June 2006 (view/solar zenith limited to < 70°, retrieved AOD < 2.5, glint angle must be > 40°)

ADM classification:

2: Clear (3.5-5.5 ms-1)

3: Clear (5.5-7.5 ms-1)

4: Clear (>7.5 ms-1)

169: Overcast (water, =0.01-1.0)



Testing aerosol model behaviour: expand sample size…


…and investigate angular coverage and behaviour

ADM =3

v: fast variation, r: slow variation


ADM=170


ADM=3

ADM=170

: fast variation, s: slow variation

Overall performance

NB: Not sampling glint region


Aerosol representation recap

• Over a 3 month period there are insufficient samples to build full empirical GERB dust ADMs, but the angular coverage can be used to test theoretical predictions over a sub-sample of geometries/s

• Similarity of simulated clear-sky conditions to CERES clear-sky ADM will place a strong dependence on the reliability of the aerosol representation:

(i) For the two representations highlighted here, at low there is little dependence on the aerosol model chosen to perform the

correction procedure: surface parameterisation dominates

(ii) As increases, comparisons of observed and retrieved BB radiances suggest that the Dubovik non-spherical dust

model gives a better match to the observations. True in terms of both the 1-1 radiance agreement, and the anisotropy of the scene


Impact of correction on GERB fluxes: clear scene IDs

Corrected flux < GERB Ed 1 flux

Impact generally increases with optical depth,

No clear s dependence in correction but suggestion of higher opticaldepths being flagged as clear at low s


Impact of correction on GERB fluxes: cloudy scene IDs

Range of dust seen in each category broadly consistent with ADM range

Corrected flux > GERB Ed 1 flux in general

Sign of impact depends on : if dust > ADM ‘mean’ then dust is less anisotropic and correction reduces the GERB flux (and vice-versa)


Impact of correction on dust radiative forcing efficiency (I): clear scene IDs

Least squares linear regression fit on flux vs ‘clear’ scenes only


Impact of correction on dust radiative forcing efficiency (II): all scene IDs

Least squares linear regression fit on flux vs all scenes


Cloudy IDClear ID

Granada AERONET site, June 14th 2006


Final Conclusions and recommendations

• CERES type correction can be used for both clear and cloudy scenes but is sensitive to RT code used to perform aerosol simulations

• For dusty ocean scenes, of the models tested the Dubovik non-spherical dust representation appears to best mimic the observed GERB ARCH radiances

• In terms of the GERB Edition 1 fluxes, dust contaminated scenes converted using clear ADMs will have strongly inflated values for all but the lowest s (<0.2)

• Conversely, dust contaminated scenes converted using cloudy ADMs will tend to be biased low

• The radiative forcing efficiency of 78 ± 5 W m-2 / calculated using the Dubovik corrected fluxes is consistent with previous estimates (e.g. Li et al., 2004: 35 ± 3 W m-2 / , diurnal mean)

• For GERB Edition 2, recommend using the CERES correction method, 6S simulations and a dust flag to obtain ‘true’ dust fluxes over ocean


Performance summary: ADM 170

s bin (degrees)

Optical depth bin

No of boxes filled

CERES ADM Tropical Maritime Dubovik dust

Mean ratio SD Mean ratio SD Mean ratio SD

0-10 0.6-0.8 51 1.798 0.163 0.882 0.038 1.014 0.020

0.8-1.0 61 1.655 0.123 0.887 0.033 1.013 0.024

1.0-1.5 61 1.393 0.081 0.874 0.027 0.983 0.023

10-20 0.6-0.8 54 1.440 0.124 0.902 0.060 0.999 0.022

0.8-1.0 65 1.320 0.092 0.900 0.053 1.000 0.023

1.0-1.5 64 1.119 0.072 0.889 0.052 0.972 0.024

1.5-2.0 56 0.922 0.060 0.866 0.037 0.930 0.019

20-30 0.6-0.8 55 1.389 0.150 0.913 0.067 0.981 0.033

0.8-1.0 65 1.231 0.116 0.897 0.064 0.975 0.030

1.0-1.5 63 1.046 0.094 0.886 0.060 0.949 0.031

30-40 0.6-0.8 55 1.407 0.133 0.916 0.056 0.967 0.031

0.8-1.0 59 1.230 0.117 0.902 0.058 0.959 0.036

1.0-1.5 57 1.044 0.100 0.891 0.060 0.934 0.032

40-50 0.6-0.8 64 1.363 0.129 0.904 0.060 0.968 0.037

0.8-1.0 64 1.209 0.098 0.896 0.058 0.956 0.038

1.0-1.5 60 1.031 0.103 0.888 0.060 0.931 0.040

50-60 0.6-0.8 81 1.282 0.116 0.891 0.065 0.982 0.051

0.8-1.0 75 1.133 0.099 0.891 0.065 0.968 0.049

1.0-1.5 59 0.985 0.113 0.875 0.067 0.943 0.057

60-70 0.4-0.6 77 1.352 0.116 0.886 0.060 0.993 0.048

0.6-0.8 84 1.194 0.094 0.878 0.064 0.987 0.046

0.8-1.0 66 1.060 0.105 0.875 0.056 0.970 0.051


s bin (degrees)

Optical depth bin

No of boxes filled

CERES ADM Tropical Maritime Dubovik dust

Mean ratio SD Mean ratio SD Mean ratio SD

0-10 0.0-0.2 27 0.885 0.040 0.855 0.017 0.959 0.020

0.2-0.4 33 0.740 0.031 0.867 0.022 1.007 0.027

0.4-0.6 32 0.636 0.029 0.877 0.013 1.032 0.031

10-20 0.0-0.2 39 0.900 0.060 0.864 0.047 0.934 0.049

0.2-0.4 57 0.765 0.060 0.884 0.045 0.979 0.045

0.4-0.6 60 0.653 0.049 0.889 0.043 1.001 0.046

20-30 0.0-0.2 40 0.921 0.045 0.886 0.045 0.926 0.060

0.2-0.4 56 0.743 0.062 0.888 0.059 0.960 0.038

0.4-0.6 62 0.651 0.060 0.899 0.058 0.988 0.036

30-40 0.0-0.2 49 0.959 0.071 0.896 0.036 0.935 0.043

0.2-0.4 54 0.750 0.071 0.907 0.055 0.969 0.043

0.4-0.6 48 0.642 0.068 0.908 0.061 0.983 0.056

40-50 0.0-0.2 52 0.974 0.060 0.904 0.028 0.949 0.043

0.2-0.4 56 0.770 0.069 0.915 0.050 0.989 0.060

0.4-0.6 41 0.647 0.072 0.926 0.077 1.011 0.077

50-60 0.0-0.2 52 0.955 0.074 0.907 0.037 0.969 0.064

0.2-0.4 56 0.761 0.069 0.918 0.054 1.011 0.078

0.4-0.6 27 0.650 0.062 0.935 0.061 1.060 0.072

60-70 0.0-0.2 53 0.952 0.087 0.909 0.049 0.983 0.074

0.2-0.4 43 0.763 0.079 0.925 0.064 1.045 0.082

Performance summary: ADM 3

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