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© Imperial College LondonPage 1 Estimating the radiative effect of mineral dust over ocean The radiance to flux problem Dust retrieval quality Applying.

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Presentation on theme: "© Imperial College LondonPage 1 Estimating the radiative effect of mineral dust over ocean The radiance to flux problem Dust retrieval quality Applying."— Presentation transcript:

1 © Imperial College LondonPage 1 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

2 © Imperial College LondonPage 2 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 =  L OBS / (Rclr x (Rth(L OBS )/Rth(L ADM )) (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

3 © Imperial College LondonPage 3 AOD ‘sanity check’ (I): GERB ARCH (SEVIRI) vs AERONET BAHRAIN FORTH CRETE DAKAR GRANADA BLIDA

4 © Imperial College LondonPage 4 AOD ‘sanity check’ (II): GERB ARCH (SEVIRI) vs MODIS (Terra) MODIS SEVIRI (GERB ARCH)

5 © Imperial College LondonPage 5 Dust forcing efficiency: GERB Edition I product (AERONET points) For a given geometry use linear interpolation in L to find F TH (L OBS ) and F TH (L ADM ). Then: R(L OBS ) =  L OBS / F TH (L OBS ) and R(L ADM ) =  L ADM / F TH (L ADM )

6 © Imperial College LondonPage 6 Sensitivity of correction to RT code Assuming L varies linearly with , can also infer a ‘broad-band’ optical depth Applying correction to all scenes

7 © Imperial College LondonPage 7 F =  L / (Rclr x (Rth(L OBS )/Rth(L ADM )) R =  L / F Black:  s :65º,  v :25º,  r :140º Blue:  s :20º,  v :65º,  r :170º Red:  s :55º,  v :45º,  r :110º L OBS L ADM

8 © Imperial College LondonPage 8 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

9 © Imperial College LondonPage 9 Sensitivity to aerosol representation 0.63  m

10 © Imperial College LondonPage 10 F =  L OBS / (Rclr x (Rth(L OBS )/Rth(L ADM )) Rclr/Rth(L ADM ) (Hess Tropical Maritime) Rclr/Rth(L ADM ) (Dubovik non-spherical dust)  s = 45°

11 © Imperial College LondonPage 11 April: 1173177 May: 729547 June: 3586795All: 5489519 ‘Dusty’ ocean points through April-June 2006 (view/solar zenith limited to 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) 170: Overcast (water,  =1.0-2.5) 171: Overcast (water,  =2.5-5.0) Testing aerosol model behaviour: expand sample size…

12 © Imperial College LondonPage 12 …and investigate angular coverage and behaviour ADM =3  v : fast variation,  r : slow variation

13 © Imperial College LondonPage 13 ADM=170

14 © Imperial College LondonPage 14 ADM=3 ADM=170  : fast variation,  s : slow variation Overall performance NB: Not sampling glint region

15 © Imperial College LondonPage 15 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

16 © Imperial College LondonPage 16 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 optical depths being flagged as clear at low  s

17 © Imperial College LondonPage 17 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)

18 © Imperial College LondonPage 18 Impact of correction on dust radiative forcing efficiency (I): clear scene IDs Least squares linear regression fit on flux vs  ‘clear’ scenes only

19 © Imperial College LondonPage 19 Impact of correction on dust radiative forcing efficiency (II): all scene IDs Least squares linear regression fit on flux vs  all scenes

20 © Imperial College LondonPage 20 Cloudy ID Clear ID Granada AERONET site, June 14 th 2006

21 © Imperial College LondonPage 21 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

22 © Imperial College LondonPage 22 Performance summary: ADM 170  s bin (degrees) Optical depth bin No of boxes filled CERES ADMTropical MaritimeDubovik dust Mean ratioSDMean ratioSDMean ratioSD 0-100.6-0.8511.7980.1630.8820.0381.0140.020 0.8-1.0611.6550.1230.8870.0331.0130.024 1.0-1.5611.3930.0810.8740.0270.9830.023 10-200.6-0.8541.4400.1240.9020.0600.9990.022 0.8-1.0651.3200.0920.9000.0531.0000.023 1.0-1.5641.1190.0720.8890.0520.9720.024 1.5-2.0560.9220.0600.8660.0370.9300.019 20-300.6-0.8551.3890.1500.9130.0670.9810.033 0.8-1.0651.2310.1160.8970.0640.9750.030 1.0-1.5631.0460.0940.8860.0600.9490.031 30-400.6-0.8551.4070.1330.9160.0560.9670.031 0.8-1.0591.2300.1170.9020.0580.9590.036 1.0-1.5571.0440.1000.8910.0600.9340.032 40-500.6-0.8641.3630.1290.9040.0600.9680.037 0.8-1.0641.2090.0980.8960.0580.9560.038 1.0-1.5601.0310.1030.8880.0600.9310.040 50-600.6-0.8811.2820.1160.8910.0650.9820.051 0.8-1.0751.1330.0990.8910.0650.9680.049 1.0-1.5590.9850.1130.8750.0670.9430.057 60-700.4-0.6771.3520.1160.8860.0600.9930.048 0.6-0.8841.1940.0940.8780.0640.9870.046 0.8-1.0661.0600.1050.8750.0560.9700.051

23 © Imperial College LondonPage 23  s bin (degrees) Optical depth bin No of boxes filled CERES ADMTropical MaritimeDubovik dust Mean ratioSDMean ratioSDMean ratioSD 0-100.0-0.2270.8850.0400.8550.0170.9590.020 0.2-0.4330.7400.0310.8670.0221.0070.027 0.4-0.6320.6360.0290.8770.0131.0320.031 10-200.0-0.2390.9000.0600.8640.0470.9340.049 0.2-0.4570.7650.0600.8840.0450.9790.045 0.4-0.6600.6530.0490.8890.0431.0010.046 20-300.0-0.2400.9210.0450.8860.0450.9260.060 0.2-0.4560.7430.0620.8880.0590.9600.038 0.4-0.6620.6510.0600.8990.0580.9880.036 30-400.0-0.2490.9590.0710.8960.0360.9350.043 0.2-0.4540.7500.0710.9070.0550.9690.043 0.4-0.6480.6420.0680.9080.0610.9830.056 40-500.0-0.2520.9740.0600.9040.0280.9490.043 0.2-0.4560.7700.0690.9150.0500.9890.060 0.4-0.6410.6470.0720.9260.0771.0110.077 50-600.0-0.2520.9550.0740.9070.0370.9690.064 0.2-0.4560.7610.0690.9180.0541.0110.078 0.4-0.6270.6500.0620.9350.0611.0600.072 60-700.0-0.2530.9520.0870.9090.0490.9830.074 0.2-0.4430.7630.0790.9250.0641.0450.082 Performance summary: ADM 3

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