© imperial college londonpage 1 inter-comparisons of monthly mean (and monthly time-step mean) gerb...

© Imperial College London

Inter-comparisons of monthly mean (and

monthly time-step mean) GERB clear sky fluxes

Joanna Futyan, Richard Allan and Jacqui RussellGIST 23DWD, Offenbach, Germany, 29/04/05


Generating monthly mean clear sky fluxes

• Flag and exclude cloudy footprints– MLE for CERES ERBE – RMIB+MPEF flags for GERB/GERB-like data– GERB data supplemented using ARCH

• CERES data collected into hour-boxes (2.5o) before processing– Maximum 2 observations day (1 in SW) for TERRA only data

• GERB/GERB-like data available at footprint scale (~0.5o)– Maximum of 96 observations per day, fewer in cloudy regions!

• Diurnal models used to fill missing data– ERBE DRMs (SW), half sine or linear interpolation (LW) for CERES– TRMM DRMs (NB problem with diurnal asymmetry) or modified sine

model for GERB/GERB-like


Comparison with CERES (ES4 FM1 only) – clear sky


• GERB-like data has problems over desert and for aerosol?

• Good SW flux agreement for CERES and GERB– Differences around coastlines/ edge of Sahara due to re-

gridding or cloud detection issues• Expect differences due to

– Calibration– Clear sky identification– Sampling


LW clear sky

• Land/ocean bias ~6Wm-2 cf 4-5Wm-2 predicted from radiance comparisons – Smaller bias for warmest desert scenes– Sampling errors in CERES data? ADM errors?– Remaining difference due to clear sky sampling?

CERES-GERB difference distributions


The satellite ‘dry bias’• In climate models clear sky flux is normally found

by making clouds transparent– Sample humid conditions under clouds which cannot be

seen from satellite– Relative to model derived value satellite flux is biased high

• Same effect makes satellite flux scale dependent

Smaller footprint enables more of the available clear sky information to be retained, reducing bias to unusually dry clear conditions


Comparisons with the UM• SINERGEE – model fluxes at 4 synoptic times

– Similar spatial resolution to GERB data– Investigate scale and cloud fraction dependence of LW

clear sky flux

Comparison of type II model clear sky flux with resolution enhanced GERB and GERB-like estimates for 1200 UTC


Sampling Effects

• Difference is an estimate of the ‘dry bias’ for GERB• Similar but smaller effects seen in observations for

resolution enhancement

12

12


• Shift in observations as more humid small clear regions sampled

• Comparable to that in the model for low cloud fractions

• Use of high resolution data acts to reduce dry bias

Region1 – Atlantic ITCZ


• Excellent agreement between model and data• No dependence on cloud fraction or scale of

clear region found• Region dominated by subsidence with dry air

aloft – free tropospheric humidity varies little with cloud fraction in the boundary layer

Region 2 – Stratocumulus deck


Summary• GERB monthly mean clear sky fluxes show

good general agreement with CERES– Biases are mostly consistent with those expected from

comparisons of lower level products– Clear sky SW agreement better than expect

• Longwave clear sky fluxes also agree well with those predicted by the UM

• Synergy/ SINERGEE allows dry bias effect to be investigated– Behaviour depends on meteorology, with largest effects

in convective regions– Using ARCH data reduces (but does not remove) bias

between GERB products and standard model diagnostics


SW flux difference

• Radiance comparison – underestimate for all scenes:– Maximum of ~7% for clear ocean, 0 difference for dark desert

• Plot differences as percentage of mean SW flux– GERB low by 4% cf CERES for ocean, high for other scenes


Comparison with CERES (ES4 FM1 only) – all sky

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