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© Imperial College LondonPage 1 Inter-comparisons of monthly mean (and monthly time-step mean) GERB clear sky fluxes Joanna Futyan, Richard Allan and Jacqui Russell GIST 23 DWD, Offenbach, Germany, 29/04/05
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© Imperial College LondonPage 2 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 (2.5 o ) before processing –Maximum 2 observations day (1 in SW) for TERRA only data GERB/GERB-like data available at footprint scale (~0.5 o ) –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
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© Imperial College LondonPage 3 Comparison with CERES (ES4 FM1 only) – clear sky
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© Imperial College LondonPage 4
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© Imperial College LondonPage 5 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
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© Imperial College LondonPage 6 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
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© Imperial College LondonPage 7 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
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© Imperial College LondonPage 8 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
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© Imperial College LondonPage 9 Sampling Effects Difference is an estimate of the ‘dry bias’ for GERB Similar but smaller effects seen in observations for resolution enhancement 1 2 1 2
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© Imperial College LondonPage 10 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
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© Imperial College LondonPage 11 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
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© Imperial College LondonPage 12 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
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© Imperial College LondonPage 13 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
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© Imperial College LondonPage 14 Comparison with CERES (ES4 FM1 only) – all sky
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© Imperial College LondonPage 15
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