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This overview discusses recent findings from the Clouds and Radiation project, specifically focusing on the apparent energy imbalance of 20 W/m² reported by CERES compared to IPCC modeling and other datasets. Analyzing solar and infrared net fluxes, the study reveals discrepancies in cloud radiative effects due to variations in water vapor and aerosol concentrations. Findings emphasize the importance of clear-sky definitions and careful data assessments, highlighting potential biases in model simulations and observational data.
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clouds and radiation … recent hot topics Stefan Kinne
348 ? CERES 165? CERES Question: how to balance the incoming extra 20W/m2 by CERES at the surface ?
overview • look at combined solar + IR netflux for • CERES SRBAVG (the new ToA reference) • SRB • ISCCP • IPCC-model median • CERES is about • 20W/m2 larger than IPCC modeling • 10W/m2 larger than SRB • 8W/m2 larger than ISCCP
diagnostics • diagnose … why ? • solar down fluxes • all-sky • CE (all-sky minus clear-sky) • IR down fluxes • all-sky • CE (all-sky minus clear-sky)
all sky cloud-free sky different definitions of the clear-sky flux • satellite clear-sky: only data from cloud-free areas • modeled clear-sky: (= cloud-free) data with cloud removed • … but in ‘cloudy columns’ there is more water vapor than in ‘clear-columns’ • model simulations underestimate the derived cloud radiative effect … as it includes the increased water vapor in cloudy regions
expected are … overestimates to OLR (IR up at ToA) IR dn at surface IR divergence solar divergence underestimates to solar transmission solar reflection modeled cloud-effect biases on fluxes OLR error ~ 10W/m2 ! B.J.Sohn (2005) OLR error (B.J. Sohn, 2010) theoretical simulations
IPCC-modeling minus CERES (obs) divergence cloud effect on up-flux cloud effect on dn-flux solar IR OLR effects are smaller due to compensating differences in cloud altitude
ISCCP (model based)minus CERES (obs) divergence cloud effect on up-flux cloud effect on dn-flux solar IR lack of absorbing aerosol in tropics for ISCCP explains unexpected sA bias
take-home messages • data products of the same name often do not mean the same (not identical by definition) • water vapor is expected often to be larger near clouds … thus clear-sky definitions in modeling • by differing from observations introduce biases • interestingly, expected differences often do not fully materialize due to other inconsistencies (e.g ancillary data of aerosol) • - careful assessments of data-products and assumptions are essential prior to conclusions