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

all-sky netflux at surface

all-sky diff-netflux at surf

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 dn-solar at surface

all-sky diff-dn-solar at surf

CE dn-solar at surface

CE diff-dn-solar at surface

all-sky dn-IR at surface

all-sky diff-dn-IR at surf

CE dn-IR at surface

CE diff dn-IR at surface

different definitions of the clear-sky fluxcloud-free skyall sky

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

JJA

CRFCERES (Wm-2)

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OLR error~ 10W/m2 !

B.J.Sohn (2005)

modeled cloud-effect biases on fluxes

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