1. clouds and radiation … recent hot topics Stefan Kinne

2. 348 ? CERES 165 ? CERES Question: how to balance the incoming extra 20W/m2 by CERES at the surface ?

3. 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

4. all-sky netflux at surface

5. all-sky diff-netflux at surf

6. 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)

7. all-sky dn-solar at surface

8. all-sky diff-dn-solar at surf

9. CE dn-solar at surface

10. CE diff-dn-solar at surface

11. all-sky dn-IR at surface

12. all-sky diff-dn-IR at surf

13. CE dn-IR at surface

14. CE diff dn-IR at surface

15. different definitions of the clear-sky flux cloud-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

16. expected are … overestimates to –OLR (IR up at ToA) –IR dn at surface –IR divergence –solar divergence underestimates to –solar transmission –solar reflection OLR error ~ 10W/m2 ! B.J.Sohn (2005) modeled cloud-effect biases on fluxes OLR error (B.J. Sohn, 2010) theoretical simulations

17. 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

18. 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

19. 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