1. Cloud fraction in midlatitude cyclones: observations vs models Catherine Naud, Columbia University Collaborators: James Booth (Columbia), Tony Del Genio (GISS), Derek Posselt (Michigan Univ.), Sue van den Heever (CSU) NASA-Terraqua NNX11AH22G, CloudSat NNX10AM20G

2. SH cloudiness in models Trenberth and Fasullo (2010): too much solar absorption in the southern oceans in CMIP3 models => less clouds when compared with ISCCP in 40- 60 o S band What about reanalysis ERA-interim and MERRA? Cloud fraction exceeds 80% in most of SH oceans according to MODIS => lower in reanalysis & ISCCP 10-year mean cloud cover: MODIS-Terra & ISCCPERA-interim and MERRA

3. SH clouds in extratropical cyclones Large frequency of occurrence of extratropical cyclones in SH oceans=> can models represent cyclone cloud fractions? Cyclone number per month per 10 o x10 o region Useful tool: cyclone centered cloud fraction composites Collect 4 summers (NDJFM) of extratropical cyclone locations in SH Associate daily MODIS, MISR and L2 CloudSat-CALIPSO cloud fraction to each cyclone Average in cyclone centered grid

4. Comparison with ERA-interim and MERRA -Apply same technique on 2D total cloud fraction from ERA-interim and MERRA (same period) -Analyze SH summer cyclones -Compare to MODIS and MISR Cloud fraction: -At least 90% along warm front and poleward of the low -At least 70% within cyclones -Both ERA-interim and MERRA produce less clouds -More drastic with MERRA South pole ^ | Equator

5. Observed Cloud fraction in SH summer cyclones Observational uncertainty: MODIS vs MISR vs CloudSat-CALIPSO: mostly within 5% except for poleward edge Poleward: MISR less than other two datasets Equatorward: MISR > CloudSat-CALIPSO > MODIS Impact of optical thickness, broken clouds, daytime vs day+night,… CloudSat-CALIPSO minus MISRMODIS minus MISRMODIS minus CloudSat-CALIPSO See Naud et al., submitted to JGR 2013

6. Difference in cloud fraction: SH summer Differences where larger than diff(MODIS-MISR)  ERA-interim 8-14% underestimate, west and equatorward of the low  MERRA ~20-35%, largest to the west of the low  MODIS CTP, CTT and MISR CTH indicate spatial correlation between ERA-I bias and CTT and between MERRA bias and CTP/CTH  Difference with obs. and between each other=> Large scale issue or parameterization?

7. Problem linked to large scale? -850 hPa horizontal winds: very similar between the two reanalysis and close to MISR cloud-top wind -Same thing for 850 hPa vertical velocities No obvious correlation with dynamics or moisture  Issue probably with parameterizations  Cloud misrepresented in MERRA at the back of cold front: -low-level clouds (MISR CTH 740 mb) -Mostly liquid: MODIS liquid cloud fraction > ice fraction < 30% -Possibly broken clouds  Problem with PBL or cloud or shallow convection scheme?

8. PBLH & LTS -PBL heights: ERA-i > MERRA everywhere in cyclones, with max difference in equator-west quadrant BUT difference definition -LTS: theta_700 – Theta_surf => virtually identical between ERA-interim and MERRA => LTS < 15 K where larger MERRA bias  Cumulus ill-represented in MERRA? Shallow convection the issue? Fig. 6, Bodas- Salcedo et al. 2012: ISCCP cloud regimes

9. Conclusions and future work Both ERA-interim and MERRA underestimate cloud fractions in SH cyclones ERA-interim bias 8-14%, low bias largest for MERRA 20-35% and max bias coincides with region of low, liquid and broken clouds Consistent with: - Williams et al. (2013): find lack of clouds at back of cold fronts in collection of GCMs - Bodas-Salcedo et al. (JCLI 2012): UK Met Office model has similar problem, not enough clouds at back of cold front (stratocumulus clouds and ‘transition’ regime in ISCCP cloud regimes), improved through modification of boundary layer scheme - New PBL scheme in GISS-modelE2 (i.e. deeper PBL) improves SH cloud cover  Are PBLH really different between the 2 models: profiles of θ e hint at 200 m difference, would affect stratocumulus cover  Or is the shallow convection different?  hit the limit of compositing usefulness