1. Important data of cloud properties for assessing the response of GCM clouds in climate change simulations Yoko Tsushima JAMSTEC/Frontier Research Center for Global Change

2. Contents Cloud feedback uncertainty in GCM global warming simulations –Uncertainty in the tropics –Uncertainty in the mid-high latitudes “Toward fusion of satellite observation and ultra- high resolution modeling” : Global cloud resolving model NICAM –Data –Workshop announcement: 3 rd -5 th, Oct, Kusatsu, Japan

3. Cloud feedback in the tropics

4. 15 AR4 coupled ocean-atmosphere GCMs +1%/yr CO 2 : (low-sensitivity models) (high-sensitivity models) The cooling effect of clouds is reduced (enhances climate sensitivity) The cooling effect of clouds is enhanced (decreases climate sensitivity) Sensitivity of the tropical NET CRF to long-term SST changes (W/m 2 /K) Bony and Dufresne, GRL (2005)

5. ISCCP cloud amounts and ERA40  500mb (tropical oceans, 1984-2000) Upper-level cloud tops Low-level cloud tops

6. Subsidence regimes HS GCMs LS GCMs HS GCMs LS GCMs HS GCMs LS GCMs Convective regimes Sensitivity of the tropical CRF to long-term SST changes in global warming experiments (W/m 2 /K) 2 OAGCM groups: High-Sensitivity models  0) Low-Sensitivity models  0) +1%/yr CO2 :

8. Interannual Climate Variations (an example, not an analogue!) 1984-2000 monthly data : ISCCP-FD radiative fluxes Reynolds SST ERA40 or NCEP2 reanalyses AR4 OAGCMs: 20 th century simulations HS (N=8) vs LS (N=7) models High-Sensitivity models Low-Sensitivity models convective regimes subsidence Sensitivity of the SW CRF to SST changes composited by dynamical regimes OBS

9. It is in regimes of large-scale subsidence (associated with low-level clouds) that the relationship between cloud radiative forcing and SST : (1) Differs the most among models in climate change (explains most of inter-model differences in cloud feedbacks) (2) Disagrees the most with observations in current interannual climate variability (models underestimate the sensitivity of clouds albedo to a change in SST) The simulation of marine boundary-layer clouds is at the heart of tropical cloud feedback uncertainties in AR4 models. Any impact on the simulation of ENSO variability ?? Needs some investigation..

10. Change in Cloud water: feedback in the mid and high latitudes

11. Cloud Feedback Model Intercomparison Project (CFMIP) McAvaney and Le Treut (2003) Outputs from IPCC models with more cloud variables than IPCC outputs. Slab ocen experiments with 1 x CO 2, 2 x CO 2. Webb et al.,2006 –The intermodel range in net cloud feedback is larger than the associated clear-sky response range: the differences in cloud response make the largest contribution to the range in climate sensitivity.

12. Climate sensitivity 6.3 ℃ 4.0 ℃ 3.6 ℃ 2.9 ℃ 2.3 ℃ Tsushima et al., 2006 Cloud water (1 x CO 2 ) 0 1.5e-4 － 90 － 60 － 30 0 30 60 90 0 200 400 600 800 1000 0 200 400 600 800 1000 0 200 400 600 800 1000 0 200 400 600 800 1000 0 200 400 600 800 1000 [hPa] Cloud ice (1 x CO 2 ) － 90 － 60 － 30 0 30 60 90 0 2.5e-5 0 200 400 600 800 1000 0 200 400 600 800 1000 0 200 400 600 800 1000 0 200 400 600 800 1000 0 200 400 600 800 1000 [hPa] Cloud liquid(2 x CO 2 － 1 x CO 2 ) -2e-5 0 2e-5 － 90 － 60 － 30 0 30 60 90 0 200 400 600 800 1000 0 200 400 600 800 1000 0 200 400 600 800 1000 0 200 400 600 800 1000 0 200 400 600 800 1000 [hPa]

13. Zonal mean profile of relative humidity, cloud water, cloud ice under 1xCO2 climate in [60S:30S] Relative humidity Cloud water Cloud ice

14. Implications from multi-GCM analysis Assessment of the mean state and sensitivity of Low clouds in the large scale subsidence region Mixed-phase level clouds (which is dominant clouds in the extra-tropics) using observational data are important for assessing GCM clouds.

15. “Toward fusion of satellite observation and ultra-high resolution modeling” : Global cloud resolving model NICAM

16. Global Cloud Resolving Model NICAM (Nonhydrostatic ICosahedral Atmospheric Model) Icosahedral grid & Nonhydrostatic model & Explicit cloud physics Development since 2000: number of test cases Problems of Current GCMs:Δx~ 20km at best & hydrostatic, cloud parameterization Horizontal resolution: up to dx=3.5km Global cloud resolving simulations with NICAM 3.5km-mesh Aqua Planet Experiment GCM expemeriments with realistic land/sea disribution 30days run through Apr. 2004 preliminary results with 14km-meshOutlines

17. Icosahedral grids Original Icosahedron Glevel-1 Glevel-3Glevel-5 Glevel-0 Glevel-9: Δx=14km Glevel-10: Δx=7km Glevel-11: Δx=3.5km

18. Condensed water distribution in Aqua planet experiment Tsushima, 2006 What are the definition of “cloud liquid”, “rain”, “cloud ice”and “snow”? Usage of observational definition is useful. “Total condensed water” data are also informative. condensed water cloud liquidrain cloud icesnow

19. Preliminary results of a global cloud-resolving simulation with realistic topography dx=14km (glevel9) L40 without parameterizationdx=14km (glevel9) L40 without parameterization dx=7, 3.5km, on goingdx=7, 3.5km, on going Apr. 2004, short-term (H.Miura)Apr. 2004, short-term (H.Miura) Perpetual July experiment, statistics (S.Iga)Perpetual July experiment, statistics (S.Iga)

20. Apr. 2004 short term exp. NICAM 14km GMS/GOES Initial condition: 2004/04/01 0UTC, 30 days simulation with 14km-mesh 2004/04/05 00UTC

21. 2004/04/02 00UTC2004/04/03 00UTC2004/04/04 00UTC GOES-9 Kochi-Univ.(http://weather.is.kochi-u.ac.jp/) NICAM gl-09

22. 2004/04/05 00UTC2004/04/06 00UTC2004/04/07 00UTC

23. 2004/04/08 00UTC2004/04/09 00UTC2004/04/10 00UTC

24. Satoh et al.,2006 Precipitation statics comparison between global cloud resolving simulation with NICAM and TRMM PR data

25. Data Summary A global cloud resolving model (GCRM) Nonhydrostatic system & Icosahedral grid: NICAM avoid ambiguity of cumulus parameterizations Use of the Earth Simulator An aqua-planet-experiment dx=3.5km and 54 layers Hierarchical structure of cloud convection Moist Kelvin wave structure with realistic phase speed Internal motions including wave structure Nasuno et al.(2006,submitted to JAS) GCRM runs on the realistic land-ocean distribution dx=14km, 30days done: dx=7, 3.5km, on-going Apr. 2004, short-term (H.Miura) Perpetual July experiment, statistics (S.Iga)

26. Announcement of a Workshop High resolution & cloud modeling workshop “toward fusion of satellite observation and ultra-high resolution modeling” 3rd-5 th, Oct, Kusatsu, Japan If you are interested in the data and/or the workshop, please contact me! Thank you. Yoko Tsushima E-mail: tsussi@jamstec.go.jptsussi@jamstec.go.jp Frontier Research Center for Global Change/JAMSTEC Japan