Aim

1. A comparison between bin and bulk models in the case of boundary layer clouds observed during RICOKozo Nakamura, Yasushi Fujiyoshi, Kazuhisa Tsuboki, Naomi Kuba (JAMSTEC)

2. Aim Aim : To improve the parameterization schemes used in warm bulk cloud microphysics model. Question? For the better simulation, • Should we divide water drops into more than 2 categories including drizzle as one of the categories ? • Should we use 3 (or more) variables for each group? Method : Using the results of a bin scheme model, we will develop a new bulk parameterization scheme. Case : RICO intercomparison case for the first case. (The scheme will be fitted for several cases in future. )

3. Fields of trade wind congestus typical cloud base 600 m typical cloud top ~ 2000-3000 m Example RF-09 17 Dec 04 2004 Model setting （RICO LES intercomparison） grid size Δｘ＝Δｙ＝100ｍ, Δｚ＝40m number of grids128 x 128 x 100 →Domain 12.8km×12.8km× 4.0km θ,qv,u,v : shown in following figures Horizontally cyclic B. C. Bottom B. C. SST 299.8Ｋ＝Ｔair＋ 0.6℃ Forcing : subsidence：w = -0.005 at 2260m constant divergence below. horizontal drying and heating. Analysis : t =20～24 hrs. １moment bulk MESO-NH SAM JAMSTEC Utah EULAG 2DSAM ２ moment bulk DALES UCLA WVU COAMPS UKMO RAMS Bin AMS@NOAA SAMEX DHARMA From http://www.knmi.nl/samenw/rico/

4. Results of RICO intercomparison (20-24hr) • 15 models : • Open circles • 1-moment bulk models • red circles • variables : QC, QR • 2-moment bulk models • green circles • var : QC, QR, NC, NR • Bin models • blue circles • var : Q1-Ｑ？, N1- Integrated liquid water（ｇ ｍ-2） Surface precipitation (Ｗ ｍ-2) From http://www.knmi.nl/samenw/rico/

5. physical process in 1-moment bulk model liquid water is divided into two groups not falling cloud and falling rain cloud amount Temp falling rain from upper grid water vapor Qsat grid model heat cloud droplets rain drops evaporation condensation Ⅰ auto-conversion (without rain) 1. condensational growth 2. collision between clouds Ⅱcollision-coalescence 　Ｒ＋Ｃ⇒Ｒ falling rain to lower grid

6. Results of RICO intercomparison • Autoconversion scheme • red marks with numbers • ２moment bulk models • green marks • Bin models • blue marks • CReSS 1-moment bulk • closed red marks with capital letters Kessler Berry Conversion rate（mg/kg/s） Mod. Berry Integrated liquid water（ｇｍ-2） cloud water（g/kg） Surface precipitation (Ｗ ｍ-2) From http://www.knmi.nl/samenw/rico/

7. Model : CReSSthe Cloud Resolving Storm Simulator developed by Dr. Tsuboki and his colleagues • Basic equations non-hydrostatic, compressible equations • advective form • Spatial discretization finite difference scheme (2,4,3) • Topography terrain following coordinate • Temporal scheme mode splitting • Slow mode　－　 explicit scheme • Fast mode －　 Horizontal Explicit Vertical Implicit scheme • Cloud physics – bulk scheme ⇒bin scheme for warm rain • vapor, cloud, rain, cloud-ice(2) snow(2) graupel(2). • Turbulence - Smagorinsky scheme or Deardorff scheme Cloud physics – bulk scheme ⇒bin scheme for warm rain 71 bins (radius of drops covers from 1μｍ to 3.5 mm) Ratio of mass between the adjacent bin is sqrt(2).

8. Aerosol size distribution and activated CCN. 1) PCASP data was used and assumed that the RH in the instrument was 0.8x the ambient RH 2) The measured wet sizes were converted to dry sizes using Kohler theory and an assumed composition of ammonium sulfate. 3) The dry size distributions were averaged over all sub-cloud legs on RF12 (Jan 11) 4) A bimodal lognormal was fitted to the spectra 5) rg1=0.03 μm, sig1=1.28, n1=90 (cm-3), rg2=0.14 μm, sig2=1.75 n2=15 (cm-3) By courtesy of Dr Hongli Jiang and Dr. Margreet van Zanten observed size distribution of CCN. Parameterization by parcel model. Kuba and Fujiyoshi (2006)

9. QC QR t=20～24hr（15 models+1） １moment bulk ２moment bulk Bin CReSS-bin Cloud water (mg/kg) Rain water(mg/kg) 雨水混合比(g/kg)

10. Results of RICO intercomparison • 1-moment bulk models • red marks with numbers • 2-moment bulk models • green marks • Bin models • blue marks • CReSS 1-moment bulk • closed red marks with capital letters • CReSS Bin model • closed blue mark Integrated liquid water（ｇ ｍ-2） Surface precipitation (Ｗ ｍ-2) From http://www.knmi.nl/samenw/rico/

11. Vertical profiles of cloud processes 33/71 Cond. Eva. Auto1>0 C →R(cond) Too large? Auto1<0 C →R (eva) Auto2 C＋C→E Coalescence R＋C→R Rain water (mg/kg/s*1.e6) Cloud water (mg/kg/s*1.e5) height (km) t=20-24hr. boundary between C&R is 47.9μｍ. i<34

12. Vertical Profiles of cloud processes Cond. Eva. Auto1>0 C →R(cond) Too large? Auto1<0 C →R (eva) Auto2 C＋C→E Collision R＋C→R Rain water (mg/kg/s*1.e6) Mod. Berry model Rain water height (km) t=20-24hr. boundary between C&R is 47.9μｍ t=20-24hr

13. autoconversion2 in terms of Qc • Colorindicates the group of number concentration of cloud 。 • Brown : the maximum number concentration group • light blue, purple, blue, green • Red : the smallest number concentration group . • （for the same mixing ratio, • the small number concentration, the larger conversion rate) autoconversion2（mg/kg/s） cloud water（g/kg）

14. Autoconversion(Qc, Nc) Autoconversion rate used in the bulk model. Kessler Berry Modified Berry • Averaged over each group • ⇒Colors • Brown : the maximum number concentration group • light blue, purple, blue, • green: the smallest number concentration group . • Black : total average. autoconv（mg/kg/s） cloud water（g/kg）

15. Parameterization of each process　１ independent variables (assuming 2-moment bulk scheme) cloud related ⇒ Qc, Nc, average mass of droplet, radius rain related ⇒ QR, NR, average mass of drop, radius environment ⇒ T, θ, Qv、Qv-Qsat、Ａ、p、e、rh、w。 Process（mass & number） variables condensation to cloud cloud & environment evaporation from cloud cloud & environment autoconv1( c -> r ) cloud & environment autoconv1 ( r -> c ) rain & environment autoconv2 cloud (& environment) collision-coalescence cloud, rain & environment 　　：

16. Parameterization of each process　２ An example autoconv( c -> r ) cloud & general Previously proposed formula (examples). Assumed formula in this work ⇒ Searching the combination of variables which gives largest correlation coefficient.

17. Parameterization of each process　３ Results of fitting parameters (few examples) Simulation results of the bulk model using these parameters ○ Conversion from cloud to rain is very small, because the large number of small value occurrence determines the fitting parameter. ○ Rain does not develop as in the bin model. ○ We need some sophisticated technique to make a bulk parameterization scheme from the bin model results.

18. Summary ○ We applied CReSS-bin model to GCSS-BLＷＧRICO intercomparison case. ○ Although the results show some difference from other model results, the results are within the range of the variation of the results of the models. (We need to compare the results with observational results and other bin model results. ) ○ We need some sophisticated technique to make a bulk parameterization scheme from the bin model results. Future work ○ to develop a 2（or more）-moment bulk scheme. ○ to apply the model to other cases and extend the model.

19. Ｂ N Ｋ Liquid water and surface precipitation 1 DYCOMS Ⅱ t=３～６hr。 Observational estimate Surface precipitation(mm day-1) 0 0 100 Integrated Liquid water(gm-2)

20. Physical process in bin model Liquid drops are divided into groups (bins). Size distribution of liquid drops is indicated by the number concentration of each bin radius rain coalescence Change of bin boundary remapping remapping boundary between cloud and rain Cond↑ equilibrium cloud mass conservation Eva↓ number autoconversion：pink and orange (C+C->R) coalescence： orange (R+C->R)