Cloud Microphysics and Climate. George A. Isaac, Ismail Gultepe and Faisal Boudala

Transcription

1 Cloud Microphysics and Climate George A. Isaac, Ismail Gultepe and Faisal Boudala

2 Parameterization of effective sizes of ice crystals in climate models and the effect of small crystals: CCCMA GCM simulations preliminary results Faisal B. (1,2), George I. (1,2), and Norm M. (3) 1) Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada 2) Cloud Physics Research Division, Meteorological Service of Canada, Toronto, Ontario, Canada 3) Canadian Centre for Climate Modeling and Analysis, Meteorological Service of Canada

3 Objective To test the sensitivity of Dge in climate models and particularly the effect of small particles 4 simulations have been conducted Dge(T) - without small particles Dge+s(T) - with small particles Dge+s(IWC,T) - with small particles Preliminary results

4 D ge (µ m) Comparisons of parameterized ice crystals mean effective sizes Tropical (M202) Boudala et al(2002) L and R (1996) CCCMA D ge D ge+s R R =100*( D ge -D ge+s ) /D ge D e (µ m) o C -55 o C -45 o C o C 0 o C Lohmann and Roeckner (1996) M (2002) Boudala et. al (2002) CCCMA Temperature ( o C) IWC (g m -3 )

5 Long wave cloud forcing: Model and satellite in Winter Direct effects (increased clouds) Indirect effect = 30µ m significant t λ = 10 µ m,12µ m ( D, λ) = A( D) Q ( D, λ) cross section s c abs s 4 πni ( λ) m( D) ( D, λ) 1 exp( ) effeciency λρ A ( D) = Arnott et al.(1994)

6 Short wave cloud forcing: Model and satellite in Winter

7 Anomaly (Dge - Dge+s) in IR flux at the top of the atmosphere. Summer (JJA), Fall (SON), winter (DJF). The IR flux is considered positive in the upward direction. ¾ Maximum near the Tropics ¾ Moves southward in NH Winter ¾ The anomaly is mostly positive ¾ Positive anomaly >Atm. Is optically thick

8 Global distribution of anomaly in energy balance TOA in Winter Mostly negative NH Winter E Dge - E Dge+s

9 Summary of preliminary observations Sensitivity to addition of small particles IR radiation Less energy flux at the top of the atmosphere (more absorption) More pronounced in the tropics Solar radiation Increased cloud forcing ( more reflection) The net effect is spatially variable Comparisons of Dge(T), Dge(T,IWC), and CCCMA Model CCCMA and Dge(T) gave similar results No significant difference between Dge+s (T) and Dge+s(T,IWC) With observation Hard to compare since CCCMA Dge is tuned, but generally including small particles seems to improve the model results particularly in the Northern hemisphere mid-latitude, but not in the tropics and the tropics is better captured by Dge(T) simulation. This suggests that Dges in the tropics are larger. General comments and future works Dge in the tropics seems to be larger Tropics and Mid-latitude need to be combined some way

10 Anomaly in cloudines in Winter

11 Cloud cover versus RH and microphysical parameters (MP), and statistical summary of MP I. Gultepe and G. Isaac Meteorological Service of Canada, Cloud Physics Research Division, Toronto, Ont. M3H5T4 E-m:

12 Objectives Cloud cover parameterization as a function of characteristics (e.g. LWC, TWC, IWC, and N d;i ) of the condensed water (ice or liquid, or mixed phase) Statistical analysis (PDFs) of cloud microphysical parameters for modeling studies

13 AIRS Cs0.005 TWC>0.005 g m -3 Cloud cover versus TWC Comparison of Xu and Randall (1996) fit and fit of present study for calculated cloud cover versus TWC. Cloud cover calculated from equation Cs = RH ρ [1- exp(-αq c )], where α and ρ are derived coefficients. q c is the condensed water content TWC=LWC+IWC

14 FIRE.ACE 1998 Cloud cover=f(q c,q s,q v ) Comparison of Xu and Randall (1996) fit and fit of present study for calculated cloud cover versus q c / (q s q v ) γ. Cloud cover is calculated from equation Cs = RH ρ [1-exp(- αq c )], where α and ρ are derived coefficients.

15 Conversion rate versus cloud cover CR = dq l / dt = q c L T 1 exp ( q / C L c w s ) 2 c A Pq L

16 Cs versus TWC (10 km versus 50 km)

17 RACE (Liquid case)

18 AIRS I (Mixed phase)

19 AIRS I (Liquid case)

20 RACE (Liquid case)

21 AIRS I (Liquid case)

22 CONCLUSIONS Cloud cover parameterizations are found comparable with these of earlier works with some differences in water and ice phase cases. PDFs are good way to validate model derived microphysical products and to test cloud development for various cloud types

23 Temperature ( O C) TWC (gm -3 ) (km -1 ) D eff ( m) N eff (cm -3 ) 0 O C<T<+10 O C O C<T< 0 O C O C<T<-10 O C O C<T<-20 O C O C<T<-30 O C O C<T<-40 O C Cloud Type St, Sc Cu Ns As, Ac Ci Korolev, Isaac, Mazin and Barker (2001): Microphysical properties of continental clouds from in-situ measurements. QJRMS, 127,

24 Korolev, Isaac, Cober and Strapp, 2001: Microstructure of mixed phase clouds. Part I: Observation Submitted to QJRMS

25 R = 4S meas /π D 2 max CFDE III FIRE.ACE AIRS I

26 Figure LNTWCN36 CFDElll + AIRS NTWC Variation of the Average Number Concentration for Liquid Phase <= NTWC < Number Concentration (m -3 microns -1 ) Points 462 Points 462 Points 370 Points 318 Points 222 Points 179 Points 103 Points = <= NTWC < <= NTWC < <= NTWC < <= NTWC < <= NTWC < <= NTWC < <= NTWC < Diameter (microns)

27 Figure GNTWCN36 CFDElll + AIRS NTWC Variation of the Average Number Concentration for Glaciated Phase Number Concentration (m -3 microns -1 ) This is to remove Data Points 317 Points 492 Points 288 Points 129 Points 52 Points = <= NTWC <= <= NTWC <= <= NTWC <= <= NTWC <= <= NTWC <= <= NTWC <= Diameter (microns)

28 TWC (g m -3 ) vs Temperature

29 TWC (g kg -1 ) vs Temperature

30 Conclusions Climate simulations are very sensitive to cloud microphysics. A greater understanding of cloud processes is necessary. Modeling our current knowledge does not fix problems. (e.g. mixed phase, small ice particles, particle shape, precipitation formation, etc). What do modelers want? (e.g. g/m -3 versus g/kg -1?) Need to test sensitivity of GCMs to cloud microphysics. What model(s) to use? How to we evaluate improvement in model performance?