1. Multi-Layer Arctic Mixed-Phase Clouds Simulated by a Cloud-Resolving Model: Comparison with ARM Observations and Sensitivity Experiments Yali Luo State Key Lab of Severe Weather (LaSW) Chinese Academy of Meteorological Sciences Co-authors: Kuan-Man Xu (LaRC), Hugh Morrison (NCAR), Greg McFarquhar (U Illinois), Zhien Wang (U Wyoming), Gong Zhang (U Illinois) Polar Cloud Working Group Breakout Session II, 4th Pan-GCSS Meeting June 4 th 2008; Toulouse, France

2. 2 Outline 1. Introduction 2. Large-scale background and observations 3. Model and simulations 4. Comparing Baseline results with observations 5. Results from sensitivity experiments

3. 3 Introduction The UCLA/CAMS CRM is used to simulate the multiple-layer mixed-phase stratiform (MPS) clouds that occurred during a 3.5-day sub-period of the M-PACE (14Z 5 Oct - 02Z 9 Oct) The UCLA/CAMS CRM is used to simulate the multiple-layer mixed-phase stratiform (MPS) clouds that occurred during a 3.5-day sub-period of the M-PACE (14Z 5 Oct - 02Z 9 Oct) The large-scale forcing data used is the same as that for the ARM inter-comparison of model simulations The large-scale forcing data used is the same as that for the ARM inter-comparison of model simulations Baseline results are compared to the M-PACE observations Baseline results are compared to the M-PACE observations Sensitivity experiments are conducted to explore the possible mechanisms for the formation and evolution of the multiple-layer MPS clouds Sensitivity experiments are conducted to explore the possible mechanisms for the formation and evolution of the multiple-layer MPS clouds

4. 4 Outline 1. Introduction 2. Large-scale background and observations 3. Model and simulations 4. Comparing Baseline results with observations 5. Results from sensitivity experiments 6. Conclusions

5. 5 Large-scale background 201 km 360 km Toolik Lake High pressure over the pack ice to the northeast of the Alaska coast North Slope of Alaska (NSA) Barrow Midlevel low pressure system drifted along the northern Alaska coast

6. 6 Observations of Cloud properties Occurrences and locations of mixed- phase cloud layers Occurrences and locations of mixed- phase cloud layers Liquid water path Liquid water path Bulk cloud microphysical properties Bulk cloud microphysical properties

7. 7 Other observations used Aerosol properties (for microphysics calculation) Aerosol properties (for microphysics calculation) Surface precipitation rate, temperature, moisture (for model evaluation; produced by the ARM analysis) Surface precipitation rate, temperature, moisture (for model evaluation; produced by the ARM analysis)

8. 8 Outline 1. Introduction 2. Large-scale background and observations 3. Model and simulations 4. Comparing Baseline results with observations 5. Results from sensitivity experiments 6. Conclusions

9. 9 UCLA/CAMS CRM (University of California at Los Angeles/Chinese Academy of Meteorological Sciences) Anelastic dynamic framework Third-order turbulence closure d -four-stream radiative transfer scheme d -four-stream radiative transfer scheme Two-moment microphysics parameterization Krueger, S. K., 1988: Numerical simulation of tropical cumulus clouds and their interaction with the subcloud layer. J. Atmos. Sci., 45, 2221-2250. Luo, Y., etc., 2008: Arctic mixed-phase clouds simulated by a cloud-resolving model: Comparison with ARM observations and sensitivity to microphysics parameterizations. J. Atmos. Sci., 65, 1285-1303.

10. 10 Large-scale forcing data Klein, S., A. Fridlind, R. McCoy, G. McFarquhar, S. Menon, H. Morrison, S. Xie, J. J. Yio, and M. Zhang (2006), Arm Cloud Parameterization and Modeling Working Group – GCSS Polar Cloud Working Group model intercomparison. Procedures for ARM CPMWG Case 5/GCSS Polar Cloud WG SCM/CRM/LES Intercomparison Case f2004: ARM Mixed-phase Arctic Cloud Experiment (M-PACE): October 5-22, 2004. Xie, S., S. A. Klein, M. Zhang, J. J. Yio, R. T. Cederwall, and R. McCoy (2006), Developing large-scale forcing data for single-column and cloud-resolving models from the Mixed-Phase Arctic Cloud Experiment, J. Geophys. Res., 111, D19104, doi:10.1029/2005JD006950.  Large-scale advection of temperature and moisture  Surface fluxes of latent and sensible heat  Skin temperature  Surface broadband albedo

11. 11 List of simulations 1. Baseline: standard baseline simulation 2. noLSforcing: neglecting large-scale advective forcing 3. noSfcFlx: neglecting surface fluxes of latent and sensible heat 4. noLWrad: neglecting longwave radiative cooling/heating 5. noIce: neglecting ice-phase microphysical processes 6. IN50th ： decreasing IFN concentration from 0.16/L to 0.003/L 7. IN50 ： increasing IFN concentration from 0.16/L to 8/L

12. 12 Outline 1. Introduction 2. Large-scale background and observations 3. Model and simulations 4. Comparing Baseline results with observations 5. Results from sensitivity experiments 6. Conclusions

13. 13 Baseline Results: Time-height distribution of horizontal-averaged LWC (shades) and IWC (lines) Time (hrs from 14Z October 5, 2004)

14. 14 Baseline Results: Occurrences of multiple- layer MPS clouds 1- layer (%)2-layer (%)3-layer (%) MMCR-MPL 10/0649419 CRM 12-36 h29647 MMCR-MPL 10/0766313 CRM 36-60 h63361 MMCR-MPL 10/0890100 CRM 60-84 h66340

15. 15 Baseline Results: Histograms of cloud-base height, cloud- top height and cloud physical thickness of the 1st MPS cloud layer ObservationsCRM Baseline Cloud Base Height Cloud Top Height Cloud Physical Thickness Lower!

16. 16 Baseline Results: Histograms of cloud-base height, cloud- top height and cloud physical thickness of the 2nd MPS cloud layer Observations CRM Baseline Cloud Base Height Cloud Top Height Cloud Physical Thickness Too homogeneous in the horizontal! Thicker!

17. 17 Baseline Results: Vertical profiles of in-cloud LWC CRM BaselineAircraft Obs. Subperiod A Subperiod B Subperiod C

18. 18 Baseline Results: Vertical profiles of in-cloud n c CRM BaselineAircraft Obs. Subperiod A Subperiod B Subperiod C ? ? CCN activation parameterization

19. 19 Baseline Results: Vertical profiles of in-cloud IWC Aircraft Obs.CRM Baseline Subperiod A Subperiod B Subperiod C but a few times smaller than observations. Reproduced the larger IWCs below 1.5 km;

20. 20 Baseline Results: Vertical profiles of in-cloud n i CRM BaselineAircraft Obs. Subperiod A Subperiod B Subperiod C Differ by one order of magnitude!

21. 21 Baseline Results: Surface precipitation Dashed line: CRM Baseline Solid line: Observations delayed underestimated 1 2 3

22. 22 Summary of baseline results The Baseline simulation reproduces the dominance of single- and double-layer MPS clouds revealed by the MMCR-MPL observations and qualitatively captures the major characteristics in the vertical distributions of LWC, nc, ISWC and nis and their interperiod differences suggested by the aircraft observations. However, The simulated first MPS cloud layer is too low and nc within the lower layer decreases with height, in contrast to the relatively constant nc revealed by the observations. These could be due to uncertainties associated with the parameterizations (e.g., turbulence, droplet activation, radiation), and the forcing data. The simulated first MPS cloud layer is too low and nc within the lower layer decreases with height, in contrast to the relatively constant nc revealed by the observations. These could be due to uncertainties associated with the parameterizations (e.g., turbulence, droplet activation, radiation), and the forcing data. The simulated second cloud layer is too thick with too large LWC, causing too strong LW cooling and negative biases in temperature. The simulated second cloud layer is too thick with too large LWC, causing too strong LW cooling and negative biases in temperature. Both simulated cloud layers contain too few ice crystal numbers and too small ice crystal masses, indicating missing of ice enhancement mechanisms in the microphysics scheme and resulting in the underestimate of surface precipitation rates. Both simulated cloud layers contain too few ice crystal numbers and too small ice crystal masses, indicating missing of ice enhancement mechanisms in the microphysics scheme and resulting in the underestimate of surface precipitation rates.

23. 23 Outline Outline 1. Introduction 2. Large-scale background and observations 3. Model and simulations 4. Comparing Baseline results with observations 5. Results from sensitivity experiments

24. 24 Time-height distribution of LWC and ISWC : Baseline vs. noLSadv noSfcFlx Baseline noLSadv T advectionq v advection coolingmoistening

25. 25 Time-height distribution of LWC and ISWC : Baseline vs. noSfcFlx Baseline noSfcFlx LH: 18  5 W m -2 SH: 3  5 W m -2

26. 26 Time-height distribution of LWC and ISWC: Baseline vs. noLWrad Baseline noLWrad LW radiative cooling/heating in Baseline

27. 27 Time-height distribution of LWC and ISWC: Baseline vs. noIce and IN50th BaselinenoIce The temporally averaged LWP is increased by a factor of 3 in noIce compared to the Baseline, suggesting depletion of liquid droplets by ice crystals in Baseline. IN50th

28. 28 Time-height distribution of LWC and ISWC: Baseline vs. IN50 Baseline IN50 No MPS clouds are formed in IN50 experiment (while magnitude of the vertically integrated ice and snow mass increases by a factor of 6).

29. 29 Summary of sensitivity experiments LW radiative cooling LW radiative warming Surface fluxes of latent and sensible heat LS advection Bergeron process

30. 30 End. Thanks for your attention!

31. 31 Summary of sensitivity experiments LW radiative cooling LW radiative warming Surface fluxes of latent and sensible heat LS advection Bergeron process

32. 32 Time-height distribution of LWC and ISWC : Baseline vs. noMicLat Baseline noMicLat Heating/cooling due to phase change in Baseline a larger magnitude of LWC in the interior of the MPS cloud layers

33. 33 Observations of Aerosol properties observed and fitted dry aerosol size distribution Aerosol composition: ammonium bisurfate (NH4HSO4) with an insoluble fraction of 30%

34. 34 Observations of Ice nulcei (IN) number concentration Active IN acting in deposition, condensation- freezing, and immersion-freezing modes: a mean of 0.16 L -1 Contact-freezing IN: a function of temperature (Meyers et al., 1992)

35. 35 Field measurements: Profiles of the sample numbers for liquid water content (solid lines) and ice water content (dashed lines), respectively, in each height bin of 400 m during the three missions that the UND Citation took on October 5 (a), October 6 (b), and October 8 (c), 2004.

36. 36 Baseline Results: Temperature and moisture CRM Baseline Baseline-Analysis water vapor mixing ratio temperature

37. 37 Baseline Results: Time series of LWP Baseline (79 g m -2 ) MWR retrieval (81 g m -2 )

38. 38 Results from sensitivity tests: eddy kinetic energy