Understanding formation and maintenance of mixed-phase Arctic stratus through longterm observation at two Arctic locations

Transcription

1 Understanding formation and maintenance of mixed-phase Arctic stratus through longterm observation at two Arctic locations Gijs de Boer E.W. Eloranta, G.J. Tripoli The University of Wisconsin - Madison

2 Introduction

3 Introduction

4 Introduction These cloud structures are extremely prevalent in the Arctic: - SHEBA: 48% occurrence in May (Rogers et al., 2001) - Low altitude stratus frequency of up to 70% during transitional seasons (Curry et al., 1996; Herman and Goody, 1976) - From Eureka: over minute cases for September 2005-December From M-PACE: over minute cases for mid September-mid November, October Single Layer Stratus Number of Cases (Barrow) 2005 (Eureka) 2006 (Eureka) Month

5 Introduction From ARM Model intercomparison (Klein et al.)

6 Introduction From ARM Model intercomparison (Klein et al.)

7 Introduction - Ice Formation (Pruppacher and Klett, 1997) Homogeneous nucleation Heterogeneous nucleation Deposition freezing Contact freezing Condensation freezing Immersion freezing Some Multiplication Processes Drop shattering Ice-ice collisions Splinter ejection during riming

8 Observations UW Arctic High Spectral Resolution Lidar NOAA ETL Millimeter Cloud Radar 12-hr. Radiosonde Frequency In-situ from M-PACE Instruments Microwave Radiometer U. Idaho Polar AERI CALIPSO CloudSAT

9 Observations Where does the ice come from? Low IN, but substantial ice... Example from M-PACE: CFDC Average out of cloud IN concentration for 9 and 10 October 2004: /L Ice particle concentrations: ~10 1/L So nucleation not by: -Deposition freezing -Condensation freezing -Contact freezing alone

10 Observations Radar Lidar backscatter cross section (Masked values shown in black and white) 1e!7 1e! e!8 1e!4 1e!9 Altitude (km) e!5 1e!10 1e!11 1e! e!12 1e!7 1e! Time (UT) 1/(m str) 1e!14 1e!8

11 Observations Cloud Mask (Masked values shown in black and white) Altitude (km) Time (UT)

12 Observations Probability Density Function Cloud Min. Temp. Cloud Max. Temp Normalized # Temperature (K)

13 Normalized # Homogeneous Freezing (< -35 C) (Hagen et al., 1981; Jensen et al., 1998) Observations Probability Density Function Cloud Min. Temp. Cloud Max. Temp Temperature (K)

14 Normalized # Homogeneous Freezing (< -35 C) (Hagen et al., 1981; Jensen et al., 1998) Observations Probability Density Function Cloud Min. Temp. Cloud Max. Temp. Splinter Ejection (> -8 C) (Heymsfield and Mossop, 1984) Temperature (K)

15 Observations (T cb!t s*c )/!< (7/km) !5! )*c. Tem1erature (7)

16 Observations Why the horizontal variability in ice production?

17 Observations Why the horizontal variability in ice production? Figures courtesy of M. Shupe (NOAA)

18 Summary Ice production likely not due to: - Homogeneous Nucleation (too warm) - Condensation, deposition or contact freezing alone (too few IN) - Drop splinter ejection during riming (too cold)

19 Summary Ice production likely not due to: - Homogeneous Nucleation (too warm) - Condensation, deposition or contact freezing alone (too few IN) - Drop splinter ejection during riming (too cold) Key to understanding ice production: - Likely lies with understanding controlling mechanisms for horizontal variability in observed precipitation

20 Summary Ice production likely not due to: - Homogeneous Nucleation (too warm) - Condensation, deposition or contact freezing alone (too few IN) - Drop splinter ejection during riming (too cold) Key to understanding ice production: - Likely lies with understanding controlling mechanisms for horizontal variability in observed precipitation Future investigation - Numerical sensitivity experiments to look at individual processes. - Evaluate role of vertical velocity in cloud layer