1. Modeling and Observations of the Impact of Pollution Aerosols on Orographic Snowfall Stephen M. Saleeby & W. Cotton, R. Borys, D. Lowenthal, M. Wetzel DRI Seminar Reno, NV Sept 21, 2007

2. Overview 1.Introduce the primary objectives of the ISPA field project held at Storm Peak Lab (SPL) 2. Provide an overview of the RAMS microphysics model 3. Describe the setup of the snowfall simulations 4. Provide results of the simulations and make some comparison to ISPA observations

3. Field project ISPA-2 (Jan-Feb 2007) Inhibition of Snowfall by Pollution Aerosols Storm Peak Lab (SPL) is situated at the top of Mt. Werner at the top of the Steamboat Springs Ski resort at (~3210m MSL). Craig power plant Hayden power plant Looking west from SPL

4. ISPA Process 1.Pollution aerosols impact total snow water equivalent (SWE) if an orographic cloud is present. Otherwise hygroscopic CCN are generally less effective in cold cloud processes. 2. Snow falling thru an orographic cloud undergoes a seeder-feeder riming process in which crystals pick up extra water mass as they fall through the orographic cloud before reaching the surface. 3. If CCN are added to the orographic cloud, the droplet number concentration increases, the mean droplet size decreases, the riming collection efficiency then decreases, and the total rimed mass decreases; thus leaving us with less SWE at the surface. Heavy Rime Event Cloud LWC up to 0.7 g/m3

5. ISPA Objectives & Questions Primary Questions: 1. Can we capture both polluted and non-polluted cases during ISPA? 2. If so, can we then make statistically significant comparisons among the cloud water, aerosols, and rime ice data to determine the relative differences due modification of the supercooled orographic cloud? 3. Can we model the ISPA process by varying the CCN profiles and determine the range of precipitation possibilities due to these different initial conditions? (this one is my part) Primary Objective: Assess the impact of pollution aerosols (CCN) on the orographic snowfall

6. Field project ISPA-2 Equipment: FSSP Cloud Probe 2DP Ice Crystal Probe Mesh rime ice collector Snow sample collector CCN counter at multiple SS SMPS and APS aerosol counter Fine and Ultra-fine CN counter Meteorological tower (temp, RH, wind, pressure) Snow depth sensor Manual daily snow depth measurement at various elevations

7. Storm Peak Lab CSU Grad Students helping with ISPA Randy, Me, and Melanie by the new CCN counter

8. Regional Atmospheric Modeling System (RAMS)

9. 1.RAMS is a 3D, non-hydrostatic, fully compressible, mesoscale model. 2. RAMS is run on a Sigma-Z terrain following coordinate using an Arakawa-C grid. 3. RAMS has been run successfully in large eddy simulation mode and as a regional climate model for periods of months. 4. It’s most desired for its sophisticated microphysics module which predicts on mixing ratio and number concentration for 8 hydrometeor species: small cloud droplets, large cloud droplets, pristine ice, snow, aggregates, graupel, and hail. It also predicts ice crystal habit from temperature and saturation. RAMS

10. RAMS Microphysics represents the 8 hydrometeor species in a “bulk” mode by assuming a generalized gamma Distribution: RAMS Microphysics

11. Microphysical Processes Cloud droplet nucleation in one or two modes Ice nucleation Vapor deposition growth Evaporation/sublimation Heat diffusion Freezing/melting Shedding Sedimentation Collisions between hydrometeors Secondary ice production

12. Cloud Droplet Nucleation Number nucleated obtained from lookup table as a function of CCN number concentration Vertical velocity Temperature Median radius of CCN distribution Lookup table generated previously (offline) from detailed parcel-bin model based on the Kohler Equations. CCN are specified with an ammonium sulfate chemistry.

13. 1. Gamma distributions are broken into bins 2. Each size bin of snow can collect each size bin of cloud droplets with a unique collection efficiency! Binned Approach to the Stochastic Collection Process for Riming Rimed Cloud Water Liquid Water Content X X BULK Riming Efficiency BINNED Riming Efficiency

14. RAMS Simulation Case Studies

15. Winter Simulations Grid Configuration

16. Simulated 2 Winter Cases Thus Far for 2007 0000 UTC Feb 11, 2007 32cm 43mm ~12.5% (Heavy riming event, less snow) 0600 UTC Feb 23, 2007 61cm 28mm ~4.3% (Light riming event, much snow) Start Time Snow The ISPA effect is maximized when the: 1. Snowfall totals are large 2. Orographic cloud is long-lived 3. Cloud liquid water content is high 4. **Cloud droplet number concentration is high** SWE Density

17. Satellite of Feb 11-13 Heavy Riming Event

18. Video of Heavy Rime and Instrumentation Feb 11-12

19. RAMS Simulations For each snow event we ran an ensemble of simulations with the following initial aerosol concentrations: CCN = 100 /cc and 1900 /cc GCCN = 0.00001 /cc and 0.5 /cc IFN = Meyers and DeMott nucleation rates (All of the following plots focus on the CCN impact only and are from the simulations with GCCN = 0.00001 /cc and IFN = Meyers Nucleation)

20. Precipitation Comparison

21. Total Simulated Snow Water Equivalent [ Simulations contained CCN = 100 | GCCN = 10^-5 | IFN = Meyers Nucleation] Orographic enhancement of snowfall is quite obvious along the ridge of the the Park Range but the local maximum can vary among simulations. (mm)

22. RAMS versus SNOTEL Snow Water Equivalent Time Series The RAMS values show the ensemble spread of model realizations that occur by varying the CCN, GCCN, and IFN nucleation rates. RAMS over-predicted at PHQ site.

23. RAMS versus SNOTEL at Various Slope Points Adjacent to SPL In the Feb 23, 2007 case, the orographic enhancement was over-eager and precip was over predicted such that the 3 rd grid point west of SPL was most representative of the obs. *Black dots represent SWE manual measurements at PHQ which whose location is equivalent to SPL-01. PHQ

24. RAMS vs. SPL Temperature & RH RAMS too warm RAMS too moist Feb 11-12, 2007Feb 23-25, 2007 Relative Humidity Temperature [ Simulations contained CCN = 100 | GCCN = 10^-5 | IFN = Meyers Nucleation]

25. Orographic Cloud Water Comparison

26. Time Averaged Cross-section of Hydrometeor Mixing Ratios Cloud mixing ratio ( shaded, g/kg ), Snow ( red lines, g/kg x 100 ) Graupel ( black dashes, g/kg x100 ) Simulations with the greater amount of average cloud water would be expected to produce the greater ISPA effect. [ Simulations contained CCN = 100 | GCCN = 10^-5 | IFN = Meyers Nucleation]

27. RAMS vs. SPL FSSP Cloud Mixing Ratio [Simulations contained CCN = 100 | GCCN = 10^-5 | IFN = Meyers Nucleation] Feb 11-12, 2007Feb 23-25, 2007 So, RAMS tends to be a bit over-eager in creating orographic cloud water in a bulk sense, but we have only 1 observation point for comparison and a model grid spacing of 750m.

28. Steamboat Springs Webcam Captures Orographic Cloud Feb 11, 2007 - 2300UTC o 1 - minute Cloud mixing Ratio (g/m3) Really localized Orographic cloud SPL

29. Pollution Aerosol Sensitivity Test Results

30. Accumulated Precipitation (SWE) along Varying Topography Snowfall DECREASE in polluted case on WINDWARD slope Snowfall INCREASE in polluted case on LEEWARD slope x Feb 11-12, 2007 x Feb 23-25, 2007

31. [GCCN = 10^-5 | IFN = Meyers Nucleation] Total Precipitation Change Due to Increased Pollution Aerosols 1.An increase in CCN leads to reduced precip along the windward slope and highest plateau, and increased snowfall to the lee of the Divide. 2. A reduction in riming decreases the average snow crystal size and fall speed, thus, leading to a blow-over advection effect that shifts the snowfall spatial distribution. (Hindman et al. 1986)

32. Modification of Ice Crystal Type Due to ISPA Effect Cloud water (g/kg, shaded) Snow (g/kg x 10, solid) Graupel (g/kg x 10, dashed) Graupel mass is reduced and pristine snow mass is greater in the polluted case due to reduced riming growth. “Clean” “Polluted” Feb 11-12, 2007

33. Can we use CCN data to guide model cloud nucleation?

34. Time Series of CCN Concentration at SPL in 2007 We would like to use such data for prescribing CCN for modeling purposes. But such variability in CCN at a single location, and the lack of vertical profiling limits us to examining a range of sensitivities rather than direct model input.

35. Rapid Variability in Aerosols Due to Weather Shifts Shaded areas highlight 2 time period of extreme variability that would be quite difficult to assimilate for model guidance or simulate at such fine scales.

36. CCN Relationships with Small Aerosol and Wind Direction Most polluted air tends to comes from the west, which is the dominant wind direction at SPL during winter months. High concentrations of sub-micron aerosols is not necessarily a good indicator of the presence of hygroscopic nucleating aerosols.

37. Summary Aerosol Impacts a. Increasing the CCN concentration alters the orographic cloud by increasing droplet number and reducing droplet size. b. Reduced riming efficiency leads to a reduction in snow growth and graupel formation within the orographic cloud. c. Smaller, slower falling crystals tend to deposit further downstream to the lee of the mountain crest. d. To better model aerosol impacts we would need a spatial network of CCN counters as well as some guidance on vertical profiles

38. THE END