Modeling and Observations of the Impact of Pollution Aerosols on Orographic Snowfall

Stephen M. Saleeby&

W. Cotton, R. Borys, D. Lowenthal,

M. Wetzel

DRI SeminarReno, NV

Sept 21, 2007

Overview1. 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

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 plantHayden powerplant

Looking west from SPL

ISPA Process1. 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 RimeEvent

Cloud LWC up to 0.7 g/m3

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

Field project ISPA-2

Equipment:

FSSP Cloud Probe2DP Ice Crystal ProbeMesh rime ice collector Snow sample collectorCCN counter at multiple SSSMPS and APS aerosol counterFine and Ultra-fine CN counterMeteorological tower (temp, RH, wind, pressure)Snow depth sensorManual daily snow depth measurement at various elevations

Storm Peak Lab

CSU Grad Studentshelping with ISPA

Randy, Me, and Melanieby the new CCN counter

Regional AtmosphericModeling System

(RAMS)

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

RAMS Microphysics represents the 8 hydrometeor speciesin a “bulk” mode by assuming a generalized gammaDistribution:

RAMS Microphysics

11

( ) exp( )t

n n n

N D Dn D

v D D D

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

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.

1. Gamma distributions are broken into bins2. 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

BULKRimingEfficiency

BINNEDRimingEfficiency

RAMS SimulationCase Studies

Winter Simulations Grid Configuration

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 large2. Orographic cloud is long-lived 3. Cloud liquid water content is high 4. **Cloud droplet number concentration is high**

SWE

Density

Satellite of Feb 11-13 Heavy Riming Event

Video of Heavy Rime and Instrumentation Feb 11-12

RAMS SimulationsFor each snow event we ran an ensemble of simulations with thefollowing initial aerosol concentrations:

CCN = 100 /cc and 1900 /ccGCCN = 0.00001 /cc and 0.5 /ccIFN = Meyers and DeMott nucleation rates

IFN Nucleation

0

0.2

0.4

0.6

0.8

1

0.2 0.3 0.4 0.5 0.6 0.7 0.8

Ice Supersaturation %

% IF

N t

o N

ucl

eate

Meyers DeMott

(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)

Precipitation Comparison

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 thethe Park Range but the local maximum can vary among simulations.

(mm)

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.

RAMS versus SNOTEL at Various Slope Points Adjacent to SPL

In the Feb 23, 2007 case, theorographic enhancement was over-eager and precip was overpredicted such that the 3rd gridpoint west of SPL was mostrepresentative of the obs.

*Black dots represent SWE manualmeasurements at PHQ which whoselocation is equivalent to SPL-01.

PHQ

RAMS vs. SPL Temperature & RH

RAMS too warm

RAMS too moist

Feb 11-12, 2007 Feb 23-25, 2007

RelativeHumidity

Temperature

[Simulations contained CCN = 100 | GCCN = 10^-5 | IFN = Meyers Nucleation]

Orographic Cloud Water Comparison

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]

RAMS vs. SPL FSSP Cloud Mixing Ratio

[Simulations contained CCN = 100 | GCCN = 10^-5 | IFN = Meyers Nucleation]

Feb 11-12, 2007 Feb 23-25, 2007

So, RAMS tends to be a bit over-eager in creating orographic cloud water in a bulksense, but we have only 1 observation point for comparison and a model grid spacing of 750m.

Steamboat Springs Webcam Captures Orographic Cloud

Feb 11, 2007 - 2300UTC

o

0

0.1

0.2

0.3

0.4

0.5

1 - minute Cloud mixingRatio (g/m3)

Really localizedOrographic cloud

SPL

Pollution Aerosol Sensitivity Test

Results

Feb 11-12, 2007 West-East Variation

0

10

20

30

40

50

60

Location

42-H

ou

r A

ccu

mu

late

d S

WE

(m

m)

CCN=100 CCN=1900

Accumulated Precipitation (SWE) along Varying Topography

Snowfall DECREASEin polluted caseon WINDWARD slope

Snowfall INCREASEin polluted caseon LEEWARD slope

x

Feb 11-12, 2007

xFeb 23-25, 2007

[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 slopeand 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)

Modification of Ice Crystal Type Due to ISPA EffectCloud 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

Feb 11-12, 2007

Can we use CCN datato guide model

cloud nucleation?

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.

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 finescales.

CCN Relationships with Small Aerosol and Wind Direction

Most polluted air tends to comes from the west, which is the dominant wind direction at SPLduring winter months.

High concentrations of sub-micronaerosols is not necessarily a goodindicator of the presence of hygroscopic nucleating aerosols.

SummaryAerosol 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

THE END