The Influence of Mountains on Airflow, Clouds, and Precipitation

Severe Downslope Windstorms

Figure 11.6. West to east vertical cross section of potential temperature across the Sierra Nevada. Dashed line represents sailplane soundings. Observed Chinook arch or Foehn wall cloud is illustrated over barrier crest, as well as rotor cloud at low levels to the east and lenticular cloud at higher levels. [From Holmboe and Klieforth (1957).]

Figure 11.9. Contours of horizontal velocity (m s-1) along an east-west line through Boulder, Colorado, as derived from the NCAR Sabreliner data on 11 January 1972. The analysis below 500 mbar was partially obtained from vertical integration of the continuity equation, assuming two-dimensional steady-state flow. [From Klemp and Lilly (1975).]

• . Over the mountain crest is a rather typical flow pattern over a broad mountain. A stationary orographic cloud exists over the highest peaks. Directly to the lee of the higher peaks, the flow descends abruptly to the plains elevation. Evidence of trapped lee waves, including a lenticular cloud, can be seen over the plains. At higher levels is a deep trough in which air originating near stratospheric heights descends to below 500 mbar. This very high-amplitude wave is believed to be instrumental in causing surface winds in excess of 50m/s.

• Maximum wind speeds occur along the lee slope of the mountain barrier at low levels.

Figure 11.11. Total horizontal velocity field for the Boulder, Colorado, windstorm simulation. Times are (a) 3200, (b) 4160, (c) 5120, (d) 6020, (e) 7040, and (f) 8000 sec. Contour interval is 8 m s-1. In (f) the horizontal wind maximum in the lee of the peak is in excess of 60 m s-1. [From Peltier and Clark (1979).]

Figure 11.12. Total horizontal velocity field for the Boulder, Colorado, windstorm simulation. Times are (a) 3200, (b) 4160, (c) 5120, (d) 6020, (e) 7040, and (f) 8000 sec. Contour interval is 8 m s-1. In (f) the horizontal wind maximum in the lee of the peak is in excess of 60 m s-1. [From Peltier and Clark (1979).]

• Klemp and Lilly (1975) first explained the severe downslope wind phenomena with a two-dimensional, linearized, hydrostatic model in isentropic coordinates.

• They concluded that the mechanism leading to strong amplification of the wave is associated with the partial reflection of upward-propagating wave energy by variations in thermal stability. They argued that a strong wave response occurs whenever the mean vertical wavelength is such that an integral number of half-wavelengths can be confined between the ground and the tropopause.

• Peltier and Clark found that the wave actually broke, leading to a local wind reversal and a layer of constant potential temperature. As a consequence, wave energy reflected from the earth's surface became trapped between the resultant critical layer and the ground. This reflection cavity produced the large-amplitude streamline deflections that resulted in the strong surface winds. When the stratospheric wind profile was modified to prevent wave breaking, the final phase of wave amplification did not occur, and the results were then similar to linear theory.

Effects of moisture on less waves

• Cloud processes can influence mountain-wave flow and thereby feed back on the formation of precipitation in orographic clouds.

Trapping of g-waves

• Recall that when l**2 is less than k**2, where l**2 is the Scorer parameter given by

Moisture alters N**2

Figure 11.14. Absolutely stable atmosphere favorable for the development of dry lee waves. (a) Temperature and wind speed profiles; dry adiabats are marked with a short-dash line; moist pseudoadiabats are marked with a Iong-dash line. (b) Scorer parameter l2 profiles; the dry l2 is marked with a solid line, the equivalent saturated l2 is a dashed line. [From Durran and Kemp (1982b).]

Figure 11.15. Streamlines produced by a 300-m-high mountain in the flow for relative humidity (RH): (a) RH =0%, (b) RH =90% (c) RH = 100%, and (d) RH = 100% with 0.2 g kg-1 of cloud, in the lowest layer upstream. Cloudy regions are shaded. [From Durran and Klemp, 1982b.]

• The inclusion of the effects of clouds results in less stable flow because the buoyancy-restoring force is decreased, the amplitude of the mountain wave under certain conditions can be significantly weakened.

Figure 11.16. Streamlines produced by a 300-m-high mountain in the flow. (a) Steady solution for RH = 0%. Time-dependent flow for RH = 90% in the lowest upstream layer at (b) t = 8000 s, (c) t = 12,000 s, and (d) t = 16,000 s. Cloud regions are shaded; dark shading indicates cloud densities exceeding 0.3 g kg-1. [From Durran and Klemp, 1982b.]

• In the dry atmosphere, a distinct trapped lee wave is evident in their solutions. The addition of a layer with 90% relative humidity results in the formationof clouds over the mountain crest and in the regions of upward motion of the trapped lee waves. The wave structure is modified somewhat. As a result of adding a 100% saturated layer, the flow is modified so that the wavelength of the partially trapped waves is increased significantly.

• Finally, by adding 0.2g/kg of liquid water to the saturated layer, a cloud could be maintained in the wave troughs as well as the wave crests. This so altered the resultant vertical profile of the Scorer parameter that the lee waves became untrapped.

• For 11 January 1972 severe downslope windstorm over Boulder, Colorado. Durran and Klemp noted the addition of moisture to their model decreased the downslope wind speed from 45 to 25m/s, a result of a weakened mountain-wave amplitude.

• Durran and Klemp also noted that lee-side warming, referred to as the Chinook or Alpine foehn, is often attributed to the release of latent heat on the windward side of the barrier in precipitating clouds and to dry adiabatic descent on the lee side. In a precipitating cloud simulation, they noted that the lee-side temperatures were several degrees cooler than those in nonprecipitating flow. This suggests that the most important factor influencing Chinook or foehn wind lee-side temperatures is the amplitude of the mountain wave, which is larger in the dry case.

• Lilly and Durran (1983) extended the Durran-Klemp calculations to precipitating clouds as well. Using a simple Kessler-type warm rain parameterization, they investigated the effects of cloud processes, including precipitation, on vertical momentum fluxes over orographic barriers. The calculated vertical momentum fluxes for (a) a case having low clouds and (b) a case saturated everywhere. The fluxes are normalized to fluxes expected for linear mountain wave theory

Figure 11.17. The effects of rain on the vertical profiles of momentum flux produced by upstream moisture profiles in which (a) there are low clouds between the heights of 667 and 3000 m, and (b) RH = 100 % everywhere. The fluxes are normalized by MLC, the flux associated with linear mountain waves. [From Lilly and Durran (1983).]

The seeder-feeder process

Figure 11.21. Conceptual model illustrating the orographic enhancement of rain. [From Browning's (1979) adaptation of Bergeron's (1965) figure.]

Blocking of low-level flow

• Froude Number: Fr=U/(N*h), where U is the speed of the incoming flow, N is the Brunt-

Vaisala frequency, and h is the height of the mountain. • The Froude number(Fr) represents the ratio of the

square root of the kinetic energy of the horizontal flow impinging on a mountain barrier to the energy required to lift an air parcel from the base of a mountain to its top in a stably stratified environment. Therefore blocking is more likely to occur when winds are weak or stabililty is large.

Figure 11.23. Conceptual model for the working hypothesis that low-level decoupled flow (stippled area) acts as an extension of the mountain barrier for orographic lift purposes which would then alter the location of condensate production and hence precipitation. A small amount of decoupled low-level flow (a) allows parcel lift to occur near the barrier while a large amount of low-level decoupled flow (b) forces parcel lift to occur upstream of the barrier. [From Peterson et al., 1991.]

Figure 11.24. A schematic depiction of the position of a cold front, at 2-h intervals, as it approaches and is influenced by a mountain range. The distortion of the frontal surface is from slowing of the low-level flow by the mountain and the acceleration aloft. This differential advection causes the cold air behind the front to override the warm air, producing an unstable air column. The resulting small-scale convection enhances precipitation upstream of the mountain and on its windward slopes. This diagram is constructed for u0 =10 m s-1, N = 0.01 s-1, b = 20 km, h = 800 m, x0 = -100 km, and a = 1/50. The vertical exaggeration is 12:1. [From Smith (1982).]

• Note that blocking of low-level flow on the windward side of the barrier causes differential thermal advection and makes the flow on the lee-side unstable as cooler air rides over relatively warmer air resulting small-scale convection

Figure 11.25. Schematic portrayal of a split front with the warm conveyor belt undergoing forward-sloping ascent, but drawing attention to the split-front characteristic and the overall precipitation distribution: (a) plan view, (b) vertical section along AB in (a). In (a) UU represents the upper cold front. The hatched shading along UU and ahead of the warm front represents precipitation associated with the upper cold front and warm front, respectively. Numbers in (b) represent precipitation type as follows: (1) warm-frontal precipitation; (2) convective precipitation-generating cells associated with the upper cold front;(3) precipitation for the upper cold-frontal convection descending through an area of warm advection; (4) shallow moist zone between the upper and surface cold fronts characterized by warm advection and scattered outbreaks of mainly light rain and drizzle;(5) shallow precipitation at the surface cold front itself. [Adapted from Browning and Monk (1982), Browning (1985), and Reynolds and Dennis (1986). Reproduced with the permission of the Controller of Her Britannic Majesty's Stationery Office.]

Precipitation Efficiency in orographic clouds

• Dirks (1973) employed aircraft data to measure directly precipitation efficiencies over a relatively isolated mountain(Elk Mountain) in Wyoming. Aircraft soundings were used to calculate condensate production and precipitation rates. Precipitation efficiencies ranged from 25 to 80%. The lowest efficiencies were at very cold temperatures and strong winds, and the highest efficiencies were under moderately cold cloud top temperatures and moderate wind speeds.

Evidence suggests that a significant contributor to the water content of the snowpack arises from rime deposits on trees and other objects located on mountain peaks; this deposit is then shed to the snow surface. Hindman(1982), for example, estimated that between 4 and 11% of the water content of snowpack near the peak of the Park Range, Colorado, was the result of rime deposits.

• Smith et al.(2003) proposed an alternate strategy for estimating precipitation efficiencies. They propose using the drying ratio (DR),  

• DR=P/I= area integrated total precipitation/impinging horizontal vapor flux

• Kirshbaum and Smith(2009) proposed an alternate expression for DR in the form

• DR=CR X PE ,

• where CR is the condensation ratio (C/I) where C is the area integrated condensation rate.

• Because PE requires an estimate of vertical air velocity at cloud base, it is subject to rather large measurement errors. Since DR uses the ratio of precipitation to water vapour flux, it is easier to quantify than PE. An important advantage of DR is that it can be estimated using hydrogen and oxygen isotope analysis, which permits evaluation using streamflow or sapwood collected near a stream.

• The idea is that condensation preferentially removes the heavier isotopes so that the remaining vapor becomes progressively lighter such that the ratios of heavy-to-light isotope concentrations in the final and initial state of vapor, is the fraction of water vapor remaining.

• The drying ratio can thus be computed using the ratios of heavy to light isotope concentrations in the upwind and downwind precipitation.

• DR estimates using isotope analysis range from 43% for the combination of the coastal and Cascade mountain ranges in western Oregon and 43% for a case in the of flow against the Alps(Smith et al., 2003) and nearly 50% for the southern Andes (Smith and Evans 2006). 

Aerosol Influences on orographic precipitation

• There is observational evidence(Borys et al.,2000 ;Borys et al.,2003) that pollution can delay precipitation in winter orographic clouds in the Rocky Mountains. They show that pollution increases the concentration of CCN and therefore cloud drops, leading to the formation of smaller cloud drops and less efficient riming. Reduced riming results in smaller, more pristine ice crystals, with smaller fall velocities, and less snowfall.

• Figure 11.36. Light riming of ice crystals in clouds affected by• pollution (left) compared to heavier riming in non-polluted clouds• (right) (Borys et al. 2003).

Effect of pollution on snow fall from orographic clouds

Note the difference in concentrations

Borys et al, GRL, 2003

Polluted case, smaller drops, less riming

Clean case,larger drops,more riming

Table 1. Chemical and Physical Properties of Cloud Droplets and Snow During Two Precipitation Events

February 15 (Polluted) 19 (clean)

Major Habit Planar Dendrite Planar Dendrite Rime Category Unrimed (0.5) Moderate (2.0) Rime Mass Frac. 5% 51% SPL Precip. Rate 0.02 mm hr1 0.38 mm hr1 ISS Precip. Rate 0 to 0.1 mm hr1 1.1 mm hr1 SPL Temperature 13C 4C Snow 18O 22.1 16.5 Cloud d18O 21.1 16.2 18O Snow Mass 14C 4.8C Temp. Of Origin

Cloud Top Temp 19C 22C Snow CAE SO4 = 0.011 mgm 3 0.072 mgm 3 Cloud CAE SO4 = 1.1 mgm 3 0.12 mgm 3 Droplet Mean Dia. 8.3 mm 13.6 mm Droplet Conc. 310 cm 3 74 cm 3 Cloud SCLW 0.13 g m 3 0.14 g m 3

Borys et al, GRL, 2003

Orographic enhancement factor (Ro)

• Orographic enhancement factor (Ro): ratio between precipitation amounts over hills to precipitation amounts in upwind lowland (Givati and Rosenfeld 2004, 2005)

• Suppression rate downwind of coastal urban areas in California and Israel 15-25% of annual precipitation in hills– Occurred mainly in relatively shallow orographic clouds

within cold air mass of cyclones• Role of pollution aerosols in decreasing Ro over

mountains to E of Salt Lake City (Griffith et al. 2005) but upwind increased!

• Similar decreasing trends in Ro (up to 30%) noted on eastern slopes of Rockies during easterly flows, downwind of Denver and Colorado Springs (Jirak and Cotton 2006)

Ro Trends• Numbers show

end/start of winter Ro (Oct-May) Ro for high rain gauge with respect to low– Red numbers are

smaller than 1.00 that indicate a statistically significant trend

• Ro has decreased significantly over all area except pristine regions of northern CA, OR, southern Idaho, and central Utah

Climatic fluctuations• Dettinger et al. (2004) found

that during negative PDO and positive SOI, westerly wind component strong so mountain-plains orographic factor higher than in the positive PDO (El Nino like) phase

– Less overall precipitation in negative than in positive PDO phase

• Ro weakly negatively correlated with PDO and even more weakly so with the SOI

– These weak relations could still explain trends if PDO and SOI would have large trends with time

Jirak and Cotton(2006)

• Effect of Air Pollution on Precipitation along the Front Range of the Rocky Mountains

Introduction

- Increased concentrations of small CCN are thought to suppress precipitation (all else being equal) and are associated with pollution in urban areas- Givati and Rosenfeld (2004) found a 15-25% reduction in orographic precipiation downwind of urban areas in California and Israel- This study investigates the same phenomenon over the Front Range by comparing trends in precipitation ratios at various pairs of sites

Analysis Methods

• Identified 3 site pairs based on length of precipitation records, correlation of precipitation, and geographic orientation:– Denver (Cherry Creek Dam/Morrison)– Colorado Springs (Colorado Springs Municipal

Airport/Ruxton)– A 'pristine' area for comparison

(Greeley/Waterdale/Estes Park)• Only considered days where wind was upslope at Denver

Stapleton (NNE to SSE)• Looked for trend on Orographic Enhancement Factor

(OEF), ratio of precipitation at higher-elevation site to lower-elevation site

Conclusions No significant trends in total precipitation, or OEF in

total precipitation Significant decreasing trends in upslope OEF for urban

areas, but not pristine Points to pollution as source of suppression

Precipitation losses on order of 1mm/year over 50 years from upslope component (but totals actually trended upward, albeit insignificantly)

Could exacerbate water shortages Greater population -> greater water demand and more

pollution More pollution -> less precipitation -> more severe water

shortages

Alpert et al,(2008)

• Does Air Pollution Really Suppress Precipitation in Israel?

• They reanalyzed Ro precip data and recalculated Ro for Israel

Central Israel

• 5 of the 11 stations included in previous analyses as coastal stations are in fact downwind of the Tel Aviv urban area.

• When including only the 6 along the coast in the analysis, the orographic ratio actually increases with time.

Northern Israel

• 2 of the stations previously included as coastal stations are downwind of the Haifa urban area.

• 4 of the coastal stations are not located along the storm track and so may not be useful in an orographic ratio.

• No change can be seen in the orographic ratio over time.

Northern Israel

• The rainfall ratio of the upslope of the mountains to the lee side has increased with time, contrary to previous studies.

• This suggests that cloud-seeding efforts in Israel are either not working properly or are dwarfed by other factors in precipitation formation.

Findings

• No mountain stations have shown a reduction in rainfall over the past 50 years.

• There is actually an increase in orographic rainfall when compared to the seashore stations.

• The decreased orographic ratio reported previously was due to larger increases in rainfall over the southern coastal plain caused by land-use and synoptic changes.

• Larger increases in rainfall also occur downwind of the urban areas, possibly due to urban heat island effects.

• Any ratio will decrease if a constant is added to both the numerator and denominator.

Summary• Use of Ro method can be misleading

• Decreasing trends can be due to other factors

Influence of CCN on Orographic Snowfall

Saleeby et al. 2008J. App. Meteor. Climat.

• Two Events Chosen– Case 1 (11-13 February 2007)

• Long lived• High liquid water content (highly rimed)• Orographic feeder cloud

– Case 2 (23-25 February 2007)• Significant snowfall accumulation• Low density snow (lightly rimed)

– A range of CCN conc’s were used in simulations to find effect of aerosol loading

• 100, 300, 500, 800, 1100, 1500, 1900 cm-3

Snow Water Equivalent Observations

Ridgeline Lee Side

Lee side SWE 11% larger for early period of 11 Feb (low riming)

Seeder-feeder enhances surface accumulation on windward slope

Decrease in total precip on upwind side of orographic barrier

Increase in total precip on the downwind side of orographic barrier

CCN changes related to the cross-section of SWE and not directly to the ridgeline (note difference in peak position of CCN concentrations)

Spatial position depends on advection

IPSA• Larger amount of CCN

results in larger number of cloud droplets with lower riming efficiencies and inhibition of snow growth rate – Inhibition of Snowfall by

Pollution Aerosols (IPSA)– Results in

• Reduced rime fraction• Smaller overall mass• Smaller fall speeds• Longer horizontal trajectories

Difference between clean and polluted simulations

Sensitivity to CCN

• Modified Ro to RWL (ratio of precip on windward side over leeward side)

• Total remains near the same value, but ratio shows change as CCN is increased

a) 100 - 300b) 100 - 500c) 100 - 800d) 100 - 1100

Sensitivity to CCN

• Vertically integrated values averaged over the domain

• Greatest change in nucleation occurs up to 800 cm-3

– Above this change is proportionally less

• Suggests a threshold of Nccn to modify some processes

Summary

• CSU RAMS simulations of two cases– Initially run with clean (100 cm-3) and polluted (1900

cm-3) vertical Nccn profiles– Range of Nccn used to identify sensitivities

• To attain maximum increase in SWE from seeder-feeder process (greatest precip efficiency for available vapor)– Long-lived supercooled orographic cloud– High LWC and large droplets– Heavy snow fall through feeder cloud

• But pollution (increased Nccn) modifies results

Summary

• Increased CCN in seeder-feeder process cause:– Greater cloud droplet number concentration– Reduction in droplet size– Reduction in droplet riming efficiency

• Unrimed crystals slower fall speeds, advected further downstream for surface deposition

• Windward precipitation decrease, leeward precipitation increase

– Apparent threshold in CCN as changing from 100 to 500 cm-3 results in greater modification of orographic clouds than a change from 1500 to 1900 cm-3

– No apparent trend domain-summed precipitation on fine-grid (±1%)

Seasonal Impacts of Cloud Condensation Nuclei on Orographic Snowfall

Stephen M. Saleeby&

William R. Cotton

RAMS Model Domain Configuration

Left panel displays the 3 grid configuration for the seasonal simulations. Grids 1, 2, & 3 have a horizontal grid spacing of 36km, 12km, & 3km. The right panel displays the topography (meters) on Grid-3 along with several abbreviated location markers for spatial reference.

Grid Configuration Topography (meters)

Accumulated Precipitation (mm) for a 60-Day Winter Period

January 1 – March 1 total precipitation (mm) on the Colorado 3km spacing grid for the labeled years on each panel.

Years of Greatest to Least total domain summed precipitation rank as:

2005, 2008, 2007, 2006

Time Series of SNOTEL Station Accumulated SWEJanuary 1 – March 1 total

SWE (cm) for select SNOTEL stations that correspond to high precipitation in the model simulations.

SKZ = SharkstoothWLF = Wolf Creek SummitCOP = Columbine PassOVR = Overland ReservoirJOE = Joe Wright ReservoirRAB = Rabbit Ears Pass

SNOTEL site locations are shown in the coming plots.

Note the dramatic difference between the 2005,2008 seasons and the 2006,2007 seasons for the SKZ, WLF, COP, and OVR sites. These are the southern-most sites.

Cumulative ISPA Effect for a 60-Day Winter Period

January 1 – March 1 total precipitation difference (mm) on the Colorado 3km spacing grid for the labeled years on each panel. The scale used here helps display the relatively small differences in the lighter

precipitation difference years of 2006 and 2007 compared to the large magnitude maxima for large difference years of 2005 and 2008.

Cumulative ISPA Effect for a 60-Day Winter Period

Same as previous slide except that the scale used here helps display differences in the lighter precipitation difference years of 2006 and 2007, but displays broad areas of scale maxima for large difference years of 2005 and 2008.

Summary

• Great variability in response to pollution aerosol from year-to-year

• Major response is a shift in precipitation downwind• This can shift water resources from one basin to the next

or even Pacific to Atlantic water basins• Total domain reductions in precip are less than a few per

cent• Southern(San Juan) mountains are more susceptible

than northern owing to storms being wetter• But interannual variability of pollution impacts is huge in

southern mountains

Recent model intercomparison study

• Three models were intercompared for idealized 2D cases with 2km grid spacing; Consortium for small-scale modeling’s(COSMO) with bulk microphysics, WRF with UTV double-moment bin micro, U of Wisconsin modeling system(UWNMS) with bin micro including ice habit prediction.

• Sensitivity of orographic precipitation to CCN variations varied greatly from case to case and model-to-model

• Neither a precipitation decrease nor a precipitation increase is found robustly in all simulations

• Estimates of CCN impacts on precip varied from -19% to 0% depending on the case and the model

• Riming is found to decrease in some cases and models whereas it increases in others which implies that a decrease in riming with higher CCN is not a robust result

• Consistent among the models is that on average, the LWP increases with greater CCN concentrations.

• The implication is that at least for warmer-based clouds, higher CCN suppresses warm-cloud collision and coalescence, thereby reducing LWC removal and resulting in higher LWP’s.

• Thus increasing CCN does not reduce riming• In some models a decrease in riming was

compensated by an increase in aggregation

• Unfortunately aggregation kernals in all models are extremely crude and clear refinements from observations and modelling is needed

• The clear picture of enhanced CCN reducing riming, which results in reduced precipitation and/or spill-over affect, is now quite muddied!