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The important physical processes controlling orographic precipitation, focusing on the enhancement and suppression of precipitation due to topography. It discusses the upslope model, airflow dynamics, convective cells, observations from rain and snow gauges, and remote sensing. The document also touches upon the limitations of predictability and the impact of climate change on orographic precipitation.
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Precipitation that has been generated or modified by topography, typically through the forcing of vertical atmospheric motions. Introduction The influence of mountains upon rain and snowfall is often profound, creating some of the Earth's wettest places (e.g. Cherrapunji in India, where monsoon flow encounters the southern Himalayas, has received 26.5 m in one year) and driest places (e.g. The central valleys of the Atacama desert, shielded by surrounding mountains, can go for decades without rainfall). Orographic effects on precipitation are also responsible for some of the planet's sharpest climatic transitions. The classic example is the so- called 'rain shadow'; for a mountain range oriented perpendicular to the prevailing winds, precipitation is greatly enhanced on the windward side and suppressed in the lee. However, the full gamut of orographic influences is much broader than this: precipitation can be enhanced in the lee, over the crest, or well upwind of a mountain. The mass balance of Earth's snow and ice is strongly affected by orographic precipitation. Accumulation of mountain snowpack and on alpine glaciers is typically dominated by orographic snowfall. The Greenland and Antarctic Ice sheets are themselves substantial topographic features responsible for orographic effects on precipitation. Avalanches are sensitive to the detailed stratigraphy of the snowpack, which is partly determined by the sequence of orographic graupel, rain, ice, and various snow crystals that fall during storms. This article will focus on the important physical processes controlling orographic precipitation, and the observational and modeling techniques that have been used to characterize and understand it. More complete reviews may be found in: Smith (1979); Roe (2005); and Smith (2006). Fundamentals Orographic precipitation is shaped by myriad non-linear processes operating on scales ranging from the 1000 km size of storms and major mountains to the sub-micron size of cloud droplets. Still, the most fundamental of these processes are thermodynamic in nature and are well understood. Almost all orographic influences on precipitation occur due to rising and descending atmospheric motions forced by topography. These motions can be forced mechanically, as air impinging on a mountain is lifted over it, or thermally, as heated mountain slopes trigger buoyancy-driven circulations. Rising motion causes the air to expand and cool, which is important since the amount of water that may exist as vapor in air is an approximately exponential function of temperature (described by the Clausius Clayperon equation). Thus if cooling is sufficient, air saturates and the water vapor condenses into cloud droplets or forms cloud ice crystals. These droplets and crystals grow by various processes until they become large enough to fall as rain and snow. It is important to emphasize that moist ascent over topography alone is typically insufficient to generate precipitation: these orographic effects mainly modify precipitation during preexisting storms (e.g. Browning et al., 1974; Smith, 2006). Conversely, when air descends it warms and dries, and both cloud and precipitation evaporate. A useful tool for understanding some of the basic controls on orographic precipitation is the āupslopeā model (e.g. Smith, 1979; Smith, 2006). This idealized and physically-based model predicts the water condensed when flow with given surface specific humidity ( qv , expressed as a mixing ratio), density ( Ļ), and uniform wind velocity ( ), impinges upon topography (with height: h(x,y) ). The model
assumes saturated air, an idealized temperature profile, and flow that parallels the topography at all heights. Under these assumptions the vertically-integrated source of condensed water per unit time is: (Eq. 1). This is also the precipitation rate at the surface if it is further assumed that conversion of cloud condensate to precipitation and fallout of precipitation are instantaneous. This model reveals some key controlling parameters: the moisture flux ( Ļ qv ), which determines the vapor available for condensation, and the topographic slope ( ) in the direction of the airflow, which determines the rate of the forced vertical motion. Airflow Dynamics Actual flow over topography during precipitation is seldom as simple as that assumed in the upslope model. Atmospheric density and temperature stratification strongly control the flow, since the typically stable stratification of the atmosphere means that a parcel of air displaced upwards becomes negatively buoyant (since it is cooler and denser than it surroundings) and is pulled back downwards. The strength of this effect may be quantified by the Brunt-Vaisala buoyancy frequency: (Eq. 2), with γ representing the observed atmospheric lapse rate (i.e. the rate of decrease of temperature with height), and Ī the theoretical dry adiabatic lapse rate for a rising air parcel (-9.8 K km
on Independent Slopes Method (PRISM; Daly et. al., 1994) uses localized regressions of elevation and precipitation to interpolate between observations. PRISM output is shown for the Cascades and Olympics in Figure 1(c). Other gridded gauge analyses from the well-instrumented European Alps (Frei and SchƤr, 1998) reveal more complex large-scale patterns than shown in Figure 1(c). The Alps receive storms arriving from a much wider range of directions, erasing any simple rain shadow and producing precipitation maxima on both sides of the range. Remote sensing offers an alternative method for studying orographic precipitation. Satellite methods are particularly useful for remote, poorly instrumented regions. For example, the Tropical Rainfall Measuring Mission (TRMM) satellite operates by emitting pulses of microwave radiation, which are reflected by precipitation. Data from TRMM have been used to characterize the pattern of precipitation over the Himalayas at 10 km scales (Anders et al., 2006), revealing a broad double-band of maximum precipitation along the southern slopes and local enhancements within windward valleys relative to the 4 km-high flanking ridges where the moisture content is quite low (Anders et al., 2006). Additional remotely-sensed data come from ground-based radars, a great number of which are deployed for weather forecasting. These can be used to make detailed observations of precipitation, including precipitation phase, with high spatial and temporal resolution. In a classic study, Browning et al. (1974) used radar over the coastal hills of Wales to show that intense periods of mountain precipitation occur when rainfall cells from upwind of the mountains are advected over the mountains and enhanced as instability is released and the seeder-feeder mechanism acts. Unfortunately, radar can be challenging to use in mountainous terrain where the beam is often blocked by topography. Both in situ and remote observations from aircraft have been a central component of several field projects devoted to better understanding orographic precipitation. The most expansive of these efforts to date, the Mesoscale Alpine Programe (MAP), focused on the southern slopes of the European Alps. Results from MAP revealed āthat detailed knowledge of the orographically-modified flow is crucial for predicting the intensity, location, and duration of orographic precipitationā (Houze and Rotunno, 2007, p.811), and that this flow is a strong function of the low level stability. Furthermore, under different flow regimes contrasting microphysical growth mechanisms become important, influencing the enhancement and distribution of precipitation (Houze and Rotunno, 2007). Models A vast array of models, each with their own advantages and drawbacks, have been used to characterize and understand orographic precipitation. The most basic of these are statistical in nature, relying upon empirical relations to estimate precipitation as is done for PRISM. Such models can be quite quantitatively successful, but need adequate data for calibration and can fail dramatically when observations are sparse or when anomalous atmospheric conditions occur. The upslope model, described above, is an example of a class of simple physically-based models that rely upon a series of idealizing assumptions to estimate precipitation with only minimal information about the incoming flow. Such models can illuminate fundamental processes and make ballpark estimates of precipitation, but neglecting airflow dynamics and cloud microphysics severely limits their physical realism. Another class models are intermediate in complexity, maintaining simplicity while incorporating more governing physics than the upslope model. An example of this is the linear theory model put forth by Smith and Barstad (2004), which builds on the upslope model to include linearized mountain wave airflow dynamics, microphysical conversion and fallout timescales, and lee side evaporation of
precipitation. Such models are useful for the same reasons as the upslope model, but offer a much more complete physical representation and better performance. Still, these models neglect important non- linear processes such as airflow blocking and microphysical collection and must be calibrated to perform well. An application of Smith and Barstad (2004)ās model to the Cascades and Olympics is shown in Figure 1(d). Mesoscale numerical weather prediction models are the most sophisticated modeling tool used in the study of orographic precipitation. They solve the full time-dependent equations of atmospheric motion and thermodynamics numerically on a three dimensional grid and use schemes that simulate the interactions occurring on the microphysical scale between vapor, clouds, and precipitation. These models are capable of realistically representing transient interactions between large-scale storms and mountains, and non-linear effects of blocking and microphysics. Yet, this physical realism comes at a computational cost, and these models can take substantial time to run even on fast computers with parallelization. Precipitation from the MM5 mesoscale model, used for operational weather forecasting, is shown in Figure 1(c) over the Olympics and Cascades. For some regions the model performance is excellent even on small scales, as shown in Figure 1 (e). However, these models cannot be taken for truth as they can be configured in a multitude of ways that give differing results. Even the best models can still have major errors, for individual storms and climatological averages, due to the challenges of simulating microphysical processes as well as inherent limits that exist on atmospheric predictability. Climate change and variability The sensitivity of orographic precipitation to large-scale climate variability and climate change is an active area of research. It is well known that year-to-year variations in mountain rain and snowfall for ranges such as the Cascades are largely due to variations in the intensity and location of the mid- latitude storminess, with some of those variations related to large-scale patterns of climate variability such as the El Nino Southern Oscillation. Understanding how orographic precipitation will be altered due to anthropogenic climate change requires understanding the temperature sensitivity of orographic precipitation processes, as well as knowledge of how storm tracks and large-scale circulation will change. The temperature dependence of orographic precipitation was investigated in depth by Kirshbaum and Smith (2008) using a mesoscale model. They found that while precipitation increases with the temperature and humidity of the atmosphere, these increases are buffered since orographic precipitation becomes less efficient at extracting moisture from the flow, due both to thermodynamic and microphysical effects. SalathĆ© et al. (2008) used a mesoscale model to downscale global climate model projections over the Cascade and Olympic mountains and showed that possible changes in the direction of airflow during storms may alter the intensity and distribution of precipitation over the region. Generally, orographic snowfall is very likely to decrease with climate warming as melting levels during storms rise and a larger fraction of precipitation falls as rain. Some loss may be offset by orographic precipitation rate increases, but for mountains like the Cascades and Olympics, where temperatures are not typically far below freezing during storms, this compensation is can be only modest due to the substantial loss of snow accumulation area. Summary Orographic precipitation processes strongly shape the climate in and around mountainous regions. Orographic influences can be pronounced on spatial scales ranging from the size of individual hills to the scale of major mountain ranges, and on temporal scales from the duration of a brief snow squall to
Smith RB, Barstad I. 2004. A linear theory of orographic precipitation. J. Atmos. Sci. 61:1377-1391. Smith RB. 1979. The influence of mountains on the atmosphere. Adv. Geophys. 21:87-230. Cross References Accumulation Atmospheric processes and snow / ice formation Cascade Mountains, USA Global warming and its affect on snow / ice / glaciers Latent heat of condensation Latent heat of fusion Precipitation Snow Line Snow Storms Snow Fall Solid precipitation Spatial / temporal variation in snow cover/snow melt Figure Caption Figure 1. Topography and precipitation for the Olympic and Cascade Mountains of northwestern Washington, USA. (a) shows elevation in grayscale (black corresponds to 3.5 km) and the location of regularly reporting precipitation gauges located above 150 m elevation (white dots).(b)-(d) shows precipitation from October 2000 ā September 2007 in gray shading, and smoothed contours of elevation every 250 m. (b) is the PRISM analysis of gauge observations (Daly et al. 1994; data from PRISM Group, Oregon State University, http://www.prismclimate.org). (c) is from the model of Smith and Barstad (2004) forced with data taken from atmospheric soundings at KUIL (shown in (a)). (d) is from the operational MM5 numerical weather predictions (e.g. Minder et al. 2008; http://www.atmos.washington.edu/mm5rt/). (e) compares the observed (gray) and MM5 (black) precipitation for a gauge transect in the southwestern Olympics (location shown with white line in (c)) for the winter of 2004-2005. The topographic profile (peak elevation of 800 m) is shaded (modified from Minder et al. (2008) and reproduced with permission from Wiley-Blackwell).
P(m) 0 10 20 1 2 SW to NE (km)^ x (km) (d) P(m/yr) (c) (a) (b) (e)