Orography

Mountains and the Atmospheric Circulation within Models

Annelize van NiekerkLeave a comment

Email: a.vanniekerk@pgr.reading.ac.uk

Mountains come in many shapes and sizes and as a result their dynamic impact on the atmospheric circulation spans a continuous range of physical and temporal scales. For example, large-scale orographic features, such as the Himalayas and the Rockies, deflect the atmospheric flow and, as a result of the Earth’s rotation, generate waves downstream that can remain fixed in space for long periods of time. These are known as stationary waves (see Nigam and DeWeaver (2002) for overview). They have an impact not only on the regional hydro-climate but also on the location and strength of the mid-latitude westerlies. On smaller physical scales, orography can generate gravity waves that act to transport momentum from the surface to the upper parts of the atmosphere (see Teixeira 2014), playing a role in the mixing of chemical species within the stratosphere.

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Figure 1: The model resolved orography at different horizontal resolutions. From a low (climate model) resolution to a high (seasonal forecasting) resolution. Note how smooth the orography is at climate model resolution.

Figure 1 shows an example of the resolved orography at different horizontal resolutions over the Himalayas. The representation of orography within models is complicated by the fact that, unlike other parameterized processes, such as clouds and convection, that are typically totally unresolved by the model, its effects are partly resolved by the dynamics of the model and the rest is accounted for by parameterization schemes.However, many parameters within these schemes are not well constrained by observations, if at all. The World Meteorological Organisation (WMO) Working Group on Numerical Experimentation (WGNE) performed an inter-model comparison focusing on the treatment of unresolved drag processes within models (Zadra et al. 2013). They found that while modelling groups generally had the same total amount of drag from various different processes, their partitioning was vastly different, as a result of the uncertainty in their formulation.

Climate models with typically low horizontal resolutions, resolve less of the Earth’s orography and are therefore more dependent on parameterization schemes. They also have large model biases in their climatological circulations when compared with observations, as well as exhibiting a similarly large spread about these biases. What is more, their projected circulation response to climate change is highly uncertain. It is therefore worth investigating the processes that contribute towards the spread in their climatological circulations and circulation response to climate change. The representation of orographic processes seem vital for the accurate simulation of the atmospheric circulation and yet, as discussed above, we find that there is a lot of uncertainty in their treatment within models that may be contributing to model uncertainty. These uncertainties in the orographic treatment come from two main sources:

  1. Model Resolution: Models with different horizontal resolutions will have different resolved orography.
  2. Parameterization Formulation: Orographic drag parameterization formulation varies between models.

The issue of model resolution was investigated in our recent study, van Niekerk et al. (2016). We showed that, in the Met Office Unified Model (MetUM) at climate model resolutions, the decrease in parameterized orographic drag that occurs with increasing horizontal resolution was not balanced by an increase in resolved orographic drag. The inability of the model to maintain an equivalent total (resolved plus parameterized) orographic drag across resolutions resulted in an increase in systematic model biases at lower resolutions identifiable over short timescales. This shows not only that the modelled circulation is non-robust to changes in resolution but also that the parameterization scheme is not performing in the same way as the resolved orography. We have highlighted the impact of parameterized and resolved orographic drag on model fidelity and demonstrated that there is still a lot of uncertainty in the way we treat unresolved orography within models. This further motivates the need to constrain the theory and parameters within orographic drag parameterization schemes.

References

Nigam, S., and E. DeWeaver, 2002: Stationary Waves (Orographic and Thermally Forced). Academic Press, Elsevier Science, London, 2121–2137 pp., doi:10.1016/B978-0-12-382225-3. 00381-9.

Teixeira MAC, 2014: The physics of orographic gravity wave drag. Front. Phys. 2:43. doi:10.3389/fphy.2014.00043 http://journal.frontiersin.org/article/10.3389/fphy.2014.00043/full

Zadra, A., and Coauthors, 2013: WGNE Drag Project. URL:http://collaboration.cmc.ec.gc.ca/science/rpn/drag_project/

van Niekerk, A., T. G. Shepherd, S. B. Vosper, and S. Webster, 2016: Sensitivity of resolved and parametrized surface drag to changes in resolution and parametrization. Q. J. R. Meteorol. Soc., 142 (699), 2300–2313, doi:10.1002/qj.2821. 

 

From foehn to intense rainfall: the importance of Alps in influencing the regional weather

Ambrogio VolontéLeave a comment

Email: a.volonte@pgr.reading.ac.uk

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Figure 1: View from Monte Lema (Italy-Switzerland) looking West. The Lake Maggiore region and the southern Alpine foothills are visible in the foreground whereas Monte Rosa and the Pennine Alps behind them are partially hidden by a characteristic foehn wall.  (A. Volonté, 4 January 2017)

The interaction between atmospheric flow and topography is at the origin of various important weather phenomena, as we have already seen in Carly Wright’s blog post. When a mountain range is particularly high and extended it can even block or deflect weather systems, as it happens with the Alps. For example, in Figure 1 we can see the main Alpine range with its over-4000m-high peaks blocking a cold front coming from the north. The main ridge acts as a wall, enhancing condensation and precipitation processes on the upstream side (stau condition) and leaving clear skies on the downstream lee side, where dry and mild katabatic foehn winds flow. The contrast is striking between sunny weather on Lake Maggiore and snowy conditions over Monte Rosa, just a few miles apart. The same phenomenon is shown in Figure 2 with a satellite image that highlights how a cold front coming from northwest gets blocked by the Alpine barrier. A person enjoying the sunny day in the southern side of the Alps, if unaware of this mechanism, would be very surprised  to know that the current weather is so different on the other side of the range.

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Figure 2: Satellite image (MODIS-NASA) over the Alps and Po Valley on 22 October 2014
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Figure 3: same as Figure 1 but on 13 December 2016

A comparison with Figure 3 helps to notice that in Figure 2 the shape of the cloud band closely mirrors the mountain range. As an additional remark,  this comparison shows that foehn bring clear skies even in the Po Valley, having blown away the typical mist/fog occurring in the region in Autumn and Winter months in high pressure regimes. The  stau/foehn dynamics is actually very fascinating, and you can read more about it in Elvidge and Renfrew (2015 ) and in Miltenberger et al. (2016), among others. Unfortunately, the interaction of weather systems with the Alps can often trigger very damaging phenomena, like heavy and long-lasting precipitation on one side of the slope, and this is what the rest of this post will be focused on. In fact, the most recent event of this kind just happened at the end of November, with intense and long-lasting rain affecting the southern slope of the Alps  and causing floods particularly in the Piedmont region, in northwestern Italy ( Figure 4).

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Figure 4: River Tanaro flooding in the town of Garessio, 24 November 2016 (Piedmont, Italy). Source: http://www.corrierenazionale.it
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Figure 5: rainfall accumulated between 21 and 26 November 2016 in the Piedmont region. Source: Regional Agency for the protection of the Environment – Piedmont

Figure 5 shows that the accumulated rainfall in the event goes over 300 mm in a large band that follows the shape of the southern Alpine slope in the region (see map of Piedmont, from Google Maps), reaching even 600 mm in a few places. This situation is the result of moist southerly flow being blocked by the Alps and thus causing ascent and consequent precipitation to persist on the same areas for up to five days. It is quite common to see quasi-stationary troughs enter the Mediterranean region during Autumn months causing strong and long-lasting moist flows to move towards the Alps. Hence, it is crucial to understand  where the heaviest precipitation will occur. In other words, will it rain the most on top of the ridge or on the upstream plain? What processes are controlling the location of heavy precipitation with respect to the slope?

The study published by Davolio et al. (2016), available here and originated from my master degree’s thesis, tackles this issue focusing on northeastern Italy. In fact, the analysis includes three case studies in which heavy and long-lasting rain affected the eastern Alps and other three case studies in which intense rainfall was mainly located on the upstream plain. Although all the events showed common large-scale patterns and similar mesoscale settings, characterised by moist southerly low-level flow interacting with the Alps, the rainfall distribution turned out to be very dissimilar. The study highlights that the two precipitation regimes strongly differ in terms of interaction of the flow with the mountain barrier. When the flow is able to go over the Alps the heaviest rain occurs on top of the ridge. When the flow is instead blocked and deflected by the ridge (flow around), creating a so-called barrier wind, intense convection is triggered on the upstream plain (Figure 6) .

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Figure 6: Schematic diagram of the key mechanisms governing the two different wind and precipitation patterns over NE Italy. (a) Blocked low-level flow, barrier wind, convergence and deep convection over the plain, upstream the orography. (b) Flow over conditions with orographic lifting and precipitation mainly over the Alps. From Davolio et al. (2016)
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Figure 7: cross section going from the Adriatic Sea to the Alps in one of the events simulated. Equivalent potential temperature is shaded, thick black lines indicate clouds while arrows show tangent wind component. See Davolio et al. (2016)

The key mechanism that explains this different evolution is connected to the thermodynamic state of the impinging flow. In fact, when the southerly moist and warm air gets close to the Alpine barrier it is lifted above the colder air already present at the base of the orography. It can be said that the colder air behaves as a first effective mountain for the incoming flow. If this lifting process triggers convection, then the persistence of a blocked-flow condition is highly favoured (see Figure 7). On the contrary, if this initial lifting process does not trigger convection the intense moist flow will eventually be able to go over the ridge, where a more substantial ascent will take place, causing heavy rain on the ridge top. This study also looks at numerical parameters used in more idealised analyses (like in Miglietta and Rotunno (2009)), finding a good agreement with the theory.

To summarise, we can say that the Alpine range is able to significantly modify weather systems when interacting with them. Thus, an in-depth understanding of the processes taking place during the interaction, along with a coherent model is necessary to capture correctly the effects on the local weather, being either a rainfall enhancement, the occurrence of foehn winds or various other phenomena.

References

Davolio, S., Volonté A., Manzato A., Pucillo A., Cicogna A. and Ferrario M.E. (2016), Mechanisms producing different precipitation patterns over north-eastern Italy: insights from HyMeX-SOP1 and previous events. Q.J.R. Meteorol. Soc., 142 (Suppl 1): 188-205. doi:10.1002/qj.2731

Elvidge A. D., Renfrew, I. A. (2015). The causes of foehn warming in the lee of mountains. Bull. Am. Meteorol. Soc. 97: 455–466, doi:10.1175/BAMS-D-14-00194.1.

Miglietta M. and Rotunno R., (2009) Numerical Simulations of Conditionally Unstable Flows over a Mountain Ridge. J. Atmos. Sci., 66, 1865–1885, doi: 10.1175/2009JAS2902.1. 

Miltenberger, A. K., Reynolds, S. and Sprenger, M. (2016), Revisiting the latent heating contribution to foehn warming: Lagrangian analysis of two foehn events over the Swiss Alps. Q.J.R. Meteorol. Soc., 142: 2194–2204. doi:10.1002/qj.2816

Stationary Orographic Rainbands

Carly Wright1 Comment

Email: c.j.wright@pgr.reading.ac.uk

Small-scale rainbands often form downwind of mountainous terrain. Although relatively small in scale (a few tens of km across by up to ~100 km in length), these often poorly forecast bands can cause localised flooding as they can be associated with intense precipitation over several hours due to the anchoring effect of orography (Barrett et al., 2013).   Figure 1 shows a flash flood caused by a rainband situated over Cockermouth in 2009.  In some regions of southern France orographic banded convection can contribute 40% of the total rainfall (Cosma et al., 2002).  Rainbands occur in various locations and under different synoptic regimes and environmental conditions making them difficult to examine their properties and determine their occurrence in a systematic way (Kirshbaum et al. 2007a,b, Fairman et al. 2016).  My PhD considers the ability of current operational forecast models to represent these bands and the environmental controls on their formation.

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Figure 1: Flash flood event caused by a rainband situated over Cockermouth, Cumbria, UK in 2009

 

What is a rainband?

  • A cloud and precipitation structure associated with an area of rainfall which is significantly elongated
  • Stationary (situated over the same location) with continuous triggering
  • Can form in response to moist, unstable air following over complex terrain
  • Narrow in width ~2-10 km with varying length scales from 10 – 100’s km

 

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Figure 2: Schematic showing the difference between cellular and banded convection

To examine the ability of current operational forecast models to represent these bands a case study was chosen which was first introduced by Barrett, et al. (2016).  The radar observations during the event showed a clear band along The Great Glen Fault, Scotland (Figure 3).  However, Barrett, et al. (2016) concluded that neither the operational forecast or the operational ensemble forecast captured the nature of the rainband.  For more information on ensemble models see one of our previous blog posts by David Flack Showers: How well can we predict them?.

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Figure 3: Radar observations of precipitation accumulation over a six hour period (between 3-9 am) showing a rainband located over The Great Glen Fault, Scotland on 29 December 2012.

Localised convergence and increased convective available potential energy along the fault supported the formation of the rainband.  To determine the effect of model resolution on the model’s representation of the rainband, a forecast was performed with the horizontal gird spacing decreased to 500 m from 1.5 km.  In this forecast a rainband formed in the correct location which generated precipitation accumulations close to those observed, but with a time displacement.  The robustness of this forecast skill improvement is being assessed by performing an ensemble of these convection-permitting simulations.  Results suggest that accurate representation of these mesoscale rainbands requires resolutions higher than those used operationally by national weather centres.

Idealised numerical simulations have been used to investigate the environmental conditions leading to the formation of these rainbands.  The theoretical dependence of the partitioning of dry flow over and around mountains on the non-dimensional mountain height is well understood.  For this project I examine the effect of this dependence on rainband formation in a moist environment.  Preliminary analysis of the results show that the characteristics of rainbands are controlled by more than just the non-dimensional mountain height, even though this parameter is known to be sufficient to determine flow behaviour relative to mountains.

This work has been funded by the Natural Environmental Research Council (NERC) under the project PREcipitation STructures over Orography (PRESTO), for more project information click here.

References

Barrett, A. I., S. L. Gray, D. J. Kirshbaum, N. M. Roberts, D. M. Schultz, and J. G. Fairman, 2015: Synoptic Versus Orographic Control on Stationary Convective Banding. Quart. J. Roy. Meteorol. Soc., 141, 1101–1113, doi:10.1002/qj.2409.

— 2016: The Utility of Convection-Permitting Ensembles for the Prediction of Stationary Convective Bands. Mon. Wea. Rev., 144, 10931114, doi:10.1175/MWR-D-15-0148.1.

Cosma, S., E. Richard, and F. Minsicloux, 2002: The Role of Small-Scale Orographic Features in the Spatial Distribution of Precipitation. Quart. J. Roy. Meteorol. Soc., 128, 75–92, doi:10.1256/00359000260498798.

Fairman, J. G., D. M. Schultz, D. J. Kirshbaum, S. L. Gray, and A. I. Barrett, 2016: Climatology of Banded Precipitation over the Contiguous United States. Mon. Wea. Rev., 144,4553–4568, doi: 10.1175/MWR-D-16-0015.1.

Kirshbaum, D. J., G. H. Bryan, R. Rotunno, and D. R. Durran, 2007a: The Triggering of Orographic Rainbands by Small-Scale Topography. J. Atmos. Sci., 64, 1530–1549, doi:10.1175/JAS3924.1.

Kirshbaum, D. J., R. Rotunno, and G. H. Bryan, 2007b: The Spacing of Orographic Rainbands Triggered by Small-Scale Topography. J. Atmos. Sci., 64, 4222–4245, doi:10.1175/2007JAS2335.1.