Future of Cumulus Parametrization conference, Delft, July 10-14, 2017

Email: m.muetzelfeldt@pgr.reading.ac.uk

For a small city, Delft punches above its weight. It is famous for many things, including its celebrated Delftware (Figure 1). It was also the birthplace of one of the Dutch masters, Johannes Vermeer, who coincidentally painted some fine cityscapes with cumulus clouds in them (Figure 2). There is a university of technology with some impressive architecture (Figure 3). It holds the dubious honour of being the location of the first assassination using a pistol (or so we were told by our tour guide), when William of Orange was shot in 1584. To this list, it can now add hosting a one-week conference on the future of cumulus parametrization, and hopefully bringing about more of these conferences in the future.

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Figure 1: Delftware.

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Figure 2: Delft with canopy of cumulus clouds. By Johannes Vermeer, 1661.

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Figure 3: AULA conference centre at Delft University of Technology – where we were based for the duration of the conference.

So what is a cumulus parametrization scheme? The key idea is as follows. Numerical weather and climate models work by splitting the atmosphere into a grid, with a corresponding grid length representing the length of each of the grid cells. By solving equations that govern how the wind, pressure and heating interact, models can then be used to predict what the weather will be like days in advance in the case of weather modelling. Or a model can predict how the climate will react to any forcings over longer timescales. However, any phenomena that are substantially smaller than this grid scale will not be “seen” by the models. For example, a large cumulonimbus cloud may have a horizontal extent of around 2km, whereas individual grid cells could be 50km in the case of a climate model. A cumulonimbus cloud will therefore not be explicitly modelled, but it will still have an effect on the grid cell in which it is located – in terms of how much heating and moistening it produces at different levels. To capture this effect, the clouds are parametrized, that is, the vertical profile of the heating and moistening due to the clouds are calculated based on the conditions in the grid cell, and this then affects the grid-scale values of these variables. A similar idea applies for shallow cumulus clouds, such as the cumulus humilis in Vermeer’s painting (Figure 2), or present-day Delft (Figure 3).

These cumulus parametrization schemes are a large source of uncertainty in current weather and climate models. The conference was aimed at bringing together the community of modellers working on these schemes, and working out which might be the best directions to go in to improve these schemes, and consequently weather and climate models.

Each day was a mixture of listening to presentations, looking at posters and breakout discussion groups in the afternoon, as well as plenty of time for coffee and meeting new people. The presentations covered a lot of ground: from presenting work on state-of-the-art parametrization schemes, to looking at how the schemes perform in operational models, to focusing on one small aspect of a scheme and modelling how that behaves in a high resolution model (50m resolution) that can explicitly model individual clouds. The posters were a great chance to see the in-depth work that had been done, and to talk to and exchange ideas with other scientists.

Certain ideas for improving the parametrization schemes resurfaced repeatedly. The need for scale-awareness, where the response of the parametrization scheme takes into account the model resolution, was discussed. One idea for doing this was the use of stochastic schemes to represent the uncertainty of the number of clouds in a given grid cell. The concept of memory also cropped up – where the scheme remembers if it had been active at a given grid cell in a previous point in time. This also ties into the idea of transitions between cloud regimes, e.g. when a stratocumulus layer splits up into individual cumulus clouds. Many other, sometimes esoteric, concepts were discussed, such as the role of cold pools, how much tuning of climate models is desirable and acceptable, how we should test our schemes, and what the process of developing the schemes should look like.

In the breakout groups, everyone was encouraged to contribute, which made for an inclusive atmosphere in which all points of view were taken on board. Some of the key points of agreement from these were that it was a good idea to have these conferences, and we should do it more often! Hopefully, in two years’ time, another PhD student will write a post on how the next meeting has gone. We also agreed that it would be beneficial to be able to share data from our different high resolution runs, as well as to be able to compare code for the different schemes.

The conference provided a picture of what the current thinking on cumulus parametrization is, as well as which directions people think are promising for the future. It also provided a means for the community to come together and discuss ideas for how to improve these schemes, and how to collaborate more closely with future projects such as ParaCon and HD(CP)2.

RMetS Impact of Science Conference 2017.

Email – j.f.talib@pgr.reading.ac.uk

“We aim to help people make better decisions than they would if we weren’t here”

Rob Varley CEO of Met Office

This week PhD students from the University of Reading attended the Royal Meteorological Society Impact of Science Conference for Students and Early Career Scientists. Approximately eighty scientists from across the UK and beyond gathered at the UK Met Office to learn new science, share their own work, and develop new communication skills.

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Across the two days students presented their work in either a poster or oral format. Jonathan Beverley, Lewis Blunn and I presented posters on our work, whilst Kaja Milczewska, Adam Bateson, Bethan Harris, Armenia Franco-Diaz and Sally Woodhouse gave oral presentations. Honourable mentions for their presentations were given to Bethan Harris and Sally Woodhouse who presented work on the energetics of atmospheric water vapour diffusion and the representation of mass transport over the Arctic in climate models (respectively). Both were invited to write an article for RMetS Weather Magazine (watch this space). Congratulations also to Jonathan Beverley for winning the conference’s photo competition!

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Jonathan Beverley’s Winning Photo.

Alongside student presentations, two keynote speaker sessions took place, with the latter of these sessions titled Science Communication: Lessons from the past, learning for future impact. Speakers in this session included Prof. Ellie Highwood (Professor of Climate Physics and Dean for Diversity and Inclusion at University of Reading), Chris Huhne (Co-chair of ET-index and former Secretary of State for Energy and Climate Change), Leo Hickman (editor for Carbon Brief) and Dr Amanda Maycock (NERC Independent Research Fellow and Associate Professor in Climate Dynamics, University of Leeds). Having a diverse range of speakers encouraged thought-provoking discussion and raised issues in science communication from many angles.

Prof. Ellie Highwood opened the session challenging us all to step beyond the typical methods of scientific communication. Try presenting your science without plots. Try presenting your work with no slides at all! You could step beyond the boundaries even more by creating interesting props (for example, the notorious climate change blanket). Next up Chris Huhne and Leo Hickman gave an overview of the political and media interactions with climate change science (respectively). The Brexit referendum, Trump’s withdrawal from the Paris Accord and the rise of the phrase “fake news” are some of the issues in a society “where trust in the experts is falling”. Finally, Dr Amanda Maycock presented a broad overview of influential science communicators from the past few centuries. Is science relying too heavily on celebrities for successful communication? Should the research community put more effort into scientific outreach?

Communication and collaboration became the two overarching themes of the conference, and conferences such as this one are a valuable way to develop these skills. Thank you to the Royal Meteorology Society and UK Met Office for hosting the conference and good luck to all the young scientists that we met over the two days.

#RMetSImpact

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Also thank you to NCAS for funding my conference registration and to all those who provided photos for this post.

Mountains and the Atmospheric Circulation within Models

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. 

 

Understanding the urban environment and its effect on indoor air.

Email: h.l.gough@pgr.reading.ac.uk

Recent estimates by the United Nations (2009) state that 50 to 70 % of the world’s population now live in urban areas with over 70 % of our time being spent indoors, whether that’s at work, at home or commuting.

We’ve all experienced a poor indoor environment, whether it’s the stuffy office that makes you sleepy, or the air conditioning unit that causes the one person under it to freeze. Poor environments make you unproductive and research is beginning to suggest that they can make you ill. The thing is, the microclimate around one person is complex enough, but then you have to consider the air flow of the room, the ventilation of the building and the effect of the urban environment on the building.

So what tends to happen is that buildings and urban areas are simplified down into basic shapes with all the fine details neglected and this is either modelled at a smaller scale in a wind tunnel or by using CFD (computer fluid dynamics). However, how do we know whether these models are representative of the real-world?

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This is Straw city, which was built in Silsoe U.K during 2014. You can just see the car behind the array (purple circle), these cubes of straw are 6 m tall, or roughly the height of an average house. Straw city is the stepping stone between the scale models and the real world, and was an urban experiment in a rural environment. We measured inside the array, outside of the array and within the blue building so we could see the link between internal and external flow: which meant the use of drones and smoke machines! The focus of the experiment was on the link between ventilation and the external conditions.

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Smoke releases, drone flying, thermal imaging and tracer gas release: some of the more fun aspects of the fieldwork

After 6 months of data collection, we took the straw cubes away and just monitored the blue cube on its own and the effect of the array can clearly be seen in this plot, where pink is the array, and blue is the isolated cube. So this is showing the pressure coefficient (Cp),  and can be thought of as a way of comparing one building to another in completely different conditions. You can see that the wind direction has an effect and that the array reduces the pressure felt by the cube by 60-90 %. Pressure is linked to the natural ventilation of a building: less pressure means less flow through the opening.

 

Alongside the big straw city, we also went to the Enflo lab at the University of Surrey to run some wind tunnel experiments of our own, which allowed us to expand the array.

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Photos of the wind tunnel arrays. Left is the biggest array modelled, centre is the Silsoe array, top right is the wind tunnel and roughness elements. Bottom right is the model of the storage shed at the full-scale site and centre is the logging system used.

So we have a data set that encompasses all wind directions and speeds, all atmospheric stabilities, different temperature differences and different weather conditions. It’s a big data set and will take a while to work through, especially with comparisons to the wind tunnel model and CFD model created by the University of Leeds. We will also compare the results to the existing guidelines out there and to other similar data sets.

I could ramble on for hours about the work, having spent far too long in a muddy field in all weathers but for more information please email me or come along to my departmental seminar on the 8th November.

This PhD project is jointly funded by the University of Reading and the EPSRC and is part of the Refresh project: www.refresh-project.org.uk