Two Weeks in Paris Learning about Fluid Dynamics and Sampling French Pastries


The Fluid Dynamics of Sustainability and the Environment (FDSE) residential summer school runs every summer for two weeks, alternating between Cambridge University and Ecole polytechnique, which run the summer school in partnership. I attended this years hosted by Ecole polytechnique, situated to the South of Paris. 40 PhD students attended from institutes around the world, all working on a range of topics who want to learn more about environmental fluid dynamics.


The lectures covered topics on fundamentals of fluid dynamics, flow instabilities, environmental fluid dynamics, cryosphere, atmosphere, physical oceanography and renewable energy. The lectures went at a very fast pace (approximately triple speed!), aiming to familiarise us with as many concepts as possible in the two weeks, resulting in everyone taking home a large overflowing folder full of lecture notes to refer back to in the future.

We were kept very busy throughout the two weeks. Each day started with breakfast (coffee and croissants) between 7.30-8.20 am, followed by two back to back lectures 8.30-10.30 am. There was then half an hour for everyone to fuel their brain with coffee and (warm!) mini pastries before another hour lecture before lunch break. Lunch was roughly 12-1.30 pm, although typically there were so many interesting questions after each lecture that we ran progressively later relative to the schedule meaning that I think we only actually started lunch on time on the first day. There were also a number of guest speakers speaking on topics such as public engagement, climate policy, meteorology on mars and air quality.

After lunch we had the final lecture of the day, followed by a short break before numerical sessions and lab experiments, which ran until roughly 6 pm. These sessions gave us the chance to really learn about a particular topic in more detail and to have a more hands on experience with some of the material being lectured. My labs were on tidal energy where we explored the energy output and efficiency of tidal turbines, and Art and Science, which encouraged us to engage with Science in new and more playful ways and also to challenge us to look at it differently.

However the day didn’t end after the labs, the evenings were also jam packed! The first evening was a poster session, giving us all the opportunity to learn more about what all of the other students work on and to mingle. Other evenings consisted of learning to row sessions, visits to the observatory, movie nights and discussions about the ‘science’ in The Day After Tomorrow movie and barbeques enjoying the warm light evenings (definitely missing those now I’m back in Reading).

During the weekend sandwiched in the middle of the two weeks, we were all transferred to a hostel in the centre of Paris, setting us all up perfectly for some weekend sightseeing in Paris. On the Friday evening there was a boat party reception on the Siene, supplying us all with lots of wine, many difference French cheeses to sample and a lively dance floor.

The school ended on Friday July 14th, Bastille Day. After a morning presenting a few slides on the labs we had completed in groups to share what we had learnt, we travelled into the centre of Paris ready for an evening enjoying the spectacular Bastille Day fireworks around the Eiffel tower, ending the summer school with a bang.

Personally the main take away from the summer school was not to learn the entirety of the lecture content, but to become familiar with a wide range of topics gain more hands on experience of laboratory experiments and to have a (rather large) folder full of lecture notes to refer back to whenever I stumble across a particular concept again in the future. And of course, it was great having the opportunity to meet lots of other PhD students from around the world working on related topics and to be able to discuss, engage and get to know each other over the two weeks. I would like to thank all of the organisers and lecturers of the summer school for a really interesting and enjoyable two weeks!


RMetS Impact of Science Conference 2017.

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“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.


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!

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.


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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


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.

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.


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

Zadra, A., and Coauthors, 2013: WGNE Drag Project. URL:

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. 


The impact of Climate Variability on the GB power system.


Bloomfield et al., 2016. Quantifying the increasing sensitivity of power systems to climate variability. View published paper.

Within the power system of Great Britain (GB), there is a rapidly increasing amount of generation from renewables, such as wind and solar power which are weather-dependent. An increased proportion of weather-dependent generation will require increased understanding of the impact of climate variability on the power system.


Figure 1: Predicted installed capacity from the National Grid Gone Green Scenario. Source: National Grid Future Energy Scenarios (2015).

Current research on the impact of climate variability on the GB power system is ongoing by climate scientists and power system modellers. The focus of the climate research is on the weather-driven components of the power system, such as the impact of climate variability on wind power generation. These studies tend to include limited knowledge of the whole system impacts of climate variability. The research by power system modellers focuses on the accurate representation of the GB power system. A limited amount of weather data may be used in this type of study (usually 1-10 years) due to the complexity of the power system models.

The aim of this project is to bridge the gap between these two groups of research, by understanding the impact of climate variability on the whole GB power system.In this project, multi-decadal records from the MERRA reanalysis* are combined with a simple representation of the GB power system, of which the weather-dependent components are electricity demand and wind power production. Multiple scenarios are analysed for GB power systems, including 0GW, 15GW, 30GW, and 45GW of installed wind power capacity in the system.

This study characterises the impact of inter-annual climate variability on multiple aspects of the GB power system (including coal, gas and nuclear generation) using a load duration curve framework. A load duration curve can be thought of as a cumulative frequency distribution of power system load. Load can be either power system demand (i.e. the NO-WIND scenario) or demand minus wind power (ie. the LOW, MED and HIGH scenarios).

The introduction of additional wind-power capacity greatly increases the year-year variability in operating opportunity for conventional generators, this is particularly evident for baseload plant (i.e. nuclear power plants). The impact of inter-annual climate variations across the power system due to present-day level of wind-farm installation has approximately doubled the exposure of the GB power sector to inter-annual climate variability. This is shown in Figure 2 as the spread between the red and blue curves (from the LOW scenario) is double that of the black curves (the NO-WIND scenario).


Figure 2: Load duration curves for the NO-WIND and LOW scenario in black and grey respectively. The two most extreme years from the LOW scenario are 1990 and 2010, plotted in red and blue respectively. Vertical dashed lines show the percentage of time that baseload-plant (91%) and peaking plant (7%) are required to operate

This work has shown that as the amount of installed wind power capacity on the power system is increased, the total amount of energy required from other generators (coal, gas, nuclear) is reduced. Wind therefore contributes to decarbonising the power system, however the reduction is particularly pronounced for plants which are operating as baseload rather than peaking plant (i.e. oil fired generation) where an increase in required production is seen.

This study adds to the literature which suggests that the power system modelling community should begin to take a more robust approach to its treatment of weather and climate data by incorporating a wider range of climate variability.

For more information contact the author for a copy of the paper with details of this work: Quantifying the increasing sensitivity of power system to climate variability (submitted to ERL).

* A reanalysis data set is a scientific method for developing a record of how weather and climate are changing over time. In it, observations are combined with a numerical model to generate a synthesised estimate of the state of the climate system.