## The advection process: simulating wind on computers

Email: js102@zepler.net   Web: datumedge.co.uk   Twitter: @hertzsprrrung

If we know which way the wind is blowing then we can predict a lot about the weather. We can easily observe the wind moving clouds across the sky, but the wind also moves air pollution and greenhouse gases. This process is called transport or advection. Accurately simulating the advection process is important for forecasting the weather and predicting climate change.

I am interested in simulating the advection process on computers by dividing the world into boxes and calculating the same equation in every box. There are many existing advection methods but many rely on these boxes having the correct shape and size, otherwise these existing methods can produce inaccurate simulations.

During my PhD, I’ve been developing a new advection method that produces accurate simulations regardless of cell shape or size. In this post I’ll explain how advection works and how we can simulate advection on computers. But, before I do, let’s talk about how we observe the weather from the ground.

In meteorology, we generally have an incomplete picture of the weather. For example, a weather station measures the local air temperature, but there are only a few hundred such stations dotted around the UK. The temperature at another location can be approximated by looking at the temperatures reported by nearby stations. In fact, we can approximate the temperature at any location by reconstructing a continuous temperature field using the weather station measurements.

So far we have only talked about temperatures varying geographically, but temperatures also vary over time. One reason that temperatures change over time is because the wind is blowing. For example, a wind blowing from the north transports, or advects, cold air from the arctic southwards over the UK. How fast the temperature changes depends on the wind speed, and the size of the temperature contrast between the arctic air and the air further south. We can write this as an equation. Let’s call the wind speed $v$ and assume that the wind speed and direction are always the same everywhere. We’ll label the temperature $T$, label time $t$, and label the south-to-north direction $y$, then we can write down the advection equation using partial derivative notation,

$\frac{\partial T}{\partial t} = - \frac{\partial T}{\partial y} \times v$

This equation tells us that the local temperature will vary over time ($\frac{\partial T}{\partial t}$), depending on the north-south temperature contrast ($- \frac{\partial T}{\partial y}$) multiplied by the wind speed $v$.

One way to solve the advection equation on a computer is to divide the world into boxes, called cells. The complete arrangement of cells is called a mesh. At a point at the centre of each cell we store meteorological information such as temperature, water vapour content or pollutant concentration. At the cell faces where two cells touch we store the wind speed and direction. The arrangement looks like this:

The above example of a mesh over the UK uses cube-shaped cells stacked in columns above the Earth, and arranged along latitude and longitude lines. But more recently, weather forecasting models are using different types of mesh. These models tesselate the globe with squares, hexagons or triangles.

Weather models must also rearrange cells in order to represent mountains, valleys, cliffs and other terrain. Once again, different models rearrange cells differently. One method, called the terrain-following method, shifts cells up or down to accommodate the terrain. Another method, called the cut-cell method, cuts cells where they intersect the terrain. Here’s what these methods look like when we use them to represent an idealised, wave-shaped mountain:

Once we’ve chosen a mesh and stored temperature at cell centres and the wind at cell faces, we can start calculating a solution to the advection equation which enables us to forecast how the temperature will vary over time. We can solve the advection equation for every cell separately by discretising the advection equation. Let’s consider a cell with a north face and a south face. We want to know how the temperature stored at the cell centre, $T_\mathrm{cell}$, will vary over time. We can calculate this by reconstructing a continuous temperature field and using this to approximate temperature values at the north and south faces of the cell, $T_\mathrm{north}$ and $T_\mathrm{south}$,

$\frac{\partial T_\mathrm{cell}}{\partial t} = - \frac{T_\mathrm{north} - T_\mathrm{south}}{\Delta y} \times v$

where $\Delta y$ is the distance between the north and south cell faces. This is the same reconstruction process that we described earlier, only, instead of approximating temperatures using nearby weather station measurements, we are approximating temperatures using nearby cell centre values.

There are many existing numerical methods for solving the advection equation but many do not cope well when meshes are distorted, such as terrain-following meshes, or when cells have very different sizes, such as those cells in cut-cell meshes. Inaccurate solutions to the advection equation lead to inaccuracies in the weather forecast. In extreme cases, very poor solutions can cause the model software to crash, and this is known as a numerical instability.

We can see a numerical instability growing in this idealised example. A blob is being advected from left to right over a range of steep, wave-shaped mountains. This example is using a simple advection method which cannot cope with the distorted cells in this mesh.

We’ve developed a new method for solving the advection equation with almost any type of mesh using cubes or hexagons, terrain-following or cut-cell methods. The advection method works by reconstructing a continuous field from data stored at cell centre points. A separate reconstruction is made for every face of every cell in the mesh using about twelve nearby cell centre values. Given that weather forecast models have millions of cells, this sounds like an awful lot of calculations. But it turns out that we can make most of these calculations just once, store them, and reuse them for all our simulations.

Here’s the same idealised simulation using our new advection method. The results are numerically stable and accurate.

A preprint of our journal article documenting the new advection method is available on ArXiv. I also have another blog post that talks about how to make the method even more accurate. Or follow me on Twitter for more animations of the numerical methods I’m developing.

## Understanding our climate with tiny satellites

Gristey, J. J., J. C. Chiu, R. J. Gurney, S.-C. Han, and C. J. Morcrette (2017), Determination of global Earth outgoing radiation at high temporal resolution using a theoretical constellation of satellites, J. Geophys. Res. Atmos., 122, doi:10.1002/2016JD025514.

The surface of our planet has warmed at an unprecedented rate since the mid-19th century and there is no sign that the rate of warming is slowing down. The last three decades have all been successively warmer than any preceding decade since 1850, and 16 of the 17 warmest years on record have all occurred since 2001. The latest science now tells us that it is extremely likely that human influence has been the dominant cause of the observed warming1, mainly due to the release of carbon dioxide and other greenhouse gases into our atmosphere. These greenhouse gases trap heat energy that would otherwise escape to space, which disrupts the balance of energy flows at the top of the atmosphere (Fig. 1). The current value of the resulting energy imbalance is approximately 0.6 W m–2, which is more than 17 times larger than all of the energy consumed by humans2! In fact, observing the changes in these energy flows at the top of the atmosphere can help us to gauge how much the Earth is likely to warm in the future and, perhaps more importantly, observations with sufficient spatial coverage, frequency and accuracy can help us to understand the processes that are causing this warming.

Observations of energy flows at the top of the atmosphere have traditionally been made by large and expensive satellites that may be similar in size to a large car3, making it impractical to launch multiple satellites at once. Although such observations have led to many advancements in climate science, the fundamental sampling restrictions from a limited number of satellites makes it impossible to fully resolve the variability in the energy flows at the top of atmosphere. Only recently, due to advancements in small satellite technology and sensor miniaturisation, has a novel, viable and sustainable sampling strategy from a constellation of satellites become possible. Importantly, a constellation of small satellites (Fig. 2a), each the size of a shoe-box (Fig. 2b), could provide both the spatial coverage and frequency of sampling to properly resolve the top of atmosphere energy flows for the first time. Despite the promise of the constellation approach, its scientific potential for measuring energy flows at the top of the atmosphere has not been fully explored.

To explore this potential, several experiments have been performed that simulate measurements from the theoretical constellation of satellites shown in Fig 2a. The results show that just 1 hour of measurements can be used to reconstruct accurate global maps of reflected sunlight and emitted heat energy (Fig. 3). These maps are reconstructed using a series of mathematical functions known as “spherical harmonics”, which extract the information from overlapping samples to enhance the spatial resolution by around a factor of 6 when compared with individual measurement footprints. After producing these maps every hour during one day, the uncertainty in the global-average hourly energy flows is 0.16 ± 0.45 W m–2 for reflected sunlight and 0.13 ± 0.15 W m–2 for emitted heat energy. Observations with these uncertainties would be capable of determining the sign of the 0.6 W m–2 energy imbalance directly from space4, even at very short timescales.

Also investigated are potential issues that could restrict similar uncertainties being achieved in reality such as instrument calibration and a reduced number of satellites due to limited resources. Not surprisingly, the success of the approach will rely on calibration that ensures low systematic instrument biases, and on a sufficient number of satellites that ensures dense hourly sampling of the globe. Development and demonstration of miniaturised satellites and sensors is currently underway to ensure these criteria are met. Provided good calibration and sufficient satellites, this study demonstrates that the constellation concept would enable an unprecedented sampling capability and has a clear potential for improving observations of Earth’s energy flows.

This work was supported by the NERC SCENARIO DTP grant NE/L002566/1 and co-sponsored by the Met Office.

Notes:

1 This statement is quoted from the latest Intergovernmental Panel on Climate Change assessment report. Note that these reports are produced approximately every 5 years and the statements concerning human influence on the climate have increased in confidence in every report.

2 Total energy consumed by humans in 2014 was 13805 Mtoe = 160552.15 TWh. This is an average power consumption of 160552.15 TWh  / 8760 hours in a year = 18.33 TW

Rate of energy imbalance per square metre at top of atmosphere is = 0.6 W m–2. Surface area of “top of atmosphere” at 80 km is 4 * pi * ((6371+80)*103 m)2 = 5.23*1014 m2. Rate of energy imbalance for entire Earth = 0.6 W m–2 * 5.23*1014 m2 = 3.14*1014 W = 314 TW

Multiples of energy consumed by humans = 314 TW / 18.33 TW = 17

3 The satellites currently carrying instruments that observe the top of atmosphere energy flows (eg. MeteoSat 8, Aqua) will typically also be hosting a suite of other instruments, which adds to the size of the satellite. However, even the individual instruments are still much larger that the satellite shown in Fig. 2b.

4 Currently, the single most accurate way to determine the top-of-atmosphere energy imbalance is to infer it from changes in ocean heat uptake. The reasoning is that the oceans contain over 90% of the heat capacity of the climate system, so it is assumed on multi-year time scales that excess energy accumulating at the top of the atmosphere goes into heating the oceans. The stated value of 0.6 W m–2 is calculated from a combination of ocean heat uptake and satellite observations.

References:

Allan et al. (2014), Changes in global net radiative imbalance 1985–2012, Geophys. Res. Lett., 41, 5588–5597, doi:10.1002/2014GL060962.

Barnhart et al. (2009), Satellite miniaturization techniques for space sensor networks, Journal of Spacecraft and Rockets46(2), 469–472, doi:10.2514/1.41639.

IPCC (2013), Climate Change 2013: The Physical Science Basis, available online at https://www.ipcc.ch/report/ar5/wg1/.

NASA (2016), NASA, NOAA Data Show 2016 Warmest Year on Record Globally, available online at https://www.nasa.gov/press-release/nasa-noaa-data-show-2016-warmest-year-on-record-globally.

Sandau et al. (2010), Small satellites for global coverage: Potential and limits, ISPRS J. Photogramm., 65, 492–504, doi:10.1016/j.isprsjprs.2010.09.003.

Swartz et al. (2013), Measuring Earth’s Radiation Imbalance with RAVAN: A CubeSat Mission to Measure the Driver of Global Climate Change, available online at https://earthzine.org/2013/12/02/measuring-earths-radiation-imbalance-with-ravan-a-cubesat-mission-to-measure-the-driver-of-global-climate-change/.

Swartz et al. (2016), The Radiometer Assessment using Vertically Aligned Nanotubes (RAVAN) CubeSat Mission: A Pathfinder for a New Measurement of Earth’s Radiation Budget. Proceedings of the AIAA/USU Conference on Small Satellites, SSC16-XII-03

## 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 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.

## Stationary Orographic Rainbands

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.

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

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?.

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.

## Showers: How well can we predict them?

Showers are one of the many examples of convective events experienced in the UK, other such events include thunderstorms, supercells and squall lines. These type of events form most often in the summer but can also form over the sea in the winter. They form because the atmosphere is unstable, i.e. warm air over a cooler surface, this results in the creation of thermals. If there is enough water vapour in the air and the thermal reaches high enough the water vapour will condense and eventually form a convective cloud. Convective events produce intense, often very localised, rainfall, which can result in flash floods, e.g. Boscastle 2004.

Flash floods are very difficult to predict, unlike flood events that happen from the autumnal and winter storms e.g. floods from Storms Desmond and Frank last winter, and the current floods (20-22 November). So often there is limited lead time for emergency services to react to flash flood events. One of the main reasons why flash floods are difficult to predict is the association with convective events because these events only last for a few hours (6 hours at the longest) and only affect a very small area.

One of the aspects of forecasting the weather that researchers look into is the predictability of certain events. My PhD considers the predictability of convective events within different situations in the UK.

The different situations I am considering are generally split into two regimes: convective quasi-equilibrium and non-equilibrium convection.

In convective quasi-equilibrium any production of instability in the atmosphere is balanced by its release (Arakawa and Schubert, 1974). This results in scattered showers, which could turn up anywhere in a region where there is large-scale ascent. This is typical of areas behind fronts and to the left of jet stream exit regions. Because there are no obvious triggers (like flow over mountains or cliffs) you can’t pin-point the exact location of a shower.  We often find ourselves in this sort of situation in April, hence April showers.

On the other hand in non-equilibrium convection the instability is blocked from being released so energy in the system builds-up over time. If this inhibiting factor is overcome all the instability can be released at once and will result in ‘explosive’ convection (Emanuel, 1994).  Overcoming the inhibiting factor usually takes place locally, such as a sea breeze or flow up mountains, etc. so these give distinct triggers and help tie the location of these events down. These are the type of situations that occur frequently over continents in the spring and often result in severe weather.

It’s useful having these regimes to categorise events to help determine what happens in the forecasts of different situations but only if we understand a little bit about their characteristics. For the initial part of my work I considered the regimes over the British Isles and found that  we mainly have convective events in convective quasi-equilibrium (showers) – on average roughly 85% of convective events in the summer are in this regime (Flack et al., 2016). Therefore it is pertinent to ask how well can we predict showers?

To see how well we can predict showers, and other types of convection, the forecast itself is examined. This is done by adding small-scale variability into the model, throughout the forecast, to determine what would happen if the starting conditions (or any other time in the model) changed. This is run a number of times to create an ensemble.

Using ensembles we can determine the uncertainty in the weather forecast, this can either be in terms of spatial positioning, timing or intensity of the event. My work has mainly considered the spatial positioning and intensity of the convection, and is to be submitted shortly to Monthly Weather Review. The intensity in my ensemble shows similar variation in both regimes, suggesting that there are times when the amount of rainfall predicted can be spot on. Most of the interesting results appear to be linked to the location of the events. The ensembles for the non-equilibrium cases generally show agreement between location of the events, so we can be fairly confident about their location (so here your weather app would be very good). On the other hand, when it comes to showers there is no consistency between the different forecasts so they could occur anywhere  (so when your app suggests showers be careful – you may or may not get one).

So I’ll answer my question that I originally posed with another question: What do you want from a forecast? If the answer to this question is “I want to know if there is a chance of rain at my location” then yes we can predict that you might get caught by a shower. If on the other hand your answer is “I want exact details, for my exact location, e.g. is there going to be a shower at 15:01 on Saturday at Stonehenge yes or no?” Then the answer is, although we are improving forecasts, we can’t give that accurate a forecast when it comes to scattered showers, simply because of their very nature.

With forecasts improving all the time and the fact that they are looking more realistic it does not mean that every detail of a forecast is perfect. As with forecasting in all areas (from politics to economy) things can take an unexpected turn so caution is advised. When it comes to the original question of showers then it’s always best to be prepared.

This work has been funded by the Natural Environmental Research Council under the project Flooding From Intense Rainfall, for more project details and project specific blogs visit: www.met.reading.ac.uk/flooding

References

Arakawa, A. and W. H. Schubert, 1974: Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment, Part I. J. Atmos. Sci., 31, 674-701.

Emanuel, K. A., 1994: Atmospheric convection, Oxford University Press, 580 pp.

Flack, D. L. A., R. S. Plant, S.L. Gray, H. W. Lean, C. Keil and G. C. Craig, 2016: Characterisation of Convective Regimes over the British Isles. Quart. J. Roy. Meteorol. Soc., 142, 1541-1553.

## Air Pollution – The Cleaner Side of Climate Change?

Air pollution is a major global problem, with the World Health Organisation recently linking 1 in 8 global deaths to this invisible problem. I say invisible, what air pollution may seem is an almost invisible problem. My PhD looks at some of the largest air pollutants, particulate matter PM10, which is still only 1/5th the width of a human hair in diameter!

My project looks at whether winter (December – February) UK PM10 concentration ([PM10]) exceedance events will change in frequency or composition in a future climate. To answer this question, a state of the art climate model is required. This model simulates the atmosphere only and is an iteration of the Met-Office HADGEM3 model. The climate simulation models a future 2050 under the RCP8.5 emissions scenario, the highest greenhouse-gas emission scenario considered in IPCC-AR5 (Riahi et al., 2011).

In an attempt to model PM10 in the climate model (a complex feat, currently tasked to the coupled UKCA model), we have idealised the problem, making the results much easier to understand. We have emitted chemically inert tracers in the model, which represent the key sources of PM10 throughout mainland Europe and the UK. The source regions identified were: West Poland, Po Valley, BENELUX and the UK. While the modelled tracers were shown to replicate observed PM10 well, albeit with inevitable sources of lost variability, they were primarily used to identify synoptic flow regimes influencing the UK. The motivation of this work is to determine whether the flow regimes that influence the UK during UK PM10 episodes, change in a future climate.

As we are unable to accurately replicate observed UK [PM10] within the model, we need to generate a proxy for UK [PM10] episodes. We chose to identify the synoptic meteorological conditions (synoptic scale ~ 1000 km) that result in UK air pollution episodes. We find that the phenomenon of atmospheric blocking in the winter months, in the Northeast Atlantic/ European region, provide the perfect conditions for PM10 accumulation in the UK. In the Northern Hemisphere winter, Rossby Wave Breaking (RWB) is the predominant precursor to atmospheric blocking (Woollings et al., 2008). RWB is the meridional overturning of air masses in the upper troposphere, so that warm/cold air is advected towards the pole/equator. The diagnostic chosen to detect RWB on is potential temperature (θ) on the potential vorticity = 2 Potential vorticity units surface, otherwise termed the dynamical tropopause. The advantages of using this diagnostic for detecting RWB have been outlined in this study’s first publication; Webber et al., (2016). Figure 1 illustrates this mechanism and the metric used to diagnose RWB, BI, introduced by Pelly and Hoskins (2003).

In Fig. 1 warm air is transported to the north of cold air to the south. This mechanism generates an anticyclone to the north of the centre of overturning (black circle in Fig 1) and a cyclone to the south. If the anticyclone to north becomes quasi-stationary, a blocking anticyclone is formed, which has been shown to generate conditions favourable for the accumulation of PM10.

To determine whether there exists a change in RWB frequency, due to climate change (a climate increment), the difference in RWB frequency between two simulations must be taken. The first of these is a free-running present day simulation, which provides us with the models representation of a present day atmosphere. The second is a future time-slice simulation, representative of the year 2050. Figure 2 shows the difference between the two simulations, with positive values representing an increase in RWB frequency in a future climate. The black contoured region corresponds to the region where the occurrence of RWB significantly increases UK [PM10].

RWB frequency anomalies within the black contoured region are of most importance within this study. Predominantly the RWB frequency anomaly, within the black contour, can be described as a negative frequency anomaly. However, there also exist heterogeneous RWB frequency anomalies within the contoured region. What is shown is that there is a tendency for RWB to occur further north and eastward in a future climate. These shifts in the regions of RWB occurrence influence a shift in the resulting flow regimes that influence the UK.

Climate shifts in flow regimes were analysed, however only for the most prominent subset of RWB events. RWB can be subset into cyclonic and anti-cyclonic RWB (CRWB and ACRWB respectively) and both have quite different impacts on UK [PM10] (Webber et al., 2016).  ACRWB events are the most prominent RWB subset within the Northeast Atlantic/ European region (Weijenborg et al., 2012). Figure 1 represents ACRWB, with overturning occurring in a clockwise direction about the centre of overturning and these events were analysed for climate shifts in resultant flow regimes.

The analysis of climate flow regime shifts, provides the most interesting result of this study. We find that there exists a significant (p<0.05) increase in near European BENELUX tracer transport into the UK and a significant reduction of UK tracer accumulation, following ACRWB events. What we therefore see is that while in the future we see a reduction in the number of RWB and ACRWB events in a region most influential to UK [PM10], there also exists a robust shift in the resulting flow regime. Following ACRWB, there exists an increased tendency for the transport of European PM10 and decreased locally sourced [PM10] in the UK. Increased European transport may result in increased long-range transport of smaller and potentially more toxic (Gehring et al., 2013) PM2.5 particles from Europe.

References

Gehring, U., Gruzieva, O., Agius, R. M., Beelen, R., Custovic, A., Cyrys, J., Eeftens, M., Flexeder, C., Fuertes, E., Heinrich, J., Hoffmann, B., deJongste, J. C., Kerkhof, M., Klümper, C., Korek, M., Mölter, A., Schultz, E. S., Simpson, A.,Sugiri, D., Svartengren, M., von Berg, A., Wijga, A. H., Pershagen, G. and Brunekreef B.: Air Pollution Exposure and Lung Function in Children: The ESCAPE Project. Children’s Health Prespect, 121,
1357-1364, doi:10.1289/ehp.1306770 , 2013.

Pelly, J. L and Hoskins, B. J.: A New Perspective on Blocking. J. Atmos. Sci, 50, 743-755, doi: http://dx.doi.org/10.1175/1520- 0469(2003)060<0743:ANPOB>2.0.CO;2, 2003.

Riahi, K., Rao S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N. and Rafaj, P.: RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change, 109, no. 1-2, 33-57, doi: 10.1007/s10584-011-0149-y, 2011.

Webber, C. P., Dacre, H. F., Collins, W. J., and Masato, G.: The Dynamical Impact of Rossby Wave Breaking upon UK PM10 Concentration. Atmos. Chem. and Phys. Discuss, doi; 10.5194/acp-2016-571, 2016.

Weijenborg, C., de Vries, H. and Haarsma, R. J.: On the direction of Rossby wave breaking in blocking. Climate Dynamics, 39, 2823- 2831, doi: 10.1007/s00382-012-1332-1, 2012.

Woollings, T. J., Hoskins, B. J., Blackburn, M. and Berrisford, P.: A new Rossby wave-breaking interpretation of the North Atlantic Oscillation. J. Atmos. Sci, 65, 609-626, doi: http://dx.doi.org/10.1175/2007JAS2347.1, 2008.

## 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.