## High-resolution Dispersion Modelling in the Convective Boundary Layer

In this blog I will first give an overview of the representation of pollution dispersion in regional air quality models (AQMs). I will then show that when pollution dispersion simulations in the convective boundary layer (CBL) are run at $\mathcal{O}$(100 m) horizontal grid length, interesting dynamics emerge that have significant implications for urban air quality.

## Modelling Pollution Dispersion

AQMs are a critical tool in the management of urban air pollution. They can be used for short-term air quality (AQ) forecasts, and in making planning and policy decisions aimed at abating poor AQ. For accurate AQ prediction the representation of vertical dispersion in the urban boundary layer (BL) is key because it controls the transport of pollution away from the surface.

Current regional scale Eulerian AQMs are typically run at $\mathcal{O}$(10 km) horizontal grid length (Baklanov et al., 2014). The UK Met Office’s regional AQM runs at 12 km horizontal grid length (Savage et al., 2013) and its forecasts are used by the Department for Environment Food and Rural Affairs (DEFRA) to provide a daily AQ index across the UK (today’s DEFRA forecast). At such horizontal grid lengths turbulence in the BL is sub-grid.

Regional AQMs and numerical weather prediction (NWP) models typically parametrise vertical dispersion of pollution in the BL using K-theory and sometimes with an additional non-local component so that

$F=-K_z \frac{\partial{c}}{\partial{z}} +N_l$

where $F$ is the flux of pollution, $c$ is the pollution concentration, $K(z)$ is a turbulent diffusion coefficient and $z$ is the height from the ground. $N_l$ is the non-local term which represents vertical turbulent mixing under convective conditions due to buoyant thermals (Lock et al., 2000; Siebesma et al., 2007).

K-theory (i.e. $N_l=0$) parametrisation of turbulent dispersion is consistent mathematically with Fickian diffusion of particles in a fluid. If $K(z)$ is taken as constant and particles are released far from any boundaries (i.e. away from the ground and BL capping inversion), the mean square displacement of pollution particles increases proportional to the time since release. Interestingly, Albert Einstein showed that Brownian motion obeys Fickian diffusion. Therefore, pollution particles in K-theory dispersion parametrisations are analogous to memoryless particles undergoing a random walk.

It is known however that at short timescales after emission pollution particles do have memory. In the CBL, far from undergoing a random trajectory, pollution particles released in the surface layer initially tend to follow the BL scale overturning eddies. They horizontally converge before being transported to near the top of the BL in updrafts. This results in large pollution concentrations in the upper BL and low concentrations near the surface at times on the order of one CBL eddy turnover period since release (Deardorff, 1972; Willis and Deardorff, 1981). This has important implications for ground level pollution concentration predicted by AQMs (as demonstrated later).

Pollution dispersion can be thought of as having two different behaviours at short and long times after release. In the short time “ballistic” limit, particles travel at the velocity within the eddy they were released into, and the mean square displacement of pollution particles increases proportional to the time squared. At times greater than the order of one eddy turnover (i.e. the long time “diffusive” limit) dispersion is less efficient, since particles have lost memory of the initial conditions that they were released into and undergo random motion.  For further discussion of atmospheric diffusion and memory effects see this blog (link).

In regional AQMs, the non-local parametrisation component does not capture the ballistic dynamics and K-theory treats dispersion as being “diffusive”. This means that at CBL eddy turnover timescales it is possible that current AQMs have large errors in their predicted concentrations. However, with increases in computing power it is now possible to run NWP for research purposes at $\mathcal{O}$(100 m) horizontal grid length (e.g. Lean et al., 2019) and routinely at 300 m grid length (Boutle et. al., 2016). At such grid lengths the dominant CBL eddies transporting pollution (and therefore the “ballistic” diffusion) becomes resolved and does not require parametrisation.

To investigate the differences in pollution dispersion and potential benefits that can be expected when AQMs move to $\mathcal{O}$(100 m) horizontal grid length, I have run NWP at horizontal grid lengths ranging from 1.5 km (where CBL dispersion is parametrised) to 55 m (where CBL dispersion is mostly resolved). The simulations are unique in that they are the first at such grid lengths to include a passive ground source of scalar representing pollution, in a domain large enough to let dispersion develop for tens of kilometres downstream.

## High-Resolution Modelling Results

A schematic of the Met Office Unified Model nesting suite used to conduct the simulations is shown in Fig. 1. The UKV (1.5 km horizontal grid length) model was run first and used to pass boundary conditions to the 500 m model, and so on down to the 100 m and 55 m models. A puff release, homogeneous, ground source of passive scalar was included in all models and its horizontal extent covered the area of the 55 m (and 100 m) model domains. The puff releases were conducted on the hour, and at the end of each hour scalar concentration was set to zero. The case study date was 05/05/2016 with clear sky convective conditions.

### Puff Releases

Figure 2 shows vertical cross-sections of puff released tracer in the UKV and 55 m models at 13-05, 13-20 and 13-55 UTC. At 13-05 UTC the UKV model scalar concentration is very large near the surface and approximately horizontally homogeneous. The 55 m model concentrations however are either much closer to the surface or elevated to great heights within the BL in narrow vertical regions. The heterogeneity in the 55 m model field is due to CBL turbulence being largely resolved in the 55 m model. Shortly after release, most scalar is transported predominantly horizontally rather than vertically, but at localised updrafts scalar is transported rapidly upwards.

By 13-20 UTC it can be seen that the 55 m model has more scalar in the upper BL than lower BL and lowest concentrations within the BL are near the surface. However, the scalar in the UKV model disperses more slowly from the surface. Concentrations remain unrealistically larger in the lower BL than upper BL and are very horizontally homogeneous, since the “ballistic” type dispersion is not represented. By 13-55 UTC the concentration is approximately uniform (or “well mixed”) within the BL in both models and dispersion is tending to the “diffusive” limit.

It has thus been demonstrated that unless “ballistic” type dispersion is represented in AQMs the evolution of the scalar concentration field will exhibit unphysical behaviour. In reality, pollution emissions are usually continuously released rather than puff released. One could therefore ask the question – when pollution is emitted continuously are the detailed dispersion dynamics important for urban air quality or does the dynamics of particles released at different times cancel out on average?

### Continuous Releases

To address this question, I included a continuous release, homogeneous, ground source of passive scalar. It was centred on London and had dimensions 50 km by 50 km which is approximately the size of Greater London. Figure 3a shows a schematic of the source.

The ratio of the 55 m model and UKV model zonally averaged surface concentration with downstream distance from the southern edge of the source is plotted in Fig. 3b. The largest difference in surface concentration between the UKV and 55m model occurs 9 km downstream, with a ratio of 0.61. This is consistent with the distance calculated from the average horizontal velocity in the BL ($\approx$7 ms-1) and the time at which there was most scalar in the upper BL compared to the lower BL in the puff release simulations ($\approx$ 20 min). The scalar is lofted high into the BL soon after emission, causing reductions in surface concentrations downstream. Beyond 9 km downstream distance a larger proportion of the scalar in the BL has had time to become well-mixed and the ratio increases.

## Summary

By comparing the UKV and 55 m model surface concentrations, it has been demonstrated that “ballistic” type dispersion can influence city scale surface concentrations by up to approximately 40%. It is likely that by either moving to $\mathcal{O}$(100 m) horizontal grid length or developing turbulence parametrisations that represent “ballistic” type dispersion, that substantial improvements in the predictive capability of AQMs can be made.

References

1. Baklanov, A. et al. (2014) Online coupled regional meteorology chemistry models in Europe: Current status and prospects https://doi.org/10.5194/acp-14-317-2014
1. Boutle, I. A. et al. (2016) The London Model: Forecasting fog at 333 m resolution https://doi.org/10.1002/qj.2656
1. Deardorff, J. (1972) Numerical Investigation of Neutral and Unstable Planetary Boundary Layers https://doi.org/10.1175/1520-0469(1972)029<0091:NIONAU>2.0.CO;2
1. DEFRA – air quality forecast https://uk-air.defra.gov.uk/index.php/air-pollution/research/latest/air-pollution/daqi
1. Lean, H. W. et al. (2019) The impact of spin-up and resolution on the representation of a clear convective boundary layer over London in order 100 m grid-length versions of the Met Office Unified Model https://doi.org/10.1002/qj.3519
1. Lock, A. P. et al. A New Boundary Layer Mixing Scheme. Part I: Scheme Description and Single-Column Model Tests https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2
1. Savage, N. H. et al. (2013) Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation https://doi.org/10.5194/gmd-6-353-2013
1. Siebesma, A. P. et al. (2007) A Combined Eddy-Diffusivity Mass-Flux Approach for the Convective Boundary Layer https://doi.org/10.1175/JAS3888.1
1. Willis. G and J. Deardorff (1981) A laboratory study of dispersion from a source in the middle of the convectively mixed layer https://doi.org/10.1016/0004-6981(81)90001-9

## Air pollution and COVID-19: is ozone an undercover criminal?

The global COVID-19 lockdown is undoubtedly resulting in curiously low levels of air pollution. Although it might seem inappropriate to seek a silver lining during a global pandemic, the fact that the air really does seem cleaner gives my PhD topic a little more everyday credibility, which – at least for me – is quite nice.

You may have already seen satellite pictures of the effects of the lockdown in northern Italy (and other major cities) on surface nitrogen dioxide (NO2) concentrations. You may have been able to breathe some cleaner air where you live these past few weeks. It feels like we are in the middle of some sort of major air quality experiment, some kind of simulation conducted by a clueless PhD student…

What you might not notice, however, is the rise in near-surface ozone (O3) during a string of warm, sunny days. While it isn’t a primary pollutant like NO2 or particulate matter, O3 is closely associated with the amount of NOx (NO + NO2) in the air. It is also invisible to the naked eye – unless it forms photochemical smog. O3 can be harmful in short bursts of elevated concentrations to people who already suffer with asthma and other respiratory problems, which could prove to be problematic since COVID-19 is itself a respiratory disease.

Weather conditions in Reading throughout the majority of April were favourable for O3 production: lots of solar radiation and weak winds. In fact, Reading experienced its sunniest April on record, along with some of the warmest April days on record. It is therefore not surprising that peak daytime concentrations of O3 creep up over a week of warm, calm weather. For example, a measuring site located between two busy roads in Reading gives a clear indication of what the mixture of favourable conditions alongside low NOx emissions can do: Figure 1 shows that peak daytime concentrations rose everyday between 02/04 – 12/04, when the air was stagnant and thus O3 tended to accumulate within the atmospheric boundary layer. The peak concentrations between 08/04 and 12/04 are typical of “moderate” levels on DEFRA’s Daily Air Quality Index (DAQI), and are close to the WHO safe concentration exceedance guidelines.

The DAQI is dictated by the highest concentration of any one of the five pollutants deemed harmful to human health: ozone, nitrogen dioxide (NO2), sulphur dioxide (SO2), and PM2.5 / PM10 (particulate matter). Figure 2 shows a moderate DAQI in parts of south and north-east England (and in fact, the map looked a lot more yellow and orange on Friday 04/04).

So although there is a clear trend in unseasonably low NOx concentrations in major cities in many parts of the world (including the UK), why can O3 concentrations rise?

The answer is probably that ozone has a “love-hate” relationship with NOx, Volatile Organic Compunds (VOCs) and the weather. It skyrockets when it’s sunny and skies are blue. High pressure systems and calm weather trap much of the existing ozone within the boundary layer, near to the ground. In particular, if easterly / southerly winds prevail, they can transport both ozone and its precursors from the continent – this often happens when there is an anticyclone over the UK. Therefore, high ozone episodes tend to occur in the spring / summer, due to the frequency of such ozone-favourable conditions.

Fossil fuel combustion releases NOx, some of which is in the form of NO and goes on to oxidise to create more NO2, or it can react with VOCs, or it can directly react with ozone. The usually abundant NO and other VOCs from vehicle emissions and industry are now significantly lower than usual, so the process of ozone scavenging by NO is minimised.

On top of that, NO has a short lifetime (maybe a few minutes), and can quickly oxidise to form NO2, which has a longer lifetime and can therefore travel on to rural areas. Often, rural regions will have higher average ozone concentrations than cities (which might seem counter-intuitive!). Although the emissions in those areas are lower, they can experience net ozone production from the additional NO2 which has travelled downwind from a nearby city.  In relatively clean tropospheric air, the production / destruction of ozone is closely linked to the ratio of NO to NO2 – an equilibrium known as the photostationary state (Leighton, 1961) – and there are some studies to show a negative correlation between annual mean NOx and O3 measurements in both rural and urban areas (e.g. Bower et al., 1989). But none of this is particularly simple, because there will always be VOCs present in air, and ozone production / destruction is also highly sensitive to the ratio of NO : VOC – this was not fully understood until Greiner (1967) and several subsequent studies, which explained the role of the hydroxyl radical OH in the reaction chain to create NO2 without destroying ozone. Another phenomenon is the ‘weekend effect’, where weekday emissions tend to be quite different from emissions during the weekend because there is no morning / evening rush-hour traffic and resultant NO (Seguel et al., 2012). If VOC levels remain high, ozone production is favoured.

Let us return to the present day. How might the weather conditions affect the delicate balance between NOx, VOCs and ozone? And what about other particles closely monitored throughout the pandemic, such as particulate matter (PM)?

February was unusually wet and windy in the UK. Strong winds can disperse both NO2 and PM, while rain is an efficient sink of PM by physically washing out the particles. Both pollutants have been monitored closely at a number of locations globally over the past few weeks, as they are good indicators of emissions (and ozone is not). Before the gloriously sunny weather came, I wondered about ways of distinguishing between causes of the unusually low NO2 / PM concentrations: what proportion is attributable to the lockdown, and what is attributable to a very wet and windy February / March period in this region? How might ambient ozone concentrations change as we move into the summer, as lockdown measures might begin to gradually relax and pollution returns to pre-lockdown levels? And what does this mean for people who are vulnerable to respiratory issues aggravated by ozone? All these questions – and many more – are currently being explored by air quality experts all over the world, hopefully reaching some conclusions in time for us all to act on them timely and appropriately.

Stay home, stay safe, and thank you for reading. Please leave a comment or send an email if you have any questions (I’ll be happy to answer) or corrections: I am a PhD student and there are probably still some gaps in my understanding.

For further reading: The Copernicus Atmosphere Monitoring Service (CAMS) are providing vital satellite observations of interest to COVID-19 matters, which I encourage you to check out if you are interested in the air quality aspects of the pandemic.

## Relationships in errors between meteorological forecasts and air quality forecasts

Exposure to pollutants in the air we breathe may trigger respiratory problems. Pollutants such as ozone ($O_{3}$) and particulate matter (PM$_{2.5}$) – particles of about 1/20th of the width of a hair strand – can get into our lungs and cause inflammation, alter their function, or otherwise cause trouble for the cardiovascular system – especially in people with existing underlying respiratory conditions. Although high pollution episodes in the UK are infrequent, the public becomes aware of the associated problems during events such as red skies, in part caused by long-range transport of Saharan dust. Furthermore, the World Health Organisation (WHO) estimates that 85% of UK towns regularly exceed the safe annual PM$_{2.5}$ limit. It is therefore important to forecast surface pollution concentrations accurately in order to enable the public to mitigate some of those adverse health risks.

In general, air pollution can be difficult to forecast near the surface because of the multitude of factors which affect it. Incorrectly modelling chemical processes within the atmosphere, surface emissions or indeed the meteorology can lead to errors in predicting ground-level pollution concentrations. It is well accepted within the literature that weather forecasting is of decisive importance for air quality. Thus, my PhD project tries to link forecast errors in meteorological processes within the atmospheric boundary layer (BL) with forecast errors in pollutants such as $O_{3}$ and $NO_{2}$ (nitrogen dioxide) using the operational air quality forecasting model in the UK, the Air Quality in the Unified Model (AQUM). This model produces an hourly air quality forecast issued to the public by DEFRA in the form of a Daily Air Quality Index (DAQI) and is verified against surface-based observations from the Automatic Urban and Rural Network (AURN).

A three-month evaluation of hourly forecasts from AQUM shows a delay in the average increase of the morning $O_{3}$ + $NO_{2}$ (‘total oxidant’) concentrations when compared to AURN observations. We also know that BL depth is important for the mixing of pollutants – it acts as a sort of lid on top of the lower part of the troposphere. Since the noted lag in total oxidant increase in our model occurs exactly at the time of the morning BL development, we can form a testable hypothesis: that an inaccurate representation of BL processes – specifically, morning BL growth – leads to a delay in entrainment of $O_{3}$-rich air masses from the layer of air above it: the residual layer. It has been suggested in the literature that when the daytime convective mixed layer collapses upon sunset, the remaining pollutants are effectively trapped in the leftover (‘residual’) layer, and thus can act as a night-time reservoir of $O_{3}$ above the stable or neutral night-time boundary layer (NBL).

To test the hypothesis, semi-idealised experiments are conducted. We simulate a one-month long release of chemically inert tracers within the Numerical Atmospheric Dispersion Environment (NAME) using different sets of numerical weather prediction (NWP) outputs. This enables a process-based evaluation of how different meteorology affects tracers within the BL. Tracers are released within the lateral boundaries of the domain centred on the UK. The idea is to separate the effects of meteorology from chemistry on the tracer concentrations. In particular, we want to understand the role of entrainment of $O_{3}$-rich air masses from the residual layer down into the developing BL during the morning hours.

We located around 50 AURN sites in urban locations and compared hourly BL depths from June 2017 in the two sets of NWP output used for the tracer simulations: the UKV and UM Global (UMG) configurations of the Met Office Unified Model. It was found that although the average diurnal profiles of BL depth were quite similar, there was a lag in the morning increase of BL depth within the UMG configuration. This may be because the representation of surface sensible heat flux (SSHF) differs in the two NWP models: the UMG uses a single tile scheme to represent urban areas, whereas the UKV uses a more realistic, two-tile scheme (‘MORUSES’) which distinguishes between roof surfaces and street canyons. SSHF is a measure of energy exchange at the ground, where positive fluxes represent a loss of heat from the surface to the atmosphere. Therefore, a more realistic representation of SSHF results in the UKV being better at capturing and storing urban heat. This leads to a faster development of the BL depth in the UKV compared to the UMG, which in turn could mean that there is more turbulent motion and mixing within the atmosphere.

Assuming that the vertical gradient in pollutant concentrations is positive between the morning BL and the free troposphere, mixing air from above should enhance pollutant concentrations nearer to the surface. Our tracer results show that during days when synoptic conditions are dominated by high pressure, the diurnal cycle in forecast and observed surface pollutant concentrations can be adequately replicated by our simplified set-up. Differences between the diurnal cycle between tracer simulations with the two different meteorological set-ups show that the UKV is not only entraining more tracer from above the boundary layer than the simulation using UMG, but also the concentrations increase on average 1 – 2 hours earlier in the morning. These results suggest that indeed the model meteorology – in particular, representation of BL processes – is important to entrainment of polluted air masses into the BL, which in turn has a significant influence on the surface pollutant concentrations.

Within the past two decades, it has been recognised by the weather and air quality modelling communities that neither type of model can truly exist without the other. This post has discussed just one aspect of how meteorology influences the air quality forecast – there are, of course, many other parameters (e.g. wind speed, precipitation, relative humidity) which affect the forecast pollutant concentrations. We therefore also evaluated night-time errors in the wind speed and found that these errors are positively correlated with the total oxidant forecast errors. This means that when the wind speed forecast is overestimated, it is likely to affect the night-time and morning forecast of both $O_{3}$ and $NO_{2}$ in a significant way.

References

Ambient Air Pollution: A global assessment of exposure and burden of disease. WHO, 2016.

Bohnenstengel S., Evans S., Clark P., Belcher S.: Simulations of the London urban heat island, Quarterly Journal of the Royal Meteorological Society, 2011 vol: 137 (659) pp: 1625-1640

Cocks A., 1993: The Chemistry and Deposition of Nitrogen Species in the Troposphere, The Royal Society of Chemistry, Cambridge 1993

Savage N., Agnew P., Davis L., Ordonez C., Thorpe R., Johnson C., O’Connor F., Dalvi M.: Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation, Geoscientific Model Development, 2013 vol: 6 pp: 353-372

Sun J., Mahrt L., Banta R., Pichugina Y.: Turbulence Regimes and Turbulence Intermittency in the Stable Boundary Layer during CASES-99, Journal of the Atmospheric Sciences, 2012 vol: 69 (1) pp: 338-351

Zhang, 2008: Online-coupled meteorology and chemistry models: History, current status, and outlook. Atmos. Chem. Phys, 2008 vol: 8 (11) pp: 2895-2932

## Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes

Williams, R. S., Hegglin, M. I., Kerridge, B. J., Jöckel, P., Latter, B. G., and Plummer, D. A.: Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes, Atmos. Chem. Phys., 19, 3589–3620, https://doi.org/10.5194/acp-19-3589-2019, 2019.

Approximately 90 % of atmospheric ozone (O3) today resides in the stratosphere, which we know as the ozone layer (extending from ~15-35 km), where it plays a critical role in filtering out most of the harmful ultraviolet (UV) rays from the sun. The gradual formation of the ozone layer from around 600 million years ago was key in Earth’s evolutionary history, as it enabled life to flourish on land. Lesser known is the importance of the remaining ~ 10 % of atmospheric ozone, which is found in the troposphere and has implications for air quality, radiative forcing and the oxidation capacity of the troposphere. Whilst ozone is a pollutant at ground level, contributing to an estimated 6 million premature deaths globally per year, it also acts to cleanse the troposphere by breaking down a large number of pollutants, along with some greenhouse gases. Ozone is however a greenhouse gas in itself – where it has a maximum radiative forcing in the upper troposphere. It is an example of a non-well mixed gas, owing to its spatially and temporally highly varying sources and sinks, as well as its relatively short global mean tropospheric lifetime of about 3 weeks.

A major source of tropospheric ozone is the photochemical reactions of emission precursors such as carbon monoxide (CO), nitrogen oxides (NOx) and volatile organic compounds (VOCs), which have both natural and anthropogenic sources, in addition to the natural influx of ozone-rich air from the stratosphere. The magnitude of these two competing influences has been poorly quantified until the recent advent of satellite observations and the development of comprehensive chemistry-climate models (CCMs), which simulate interactive chemistry and are stratospherically well-resolved.

Our study aimed to update and extend the knowledge of a previous key study (Lamarque et al., 1999), that investigated the role of stratosphere-troposphere exchange (STE) on tropospheric ozone, using two contemporary state-of-the-art CCMs (EMAC and CMAM) with stratospheric-tagged ozone tracers as a diagnostic. We first sought to validate the realism of the model ozone estimates with respect to satellite observations from the Ozone Monitoring Instrument (OMI), together with spatially and temporally limited vertical profile information provided from ozonesondes, which we resolved globally on a seasonal basis for the troposphere (1000-450 hPa) (Figure 1).

Whilst we found broad overall agreement with both sets of observations, an overall systematic bias in EMAC of + 2-8 DU (Dobson Units) and regionally and seasonally varying biases in CMAM (± 4 DU) can be seen in the respective difference panels (Figure 1b and 1c). A height-resolved comparison of the models with respect to regionally aggregated ozonesonde observations helped us to understand the origin of these model biases. We showed that apparent closer agreement in CMAM arises due to compensation of a low bias in photochemically produced ozone in the troposphere, resulting from the omission of a group of emission precursors in this model, by excessive smearing of ozone from the lower stratosphere due to an inherent high bias. This smearing is induced when accounting for the satellite observation geometry of OMI, necessary to ensure a direct comparison with vertically well-resolved models, which has limited vertical resolution due to its nadir field of view. The opposite was found to be the case in EMAC, with a high (low) bias in the troposphere (lower stratosphere) relative to ozonesondes. Given the similarity in the emission inventories used in both models, the high bias in this model indicates that excess in situ photochemical production from emission precursors is simulated within the interactive chemistry scheme. These findings emphasise the importance of understanding the origin of such biases, which can help prevent erroneous interpretations of subsequent model-based evaluations.

Noting these model biases, we next exploited the fine scale vertical resolution offered by the CCMs to investigate the regional and seasonal variability of the stratospheric influence. Analysis of the model stratospheric ozone (O3S) tracers revealed large differences in the burden of ozone in the extratropical upper troposphere-lower stratosphere (UTLS) region, with some 50-100 % more ozone in CMAM compared to EMAC. We postulated that CMAM must simulate a stronger lower branch of the Brewer-Dobson Circulation, the meridional stratospheric overturning circulation, since the stratospheric influence is isolated using these simulations. This has implications for the simulated magnitude and distribution of the downward flux of ozone from the stratosphere in each model. Shown in Figure 2 is the zonal-mean monthly evolution of ozone volume mixing ratio (ppbv) from ozonesondes and EMAC over the period 1980-2013 for the upper (350 hPa), middle (500 hPa) and lower (850 hPa) troposphere, together with the EMAC O3S and derived fraction of ozone of stratospheric origin (O3F) (%) evolution.

We found that the ozonesonde evolution closely resembles that of both EMAC and CMAM (not shown) throughout the troposphere. A clear correspondence in the seasonality of ozone is also evident for the EMAC O3S tracer, and in turn the O3F evolution, particularly towards the upper troposphere. Nonetheless, both models imply that over 50 % of near-surface ozone is derived from the stratosphere during wintertime in the extratropics, which is substantially greater than that estimated by Lamarque et al. (1999) (~ 10-20 %), and still considerably higher than more recent studies (~ 30-50 %) (e.g. Banarjee et al., 2016). This indicates that the stratospheric influence may indeed be larger than previously thought and is thus an important consideration when attempting to understand past, present and future trends in tropospheric ozone.

Finally, we analysed height-resolved seasonal changes in both the model O3 and O3S between 1980-89 and 2001-10. The calculated hemispheric springtime (MAM/SON) changes in ozone are shown in Figure 3, and equivalently for O3S in Figure 4, for the upper and middle troposphere (350 and 500 hPa), as well as for the surface model level. A general increase in tropospheric ozone was found worldwide in all seasons, which is maximised overall during spring in both the Northern Hemisphere (~ 4-6 ppbv) and the Southern Hemisphere subtropics (~ 2-6 ppbv), corresponding to a relative increase of about 5-10 %. Respectively, a significant stratospheric contribution to this change of ~ 3-5 ppbv and ~ 1-4 ppbv is estimated using the model O3S tracers (~ 50-80 % of the total change), although with substantial inter-model disagreement over the magnitude and sometimes the sign of the attributable change for any given region or season from the stratosphere.

Although surface ozone changes are dominated by regional changes in precursor emissions between the two periods – the largest, statistically significant increases (> 6 ppbv) being over south-east Asia – the changing influence from the stratosphere were estimated to be up to 1–2 ppbv between the two periods in the Northern Hemisphere, albeit with high regional, seasonal and inter-model variability. In relative terms, the stratosphere can be seen to typically explain 25-30 % of the surface change over regions such as the Himalayas, although locally it may represent the dominant driver (> 50 %) where changes in emission precursors are negligible or even declining due to the enforcement of more stringent air quality regulations over regions such as western Europe and eastern North America in recent years.

To summarise, our paper highlights some of the shortcomings of the EMAC and CMAM CCMs with respect to observations and we emphasise the importance of understanding model bias origins when performing subsequent model-based evaluations. Additionally, our evaluations highlight the necessity of a well-resolved stratosphere in models for quantifying the stratospheric influence on tropospheric ozone. We find evidence that the stratospheric influence may be larger than previously thought, compared with previous model-based studies, which is a highly significant finding for understanding tropospheric ozone trends.

References:
Lamarque, J. F., Hess, P. G. and Tie, X. X.: Three‐dimensional model study of the influence of stratosphere‐troposphere exchange and its distribution on tropospheric chemistry., J. Geophys. Res. Atmos., 104(D21), 26363-26372, https://doi:10.1029/1999JD900762, 1999.

Banerjee, A., Maycock, A. C., Archibald, A. T., Abraham, N. L., Telford, P., Braesicke, P., and Pyle, J. A.: Drivers of changes in stratospheric and tropospheric ozone between year 2000 and 2100., Atmos. Chem. Phys., 16, 2727-2746, https://doi.org/10.5194/acp-16-2727-2016, 2016.

## On relocating to the Met Office for five weeks of my PhD

Some PhD projects are co-organised by an industrial CASE partner which provides supervisory support and additional funding. As part of my CASE partnership with the UK Met Office, in January I had the opportunity to spend 5 weeks at the Exeter HQ, which proved to be a fruitful experience. As three out of my four supervisors are based there, it was certainly a convenient set-up to seek their expertise on certain aspects of my PhD project!

One part of my project aims to understand how certain neighbourhood-based verification methods can affect the level of surface air quality forecast accuracy. Routine verification of a forecast model against observations is necessary to provide the most accurate forecast possible. Ensuring that this happens is crucial, as a good forecast may help keep the public aware of potential adverse health risks resulting from elevated pollutant concentrations.

The project deals with two sides of one coin: evaluating forecasts of regional surface pollutant concentrations; and evaluating those of meteorological fields such as wind speed, precipitation, relative humidity or temperature. All of the above have unique characteristics: they vary in resolution, spatial scale, homogeneity, randomness… The behaviour of the weather and pollutant variables is also tricky to compare against one another because the locations of their numerous measurement sites nearly never coincide, whereas the forecast encompasses the entirety of the domain space. This is kind of the crux of this part of my PhD: how can we use these irregularly located measurements to our advantage in verifying the skill of the forecast in the most useful way? And – zooming out still – can we determine the extent to which the surface air pollution forecast is dependent on some of those aforementioned weather variables? And can this knowledge (once acquired!) be used to further improve the pollution forecast?

While at the Met Office, I began my research specifically into methods which analyse the forecast skill when a model “neighbourhood” of a particular size around a particular point-observation is evaluated. These methods are being developed as part of a toolkit for evaluation of high resolution forecasts, which can be (and usually are) more accurate than a lower resolution equivalent, but traditional metrics (e.g. root mean square error (RMSE) or mean error (ME)) often fail to demonstrate the improvement (Mittermaier, 2014). They can also fall victim to various verification errors such as the double-penalty problem. This is when an ‘event’ might have been missed at a particular time in the forecast at one gridpoint because it was actually forecast in the neighbouring grid-point one time-step out, so the RMSE counts this error both in the spatial and temporal axes. Not fair, if you ask me. So as NWP continues to increase in resolution, there is a need for robust verification methods which relax the spatial (or temporal) restriction on precise forecast-to-observation matching somewhat (Ebert, 2008).

One way to proceed forward is via a ‘neighbourhood’ approach which treats a deterministic forecast almost as an ensemble by considering all the grid-points around an observation as an individual forecast and formulating a probabilistic score. Neighbourhoods are made of varying number of model grid-points, i.e. a 3×3 or a 5×5 or even bigger. A skill score such as the ranked probability score (RPS) or Brier Score is calculated using the cumulative probability distribution across the neighbourhood of the exceedance of a sensible pollutant concentration threshold. So, for example, we can ask what proportion of a 5×5 neighbourhood around an observation has correctly forecasted an observed exceedance (i.e. ‘hit’)? What if an exceedance forecast has been made, but the observed quantity didn’t reach that magnitude (i.e. ‘false alarm’)? And how do these scores change when larger (or smaller) neighbourhoods are considered? And, if these spatial verification methods prove informative, how could they be implemented in operational air quality forecast verification? All these questions will hopefully have some answers in the near future and form a part of my PhD thesis!

Although these kind of methods have been used for meteorological variables, they haven’t yet been widely researched in the context of regional air quality forecasts. The verification framework for this is called HiRA – High Resolution Assessment, which is part of the wider verification network Model Evaluation Tools (which, considering it is being developed as a means of uniformly assessing high-resolution meteorological forecasts, has the most unhelpful acronym: MET). It is quite an exciting opportunity to be involved in the testing and evaluation of this new set of verification tools for a surface pollution forecast at a regional scale, and I am very grateful to be involved in this. Also, having the opportunity to work at the Met Office and “pretend” to be a real research scientist for a while is awesome!

## Experiences of the NERC Atmospheric Pollution and Human Health Project.

One of the most exciting opportunities of my PhD experience to date has been a research trip to Beijing in June, as part of the NERC Atmospheric Pollution and Human Health (APHH) project. This is a worldwide research collaboration with a focus on the way air pollution in developing megacities affects human health, and the meeting in Beijing served as the 3rd project update.

Industrialisation of these cities in the last couple of decades has caused air pollution to rise rapidly and regularly exceed levels deemed safe by the World Health Organisation (WHO).  China sees over 1,000,000 deaths annually due to particulate matter (PM), with 76 deaths per 100,000 capita. In comparison, the UK has just over 16,000 total deaths and 26 per capita. But not only do these two countries have very different climates and emissions; they are also at very different stages of industrial development. So in order to better understand the many various sources of pollution in developing megacities – be they from local transport, coal burning or advected from further afield – there is an increased need for developing robust air quality (AQ) monitoring measures.

The APHH programme exists as a means to try and overcome these challenges. My part in the meeting was to expand the cohort of NCAS / NERC students researching AQ in both the UK and China, attending a series of presentations in a conference-style environment and visiting two sites with AQ monitoring instruments. One is situated in the Beijing city centre while the other in the rural village of Pinggu, just NW of Beijing. Over 100 local villagers take part in a health study by carrying a personal monitor with them over a period of two weeks. Their general health is monitored at the Pinggu site, alongside analysis of the data collected about their personal exposure to pollutants each day, i.e. heatmaps of different pollutant species are created according to GPS tracking. Having all the instruments being explained to us by local researchers was incredibly useful, because since I work with models, I haven’t had a great deal of first hand exposure to pollutant data collection. It was beneficial to get an appreciation of the kind of work this involves!

In between all our academic activities we also had the chance to take some cultural breaks – Beijing has a lot to offer! For example, our afternoon visit to the Pinggu rural site followed the morning climb up the Chinese Great Wall. Although the landscape was somewhat obscured by the pollution haze, this proved to be a positive thing as we didn’t have to suffer in the direct beam of the sun!

I would like to greatly thank NERC, NCAS and University of Leeds for the funding and organisation of this trip. It has been an incredible experience, and I am looking forward to observing the progess of these projects, hopefully using what I have learnt in some of my own work.

## 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.