The Scandinavia-Greenland Pattern: something to look out for this winter

Simon Lee, s.h.lee@pgr.reading.ac.uk

The February-March 2018 European cold-wave, known widely as “The Beast from the East” occurred around 2 weeks after a major sudden stratospheric warming (SSW) event on February 12th. Major SSWs typically occur once every other winter, involving significant disruption to the stratospheric polar vortex (a planetary-scale cyclone which resides over the pole in winter). SSWs are important because their occurrence can influence the type and predictability of surface weather on longer timescales of between 2 weeks to 2 months. This is known as subseasonal-to-seasonal (S2S) predictability, and “bridges the gap” between typical weather forecasts and seasonal forecasts (Figure 1).  

Figure 1: Schematic of medium-range, S2S and seasonal forecasts and their relative skill. [Figure 1 in White et al. (2017)] 

In general, S2S forecasts suffer from relatively low skill. While medium-range forecasts are an initial value problem (depending largely on the initial conditions of the forecast) and seasonal forecasts are a boundary value problem (depending on slowly-varying constraints to the predictions, such as the El Niño-Southern Oscillation), S2S forecasts lie somewhere between the two. However, certain “windows of opportunity” can occur that have the potential to increase S2S skill – and a major SSW is one of them. Skilful S2S forecasts can be of particular benefit to public health planners, the transport sector, and energy demand management, among many others.  

Following an SSW, the eddy-driven jet stream tends to weaken and shift equatorward. This is characteristic of the negative North Atlantic Oscillation (NAO) and negative Arctic Oscillation (AO), and during these patterns the risk of cold air outbreaks significantly increases in places like northwest Europe. So, by knowing this, S2S forecasts issued during the major SSW were able to highlight the increased risk of severely cold weather.  

Given that we know that following an SSW certain weather types are more likely for several weeks, and forecasts may be more skilful, it might seem advantageous to know an SSW was coming at a long lead-time in order to really push the boundaries of S2S prediction. So, what about in 2018?  

In the first paper from my PhD, published in July 2019 in JGR-Atmospheres, we explored the onset of predictions of the February 2018 SSW. We found that, until about 12 days beforehand, extended-range forecasts that contribute to the S2S database (an international collaboration of extended-range forecast data) did not accurately predict the event; in fact, most predictions indicated the vortex would remain unusually strong! 

We diagnosed that anticyclonic wave breaking in the North Atlantic was a crucial synoptic-scale “trigger” event for perturbing the stratospheric vortex, by enhancing vertically propagating Rossby waves (which weaken the vortex when they break in the stratosphere). Forecasts struggled to predict this event far in advance, and thus struggled to predict the SSW. We called the pattern the “Scandinavia-Greenland (S-G) dipole” – characterised by an anticyclone over Scandinavia and a low over Greenland (Figure 2), and we found it was present before 35% of previous SSWs (1979-2018). The result agrees with several previous studies highlighting the role of blocking in the Scandinavia-Urals region, but was the first to suggest such a significant impact of a single tropospheric event.  

Figure 2: Correlation between mean sea level pressure forecasts over 3-5 February 2018 and subsequent forecasts of 10 hPa 60°N zonal-mean zonal wind on 9-11 February, in (a) NCEP and (b) ECMWF ensembles launched between 29 January and 1 February 2018. White lines (dashed negative) indicate correlations exceeding +/- 0.7, while the black dashed lines indicate the nodes of the S-G dipole. [Figure 3 in Lee et al. (2019)] 

So, we had established the S-G dipole was important in the predictability onset in 2018, and important in previous cases – but how well do S2S models generally capture the pattern?  

That was the subject of our recent (open-access) paper, published in August in QJRMS. We define a more generalised pattern by performing empirical orthogonal function (EOF) analysis on mean sea-level pressure anomalies in a region of the northeast Atlantic during November-March in ERA5 reanalysis (Figure 3).  While the leading EOF (the “zonal pattern”) resembles the NAO, the 2nd EOF resembles the S-G dipole from our previous paper – so we call it the “S-G pattern”.  

Figure 3: The first two leading EOFs of MSLP anomalies in the northeast Atlantic during November-March in ERA5, expressed as hPa per standard deviation of the principal component timeseries. The percentage of variance explained by the EOF is also shown. [Figure 1 in Lee et al. (2020) 

We then establish, through lagged linear regression analysis, that the S-G pattern is associated with enhanced vertically propagating wave activity (measured by zonal-mean eddy heat flux) into the stratosphere, and a subsequently weakened stratospheric vortex for the next 2 months. Thus, it supports our earlier work, and motivates considering how the pattern is represented in S2S models. To do this, we look at hindcasts – forecasts initialised for dates in the past – from 10 different prediction systems from around the world.  

We find that while all the S2S models represent the spatial pattern of these two EOFs very well, some have biases in the variance explained by the EOFs, particularly at weeks 3 and 4 (Figure 4). Broadly, all the models have more variance explained by their first EOF compared with ERA5, and less by the second EOF – but this bias is particularly large for the three models with the lowest horizontal resolution (BoM, CMA, and HMCR).  

Figure 4: Weekly-mean ratio between the variance explained by the EOFs in each model and the ERA5 EOF. [Figure 6 in Lee et al. (2020)] 

Additionally, we find that the deterministic prediction skill in the S-G pattern (measured by the ensemble-mean correlation) can be as small as 5-6 days for the BoM model – and only as high as 11 days in the higher resolution models. Extending this to probabilistic skill in weeks 3 and 4, we find models have only limited (if any) skill above climatology in weeks 3 and 4 (and much less than the skill in the leading EOF, the NAO-like pattern).  

Furthermore, we find that the relationship between the S-G pattern and the enhanced heat flux in the stratosphere decays with lead-time in most S2S models, even in the higher-resolution models (Figure 5). Thus, this suggests that the dynamical link between the troposphere and stratosphere weakens with lead time in these models – so even a correct tropospheric prediction may not, in these cases, have a subsequently accurate extended-range stratospheric forecast. 

Figure 5: Weekly mean regression coefficients between the S–G index and the corresponding eddy heat flux anomalies at (a) 300 hPa on the same day, (b) 100 hPa three days later, and (c) 50 hPa four days later. The lags correspond to days with maximum correlation in ERA5. Stippled bars indicate a significant difference from ERA5 at the 95% confidence level. [Figure 11 in Lee et al. (2020)] 

So, when taking this all together, we have: 

  • The S-G pattern is the second-leading mode of MSLP variability in the northeast Atlantic during winter. 
  • It is associated with enhanced vertically propagating wave activity into the stratosphere and a weakened polar vortex in the following weeks to months. 
  • S2S models represent the spatial patterns of the two leading EOFs well. 
  • Most S2S models have a zonal variability bias, with relatively more variance explained by the leading EOF and correspondingly less in the second EOF.  
  • This bias is largest in the lowest-resolution models in weeks 3 and 4.  
  • Extended range skill in the S-G pattern is low, and lower than for the NAO-like zonal pattern. 
  • The linear relationship between the S-G pattern and eddy heat flux in the stratosphere decays with lead-time in most S2S models.  

The zonal variance bias is consistent with S2S model biases in Rossby wave breaking and blocking, while these biases have been widely found to be largest in the lowest resolution models. The results suggest that the poor prediction of the S-G event in February 2018 is not unique to that case, but a more generic issue. Overall, the combination of the variability biases, the poor extended-range predictability, and the poor representation of its impact on the stratospheric vortex at longer lead-times likely contributes to limiting skill at predicting major SSWs on S2S timescales – which remains low, despite the stratosphere’s much longer timescales. Correcting the biases outlined here will likely contribute to improving this skill, and subsequently increasing how far we are able to predict real-world weather.   

A Journey through Hot British Summers

Email: s.h.lee@pgr.reading.ac.uk

The phrase “British summer” tends to evoke images of disorganised family barbecues being interrupted by heavy rain, or the covers coming on at Wimbledon, or the saying “three fine days and a thunderstorm”. Yet in recent years, hot weather has become an increasingly regular occurrence. Let me take you on a brief tour of notably hot summers in the UK. I’ll largely draw on the Met Office HadUK-Grid dataset, shown in Figure 1.

Figure 1: Nationally-averaged daily maximum temperatures for June-July-August from HadUK-Grid. In red is a 30-year centred running mean, which has risen by 1°C since the mid-20th century.

HadUK-Grid begins in 1884, but thanks to the Central England Temperature dataset (which extends back to 1659), we do have records of earlier heatwaves.  These include the hot summer of 1666, which set the scene for the Great Fire of London in September. The summers of 1781, 1826 and 1868 were also particularly hot. The first hot summer in the HadUK-Grid series is 1899, which was the warmest summer by average maxima in that series until 1976!

But our journey properly begins in 1911, when the temperature reached 36.7°C on August 9th. At the time, this was the highest reliably recorded temperature measured in the UK. It is hard to imagine how this summer must have felt at the time – not least in the cooler average climate, but also with the less developed infrastructure and clothing customs of the time. As with any heatwave, its impacts were large with increased death, drought, and agricultural impacts. The summer of 1911 was followed by the summer of 1912, which was the 2nd wettest on record for the UK. Such a turnaround must have been equally hard to believe and does highlight that extreme swings in the British weather are not, in themselves, new. In a series from 1884, the summer of 1911 is the 8th warmest in terms of the UK average maximum temperature (at the time, it would have been 2nd, with only 1899 warmer).

Stopping briefly in 1933 (which eclipsed 1911, but pales in comparison with the dustbowl conditions being experienced in the US at the time) and then again in August 1947 (which remains 2nd warmest for UK average maxima and the nation’s driest, and was in huge contrast to the tremendously snowy and cold February), our next destination is 1975.

1975 currently ranks as the 11th warmest for UK average maxima but is also the 19th driest. This, when combined with the dry winter that followed, set the scene for the infamous summer of 1976. Both these summers followed a spell of very cool summers, with no particularly remarkable summers in the 1960s, while the UK did not see a temperature above 28°C in 1974 (almost unthinkable nowadays). I won’t go into huge detail about the 1976 summer, but it is engrained in the minds of a generation thanks not only to its remarkable June heatwave (which has never been matched) but also the cool climate in which it occurred. It ranks as the 2nd driest summer for the UK and remains the warmest on record in terms of average maxima – though no individual month holds the number 1 spot.

Let us next whizz off to July 1983, which at the time had the warmest nationally averaged maxima for the month (it now ranks 3rd). Oddly enough, while the UK baked in heat, the temperature at Vostok, Antarctica dropped to -89.2°C on the 21st – the lowest surface-based temperature ever recorded. I am keeping the topic of this blog to hot summers, but I want to give 1985 a special mention – the most recent summer when the UK-average maxima were less than 17°C, a formerly frequent occurrence.

As we hot-foot it toward the end of the 20th century (pun intended), we arrive at 1990. Liverpool had just won the First Division (sound familiar?) and on August 3rd the temperature at Cheltenham, Gloucestershire reached 37.1°C – beating the record set in 1911 after 79 years. That night, the temperature fell to only 23.9°C in Brighton – the warmest night on record. However, the heatwave was rather brief but intense (3 consecutive days exceeded 35°C, the only other occurrences were in 1976). For a prolonged heatwave, we jump to August 1995. With a UK average maximum of 22.8°C, it remains the UK’s warmest August by that metric, and the 2nd driest. The summer ranks 2nd warmest by maxima. Soon after, the August of 1997 (4th warmest) added to growing evidence of a change to the British climate.

But it was in the August of 2003 when things really kicked off. In the earliest heatwave I remember, the temperature hit 38.5°C on the 10th at Faversham, Kent (satellite image in Figure 2) – the first time the UK had surpassed 37.8°C (100°F) and breaking the record from 1990 after only 23 years. 30°C was exceeded somewhere for 10 consecutive days. The summer of 2003 ranks nowadays as 6th warmest by average maxima; across Europe conditions were more extreme with a huge estimated death toll.

Figure 2: Terra-MODIS imagery from 10 August 2003, when the UK first surpassed 100°F and most of Europe was experiencing an intense heatwave (via https://worldview.earthdata.nasa.gov/)

Only 3 years later, July 2006 set the record for the hottest month for the UK-average maxima (23.3°C), and set – at the time – a record for the highest-recorded July temperature (36.5°C at Wisley on the 19th). Ranking 4th warmest by average maxima, the summer was even more extreme across mainland Europe.

What followed from 2007 through 2012 was a spell of wet summers, but we shrug off all that Glastonbury mud to arrive at July 2013, which currently ranks as 4th warmest by average maxima and saw the longest spell of >28°C weather since 1997.

Skipping through in increasingly short steps, we arrive for a brief blast on July 1st, 2015 – when the July record from 2006 fell, with 36.7°C at Heathrow in an otherwise cool month. We hop over now to 2018…

The summer of 2018, memorable for England’s performance in the World Cup, saw very warm temperatures in June and July. By nationally averaged maxima, June 2018 ranks 2nd behind 1940, and July sits 2nd behind 2006. The summer ranks 3rd, but by mean temperature is the warmest. Though not reaching the dizzying highs of 2003 (“only” 35.3°C was reached on July 26th), the prolonged dry conditions which began in May across England led to parched grasses (Figure 3), wildfires, and low river levels. I may have also had a viral tweet.

Figure 3: Brown grass during summer 2018 at the University of Reading, as seen in Google Earth.

With the present day in sight, our journey is not yet over. Stepping into 2019, an otherwise unremarkable summer was characterised with huge bursts of heat – setting records across Europe – which on July 25th saw the temperature reach 38.7°C at Cambridge Botanic Gardens. This eclipsed the 2003 record and became only the 2nd day – at the time – when 100°F or more had been reached in the UK.

But that is still not the end of the story! After a record-setting sunny spring followed by a mixed first half of summer, on July 31st 2020 the temperature at Heathrow hit 37.8°C – becoming the UK’s third warmest day on record and the third time 100°F had been recorded. The following Friday, 36.4°C was reached at Heathrow and Kew – the UK’s 9th warmest day on record, and highest temperature in August since 2003. Figure 4 shows the view at the University atmospheric observatory shortly after 34.8°C was reached, Reading’s 4th highest in August since records began in 1908.

Figure 4: The University of Reading Atmospheric Observatory on the afternoon of August 7th, shortly after 34.8°C had been recorded by the automatic sensor.

Forecasts suggest a continuation of hot weather through the next week or so, with many records up for grabs. However, we should be mindful that heatwaves cause suffering and excess deaths, too. And, with the evidently increasing frequency with which these hot extremes are occurring (note how so many of the stops on my tour were clustered in the last 30 years), they are not good news, but another sign that our climate is changing.

Now that we have blasted through the 100°F barrier, our attention turns to 40°C. Research suggests this is already becoming much more likely thanks to climate change and will continue to do so. Reaching such extremes in the UK requires a unique combination of factors – but when these do come together, expect yet more records to fall.

Thanks to Stephen Burt for useful discussions.

Further Reading:

McCarthy, M., et al. 2019: Drivers of the UK summer heatwave of 2018. Weather, https://doi.org/10.1002/wea.3628.

Black, E., et al. 2006: Factors contributing to the summer 2003 European heatwave. Weather, https://doi.org/10.1256/wea.74.04

Burt, 2006: The August 2003 heatwave in the United Kingdom: Part 1 – Maximum temperatures and historical precedents. Weather, https://doi.org/10.1256/wea.10.04A

Burt and Eden, 2007: The August 2003 heatwave in the United Kingdom: Part 2 – The hottest sites. Weather, https://doi.org/10.1256/wea.10.04B

Brugge, 1991: The record-breaking heatwave of 1-4 August 1990 over England and Wales. Weather, https://doi.org/10.1002/j.1477-8696.1991.tb05667.x

Tips for working from home as a PhD student

As PhD students, working from home is an option for many of us on a “normal” day – as indeed is increasingly the case with jobs which primarily need just an Internet connection. But, thanks to COVID-19, working from home (WFH) is our new collective reality. So how can we make this work well, when for many, our offices may only now be a few steps away from our beds? We asked around for advice on this matter from current PhD students.

Remember to take a break every half an hour or so. Go away from the desk!

It can be easy to forget to take a break when you’re “at home”, even if you’re also “at work”, and especially when you’re likely closer to the kettle/food/toilet than you would be otherwise. Get up, move around!

Stick to a regular schedule: when you wake up, go to sleep, work, relax, etc.

This is great advice for doing a PhD in general, but even more pertinent now that our routines have been turned upside down.

Pretend that you “go to and from work”, i.e take a morning and afternoon walk/cycle to mark the start and end of your work day.

A commute can be a great time to wake up in the morning and wind down in the evening. Get creative with what you can (safely, and in accordance with government guidance) do to replace your commute during this time.

Pretend that you go to work by dressing accordingly, it makes the brain active and makes you stronger against the ‘do something else’  or ‘ relax’ mode activated by the comfy at home clothes.

It’s tempting to work wearing pyjamas, but will this help your productivity and mindset? Getting dressed for work can also help to maintain your work-life balance.

Look after your posture. If possible, sit at a desk with a screen at the right height. 

Try to follow standard health and safety advice when it comes to working long hours at a desk. If possible, invest time and money in making your home working environment a comfortable and non-straining place to be.

If you can at all help it, don’t work in the room where you sleep. It can cause difficulties sleeping.

This also helps add some breaks and changes in your day, which can help to maintain focus and motivation.

Enjoy the benefits of working from home: take a break to actually cook lunch, get things done around the house. Let yourself appreciate the things that are handy about it as well as the negatives. 

Being able to get away from your work and do something like ironing, cooking, baking or cleaning might actually help your productivity and concentration by providing a better break than you might otherwise get in an office. Embrace it!

Schedule social e-contact. Don’t let yourself go more than a day without at least hearing someone’s voice on the phone. Use the opportunity to reconnect with old friends. 

In Reading, we’re making extensive use of Microsoft Teams to remain in contact with each other and try to mimic our vibrant work atmosphere.

Do (as long as it’s safe to do so) go for walks, head outside, make sure you do some exercise twice a week. 

Luckily, we’ve got some very nice weather this week in most of the UK. But do please adhere to social distancing guidelines when you do go outside.

It can be easy for the lines between work and life outside of work to be blurred during a PhD at the best of times, and WFH can make this more problematic. Set your hours, and stick to it.

If you work 8-4, work 8-4! At 4pm, switch your computer off and do something different. Without an evening commute, it can be trickier to bring an end to your working day, but this is probably one of the most important things to maintain.

Most operating systems, including Windows 10, support multiple virtual desktops. Try using one of those for your virtual “work” PC, and another as your virtual “home” PC. Then you can keep the two segregated. 

At the end of the day you can switch to your “home” desktop, and then return to “work” the following day.

This Twitter thread has some great advice: https://twitter.com/ProfAishaAhmad/status/1240284544667996163?s=19

Twitter is of course full of great (and not so great) advice. It can keep people connected but also increase anxiety. Be cautious with it, along with all social media during this time.

Allow yourself ample time to adjust, get the important things in order first (friends/family/food/fitness), and build a regular schedule.

This is a huge change. It’s not just a huge change to work, it’s a huge change to our entire lives. Go easy on yourself as you get into the swing of things.

Fill the space around you with plants – it’ll make you feel like you’re outside if you don’t have that luxury – and open your windows every morning (you’ll appreciate the fresh air!) 

Nature is very calming. Open the window, listen to the birds (you might hear them more than you used to nowadays).

Extending our best wishes to all from everyone in Reading Meteorology during this challenging time.

North American weather regimes and the stratospheric polar vortex

s.h.lee@pgr.reading.ac.uk

The use of weather regimes offers the ability to categorise the large-scale atmospheric circulation pattern over a region on any given day. One way of doing this is through k-means clustering of the 500 hPa geopotential height anomaly field. Cassou (2008) determined the lagged influence of the Madden-Julian Oscillation (MJO) on four wintertime regimes over the North Atlantic; these regimes have subsequently become commonly used (e.g. they are in use operationally at ECMWF). Charlton-Perez et al. (2018) used the same four regimes to describe the influence of the stratospheric polar vortex on Atlantic circulation patterns.

Stratosphere-troposphere coupling is often described in terms of either the annular modes (the leading principal component (PC) of hemisphere-wide variability, often known as the Arctic and Antarctic Oscillations (AO/AAO) when discussing the lower-troposphere) or regional leading principal components (such as the North Atlantic Oscillation (NAO)). However, by their definition, this doesn’t tell the full story – only some percentage of it (around 1/3 for the NAO). The downward coupling of stratospheric circulation anomalies onto tropospheric weather patterns is an area of active research. For example, not every sudden stratospheric warming (SSW) event exhibits the “canonical” response in the troposphere of a strongly negative NAO-type pattern (Karpechko et al. 2017, Domeisen et al. 2020).

Could regimes tell us something more? Specifically – could they shed light onto the impact of the stratosphere on North America, which has been under-explored compared with Europe? In a recent paper (Lee et al. 2019), we look at just that.

We use 500 hPa geopotential height anomalies in the sector 20-80°N 180-30°W from ERA-Interim reanalysis for December—March 1979—2017. In order to describe only the large-scale variability, we first reduced the dimensionality of the problem by performing the clustering on a filtered dataset – achieved by retaining only the first 12 PCs which explain 80% of the variance in the dataset. We set k a priori to be 4 in the ­k-means clustering, following Vigaud et al. (2018). The number of clusters is somewhat arbitrary, but 4 has been shown to be significant when comparing with a reference noise model (i.e., testing if the clusters are just the result of forcefully clustering noise, or something meaningful). Once the clusters have been determined from analysis of the dataset – the “centroids” – each day in the dataset is assigned to one of the clusters. The patterns produced (Figure 1) are like a similar analysis in Straus et al. (2007) so we adopt their names.

Figure 1: 500 hPa geopotential height anomalies for the four North American weather regimes. Anomalies are expressed with respect to a linearly de-trended 1979-2017 base period. Stippling indicates significance at the 95% confidence level according to a two-sided bootstrap re-sampling test.

To diagnose how these regimes vary with the state of the stratospheric vortex, we compute some statistics (Figure 2) based on the tercile category of the 100 hPa 60°N zonal-mean zonal wind on the preceding day (“strong”, “neutral”, and “weak”). 100 hPa is used as a lower-stratospheric measure (compared with 10 hPa used for diagnosing major sudden stratospheric warmings) to assess only those anomalies in the stratosphere which have the potential to influence tropospheric weather.

Figure 2: Probabilities of (a) occurrence, (b) persistence, and (c) transition from another regime into each regime stratified by the tercile anomaly categories of 100 hPa 60°N zonal-mean zonal wind. Error bars indicate 95% binomial proportion confidence intervals where the sample size has been scaled to account for lag-1 autocorrelation.

Evidently, the Arctic High regime is strongly sensitive to the strength of the stratospheric winds, being 7 times more likely following a weak vortex versus a strong vortex. The Arctic Low regime displays the opposite sensitivity, being more likely following a strong vortex. A similar but weaker relationship is found for the Pacific Trough. The Alaskan Ridge regime, however, does not display a sensitivity to the vortex strength. This result was somewhat surprising as the Alaskan Ridge regime resembles a pattern which became known as a “polar vortex outbreak”, but we suggest that (a) the similarity of the pattern to the Tropical-Northern Hemisphere pattern may indicate tropospheric forcing exhibits greater control on this regime, and (b) a possible influence through a barotropic anomaly exists from a distortion of the stratospheric vortex (which is not manifest in the zonal-mean zonal wind).

We relate these regimes to impactful real-world weather by computing the probability of an extreme cold temperature (defined as 1.5 standard deviations below normal) in each regime (Figure 3). We find that the greatest likelihood of widespread extreme cold in North America is during the Alaskan Ridge regime, with secondary likelihood of extreme cold for the west coast during the Arctic Low (recall that this pattern is more likely following a strong vortex), and only a low probability during the Arctic High regime (which is strongly associated with extreme cold in Europe).

Figure 3: Proportion of days assigned into each regime over the period 1 January 1979-31 December 2017 (DJFM days only) where normalised temperatures dropped below -1.5 standard deviations. Stippling indicates 95% confidence according to a one-sided bootstrap re-sampling test.

Our results therefore suggest that the strength of the stratospheric polar vortex does not change the likelihood of the circulation pattern with the greatest potential for driving extreme cold weather in North America (in stark contrast to Europe), and that prediction of this pattern should look elsewhere – either to the tropics, or to changes in the shape of the stratospheric vortex – including wave reflection events (Kodera et al. 2008, Kretschmer et al. 2018).

Further work will investigate how well these regimes and their response to changes in the stratosphere are captured by the extended-range forecasting models which comprise the S2S database.

This work was funded by the NERC SCENARIO doctoral training partnership.

2019 on The Social Metwork

It’s been quite a busy and successful year here on The Social Metwork, and my first full calendar year as Editor after taking over in October 2018. We’ve had some great contributions on all sorts of topics, from published research to summer schools, conferences, and PhD tips. I’d like to extend my thanks and praise to everyone who has contributed a post or reviewed a submission this year – thank you for taking the time out from your busy PhD life! To those of you who have since finished your PhD, congratulations and all the best for the future. I’d also like to thank everyone who visited the site from around the world (over 5000 of you) and read our blog posts – you’re the reason we do this! – Simon, Editor.

To wrap up 2019, here is a list of all this year’s 32 posts, in case you missed any.

AMS Annual Meeting 2019 – Lewis Blunn

My tips, strategies and hacks as a PhD student – Mark Prosser

Going Part-time… – Rebecca Couchman-Crook

Quantifying the skill of convection-permitting ensemble forecasts for the sea-breeze occurrence – Carlo Cafaro

Is our “ECO mode” hot water boiler eco-friendly? – Mark Prosser

Evaluating aerosol forecasts in London – Elliott Warren

APPLICATE General Assembly and Early Career Science event – Sally Woodhouse

The Circumglobal Teleconnection and its Links to Seasonal Forecast Skill for the European Summer – Jonathan Beverley

Extending the predictability of flood hazard at the global scale – Rebecca Emerton

On relocating to the Met Office for five weeks of my PhD – Kaja Milczewska

Workshop on Predictability, dynamics and applications research using the TIGGE and S2S ensembles – Simon Lee

Representing the organization of convection in climate models – Mark Muetzelfeldt

EGU 2019 – Bethan Harris and Sally Woodhouse

Investigating the use of early satellite data to test historical reconstructions of sea surface temperature – Thomas Hall

Island convection and its many shapes and forms: a closer look at cloud trails – Michael Johnston

PhD Visiting Scientist 2019: Prof. Cecilia Blitz – Rebecca Frew

Met Department Summer BBQ 2019 – Mark Prosser

Simulating measurements from the ISMAR radiometer using a new light scattering approximation – Karina McCusker

RMetS Student and Early Career Scientists Conference 2019 – Dom Jones

The 2nd ICTP Summer School in Hierarchical Modelling of Climate Dynamics – Kieran Pope

The 27th General Assembly of the International Union of Geodesy and Geophysics (IUGG) in Montreal, Canada – Tsz Yan (Adrian) Leung

The Colour of Climate – Jake Gristey

Fluid Dynamics of Sustainability and the Environment Summer School – Mark Prosser

SWIFT and YESS International Summer School, Kumasi, Ghana – Alex Doyle

Wisdom from experience: advice for new PhD students – Simon Lee and Sally Woodhouse

On relocating to Oklahoma for 3.5 months – Simon Lee

Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes – Ryan Williams

Combining multiple streams of environmental data into a soil moisture dataset – Amsale Ejigu

How much energy is available in a moist atmosphere? – Bethan Harris

The Variation of Geomagnetic Storm Duration with Intensity – Carl Haines

The impact of atmospheric model resolution on the Arctic – Sally Woodhouse

Sudden Stratospheric Warming does not always equal Sudden Snow Shoveling – Simon Lee

Sudden Stratospheric Warming does not always equal Sudden Snow Shoveling

Email: s.h.lee@pgr.reading.ac.uk

During winter, the poles enter permanent darkness (“the polar night”) and undergo strong radiative cooling. In the stratosphere – a dry, stable layer of the atmosphere around 10-50 km above the surface – this cooling is particularly effective. By thermal wind balance, the strong polar cooling leads to the formation of the stratospheric polar vortex (SPV), a planetary scale westerly circulation that sits atop each winter pole (Figure 1).

Figure 1: The Arctic stratospheric polar vortex, here shown at 10 hPa, on March 12, 2019. Geopotential height is contoured, and filled colours show the wind speed in m/s. The zonal-mean zonal wind at 60°N is shown in the bottom left, a commonly used diagnostic of the strength of the SPV. After Figure 6 in Lee and Butler (2019).

In the Northern Hemisphere, the SPV is highly variable, thanks to the generation of large planetary waves in the mid-latitude westerly flow (driven primarily by mountains and land-sea contrast around the continents), which can propagate vertically into the stratosphere and break there, decelerating and deforming the SPV and warming the stratosphere.  In the Antarctic, the presence of the Southern Ocean in the mid-to-high latitudes encircling Antarctica means no similar waves are typically produced. The Antarctic SPV is therefore much stronger than its Arctic counterpart, which is why the ozone hole developed there rather than over the Arctic – with the colder temperatures inside the vortex allowing for the formation of polar stratospheric clouds, which catalyse the reactions that deplete ozone.

Now, since all the weather we experience takes place in the troposphere, you might wonder why we should worry about what happens in the layer above that. In the past, numerical weather prediction models did not resolve the stratosphere, because it wasn’t considered worth the extra computational resources. However, it is now known that the state of the SPV can act as a boundary condition to weather forecasts (especially long-range forecasts that extend beyond 2 weeks ahead, e.g. Scaife et al. (2016)) in a similar way to sea surface temperatures (SSTs). One of the reasons for this is the longer timescales present in the stratosphere (also analogous to SSTs) compared with tropospheric weather systems – an anomaly present in the stratosphere has a long persistence time. But how do these stratospheric anomalies influence the weather we experience?

Let’s take one particularly exciting case of SPV variability: major sudden stratospheric warmings (SSWs). SSWs (defined by the 10 hPa 60°N zonal-mean zonal wind reversing from westerlies to easterlies) occur on average 6 times per decade (Butler et al. 2017) and are associated with either a displacement of the SPV off the Pole, or a split of the SPV into two daughter vortices. Coincident with this is a rapid heating of the polar stratosphere (~50°C in a few days) due to adiabatic warming of descending air – hence the name. The most recent major SSW occurred on 2 January 2019 (Figure 2), but one also occurred on 12 February 2018.

Figure 2: As in Figure 1 but for 2 January 2019 (after Figure 4 in Lee and Butler (2019)).

Following a major SSW, the easterly winds descend through the stratosphere over the next few weeks and tend to persist for weeks to months in the lower stratosphere. What happens beneath that in the troposphere is then more varied, but on average there is a transition to a negative Northern Annular Mode (NAM). In a negative NAM, the mid-latitude westerlies associated with the tropospheric jet stream weaken and shift equatorward, increasing the likelihood of cold air outbreaks (and, yes, snow!) in places like the UK and northern Europe (Figure 3). However, that’s only the average response!

Figure 3: Average surface temperature anomaly for days 0-30 following all major SSWs in ERA-Interim 1979-2014. [Source: SSW Compendium]

In February-March 2018, we did indeed see this response following a major SSW – immortalised as the ‘Beast from the East’ with record-breaking cold weather and heavy snowfall in the UK (e.g. Greening and Hodgson 2019). But following the January 2019 SSW, there was no similar weather pattern. Figure 4 shows a cross-section of polar cap geopotential height anomalies (analogous to the NAM). Reds effectively indicate weaker westerly winds, and the major SSW is evident in the centre (second dashed line from the left). However, it doesn’t persistently “drip” down into the troposphere below 200 hPa, with only a brief “drip” in early February 2019. For the most part, the stratosphere and troposphere did not “talk” to each other.

Figure 4: 60-90°N geopotential height anomaly time-height cross-section for August 2018-May 2019. Vertical dashed lines indicate (left-right) the SPV spin-up, the major SSW, a strong vortex event (Tripathi et al. 2015), and the vortex dissipation. (After Figure 8 in Lee and Butler (2019).)

This SSW was thus “non-downward propagating” (Karpechko et al. 2017), which is the case with somewhere close to half of the observed events.

Why?

Some research suggests this may be due to the type of SSW (split vs. displacement, e.g. Mitchell et al. 2013), the tropospheric weather regimes present following the SSW (e.g. Charlton-Perez et al. 2018), the evolution of the SSW (e.g. Karpechko et al. 2017), the interaction of the vertically-propagating waves with the SPV at the time of the SSW (e.g. Kodera et al. 2016), or some combination of those. Perhaps other forcing from the troposphere may dominate over the signal from the stratosphere – such as the teleconnection of the Madden-Julian Oscillation (MJO) to the North Atlantic weather regimes (e.g. Cassou 2008).

Thus, whilst an SSW may make cold weather more likely, it’s by no means guaranteed – and we still don’t fully understand the mechanisms involved with downward coupling. That’s one of the reasons why, regardless of what the tabloids may tell you, sudden stratospheric warming does not always guarantee sudden snow shoveling!

References

Butler, A. H., J. P. Sjoberg, D. J. Seidel, and K. H. Rosenlof, 2017: A sudden stratospheric warming compendium. Earth System Science Data, https://doi.org/10.5194/essd-9-63-2017

Cassou, C., 2008: Intraseasonal interaction between the Madden–Julian Oscillation and the North Atlantic Oscillation. Nature, https://doi.org/10.1038/nature07286

Charlton-Perez, A. J., L. Ferranti, and R. W. Lee, 2018: The influence of the stratospheric state on North Atlantic weather regimes. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.3280

Greening, K., and A. Hodgson, 2019: Atmospheric analysis of the cold late February and early March 2018 over the UK. Weather, https://doi.org/10.1002/wea.3467

Karpechko, A. Yu., P. Hitchcock, D. H. W. Peters, and A. Schneidereit, 2017: Predictability of downward propagation of major sudden stratospheric warmings. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.3017

Kodera, K., H. Mukougawa, P. Maury, M. Ueda, and C. Claud, 2016: Absorbing and reflecting sudden stratospheric warming events and their relationship with tropospheric circulation. Journal of Geophysical Research: Atmospheres, https://doi.org/10.1002/2015JD023359

Lee, S. H., and A. H. Butler, 2019: The 2018-2019 Arctic stratospheric polar vortex. Weather, https://doi.org/10.1002/wea.3643

Mitchell, D. M., L. J. Gray, J. Antsey, M. P. Baldwin, and A. J. Charlton-Perez, 2013: The Influence of Stratospheric Vortex Displacements and Splits on Surface Climate. Journal of Climate, https://doi.org/10.1175/JCLI-D-12-00030.1

Scaife, A. A., A. Yu. Karpechko, M. P. Baldwin, A. Brookshaw, A. H. Butler, R. Eade, M. Gordon, C. MacLachlan, N. Martin, N. Dunstone, and D. Smith, 2016: Seasonal winter forecasts and the stratosphere. Atmospheric Science Letters, https://doi.org/10.1002/asl.598

Tripathi, O. P, A. Charlton-Perez, M. Sigmond, and F. Vitart, 2015: Enhanced long-range forecast skill in boreal winter following stratospheric strong vortex conditions. Environmental Research Letters, https://doi.org/10.1088/1748-9326/10/10/104007

On relocating to Oklahoma for 3.5 months

Email: s.h.lee@pgr.reading.ac.uk

From May 4th through August 10th 2019, I relocated to Norman, Oklahoma, where I worked in the School of Meteorology in the National Weather Center (NWC) at the University of Oklahoma (OU). I’m co-supervised by Jason Furtado at OU, and part of my SCENARIO-funded project plan involves visiting OU each summer to work with Dr. Furtado’s research group, while using my time in the U.S. to visit relavant academics and conferences. Prior to my PhD, I studied Reading’s MMet Meteorology and Climate with a Year in Oklahoma degree, and spent 9 months at OU as part of that – so it’s a very familiar place! The two departments have a long-standing link, but this is the first time there has been PhD-supervision collaboration.

The National Weather Center in Norman, Oklahoma – home to the School of Meteorology.

The National Weather Center (NWC) [first conceived publicly in a 1999 speech by President Bill Clinton in the aftermath of the Bridge Creek-Moore tornado] opened in 2006 and is a vastly bigger building than Reading Meteorology! Alongside the School of Meteorology (SoM), it houses the Oklahoma Mesonet, the NOAA Storm Prediction Center (SPC) (who are responsible for operational severe weather and fire forecasting in the U.S.) and the NOAA National Severe Storms Laboratory (NSSL). SPC and NSSL will be familiar to any of you who have seen the 1996 film Twister. You could think of it as somewhat like a smaller version of the Reading Meteorology department being housed in the Met Office HQ in Exeter.

Inside the NWC.

The research done at SoM is mostly focussed on mesoscale dynamics, including tornadogenesis, thanks to its location right at the heart of ‘tornado alley’. It’s by no means a typical haunt of someone who researches stratosphere dynamics like I do, but SoM has broadened its focus in recent years with the inception of the Applied Climate Dynamics research group of which I’m a part. Aside from the numerous benefits of being able to speak face-to-face with a supervisor who is otherwise stuck on a TV screen on Skype, I also learnt new skills and new ways of thinking – purely from being at a different institution in a different country. I also used this time to work on the impact of the stratosphere on North America (a paper from this work is currently in review).

I also visited the NOAA Earth System Research Laboratory (ESRL) in Boulder, Colorado to present some of my work, and collaborate on some papers with scientists there. Boulder is an amazing place, and I highly recommend going and hiking up into the mountains if you can (see also this 2018 blog post from Jon Beverley on his visit to Boulder).

As for leisure… I chose to take 2 weeks holiday in late May to, let’s say, do “outdoor atmospheric exploration“. This happened to coincide with the peak of one of the most active tornado seasons in recent years, and I just so happened to see plenty of them. I’m still working on whether or not the stratosphere played a role in the weather patterns responsible for the outbreak!

An EF2-rated wedge tornado on 23 May near Canadian, Texas.

Wisdom from experience: advice for new PhD students

The new academic year is now underway, and a new bunch of eager first year PhD students are dipping their toes into a three-to-four year journey to their doctorate. So, we’ve collated some advice from the more experienced among us! The idea behind the following tidbits of advice is that they are things we would tell our younger selves if we could go back to day 1…

Work

“Make sure you and your supervisor set out expectations and at least a vague timeline at the start, that way you will know you’re on track.”

“Write code as if you’re giving it to someone else – one day you might have to.”

Even if you don’t give your code to another use, in a year’s time you’ll have forgotten what it does! Related to this, it’s useful to keep good “readme” documents to note where all your code is, how to run things, etcetera. Also, if you think you’re going to present a plot at some point – in a talk, paper, or even your thesis, make a final version at the time (using appropriately accessible colour maps and big enough labels), plus note down where you’ve stored the code you used to make it.

“Learn and use git/github (or at least get familiar with the 3 basic commands of: git add, commit, push) ASAP! This means that if you take a wrong turn in your code (you will), you can painlessly ‘revert’ to a stage before you made a mess.”

“Read papers with your literature review in mind. If you can’t see where the paper will fit in your literature review, either reconsider your literature review… or find a more relevant paper.”

“Write down everything you learn, or facts you are told – you never know when you’ll need a piece of information again.”

But also be prepared to have not really followed any of this advice properly until you regurgitate it to new students in your fourth year and wonder why you haven’t been doing any of it up until now.

“Try to keep up a good routine – it’s much easier to get out of bed when you’re having a slow work week if that’s what your body is used to.”

“You’ll be amazed at how much you’ll learn and master without even realising.”

“Don’t compare yourself to others.”

Every PhD project is unique, as is every student. During a PhD, you’re looking into the unknown. Maybe you’ll get lucky (with some hard work) and have some really interesting results, or it might be a bit of a battle. Some projects are more suited to regular publications, others less so – this doesn’t necessarily reflect your individual abilities. In addition, everyone has different background knowledge and motivation for doing a PhD.

“Not every day has to be maximum productivity, that’s okay!”

“Some days are great, others are rubbish. Like life, really.”

Life

“Make friends with other PhD students. It’s nice to have someone who might make you cake when you feel sad, or happy.”

This is so true. A PhD is quite a unique experience and lots of people don’t really get it, thinking it’s just like another undergrad. Sometimes it’s really useful to have someone who understands the stress of some code just not working, or the dread of a blank page where your monitoring committee report should be. It’s also helpful to get to know people in the years above, or even post-docs, since they’ve probably already gone through what you’re experiencing.

“Make friends and join clubs and societies with people that aren’t doing PhDs.”

Sometimes it’s important to get out of the PhD “bubble” and put things in perspective. Keeping in touch with friends that have “real” jobs (for want of a better word) can be a nice reminder of some of the benefits of PhD life – such as flexible hours (you don’t have to be in before 9 every day) or not having to wear formal business attire.

Wellbeing

“Try to keep your weekends free – it’s great for your sanity!”

“Take holiday! You are expected to.”

“Don’t feel guilty for not cheering up when people tell you everything’s okay. It almost invariably is, but sometimes it all gets a bit much and you’ll feel bad for a while, that’s totally normal!”

Yes, it’s totally okay to have a couple of bad days. Remember, this can often be true of people with ‘real’ jobs, it isn’t just unique to the PhD experience! However, if you’re feeling bad for a long period of time, it’s important to acknowledge that this isn’t okay and you don’t have to feel like that. It might be helpful to let your supervisor know that you’re having a bit of a hard time, for whatever reason, and work might be slow for a while. There are also lots of support systems available. For students at Reading, you can find out more about the Counselling and Wellbeing Service here (http://www.reading.ac.uk/cou/counselling-services-landing.aspx). A PhD is hard work, but it should be a fundamentally enjoyable experience!

Finally:

“No poking your supervisor with a stick. They don’t appreciate it.”

(…no, we don’t get it either)


Co-written by Simon Lee and Sally Woodhouse, with anonymous pieces of advice collected from various PhD students in the Department of Meteorology.

Workshop on Predictability, dynamics and applications research using the TIGGE and S2S ensembles

Email: s.h.lee@pgr.reading.ac.uk

From April 2nd-5th I attended the workshop on Predictability, dynamics and applications research using the TIGGE and S2S ensembles at ECMWF in Reading. TIGGE (The International Grand Global Ensemble, formerly THORPEX International Grand Global Ensemble) and S2S (Sub-seasonal-to-Seasonal) are datasets hosted at primarily at ECMWF as part of initiatives by the World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP). TIGGE has been running since 2006 and stores operational medium-range forecasts (up to 16 days) from 10 global weather centres, whilst S2S has been operational since 2015 and houses extended-range (up to 60 days) forecasts from 11 different global weather centres (e.g. ECMWF, NCEP, UKMO, Meteo-France, CMA…etc.). The benefit of these centralised datasets is their common format, which enables straightforward data requests and multi-model analysis with minimal data manipulation allowing scientists to focus on doing science!

Attendees of the workshop came from around the world (not just Europe) although there was a particularly sizeable cohort from Reading Meteorology and NCAS.

Figure 1: Workshop group photo featuring the infamous ECMWF ducks!

In my PhD so far, I have been making extensive use of the S2S database – looking at both operational and re-forecast datasets to assess stratospheric predictability and biases – and it was rewarding to attend the workshop and see what a diverse range of applications the datasets have across the world. From the oceans to the stratosphere, tropics to poles, predictability mathematics to farmers and energy markets, it was immediately very clear that TIGGE and S2S are wonderfully useful tools for both the research and applications communities. A particular aim of the workshop was to discuss “user-oriented variables” – derived variables from model output which represent the meteorological conditions to which a user is sensitive (such as wind speed at a specific height for wind power applications).

The workshop mainly consisted of 15-minute conference-style talks in the main lecture theatre and poster sessions, but the final two days also featured parallel working group sessions of about 15 members each. The topics discussed in the working groups can be found here. I was part of working group 4, and we discussed dynamical processes and ensemble diagnostics. We reflected on some of the points raised by speakers over the preceding days – particular attention was given to diagnostics needed to understand dynamical effects of model biases (such as their influence on Rossby wave propagation and weather-regime transition) alongside what other variables researchers needed to make full use of the potentials S2S and TIGGE offer (I don’t think I could say “more levels in the stratosphere!” loudly enough – TIGGE does not go above 50 hPa, which is not useful when studying stratospheric warming events defined at 10 hPa).

Data analysis tools are also becoming increasingly important in atmospheric science. Several useful and perhaps less well-known tools were presented at the workshop – Mio Matsueda’s TIGGE and S2S museum websites provide a wide variety of pre-prepared plots of variables like the NAO and MJO which are excellent for exploratory data analysis without needing many gigabytes of data downloads. Figure 2 shows an example of NAO forecasts from S2S data – the systematic negative NAO bias at longer lead-times was frequently discussed during the workshop, whilst the inability to capture the transition to a positive NAO regime beginning around February 10th is worth further analysis. In addition to these, IRI’s Data Library has powerful abilities to manipulate, analyse, plot, and download data from various sources including S2S with server-side computation.


Figure 2: Courtesy of the S2S Museum, this figure shows S2S model forecasts of the NAO launched on January 31st 2019. The verifying scenario is shown in black, with ensemble means in grey. All models exhibited a negative ensemble-mean bias and did not capture the development of a positive NAO after February 10th.

It’s inspiring and motivating to be part of the sub-seasonal forecast research community and I’m excited to present some of my work in the near future!

TIGGE and S2S can be accessed via ECMWF’s Public Datasets web interface.

Night at the Museum!

On Friday November 30th, Prof. Paul Williams and I ran a ‘pop-up science’ station at the Natural History Museum’s “Lates” event (these are held on the last Friday of each month; the museum is open for all until 10pm, with additional events and activities). Our station was entitled “Turbulence Ahead”, and focused on communicating research under two themes:

  1.  Improving the predictability of clear-air turbulence (CAT) for aviation
  2.  The impact of climate change on aviation, particularly in terms of increasing CAT

There were several other stations, all run by NERC-funded researchers. Our stall went ‘live’ at 6 PM, and from that point on we were speaking almost constantly for the next 3.5 hours – with hundreds (not an exaggeration!) of people coming to our stall to find out more. Neither of us were able to take much of a break, and I’ve never had quite such a sore voice!

IMG_1769
Turbulence ahead? Not on this Friday evening!

Our discussions covered:

  • What is clear-air turbulence (CAT) and why is it hazardous to aviation?
  • How do we predict CAT? How has Paul’s work improved this?
  • How is CAT predicted to change in the future? Why?
  • What other ways does climate change affect aviation?

Those who came to our stall asked some very intelligent questions, and neither of us encountered a ‘climate denier’ – since we were speaking about a very applied impact of climate change, this was heartening. This impact of climate change is not often considered – it’s not as obvious as heatwaves or melting ice, but is a very real threat as shown in recent studies (e.g. Storer et al. 2017). It was a challenge to explain some of these concepts to the general public – some had heard of the jet stream, others had not, whilst some were physicists… and even the director of the British Geological Survey, John Ludden, turned up! It was interesting to hear from so many people who were self-titled “nervous flyers” and deeply concerned about the future potential for more unpleasant journeys.

I found the evening very rewarding; it was interesting to gauge a perspective of how the public perceive a scientist and their work, and it was amazing to see so many curious minds wanting to find out more about subjects with which they are not so familiar.

My involvement with this event stems from my MMet dissertation work with Paul and Tom Frame looking at the North Atlantic jet stream. Changes in the jet stream have large impacts on transatlantic flights (Williams 2016) and the frequency and intensity of CAT. Meanwhile, Paul was a finalist for the 2018 NERC Impact Awards in the Societal Impact category for his work on improving turbulence forecasts – he finished as runner-up in the ceremony which was held on Monday December 3rd.

So, yes, there may indeed be turbulent times ahead – but this Friday evening certainly went smoothly!

Email: s.h.lee@pgr.reading.ac.uk

Twitter: @SimonLeeWx

References

Storer, L. N., P. D. Williams, and M. M. Joshi, 2017: Global Response of Clear-Air Turbulence to Climate Change. Geophys. Res. Lett., 44, 9979-9984, https://doi.org/10.1002/2017GL074618

Williams, P. D., 2016: Transatlantic flight times and climate change. Environ. Res. Lett., 11, 024008, https://doi.org/10.1088/1748-9326/11/2/024008.