Latest Publications

GUEST BLOG: Talking sensible science when wondering about the weather

By Georgina Glaser, Voice of Young Science member

Talking about science when you’re a PhD student seems like it should be an obvious prerequisite and an easy task. For some people, it is. For others, it is far more difficult. Reasons for this can be the intimidation of other scientists who you believe ‘know more than you do’ and so you feel your opinion isn’t valid. But these barriers need to be breached, and the sooner the better.

As a member of Voice of Young Science (VoYS), (a network of early career researchers who stand up for science), I am encouraged to think critically about science and to feel confident in immersing myself in scientific discussions whilst also challenging claims which I think are unreliable. This can be a daunting task, but it is a worthy and rewarding pursuit, not least of all because it equips us with the confidence to talk about science. Voice of Young Science is run by Sense About Science, a charity that aims to put science and evidence in the hands of the public. When the VoYS network suggested creating a Weather Quiz to address misused and sometimes misleading meteorological terminology used by the media, it seemed like a fantastic opportunity to get involved with some of the great work that they do. The aim of the quiz is to test your knowledge on the definitions and details of ‘well-known’ meteorological terms. That is, terms we are familiar with and have a vague notion of the definition, but which are not necessarily recognised as real or correct by meteorologists. And herein lies the problem. We all know what a heatwave is, in the sense that it is a period of warmth, but I would have no idea how to identify a heatwave on a technical basis. So, the question here might be: does that matter? When the media uses the term heatwave, and they’re just telling us that it’s going to be warmer, does it really matter if we know the details?

The answer to both of these questions should be yes. Not only because it is frustrating for meteorologists, but because it represents somewhat careless reporting on the side of journalists, and shows a clear misdirection or miscommunication of science to the public.

Although meteorology is not my field of expertise (I’m in the School of Biology at the University of St Andrews), it is still something that affects me as a member of the general public. In fact, a few months before I contributed to the project, my friend and I were discussing what exactly is meant by the term ‘80% chance of rain’ (which I therefore made an effort to include in the quiz in case anyone else has difficulty with this one as well). This is one aspect that I felt needed clarifying, but there are others where the media just misuses terms completely (I would give more detail here but obviously I don’t want to ruin the fun of the quiz!). Working on this project was a real eye opener, and I truly appreciated the chance to make contact with meteorologists who made me aware of just how many terms are misused, made up, or are misleading in the media (and not all of them made the cut, so there are still terms out there that we weren’t able to address in our quiz). What’s more worrying is that I was not previously aware of the extent of the problem before engaging with the project. Further to this, other people were aware of the issues, but perhaps felt that it was unimportant, or not their place to say, or not worth the effort. However, it is vital to encourage scientists, early career or not, that it is important, it is your place to say, and it is definitely worth the effort. VoYS provides us with such a valuable platform where we have the opportunity to clarify and address the issues of how science is reported, benefitting scientists and members of the public in the process, and even improving the quality of media coverage.

Meteorology is not my specialist area, but I was still able to engage with this aspect of science by simply understanding where the problems were and how they could be addressed. With help from early career researchers who are indeed meteorologists (including those at the University of Reading), we have been able to produce something which will hopefully not only highlight the issues with how the weather is reported in the media, but also highlight the need for scientists to step forward with other issues in their own field so that we can continue to address the misrepresentation of science.

The Voice of Young Science weather quiz Haven’t the foggiest

Not yet Christmas, but has spring sprung?

By Roger Brugge

This may seem a strange question, but it was prompted by the sight of flowering daffodils alongside the A4 through Maidenhead on 12 December 2015 (Figure 1).

Figure 1. Flowering daffodils along the A4 in Maidenhead, on 12 December 2015

In order to bloom, daffodils and other spring-flowering bulbs must be exposed to a drop in temperature after the previous flowering period – often a temperature below 8 °C is quoted for this threshold. However, once spring arrives it has been shown that a cool/cold winter will lead to a delayed onset in flowering (Seasonal weather, by Lionel P. Smith, published in 1968 by George Allen and Unwin Ltd). Warmer weather in spring has, according to plant scientists at Kew Gardens and Wakehurst (Sussex), led to an earlier onset of the flowering dates of daffodils in recent years: in the 1980s daffodils commonly flowered around 12 February, but by 2008 this date had shifted to 27 January, 16 days earlier. In the 1960s the flowers appeared some 3-4 weeks later than they do nowadays, so an appearance of the flowers in mid-December is remarkable.

Has the warmth of recent weeks been to blame for the flowering observed in Maidenhead? Weather conditions in autumn and winter so far (17 December) have been unusually mild. In Reading, at the University’s climatological station, just two air frosts have been recorded since the beginning of September (see Figure 2) while the mean air temperature has been about 5 degC above average for December so far.

Figure 2. Red: mean air temperature for 1-17 December during each year from 1918 to 2015. Black: The number of air frosts recorded during autumn and winter up to 17 December for the same years. Dashed lines show the 30 year averages for 1981-2010. Note that the air frost axis on the right has been reversed so that a low incidence of air frost and higher temperatures tend to coincide. Observations made at the University of Reading.

Following a mild November (the third mildest in the period 1908-2015) the first 17 days of December have been the mildest on record – see Figure 2 – by a truly remarkable 1.1 degC. The average temperature during this period of 10.6 °C in 2015 is similar to the mean temperature normally to be expected around the beginning of May! During the afternoon of the 17th, the air temperature rose to 15.5 °C at the University, the second highest ever recorded here in December (after 15.8 °C in 1985).

As mentioned earlier, winter bulbs usually need a period of cold weather in order for the shoots of the bulb to sprout and produce flowers; such a spell has not occurred yet – since late September average temperatures have remained fairly constant, give or take a degree or so (Figure 3). The only exception to this was a cold spell around 20-25 November, when the two air frosts and the only snow shower of the season (so far) occurred in Reading.

Figure 3. Five day running mean temperatures °C for 2015 (blue) and the 1981-2010 average (red) at the University of Reading.

Of course, soil temperatures may also be playing a role in encouraging the bulbs to produce growth and flowers. Table 1 shows the anomalous warmth of the soil during December so far.

Measurement (depth) 10 cm 30 cm 100 cm
1981-2010 climatological averages
1st-10th 4.9 °C 7.1 °C 9.1 °C
11th-17th 4.1 6.1 8.2
December 4.3 6.3 8.3
2015 averages
1st-10th 9.2 °C 10.3 °C 11.1 °C
11th-17th 8.7 10.1 10.9
December 1st-17th 9.0 10.2 11.0
2015 anomalies
1st-10th +4.3 degC +3.2 degC +2.0 degC
11th-17th +4.6 +4.0 +2.7
December 1st-17th +4.7 +3.9 +2.8

Table 1. Soil temperatures during 1-17 December 2015 in Reading.

The soil temperature averages for the month of December 2015 so far are currently averaging close to 4.5 degC above average at daffodil bulb depth; in fact, the soil temperatures so far this month are currently those normally seen in mid-October/mid-April (at 10 cm depth), late-October/late April (30 cm) and mid-November/mid-May (100 cm). Peak temperatures at depths of 30 and 50 cm this month have also exceeded anything previously measured at this time of year since observations at these depths began in 1910 and 1971, respectively.

This unusually persistent warmth has been the result of a higher-than-normal frequency of winds from the south-west quadrant (a mild direction in autumn/winter) across southern England – see Figure 4. With Christmas fast approaching, there is no sign of any change to this pattern of winds and temperatures. Indeed, the chances of a White Christmas in Reading seem very slim. Possibly a rerun of the Christmases of 1911 (when temperatures rose to 15.0 °C on Boxing Day) or 1935 (when the temperatures reached at least 10 °C every day from Christmas Day until New Year’s Eve) might be more likely this year? Even another air frost looks unlikely in Reading before the start of 2016.

Figure 4. Wind rose of winds at the University of Reading based on observations every 5 minutes during 1 November – 16 December 2015. Mean wind speeds: 0-2 m/s (red), 2-4 m/s (magenta), 4-6 m/s (cyan) and 6 m/s and above (blue). Radial axis shows the percentage occurrence during the period in 30 degree-wide bands.

It will be interesting to see how the Maidenhead daffodil clump copes with any cold spells this winter. Most flowering daffodils can handle a small amount of frost – but if the winter does produce a long, cold snap …

Further reading about Reading’s weather:

R Brugge and S Burt, 2015. One hundred years of Reading weather

Recent changes in Africa rainfall

By Richard Allan

Changing rainfall can have profound societal consequences across Africa where it plays a crucial role in sustaining livelihoods and economic development. Predicting how rainfall patterns will alter as the planet warms in response to human-caused greenhouse gas emissions is therefore of great importance; for this both observations and simulations are vital. A new analysis of observed and simulated changes in rainfall over Africa has been published by the University of Reading TAMSAT group: the main results along with discussion of some other important studies are summarised below:

  • Southen Africa has experienced an increase in rainfall during December to January (DJF) that contributed to an increase in annual rainfall of 32-41 mm/year per decade (1983-2010). We argue that this increase is linked to a strengthening of the Pacific Walker circulation and since this is most likely related to a decadal climate fluctuation we do not anticipate this trend will be sustained into the future (a recent reversal in the Pacific Decadal Variability index has coincided with a developing drought in South Africa).
  • East Africa has suffered a decline in the “long rains” (March-May, MAM) over the past 3 decades. New research shows drying in this region is unusual in the context of the last 2000 years yet climate model simulations struggle to capture this rainy season.
  • Sahel rainfall has increased by 29-43 mm/year per decade (1983-2010), in particular during the June-August (JJA) wet season. Recent research has implicated increasing greenhouse gas concentrations as the primary cause of this recovery in rainfall from the 1980s drought. Particulate aerosol pollution and amplifying feedback loops from water vapour in the air are thought to also play a role.
  • Figure 1: Rainfall trends (in mm/day per year) for 3 seasons over the period 1983-2008 in (1) atmosphere climate models (AMIP) that are fed with the observed sea surface temperature and sea ice, (2) the Climate Research Unit (CRU) rain gauge dataset, (3) the Global Precipitation Climatology project combined gauge and satellite dataset and the (4) TARCAT satellite-based dataset. See Maidment et al. (2015) for more details.

    In our analysis, we excluded datasets which contained unrealistic jumps in the time series (attributable to changing coverage of rain gauge measurements or other factors). For the remaining datasets, the simulations broadly capture the observed changes in rainfall in the regions outlined above indicating that they can be explained by changes in sea surface temperature, the radiative forcings or a combination. They also add confidence in the robust nature of the observed rainfall trends. Projecting future climate change requires fully coupled climate simulations in which the ocean temperature and circulation are free to vary (unlike in the AMIP experiments we used to assess current changes). It is important that the seasonal characteristics of rainfall patterns simulated by these fully coupled models are realistic. This requires improved understanding of the physical processes that determine the climate in key regions such as West Africa and the Horn of Africa which involves combining satellite and conventional observations with detailed modelling as for example planned under current missions such as the DACCIWA project.


    • Dong and Sutton (2015) Dominant role of greenhouse-gas forcing in the recovery of Sahel rainfall,Nature Clim. Ch., doi: 10.1038/nclimate2664
    • Evan et al. (2015) Water Vapor-Forced Greenhouse Warming over the Sahara Desert and the Recent Recovery from the Sahelian Drought, J. Climate, doi: 10.1175/JCLI-D-14-00039.1
    • James et al. (2015) Process-based assessment of an ensemble of climate projections for West Africa, J. Geophys. Res., doi: 10.1002/2014JD022513
    • Knippertz et al. (2015) The DACCIWA Project: Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa, Bull. American Meteorol. Soc., doi: 10.1175/BAMS-D-14-00108.1
    • L’Heureux et al. (2013) Recent multidecadal strengthening of the Walker circulation across the tropical Pacific, Nature Clim. Ch., doi: 10.1038/nclimate1840
    • Maidment et al. (2015) Recent observed and simulated changes in precipitation over Africa,Geophys. Res. Lett., doi: 10.1002/2015GL065765
    • Tierney et al. (2015) Past and future rainfall in the Horn of Africa, Science Advances, doi:10.1126/sciadv.1500682
    • Trenberth (2015) Has there been a hiatus? Science, 349, p. 691-692, doi:10.1126/science.aac9225
    • Yang et al. (2015) The Rainfall Annual Cycle Bias over East Africa in CMIP5 Coupled Climate Models, J. Climate, doi: 10.1175/JCLI-D-15-0323.1

    An unusually dull, but mild, start to November

    By Roger Brugge

    “Persistent low cloud, spells of fog, minimal amounts of sunshine and night-time temperatures more suited to September.”  Such a description of the weather of the past ten days or so had me reaching for the records to try to place the recent weather conditions into a historical context.


    Daytime temperatures of 16.8 °C and 16.7 °C on the 6th and 7th at the University’s climatological weather station are not that unusual for early November; 16.8 °C was reached in November 2014 with 18.1 °C in November 2010 – the latter has only been surpassed in November by 20.4 °C on 5 November1938, 18.7 °C on 5 November 1946 and 18.2 °C on 4 November 1946 (figures taken from One hundred years of Reading weather). Even more unusual, the minimum temperature of 13.3 °C on Saturday 7th in 2015 has only been bettered on five nights at the University in the past century, and in fact some nights this month so far have seen higher temperatures than might be expected by day – see Figure 1.

    Figure 1. Daily maximum and minimum temperatures during 1-10 November 2015. Updated daily at

    However, not only is it in Reading that the temperatures at the start of November 2015 have been unusual. Trawscoed in mid-Wales broke the UK’s November temperature record on Sunday 1st, with 22.4 °C being reached. The previous November record was also in Wales, when temperatures reached 21.7 °C at Prestatyn in 1946. On Monday 2nd this year Trawscoed reached 22.3 °C, while the Eskdalemuir observatory in Dumfries and Galloway had its highest November temperature on record at 16.3 °C. It is hardly surprising then, that average temperatures so far this month have been typically 3-5 degC above the average for November (admittedly this is the average for the whole of November, but nevertheless…) – see Figure 2. In Reading the average temperature of 11.9 °C over the first nine days of November amounted to the mildest such spell on these dates since 1982 when the average temperature was 12.0 °C – no other November in the 1908-2015 station record has started as warm as these two.

    Figure 2. Mean temperature anomaly (with respect to 1981-2015) for November 2015 (up to 0600 GMT/10th) over the British Isles with respect to the monthly mean for the whole month. This image (and other illustrating the current status of the month) are updated three times each day at


    Just 30 minutes of bright sunshine has been recorded during the first nine days of November 2015 at the University. This dull spell is one of the dullest on record at this time of year – not a record but certainly worth a mention as this list of the lowest 8-day (or longer) sunshine totals at the University involving at least 7 November days shows:

    17-29 Nov 1958 nil sunshine (13 days)
    1-9 Nov 2015 0.5 h (9 days)
    20 Nov – 1 Dec 1963 0.4 h (12 days)
    29 Oct – 7 Nov 1982 0.3 h (10 days)
    31 Oct – 7 Nov 1972 0.1 h (8 days)

    Again, these dull conditions have been widespread across the British Isles this month – initially due to high pressure and fog/low cloud and then to cloud-bearing areas of low pressure; sunshine totals as of 0600 GMT on the 10th include (values in parentheses are the percentage of the total for the whole of November recorded so far):

    Leeming, North Yorkshire 3.8 h (6%)
    Hawarden, Clwyd 3.7 h
    Shobdon, Herefordshire 3.0 h
    Shoeburyness, Essex 1.3 h
    Odiham, Hampshire 2.1 h (3%)
    Charlwood, Surrey 2.5 h (3%)

    These are in contrast to parts of northern Scotland where the sunshine total so far amounts to over 40% of the average for the whole of November. While there are still a lot of November days to come yet in 2015, it is worth noting that three dullest Novembers at the University have been those of 1962 (32.6 h), 1994 (34.6 h) and 2014 (38.2 h) – so maybe the present month might be the second successive dull November in Reading?

    Mean sea level air pressure so far this November has been slightly above average – and this has helped to give persistent low cloud and poor visibility. Reading has had two days with fog (visibility below 1000 metres) at 0900 GMT – November 2014 had 5 days with fog and there were 8 days with fog at this time of day in 2011 – so the total so far in November 2015 is not that unusual. But many mornings this month have also been quite misty and ‘grey’.

    So, with no air frost so far this autumn many will be wondering when it will turn cold. Statistically, the median (i.e. middle) date for the first air frost of the autumn/winter during the period 1925-2014 at the University was 3 November; only in 10 per cent of the years did the first air frost occur after 24 November.

    Time will tell …

    This blog was originally posted on 10 November 2015. It was reposted on 4 December following computer system issues.

    Phew! What a scorcher!

    By Stephen Burt

    These were typical newspaper headlines earlier this week. So how did we fare in Reading?

    We did set one extraordinary record during this short-lived hot spell. We have daily temperature records for the university back to 1908, and as well as maximum and minimum temperatures these include the air temperature observed at 0900 UTC (10 a.m. in British Summer Time). Until Wednesday, the hottest morning on our records was Sunday 10 August 2003, when the 0900 UTC temperature stood at 28.4 °C. That particular day went on to become the hottest day on Reading’s long record, when the temperature rose to 36.4 °C later that afternoon.

    On Wednesday morning, our principal observer Mike Stroud could hardly believe his eyes when the dry-bulb thermometer at 0900 UTC stood at 30.6 °C, by more than 2 degC the hottest morning in Reading in more than a century.

    Fortunately or unfortunately – depending upon your viewpoint – extensive cirrus and altocumulus then largely obscured the Sun for the next couple of hours, and the temperature fell back to 29 °C, before rising quite late in the afternoon to the day’s maximum of 33.6 °C. This is of course by Reading’s standards a very hot day, but it ranked only fifth in the hottest July days on record (incidentally, displacing the hottest day of the legendary 1976 summer in doing so) … and it failed to make the ‘Top Ten’ hottest days since our records commenced. Had the rise in temperature from 0900 UTC to day’s maximum been more typical of previous heatwaves, we could well have broken the August 2003 record, and just possibly surpassed 38 °C.

    Only a little further east, the cloud cover during the morning was less thick, and this led to a pronounced east-west gradient in maximum temperature. A new UK July record was set at London’s Heathrow Airport at 1413 UTC when the temperature reached 36.7 °C, just surpassing the previous record of 36.5 °C set at the RHS Gardens in Wisley, Surrey, only nine years ago on 19 July 2006. Further west along the Thames Valley, 34.9 °C was attained in Maidenhead, 33.6 °C in Wokingham and here at the University, 32.7 °C at CEH Wallingford and 32.3 °C at Stratfield Mortimer, between Reading and Basingstoke.

    Some Met Office climatological stations report only once per month, so it may yet transpire when the returns arrive that somewhere was even hotter than Heathrow. A few locations are already known to have established all-time records last Wednesday, eclipsing the many notable heatwaves of recent years – we can include the summers of 1976, 1983, 1989, 1990, 1995, 2003 and 2006.

    What was all the more surprising was that Wednesday’s great heat was only the second day this year to have surpassed 25 °C. It will be interesting to see what the rest of the summer brings – more hot weather, or was Wednesday our ration of summer for this year?

    For more on notable heatwaves in Reading over the last century or so, including tables of the hottest days in each month and the ‘Top Ten’ for the whole record, see the newly-published One hundred years of Reading weather.

    Complexity of surface temperatures in cities – let’s talk about what we don’t know

    By Simone Kotthaus

    With summer upon us we want to spend more time outdoors. However, as temperatures rise, conditions in cities may become uncomfortable. The urban heat island (UHI) effect, whereby cities are warmer than their surroundings, may exacerbate the higher temperatures. This UHI is amongst the best known phenomena of climate conditions in cities.

    A number of processes contribute to the distinct climates in cities and many environmental variables provide useful insights. One such variable is the surface (skin) temperature of the urban canopy (the temperatures of all the surface of all the different facets – roads, buildings, gardens, etc). It responds to the amount of solar radiation absorbed by the surface and the anthropogenic heat emitted (from buildings, vehicles, people), and serves as an indicator for the amount of energy being stored in the urban fabric which in turn is used to drive turbulent surface exchanges (heating the air and evaporating water). Given the significance of surface temperature to the heating of the lowermost atmosphere, where people live and work, it is a critical variable in many model parameterisations of energy exchange processes.

    For larger areas, satellite remote sensing techniques have proven extremely useful in observing thermal contrasts, e.g. urban vs rural differences for large cities at a spatial resolution common to thermal infrared satellite images (~ 1 km). However, thermal patterns are increasingly being investigated at more detailed scales, either using remotely sensed imagery from airborne platforms, ground based imagers or by decomposing mixed-pixel information in satellite imagery. Despite great advances in thermal remote sensing methods over the last three decades, many challenges remain when studying the thermal response of the urban surface in detail given the huge heterogeneity of the urban canopy layer (i.e. the three-dimensional urban surface composed of buildings, roads, vegetation and potentially open water surfaces).

    Given the 3D-structure of the urban surface the observed surface temperature depends on the viewing geometry and the solar position. This is referred to as thermal anisotropy. Shaded areas are usually much cooler than the average temperatures, while sunlit areas are warmer. The average surface temperature in a pixel (the smallest area a satellite ‘sees’), is a function of the shadows ‘seen’ by the remote sensor but also the type of facets sampled (Figure 1). Another challenge is the complexity of construction materials used in urban areas. These have a wide range of radiative and conductive properties. Hence they not only absorb and store the solar energy in different ways, but also require different corrections to be applied when processing the remotely sensed imagery. Significant amounts of research currently are focused on this topic.

    This week, a range of experts in the fields of thermal remote sensing and urban climate will come together at the University of Reading to discuss this hot topic. This is particularly important given current rates of urbanization world-wide, climate change and predicted increases in the frequency and duration of heatwaves. The Department of Meteorology hosts the Fourth International Workshop of the EarthTEMP Network, an initiative to stimulate new international collaboration in measuring and understanding the surface temperatures of Earth across all domains and methods. Previous meetings discussed the characterisation of surface temperatures in key land regions, extreme regions or specifically data sparse regions.

    This year’s meeting aims to addresses the ‘Complexity of Urban Surface Temperatures’. International scientists with various backgrounds have been invited to identify the key challenges in quantifying surface temperatures in urban areas. The workshop provides a platform for establishing new collaborations with the objective to advance thermal research in urban areas. The programme starts today, Monday 8 June, with a practical exercise taking place at University of Reading’s Whiteknights campus. This is followed by two days with a range of high-profile keynote presentations that will provide the background for fruitful discussions.

    Figure 1. (Upper) Brightness temperature image and (lower) visible image of an urban surface in central London with a westward view, taken in November 2010 around midday. The roof surfaces (~ 20 °C) are clearly hotter than the east-facing walls (~ 5 °C) and windows appear warmer (~ 9 °C) due to their differing radiative properties.

    Watering the garden in April – how unusual is this?

    Gardeners among the readership of this blog may already be watering their seeds in an attempt to ensure germination and early growth, and may be wondering how common this activity is (over the years) given the fact that spring is only halfway sprung and the past winter was not that dry overall.

    One observation carried out each morning in the Reading University Atmosphere Observatory is that of determining the ‘state of the ground’, the observations being represented by one of 20 integer codes.  Some of these observations are made using a weeded, plot of bare soil in the Observatory enclosure.

    Thus, as well as determining whether the ground is snow-covered, the duty observer will, in the absence of any snow or ice cover, decide whether the bare soil patch can be described a dry, moist, wet, flooded or cracked, for example. The World Meteorological Organization specifies 10 code figures for such descriptions – see Table 1. A similar table of codes exist to describe conditions when there is ice or snow on the ground – the observer uses one, but not both codes – on any given day.

    Table 1. Codes use to specify the state of the ground when no ice or snow is lying (WMO code 0901 on page A-274 of These codes have been in use at the University since January 1982.

    Code Description
    0 Surface of ground dry (without cracks and no appreciable amount of dust or loose sand)
    1 Surface of ground moist
    2 Surface of ground wet (standing water in small or large pools on surface)
    3 Flooded
    4 Surface of ground frozen
    5 Glaze on ground
    6 Loose dry dust or sand not covering ground completely
    7 Thin cover of loose dry dust or sand covering ground completely
    8 Moderate or thick cover of loose dry dust or sand covering ground completely
    9 Extremely dry with cracks

    Recently (as of 20 April 2015) the Reading observations indicate 8 consecutive mornings with the ground being ‘extremely dry with cracks’ (there had also been 2 similar observations earlier in the month. No doubt this total has been helped by a fall of just 6.3 mm of rain so far in April 2015 – well below the average of 48 mm expected for the month.

    Of course, soils vary and the response of a particular soil to the effects of wind/rain/sunshine/air humidity and temperature will depend upon the mineral and organic matter composition of the soil (gardeners will be well-aware of the benefits of an organic mulch, for example, and of the way that clay-rich soils tend to be heavy and retain moisture).

    Clay soils are also more prone to cracking than are soils with a fine tilth. The clay particles act just like a sponge – they swell as they soak up water, and then they shrink as they dry out. In a spell of dry weather, the shrinkage can result in large cracks. In severe cases, these soils can undermine the foundations of buildings because they swell and shrink so much. Such conditions were widely reported during the dry spell of 1975-1976 in parts of the UK. In Reading during April-July 1976 (a different set of codes was in use then) 106 of the 122 days were reported as having ‘dry’ soil and only 16 were classified as ‘moist’. During 1982-2010 the number of days with moist soil was almost exactly the same as the count of dry or cracked soils during these four months on average – largely thanks to April 2007 (see Figure 1 below) when less than 1 mm of rain fell all month.

    So how do the ’10 soil cracked days’ of 2015 compare with measurements made in April over the years? The answers are in Table 2.

    Table 2. Averages of daily count of measurements of the state of the bare soil patch (1982-2010) when snow/ice-free, and the maximum count of given conditions in a single calendar month (1982-2014). These are compared to the observations of 1-20 April 2015.

    Code Average occurrence in April, 1982-2010 Greatest number of occurrences in April, 1982-2014 1-20 April 2015 total
    0 8.0 21 3
    1 19.5 30 6
    2 0.2 3 1
    3 0 0 0
    4 0 1 0
    5, 6, 7, 8 0 0 0
    9 2.1 14 10
    Mornings with snow/ice cover 0.2 2 0

    Table 2 shows that a cracked ground is reported in April on about two days each April on average. In fact, April tends to account for about half the annual count of cracked soils in Reading on average during 1981-2010.

    Figure 1. Year-by-year count of number of days with cracked ground in April at the University of Reading, 1982-2014.

    As with many on/off types of meteorological observations, the complete observational database indicates quite a variation in this count, however (Figure 1). Just 7 out of the 33 years had soil this dry, with only 5 of those having cracked soil on 5 mornings or more. Maybe there is a suggestion of an increased frequency of such events since 2002 – gardeners beware!

    Spilling the beans on climate change

    By Hannah Parker

    Geography students studying ‘Resilience for Sustainable Development’ had a change from their normal lecture format recently, and instead played a game. This wasn’t just for fun though, as ‘serious gaming’ is becoming a popular way of sharing complex information with a range of potential users and giving them opportunity to discuss its use. The students played CAULDRON, a game developed by members of the ACE-Africa project (University of Reading (Parker, Cornforth and Boyd) and Oxford University) together with the Red Cross/ Red Crescent Climate Centre, who have lots of experience designing games to communicate climate information. This game was developed to present the science of extreme weather event attribution in an accessible way, and provide space for discussion about whether it could be used in climate policy.

    CAULDRON stands for Climate Attribution Under Loss and Damage: Risking, Observing, Negotiating. This reflects the fact that loss and damage due to extreme weather events is occurring all over the world and people are taking an interest in whether this is due to climate change. Negotiations are also currently taking place to work out how to address this loss and damage under the United Nations Framework Convention on Climate Change (UNFCCC). The game gives players the chance to experience having to make decisions under uncertain climate risk, something many people have to do in reality every day. They also have to analyse changes in risk with only limited data and deal with the difficulties of negotiating with other players with different interests.

    The game began with players given the role of farmers who had to plant crops each season. They were each given beans to symbolise their crops and a ‘rainmaker’, which was a small pot containing a dice, to shake to determine their rainfall each season. Players who had good rains gained more beans, while those with drought years lost beans. Some players ended up in crisis with too few beans to be able to plant, so had to try and strike up deals with fellow players to be lent beans so they could keep playing!

    Climate change can affect the probabilities of extreme weather events occurring, so for the next part of the game players were given new rainmakers. Some of these contained dice with increased probability of drought which would ruin crops, but players didn’t know which … Suddenly, there seemed to be more droughts happening and more players getting into crisis.

    Players try to figure out their best farming strategy

    For the next part of the game, players became scientists. Using new rainmakers as ‘climate models’, they produced more statistics to help them work out whether their risk of drought had been altered by climate change. How trustworthy were the results provided by their models though?

    Players became negotiators at the UN climate negotiations for the final part of the game. They had to work out how they were going to deal with the fact that some players had collected more beans than others. Some players had been acting as developed countries and so, along with fewer losses, they had greater historical emissions. Were they to blame for losses in the developing countries? After much debate, each group managed to come up with an agreement that all players were happy to sign. However, some players did say they felt they had been bullied into making agreements and noted that the developing countries were denying that climate change had happened at all! Solutions presented to address the loss and damage at the end of the game included clearance of debts that had accumulated between players, rules on farming strategies that would be used in the future, and agreements on transfer of beans for when players got into crisis. With such a range of ideas diplomatically expressed, maybe we have uncovered some of the negotiators of the future …

    A spokesperson reads out his region’s signed agreement to address loss and damage

    By the end of the game, all the players said their knowledge of extreme event attribution had been improved. One player said their understanding had been improved ‘by creating a situation where extreme events had “real” consequences and a political “reality” ‘. This is the key feature of participatory gaming, that players can experience the emotions involved and have to act under uncertainty rather than just learning about it theoretically. Furthermore, it provides insights into the challenges of climate negotiations and the inequality between developing and developed countries, along with the difficulties in separating the impacts of climate change from other factors.

    This has been just one of the many times the CAULDRON game has been played, which have included players from sectors ranging from climate science to civil society. Each time the game has prompted lively discussion about event attribution science and dealing with the impacts of climate change and demonstrated that ‘serious gaming’ can be an effective, but also fun, way of sharing climate research.

    A career in environmental science research … ?

    The SCENARIO NERC Doctoral Training Partnership (DTP) at the Universities of Reading and Surrey is advertising 12-16 fully funded PhD studentships starting in September 2015.  SCENARIO seeks to attract high-quality graduates from science, mathematics and engineering degrees.  For a full list of available PhD projects and details on how to apply, please visit our website: .

    The scope of SCENARIO is broad, spanning the science of the atmosphere, oceans, ice, hydrology, soil, biosphere and space weather. As a SCENARIO DTP student you can expect to receive excellent training in quantitative environmental science, research skills and a wider set of professional skills in preparation for a leading role in science, industry, the public sector or academia. SCENARIO has many partners from industry and the public sector who offer co-supervision and opportunities for placement work related to your PhD research. Look for details of CASE sponsorship in the project adverts.

    NERC funding is only available to UK citizens and other EU citizens meeting the RCUK residence requirements. The studentships cover fees, training, research expenses, conference attendance and a tax free maintenance grant.

    The deadline for applications is approaching soon – it’s 2 February 2015.

    For further queries please contact Jill Hazleton, (SCENARIO DTP Administrator)

    Solar Stormwatch

    By Luke Barnard

    Coronal mass ejections (CMEs) are eruptions of coronal plasma and magnetic flux from the Sun’s corona, out into interplanetary space. CMEs are widely recognised as being a main driver of space weather and those CMEs that travel on a trajectory that intersects Earth’s orbit can be highly “geo-effective”, potentially generating geomagnetic storms and affecting Earth’s radiation belts. The risks associated with CME-driven space weather hazards can, to some extent, be mitigated by accurately forecasting the time at which a CME will interact with the Earth system – more specifically, what time it will “hit” Earth’s magnetic field. Therefore, a major theme in space weather research is developing a better understanding of the physics of CMEs, especially the dynamics of CME propagation from the Sun to Earth.

    We use the Heliospheric Imager (HI) instruments aboard the twin STEREO satellites to study the dynamics of CMEs. These are white-light cameras with a wide field-of-view that can image plasma motions such as CMEs all the way from the outer edge of the Sun’s corona to near-Earth space. The two STEREO satellites, each carrying a HI instrument, are in Earth like orbits, but one drifts ahead of Earth (STEREO-A) and one drifts behind (STEREO-B), separating from Earth by about 20 degrees per year. Therefore, HI images allow us to study CMEs travelling towards Earth from two different vantage points.


    Figure 1: (A) An example of an image taken by the HI instrument aboard STEREO-A. Each HI consists of two separate cameras, HI1 with a 20 degree field-of-view (on the right), and HI2 with a 70 degree field-of-view (on the left). The cameras are aligned so that the ecliptic plane runs horizontally along the center; the Sun is located just outside the rightmost edge of the HI1 image, whilst Earth is located just off the leftmost edge of the HI2 image. (B) An image from HI1 that has been processed to remove the background stars and enhance the visibility of a CME, which can be observed on the right hand side as the higher contrast white and black regions.

    However, there are challenges in using the HI data to study CMEs. Firstly, there is no absolute definition of what constitutes a CME and so their identification and characterisation is subjective. Secondly, the CME characterisation is typically done by manually analysing images, which is very time consuming. Finally, CMEs are sufficiently complex and variable that it is difficult to automate this analysis, which would reduce the subjectiveness and labour of our research.

    Solar Stormwatch ( is a citizen science project that solves many of these problems. The project consists of several activities, completed via a web interface, where anyone who is interested can identify and characterise CMEs visible in the HI images.


    Figure 2: An example of the Solar Stormwatch web interface in which CMEs are identified in the HI1 cameras aboard STEREO-A and STEREO-B.

    For example, in one activity participants are asked to view a movie of HI images and to record the time at which they can see a CME enter the HI field-of-view from either STEREO satellite. When many participants tell us they can see a CME entering the HI field-of-view, we can be confident they are probably correct. In a second task, we direct participants to images in which the profile of a CME should be visible, and ask them to locate the front of the CME in the image. When many participants characterise the same CME, we can average their individual estimates to produce a consensus profile of the CME. This consensus profile does not suffer from the subjectiveness of an individual expert’s identification, and the variability of this average gives us new quantitative information about how well defined the event is. You can see the results of this process in the animation in Figure 3, which shows the propagation of a CME through the HI1 field-of-view, upon which the CME front identified by the Solar Stormwatchers has been overlaid in red. The yellow lines mark the outer-limits of the HI1 field-of-view analysed by Solar Stormwatch.

    Figure 3 (link to animated GIF): This movie shows a sequence of HI1 images, processed similarly to Figure 1B, in which a CME can be seen to enter and propagate across the HI1 field-of-view. The yellow lines mark the outer limits of the image region analysed. The red lines mark the location of the consensus profile of the CME front, calculated by averaging the observations of many Solar Stormwatchers.

    Solar Stormwatch has now been running for approximately 4 years, with input from more than 16,000 citizen scientists, resulting in a data set in excess of 38,000 characterisations of CME trajectories. We have recently turned these observations into a catalogue of CMEs observed by the HI instruments. This is a new and unique catalogue, providing information about CMEs at distances away from the Sun not presently covered by other widely used CME catalogues. These data are all publicly available, and we hope they will aid new research into the dynamics of CMEs – some of which is already being done here in Reading. The next stage of Solar Stormwatch is to update it with new data, as presently only the HI images from 2007 to 2010 have been analysed – with 2010 to 2014 left to analyse there is much more data to process!

    To read more:

    To take part:

    ACKNOWLEDGEMENTS Many thanks to Chris Scott for Figure 1A.