Meteorological factors involved in the genesis of the 2012/2013 fodder crisis

 

This post contains part of the work performed by Robyn Dennehy during her SEFS (College of Science, Engineering and Food Science) undergraduate summer bursary, under the supervision of Dr. Lucía Hermida González and Dr. Paul Leahy.

Introduction

The fodder crisis of 2012/2013 had massive impacts on the Irish agricultural sector and affected most farmers, particularly in the midlands, south and southwest of the country. Many farmers struggled to gain access to fodder resulting in increased animal deaths and decreased farm output (Figure 1).

Fodder crises are not uncommon in Ireland with one occurring in 1998/1999 and most recently as 2018. A deeper understanding of these fodder crises is needed to assist farmers in farm planning and mitigating the effects of reduced output. A step towards this is the analysis of the meteorological factors involved in the genesis of such crises.

Figure 1. Dairy cows on a farm in Clonakilty, Cork. Picture courtesy of Dr. Greg Beechinor.

Concentrated feed and fodder prices increased significantly through 2012 and into 2013, due to the lack of grass growth, which in turn led to the early slaughtering of 23,000 extra livestock, particularly from beef farming (Agriland.ie, 2013). Some long-term effects of such a fodder crisis on farms include soil damage and decreased fertility of the livestock. Even though some of the losses incurred were offset by increased dairy prices in 2013, the official cost of the fodder crisis to the farming community was estimated at €500 million (Agriland.ie, 2013).

Meteorological Conditions and Grass Growth

Meteorological conditions are critical to grass growth in Ireland. Particularly so because 81% of Ireland’s agricultural land is used for pasture, hay and grass silage (European Commission-Ireland, 2018). As is common in temperate climates, Ireland’s dominant grass species is perennial ryegrass. Different authors have already explained how factors including solar radiation, temperature and rainfall affect the rate of grass growth (e.g.: Hurtado-Uria et al., 2013; Brereton et al., 1996).

1.-Temperature

Temperature affects photosynthesis, respiration, spring growth and senescence of grass (Hurtado-Uria et al., 2013). Temperature varies by season and by location based on latitude, longitude and altitude. In north-west Europe, winter temperatures tend to vary in a west-east direction due to the isotherms being positioned in a north-south direction (Brereton et al., 1996). Conversely, in summer, temperatures tend to vary in a north-south direction due to the isotherms lying west-east. Temperature also decreases with height at a rate of approximately 0.6°C per 100m rise in altitude (Brereton et al., 1996). In Ireland, perennial ryegrass does not grow until temperatures exceed 5°C (Hurtado-Uria et al., 2013; Brereton et al., 1996). Winter temperatures average between 4°C and 7°C, while summer temperatures average between 14°C and 18°C. Therefore, in winter months, little to no grass growth occurs. Temperature tends to vary from west to east but also from coast to further inland in Ireland. This causes spring growth of grass to begin at different times of the year depending on location within the country. The length of the growing season in coastal areas of the south of Ireland is approximately 340 days, in comparison to northern inland locations where it is approximately 240 days (Connaughton, 1973).

2.-Rainfall

Rainfall determines the soil moisture and thus the rate of growth of grass (Brereton et al., 1996). Plants require water in the soil to grow and to carry out the processes of evapotranspiration and nutrient uptake (Hurtado-Uria et al, 2013). Available moisture in the soil depends on the soil type which determines that rainfall is retained differently. Rainfall is particularly important to maintain soil moisture in summer months and in areas prone to drought (Hurtado-Uria et al., 2013).

Data and methodology

We examined the meteorological factors that determined the poor growing season in 2012 and that triggered the severe fodder crisis of 2012/2013. In order to do so, we obtained daily minimum air temperature (limiting for grass growth) and daily rainfall data from Met Éireann.

Five locations across Ireland were chosen to be used in this analysis: Cork Airport in Co. Cork, Mallow (Hazelwood) in Co. Cork, Shannon Airport in Co. Clare, Newport (Furnace) in Co. Mayo and Mullingar in Co. Westmeath (Figure 2). The stations were selected to reflect different regions of the country and provide an overview of the representative conditions seen nationwide in 2012. No air temperature data were available for analysis at Mallow Hazelwood. The daily data were converted into annual and weekly data. When possible two different long-term averages (LTA) were considered: 1961-1990 and 1981-2010.

Figure 2. Locations of the meteorological stations used in this study to analyse meteorological variables and  grass growth.

Soil temperature was converted into weekly averages for 2012. Due to productive grass growing weeks beginning when the weekly average soil temperature exceeds 5°C, weekly soil temperatures were plotted from January in order to determine when the farming weeks began in the year 2012.

A long-term continuous record of grass growth is available for Moorepark agricultural station in southwestern Ireland. The record extends from 1982 to 2015 and has been collected by Teagasc, the Irish food and agriculture development agency and described in (Hennessy et al, 2010). The unit of measurement for this grass growth record is tonnes of dry matter per hectare (t DM/ha). Grass growth for the Moorepark station for the year 2012 is shown in Figure 2.

Results

1. – Minimum air temperatures and rainfall anomalies

a) May

For the year 2012, two locations -Shannon Airport and Mullingar- have an average temperature in May lower than the LTA of 1981-2010 (and the 1961-1990 LTA) (figure 3). All stations examined experienced a lower than average amount of rainfall for the month of May in 2012, with anomaly values ranging from 60% to 80% of the LTA (1981-2010).

Figure 3. Average May minimum air temperatures for Shannon Airport (left) and Mullingar (centre). Continuous line represents the LTA of 1961-1990; the dashed line is the LTA for 1981-2010. On the right, rainfall anomaly for May expressed as percentage (%) with respect to the LTA 1981-2010.

b) June

For 2012, all stations except for Cork Airport had an average June minimum temperature on or above the 1981-2010 LTA (figure 4). Furthermore, the year 2012 was significant in terms of average rainfall at all five of the locations analysed. Newport (Furnace) had the highest amount of rainfall in the period 1960-2016. The anomaly values range from almost 200% of the LTA at Newport (Furnace) to 340% at Mallow (Hazelwood) (figure 4).

Figure 4. Average June minimum air temperatures for Cork Airport (top left) and Mullingar (top right). Continuous line represents the LTA of 1961-1990; the dashed line is the LTA for 1981-2010. Bottom, rainfall anomaly for June expressed as percentage (%) with respect to the LTA 1981-2010 for the stations of Mallow (Hazelwood) and Newport (Furnace) (left and right, respectively).

c) July

Three of the four stations had an average minimum temperature below both LTAs during 2012 (figure 5). The rainfall of July 2012 was above the LTA of 1981-2010 at all locations, with anomalies between 115% of the LTA at Newport (Furnace) and 170% of the LTA at Shannon Airport.

Figure 5. Average July minimum air temperatures for Cork Airport (top left), Shannon Airport (centre) and Newport (Furnace) (top right). Continuous line represents the LTA of 1961-1990; the dashed line is the LTA for 1981-2010. Bottom, rainfall anomaly for June expressed as percentage (%) with respect to the LTA 1981-2010 for the stations of Mullingar, Shannon Airport and Newport (Furnace) (from left to right).

2.- 2012 weekly variables

The average weekly values of soil temperatures for week 1 vary  between 5.0°C and 6.5°C (figure 6). Temperatures remain above 5°C for the following two weeks suggesting that the beginning of the growing season in 2012 was in fact at the beginning of January. Temperatures drop in week 5 to as low as 3.1°C in Mullingar, but remain on or above the 5°C threshold for the remaining weeks. The sharp drop in temperature in week 5 is common to both soil and minimum air temperature.

Figure 6. Left, average weekly soil temperature for the five stations from January to July. Right, weekly rainfall.

The 2012 weekly rainfall graph (figure 5, right) shows huge variability between weeks at Newport (Furnace). The considerably more amount of rainfall in that station may be due to its location on the western coast of the country. The larger quantities of rainfall seen between weeks 22-26 at all locations echo the previous data in this report highlighting the enormous rainfall levels of June 2012.

3.- Grass Growth at Moorepark

Weeks 6 to 13 show above average grass growth at Moorepark during 2012 (figure 7). A huge drop in grass growth occurs through weeks 24-28. The grass growth increases above average from weeks 30-38. The cumulative weekly sum of grass growth at Moorepark illustrates clearly how grass growth varied in 2012 compared to the LTA (1981-2010) and the average of the period. Grass growth begins above average for 2012 but the total grass growth by the end of the season in 2012 is approximately 18750 t DM/ha, while the LTA and average of the period read approximately 2020 t DM/ha.

Figure 7. Comparison of the weekly grass growth of 2012, the average of the period analysed (1981-2015), and the LTA 1981-2010.

Conclusions

There is no evidence to suggest that significantly colder than average minimum air temperatures in the month of June were the sole cause of the decreased grass growth observed in the summer of 2012.

The rainfall experienced in the months of June and July 2012 were significant, with June seeing a record breaking amount of rainfall at all locations.

An examination of the patterns of grass growth against the recorded rainfall shows a strong link between the excessive amount of rain and the quantity of grass which grew during the same period. It is very plausible that the heavy rainfall of June followed by the above average rainfall of July may have been instrumental in the decreased crop growth of that year.

Rainfall is a significant factor controlling grass growth. Too much rainfall, as seen in June and July 2012, can hinder grass growth. However, too little rainfall may also hinder grass growth, as appears to be the case in 2018, although further research need to be carried out.

Acknowledgements

We would like to thank the SEFS Undergraduate Summer Research Bursary 2018 for providing the funding for this research.

REFERENCES

  • Agriland.ie (2013). Fodder Crisis Cost Irish Farming €500m [Accessed 14th August 2018].
  • Brereton, A.J., Danielov, S.A. and Scott, D. (1996). Agrometeorology of Grass and Grasslands for Middle Latitudes. World Meteorological Organisation, 839(197).
  • Connaughton, M.J. (1973). The Grass Growing Season in IrelandAgrometeorological Memorandum, 5(1).
  • European Commission-Ireland (2018). Agriculture|Ireland [Accessed 14th August 2018].
  • Hennessy, D., Hurtado-Uria, C.,  Shaloo, L., Schulte, R., Delaby, L., O’Connor, D. (2010) Evaluating grass growth models to predict grass growth in Ireland. Proceedings of the 1st EGF Working Group on Grazing, Kiel, 29 August 2010. Available: http://www.europeangrassland.org/working-groups/grazing.html
  • Hurtado-Uria, C., Hennessy, D., Shalloo, L., O’Connor, D. and Delaby, L. (2013). Relationships between meteorological data and grass growth over time in the south of Ireland. Irish Geography, DOI: 10.1080/00750778.2013.865364

Reanalysis data

Reanalysis data are extensively used in atmospheric science. Briefly, and in order to obtain a first general idea, reanalysis involves analysing past observational data again. These historical observational data are available from different sources such as buoys, radiosondes, ships, meteorological stations or satellites. They are used to feed numerical weather prediction models through different advanced methods of assimilation, and so transforming an irregularly distributed network of observations into a three-dimensional grid (Bengtsson et al., 2004) of the best estimate of the state of the atmosphere for any period and place (Thorne and Vose, 2010), at different vertical layers of the atmosphere and time steps.

First of all observations are needed. Observational data are employed in model weather forecasts in order to provide the best description possible of the state of the atmosphere at an initial moment. This requires a careful process to recover data and to carry out quality control before data can be used. For example, meteorological stations may be subject to changes in instrumentation, methods of measurement, personnel in charge, changes in location or in the environment of the station such as the effect of urbanisation. However, the station records should ideally reflect only the variation of the weather and climate.

Figure 1. Correction of the model with the observations introduced by assimilation. Source: Met Office.

Furthermore, the available observational data change qualitatively and quantitatively between countries and inside them, along with changes over the periods they are  recorded and in their spatial distribution. Since 1979 the use of weather satellites has allowed more data to be incorporated in the assimilation process. If the observational systems, the model or the method of assimilation are improved, the new reanalysis data obtained corresponds to a new generation. That way it is possible to distinguish between 1st generation reanalysis data such as NCEP/NCAR (National Center for Environmental Prediction / National Center for Atmospheric Research), 2nd generation such as ERA40 data from the ECMWF (European Centre for Medium-Range Weather Forecasts) and 3rd generation like the MERRA (Retrospective analysis for Research and Applications). The ERA-40 was surpassed by the ERA-Interim data (3rd generation) and currently, by the recently launched ERA5 (4th generation reanalysis). Each generation implies an improvement in the data used to characterise the state of the atmosphere.

The incorporation of observations into the model is done by a process called assimilation. The observations are introduced at different times to correct the model in order to ensure that the evolution of the atmosphere during the simulation is as close as possible to the real observed conditions of the atmosphere. The initial forecast created by the model is corrected with the observational data during the execution of the model (figure 1). The output of the model is fitted to those observations to obtain a corrected forecast.

The observations can be introduced into the model at different stages while the model is running, by “running” we mean while the model is executing all the instructions that we have given to it. It can be thought of as similar to the calculations performed by a calculator after typing in “2+2” and before the result of 4 is displayed. Aside from observations, the forecast obtained in the previous time step by the model is used as input in the following time step analysis.

There are different types of assimilation (figure 2).

Figure 2. Methods for the reanalysis data assimilation process. Source: National Center for Atmospheric Research.

The assimilation is intermittent when the available observations over a range of time are introduced at regular intervals into the model. On the other hand, if the assimilation is continuous, every time there is an observation, it is introduced into the model at the time when the observation was registered.

In the sequential assimilation method the simulation is always in a forward direction, using the corrected outputs of the model in each stage as input for the next forecast or using only observations as inputs. In any case, the assimilation is done in the same direction in time. However, if the method is non-sequential, at each step of the model or at the end of the simulation, the corrected output constitutes again an input at the beginning of each time step or at the very beginning of the simulation.

When the available observations are scarce, the model accuracy is low, and the output of the model reflects mainly the variability of the model being used. If we increase the observations, the model is forced to follow the observations (Sterl, 2004). So, the more observations we have, the less important the model becomes. In any case, we would still have different sources of uncertainty or errors, such as the quality of the observations, the method of assimilation or the particular model used and its characteristics such as the parameterisations chosen.

Numerical Weather Prediction (NWP) models are physico-mathematical models used to reproduce the evolution of the atmosphere over time. NWP models calculate the state of the atmosphere at discrete locations in a three-dimensional grid. Some of the meteorological processes and phenomena in the atmosphere take place in a lower spatial resolution than the size of NWP models’ grid cells, for example convection or cloud microphysics. The NWP models use parameterisations to reproduce the dynamic and thermodynamic conditions for these phenomena to occur. They can be thought as formulae that use the information available at a coarse resolution to try to reproduce those phenomena at a finer resolution.

Figure 3. Ensemble members (from P1 to P20 (perturbation)) of the NWP model GEFS (Global Ensemble Forecast System). Forecast of precipitation made at 06UTC on the 14th May 2018, for the same day at 14:00 UTC. Source: Meteociel.

The model can be run several times to obtain different possible solutions to the meteorological evolution of the atmosphere. The group of all those outputs is called ensemble (figure 3). This way it is possible to take into account the chaotic nature of the atmosphere and the uncertainty of the initial state of the atmosphere.

Another way to visualise the ensembles is through diagrams (figure 4). The uncertainty of the forecast increases with time, as is indicated by the increasing spread of the ensembles.

 

 

Figure 4. Diagram of ensembles for a point in the south of Ireland (coordinates 51.9N and 8.9W). From top to bottom, ensembles for the temperature at 850 hPa and at 500 hPa, and precipitation.

Reanalysis data offer valuable information about the atmospheric conditions necessary or favourable for a particular phenomenon to happen, for example, to determine the position of the jet stream or the location of low and high pressure systems. Reanalysis data can also be employed for the study of trends or in attribution studies.

Given the disparity in the availability of observational data, is not uncommon that there is no such data available for use in climatological studies due to a low spatial and/or temporal resolution. Moreover, when dealing with extreme weather and climate events, which by definition are rare at any point, it is even more difficult to collate long and homogeneous records from observational data. Reanalysis data also offer the possibility to construct proxies for parameters or indices that could be used to characterise, either from the thermodynamic or the dynamic point of view, the event being studied in the case of that a useful direct output from the model is not available.

Figure 5. Precipitation anomaly (mm/day) in winter 2013/2014 respect to the period of reference 1981-2010. Output from four different reanalysis data: NCEP/NCAR, ERA-Interim, 20CRV2c and JRA-55. Maps created with the online tool: https://www.esrl.noaa.gov/psd/cgi-bin/data/testdap/plot.comp.pl.

When using reanalysis data is important to choose the best option for the objective of our study (figure 5 and 6). Different datasets are based on different historical observational data, number of vertical levels or different temporal and spatial resolution, among other factors.

Figure 6. Precipitation anomaly (mm/day) in winter 2013/2014 respect to the period of reference 1981-2010. Output from four different reanalysis data: NCEP/NCAR, ERA-Interim, 20CRV2c and JRA-55. Maps created with the online tool: http://cci-reanalyzer.org/

Met Éireann, the Irish Meteorological service, has launched a specific reanalysis product for Ireland called MÉRA. These data have an excellent horizontal resolution of 2.5 km and go back to 1981.

So, what are you waiting for to start working with reanalysis data? Do you want to explore the possibilities to complement your research results? You can become familiar with this kind of data by using very simple web-based tools to plot the data of your interest.

REFERENCES

European Geoscience Union 2018

During the week 9th – 13th of April 2018, ClimAtt was at the European Geoscience Union (EGU) at the Austria Center Vienna. The ClimAtt team presented three posters at the conference in the sessions:

Figure 1. On the left, ClimAtt flyers during the poster session and view of the hall of the Austria Center Vienna. On the right, sunny coffee break in the outside of the EGU venue during the 9th of April.

Evaluation of observational data sources and attribution studies of precipitation extreme indices in Ireland

Lucia Hermida, Gerard Kiely, Kieran Hickey, Myles Allen and Paul Leahy

The warming of the climate system due to anthropogenic climate change not only leads to slow changing events such as the increase in the mean sea surface temperature (SST), but is also expected to cause changes in the magnitude and/or frequency of extreme weather extreme events, e.g. precipitation, floods, heatwaves. Many of these changes have, to some extent, been attributed to human influence in terms of probabilities.

Different studies have analysed the variations in indices of extreme temperature and precipitation between historical and simulated future datasets under different IPCC carbon emission scenarios. However, to the best of our knowledge, no previous study has investigated possible changes of such indices in Ireland between the actual world (factual) and the counterfactual world (i.e. a world without the anthropogenic human influence).

Extreme precipitation indices were obtained from daily observational time series of precipitation provided by Met Éireann, the Irish Meteorological Service. Sources such as GPCC, GHCNDEX or HadGHCND were also considered. The RClimDex package, from the Expert Team on Climate Change Detection and Indices (ETCCDI), was used to verify the quality and homogeneity of the time series and the calculation of extreme indices related to precipitation.

The evaluation of the performance of the different datasets for the extreme precipitation indices over Ireland resulted in the selection of a cluster of best-suited data for attribution studies of extreme weather events with the biggest impacts on Ireland, namely long duration precipitation (typically greater than 24 hours) and associated flooding.

Extreme indices of precipitation were then used to validate outputs of climate models such as HadGEM3 for the actual world. Indices for the counterfactual world are also obtained using the climate models. Finally, changes in extreme precipitation between the two scenarios were studied.

The results will be further used for an attribution study from a dynamic point of view, analysing the changes in the synoptic patterns associated with those extreme indices of precipitation between both worlds.

Figure 2. On the left, Adam Pasik, master student of the ClimAtt project, during the poster session. On the right, Dr. Kieran Hickey answering some questions about the Fodder Crisis at the EGU.

Attribution study of the December 1998 flooding in the South of Ireland

Lucia Hermida, Gerard Kiely, Kieran Hickey, Parvaneh Nobakht, Adam Pasik, Myles Allen and Paul Leahy

Increases in annual precipitation and in the occurrence of extreme precipitation events have already been detected in Ireland since the middle of the 20th century. The changes in precipitation are primarily associated with the number of rain days, rather than the amount of rain. Many parts of Ireland are vulnerable to pluvial floods predominantly caused by longer periods (greater than 24 hours) of low intensity (less than 5mm/hour) precipitation. However, antecedent weather and environmental conditions influence the magnitude of the flooding.

The Munster Blackwater Catchment, in the south of Ireland, is one of the least modified major catchments in the country, with a low level of urbanisation. The catchment has an area of approximately 3324 km2.

The year 1998 is selected as a study case for being wetter than normal and the wettest for 30 years in some places. On the 29th December over 30 mm were registered in southern areas. On the 30th and 31st another depression affected the catchment area. During these three consecutive days of rainfall, more than 50 mm were recorded at Cork. Mount Russell registered the highest daily rainfall value of December on the 29th with a total of 35 mm.

The heavy rainfall between the 29th and the 31st of December 1998 led to a major flood on the Munster Blackwater, with a peak flow occurring on the 29th (the highest of the year and the fourth-highest since 1977). The rainfall caused a quick response of the river level due to the near-saturation of the soils in the catchment, with the river overtopping its banks on the 30th and lasting more than 48 hours. More than 5 IR£ million (c. €.35 million) damage was caused to property.

The historical precipitation record for the Blackwater catchment was analysed with observations data provided by two government agencies, Met Éireann and the Office of Public Works (OPW). Both datasets were subjected to a process of quality control (QC) and homogenization when required. The results of the QC exercise led to the selection of a database of suitable stations and a historical period with a sufficient number stations of long records to contextualize the event and validate models.

Models comprising two different experiments were examined: one, which represents the world as we know it today (factual), and other with the world as it would have been without anthropogenic climate change (counterfactual). The probabilities of events similar to that which led to the 1998 floods in both scenarios were calculated. The values were used to determine if this kind of event is more likely to happen due to climate change through the calculation of the risk ratio (RR).

The meteorological and climatological context of the fodder crises in Ireland in2012-2013

Kieran Hickey, Paul Leahy, Gerard Kiely, Myles Allen, Adam Pasik and Lucía Hermida

Ireland was affected by a very significant fodder crises in Ireland in late 2012 but particularly in early 2013.

Irish agriculture is primarily based on grass production for dairy and livestock. There is constant pressure on farmers to ensure that sufficient hay and silage is harvested and stored for the winter months to ensure animals are fed when grass growth is minimal at best.

The summer of 2012 saw below-average temperatures and sunshine hours and higher than average rainfall in June and July resulting in poor quantity and quality of fodder going into storage for the winter of 2012-13. This was followed by a cool and dry autumn which in turn was followed by a cold and wet winter exacerbating the fodder situation as farmers used up their available stocks quite quickly due to limited grass growth. The situation started to become critical with a very cold spring with mean seasonal temperatures up to 1.8oC below the long-term average and variable rainfall. The outcome of this exceptionally cold spring was a delay to the start of any significant grass growth. This in turn resulted in farmers running out of fodder. Many farmers become reliant on purchased fodder and feed until they no longer could afford it or in the case of fodder they could not find any to purchase. Only with a return to above average temperatures in June and July did the fodder crises abate.

The fodder crises is estimated to have cost Irish agriculture at least 500 million euro. This cost was made up of additional expenditure on feed and fodder including the import of fodder from France, a reduction in milk production, early slaughtering of cattle due to lack of feed and approximately 23,000 livestock deaths. This paper will analyse the meteorological contribution to the fodder crisis of 2012-2013 and will provide an initial assessment of this complex event in the context of recent climate change and attribution.

It was a long but great week where we had good chats, made interesting contacts and leave the door open to new promising collaborations. We will keep this thread updated if there are further developments!

Emma and The Beast

The weather that we experienced last week is not atypical in its origins. It was caused by an anomaly in the dynamic of the atmosphere that usually happens in our latitudes during the winter season (Met Éireann, 2018).

There are other factors that can enhance a meteorological situation like this one such as the Artic Oscillation (AO) and the North Atlantic Oscillation (NAO), volcanic eruptions or low sunspot numbers that can act alone or through the modulation of the AO and NAO (Hickey, 2011).

This event of snow showers, low temperatures, gales and blizzards, is similar to other past events. For example, the one in January-March 1947 with a “Great Blizzard”, January 1982 named the “Big Snow”, or December 1962-February 1963 called the “Big Freeze”. A more recent cold spell was the one during  November-December 2010.

The Sudden Stratospheric Warming

The cause of this phenomenon is a so-called Sudden Stratospheric Warming (SSW). According with the World Meteorological Organization (WMO) SSWs may be classified as minor or major. The minor warmings occur when a temperature increase of at least 25ºC is observed in a period of a week or less at any stratospheric level. Major warmings are those with a temperature increase of at least 30ºC in a week or less at 10 mb (around 30 km height, in the stratosphere) or below, or by at least 40ºC above 10 mb (WMO CAS, 1978).

The stratosphere is the layer of the atmosphere situated between 10 and 50 km (figure 1). This layer is just above the troposphere, the lowest part of the atmosphere where most of the weather that we experience is developed. These two layers are not isolated from each other. They form a continuum where changes are propagated from one layer to another instead of being confined to a specific part of the atmosphere.

Figure 1. Layers of the atmosphere. Source: NASA ESPO/INTEX-NA Educational Outreach.

However, even though SSWs are not uncommon, the recent SSW set some historical records, with temperature increases up to 50ºC in the Arctic in the stratosphere and so it has been classified as a major SSW.

The warming that released the weather of last week was also coupled with an important decrease in the sea ice concentration just to the north of Greenland. This is fostering debate among scientists about the extraordinary character of this event and the potential role of climate change in the Arctic and consequently in the climate of northern Europe (Watts, 2018) (figure 2).

 Figure 2. Variation of the sea ice concentration (%) north of Greenland during the period 20th– 25th February 2018. Source: reproducible without permission from @seaice_de.

The Polar Vortex

The stratospheric warming has a direct effect on the polar vortex. This vortex is a surface of cold air in the stratosphere around the Polar High (a high pressure centre over the North Pole), that is confined to high latitudes during winter by the polar night jet. This is a jet stream, a belt of high-speed winds that circulates from west to east around the Arctic, and it constitutes a natural frontier between cold air to the north and warm air to the south.

The SSW alters the vortex due to the weakening or even the complete cessation of this belt of high-speed winds. The interruption of the wind circulation from west to east fosters the development of waves in this boundary which separates the air masses of opposite temperature. Furthermore, globally, anthropogenic climate change is known to have already reduced the differences in temperatures between different latitudes, thereby decreasing the existent gradient between the equator and the pole that, as consequence, could attenuate the net cold-warm air division in the vicinity of the Arctic.

The deceleration of the polar vortex caused by the warming has two possible different outcomes: either the weakened vortex moves southwards or, as has happened on this occasion, that it splits into two vortices (Butler et al., 2014) (figure 3). The vortex situated over northern Europe has a retrograde circulation, that is, it blows from east to the west, a clockwise anti-cyclonic flow. This system of high pressure was located around Scandinavia, and channelled cold polar continental air from the east of Europe towards the west in a northeasterly direction. This dry and cold air mass caused the thermometer to plunge. This is “the Beast from the East”. The exact trajectory of this cold air, together with its interaction with other air masses of different characteristics coming from the southwest, i.e. from the Atlantic, determined the magnitude of the events that we experienced.

Figure 3. ERA-Interim temperature anomalies and circulation for a) displacement and b) split of the SSW. Source: Butler et al., 2014.

When Emma met The Beast

At the beginning of the last week, the Polar Front was clearly distinguishable in the North Atlantic, as a north-south band just west of Europe (figure 4, top panel). This Front acted as a frontier between systems with different characteristics situated on both sides of it.

In the air mass satellite images cold air masses are represented by blue, while the warm ones appear in green. As the week advances the Front moves northward and it is influenced later by the cold air mass situated over Scandinavia, which forces it further into the Atlantic (figure 4, top right). As the low pressure system over the west of the Iberian Peninsula, named Emma by the Portuguese Meteorological Service, continues its trajectory towards the northeast of Europe, it forces the warm and humid mass from the Atlantic to move also to the northeast (figure 4, bottom panel).

The position of these low and high pressure systems, their trajectories, and the place where both collide, determine the intensity and type of precipitation, the wind, the temperatures, and their magnitudes.

Figure 4. Air mass satellite images for the 26th February 2018 at 12:00 and 00:00 UTC (top panel, from left to right), and for the 27th at 12:00 UTC and the 28th at 00:00 UTC (bottom panel, from left to right). Source: Wetterzentrale.

In figure 5, we examine the atmospheric configuration and its evolution with the output of the models from the European Centre for Medium-Range Weather Forecasts (ECMWF). In the top panel, the shadowed colours represent the geopotential height at 500 hPa. We could say that this is the altitude in the atmosphere where the pressure is 500 hPa. This is approximately the middle of the atmosphere, around 5,500 m. However, depending on the temperature of the air mass, the 500 hPa level may be lower (cold air that sinks) or higher (warm air that expands) in the atmosphere.  A low geopotential height can be observed in this case expanding from central Europe and that continued its way down to the east reaching Ireland and the United Kingdom.

In the middle panel of figure 5, the pressure (contours) and the temperature (shading) at 850 hPa (1,500 m approximately) are presented. The bottom panel of the same figure, shows the wind direction (stream lines) and intensities (shading). In the high pressure over Scandinavia, wind flow moves in an anti-clockwise direction, bringing the Beast, the cold and dry air, towards Ireland. Southwest of the Iberian Peninsula, Emma moved in a northeast direction, to be finally located in the Bay of Biscay on Thursday morning. There is little change in its position for the rest of the day as well as on Friday, staying south of Ireland.

Figure 5. Forecast from the model of the ECMWF. Top panels, geopotential height at 500 hPa (shading) and surface pressure (contours) from Wednesday to Friday at 00:00 UTC. Middle panels, temperature at 850 hPa (shading) and surface pressure (contours). Bottom panel, direction (stream lines) and intensity (shading) of the wind (in knots). Source: Wetterzentrale.

Wind starts blowing from the northeast, changing to east and southeast as Emma approaches Ireland. The progression of the cold temperatures from the east can also be seen in the middle panel of figure 5, as well as the warmer temperatures of the air mass that accompanies Emma and that finally met the Beast.

The convergence of both of these air masses with opposite characteristics, results in precipitation due to the contribution of Emma with the warm and humid air from the Atlantic. However, the cold temperatures associated with the Beast turns this precipitation into snow.

Furthermore, due to the collision of the two different air masses, the temperature changes drastically in a very short distance in the atmosphere, promoting high intensity winds over Ireland and the United Kingdom. This can be seen by the proximity of the lines of the surface pressure (figure 5). The closer they are, the higher the intensity of the winds expected, due to a stronger gradient of temperature. These winds are called gales, a wind with a speed between 34 and 40 knots (around 63 to 74 km/h) (Beaufort scale wind force 8) (WMO, 1992).

And so, the gale accompanied the snow. The result was blizzards, defined by the WMO as a violent winter storm lasting at least 3 hours, which combines below freezing temperatures and very strong wind laden with blowing snow that reduces visibility to less than 1 km (WMO, 1992).

This situation can last for days or weeks, due to a blocking situation as a result of the persistence location of the high pressure system over Scandinavia and so, providing a continuous source of cold air form the east. Moreover, the southward displacement of the trajectory of the storms in the Atlantic, will continue providing a cluster of low pressure systems that will influence Ireland in the next days, bringing more rainfall.

Figure 6. Left, Wellington Bridge (Cork) on Wednesday morning, 28th February, after the first showers of snow. Right, bridge in the Main Gate of UCC campus on Wednesday evening.

Around 12:00 on Friday morning, the low pressure situated just south of Ireland (Emma), together with the cold air from the east (figure 7) continues to give us snow during most of the rest of the day (figure 8).

Figure 7. Surface pressure chart forecast for Friday 2nd at 12:00 UTC (left image). Low pressure centre (aka Emma) indicated with L Source: Met Office. Air mass satellite image observed for the same time and day (right image). Source: Wetterzentrale.

 

Figure 8. Weather Research and Forecasting (WRF) model  showing the different types of precipitation, from 02:00 on Friday to 00:00 on Saturday. Pink and red colours represent snow. Source: Meteociel.

If the decrease of the sea ice in the Arctic continues due to anthropogenic climate change, together with the increased mean global temperatures that attenuates the gradient between the equator and the pole, it is highly probable that the mid latitudes (where Ireland is) will experience more events like this one during the winter. Now that the debate is open, further discussion and research need to be carried out.

REFERENCES

Ex-hurricane Ophelia

In a period of ten weeks, ten hurricanes including Maria, Irma, Harvey, and Jose have hit Central America, the Caribbean and U.S. Gulf Coast. This Atlantic Hurricane season has the dubious privilege of being the 7th most intense since 1850 to date. Furthermore, there is no precedent in the era of modern satellite imagery and only in 1878, 1886 and 1893 can we find a comparable situation.  So far this season there have been 357 deaths and an estimated $186.8 billion in damages, and as the hurricane season doesn’t finish until the end of November, what else can we expect?

In this context, Ophelia, the 15th named storm of the season (Humphries, 2017), has stood out as a stubborn hurricane with a trajectory bound for the mid-latitudes of Western Europe. Although it is not impossible for a hurricane to form in the region south of the Azores Islands, it is quite unusual. We have to look back to 1851 for such an event to happen (Henson, 2017). Even Michael in 2012 was 1.450 km further into the Atlantic (Henson, 2017). This makes Ophelia the hurricane with the furthest easterly position in the Atlantic and also the furthest north recorded since 1939 (Humphries, 2017).

Usually cyclones such as this one impact Ireland after following a long trajectory through the Atlantic, from west to east. However, if they are formed off western Africa they do not have to travel as long a distance to reach us as their American relatives, and therefore even when environmental conditions are not equal to those on the other side of the Atlantic, these cyclones can conserve most of their damaging characteristics (Haarsma et al., 2013).

For such a cyclone to develop the surface water temperature needs to be at around 27ºC. The warmer water 1.400 km off the coast of the Azores made possible the formation of “Tropical Depression Seventeen” (NHC, 2017), as it was called by then, on October 9th (Echo News, 2017). The depression strengthened and obtained the right to be given a name: Ophelia, although it wasn’t until the 11th that she became a hurricane with winds of 105 km/h.

On the 12th October the approach of Ophelia to Ireland gained media and public interest, since Met Éireann started to seriously warn about the characteristics of Ophelia and its potential for damage. Winds of 170 km/h were recorded at that stage and from 50 to 100 mm of rain was forecast for the Azores that weekend (NHC, 2017).

In the beginning of her journey, Ophelia moved to the southeast and veered later towards the northeast. After remaining Category 1 and being static between the trade winds and the westerlies (Fonseca, 2017), she started moving towards the northeast, with the maximum winds up to 170 km/h. On Saturday 14th she was considered Category 2 and was subsequently reclassified as Category 3 in the same day, when she passed the Azores.

During the night from Saturday to Sunday, the eye of the hurricane became indistinguishable. Nonetheless, the core of the storm was what impacted on Ireland, and not the tail. This, together with the fact that Ophelia was caught by a trough propelling her to move north and northeast, made Ireland more vulnerable to the high winds, precipitation and storm surge in the southwestern and southern areas. Furthermore, the upper jet streak helped to deepen the low pressure (NHC, 2017).

As the hurricane moved above colder waters, even despite the positive temperature anomaly of around 2ºC, she changed to an extra-tropical depression or post-tropical cyclone. As such, she transitioned from having a warm core and axial symmetric structure to being asymmetrical, with the low and mid-level centers separated (NHC, 2017), and lower core temperatures.

Sea Surface Temperature Anomaly in the Atlantic Ocean the 14th October 2017. Source: NCEP/NOAA.

Her movement was influenced by the Coriolis force and the trade winds from the west, and she was soon being driven by the westerly winds (Haarsma et al., 2013). Although now an ex-hurricane, the winds were still close to hurricane level once she landed, in particular in the sting jet, which is the frequent cause of windstorms. Furthermore, higher winds were present in the eastern and southern flank of the low, which changed direction as the extra-tropical cyclone passed through the country.

Air masses satellite image showing the position of the sting jet in ex-hurricane Ophelia. Source: @KeraunosObs.

It is true that positive anomalies in SST (Sea Surface Temperature) could slow the transition (see picture above). However, the baroclinic instability of mid-latitudes can promote a re-intensification of the depression (Haarsma et al., 2013). Indeed, this instability is the one which led to a previous formation of a similar storm in that area (Haarsma et al., 2013).

On Sunday morning the National Emergency Coordination Group was in place to evaluate the situation and put into practice all the appropriate measures according to the red wind warning for the coastal areas and orange for the rest of Ireland issued by Met Éireann.

The predictions came true when on Sunday it was claimed to be the strongest storm in Ireland since Debbie (in that occasion, in 1961, 18 people died), or Charley in 1986, with 11 deaths (Evening Echo, 2017).

The initial warnings were finally extended to all the country from 9 am on Monday to 3 am on Tuesday as Ophelia approached.

Red warning for winds (a) and orange and yellow warning for rain (b) issued by Met Éireann due to the expected high probability of very strong winds directly impacting Ireland.

On that same day, Met Éireann informed the public about the approximate times of Ophelia’s arrival for different areas:

  • From 7 am: Coastal areas of counties Cork and Kerry.
  • From 9 am: Remaining parts of Munster.
  • From midday: South Leinster and Galway.
  • From 1 pm: Dublin and remaining Leinster.
  • From 3 pm: North Connacht and Ulster.

Once Ophelia made land she crossed the country causing damage on her way northwards. At 2 pm on Monday 16th the NHC (National Hurricane Center) stopped following Ophelia and Met Éireann published some of the strongest gusts and rainfalls recorded by that time:

  • 191 km/h at Fastnet Rock (6.5km SW of Cape Clear Co. Cork, at a height of 200ft).
  • 156 km/h at Roches Point.
  • 135 km/h at Sherkin Island.
  • 126 km/h at Cork Airport.
  • 122 km/h at Shannon Airport.

Rainfall:

  • 17 mm at Valentia, including 9mm in one hour.
  • 17 mm at Mace Head, including 8mm in one hour.

The warnings encouraged people to stay safe indoors while Ophelia was still impacting the country in order to avoid fallen trees or power lines, closed roads, debris or high waves. Moreover, some places were provided with sandbags. However, the response from the government has raised some criticism for its slow reaction (Daly, 2017).

Pictures of the aftermath of ex-hurricane Ophelia in College Road and high water levels in the south branch of the river Lee passing through the Brookfield bridge, Cork.

Authorities urged people in low coastal areas to be prepared in light of the 4 pm high tide. Fortunately, no significant tidal flooding was reported (Niamh et al., 2017).

However, there were accidents which ended tragically. A woman died in Aglish, Waterford, when a tree fell and hit her car, as wind gusts of more than 130 km/h reached the south (The Independent, 2017). Two more people died during the day and there were also reports of injured people. Intervention from the Coast Guard was needed to rescue kite surfers.

Numerous flights, more than 200, (The Independent, 2017) were cancelled on Monday, schools and institutions such as University College Cork (UCC) closed, public transport (bus, train, ferries) and other events were cancelled or rescheduled. Postal services, courts, government institutions, businesses, small and large shops,… all announced that they would remain closed in light of the high winds and the resulting risk for their employees. Also, Cork University Hospital (CUH) cancelled procedures. Measures were taken to offer assistance to elderly and homeless people. Hotels were recognised for their services and many farmers had to resort to generators for electricity.

The ESB (Electricity Supply Board) was updating the list of incidents as Ophelia advanced to the north of the country. Power outages increased continuously until an estimated figure of 360.000 customers were without power, 450.000 homes and businesses. They also warned that it could take up to 10 days before electricity was restored. Broadband and telephone services were interrupted too (The Independent, 2017). Water supply was also disrupted in some areas.

Service interruptions updates during storm Ophelia. Source: ESB.

The economic damage was forecast to be around 700 m euros, but this figure increased as the storm advanced up to 1.5bn euros (The Independent, 2017).

What about an attribution study of Ophelia?

The great majority of attribution studies focus on extreme temperatures and precipitation since these have greater reliability. Events such as tropical and extra-tropical cyclones, together with wildfires and severe convective storms (NASEM, 2016), experiment changes in their physical mechanisms due to climate change that are less understood.

Prediction of this kind of weather system is extremely difficult. First of all because there is a combination of various specific ingredients needed for a hurricane to develop and in order to determine its intensity and trajectory. Besides, these events lack continuous observational time series with the necessary quality and duration. Rather, we have discrete events whose fragmented time series hinder the application of a proper study of extreme values. Even more, when we use models it is important to be aware of their limitations in the reproduction of extra-tropical cyclones. Their performance is even worse when referring to tropical cyclones (NASEM, 2016). All those factors lead to a low confidence when performing attribution studies of anthropogenic climate change for extra-tropical cyclones.

Nonetheless, the ingredients necessary for a hurricane or an extra-tropical cyclone to develop can shed some light about their potential changes in a warming climate. Yet, there is an implicit assumption that the underlying physical mechanisms are well-understood and that current relationships between variables and mechanisms will be maintained in the future.

Even so, there is already some evidence that anthropogenic climate change has potential impacts in terms of the frequency and/or intensity of tropical and extra-tropical cyclones. First of all, the increase in the mean global SST will have multiple different consequences. On one hand, the thermal expansion of the water in seas and oceans together with the melting of the main ice cores constitutes an increased risk of coastal flooding due to higher sea levels and storm surges. Extreme sea levels during storm surges have already been observed since 1970 (IPCC, 2014). The SST difference between the Pacific and Atlantic oceans also affects the wind shear, although with considerable uncertainty (Haarsma et al., 2013). On the other hand, warmer water leads to an increase in the moisture holding capacity of the atmosphere, which increases exponentially according to the Clausius-Clapeyron relation (7% per degree temperature).

It is also necessary to take into consideration that an increase in mean global temperatures decreases the temperature gradient between the equator and the poles, creating the instability that drives air masses from south to north in the Northern Hemisphere. Therefore, a smaller  gradient would be expected to imply less instability. However, a warmer atmosphere expands, increasing the height of the tropopause and the latent heat and therefore contributing to the instability.

As a result of considering some of these various factors, longer sustained systems are expected to move through the Atlantic, and some authors have already obtained a decrease in the number of hurricanes although with a higher intensity.  According to Haarsma et al., (2013) an increasing number of tropical hurricanes arriving to western Europe is also probable in a future scenario.

REFERENCES

ClimAtt project flyer

We have been making some aesthetic changes to our web site and Twitter account @ClimAtt_Project. ClimAtt is a dynamic project and we are working constantly to study the human influence in the extreme weather events in Ireland.

We are increasing our media presence day by day, in order to reach scientists and also the general public. We hope to communicate the objectives of the project as well as the progress, results and current events and news.

We will be perfoming studies of the extent of anthropogenic climate change influence on extreme weather events, so that this information can be used for taking objective and effective adaptation and mitigation measures. This is even more important because islands are being particularly impacted by human warming.

The new ClimAtt project flyer offers an overview of the project. It can be downloaded from here: Flyer_ClimAtt_Project

The ClimAtt logo represents the identity of the project: a swirl combining green and grey colours. The message we want to transmit with this logo is dual. Firstly, the complexity of the climate system,  the chaotic nature of the atmosphere, and the combination of dynamic and thermodynamic factors all influence specific extreme weather events. Secondly, it is necessary to evaluate and interpret all the pieces of this complex world of interactions in order to obtain robust estimates of the human influence on extreme weather events. This is done by comparing the actual world (factual) with another world scenario without human impact (counterfactual).

This project has been made possible through a grant awarded by the Environmental Protection Agency.

If you would like printed copies of the flyer, please contact us . We particularly welcome enquiries from schools.

 

 

Donegal Extreme Rainfall and Floods of August 2017

By Adam Pasik, ClimAtt Master’s Student, Department of Geography, UCC

The Weather of August 2017

August 2017 in Ireland was rather cold and dull, yet for the most part well within its normal scope of variability. All twenty five principal weather stations recorded mean temperatures somewhat below their 1981-2010 long term average (LTA) and the number of recorded sunshine hours was also below the LTA at most stations. Overall, August was unexceptional in terms of precipitation with monthly totals ranging from 75% to 185% of the LTA across the country, and only one day with gale force winds was recorded (Met Éireann, 2017a).
However, one event of localised extreme rainfall took place in the north western part of the country, causing extensive and severe flooding and landslides (Donegal Now, 2017; Maguire, 2017a).

Meteorological Background

In the early morning of the 13th of August the United States National Hurricane Center (NHC) issued its first public advisory notice on the tropical depression no. 8. The NHC continued to issue updates on the storm four times daily until the evening of the 17th of August, when the storm moved away and no longer endangered the East Coast of the United States (NHC, 2017).

The initial tropical depression developed east of the Bahamas and began to travel north-northwest towards the United States. It had an estimated minimum central pressure of 1,011mb and sustained winds of up to 35mph. With the decreasing pressure and strengthening winds, the depression evolved into a tropical storm, and received the name Gert on the evening of August 13th (NHC, 2017). Gert attained hurricane force in the early morning hours of August 15th, and began to veer northeast. Now travelling away from the continent, Gert continued to increase in strength and attained its maximum strength around 3am on August 17th, reaching 105mph in sustained winds with stronger gusts and a minimum central pressure of 967mb. From there on, the hurricane began to weaken quickly as it continued to move northeast into the colder waters of the North Atlantic, and was reduced to a post-tropical storm by the evening of August 17th (NHC, 2017).

The remnants of Gert became absorbed by another low pressure system travelling across the Atlantic, before making landfall in the northwest of Ireland in the afternoon of the 22nd of August. The low pressure weather system brought extremely heavy, although very localised, rainfall yet no significant winds. North Co. Donegal was most affected with an extremely high 77.2mm of rain being recorded at Malin Head weather station, most of which fell in the space of just 8 hours (Fleming, 2017). This was the second wettest day (and the wettest August day) recorded at Malin Head since 1955. The only wetter day recorded was December 5th 2015 with 80.6mm of rain. However, on that day the precipitation was more evenly spread over a 24 hour period (Met Éireann, 2017b). At Malin Head, August 2017 as a whole received 185% of the LTA rainfall, where the above mentioned event was responsible for 83% of this total (Met Éireann 2017c).

Impacts: Flooding in the Northwest

This downpour resulted in flash flooding in the eastern part of Co. Donegal, Co. Tyrone and Co. Derry/Londonderry. Flood waters caused severe structural damage to major roads and destroyed bridges (McClements, 2017; McClements et al., 2017). Many homes and businesses were damaged and local farmers reported losing farm animals to the flood waters (Highland Radio, 2017a). Tens of families registered as misplaced and worked with the local council to avail of temporary accommodation due to their homes being inundated (Maguire, 2017b). The city of Derry was virtually inaccessible by road and its airport had to temporarily shut down and cancel all flights (Highland Radio, 2017b).

Severe flood damage to the Old Mill bridge, Buncarna, Co. Donegal. Photograph courtesy of Pat Colhoun https://500px.com/paddyc

Worst affected however was the Inishowen Peninsula in Co. Donegal, where the damages included collapsed bridges and some roads being simply washed away. Some 1,500km of the road network were affected by the disaster on the Peninsula alone, parts of which are expected to remain impassable for weeks (Maguire, 2017b).

Restoration works at the Cockhill Bridge, Buncarna, Co. Donegal.
Photograph courtesy of Pat Colhoun https://500px.com/paddyc

There were power shortages following the rain, caused by the flooding as well as lightning strikes. The Electricity Supply Board (ESB) estimated that at the height of the storm some 25,000 dwellings were without power throughout the country. On Inishowen 1,600 homes were still without power the following afternoon. In many cases it was deemed unsafe to restore the power until the flood waters have receded (McNeice, 2017). Irish Water has announced several burst mains and damages to wastewater infrastructure due to flooding, causing shortages in freshwater supply on the Peninsula (McNeice, 2017). At least two instances of landslides were also reported occurring at Grainne’s Gap, near Muff, and a smaller one in Urris (Donegal Now, 2017; Maguire, 2017a).

Flood damage to the football pitch of Cockhill Celtic, Buncarna, Co. Donegal. Photograph courtesy of Pat Colhoun https://500px.com/paddyc

With more than 100 people having to be rescued by the emergency services from their stranded cars or flooded properties, it is surprising that no serious injuries or deaths resulting from this event were recorded (McClements et al., 2017).

REFERENCES

 

 

Analogies for attribution of extreme events

Unlike other research fields where the work is performed with tangible samples and materials, attribution studies of extreme weather events don’t offer that possibility, which make them sometimes quite difficult to understand.

The analyses carried out by meteorologists and climatologists usually involve huge amounts of data which seem abstract until you make sense of them, identify what you are looking for, and present the results in a visual and comprehensible way.

Through the review of the literature of attribution studies of extreme weather events, it is possible to find analogies to make the basis and methodology of theses studies easier to understand by comparing them with possible everyday  activities or actions.

Maybe the first and most well known analogy is the one of Professor Myles Allen of the loaded dice (you can watch the seminar Loading the dice: climate change and extreme weather in our former post). If you repeatedly roll several dice, it can happen that initially everything seems normal, nothing extraordinary. However, if you look twice, more carefully, you will soon realize that the number six seems to occur frequently. Of course, this could be by chance, but it could also be associated to other factor/s which would explain this apparently extraordinary number of sixes. But in order to discover that, you would have to roll the dice quite a few times to get to know the chances of obtaining so many sixes from all the rolls of the dice. So, in attribution of extreme events, it is also needed to run the model over and over again in order to obtain multiple results (ensembles) that can be analyzed in order to be able to draw some conclusions.

Another analogy appeared in the Bulletin of the American Meteorological Society (BAMS) of 2014 (Herring et al., 2014), whose vision of how this new research field works is reflected in a video entitled Steroids, baseball, and climate change. Before you watch it, here is the outline. Imagine a baseball player who starts taking steroids. After that, he hits an average of 20% more home runs than before and so probably you would attribute this amazing improvement to the steroids. But was this the only factor which changed during that period? Maybe, he was able to spend more hours training, hired a new trainer, or changed his diet. But if all those factors didn’t change, we can say that the steroids are the responsible for that increase in the probability of him hitting more home runs.

(©UCAR. Video by Noah Besser, produced by UCAR Communications for AtmosNews: NCAR & UCAR Science. This video is freely available for media and nonprofit use.)

The BAMS issue of 2013 (Peterson et al., 2013) echoed another analogy from the University Corporation for Atmospheric Research (UCAR, 2012). In this case they use a car instead of a baseball player. If we go every day to work by car, but we increase the speed of our journey, the greater the speed, the more the likelihood of us having an accident. However, if this event unfortunately happens, it may not be due to the speed. It could be related to bad weather, another driver speaking by phone, an obstacle in the road… In this case atmospheric greenhouse gases are analogous to the speed. We can study the odds of having such an accident at a particular speed but also taking into account some of the other factors. That’s to say, maybe the speed was a key factor in the accident, but some other aspect could have played a role too (this is akin to natural climatic variability).

In these two cases is clear that greenhouse gases could have being playing a role (steroids, speed), but that there are many other factors which could be influencing the result to some extent. However, understanding how greenhouse gases affect the likelihood or magnitude of an extreme weather event is a key step in the decision making process and the adaptation and mitigation measures for the long term, which should be still effective in 50 years’ time or more.

The third and last analogy is that of the cookies. This more recent analogy is based on the book Attribution of Extreme Weather Events in the Context of Climate Change from The National Academies of Sciences, Engineering and Medicine, about which we have already talked in our post Getting to know attribution studies: basic concepts and references. Dave Titley, Committee Chair of the Board on Atmospheric Science and Climate, gave a presentation entitled: Attribution of Extreme Weather Events in the Context of Climate Change within the framework of the Report Release Briefing on the 11th March 2016. You can read and download Dave Titley’s  presentation in the following link:Also, here is the fragment of Dave’s talk so you can understand better the slides:

One of the slides it is called A Banking Analogy. In this case one cookie is the event, similar to the home run or the accident by car in the other cases. All the factors that are involved in this case are the ingredients of the recipe, and they may or may not be responsible to some extent for the output, which can be different according with the proportions of the different constituents involved: quantities of the ingredients, temperature of the oven,… This analogy is fully explained in the following video:

Even though climate models may be intangible, I hope you can have a better understanding of attribution science of extreme weather events. So, here is a video where you can apply what you have learned so far and put all the pieces together to become aware of the importance of this methodology and the outcomes it can offer us in order to be prepared for a changing climate.

 This video is presented by the Climate & Development Knowledge Network (CDKN), who has an initiative to raise risk awareness in the developing countries. This Raising Risk Awareness initiative uses “state-of-the-art science to help Asian and African societies to understand the role of climate change in extreme weather events and prepare for future ones“.

If you feel now that you can be a part of the development of this exciting new field of research and want to contribute to the objective of ClimAtt, you can do it through the World Weather Attribution (WWA) project. Please, just click on the following image:

This project depends upon enthusiastic volunteers, citizen scientists who contribute to improvements in the science of attribution of extreme weather events and make a positive impact in societies around the world by offering some computing time.

 REFERENCES

Getting to know attribution studies: basic concepts and references

Attribution is considered the process of evaluating the relative contribution of multiple causal factors to a change or event with an assignment of statistical confidence (Hergerl et al., 2010).

You can learn some basic concepts about attribution and on how it works with this short but very instructive video produced by Professor Myles Allen (Oxford Martin School).

In 2012 the Bulletin of the American Meteorological Society (BAMS) published its first annual report on climate change attribution of extreme weather events. The authors submitted their works on attribution studies of extreme events which occurred across the world during the previous year. Although some skew can be perceived in relation to the areas studied or the interests of scientists, the number of papers has been increasing since then, as well as the interest due to applications of the findings in areas such as the management of land use or the development of improved forecast systems.

The book ATTRIBUTION OF EXTREME WEATHER EVENTS IN THE CONTEXT OF CLIMATE CHANGE, published in 2016 by the National Academies of Sciences, Engineering and Medicine, integrates all the basic concepts and approaches used in this relatively new field of research, Probabilistic Event Attribution (PEA)*. It offers guidance on how to carry out an end-to-end attribution study. For example, it addresses the importance of correctly framing the attribution question in order to clarify whether the frequency and/or magnitude of the event that we are going to study is experiencing changes due to a specific driver and to what extent. It also provides full information of the different methods that can be applied, as well as an overview for various event types, along with some results, such as extreme heat, extreme cold, droughts and extreme rainfall. These events, as they highlight, are also those which offer the highest confidence in attribution studies (as you can see in the figure on the right, below, reproduced from the book), but also the events which attract the most media and public attention.

It is therefore, a good point to start to go into greater depth on attribution studies and how is possible to probabilistically determine the contribution of a driver, such as the human greenhouse gases emissions, to an extreme event.

Today, a new book about the impact of climate change on weather and climate events has been launched by the American Geophysical Union, CLIMATE EXTREMES: PATTERNS AND MECHANISMS. To accompany the launch, the editors offered an interview. They spoke about the importance of attribution studies in the forecasting of weather and climate extremes, the regions of the world most vulnerable to extremes, and the benefits of increasing the resolution of the models, which could help to better understand the events from a physical point of view. However, they share their concerns about the continuing decrease in the number of weather stations and other observational data, which are necessary to validate climate models.

Here you will find the interview: https://eos.org/editors-vox/how-does-changing-climate-bring-more-extreme-events

We hope you enjoy these (maybe summer) readings.

 * Also called Attribution of Climate-Related Events (ACE).

REFERENCES

  • Hegerl, G.C., Hoegh-Guldberg, O., Casassa, G., Hoerling, M.P., Kovats, R.S., Parmesan, C., Pierce, D.W. and Stott, P.A., 2010. Good practice guidance paper on detection and attribution related to anthropogenic climate change. In Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Detection and Attribution of Anthropogenic Climate Change. IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland.
  • National Academies of Sciences, Engineering, and Medicine, 2016. Attribution of Extreme Weather Events in the Context of Climate Change. Washington, DC: The National Academies Press.
  • S.-Y. Simon Wang (Editor), Jin-Ho Yoon (Editor), Christopher C. Funk (Editor), Robert R. Gillies (Editor), 2017. Climate Extremes: Patterns and Mechanisms. American Geophysical Union. ISBN: 978-1-119-06784-9. 400 pages.

 

Loading the dice: climate change and extreme weather

On the 27th February 2017, the University College Cork  organized a seminar where professor Myles Allen, of the Environmental Change Institute at the University of Oxford, explained in what consists the Probabilistic Event Attribution. The objective of this research is to determine the anthropogenic climate change on extreme weather events.

Professor Myles Allen is at the forefront of these studies whose cornerstone is the computing time offered by volunteers in order to run multiple ensembles for two different scenarios, with and without human influence (the factual and counterfactual world, respectively). This is done through the platform weather@home of the climateprediction.net project.  Once the variable is obtained under both scenarios, it is possible to calculate the changes in the probability of occurrence of an extreme event.

From left to right: Dr. Kieran Hickey, Dr. Paul Leahy, Prof. Myles Allen and Prof. Ger Kiely, during the seminar: Loading the dice: climate change and extreme weather.

Here you can access to the full seminar of Professor Myles Allen:

https://ucc.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=55f9f95a-54f5-4640-a940-05e1a21929ac