Development and analysis of a homogeneous long-term precipitation network (1850-2015) and assessment of historic droughts for the island of Ireland.

Following up on last week’s blog I would like to present a short overview of my PhD thesis. The overarching aim of my research was to rescue and transcribe (key into excel) hard copy long-term monthly precipitation records for the island of Ireland (see Figure 1). To quality check and assess the long-term precipitation records for variability and change and analyse the records to identify past drought events. In addition, my research aimed to integrate past documentary evidence into the analysis to add confidence to the data and present some of the social and economic impacts from past drought events. Finally, I aimed to reconstruct long-term river flow records utilising the good quality monthly precipitation records and asses the flow for past drought events.

Figure 1. Hard copies of rainfall records held in Met Éireann archives (Photos taken by S.Noone, 2012)

 Summary of key findings:

This research produced a quality assured Island of Ireland precipitation (IIP) network of 25 stations dating back to 1850. The results of the analysis show that the years 1891 and 1964 stand out as the driest winters at nine and six of the IIP stations respectively. The wettest ranked winters across 12 stations occurred in 1877, 1994 and 1995. The summer of 1995 was the driest at 6 stations (east and southeast) while 1976 was driest at 3 stations (midlands and northeast) since 1850. 1861 ranks as the wettest summer for 8 stations located along the west coast while 1958 is wettest for stations in the east. The 2000s also stand out because of wet summers (in 2007, 2008 and 2009). Spring 1947 was the wettest for 15 stations with both 1995 and 1976 notable as the driest springs.

The Mann Kendall trend test results indicate positive trends in winter precipitation and negative trends in summer over the period 1850-2010 (see Figure 2 and 3). The trend results following data homogenisation showed changes in magnitude and direction in trends at some stations. Malin Head has been analysed in previous studies (e.g. McElwain and Sweeney, 2007) and significant annual increasing trends were detected 1890-2003. However, post homogenisation no annual trends were present at Malin Head, with similar results found for winter. In addition, summer trends pre-homogenisation at Malin Head indicates no trend while post homogenisation trends show significant decreasing trends.

These results show the importance of assuring that climate records are homogenous as misleading trends can be present. The trends in shorter records commencing post 1940 are not representative of the detected trends since 1850. The results show that in most cases trends over the period 1940-2010 contradict the trends detected over the period 1850-2015, highlighting the importance of long-term records.

Figure 2 Homogenised winter time series for all stations smoothed with an 11-year moving average (black line). MK Z scores are shown before and after homogenization where applicable (red: unhomogenised; blue: homogenised/no breaks detected) calculated for varying start years (which are given by the x-coordinate). The grey lines indicate ±1.96; absolute values exceeding these bounds are interpreted as significant at the 0.05 level.

Figure 3 same as Figure 2 only for summer.

This research also produced a 250-year detailed historical drought catalogue for Ireland and integrated qualitative historical documentary evidence. The results identified seven major drought rich periods in the IIP station network during 1850-2015 with drought events lasting (>18 months) impacting simultaneously at least 40% of the study sites in 1854-1860, 1884-1896, 1904-1912, 1921-1924, 1932-1935, 1952-1954 and 1969-1977 (see Figure 4). 

Figure 4 Drought signatures showing SPI-12 values for all stations in the Island of Ireland Precipitation (IIP) network for the 7 drought periods identified with island wide impact. Negative SPI-12 values are colour coded according to severity thresholds to highlight periods of moderate to extreme drought conditions.

Results for an extended precipitation series (1765-1849) identified a further seven long duration droughts (>18 months) during 1784-1786, 1800-1804, 1805-1806, 1807-1809 1813-1815, 1826-1827 and 1838-1939. Many of these drought events occurred during or immediately prior to Irish famine events, most notably the Great Irish Famine 1845-1849 (Ó Gráda, 2015).  

An online drought mapping application which highlights three of the droughts identified and their impacts, the map also provides a summary of all droughts and can be accessed at:

Historical Irish Drought Maps

Documentary evidence has provided important insights into the impacts from past severe drought in Ireland while highlighted interesting societal responses (See Figure 5 and 6). Impacts include reduced or failed crop yields, increased crop and dairy prices, human and livestock  health  issues,  water  restrictions,  low  reservoir  levels,  water  supply  failures  and hydro-power  reductions. The  work  shows  the  importance  of  combining  qualitative  and  quantitative evidence  of  historical  droughts,  which  provides  crucial  information  allowing  for  a  much clearer understanding of drought development and impacts. 

Figure 5 Letter to the Irish Times published 16th September 1893 proposing exploding dynamite over Dublin to try and induce rainfall.

Figure 6 Circular from the Bishop of Meath authorising the prayer for rain, published in the Irish Times on 2nd July 1887.

 My thesis produced (for the first time) a homogenised precipitation network of 25 stations for the island of Ireland. The precipitation network analysis has contributed considerably to knowledge by providing important insights into variability and change over the longer term. In addition, this thesis has produced (for the first time) a detailed 250-year drought catalogue for the island of Ireland. This work contributes significant new knowledge that can be used for stress-testing the resilience of planned Irish water resource developments. By combining qualitative and quantitative evidence of historical droughts this thesis has provided a more coherent understanding of drought development and historic impacts. Finally, several peer reviewed journals have also stemmed from my research (Murphy et al., 2016; Noone et al., 2015; Noone et al., 2016; Wilby et al., 2015).

References

McElwain L, Sweeney J. 2007. Key Meteorological Indicators of Climate Change in Ireland. Environmental Research Centre Report available online from: https://www.epa.ie/pubs/reports/research/climate/ERC-6-Climate%20Change-web-ready3.pdf .

Murphy C, Noone S, Duffy C, Broderick C, Matthews T, Wilby RL. 2016. Irish droughts in newspaper archives: Rediscovering forgotten hazards? Weather. Accepted.

Noone S, Murphy C, Coll J, Matthews T, Mullan D, Wilby RL, Walsh S. 2015. Homogenization and analysis of an expanded long-term monthly rainfall network for the Island of Ireland (1850–2010). Int. J. Climatol. doi: 10.1002/joc.4522.

Noone S, Broderick  C,  Duffy C, Matthews T,  Wilby RL, Murphy C. 2016. A 250 year drought catalogue for the island of Ireland (1765-2015) Int. J. Climatol. Accepted

O’ Gráda C. 2015. Famine in Ireland, 1300-1900, UCD Centre For Economic Research Working Paper Series 2015, UCD School Of Economics, University College Dublin. Belfield, Dublin 4.

Wilby RL, Noone S, Murphy C, Matthews T, Harrigan S, Broderick C. 2015. An evaluation of persistent meteorological drought using a homogeneous Island of Ireland precipitation network. Int. J. Climatol. doi:10.1002/joc.4523

The "PhD experience" at ICARUS

Introduction

My name is Simon Noone and I recently successfully defended my PhD thesis. I am writing this blog to share some of my experiences throughout my PhD which might help prospective PhD candidates. I undertook the Certificate Return to Learning Course in Maynooth University in 2008 as a mature student after spending 18 years running my own business. I really enjoyed this course and learned important new skills which help prepare me for academia. The following year I began my undergraduate degree and studied Geography (major) and Spanish (minor). I had always been interested in the climate so during the 3 years as an undergraduate I focused on taking physical Geography modules. After my undergrad I was accepted on the MSc in Climate Change and on completion I was awarded the John Hume Scholarship to begin my PhD at ICARUS in 2012 entitled "Development and analysis of a homogeneous long-term precipitation network (1850-2015) and assessment of historic droughts for the island of Ireland". I applied for the Irish Research Council Postgraduate funding and on my second attempt I was successful, which took over my funding for the final two years of my PhD.

PhD Experiences

I feel that obtaining a PhD is a "process" and not just about conducting your research. Firstly, it is very important that you choose a supervisor that you can work and get on well with. My advice would be to speak to PhD students and others who have finished and ask about their supervisor experiences, so you can make an informed decision. I was lucky enough to have an excellent supervisor, Dr. Conor Murphy who expertly guided and advised me throughout my PhD. Secondly, make sure choose a topic that you are passionate and enthusiastic about, otherwise you won’t enjoy your research or even risk losing interest. 

Early in the “PhD process” I was asked to get involved in tutoring, the pay wasn’t great and it was challenging, but it gave me confidence teaching and was very rewarding. I was regularly expected to present my work at conferences and workshops or even just to my colleagues, which really took me out of my comfort zone. However, presenting my work added to my confidence and gave me great experience in speaking to large groups. I was also invited to sit in on research meetings with my supervisor and the research team at ICARUS, this was an important part of my development and where I gained crucial knowledge. 

It is important that you try and publish your work as early in your PhD as possible. During my PhD research I was involved in the peer reviewed publications listed below as either lead author or as collaborating senior author. This was a great way to help you really focus on a specific part of your research, learn new skills, work with other researchers, get your work disseminated and understand the peer review process while building on your writing skills. In addition, it gives you a huge advantage when applying for academic positions after you have finished your PhD, as publications are crucial in this very competitive employment environment.

Finally, I have a great relationship with my PhD colleagues; they have been a great source of advice, encouragement and friendship. Most of all I really enjoyed the “PhD process” it has been a tough journey, but with huge rewards.

Publications that have stemmed from my research.

Noone S, Broderick  C,  Duffy C, Matthews T,  Wilby RL, Murphy C. 2016. A 250 year drought catalogue for the island of Ireland (1765-2015) Int. J. Climatol. Accepted with minor corrections.

Murphy C, Noone S, Duffy C, Broderick C, Matthews T, Wilby RL. 2016. Irish droughts in newspaper archives: Rediscovering forgotten hazards? Weather. Accepted.

Wilby RL, Noone S, Murphy C, Matthews T, Harrigan S, Broderick C. 2015. An evaluation of persistent meteorological drought using a homogeneous Island of Ireland precipitation network. Int. J. Climatol. doi:10.1002/joc.4523

Noone S, Murphy C, Coll J, Matthews T, Mullan D, Wilby RL, Walsh S. 2015. Homogenization and analysis of an expanded long-term monthly rainfall network for the Island of Ireland (1850–2010). Int. J. Climatol. doi: 10.1002/joc.4522.

Homogenisation of temperature and precipitation time series with ACMANT3: method description and efficiency tests

Paper featuring John Coll of ICARUS. Post written by John Coll

International Journal of Climatology July 2016 doi: 10.1002/joc.4822

http://onlinelibrary.wiley.com/doi/10.1002/joc.4822/abstract

Time series homogenisation and the multiple breaks problem

During the long period of climatic observations, station location, instrumentation and several other conditions of the observation may change, resulting in non-climatic temporal variation in the observed data.  Such non-climatic changes “inhomogeneities” affect the usability of observed data to the detection of climate change and climate variability.  One important present task of climate science is to provide accurate regional and global mean temperature trend estimates (Rohde et al., 2013; Rennie et al., 2014; Venema et al., 2015), and homogenisation significantly contributes to that. The most frequent way of time series homogenisation is the use of statistical procedures.  The direct aim is to identify and correct statistically significant shifts in the section means (they are the estimated timings of technical changes, often referred as breaks or change points).  To separate the inhomogeneities from the true climatic variation (the latter never should be removed from the data), homogeneity tests are usually applied to the differences between a candidate series and other series of the same climatic area (“relative homogenisation”), rather than directly to the candidate series (“absolute homogenisation”).

We have the general experience that climatic time series contain about 5 breaks per 100 years on average (Venema et al. 2012).  Although statistical homogenisation has a century long history, the theory and development of multiple break homogenisation offering mathematically higher level solutions appeared only in the 1990s coincident with the more widespread use of personal computers.  One early representative of multiple break methods was PRODIGE (Caussinus and Mestre 2004).

During the European project COST ES0601 (known as ‘HOME’, 2007–2011)  two new multiple break methods were created based on PRODIGE: one is the fully automatic ACMANT (Adapted Caussinus–Mestre Algorithm for homogenising Networks of Temperature series, Domonkos, 2011) and the other is Homogenisation software in R (HOMER, Mestre et al., 2013), the interactive homogenisation method officially recommended by HOME.  Both HOMER and ACMANT include the optimal step function fitting with dynamic programming for break detection and the network wide minimization of residual variance  for correcting inhomogeneities (ANOVA, Caussinus and Mestre, 2004; Domonkos, 2015).  Both HOMER and ACMANT provide additional functionality relative to the parent method PRODIGE, and they are assumed to be the most efficient homogenization methods nowadays.

ACMANT3 development and discussion

This paper describes the theoretical background of ACMANT and the recent developments, which extend the capabilities, and hence, the application of the method.  The most important novelties in ACMANT3 are: the ensemble pre-homogenisation with the exclusion of one potential reference composite in each ensemble member; the use of ordinary kriging for weighting reference composites; the assessment of seasonal cycle of temperature biases in case of irregular-shaped seasonal cycles. ACMANT3 also allows for homogenisation on the daily scale including for break timing assessment, gap filling and analysis of ANOVA application on the daily time scale.

ACMANT3 is a complex software package incorporating six programmes, these are: temperature homogenisation with a sinusoid annual cycle of biases; temperature homogenisation with an irregular annual cycle of biases; precipitation homogenisation.  Each of the preceding three has monthly and daily homogenisation versions (http://www.c3.urv.cat/data.html); and in total the six programmes incorporate 174 sub-routines.  The software package also includes auxiliary files to support network construction. However, despite its complicated structure, ACMANT provides the fastest method implementation among all the available automatic homogenisation methods.

Considering the similarities of the theoretical background of HOMER and ACMANT, the choice between HOMER and ACMANT for particular homogenisation tasks should be based on the dataset characteristics.  The use of ACMANT is particularly recommended for (1) datasets with little or no metadata; (2) datasets from dense networks with large numbers of time series and where there are high spatial correlations; (3) very large datasets (>∼200 time series) for which the use of automatic methods is the most feasible and easily managed solution.   

Figure 1: Errors of raw data and residual errors of ACMANT homogenised data in a test dataset of simulated air surface temperatures. AC1, AC2, AC3 mean the first, second and third generation of ACMANT. Upper left: root mean squared error (RMSE) of monthly values, upper right: RMSE of annual values, bottom left: trend bias for individual series, bottom right: network mean trend bias. Smean means systematic trend bias. 

The efficiency tests presented in this paper provide firm indications that ACMANT3 can considerably reduce initial regional trend biases at any spatial scale, although the efficiency achieved depends both on the spatial density and the extent of the intact record of the observational data.  Further research is needed in this important and emerging area, for both the development and testing of statistical methods (Domonkos and Guijarro, 2015) and alongside an analysis of the causes of possible systematic biases in temperature records, with parallel measurements (http://www.surface

temperatures.org/databank/parallel_measurements).

The authors also have another ongoing collaboration as part of the Irish Environmental Protection Agency funded “HOMERUN” project (e.g. Coll et al., 2015a,b) which aims to homogenise the large and dense Irish precipitation dataset with ACMANT and HOMER and explore more details about the practical application of these methods.  More details are available from

 https://www.researchgate.net/project/HOMERUN-homogenisation-of-Irish-precipitation-data

The ACMANT3 software package together with its manual is freely accessible from http://www.c3.urv.cat/ data.html.

References

Caussinus H, Mestre O. 2004. Detection and correction of artificial shifts in climate series. J. R. Stat. Soc. C 53: 405–425, doi: 10.1111/j.1467-9876.2004.05155.x.

Coll J, Curley C, Domonkos P, Aguilar, E, Walsh S, Sweeney J, 2015a.  An application of HOMER and ACMANT for homogenising monthly precipitation records in Ireland.  Geophysical Research Abstracts 17: EGU2015-15502.

Coll J,  Domonkos P, Curley, M, Aguilar E, Walsh S, Sweeney J, 2015b.  IENet: A homogenised precipitation network for Ireland – preliminary results.  10^th EUMETNET Data Management Workshop. St Gallen, Switzerland.

Domonkos P. 2011. Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT). Int. J. Geosci. 2: 293–309, doi: 10.4236/ijg.2011.23032.

Domonkos P. 2015 Homogenization of precipitation time series with ACMANT. Theor. Appl. Climatol. 122: 303–314, doi: 10.1007/s00704-014-1298-5.

Domonkos P, Guijarro JA. 2015. Efficiency tests for automatic homogenization methods of monthly temperature and precipitation series. 10th EUMETNET Data Management Workshop Oct 28-30, St. Gallen, Switzerland.

Mestre O, Domonkos P,  Picard F,  Auer I, Robin S, Lebarbier E, Böhm R, Aguilar E,  Guijarro J, Vertacnik G,  Klancar M, Dubuisson B, Štepánek P. 2013. HOMER: homogenization software in R – methods and applications. Idojaras Q. J. Hung. Meteorol. Serv. 117: 47–67.

Rennie JJ, Lawrimore JH, Gleason BE, Thorne PW and others. 2014. The international surface temperature initiative global land surface databank: monthly temperature data release description and methods. Geosci. Data J. 1/2: 75–102, doi: 10.1002/gdj3.8.

Rohde R, Muller R, Jacobsen R, Perlmutter S, Rosenfeld A, Wurtele J, Curry J, Wickham C, Mosher S. 2013. Berkeley Earth temperature averaging process. Geoinform. Geostat. 1: 2, doi: 10.4172/gigs.1000103.

Venema V, Jones P, Lindau R, Osborn T. 2015. Is the global mean land surface temperature trend too low? 15th Annual Meeting of the European Meteorological Society Sofia (Bulgaria), EMS2015-557.

Venema V, Mestre, O, Aguilar E, Auer I, Guijarro JA, Domonkos P, Vertacnik G, Szentimrey T, Stepanek P, Zahradnicek P, Viarre J, Müller-Westermeier G, Lakatos M, Williams CN, Menne M, Lindau R, Rasol D, Rustemeier E, Kolokytha, K, Marinova T, Andresen L, Acquaotta F, Fratianni S, Cheval  S, Klancar M, Brunetti M, Gruber C, Duran MP, Likso T, Esteban P and Brandsma T. 2012: Benchmarking monthly homogenization algorithms. Climate of the Past, 8, 89-115, doi:10.5194/cp-8-89-2012.

New paper by John Coll et al.: Projected climate change impacts on upland heaths in Ireland

Projected climate change impacts on upland heaths in Ireland

Climate Research July 2016 doi: 10.3354/cr01408

Link:  http://www.int-res.com/abstracts/cr/v69/n2/p177-191

Ireland has a high proportion of the northern Atlantic wet and alpine and boreal heaths of high conservation value within Europe.  These upland habitats of and their associated oceanic species and vegetation are of high conservation value, but are also considered vulnerable to climate change.  For example, is anticipated that an amplification of the elevation-dependant warming already detected will accelerate the rate of change in mountain ecosystems, with the potential to exacerbate both the pace and the amplitude of extinctions of vulnerable upland species uniquely adapted to these habitats.

However, projections from different climate models vary markedly and local processes for upland regions are poorly captured, hence more localised modelling studies are required to inform management decisions.  Various modelling approaches have been used to convert species distributions into predictive maps, and bioclimatic envelope models (BEMs) are widely used.   However, confidence in the predictive power of BEMs is compromised by conceptual, biotic and algorithm flaws. Arising from this, the use of consensus methods is popular on the basis that they decrease the predictive uncertainty of single-models to give a probability distribution per pixel as opposed to a single value. 

The use of BEMs for habitats is novel, and only a limited number of studies have applied these methods to landforms and habitats. In this work seven bioclimatic envelope modelling techniques implemented in the BIOMOD modelling framework were used to model Wet and Alpine and Boreal heath distributions in Ireland.  An ensemble prediction from all the models was used to project changes based on a climate change scenario for 2031 to 2060 dynamically downscaled from the Hadley Centre HadCM3-Q16 global climate model. The climate change projections for the individual models change markedly from the consistent baseline predictions.  Projected climate space losses (gains) from the BIOMOD consensus model are -40.84% (limited expansion) and -10.38% (full expansion) for Wet heath (Figure 1a); and -18.31% (limited expansion) and +28.17% (full expansion) for Alpine and Boreal heath (Figure 1b).

Fig. 1. Mapped BIOMOD consensus model outputs for (a) wet heath and (b) alpine and boreal heath habitats based on median probability ensemble forecasting method values using the true skill statistic threshold. Red squares denote projected losses of climate space for the A1B 2031−2060 scenario relative to the baseline; blue squares denote stable climate space grids (areas of suitable climate under a no dispersal—no habitat expansion—scenario); green squares denote potential climate space gains relative to the baseline; blue and green squares combined indicate areas of suitable climate under a full dispersal (habitat expansion) scenario.

The projected decline and fragmentation of the climate space associated with heath habitats would

have significant implications for the ecology of these complex upland ecosystems and their associated species.  Results indicate that the distribution of wet heath habitats in Ireland is regionally sensitive to climate change, most notably for lower-lying areas in the south and west of the country. Increasing temperature and precipitation changes may reduce and fragment the area that is suitable for heath development.  Degrading heaths will also have an impact on the wider structure and function of the uplands as the overall mosaic of habitat types respond to climate change. For example, drier and warmer summers may increase the frequency, size and severity of uncontrolled fires, and drought effects may become more common later in the year. This may have severe impacts in areas already subject to pressures such as overgrazing, inappropriate burning, and loss of vegetation cover combined with erosion of the peat or soil.

Some attempt has been made to deal with uncertainty, at least in relation to differing results between the model categories, by providing the results from the individual BEMs implemented in the BIOMOD framework alongside the ensemble projection. Certainly, there is substantial variation in the results between the individual BEM types when the A1B scenario data are projected through the models.  Although only the downscaled output from 1 GCM and scenario has been used to project climate space changes, the methods lend themselves to using different GCM and RCM outputs from a range of scenarios to better encapsulate uncertainty. Thus e.g., given the importance of mean winter precipitation in all the BEM model families, if a wetter or dryer model or scenario had been used from the ENSEMBLES RCMs, the results projected via the BEMs could have varied further. 

Such an expanded framework would allow the identification of adaptation strategies that are robust (i.e. insensitive) to climate change uncertainties, and would allow more confidence in identifying and targeting vulnerable areas of heath habitat for priority conservation management measures.  These sort of refinements would also help inform best practice conservation management, whereby limited resources could be directed to areas coincident with healthy and functional heath communities and projected future climate suitability.

Open Session 9-10am June 20th to open the 9th ACRE conference

ICARUS and Maynooth University shall be hosting the 9th Meeting of the Atmospheric Circulation Reconstructions over the Earth project next week (20th-24th June). The opening session will be open to the public and media and be held in Renehan Hall on the South Campus from 9 to 10am.

The Session shall be chaired by Dr. Kate Willett of the UK Met Office who has created numerous datasets and edited for the past several years the annual State of the Climate Series in the Bullettin of the American Meteorological Society.

Seamus Walsh the Head of Climatology and Observations Division at Met Eireann shall welcome attendees to Ireland and provide a high level overview of the importance of data rescue and analysis to their mission.

Dr. Philip Brohan of the UK Met Office and Dr. Gil Compo of the Cooperative Institute for Environmental Sciences shall provide a talk on data rescue using citizen science and its application to historical renalayses products.

Finally, Dr. Conor Murphy of ICARUS shall provide an overview of several recent pieces of research on long-term changes in Irish climate including, droughts, floods, storminess and temperature.

All are welcome to attend and seating is on a first come first served basis. Renehan Hall is a ten minute walk from both the bus stop and the train station. For those driving onto campus the parking restrictions are lifted presently.

Details on the remainder of the program can be made available on request and a small number of seats may be available for some sessions.

Re-examining changes in Diurnal Temperature Ranges

This is a repost for the International Surface Temperature Initiative blog

A recently published pair of papers in JGR ($, sorry) reassessing changes in observed Diurnal Temperature Range changes has been recently highlighted in EOS and Nature Climate Change.

The analyses have been extremely long in the making. They started out back in 2010 as 'hobby' papers and never got explicit funding so trundled along very very slowly indeed. The release of the ISTI databank provided an opportunity to create a new estimate of DTR changes and compare it to several pre-existing estimates.

The first paper details the construction of the new dataset of DTR changes. This takes the version 1 release of the ISTI databank and applies the pairwise homogenisation algorithm (PHA) used by NOAA NCEI to these holdings. The paper deals with the homogenisation processing, analyses the resulting dataset estimates and discusses aspects of the underlying metrology (not a typo). Below are the gridded trends over 1951-2012 and the global timeseries. The 'raw' data is the basic data held in the databank. Directly adjusted is where the DTR series were presented to the PHA algorithm. Indirectly adjusted is where, instead, the adjustments to Tmin and Tmax are used.

We found that more breaks are returned for DTR than is the case for Tmax or Tmin, for which more breaks are returned again than Tmean. This has potential implications for future homogenisation strategies in that searching for breaks in Tmean appears sub-optimal. Potential reasons for this were detailed in a prior ISTI blogpost and are further elucidated upon in the paper itself.

The second paper takes the new analysis and compares it to several pre-existing analysis and then attempts to reformulate the findings on DTR from the IPCC Fifth Assessment Report (which assessed only medium confidence). The new analyses provide considerable confidence in a finding that DTR has decreased globally since the mid-twentieth Century, with most of that decrease occuring prior to 1980. Data are too sparse and uncertain to make meaningful conclusions about DTR changes prior to the mid-twentieth Century, at least globally. The compared datasets show very distinct coverage and somewhat divergent trends since the mid-twentieth Century:

Much of the divergence between estimates results from the disparate approaches taken to accounting for incomplete sampling by the underlying data through interolating (or not) into data sparse regions. Using the native coverage (top) or the estimates restricted to common coverage (bottom) greatly alters the perceived degree of agreement between the independently produced products from various groups:

The conclusion of the second paper was as follows:

The driving rationale behind this work was the lack of explicit progress in the literature in assessing DTR changes between the fourth and fifth assessment reports of the IPCC. Based upon the findings herein, where a new assessment to be performed by IPCC of the observational DTR record at this time the text might read as follows (use of IPCC carefully calibrated uncertainty language and italicization [Mastrandrea et al., 2010] is intended).

It is virtually certain that globally averaged DTR has significantly decreased since 1950. This reduction in DTR is robust to both choice of data set and to reasonable variations in station selection and gridding methodology. However, differences between available estimates mean that there is only medium confidence in the magnitude of the DTR reductions. It is likely that most of the global-mean decrease occurred between 1960 and 1980 and that since then globally averaged DTR has exhibited little change. Because of current data sparsity in the digitized records, there is low confidence in trends and multidecadal variability in DTR prior to the middle twentieth century. It is likely that considerable pre-1950 data exist that could be shared and/or rescued and used in future analyses. All assessed estimates of global DTR changes are substantially smaller than the concurrently observed increases in mean and maximum and minimum temperatures (high confidence, virtually certain).

The datasets and code used are available via https://www.maynoothuniversity.ie/icarus/icarus-data

ICARUS researchers give public lecture last month in Customs House , Dublin.

Recent Flooding in Ireland: Hydro-Climatic aspects and associated impacts

Link to slides: http://www.irishmetsociety.org/images/stories/IMS/PDF/Flooding%20Talk%20March%202016.pdf

Darren Clarke and myself Simon Noone gave a recent talk to the Irish Meteorological Society (IMS) in the Customs House, Dublin. The public lecture gave some insights into the causes and effects of the recent widespread flooding by looking through the lens of each of their areas of expertise. There was a good attendance of about 40 people from Met Éireann staff, engineers and even pilots to climate enthusiasts. After the talk we both responded to some interesting comments and received very positive feedback. We would like to thank Paul Halton and his colleagues at the IMS for inviting us and for their hospitality. For the lecture Darren focused on the social impacts of flooding and asked whether the current national approach to managing flood risks is fit for purpose. Darren is examining how societies adapt to climate change, specifically focusing on community adaptation to flood risk management strategies in Ireland.  I discussed the flooding in the context of the long-term hydrological cycle, how recent events fit into the historical record and what, if any, trends are emerging.  The recent rainfall extremes of late 2013/14 and 2015 were put into context over the longer term, using the recently updated Island of Ireland Precipitation (IIP) series 1850-2015.

 Key hydro-climatic results presented:

•     

•      Results indicate statistically significant increasing trends in winter and decreasing trends in summer precipitation.

•      Across 13 IIP stations (mainly in west) so far 2014 was the wettest winter (Dec 2014, Jan/ Feb 2015) in Ireland over the past 165 years (see table 1).

•      2015 has seen the wettest December at 16 IIP stations over the past 165 years and 2015 ranked either 2^nd or 3^rd for the other 9 IIP stations (see table 2).

•      However, December 1978 was the wettest across eastern IIP stations (see table 2).

Table 1. Presents the wettest and driest seasons from 1850-2015 using the IIP series.

Table 2.  Presents the wettest December from 1850-2015 and corresponding standardized monthly anomaly for each of the IIP stations.

Key results on social impacts presented:

·         The economic impacts of flooding currently dominate policy, political and media debates at the expense of social costs such as physical and mental well-being. However, understanding the social impacts of flooding is crucial as the effects last long after a flood has receded

·         Greater attention to non-engineered flood defences will be needed in the coming decades if flood risks are to be managed sustainably and fairly.

·         The provision of flood insurance is likely to remain a contentious issue for those unable to avail of flood insurance nationally. The implementation of a flood re-insurance scheme as exists in the UK and France may be difficult to implement in Ireland for social, political and economic reasons.

·         Understanding of the local context in which new flood defences are to be located and the value of community input will be crucial if flood authorities are to be considered as legitimate both now and in the future.