Sunday, February 12, 2017

Too fast or too slow?

The discussion around Karl et al., 2015 continues in certain segments of the media.

Last week, the allegations were that the paper was rushed to influence the Paris agreement. That the multi-year multi-party talks could be swayed by a single paper is of course pure bunkum (as an aside Bunkum arises from the behaviour of a politician from Buncombe County where NCEI is located). The Paris climate agreement was the culmination of thousands of individual studies and painstaking, careful, assessments of a wealth of scientific evidence that led to a conclusion by the governments of the world that action was imperative. That any single study was, or could ever be, a ‘clincher’ is pure fantasy. The weight and breadth of evidence is what convinced all parties and is the work of many thousands of experts.

This week, there has been a volte face (about face) and the allegation instead is that Karl et al. led to a delay in the release of ERSSTv4 which was ‘unacceptable’. You’ll forgive me for a second while I sweep up the remains of my shattered irony-meter strewn about over the floor.

Right, where was I? Well, firstly, the new allegation fundamentally clears up one allegation in that the ERSSTv4 product was in no sense experimental and had undergone full internal review as well as having (at the time) two peer-reviewed published papers describing it.

Huang et al. put out a technical paper, published in the Journal of Climate in February 2015, on the new dataset, accompanied by a paper describing its uncertainties by Liu et al.  The Huang et al. paper covered in detail the dataset specification.  It did not address the implications of the dataset related to temperature trends over the past several decades. It was a highly technical paper focused on explaining the details of new corrections to sea surface temperatures. It was not intended for general public communication.  

At the same time, NOAA and ISTI had in late 2014 released a major upgrade to monthly global land data holdings source that is updated each month in NOAA’s regular global temperature monitoring.  It has extra stations in many regions of the world and improved coverage , and that is why Karl et al. took advantage of this for a snapshot data set analysis that ended in 2014. The envisaged upgrade to GHCNM will regularly add new data and process these data through the existing land analysis processing suite.

Whenever NOAA puts out new data sets, that will be updated each month for tracking global temperatures they always get numerous questions that they have to be able to answer. So, NOAA waited on releasing this new ERSSTv4 data set until the Karl et al. paper was out because the Karl et al. paper presented the implications of the new corrections and new land data sources to previously reported trends appearing in prominent works such as the Fifth IPCC assessment including, but not limited to, the recent behaviour. NOAA took extra steps to ensure the impact of the new corrections and data could be readily explained. It took an extra 4 months to accomplish this. If the Karl et al. paper had not been in the pipeline, arguably NOAA would have had to write up a stand-alone paper to explain the implications of the updated data set, essentially the “Karl et al. paper.”

And there is still nothing in these process complaints that substantiates anything but high-quality science that has already been reproduced by other scientists in peer-reviewed scientific articles. Those studies validated NOAA's work. They validated the science, the data provided to them by NOAA, and the quality of the work.

Thursday, February 9, 2017

Week of International Scientific Young Talents

A few months back I entered a competition to participate in a ‘week of scientific young talents’ in Paris. To my surprise a few weeks later I received an email letting me know I had won! Myself and two other Irish scientists were chosen by the French embassy to travel to Paris for a week of activities.

Scientists from Ireland, Portugal and Norway

The competition was organised by Universcience, a French organisation based in the Cité de Sciences et de l’Industrie and the Palais de la Découverte. Their goal is to encourage people to engage with science and scientific reasoning and to inspire people to be curious about the world around them. The prize was arranged to celebrate the 30th anniversary of the Cité de Sciences and the 80th anniversary of the Palais de la Découverte this year. 
The Young Scientists outside the Palais de la Découverte

On arrival we were greeted by the organisers at the Cité de Sciences and introduced to the hugely varied group. 42 people from 26 countries spanning every continent. That evening we were treated to a screening of the movie ‘A Beautiful Planet’ in La Géode, an incredible 180 degree cinema which creates a truly immersive experience (and can induce minor motion sickness). The movie shows films of the Earth taken from the International Space Station and highlights the international collaboration of space agencies who transcend politics and borders to achieve common goals – an appropriate introduction to the week ahead!

La Géode - cinema sphere

On Monday we were given a private tour of the Cité de Sciences as it was closed to the public, allowing us free reign over the exhibitions and a chance to see how children learn in the kids section of the museum. This fun experience saw us playing with TV, water, robots, plants and ants. That evening we travelled as a group on the metro (easier said than done) to the Ministry of Higher Education and Research, where we had champagne and canapés with the Minister’s top aide and the president of Universcience, among others.
The scientists and dignitaries

On Tuesday we visited the headquarters of L’Oréal Paris, who co-sponsored the week. The morning consisted of a workshop on women in science with some fascinating discussions arising from all the different experiences of scientists from around the world. A luxurious lunch of sushi and French patisserie followed - where we all felt that we could get used to this kind of treatment. After lunch we toured the L’Oréal factory and learned about their outreach, manufacturing and development processes and met a lovely Irish woman called Maureen who showed us how hair dye lives up to what it says on the tin.  
Women in Science
Food, glorious food

Wednesday saw our first visit to the Palais de la Découverte, a beautiful building temporarily obscured by some scaffolding as they fix the roof. We visited the planetarium and while some toured the night sky, others took the opportunity to take a nap and sleep off the French wine from the night before. That afternoon all 42 young scientists presented our research projects in three minutes each. Topics included everything from climate change, engineering, computer science, fibre optics, biology, communication, chemistry, physics. medicine, and bird sperm analysis.

What am I doing? A very good question...
On Thursday we returned to the Cité de Sciences and participated in workshops about museology, spending the afternoon designing and building our own creations in the museum’s FabLab. In the evening we visited the Musée d’Orsay where we viewed the exhibitions in the gorgeous museum and had dinner in the petit salon with members of Universcience, L’Oréal and government representatives. Again, we felt we could get used to this treatment.

Petit Salon of the Musée d'Orsay


On Friday in the Cité de Sciences we listened to presentations on spatial physics, maths and magic, and climate change, before being transferred to the Musée de l’Homme where we saw the history of mankind, our current state, and where we are going in the future. We continued on to the Foundation Louis Vuitton to witness the collection of Sergei Schukin – ‘Icons of Modern Art’. The exhibition chronicled the development of modern art from impressionism to cubism, expertly explained by a guide from the Louis Vuitton foundation. 
Frank Gehry's original architectural drawing for the Foundation Louis Vuitton (seriously)

The finished building
That was sadly our last night in Paris and after a fantastic week, a quick look at the Eiffel tower, and stocking up on cheese - on Saturday morning we headed home. 
Obligatory selfie

I’d like to express a huge thank you to the all the staff of Universcience (particularly Flavie who was always there to help!), the French embassy in Ireland (FranceinIreland) for funding the programme and allowing three Irish people to go, and of course the 41 other young scientists who made the experience such a pleasure to be part of. 

Sunday, February 5, 2017

On the Mail on Sunday article on Karl et al., 2015

There is an "interesting" piece (use of quotes intentional) in the Mail on Sunday today around the Karl et al., 2015 Science paper.

There are a couple of relevant pieces arising from Victor Venema and Zeke Hausfather already available which cover most of the science aspects and are worth a read. I'm adding some thoughts because I worked for three and a bit years in the NOAA group responsible in the build-up to the Karl et al. paper (although I had left prior to that paper's preparation and publication). I have been involved in and am a co-author upon all relevant underlying papers to Karl et al., 2015.

The 'whistle blower' is John Bates who was not involved in any aspect of the work. NOAA's process is very stove-piped such that beyond seminars there is little dissemination of information across groups. John Bates never participated in any of the numerous technical meetings on the land or marine data I have participated in at NOAA NCEI either in person or remotely. This shows in his reputed (I am taking the journalist at their word that these are directly attributable quotes) mis-representation of the processes that actually occured. In some cases these mis-representations are publically verifiable.

I will go through a small selection of these in the order they appear in the piece:

1. 'Insisting on decisions and scientific choices that maximised warming and minised documentation'

Dr. Tom Karl was not personally involved at any stage of ERSSTv4 development, the ISTI databank development or the work on GHCN algorithm during my time at NOAA NCEI. At no point was any pressure bought to bear to make any scientific or technical choices. It was insisted that best practices be followed throughout. The GHCN homogenisation algorithm is fully available to the public and bug fixes documented. The ISTI databank has been led by NOAA NCEI but involved the work of many international scientists. The databank involves full provenance of all data and all processes and code are fully documented. The paper describing the databank was held by the journal for almost a year (accepted October 2013, published September 2014) to allow the additional NOAA internal review processes to complete. The ERSSTv4 analysis also has been published in no fewer than three papers. It also went through internal review and approval processes including a public beta release prior to its release which occurred prior to Karl et al., 2015.

2. 'NOAA has now decided the sea dataset will have to be replaced and revised just 18 months after it was issued, because it used unreliable methods which overstated the speed of warming' 

While a new version of ERSST is forthcoming the reasoning is incorrect here. The new version arises because NOAA and all other centres looking at SST records are continuously looking to develop and refine their datasets. The ERSSTv4 development completed in 2013 so the new version reflects over 3 years of continued development and refinement. All datasets I have ever worked upon have undergone version increments. Measuring in the environment is a tough proposition - its not a repeatable lab experiment - and measurements were never made for climate. It is important that we continue to strive for better understanding and the best possible analyses of the imperfect measurements. That means being open to new, improved, analyses. The ERSSTv4 analysis was a demonstrable improvement on the prior version and the same shall be true in going to the next version once it also has cleared both peer-review and the NOAA internal process review checks (as its predecessor did).

3. 'The land temperature dataset used by the study was afflicted by devestating bugs in its software that rendered its findings unstable' (also returned to later in the piece to which same response applies)

The land data homogenisation software is publically available (although I understand a refactored and more user friendly version shall appear with GHCNv4) and all known bugs have been identified and their impacts documented. There is a degree of flutter in daily updates. But this does not arise from software issues (running the software multiple times on a static data source on the same computer yields bit repeatability). Rather it reflects the impacts of data additions as the algorithm homogenises all stations to look like the most recent segment. The PHA algorithm has been used by several other groups outside NOAA who did not find any devestating bugs. Any bugs reported during my time at NOAA were investigated, fixed and their impacts reported.

4. 'The paper relied on a preliminary alpha version of the data which was never approved or verified'

The land data of Karl et al., 2015 relied upon the published and internally process verified ISTI databank holdings and the published, and publically assessable homogenisation algorithm application thereto. This provenance satisfied both Science and the reviewers of Karl et al. It applied a known method (used operationally) to a known set of improved data holdings (published and approved).

5. [the SST increase] 'was achieved by dubious means'

The fact that SST measurements from ships and buoys disagree with buoys cooler on average is well established in the literature. See IPCC AR5 WG1 Chapter 2 SST section for a selection of references by a range of groups all confirming this finding. ERSSTv4 is an anomaly product. What matters for an anomaly product is relative homogeneity of sources and not absolute precision. Whether the ships are matched to buoys or buoys matched to ships will not affect the trend. What will affect the trend is doing so (v4) or not (v3b). It would be perverse to know of a data issue and not correct for it in constructing a long-term climate data record.

6. 'They had good data from buoys. And they threw it out [...]'

v4 actually makes preferential use of buoys over ships (they are weighted almost 7 times in favour) as documented in the ERSSTv4 paper. The assertion that buoy data were thrown away as made in the article is demonstrably incorrect.

7. 'they had used a 'highly experimental early run' of a programme that tried to combine two previously seperate sets of records' 

Karl et al used as the land basis the ISTI databank. This databank combined in excess of 50 unique underlying sources into an amalgamated set of holdings. The code used to perform the merge was publically available, the method published, and internally approved. This statement therefore is demonstrably false.

There are many other aspects of the piece that I disagree with. Having worked with the NOAA NCEI team involved in land and SST data analysis I can only say that the accusations in the piece do not square one iota with the robust integrity I see in the work and discussions that I have been involved in with them for over a decade. 

Monday, December 19, 2016

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:
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).

McElwain L, Sweeney J. 2007. Key Meteorological Indicators of Climate Change in Ireland. Environmental Research Centre Report available online from: .
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

Thursday, December 8, 2016

The "PhD experience" at ICARUS

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.

Monday, September 12, 2016

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
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 (; 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

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

The ACMANT3 software package together with its manual is freely accessible from data.html.

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.  10th 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.

Thursday, September 1, 2016

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

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.