In summary:
- We want to build a global database of parallel measurements: observations of the same climatic parameter made independently at the same site
- This will help research in many fields
- Studies of how inhomogeneities affect the behaviour of daily data (variability and extreme weather)
- Improvement of daily homogenisation algorithms
- Improvement of robust daily climate data for analysis
- Please help us to develop such a dataset
Introduction
 One way to study the influence of changes in measurement techniques is by making simultaneous measurements with historical and current instruments, procedures or screens. This picture shows three meteorological shelters next to each other in Murcia (Spain). The rightmost shelter is a replica of the Montsouri screen, in use in Spain and many European countries in the late 19th century and early 20th century. In the middle, Stevenson screen equipped with automatic sensors. Leftmost, Stevenson screen equipped with conventional meteorological instruments. |
We intend to build a database with parallel measurements to study non-climatic changes in the climate record. This is especially important for studies on weather extremes where the distribution of the daily data employed must not be affected by non-climatic changes.
There are many parallel measurements from numerous previous studies analysing the influence of different measurement set-ups on average quantities, especially average annual and monthly temperature. Increasingly, changes in the distribution of daily and sub-daily values are also being investigated (Auchmann and Bönnimann, 2012; Brandsma and Van der Meulen, 2008; Böhm et al., 2010; Brunet et al., 2010; Perry et al., 2006; Trewin, 2012; Van der Meulen and Brandsma, 2008). However, the number of such studies is still limited, while the number of questions that can and need to be answered are much larger for daily data.
Unfortunately, the current common practice is not to share parallel measurements and the analyses have thus been limited to smaller national or regional datasets, in most cases simply to a single station with multiple measurement set-ups. Consequently there is a pressing need for a large global database of parallel measurements on a daily or sub-daily scale.
Also datasets from pairs of nearby stations, while officially not parallel measurements, are interesting to study the influence of relocations. Especially, typical types of relocations, such as the relocation of weather stations from urban areas to airports, could be studied this way. In addition, the influence of urbanization can be studied on pairs of nearby stations.
Daily data
Daily datasets are essential for studying the variability of and extremes in weather and climate. Looking at the physical causes of inhomogeneities, one would expect that many of the effects are amplified on days with special weather conditions and thus especially affect the tails of the distribution of the daily data. Now that the interest in extreme weather and thus in daily data has increased, more and more people are also working on the homogenization of daily data. Increasingly, developers of national and regional temperature datasets have homogenised the temperature distribution (see e.g., Nemec et al., 2012; Auer et al., 2010; Brown et al., 2010; Kuglitsch et al., 2009, 2010). Further improvements in the quantity and quality of such datasets, and a deeper understanding of remaining deficiencies, are important for climatology.Application possibilities of parallel measurements
The most straightforward application of such a dataset would be a comparison of the magnitude of the non-climatic changes to the magnitude of the changes found in the climate record. We need to know whether the non-climatic changes are large enough to artificially hide or strengthen any trends or perturb decadal variability. In addition, such a dataset would help us to better understand the physical causes of inhomogeneities. A large and quasi-global dataset would enable to analyse how the magnitude and nature of inhomogeneities differ depending on the geographical region and the microclimate.The dataset would also benefit homogenisation science in multiple ways. It may reveal typical statistical characteristics of inhomogeneities that would allow for a more accurate detection and correction of breaks. The dataset would facilitate the development of physical homogenisation methods for specific types of breaks that are able to take the weather conditions into account; similar to the method developed for the transition of Wild screens to Stevenson screens for Switzerland by Auchmann and Brönnimann (2012). It would also allow for the development of generalised physical correction methods suitable for multiple climatic regions. Finally, the dataset would improve the ability to create realistic validation datasets, thus improving our estimates of the remaining uncertainties. This in turn again benefits the development of better homogenisation methods.
Organisational matters
As an incentive to contribute to the dataset, initially only contributors will be able to access the data. After joint publications, the dataset will be opened for academic research as a common resource for the climate sciences. These two stages will also enable us to find errors in the dataset before the dataset is published.The International Surface Temperature Initiative (ISTI) and the European Climate Assessment & Dataset (ECA&D) are willing to host the dataset. This is great, because it makes the dataset more visible for contributors and users alike. We are still looking for an organisational platform that could facilitate the building of such a dataset. Any ideas for this are appreciated.
A preliminary list with parallel measurements can be found in our Wiki.
If you have any ideas or suggestions for such an initiative, if you know of further parallel datasets, or if you just want to be kept informed, please update our Wiki, comment at Variable Variability or send an email to Victor.Venema@uni-bonn.de. Furthermore, if you know someone who might be interested, please inform him or her about this initiative. Thank you.
Scientists involved in this initiative are:
- Enric Aguilar (University of Tarragona, Spain)
- Renate Auchmann (University of Bern, Switzerland)
- Ingeborg Auer (Zentralanstalt für Meteorologie und Geodynamik, Austria)
- Andreas Becker (Global Precipitation Climatology Centre, Deutscher Wetterdienst, Germany)
- Stefan Brönnimann (Institute of Geography, University of Bern, Switzerland)
- Michele Brunetti (Institute of Atmospheric Sciences and Climate of the National Research Council, Italy)
- Sorin Cheval (National Meteorological Administration, Romania)
- Peter Domonkos (University of Tarragona, Spain)
- Aryan van Engelen (Royal Netherlands Weather Service, The Netherlands)
- José Guijarro (Agencia Estatal de Meteorología, Spain)
- Franz Gunther Kuglitsch (GFZ German Research Centre for Geosciences, Germany)
- Monika Lakatos (Hungarian Meteorological Service, Hungary)
- Øyvind Nordli (Meteorologisk institutt, Norway)
- David Parker (UK MetOffice, United Kingdom)
- Mário Gonzalez Pereira (Universidade de Trás-os-Montes e Alto Douro, Portugal)
- Tamas Szentimrey (Hungarian Meteorological Service, Hungary)
- Peter Thorne (National Climatic Data Center, USA; International Surface Temperature Initiative)
- Victor Venema (University of Bonn, Germany)
- Kate Willett (UK MetOffice, United Kingdom)
Related posts
- Future research in homogenisation of climate data – EMS 2012 in Poland
- A discussion on homogenisation at a Side Meeting at EMS2012
- What is a change in extreme weather?
- Two possible definitions, one for impact studies, one for understanding.
- HUME: Homogenisation, Uncertainty Measures and Extreme weather
- Proposal for future research in homogenisation of climate network data.
- Homogenization of monthly and annual data from surface stations
- A short description of the causes of inhomogeneities in climate data (non-climatic variability) and how to remove it using the relative homogenization approach.
- New article: Benchmarking homogenization algorithms for monthly data
- Raw climate records contain changes due to non-climatic factors, such as relocations of stations or changes in instrumentation. This post introduces an article that tested how well such non-climatic factors can be removed.
References
Auchmann, R., and S. Brönnimann. A physics-based correction model for homogenizing sub-daily temperature series, J. Geophys. Res., 117, art. no. D17119, doi: 10.1029/2012JD018067, 2012.Auer I., Nemec J., Gruber C., Chimani B., Türk K. HOM-START. Homogenisation of climate series on a daily basis, an application to the StartClim dataset. Wien: Klima- und Energiefonds, Projektbericht, 34 p., 2010.
Brandsma, T. and J.P. van der Meulen, Thermometer Screen Intercomparison in De Bilt (the Nether-lands), Part II: Description and modeling of mean temperature differences and extremes. Int. J. Climatology, 28, pp. 389-400, 2008.
Brown, P. J., R. S. Bradley, and F. T. Keimig. Changes in extreme climate indices for the northeastern United states, 1870–2005, J. Clim., 23, 6555–6572, doi: 10.1175/2010JCLI3363.1, 2010.
Böhm, R., P.D. Jones, J. Hiebl, D. Frank, M. Brunetti, M. Maugeri. The early instrumental warm-bias: a solution for long central European temperature series 1760–2007. Climatic Change, 101, pp. 41–67, doi: 10.1007/s10584-009-9649-4, 2010.
Brunet, M., J. Asin, J. Sigró, M. Banón, F. García, E. Aguilar, J. Esteban Palenzuela, T.C. Peterson and P. Jones. The minimization of the screen bias from ancient Western Mediterranean air temperature records: an exploratory statistical analysis. Int. J. Climatol., doi: 10.1002/joc.2192, 2010.
Klein Tank, A.M.G., Wijngaard, J.B., Können, G.P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., Heino, R., Bessemoulin, P., Müller-Westermeier, G., Tzanakou, M., Szalai, S., Pálsdóttir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., van Engelen, A. F.V., Forland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T., Antonio López, J., Dahlström, B., Moberg, A., Kirchhofer, W., Ceylan, A., Pachaliuk, O., Alexander, L.V. and Petrovic, P. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol., 22, pp. 1441–1453. doi: 10.1002/joc.773, 2002. Data and metadata available at http://www.ecad.eu.
Kuglitsch F.G., Toreti A., Xoplaki E., Della-Marta P.M., Luterbacher J., Wanner H. Homogenisation of daily maximum temperature series in the Mediterranean. Journal of Geophysical Research, 114, art. no. D15108, doi: 10.1029/2008JD011606, 2009.
Kuglitsch F.G., Toreti A., Xoplaki E., Della-Marta P.M., Zerefos C.S., Türkes M., Luterbacher J. Heat wave changes in the eastern Mediterranean since 1960. Geophysical Research Letters, 37, art.no. L04802, doi: 10.1029/2009GL041841, 2010.
Meulen, van der, JP, T Brandsma. Thermometer screen intercomparison in De Bilt (The Netherlands), part I: Understanding the weather-dependent temperature differences. Int. J. Climatol., 28, 371-387, 2008.
Nemec, J., Ch. Gruber, B. Chimani, I. Auer. Trends in extreme temperature indices in Austria based on a new homogenised dataset. Int. J. Climatol., doi: 10.1002/joc.3532, 2012.
Perry, M., Prior, J. and Parker, D.E., 2006: An assessment of the suitability of a plastic thermometer screen for climatic data collection. Int. J. Climatol., 27, 267-276.
Trewin, B. A daily homogenized temperature data set for Australia. Int. J. Climatol., doi: 10.1002/joc.3530, 2012.
Thorne, Peter W., Kate M. Willett, Rob J. Allan, Stephan Bojinskski, John R. Christy, Nigel Fox, Simon Gilbert, Ian Jolliffffe, John J. Kennedy, Elizabeth Kent, Albert Klein Tank, Jay Lawrimore, David E. Parker, Nick Rayner, Adrian Simmons, Lianchun Song, Peter A. Stott, and Blair Trewin 2011: Guiding the Creation of A Comprehensive Surface Temperature Resource for Twenty-First-Century Climate Science. Bull. Amer. Meteor. Soc., 92, ES40–ES47. doi: 10.1175/2011BAMS3124.1. More information at: http://www.surfacetemperatures.org
