Homogenization is necessary because much has happened in the world between the French and industrial revolutions, two world wars, the rise and fall of communism, and the start of the internet age. Inevitably many changes have occurred in climate monitoring practices.

As a consequence, the instruments used to measure temperature have changed, the screens to protect the sensors from the weather have changed and the surrounding of the stations has often been changed and stations have been moved in response. These non-climatic changes in temperature have to be removed as well as possible to make more accurate assessments of how much the world has warmed.

Removing such non-climatic changes is called homogenization. For the land surface temperature measured at meteorological stations, homogenization is normally performed using relative statistical homogenizing methods. Here a station is compared to its neighbours. If the neighbour is sufficiently nearby, both stations should show about the same climatic changes. Strong jumps or gradual increases happening at only one of the stations indicate a non-climatic change.

If there is a bias in the trend, statistical homogenization can reduce it. How well trend biases can be removed depends on the density of the network. In industrialised countries a large part of the bias can be removed for the last century. In developing countries and in earlier times removing biases is more difficult and a large part may remain. Because many governments unfortunately limit the exchange of climate data, the global temperature collections can also remove only part of the trend biases.

Software to homogenize climate data can be found here.

My posts on homogenization

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.
Five statistically interesting problems in homogenization
Series written for statisticians and climatologists looking for interesting problems.
Just the facts, homogenization adjustments reduce global warming
Many people only know that climatologists increase the land surface temperature trend, but do not know that they also reduce the ocean surface trend and that the net effect is a reduction of global warming. This does not fit to well to the conspiracy theories of the mitigation sceptics.
Why raw temperatures show too little global warming
The raw land surface temperature probably shows too little warming. This post explains the reasons why: thermometer screen changes, relocations and irrigation.
Statistical homogenisation for dummies
A primer on statistical homogenisation with many pictures.
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.
A short introduction to the time of observation bias and its correction
The time of observation bias is an important cause of inhomogeneities in temperature data.
Do you want to help with data discovery?
Repost on crowd sourcing to digitise climate data by the International Surface Temperature Initiative.
HUME: Homogenisation, Uncertainty Measures and Extreme weather
Proposal for future research in homogenisation of climate network data.
What distinguishes a benchmark?
Main answer: benchmarking is a community effort.

Scientific meetings

The main meetings of the homogenization community are the Seminar in Budapest and the Data Management Workshops. The Homogenization Seminars are more theoretical; the last one was in 2014. The EUMETNET Data Management Workshops are more practical; the upcoming one is in October 2015.

Next to these meetings, we have two sessions at two main conferences. The session "Climate monitoring; data rescue, management, quality and homogenization" on at the EMS in September 2015. The session "Climate Data Homogenization and Climate Trend and Variability Assessment" at EGU in April 2016.

Classic scientific literature


Blair Trewin, 2010: Exposure, instrumentation, and observing practice effects on land temperature measurements. WIRES Climate Change, 1, doi: 10.1002/wcc.46.

Enric Aguilar, Inge Auer, Manola Brunet, Thomas C. Peterson, and Jon Wieringa, 2003: Guidelines on climate metadata and homogenization. Report no. WMO/TD No. 1186, World Meteorological Organization, Geneva.

Thomas C. Peterson, David R. Easterling, Thomas R. Karl, Pavel Groisman, Neville Nicholls, Neil Plummer, Simon Torok, Ingeborg Auer, Reinhard Böhm, Donald Gullett, Lucie Vincent, Raino Heino, Heikki Tuomenvirta, Olivier Mestre, Tamás Szentimrey, James Salinger, Eirik J. Førland, Inger Hanssen-Bauer, Hans Alexandersson, Philip Jones and David Parker, 1998: Homogeneity adjustments of in situ atmospheric climate data: A review. International Journal of Climatology, 18, pp. 1493-1517.

Homogenization methods

The five modern methods below are the most accurate ones and much preferred over traditional methods. See also HOME benchmarking study.

Olivier Mestre, Peter Domonkos, Franck Picard, Ingeborg Auer, Stéphane Robin, Emilie Lebarbier, Reinhard Böhm, Enric Aguilar, Jose Guijarro, Gregor Vertachnik, Matija Klancar, Brigitte Dubuisson, and Petr Stepanek, 2013: HOMER: a homogenization software – methods and applications. Idojaras, Quarterly journal of the Hungarian Meteorological Service, 117, no. 1.

Peter Domonkos, 2011: Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT). International Journal of Geosciences, 2, no. 3, pp. 293-309. doi: 10.4236/ijg.2011.23032.

Henry Caussinus and Olivier Mestre, 2004: Detection and correction of artificial shifts in climate series. Applied Statistics, 53, part 3, pp. 405-425, doi: 10.1111/j.1467-9876.2004.05155.x.

Tamás Szentimrey, 2008: Development of MASH homogenization procedure for daily data. Proceedings of the fifth seminar for homogenization and quality control in climatological databases, Budapest, Hungary, 2006; WMO Reports no. WCDMP-No. 71, pp. 123-130.

Matthew J. Menne, Claude Williams jr., and Vose, R. S., 2009: The U.S. historical climatology network monthly temperature data, version 2. Bulletin American Meteorological Society, 90, no.7, pp. 993-1007, doi: 10.1175/2008BAMS2613.1

Michele Brunetti, Maurizio Maugeri, Fabio Monti, and Teresa Nanni, 2006: Temperature and precipitation variability in Italy in the last two centuries from homogenized instrumental time series. International Journal of Climatology, 26, pp. 345–381, doi: 10.1002/joc.1251.


Victor Venema, Olivier Mestre, Enric Aguilar, Ingeborg Auer, José A. Guijarro, Peter Domonkos, Gregor Vertacnik, Tamás Szentimrey, Petr Stepanek, Pavel Zahradnicek, Julien Viarre, Gerhart Müller-Westermeier, Monika Lakatos, Claude N. Williams Jr., Matthew Menne, Ralf Lindau, Dubravka Rasol, Elke Rustemeier, Kostas Kolokythas, Tania Marinova, Lars Andresen, Fiorella Acquaotta, Simona Fratianni, Sorin Cheval, Matija Klancar, Michelle Brunetti, Christine Gruber, Marc Prohom Duran, Tanja Likso, Pere Esteban, Theo Brandsma, 2012: Benchmarking homogenization algorithms for monthly data. Climate of the Past, 8, pp. 89-115, doi: 10.5194/cp-8-89-2012.

Claude Williams Jr, Matthew J. Menne, and Peter W. Thorne, 2012: Benchmarking the performance of pairwise homogenization of surface temperatures in the United States. Journal Geophysical Research, 26, no. 3, pp. 345–381, doi: 10.1029/2011JD016761.