This picture shows three meteorological shelters next to each other in Murcia (Spain). The rightmost shelter is a replica of the Montsouri (French) screen, in use in Spain and many European countries in the late 19th century and early 20th century. Leftmost, Stevenson screen equipped with conventional meteorological instruments, a set-up used globally for most of the 20th century. In the middle, Stevenson screen equipped with automatic sensors. The Montsouri screen is better ventilated, but because some solar radiation can get onto the thermometer it registers somewhat higher temperatures than a Stevenson screen. Picture: Project SCREEN, Center for Climate Change, Universitat Rovira i Virgili, Spain.
The
instrumental climate record is human cultural heritage, the product
of the diligent work of many generations of people all over the
world. But changes in the way temperature was measured and in the
surrounding of weather stations can produce spurious trends. An
international team, with participation of the University Rovira i
Virgili (Spain), State Meteorological Agency (AEMET, Spain) and
University of Bonn (Germany), has made a great endeavour to provide
reliable tests for the methods used to computationally eliminate such
spurious trends. These so-called “homogenization methods“ are a
key step to turn the enormous effort of the observers into accurate
climate change data products. The results have been published in the
prestigious Journal of Climate of the American Meteorological
Society. The research was funded by the Spanish Ministry of Economy
and Competitiveness.
Climate
observations often go back more than a century, to times before we
had electricity or cars. Such long time spans make it virtually
impossible to keep the measurement conditions the same across time.
The best-known problem is the growth of cities around urban weather
stations. Cities tend to be warmer, for example due to reduced
evaporation by plants or because high buildings block cooling. This
can be seen comparing urban stations with surrounding rural stations.
It is less talked about, but there are similar problems due to the
spread of irrigation.
The
most common reason for jumps in the observed data are relocations of
weather stations. Volunteer observers tend to make observations near
their homes; when they retire and a new volunteer takes over the
tasks, this can produce temperature jumps. Even for professional
observations keeping the locations the same over centuries can be a
challenge either due to urban growth effects making sites unsuitable
or organizational changes leading to new premises. Climatologist from
Bonn, Dr. Victor Venema, one of the authors: “a quite typical
organizational change is that weather offices that used to be in
cities were transferred to newly build airports needing observations
and predictions. The weather station in Bonn used to be on a field in
village Poppelsdorf, which is now a quarter of Bonn and after several
relocations the station is currently at the airport Cologne-Bonn.”
For
global trends, the most important changes are technological changes
of the same kinds and with similar effects all over the world. Now we
are, for instance, in a period with widespread automation of the
observational networks.
Appropriate
computer programs for the automatic homogenization of climatic time
series are the result of several years of development work. They work
by comparing nearby stations with each other and looking for changes
that only happen in one of them, as opposed to climatic changes that
influence all stations.
To
scrutinize these homogenization methods the research team created a
dataset that closely mimics observed climate datasets including the
mentioned spurious changes. In this way, the spurious changes are
known and one can study how well they are removed by homogenization.
Compared to previous studies, the testing datasets showed much more
diversity; real station networks also show a lot of diversity due to
differences in their management. The researchers especially took care
to produce networks with widely varying station densities; in a dense
network it is easier to see a small spurious change in a station. The
test dataset was larger than ever containing 1900 station networks,
which allowed the scientists to accurately determine the differences
between the top automatic homogenization methods that have been
developed by research groups from Europe and the Americas. Because of
the large size of the testing dataset, only automatic homogenization
methods could be tested.
The
international author group found that it is much more difficult to
improve the network-mean average climate signals than to improve the
accuracy of station time series.
The
Spanish homogenization methods excelled. The method developed at the
Centre for Climate Change, Univ. Rovira i Virgili, Vila-seca, Spain,
by Hungarian climatologist Dr. Peter Domonkos was found to be the
best at homogenizing both individual station series and regional
network mean series. The method of the State Meteorological Agency
(AEMET), Unit of Islas Baleares, Palma, Spain, developed by Dr. José
A. Guijarro was a close second.
When
it comes to removing systematic trend errors from many networks, and
especially of networks where alike spurious changes happen in many
stations at similar dates, the homogenization method of the American
National Oceanic and Atmospheric Agency (NOAA) performed best. This
is a method that was designed to homogenize station datasets at the
global scale where the main concern is the reliable estimation of
global trends.
The earlier used Open Screen used at station Uccle in Belgium, with two modern closed thermometer Stevenson screens with a double-louvred walls in the background.
Quotes from participating researchers
Dr.
Peter Domonkos, who earlier was a weather observer and now writes a
book about time series homogenization: “This study has shown the
value of large testing datasets and demonstrates another reason why
automatic homogenization methods are important: they can be tested
much better, which aids their development.”
Prof.
Dr. Manola Brunet, who is the director of the Centre for Climate
Change, Univ. Rovira i Virgili, Vila-seca, Spain, Visiting Fellow at
the Climatic Research Unit, University of East Anglia, Norwich, UK
and Vice-President of the World Meteorological Services Technical
Commission said: “The study showed how important dense station
networks are to make homogenization methods powerful and thus to
compute accurate observed trends. Unfortunately, still a lot of
climate data needs to be digitized to contribute to an even better
homogenization and quality control.”
Dr.
Javier Sigró from the Centre for Climate Change, Univ. Rovira i
Virgili, Vila-seca, Spain: “Homogenization is often a first step
that allows us to go into the archives and find out what happened to
the observations that produced the spurious jumps. Better
homogenization methods mean that we can do this in a much more
targeted way.”
Dr.
José A. Guijarro: “Not only the results of the project may help
users to choose the method most suited to their needs; it also helped
developers to improve their software showing their strengths and
weaknesses, and will allow further improvements in the future.”
Dr.
Victor Venema: “In a previous similar study we found that
homogenization methods that were designed to handle difficult cases
where a station has multiple spurious jumps were clearly better.
Interestingly, this study did not find this. It may be that it is
more a matter of methods being carefully fine-tuned and tested.”
Dr.
Peter Domonkos: “The accuracy of homogenization methods will likely
improve further, however, we never should forget that the spatially
dense and high quality climate observations is the most important
pillar of our knowledge about climate change and climate
variability.”
Press releases
Spanish weather service, AEMET: Un equipo internacional de climatólogos estudia cómo minimizar errores en las tendencias climáticas observadas
URV university in Tarragona, Catalonian: Un equip internacional de climatòlegs estudia com es poden minimitzar errades en les tendències climàtiques observades
URV university, Spanish: Un equipo internacional de climatólogos estudia cómo se pueden minimizar errores en las tendencias climáticas observadas
URV university, English: An international team of climatologists is studying how to minimise errors in observed climate trends
Articles
Tarragona 21: Climatòlegs de la URV estudien com es poden minimitzar errades en les tendències climàtiques observades
Genius Science, French: Une équipe de climatologues étudie comment minimiser les erreurs dans la tendance climatique observée
Phys.org: A team of climatologists is studying how to minimize errors in observed climate trend
