Cooling moves of urban stations

It has been studied over and over again, in very many ways: in global temperature datasets urban stations have about the same temperature trend as surrounding rural stations.

There is also massive evidence that urban areas are typically warmer than their surroundings. For large urban areas the Urban Heat Island (UHI) effect can increase the temperature by several degrees Celsius.

A constant higher temperature due to the UHI does not influence temperature changes. However, when cities grow around a weather station, this produces an artificial warming trend.

Why don’t we see this in the urban stations of the global temperature collections? There are several reasons; the one I want to focus on in this post is that stations do not stay at the same place.

Urban stations are often relocated to better locations, more outside of town. It is common for urban stations to be moved to airports, especially when meteorological offices are moved to the airport to assist in airport safety. Also when meteorological offices can no longer pay the rent in the city center, they are forced to move out and take the station with them. When urban development makes the surrounding unsuited or when a volunteer observer retires, the station has to move, it makes sense to then search for a better location, which will likely be in a less urban area.

Relocations are nearly always the most frequent reason for inhomogeneities. For example, Manola Brunet and colleagues (2006) write about Spain:

“Changes in location and setting are the main cause of inhomogeneities (about 56% of stations). Station relocations have been common during the longest Spanish temperature records. Stations were moved from one place to another within the same city/town (i.e. from the city centre to outskirts in the distant past and, more recently, from outskirts to airfields and airports far away from urban influence) and from one setting (roofs) to another (courtyards).”

Since relocations of that kind are likely to result in a cooling, the Parallel Observations Science Team (ISTI-POST) wants to have a look at how large this effect is. As far as we know there is no overview study yet, but papers on the homogenization of a station network often report on adjustments made for specific inhomogeneities.

We, that is mainly Jenny Linden of Mainz University, had a look in the scientific literature. Let’s start in China were urbanization is strong and can be clearly seen in the raw data of many stations. They also have strong cooling relocations. The graph below from Wenhui Xu and colleagues (2013) shows the distribution of breaks that were detected (and corrected) with statistical homogenization for which the station history indicated that they were caused by relocations. Both the minimum and the maximum temperature cool by a few tenth of a degree Celsius due to the relocations.

The distribution of the breaks that were due to relocations for the maximum temperature (left) and minimum temperature (right). The red line is a Gaussian distribution for comparison.


Going more in detail, Zhongwei Yan (2010) and colleagues studied two relocations in Beijing. They found that the relocations cooled the observations by −0.81°C and −0.69°C. Yuan-Jian Yang and colleagues (2013) find a cooling relocation of 0.7°C in the data of Hefei. Clearly for single urban stations, relocations can have a large influence.

Fatemeh Rahimzadeh and Mojtaba Nassaji Zavareh (2014) homogenized the Iranian temperature observations and observed that relocations were frequent:

“The main non-climatic reasons for non-homogeneity of temperature series measured in Iran are relocation and changes in the measuring site, especially a move from town to higher elevations, due to urbanization and expansion of the city, construction of buildings beside the stations, and changes in vegetation.”

They show an example with 5 stations where one station (Khoramabad) has a relocation in 1980 and another station (Shahrekord) has two relocation in 1980 and 2002. These relocations have a strong cooling effect of 1 to 3 degrees Celsius.


Temperature in 5 stations in Iran, including their adjusted series.


The relocations do not always have a strong effect. Margarita Syrakova and Milena Stefanova (2009) do not find any influence of the inhomogeneities on the annual mean temperature averaged over Bulgaria. This while “Most of the inhomogeneities were caused by station relocations… As there were no changes of the type of thermometers, shelters and the calculation of the daily mean temperatures, the main reasons of inhomogeneities could be station relocations, changes of the environment or changes of the station type (class).”

In Finland, Norway, Sweden and the UK the relocations produced a cooling bias of -0.11°C and relocations appear to be the most common cause of inhomogeneities (Tuomenvirta, 2001). The table below summarises the breaks that were found and what the reasons for them were if this was known from the station histories. They write:

“[Station histories suggest] that during the 1930s, 1940s and 1950s, there has been a tendency to move stations from closed areas in growing towns to more open sites, for example, to airports. This can be seen as a counter-action to increasing urbanization.”

Table with the average bias of inhomogeneities found in Finland, Sweden, Norway and the UK in winter (DJF), spring (MAM), summer (JJA) and autumn (SON) and in the yearly average. Changes in the surrounding, such as urbanization or micro-siting changes, made the temperatures higher. This was counteracted by more frequent cooling biases from changes in the thermometers and the screens used to protect the thermometers, by relocations and by changes in the formula used to compute the daily mean temperature.


Concluding, relocations are a frequent type of inhomogeneity. They produce a cooling bias. For urban stations the cooling can be very large. For the average over a region, the values are smaller, but especially because they are so common, they will have most likely a clear influence on global warming in raw temperature observations.

Future research

One problem with studying relocations is that they are frequently accompanied by other changes. Thus you can study them in two ways: study only relocations where you know that no other changes were made or study all historical relocations whether there was another change or not.

The first set-up allows us to characterize the relocations directly, to understand the physical consequences to move for example a station from the center of a city / village to the airport. In this way the differences are not subject to other changes specific to a network. So, the results can be easily compared between regions. The problem is that only a part of the parallel measurements available satisfy these strict conditions.

Conversely, for the second design (taking all historical relocations, also when they have another change) the characterization of the bias will be limited to the datasets studied and we will need a large sample to say something about the global climate record. But on the other hand, we can also analyze more data this way.

There are also two possible sources of information. The above studies relied on statistical homogenization comparing a candidate station to its neighbors. All you need to know for this is which inhomogeneities belong to a relocation. A more direct way to study these relocations is by using parallel measurements at both locations. This is especially helpful to study changes in the variability around the mean and in weather extremes. That is where the Parallel Observation Science Team (ISTI-POST) comes into play.

It is also possible to study specific relocations. The relocation of stations to airports was an important transition, especially around the 1940s. This temperature change is likely large and this transition quite frequent and well documented. One could focus on urban stations or on village stations, rather than studying all stations.

One could make a classification of the micro and macro siting before and after the relocation. For micro-siting the Michel Leroy (2010) classification could be interesting; as far as I know this classification has not been validated yet, we do not know how large the biases of the 5 categories are and how well-defined these biases are. Ian Stewart and Tim Oke (2012) have made a beautiful classification of the local climate zones of (urban) areas, which can also be used to classify the surrounding of stations.


Example of various combinations of building and land use of the local climate zones of Stewart and Oke.


There are many options and what we choose will also depend on what kind of data we can get. Currently our preference is to study parallel data with identical instrumentation at two locations, to understand the influence of the relocation itself as well as possible. In addition to study the influence on the mean, we are gathering data on the break sizes found by statistical homogenization for breaks due to relocations. The station histories (metadata) are crucial for this in order to clearly assign breakpoints to relocation activities. It will also be interesting to compare those two information sources where possible. This may become one study or two depending on how involved the analysis will become.

This POST study is coordinated by Alba Guilabert and Jenny Linden and Manuel Dienst are very active. Please contact one of us if you would like to be involved in a global study like this and tell us what kind of data you would have. Also if anyone knows of more studies reporting the size of inhomogeneities due to relocations, please let us know. I certainly have seen more such tables at conferences, but they may not have been published.

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Related reading

Parallel Observations Science Team (POST) of the International Surface Temperature Initiative (ISTI).

The transition to automatic weather stations. We’d better study it now.

Changes in screen design leading to temperature trend biases.

Early global warming.

Why raw temperatures show too little global warming.

References

Brunet M., O. Saladie, P. Jones, J. Sigró, E. Aguilar, et al., 2006: The development of a new daily adjusted temperature dataset for Spain (SDATS) (1850–2003). International Journal of Climatology, 26, pp. 1777–1802, doi: 10.1002/joc.1338.
See also: a case-study/guidance on the development of long-term daily adjusted temperature datasets.

Leroy, M., 2010: Siting classifications for surface observing stations on land. In WMO Guide to Meteorological Instruments and Methods of Observation. "CIMO Guide", WMO-No. 8, Part I, Chapter 1, Annex 1B.

Rahimzadeh, F. and M.N. Zavareh, 2014: Effects of adjustment for non‐climatic discontinuities on determination of temperature trends and variability over Iran. International Journal of Climatology, 34, pp. 2079-2096, doi: 10.1002/joc.3823.

Stewart, I.D. and T.R. Oke, 2012: Local climate zones for urban temperature studies. Bulletin American Meteorological Society, 93, pp. 1879–1900, doi: 10.1175/BAMS-D-11-00019.1.
See also the World Urban Database.

Tuomenvirta, H., 2001: Homogeneity adjustments of temperature and precipitation series - Finnish and Nordic data. International Journal of Climatology, 21, pp. 495-506, doi: 10.1002/joc.616.

Xu, W., Q. Li, X.L. Wang, S. Yang, L. Cao, and Y. Feng, 2013: Homogenization of Chinese daily surface air temperatures and analysis of trends in the extreme temperature indices. Journal Geophysical Research Atmospheres, 118, doi: 10.1002/jgrd.50791.

Syrakova M. and Stefanova M., 2009: Homogenization of Bulgarian temperature series. International. Journal Climatology, 29, pp. 1835-1849, doi: 10.1002/jov.1829.

Yan ZW; Li Z; Xia JJ. 2014. Homogenisation of climate series: The basis for assessing climate changes. Science China: Earth Sciences, 57, pp 2891-2900, doi: 10.1007/s11430-014-4945-x.

* Photo at the top "High Above Sydney" by Taro Taylor used with a Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0) license.

Irrigation and paint as reasons for a cooling bias

In previous posts on reasons why raw temperature data may show too little global warming I have examined improvements in the siting of stations, improvements in the protection of thermometers against the sun, and moves of urban stations to better locations, in particular to airports. This post will be about the influence of irrigation and watering, as well as improvements in the paints used for thermometer screens.

Irrigation and watering

Irrigation can decrease air temperature by up to 5 degrees and typically decreases the temperature by about 1°C (Cook et al., 2014). Because of irrigation more solar energy is used for evaporation and for transpiration by the plants, rather than for warming of the soil and air.

Over the last century we have seen a large 5 to 6 fold global increase in irrigation; see graph below.



The warming by the Urban Heat Island (UHI) is real. The reason we speak of a possible trend bias due to increases in the UHI is that an urban area has a higher probability of siting a weather station than rural areas. If only for the simple reason that that is where people live and want information on the weather.

The cooling due to increases in irrigation are also real. It seems to be a reasonable assumption that an irrigated area again has a higher probability of siting a weather station. People are more likely to live in irrigated areas and many weather stations are deployed to serve agriculture. While urbanization is a reason for stations to move to better locations, irrigation is no reason for a station to move away. On the contrary maybe even.

The author of the above dataset showing increases in irrigation, Stefan Siebert, writes: "Small irrigation areas are spread across almost all populated areas of the world." You can see this strong relation between irrigation and population on a large scale in the map below. It seems likely that this is also true on local scales.



Many stations are also in suburbs and these are likely watered more than they were in the past when water (energy) was more expensive or people even had to use hand pumps. In the same way as irrigation, watering could produce a cool bias due to more evaporation. Suburbs may thus be even cooler than the surrounding rural areas if there is no irrigation. Does anyone know of any literature about this?

I know of one station in Spain where the ground is watered to comply with WMO guidelines that weather stations should be installed on grass. The surrounding is dry and bare, but the station is lush and green. This could also cause a temperature trend bias under the reasonable assumption that this is a new idea. If anyone knows more about such stations, please let me know.

From whitewash to latex paint

Also the maintenance of the weather station can be important. Over the years better materials and paints may have been used for thermometer screens. If this makes the screens more white, they heat up less and they heat up the air flowing through the Louvres less. More regular cleaning and painting would have the same effect. It is possible that this has improved when climate change made weather services aware that high measurement accuracies are important. Unfortunately, it is also possible that good maintenance is nowadays seen as inefficient.

The mitigation skeptics somehow thought that the effect would go into the other direction. That the bad paints used in the past would be a cooling bias, rather than a warming bias. Something with infra-red albedo. Although most materials used have about the same infra-red albedo and the infra-red radiation fluxes are much smaller than the solar fluxes.

Anthony Watts started a paint experiment in his back garden in July 2007. The first picture below shows three Stevenson screens, a bare one, a screen with modern latex paint and one with whitewash, a chalk paint that quickly fades.



Already 5 months later in December 2007, the whitewash had deteriorated considerably; see below. This should lead to a warm bias for the whitewash screen, especially in summer.

Anthony Watts:

Compare the photo of the whitewash paint screen on 7/13/07 when it was new with one taken today on 12/27/07. No wonder the NWS dumped whitewash as the spec in the 70’s in favor of latex paint. Notice that the Latex painted shelter still looks good today while the Whitewashed shelter is already deteriorating.

In any event the statement of Patrick Michaels “Weather equipment is very high-maintenance. The standard temperature shelter is painted white. If the paint wears or discolors, the shelter absorbs more of the sun’s heat and the thermometer inside will read artificially high.” seems like a realistic statement in light of the photos above.

I have not seen any data from this experiment beyond a plot with one day of temperatures, which was a day one month after the start, showing no clear differences between the Stevenson screens. They were all up to 1°C warmer than the modern ventilated automatic weather station when the sun was shining. (That the most modern ventilated measurement had a cool bias was not emphasized in the article, as you can imagine.) Given that Anthony Watts maintains a stealth political blog against mitigation of climate change, I guess we can conclude that he probably did not like the results, that the old white wash screen was warmer and he did not want to publish that.

We may be able to make a rough estimate the size of the effect by looking at another experiment with a bad screen. In sunny Italy Giuseppina Lopardo and colleagues compared two old aged, yellowed and cracked screens of unventilated automatic weather stations that should have been replaced long ago with a good new screen. The picture to the right shows the screen after 3 years. They found a difference of 0.25°C after 3 years and 0.32°C after 5 years.

The main caveat is that the information on the whitewash comes from Anthony Watts. It may thus well misinformation that the American Weather Bureau used whitewash in the past. Lacquer paints are probably as old as 8000 years and I see no reason to use whitewash for a small and important weather screen. If anyone has a reliable source about paints used in the past, either inside or outside the USA, I would be very grateful.

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Related posts

Changes in screen design leading to temperature trend biases

Temperature bias from the village heat island

Temperature trend biases due to urbanization and siting quality changes

Climatologists have manipulated data to REDUCE global warming

Homogenisation of monthly and annual data from surface stations

References

Cook, B.I., S.P. Shukla, M.J. Puma, L.S. Nazarenko, 2014: Irrigation as an historical climate forcing. Climate Dynamics, 10.1007/s00382-014-2204-7.

Siebert, Stefan, Jippe Hoogeveen, Petra Döll, Jean-Marc Faurès, Sebastian Feick and Karen Frenken, 2006: The Digital Global Map of Irrigation Areas – Development and Validation of Map Version 4. Conference on International Agricultural Research for Development. Tropentag 2006, University of Bonn, October 11-13, 2006.

Siebert, S., Kummu, M., Porkka, M., Döll, P., Ramankutty, N., and Scanlon, B.R., 2015: A global data set of the extent of irrigated land from 1900 to 2005. Hydrology and Earth System Sciences, 19, pp. 1521-1545, doi: 10.5194/hess-19-1521-2015.

See also: Zhou, D., D. Li, G. Sun, L. Zhang, Y. Liu, and L. Hao (2016), Contrasting effects of urbanization and agriculture on surface temperature in eastern China, J. Geophys. Res. Atmos., 121, doi: 10.1002/2016JD025359.

Temperature trend biases due to urbanization and siting quality changes

The temperature in urban areas can be several degrees higher than their surrounding due to the Urban Heat Island (UHI). The additional heat stress is an important medical problem and studied by bio-meteorologists. Many urban geographers study the UHI and ways to reduce the heat stress. Their work suggests that the UHI is due to a reduction in evaporation from bare soil and vegetation in city centers. The solar energy that is not used for evaporation goes into warming of the air. In case of high-rise buildings there are, in addition, more surfaces and thus more storage of heat in the buildings during the day, which is released during the night. High-rise buildings also reduce radiative cooling (infrared) at night because the surface sees a smaller part of the cold sky. Recent work suggests that cities also influence convection (often visible as cumulus (towering) clouds).

To study changes in the temperature, a constant UHI bias is no problem. The problem is an increase in urbanization. For some city stations this can be clearly seen in a comparison with nearby rural stations. A clear example is the temperature at the station in Tokyo, where the temperature since 1920 rises faster than in surrounding stations.



Scientists like to make a strong case, thus before they confidently state that the global temperature is increasing, they have naturally studied the influence of urbanization in detail. An early example is Joseph Kincer of the US Weather Bureau (HT @GuyCallendar) who studied the influence of growing cities in 1933.

While urbanization can be clearly seen for some stations, the effect on the global mean temperature is small. The Fourth Assessment Report from the IPCC, states the following.

Studies that have looked at hemispheric and global scales conclude that any urban-related trend is an order of magnitude smaller than decadal and longer time-scale trends evident in the series (e.g., Jones et al., 1990; Peterson et al., 1999). This result could partly be attributed to the omission from the gridded data set of a small number of sites (<1%) with clear urban-related warming trends. ... Accordingly, this assessment adds the same level of urban warming uncertainty as in the TAR: 0.006°C per decade since 1900 for land, and 0.002°C per decade since 1900 for blended land with ocean, as ocean UHI is zero.

Next to the removal of urban stations, the influence of urbanization is reduced by statistical removal of non-climatic changes (homogenization). The most overlooked aspect may, however, be that urban stations do not often stay at the same location, but rather are relocated when the surrounding is seen to be no longer suited or the meteorological offices simply cannot pay the rent any more or the offices are relocated to airports to help with air traffic safety.

Thus urbanization does not only lead to an gradual increase in temperature, but also to downward jumps. Such a non-climatic change often looks like an (irregular) sawtooth. This can lead to artificial trends in both directions; see sketch below. In the end, what counts is how strong the UHI was in the beginning and how strong it is now.



The first post of this series was about a new study that showed that even villages have a small "urban heat island". For a village in Sweden (Haparanda) and Germany (Geisenheim) the study found that the current location of the weather station is about 0.5°C (1°F) colder than the village center. For cities you would expect a larger effect.

Around the Second World War many city stations were moved to airports, which largely takes the stations out of the urban heat island. Comparing the temperature trend of stations that are currently at airports with the non-airport stations, a number of people have found that this effect is about 0.1°C for the airport stations, which would suggest that it is not important for the entire dataset.

This 0.1°C sounds rather small to me. If we have urban heat islands of multiple degrees and people worry about small increases in the urban heat island effect, then taking a station (mostly) out of the heat island should lead to a strong cooling. Furthermore, cities are often in valleys and coasts and the later build airports thus often are at a higher and thus cooler location.

A preliminary study by citizen scientist Caerbannog suggests that airport relocations can explain a considerable part of the adjustments. These calculations need to be performed more carefully and we need to understand why the apparently small difference for airport stations translates to a considerable effect for the global mean. A more detailed scientific study on relocations to airports is unfortunately still missing.

Also the period where the bias increases in GHCNv3 corresponds to the period around the second world war where many stations were relocated to airports, see figure below. Finally, also that the temperature trend bias in the raw GHCNv3 data is larger than the bias in the Berkeley Earth dataset suggests that airport relocations could be important. Airport stations are overrepresented in GHCNv3, which contains a quite large fraction of airport stations.



With some colleagues we have started the Parallel Observations Science Team (POST) in the International Surface Temperature Initiative. There are some people interested in using parallel measurements (simultaneous measurements at cities and airports) to study the influence of these relocations. There seems to be more data than one may think. We are, however, still looking for a leading author (hint).

If the non-climatic change due to airport relocations is different (likely larger) than the change implemented in GHCNv3, that would give us an estimate of how well homogenization methods can reduce trend biases. Williams, Menne, and Thorne (2012) showed that homogenization can reduce trend errors, that they improve trend estimates, but also that part of the bias remains.



In the 19th century and earlier, thermometers were expensive scientific instruments and meteorological observations were made by educated people, apothecaries, teachers, clergymen, and so on. These people lived in the city. Many stations have subsequently been moved to better and colder locations. Whether urbanization produces a cold or a warm bias is thus an empirical and historical question. The evidence seems to show that on average the effect is small. It would be valuable when the effect of urbanization and relocations would be studies together. That may lead to an understanding of this paradox.

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Related posts

Changes in screen design leading to temperature trend biases

Temperature bias from the village heat island

Climatologists have manipulated data to REDUCE global warming

Homogenisation of monthly and annual data from surface stations

Temperature bias from the village heat island

The most direct way to study how alterations in the way we measure temperature affect the registered temperatures is to make simultaneous measurements the old way and the current way. New technological developments have now made it much easier to study the influence of location. Modern batteries have made it possible to just install an automatically recording weather station anywhere and obtain several years of data. It used to be necessary to have nearby electricity access, permissions to use it and dig cables in most cases.

Jenny Linden used this technology to study the influence of the siting of weather stations on the measured temperature for two villages. One village was in North Sweden, one in the West of Germany. In both cases the center of the village was about half a degree Centigrade (one degree Fahrenheit) warmer than the current location of the weather station on grassland just outside the villages. This is small compared to the urban heat island found in large cities, but it is comparable in size to the warming we have seen since 1900 and thus important for the understanding of global warming. In urban areas, the heat island can be multiple degrees and is studied much because of the additional heat stress it produces. This new study may be the first for villages.

Her presentation (together with Jan Esper and Sue Grimmond) at EMS2014 (abstract) was my biggest discovery in the field of data quality in 2014. Two locations is naturally not not enough for strong conclusions, but I hope that this study will be the start of many more, now that the technology has been shown to work and the effects to be significant for climate change studies.

The experiments

A small map of Haparanda, Sweden, with all measurement locations indicated by a pin. Mentioned in the text are Center and SMHI current met-station.

The Swedish case is easiest to interpret. The village [[Haparanda]] with 5 thousand inhabitants is in the North of Sweden, on the border with Finland. It has a beautiful long record, measurements started in 1859. Observations started on a North wall in the center of the village and were continued there until 1942. Currently the station is on the edge of the village. It is thought that the center did not change much any more since 1942. Thus the difference could be interpreted as the cooling bias due to the relocation from the center to its current location in the historical observations. The modern measurement was not at the original North wall, but free standing. Thus only the difference of the location can be studied.

As so often, the minimum temperature at night is affected most. It has a difference of 0.7°C between the center and the current location. The maximum temperature only shows a difference of 0.1°C. The average temperature has a difference of 0.4°C.

The village [[Geisenheim]] is close to Mainz, Germany, and was the first testing location for the equipment. It has 11.5 thousand inhabitants and is on the right bank of the Rhine. Also this station has a quite long history and started in 1884 in a park and stayed there until 1915. Now it is well-sited outside of the village in the meadows. A lot has changed in Geisenheim between 1915 and now. So we cannot make any historical interpretation of the changes, but it is interesting to compare the measurements in the center with the current ones to compare with Haparanda and to get an idea how large the maximum effect would theoretically be.

A small map of Geisenheim, Germany. Compared in the text are Center and DWD current met-station. The station started in Park.

The difference in the minimum temperature between the center and the current location is 0.8°C. In this case also the maximum temperature has a clear difference of 0.4°C. The average temperature has a difference of 0.6°C.

The next village on the list is [[Cazorla]] in Spain. I hope the list will become much longer. If you have any good suggestions please comment below or write Jenny Linden. Especially locations where the center is still mostly like it used to be are of interest. And as much different climate regions should be sampled as possible.

The temperature record

Naturally not all stations started in villages and even less exactly in the center. But this is still a quite common scenario, especially for long series. In the 19th century thermometers were expensive scientific instruments. The people making the measurements were often the few well-educated people in the village or town, priests, apothecaries, teachers and so on.

Erik Engström, climate communicator of the Swedish weather service (SMHI) wrote:

In Sweden we have many stations that have moved from a central location out to a location outside the village. ... We have several stations located in small towns and villages that have been relocated from the centre to a more rural location, such as Haparanda. In many cases the station was also relocated from the city centre to the airport outside the city. But we also have many stations that have been rural and are still rural today.

Improvements in siting may be even more interesting for urban stations. Stations in cities have often been relocated (multiple times) to better sited locations, if only because meteorological offices cannot afford the rents in the center. Because the Urban Heat Island is stronger, this could lead to even larger cooling biases. What counts is not how much the city is warming due to its growth, but the siting of the first station location versus its current one.

More specifically, it would be interesting to study how much improvements in siting have contributed to a possible temperature trend bias in the recent decades. The move to the current locations took place in 2010 in Haparanda and in 2006 in Geisenheim. Where it should be noted that the cooling bias did not take place in one jump: decent measurements are likely to have been recorded since 1977 in Haparanda, and since 1946 in Geisenheim; For Geisenheim the information is not very reliable).

It would make sense to me that the more people started thinking about climate change, the more the weather services realized that even small biases due to imperfect siting are important and should be avoided. Also modern technology, automatic weather stations, batteries and solar panels, have made it easier to install stations in remote locations.

An exception here is likely the United States of America. The Surface Stations project has shown many badly sited stations in the USA and the transition to automatic weather stations is thought to have contributed to this. Explanations could be that America started early with automation, the cables were short and the technician had only one day to install the instruments.

When also villages have a small urban effect, it is also possible that this gradually increases while the village is growing. Such a gradual increase can also be removed by statistical homogenization by comparison with its neighboring stations. However, if too many stations have a such a gradual inhomogeneity, the homogenization methods will no longer be able to remove this non-climatic increase (well). Thus this finding makes it more important to make sure that sufficient really rural stations are used for comparison.

On the other hand, because a village is smaller, one may expect that the "gradual" increases are actually somewhat jumpy. Rather than being due to many changes in a large area around the station, in case of a village the changes may be expected to be more often nearer to the station and produce a small jump. Jumps are easier to remove by statistical homogenization than smooth gradual inhomogeneities, because the probability of something happening simultaneously in the neighboring station is smaller.

A parallel measurement in Basel, Switzerland. A historical Wild screen, which is open to the bottom and to the North and has single Louvres to reduce radiation errors, measures in parallel with a Stevenson screen (Cotton Region Shelter), which is close to all sides and has double Louvres.

Parallel measurements

These measurements at multiple locations are an example of parallel measurements. The standard case is that an old instrument is compared to a new one while measuring side by side. This helps us to understand the reasons for biases in the climate record.

From parallel measurements we, for example, also know that the way temperature was measured before the introduction of Stevenson Screens has caused a bias in the old measurements of up to a few tenth of a degree. With differences of 0.5°C being found for two locations Spain and two tropical countries, while the differences in North West Europe are typically small.

To be able to study these historical changes and their influence on the global datasets, we have started an initiative to build a database with parallel measurements under the umbrella of the International Surface Temperature Initiative (ISTI), the Parallel Observations Science Team (POST). We have just started and are looking for members and parallel datasets. Please contact us if you are interested.

[UPDATE. The above study is now published as. Lindén, J., C.S.B. Grimmond, and J. Esper: Urban warming in villages, Advances in Science and Research, 12, pp. 157-162, doi: 10.5194/asr-12-157-2015, 2015.]

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