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.
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 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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This is an interesting issue in homogenisation of climate series, however in my opinion these cases are unfortunately quite ideal cases.
ReplyDeleteIn many cases, the difference between the first and the last site of a very long time series is a complex issue (especially in urban areas) due to:
1) different screen and thermometer (sometimes it is impossible to make a replica of the old screen)
2) different elevation above the ground, different ground, different siting (i.e. north wall vs. free stading screen)
3) different land use or different physical properties of the neighbourhood (i.e. building heights, roads etc.)
4) different amount of waste heat (traffic) and household use of energy (heating, electricity)
In some cases if only one of the four points is unknown, then it is hard to say anything reliable about the total inhomogeneity merely from parallel measurements. For example in Ljubljana we know all the locations of measurements but we don't know enough about the screens and also cannot imitate the old conditions due to point 3 and 4.
Hopefully in future there will be any smart idea how to statistically bypass these problems (to deduce the magnitude of inhomogeneity from many statistical properties).
Gregor, you are right that it will often not be possible to find locations that are similar to the surrounding in the past when the observations were made.
ReplyDeleteIn such cases you could look for parallel measurements that were made at the time of the relocation. I just got a mail from a colleague who it working on such a problem for several relocations.
We would like to have a look in the large international collections, whether we can find instances where an old measurement overlaps with a new one. Then one still has to gather metadata to see what actually happened. And that would often mix a change in location with a change in relocation. That is what I liked about the above study, that it isolated the effect of the relocation.
I think that having a sufficiently large dataset will reduce much of your problems and in the best case would allow us to study such additional factors. Let's see. I am hopeful, but as with anything new, you first have to do before you can show it works.
Interesting, although I was rather amused by the population of the 'villages' - in many parts of Australia, 11,000 is a large town :-).
ReplyDeleteI've long thought that what's generally referred to as the 'urban heat island' is largely driven by conditions near the observation site (say, within 200-300 metres), with the overall size of the urban area being of much lesser importance, so it is interesting to see some evidence in support of that. We have numerous cases in Australia of sites relocating from built-up sites in the centre of small towns (sometimes very small towns - one of the most built-up sites in our long-term network in Australia was in a 'town' with a population of 150 in the South Australian outback) to airports or similar outside the town, and even for very small populations the out-of-town sites usually have cooler overnight temperatures (assuming they are topographically similar).
Conversely, the central Sydney observation site, which is in an area which has been heavily built-up since the 19th century, shows no evidence of anomalous warming post-1910 relative to rural sites in the region, indicating that whatever urban influence existed on temperatures at that site was already fully developed by 1910 (despite the fact that the population of the Sydney metropolitan area has increased from about 500,000 in 1910 to 4 million now).
Nice Victor.
ReplyDeleteA while back I started a study of small villages using MODIS LST, looks like I should go finish that work
Hi Blair, some Germans coming from the country side also do not agree with my personal definition of a village.
ReplyDeleteComing from a densely populated country, The Netherlands, my definition may be somewhat bias. Especially as I come from a "village" of 8 thousand inhabitants next to a city of 300 thousand. Such a village feels more like a village than one which is isolated. Most of the services were in the city next door.
Blog science shows that the relocations to airports only lead to a bias of 0.1°C. This somewhat surprises me, if the urban heat island is a real problem and produces a bias of multiple degrees, one would expect that taking a station out of a city and moving it to an airport would mean a clear cooling. (Then at the airport, you may see some warming again as the airport is growing.)
Also in London and in Vienna it is observed that the temperature trend of the rural stations is similar tot the trend inside the city. (The temperature in the city is absolutely larger, but the trend is similar.) Thus it seems that for large cities, the urban heat island can saturate.
Steven Mosher, you should! That would be an interesting and nicely complementary study. Looking forward to the results.
ReplyDeleteThen we would still need to understand the relationship with the surface temperature and the air temperature.
In our 2013 paper we looked in detail at the impact of urbanization on U.S. temperatures pre- and post-homogenization. We also ran a special version of the pairwise homogenization algorithm using only rural stations to homogenize, to show that homogenization wasn't simple spreading urban warmth to rural stations.
ReplyDeleteThe challenge, of course, is that the definition of urbanity is somewhat arbitrary. We looked at four different urbanity proxies, and each could be tightened or loosened if needed. We even did some sensitivity analysis of the results to the strictness of the urbanity definition, though I don't think that actually made it into the final paper. The challenge is that if you choose too strict a definition of urbanity, you don't have enough neighbors to effectively detect inhomogeneities, particularly in the early years (pre-1940s) when there were less co-op stations.
ftp://ftp.ncdc.noaa.gov/pub/data/ushcn/papers/hausfather-etal2013.pdf
I was aware of the London and Vienna studies. A difference with Sydney is that Sydney has grown greatly in area since 1910, much more so than London and Vienna have, but still shows no sign of an anomalous trend over that time.
ReplyDeleteMay be Sydney is a well ventilated (windy) place? UHI could be less noticeable in coastal towns or cities than in continental ones, that can be more affected by nocturnal calms...
ReplyDelete