Upcoming meetings on climate data quality

It looks like 2016 will be a year full of interesting conferences. I already pointed you to EGU, IMSC and EMS before. Here is an update with three upcoming European meetings, including two close deadlines.

The marine climate data community will hold its main workshop this July (18 to 22). The deadline has just been prolonged to the 6th of April, Wednesday next week.

The metrologists (no typo) organize a meeting on climate data, MMC2016. It will take place from 26 to 30 September and will be organized together with the WMO TECO conference.

And naturally we will have the European Meteorological Society meeting in Autumn. This year is an ECAC year (European Conference on Applied Climatology). The abstract submission deadline is in about three weeks, 21 Apr 2016, during EGU. So start writing soon. As always we will have a session on "Climate monitoring; data rescue, management, quality and homogenization" for the homogenization addicted readers of this blog.

If you know of more interesting conferences, do add them in the comments.

MARCDAT-IV

The Fourth International Workshop on the Advances in the Use of Historical Marine Climate Data (MARCDAT-IV) will be held at the National Oceanography Centre, Southampton, UK between the 18th and 22nd July 2016. The workshop will be arranged around the following themes:

• Data homogenization (benchmarking, bias adjustments, step change analysis, metadata)
• Quantification and estimation of uncertainty
• Data management, recovery and reprocessing (digitisation efforts and reprocessing of previously digitised data)
• Reconstructing past climates
• Integrating In-situ / satellite data sources
• Consistency of the climate across domain boundaries (land, ocean, surface, subsurface, atmosphere)
• The role of ICOADS and applications of marine climate data
• Review of the 10-year action plan

This looks like an invitation of people working on land data to also participate. I just asked some colleagues on the homogenization list and it looks like there are a decent number of weather stations near the coast. We could compare them to marine observations, I would especially be interested in comparing sea surface temperature observations.

This workshop is free to attend, but participants must register.

Key Dates:
Abstract submission deadline: 8th April 2016
Registration closes: 31st May 2016

MMC-2016

International workshop on Metrology for Meteorology and Climate in conjunction with WMO TECO 2016 conference & Meteorological Technology World Expo 2016. It will be held in Madrid Spain from 26 to 30 September 2016.

During the last years an increasing collaboration has been established between the Metrology and Meteorology communities. EURAMET, the European association of metrology Institutes, is funding several projects aiming at delivering results of valuable impact for the meteorology and climatology science. The key aspect of such projects is the traceability of measurements and uncertainties of measured physical and chemical quantities describing the earth atmosphere. The MMC conference aims to give an opportunity to those two communities to present and discuss needs, methods, expertise and devices for cooperating in producing better data. The invitation is addressed to the metrology, meteorology and climate scientific communities and operators. Starting with the first MMC 2014 in Brdo, Slovenia, this time the MMC 2016 is organized Madrid, Spain, in conjunction with CIMO-TECO conference.

As far as I know the abstract deadline has not been determined yet, but write the date into your agenda, bookmark the homepage and contact the organizers to give you a notice once the deadline is known.

EMS2016

The conference theme of the Annual Meeting of the European Meteorological Society is: Where atmosphere, sea and land meet: bridging between sciences, applications and stakeholders. It will be held from 12 to 16 September 2016 in Trieste, Italy. The abstract submission deadline is 21 April 2016, during EGU2016.

MC1 Climate monitoring; data rescue, management, quality and homogenization
Convener: Manola Brunet-India
Co-Conveners: Hermann Mächel, Victor Venema, Ingeborg Auer, Dan Hollis 

Robust and reliable climatic studies, particularly those assessments dealing with climate variability and change, greatly depend on availability and accessibility to high-quality/high-resolution and long-term instrumental climate data. At present, a restricted availability and accessibility to long-term and high-quality climate records and datasets is still limiting our ability to better understand, detect, predict and respond to climate variability and change at lower spatial scales than global. In addition, the need for providing reliable, opportune and timely climate services deeply relies on the availability and accessibility to high-quality and high-resolution climate data, which also requires further research and innovative applications in the areas of data rescue techniques and procedures, data management systems, climate monitoring, climate time-series quality control and homogenisation.

In this session, we welcome contributions (oral and poster) in the following major topics:

• Climate monitoring , including early warning systems and improvements in the quality of the observational meteorological networks
• More efficient transfer of the data rescued into the digital format by means of improving the current state-of-the-art on image enhancement, image segmentation and post-correction techniques, innovating on adaptive Optical Character Recognition and Speech Recognition technologies and their application to transfer data, defining best practices about the operational context for digitisation, improving techniques for inventorying, organising, identifying and validating the data rescued, exploring crowd-sourcing approaches or engaging citizen scientist volunteers, conserving, imaging, inventorying and archiving historical documents containing weather records
• Climate data and metadata processing, including climate data flow management systems, from improved database models to better data extraction, development of relational metadata databases and data exchange platforms and networks interoperability
• Innovative, improved and extended climate data quality controls (QC), including both near real-time and time-series QCs: from gross-errors and tolerance checks to temporal and spatial coherence tests, statistical derivation and machine learning of QC rules, and extending tailored QC application to monthly, daily and sub-daily data and to all essential climate variables
• Improvements to the current state-of-the-art of climate data homogeneity and homogenisation methods, including methods intercomparison and evaluation, along with other topics such as climate time-series inhomogeneities detection and correction techniques/algorithms (either absolute or relative approaches), using parallel measurements to study inhomogeneities and extending approaches to detect/adjust monthly and, especially, daily and sub-daily time-series and to homogenise all essential climate variables
• Fostering evaluation of the uncertainty budget in reconstructed time-series, including the influence of the various data processes steps, and analytical work and numerical estimates using realistic benchmarking datasets

Next to this session, readers of this blog may also like: Climate change detection, assessment of trends, variability and extremes. And I personally like the session on Spatial Climatology, which has a lot to do with structure and variability.

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Top photo by Martin Duggan, which has a CC BY 2.0 license.

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.

Bernie Sanders is more electable

The bias in the American mass media are driving me crazy. So let me get onto my little soap box.

You can see the bias when they add the super-delegates to the pledged delegates to pretend that Clinton has a higher lead. If these super-delegates would actually vote against the wish of the primary voters, the disgust will be large enough to make sure that the Democrats would lose.

You can see the bias in that the media hardly speaks about money in politics. A main topic in both primaries.

What I want to talk about today is that the media generally assumes that Hillary Clinton is more electable in the general presidential election than Bernie Sanders. Often in the dismissive implicit way of the establishment. That can be a real opinion, but the evidence goes into another direction.

Money in politics

I am happy to admit that I am also biased. For me Bernie Sanders is clearly the best candidate, mainly because he wants to get money out of politics. Money in politics is bad for the public debate, for democracy, for the environment and for the economy.

Politicians who have to do what their donors tell them have no flexibility to compromise and get things done; they have to defend indefensible positions. This leads to a childish political debate. Nearly all Republican representatives in Washington reject basic scientific findings on climate change. This is childish. It is equivalent to putting your fingers in your ears and singing la, la, la, la. It matches the donor's preferences, but does not match their voter base. Half of the Republican voters support policies to reduce greenhouse gasses.