The Parallel Observations Science Team (POST) is compiling a database with parallel measurements, which is important for a better understanding of
non-climatic changes (inhomogeneities) affecting the evaluation of long term changes in daily climate
Long instrumental climate records are usually affected by non-climatic changes due to, for example, relocations and changes in instrumentation, instrument height or data collection and manipulation procedures. These so-called inhomogeneities distort the climate signal and can hamper the assessment of trends and variability. Thus to study climatic changes we need to accurately distinguish non-climatic and climatic signals.
Inhomogeneities are especially important for studies on changes in extremes and weather variability using daily data. Our abilities to statistically homogenise daily data are very limited, while at least for temperature the non-climatic changes in the tails of the distribution are expected to be stronger than the changes in the mean state. This expectation is based on a very limited number of studies on daily parallel measurements and our understanding of their causes. For example, temperature measurements at Kremsmünster, Austria, on a north-facing wall show a mean bias of 2°C in June, with a bias of 5°C in the 99th percentile due to insolation at dawn.
The most direct way to study the influence of these non-climatic changes on the distribution and to understand the reasons for these biases is the analysis of parallel measurements representing the old and new situation (in terms of e.g. instruments, location).
A global parallel climate dataset
Current studies of non-climatic changes using parallel data are limited to local and regional case studies. However, the effect of specific transitions depends on the local climate and the most interesting questions are about the systematic large-scale biases produced by transitions that occurred in many regions. Important potentially biasing transitions are the adoption of Stevenson screens, efforts to reduce undercatchment of precipitation or the move to automatic weather stations. Thus a large global parallel dataset is highly desirable as it allows for the study of systematic biases in the global record.
The information from parallel measurements is also necessary to produce realistic validation datasets for homogenization methods and thus to be able to estimate the contribution of non-climatic changes to the uncertainty budget. Furthermore, a large dataset is needed to use parallel data to validate homogenization adjustments directly.
The WMO has recently called on all members to assist in gathering parallel datasets for an international dataset. The database is supported by the Task Team on homogenisation (TT-HOM) of the Commission for Climatology (CCl). The International Surface Temperature Initiative (ISTI) host a copy of the parallel dataset, as well as the European Climate Assessment & Dataset project (ECA&D). This will ensure professional and permanent archiving and thus the long-term use of these important datasets.
The dataset will be mainly build by executing studies using parallel data. We have several ongoing studies for which we are searching for collaborators.
a. POST-AWS-temp. The influence of automation on temperature, which is coordinated by Enric Aguilar.
b. POST-AWS-precip.The influence of automation on precipitation, coordinated by Petr Stepanek.
c. POST-early.The temperature change due to the transition of early screens to Stevenson screens, coordinated by Renate Auchmann and Victor Venema and with the help of Theo Brandsma.
d. POST-move.The influence of relocations on temperature, coordinated by Alba Gilabert with the help of Jenny Lindén and Manuel Dienst.
Some first results of POST-AWS.
We may work on humidity and sun shine duration in future and are open for suggestions for further studies in the framework of POST.
What we need
In the ISTI Parallel Observations Science Team (POST), we will gather parallel data in their native format (to avoid undetectable conversion errors we will convert it to a standard format ourselves). We are interested in data from all climate variables at all time scales; from annual to sub-daily.
High-resolution data is important for understanding the physical causes for the differences between the parallel measurements. This is an important application. Thus we are also interested in other climate variables measured at the same station. For example, in case of parallel temperature measurements, the influencing factors are expected to be insolation, wind and clouds cover; in case of parallel precipitation measurements, wind and temperature are potentially important.
For the same reason metadata that describe the parallel measurements is as important as the data itself and will be collected as well. For example, the types of the instruments, their siting, height, maintenance, etc.
The minimum length of the overlapping period is one season. Because they are widely used to study moderate extremes we will compute the indices of the Expert Team on Climate Change Detection and Indices. In case the daily data cannot be shared, we would appreciate these indices from parallel measurements.
Here is our current list with members. If you are interested in becoming a member, do contact us.
We also have a distribution list to keep people informed about the database and its analysis. If you would like to join, please send me a mail: Victor.Venema@uni-bonn.de.
The Terms of Reference give our rationale and our internal organisation.
Blog post on POST-AWS: The transition to automatic weather stations. We’d better study it now.
The blog post, A database with parallel climate measurements, describes the current state of the project and especially makes a proposal for the structure of the database (feedback welcome).
A more detailed description of the proposed database structure and the file formats.
The first blog post: A database with daily climate data for more reliable studies of changes in extreme weather gives somewhat more background.
An important first task is to inventory possible sources of parallel data. If you know of any additional sources or have access to these sources, do let us know in the comments below or by sending a mail to: Victor.Venema@uni-bonn.de.