A database with daily climate data for more reliable studies of changes in extreme weather

In summary:

• We want to build a global database of parallel measurements: observations of the same climatic parameter made independently at the same site
• This will help research in many fields
• Studies of how inhomogeneities affect the behaviour of daily data (variability and extreme weather)
• Improvement of daily homogenisation algorithms
• Improvement of robust daily climate data for analysis
• Please help us to develop such a dataset

Introduction

One way to study the influence of changes in measurement techniques is by making simultaneous measurements with historical and current instruments, procedures or screens. This picture shows three meteorological shelters next to each other in Murcia (Spain). The rightmost shelter is a replica of the Montsouri screen, in use in Spain and many European countries in the late 19th century and early 20th century. In the middle, Stevenson screen equipped with automatic sensors. Leftmost, Stevenson screen equipped with conventional meteorological instruments. Picture: Project SCREEN, Center for Climate Change, Universitat Rovira i Virgili, Spain.

We intend to build a database with parallel measurements to study non-climatic changes in the climate record. This is especially important for studies on weather extremes where the distribution of the daily data employed must not be affected by non-climatic changes.

There are many parallel measurements from numerous previous studies analysing the influence of different measurement set-ups on average quantities, especially average annual and monthly temperature. Increasingly, changes in the distribution of daily and sub-daily values are also being investigated (Auchmann and Bönnimann, 2012; Brandsma and Van der Meulen, 2008; Böhm et al., 2010; Brunet et al., 2010; Perry et al., 2006; Trewin, 2012; Van der Meulen and Brandsma, 2008). However, the number of such studies is still limited, while the number of questions that can and need to be answered are much larger for daily data.

Unfortunately, the current common practice is not to share parallel measurements and the analyses have thus been limited to smaller national or regional datasets, in most cases simply to a single station with multiple measurement set-ups. Consequently there is a pressing need for a large global database of parallel measurements on a daily or sub-daily scale.

Also datasets from pairs of nearby stations, while officially not parallel measurements, are interesting to study the influence of relocations. Especially, typical types of relocations, such as the relocation of weather stations from urban areas to airports, could be studied this way. In addition, the influence of urbanization can be studied on pairs of nearby stations.

HUME: Homogenisation, Uncertainty Measures and Extreme weather

Proposal for future research in homogenisation

To keep this post short, a background in homogenisation is assumed and not every argument is fully rigorous.

Aim

This document wants to start a discussion on the research priorities in homogenisation of historical climate data from surface networks. It will argue that with the increased scientific work on changes in extreme weather, the homogenisation community should work more on daily data and especially on quantifying the uncertainties remaining in homogenized data. Comments on these ideas are welcome as well as further thoughts. Hopefully we can reach a consensus on research priorities for the coming years. A common voice will strengthen our voice with research funding agencies.

State-of-the-art

From homogenisation of monthly and yearly data, we have learned that the size of breaks is typically on the order of the climatic changes observed in the 20th century and that period between two detected breaks is around 15 to 20 years. Thus these inhomogeneities are a significant source of error and need to be removed. The benchmark of the Cost Action HOME has shown that these breaks can be removed reliably, that homogenisation improves the usefulness of the temperature and precipitation data to study decadal variability and secular trends. Not all problems are already optimally solved, for instance the solutions for the inhomogeneous reference problem are still quite ad hoc. The HOME benchmark found mixed results for precipitation and the handling of missing data can probably be improved. Furthermore, homogenisation of other climate elements and from different, for example dry, regions should be studied. However, in general, annual and monthly homogenisation can be seen as a mature field. The homogenisation of daily data is still in its infancy. Daily datasets are essential for studying extremes of weather and climate. Here the focus is not on the mean values, but on what happens in the tails of the distributions. Looking at the physical causes of inhomogeneities, one would expect that many of them especially affect the tails of the distributions. Likewise the IPCC AR4 report warns that changes in extremes are often more sensitive to inhomogeneous climate monitoring practices than changes in the mean.