Detection of inhomogeneities in daily climate records to study trends in extreme weather (daily stew project)

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Global change may not only affect the long-term mean temperatures and precipitation rates, but may also lead to changes in weather extremes, i.e. heat and cold waves, droughts, floods and storms may become more common. The measurement record gives some evidence that trends in extreme precipitation can be found for the last century. However, it is not clear whether the current quality of the climate record is sufficient to draw firm conclusions. Long climate records are known to contain inhomogeneities, i.e. changes that do not represent climatic changes, but changes in the measurement conditions (relocations, changes in shelters and instruments, etc.). These inhomogeneities can be removed if they are known from meta data, but in most cases need to be identified by a comparison with neighbouring stations (statistical relative homogenisation). Currently inhomogeneities are typically analysed in monthly to yearly means of climatic variables. We will argue that many inhomogeneities mostly affect the tails of the distribution of the daily data and may thus not be detectable in aggregated data. Consequently, the inhomogeneities that are of most interest for trends in extremes are poorly detected. Corrections are typically limited to changing the means. Correction algorithms for the bulk of the temperature distribution are available, but require highly correlated neighbouring stations. Since changes, e.g. in instrumentation, are often spread through the entire network inhomogeneities may not only lead to larger uncertainties in trend estimates, but may also lead to biases. In order to provide more reliable estimates of trends in extremes, we will develop a new inhomogeneity detection algorithm for temperature and precipitation, which targets breaks in the tails of the distribution. We propose not to correct the data, but rather to develop trend tests that are aware of the breaks and explicitly ignore them. Thus, we circumvent correcting inhomogeneous data with imperfect methods. This will allow us to estimate trends in extremes much more reliably. With these tools, we will study climate trends for Germany.