Pan-European homogenization of daily multi-decadal temperature series from station-based observations

The changes of European climate have serious effects on society and economy. A thorough climatological analysis is fundamental to provide reliable and accurate assessments of these changes. Extreme temperature events, such as heatwaves and cold spells, have considerable effects on e.g. health systems, energy consumption and phenological cycles. Their changes in frequency and severity over the last centuries can be studied using daily temperature series from in-situ weather stations. However, these series suffer from external interventions to the measuring stations, such as relocations and modifications to the instruments, and from changes in their surroundings (growing trees, new buildings). These induced changes in the recorded values are not related to climatic events, making the series inhomogeneous and unreliable. With the aim of producing solid temperature databases, several works in the past decades have introduced techniques for the identification of such changes and their correction (homogenization). Within this thesis, a new procedure has been developed, taking inspiration from the Quantile Matching method [Trewin, 2013]. This is based on the calculation of different adjustments for average and extreme values and, in this project,  has  been  revisited  and  modified,  introducing  new aspects  aimed at making it more flexible, more heuristic and more faithful to the originally observed data. The new homogenization is applied to the European Climate Assessment & Dataset (ECA&D), a pan-European dataset providing observations from all European National Meteorological Services. The method is validated with a comparison to acknowledged homogenization methods against a benchmark dataset, proving its robustness and the quality of the results. The homogenized temperature series, thanks to their high reliability, are then analyzed, performing trend analyses focused on the extreme events. Finally the homogenized series are used to create a homogenized version of E-OBS, gridded dataset obtained with the interpolation of ECA&D station data. The homogenized E-OBS is then employed to compare the trends on average and extreme values of the last decades with those simulated in the same period by climate models, which in other studies are used for predictions of future climate under different emission scenarios.The Introduction (Chapter 1) explains the context in which this thesis is developed. The current state and knowledge about climate change is introduced, followed by the issues implied by the presence of inhomogeneities in temperature series. The aims of the thesis and the expected results and further applications are then exposed in detail.Chapter 2 describes in detail the statistical bases of the homogenization method. It is composed by a break detection that statistically identifies the timing of the occurred inhomogeneities. This is needed since the dataset is too big to handle the available docu- mentation of reported changes occurred to the stations (metadata),  which would require a long and labourious process. The second step is the adjustment calculation, developed following previous studies on the technique of Quantile Matching. This calculates different adjustments for the daily data according to their position in the temperature distribution, thus handling differently average and extreme values. The reported case studies prove   the effectiveness of the routine, showing clear improvement in the quality of the series. The difference in the trends of several indices of minimum and maximum temperature between homogenized and raw series show limited changes in average (between +0.01 and +0.02) and no geographical patterns. Moreover, the trends of homogenized series present a clear improvement of the geographical consistency and a considerable narrowing of their distribution. This proves the increased quality of the dataset.The work reported in Chapter 3 describes the process of blending of series. This involves, for example,  the series of the station in a city centre that was ended and the new one  that was started in a close-by rural area or in an airport. The blending procedure here described joins these series by concatenating them and by mutually filling their gaps. While on one side this process generates long series,  on the other hand the blended  series are not necessarily homogeneous. For this reason, the homogenization process exposed in Chapter 2 is adapted and applied to these series.  The results of this process   is a set of long and homogeneous series that are fundamental for thorough historical climatic inspections.   Three case studies help exposing the complexity of the process   and its benefits. Finally a trend assessment on the new homogenized blended series has been performed. Similarly to what reported by previous studies, this has revealed steep trends in summer maximum temperatures over the Mediterranean and in winter minimum temperatures in Eastern Europe. The latter is connected with a narrowing of the winter minimum temperatures, while in Central Europe a relevant widening of summer maximum temperatures is observed.The Quantile Matching homogenization procedure is compared with other methods in Chapter 4. Here two benchmark datasets are generated, concatenating data from homo- geneous neighbouring series in the national network of Czech Republic and among series specifically selected within the ECA&D. Two benchmark datasets allow to compare situ- ations with very good data quality and station density (Czech dataset) and with scarcer station density and presence of missing data (European dataset). Three well known methods (DAP, HOM, SPLIDHOM) are evaluated together with the Quantile Matching, making use of a set of metrics, such as Root Mean Square Error, percentage of adjusted data and evaluation of trends in average and extreme values. On the Czech Dataset almost all methods perform very well, proving the quality of their statistical features in favourable conditions. The European Dataset allows to test the robustness of the methods in challenging conditions. Here some methods show difficulties in the homogenization of warm extremes and large percentages of missed adjustments of biased data. The Quantile Matching works very well in both cases, showing good performances, comparable to the results of a prestigious method as SPLIDHOM.The homogeneous blended series are the bases for a new version of the gridded dataset E-OBS, which is a valuable tool for the validation of climate simulations, such as the ones developed in the frame of the High Resolution Model Intercomparison Project. These models aim at simulating the climate of the period after 1950 and can be compared to observed values to detect how well they reproduced climate variability and trends. Studies of previous versions of climate simulations highlighted underestimations in the trends of (especially warm) extreme events. In Chapter 5 this comparison is performed taking the difference of the trends in average values and in the number of warm (or cold) extreme events above (or below) percentile-based thresholds. The studied models simulate the trends generally well, though they show underestimation of the strong reduction of cold events in Eastern Europe and of the steep increase of warm events in the Mediterranean area.In the Synthesis (Chapter 6) the obtained results are summarized and discussed, focusing on how they have accomplished the aims of the research. The homogenization method based on the Quantile Matching has shown to work very well on the individual series and on the whole network, reducing the presence of anomalous trends and increasing spatial coherence of the data. The comparison with other methods against a benchmark dataset has validated the quality of the new method and given reliability to the studies performed on the homogenized dataset. These have confirmed the severe warming processes over Europe, highlighting the increased distribution width of summer daily temperatures over Central Europe and the narrowing of the distribution of winter daily temperatures over  the Alps and Eastern Europe. Finally,  one of the very powerful uses of the results of   this thesis has been shown. This is the evaluation of climate simulations against a ho- mogenized gridded dataset, which has allowed to inspect how well the models are able to reproduce the statistical features of the extreme temperature events over the last decades. Moreover, in the Synthesis possible improvements for the homogenization method are ex- posed together with concluding remarks. The main conclusions of this thesis are the acknowledgement of the high efficiency of the developed method, of the high quality of the obtained dataset and of the important added value  that homogenization processes like this provide to climatological analyses and to the solidity of the evidences of climate change.