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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 use

Accumulated snow amounts are a key climate change indicator. It combines the competing effects of climate change-driven changes in precipitation and stronger snowmelt related to increasing temperatures. Here we provide observational evidence from a pan-European in situ data set that mean snow depth generally decreases stronger than extreme snow depth. Widespread decreases in maximum and mean snow depth were found over Europe, except in the coldest climates, with an average decrease of −12.2%/decade for mean snow depth and −11.4%/decade for maximum snow depth since 1951. These trends accelerated after the 1980s. This has strong implications for the availability of freshwater in spring, while extremes in snow depth, usually very disruptive to society, are decreasing at a slower pace.

Changes in diurnal temperature range (DTR) over global land areas are compared from a broad range of independent data sets. All data sets agree that global‐mean DTR has decreased significantly since 1950, with most of that decrease occurring over 1960–1980. The since‐1979 trends are not significant, with inter‐data set disagreement even over the sign of global changes. Inter‐data set spread becomes greater regionally and in particular at the grid box level. Despite this, there is general agreement that DTR decreased in North America, Europe, and Australia since 1951, with this decrease being partially reversed over Australia and Europe since the early 1980s. There is substantive disagreement between data sets prior to the middle of the twentieth century, particularly over Europe, which precludes making any meaningful conclusions about DTR changes prior to 1950, either globally or regionally. Several variants that undertake a broad range of approaches to postprocessing steps of gridding and interpolation were analyzed for two of the data sets. These choices have a substantial influence in data sparse regions or periods. The potential of further insights is therefore inextricably linked with the efficacy of data rescue and digitization for maximum and minimum temperature series prior to 1950 everywhere and in data sparse regions throughout the period of record. Over North America, station selection and homogeneity assessment is the primary determinant. Over Europe, where the basic station data are similar, the postprocessing choices are dominant. We assess that globally averaged DTR has decreased since the middle twentieth century but that this decrease has not been linear.

Projections of extreme precipitation are of great importance, considering the potential severe impacts on society. In this study a recently developed regional, non-stationary peaks-over-threshold approach is applied to two transient simulations of the RACMO2 regional climate model for the period 1950–2100, driven by two different general circulation models. The regional approach reduces the estimation uncertainty compared to at-site approaches. The selection of a threshold for the peaks-over-threshold model is tackled from a new perspective, taking advantage of the regional setting. Further, a regional quantile regression model using a common relative trend in the threshold is introduced. A considerable bias in the extreme return levels is found with respect to gridded observations. This bias is corrected for by adjusting the parameters in the peaks-over-threshold model. In summer a significant increase in extreme precipitation over the study area is found for both RACMO2 simulations, mainly as a result of an increase of the variability of the excesses over the threshold. However, the magnitude of this trend in extreme summer precipitation depends on the driving general circulation model. In winter an increase in extreme precipitation corresponding with an increase in mean precipitation is found for both simulations. This trend is due to an increase of the threshold., Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

Described herein is the first version release of monthly temperature holdings of a new Global Land Surface Meteorological Databank. Organized under the auspices of the International Surface Temperature Initiative (ISTI), an international group of scientists have spent three years collating and merging data from numerous sources to create a merged holding. This release in its recommended form consists of over 30 000 individual station records, some of which extend over the past 300 years. This article describes the sources, the chosen merge methodology, and the resulting databank characteristics. Several variants of the databank have also been released that reflect the structural uncertainty in merging datasets. Variants differ in, for example, the order in which sources are considered and the degree of congruence required in station geolocation for consideration as a merged or unique record. Also described is a version control protocol that will be applied in the event of updates. Future updates are envisaged with the addition of new data sources, and with changes in processing, where public feedback is always welcomed. Major updates, when necessary, will always be accompanied by a new journal paper. This databank release forms the foundation for the construction of new global land surface air temperature analyses by the global research community and their assessment by the ISTI's benchmarking and assessment working group.

Although climate change is expected to continue, its negative impacts for the Netherlands appear manageable. Here, most climatic changes will take place more gradual than in many other regions, thus enabling citizens, companies and authorities to adapt. Furthermore, current policies focus attention on important aspects of climate change, such as the risk of flooding, drought and precipitation extremes.

Regional frequency analysis is often used to reduce the uncertainty in the estimation of distribution parameters and quantiles. In this paper a regional peaks-over-threshold model is introduced that can be used to analyze precipitation extremes in a changing climate. We use a temporally varying threshold, which is determined by quantile regression for each site separately. The marginal distributions of the excesses are described by generalized Pareto distributions (GPD). The parameters of these distributions may vary over time and their spatial variation is modeled by the index flood (IF) approach. We consider different models for the temporal dependence of the GPD parameters. Parameter estimation is based on the framework of composite likelihood. Composite likelihood ratio tests that account for spatial dependence are used to test the significance of temporal trends in the model parameters and to test the IF assumption. We apply the method to gridded, observed daily precipitation data from the Netherlands for the winter season. A general increase of the threshold is observed, especially along the west coast and northern parts of the country. Moreover, there is no indication that the ratio between the GPD scale parameter and the threshold has changed over time, which implies that the scale parameter increases by the same percentage as the threshold. These positive trends lead to an increase of rare extremes of on average 22% over the country during the observed period., Delft Institute of Applied Mathematics, Electrical Engineering, Mathematics and Computer Science

De KNMI klimaatscenario’s voor Nederland uit 2006 zijn getoetst aan de nieuwste nationale en internationale inzichten. De resultaten daarvan staan beschreven in een brochure die het KNMI onlangs publiceerde. Uit deze evaluatie blijkt dat het klimaat in Nederland sterk verandert én dat de toekomstbeelden van het KNMI uit 2006 de meest waarschijnlijke veranderingen goed weergeven.

Staatssecretaris Schultz van Haegen nam op 30 mei jl. de nieuwe klimaatscenario's van het KNMI in ontvangst. Ze zijn een actualisering van de vorige scenario's die voor de commissie Waterbeheer 21e eeuw waren opgesteld. De nieuwe klimaatscenario's zijn niet ingrijpend anders dan de oude, wel beter onderbouwd