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The Pan European Phenology (PEP) project is a European infrastructure to promote and facilitate phenological research, education, and environmental monitoring. The main objective is to maintain and develop a Pan European Phenological database (PEP725) with an open, unrestricted data access for science and education. PEP725 is the successor of the database developed through the COST action 725 “Establishing a European phenological data platform for climatological applications” working as a single access point for European-wide plant phenological data. So far, 32 European meteorological services and project partners from across Europe have joined and supplied data collected by volunteers from 1868 to the present for the PEP725 database. Most of the partners actively provide data on a regular basis. The database presently holds almost 12 million records, about 46 growing stages and 265 plant species (including cultivars), and can be accessed via http://www.pep725.eu/. Users of the PEP725 database have studied a diversity of topics ranging from climate change impact, plant physiological question, phenological modeling, and remote sensing of vegetation to ecosystem productivity.

Most studies on the impact of climate change on regional water resources focus on long-term average flows or mean water availability, and they rarely take the effects of altered human water use into account. When analyzing extreme events such as floods and droughts, the assessments are typically confined to smaller areas and case studies. At the same time it is acknowledged that climate change may severely alter the risk of hydrological extremes over large regional scales, and that human water use will put additional pressure on future water resources. In an attempt to bridge these various aspects, this paper presents a first-time continental, integrated analysis of possible impacts of global change (here defined as climate and water use change) on future flood and drought frequencies for the selected study area of Europe. The global integrated water model WaterGAP is evaluated regarding its capability to simulate high and low-flow regimes and is then applied to calculate relative changes in flood and drought frequencies. The results indicate large \"critical regions\" for which significant changes in flood or drought risks are expected under the proposed global change scenarios. The regions most prone to a rise in flood frequencies are northern to northeastern Europe, while southern and southeastern Europe show significant increases in drought frequencies. In the critical regions, events with an intensity of today's 100-year floods and droughts may recur every 10-50 years by the 2070s. Though interim and preliminary, and despite the inherent uncertainties in the presented approach, the results underpin the importance of developing mitigation and adaptation strategies for global change impacts on a continental scale. [PUBLICATION ABSTRACT]

Mittelfristige Klimaprognose (MiKlip), an 8-yr German national research project on decadal climate prediction, is organized around a global prediction system comprising the Max Planck Institute Earth System Model (MPI-ESM) together with an initialization procedure and a model evaluation system. This paper summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern strategies and structures of research that target future operational use. Three prediction system generations have been constructed, characterized by alternative initialization strategies; the later generations show a marked improvement in hindcast skill for surface temperature. Hindcast skill is also identified for multiyear-mean European summer surface temperatures, extratropical cyclone tracks, the quasi-biennial oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly enhances the skill in European surface temperature inherited from the global model and also displays hindcast skill for wind energy output. A new volcano code package permits rapid modification of the predictions in response to a future eruption. MiKlip has demonstrated the efficacy of subjecting a single global prediction system to a major research effort. The benefits of this strategy include the rapid cycling through the prediction system generations, the development of a sophisticated evaluation package usable by all MiKlip researchers, and regional applications of the global predictions. Open research questions include the optimal balance between model resolution and ensemble size, the appropriate method for constructing a prediction ensemble, and the decision between full-field and anomaly initialization. Operational use of the MiKlip system is targeted for the end of the current decade, with a recommended generational cycle of 2–3 years.

During the last interglacial period, the volume of the Greenland ice sheet was up to 60% smaller than today. Climate and ice-sheet modelling suggests that about 55% of this change was caused by higher ambient temperatures and the remaining 45% was a result of higher insolation and the associated climate feedbacks. During the Eemian interglacial period, 130,000 to 114,000 years ago, the volume of the Greenland ice sheet was about 30–60% smaller than the present-day volume 1 , 2 . Summer temperatures in the Arctic region were about 2–4 K higher than today 3 , 4 , 5 , leading to the suggestion that Eemian conditions could be considered an analogue for future warming 6 , particularly for the future stability of the Greenland ice sheet. However, Northern Hemisphere insolation was much higher during the Eemian than today, which could affect the reliability of this analogy. Here we use a high-resolution regional climate model with a realistic ice-sheet surface representation to assess the surface mass balance of the Greenland ice sheet during the Eemian. Our simulations show that Eemian climate led to an 83% lower surface mass balance, compared with the preindustrial simulation. Our sensitivity experiments show that only about 55% of this change in surface mass balance can be attributed to higher ambient temperatures, with the remaining 45% caused by higher insolation and associated nonlinear feedbacks. We show that temperature–melt relations are dependent on changes in insolation. Hence, we suggest that projections of future Greenland ice loss on the basis of Eemian temperature–melt relations may overestimate the future vulnerability of the ice sheet.

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 ∘C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region.

The weather extremity index (WEI) and the cross-scale WEI (xWEI) are useful for determining the extremeness of precipitation events. Both require the estimation of return periods across different precipitation duration levels. For this purpose, previous studies determined annual precipitation maxima from radar composites in Germany and estimated the parameters of a generalized extreme value (GEV) distribution. Including the year 2021 in the estimation of GEV parameters, the devastating event in July 2021 drops from a rank of first to fourth regarding the WEI compared to all events between 2001 and 2020 but remains the most extreme regarding the xWEI. This emphasizes that it was extreme across multiple spatial and temporal scales and the importance of considering different scales to determine the extremeness of rainfall events.

Incomplete spatiotemporal meteorological observations can result in misinterpretations of the current climate state, uncertainties in early warning systems, or inaccuracies in nowcasting models and can thereby pose significant challenges in hydrology research or similar applications. Traditional statistical methods for infilling missing precipitation data demand substantial computational resources and fail over large areas with sparse data – like temporary outages of weather radars. Although recent machine learning advancements have shown promise in addressing missing meteorological or satellite observations, they typically focus on spatial aspects, overlooking the complex spatiotemporal variability characteristic of precipitation, especially during extreme events. We propose a deep convolutional neural network enhanced with a memory component to better account for temporal changes in precipitation fields. This approach can analyse arbitrary sequences from before and/or after the incomplete observation of interest. Our model is trained and evaluated on the hourly RADKLIM dataset, which features 1 km resolution precipitation data derived from combined radar and weather stations across Germany. By infilling both artificial and actual data gaps of RADKLIM, we demonstrate the model's effectiveness, providing detailed insights into its capabilities during significant rainfall events, such as those in May 2012 and July 2021, including those responsible for the Ahrtal flood. This novel approach represents a step forward in hydrological applications, potentially improving the way we predict and manage water-related events by increasing the accuracy and reliability of precipitation data analysis.

The regional reanalysis COSMO-REA6 has frequently been used for applications in the energy sector, but there were also known deficits for some relevant features. A successor is currently produced and this manuscript provides a first initial technical validation, with a focus on those features. The dataset is available under an open data licence and is expected to be of interest for energy-related applications. Relevant meteorological variables for the renewable energy generation like Global Horizontal Irradiance (GHI, often also referred as global radiation) and wind speed at altitudes between 10 and 258 m of the new regional reanalysis COSMO-REA6 Generation 2 (COSMO-R6G2) by Deutscher Wetterdienst (DWD) are investigated with a focus on Germany using the regional reanalysis COSMO-REA6 as a benchmark. COSMO-R6G2 shows slightly lower values of GHI compared to COSMO-REA6, thus increasing the bias already present in COSMO-REA6. In terms of wind speed, comparisons to data from measurement masts (e.g. at sites Hamburg, Lindenberg and Karlsruhe) show good quality of COSMO-R6G2 in flatlands. As deficits in the representation of the daily cycle of wind speed at hub height were identified for COSMO-REA6, these characteristics were analysed in more detail. COSMO-R6G2 underestimates the night-time maximum over mountainous regions stronger than COSMO-REA6, but differences over low terrain are small and broadly comparable to COSMO-REA6. In summary, this initial assessment shows that users can use COSMO-R6G2 as an extended successor of COSMO-REA6, but should carefully consider the results of all quality assessments and known weaknesses of the COSMO-based datasets.

Extreme precipitation is a weather phenomenon with tremendous damaging potential for property and human life. As the intensity and frequency of such events is projected to increase in a warming climate, there is an urgent need to advance the existing knowledge on extreme precipitation processes, statistics and impacts across scales. To this end, a working group within the Germany-based project, ClimXtreme, has been established to carry out multidisciplinary analyses of high-impact events. In this work, we provide a comprehensive assessment of the 29 June 2017 heavy precipitation event (HPE) affecting the Berlin metropolitan region (Germany), from the meteorological, impacts and climate perspectives, including climate change attribution. Our analysis showed that this event occurred under the influence of a mid-tropospheric trough over western Europe and two shortwave surface lows over Britain and Poland (Rasmund and Rasmund II), inducing relevant low-level wind convergence along the German–Polish border. Over 11 000 convective cells were triggered, starting early morning 29 June, displacing northwards slowly under the influence of a weak tropospheric flow (10 m s−1 at 500 hPa). The quasi-stationary situation led to totals up to 196 mm d−1, making this event the 29 June most severe in the 1951–2021 climatology, ranked by means of a precipitation-based index. Regarding impacts, it incurred the largest insured losses in the period 2002 to 2017 (EUR 60 million) in the greater Berlin area. We provide further insights on flood attributes (inundation, depth, duration) based on a unique household-level survey data set. The major moisture source for this event was the Alpine–Slovenian region (63 % of identified sources) due to recycling of precipitation falling over that region 1 d earlier. Implementing three different generalised extreme value (GEV) models, we quantified the return periods for this case to be above 100 years for daily aggregated precipitation, and up to 100 and 10 years for 8 and 1 h aggregations, respectively. The conditional attribution demonstrated that warming since the pre-industrial era caused a small but significant increase of 4 % in total precipitation and 10 % for extreme intensities. The possibility that not just greenhouse-gas-induced warming, but also anthropogenic aerosols affected the intensity of precipitation is investigated through aerosol sensitivity experiments. Our multi-disciplinary approach allowed us to relate interconnected aspects of extreme precipitation. For instance, the link between the unique meteorological conditions of this case and its very large return periods, or the extent to which it is attributable to already-observed anthropogenic climate change.

Based on the numerical weather prediction model COSMO of Germany's national meteorological service (Deutscher Wetterdienst, DWD), regional reanalysis datasets have been developed with grid spacing of up to 2 km. This development started as a fundamental research activity within the Hans-Ertel-Centre for Weather Research (HErZ) at the University of Bonn and the University of Cologne. Today, COSMO reanalyses are an established product of the DWD and have been widely used in applications on European and national German level. Successful applications of COSMO reanalyses include renewable energy assessments as well as meteorological risk estimates. The COSMO reanalysis datasets are now publicly available and provide spatio-temporal consistent data of atmospheric parameters covering both near-surface conditions and vertical profiles. This article reviews the status of the COSMO reanalyses, including evaluation results and applications. In many studies, evaluation of the COSMO reanalyses point to an overall good quality and often an added value compared to different contemporary global reanalysis datasets. We further outline current plans for the further development and application of regional reanalyses in the HErZ research group Cologne/Bonn in collaboration with the DWD.