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The concept of plant functional types (PFTs) is shown to be beneficial in representing the complexity of plant characteristics in land use and climate change studies using regional climate models (RCMs). By representing land use and land cover (LULC) as functional traits, responses and effects of specific plant communities can be directly coupled to the lowest atmospheric layers. To meet the requirements of RCMs for realistic LULC distribution, we developed a PFT dataset for Europe (LANDMATE PFT Version 1.0; http://doi.org/10.26050/WDCC/LM_PFT_LandCov_EUR2015_v1.0, Reinhart et al., 2021b). The dataset is based on the high-resolution European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset and is further improved through the additional use of climate information. Within the LANDMATE – LAND surface Modifications and its feedbacks on local and regional cliMATE – PFT dataset, satellite-based LULC information and climate data are combined to create the representation of the diverse plant communities and their functions in the respective regional ecosystems while keeping the dataset most flexible for application in RCMs. Each LULC class of ESA-CCI is translated into PFT or PFT fractions including climate information by using the Holdridge life zone concept. Through consideration of regional climate data, the resulting PFT map for Europe is regionally customized. A thorough evaluation of the LANDMATE PFT dataset is done using a comprehensive ground truth database over the European continent. The assessment shows that the dominant LULC types, cropland and woodland, are well represented within the dataset, while uncertainties are found for some less represented LULC types. The LANDMATE PFT dataset provides a realistic, high-resolution LULC distribution for implementation in RCMs and is used as a basis for the Land Use and Climate Across Scales (LUCAS) Land Use Change (LUC) dataset which is available for use as LULC change input for RCM experiment set-ups focused on investigating LULC change impact.

Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS - "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC dataset Version 1.1 at 0.1° resolution for Europe with annual LULC maps from 1950–2100 (Hoffmann et al., 2022b, a), which is tailored towards the use in state-of-the-art RCMs. The plant functional type distribution (PFT) for the year 2015 (i.e., LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method, cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25° resolution as input for CMIP6 experiments, to derive LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide.

Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperatures (i.e. heat and cold) may increase the risk of MI. The relationship between health impacts and climate is complex and influenced by a multitude of climatic, environmental, socio-demographic and behavioural factors. Here, we present a machine learning (ML) approach for predicting MI events based on multiple environmental and demographic variables. We derived data on MI events from the KORA MI registry dataset for Augsburg, Germany, between 1998 and 2015. Multivariable predictors include weather and climate, air pollution (PM10, NO, NO2, SO2 and O3), surrounding vegetation and demographic data. We tested the following ML regression algorithms: decision tree, random forest, multi-layer perceptron, gradient boosting and ridge regression. The models are able to predict the total annual number of MIs reasonably well (adjusted R2=0.62–0.71). Inter-annual variations and long-term trends are captured. Across models the most important predictors are air pollution and daily temperatures. Variables not related to environmental conditions, such as demographics need to be considered as well. This ML approach provides a promising basis to model future MI under changing environmental conditions, as projected by scenarios for climate and other environmental changes.

This paper presents a novel dataset of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. This is a unique and physically consistent dataset, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100×10-year simulations and 25×10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. To demonstrate how adaptation-relevant information can be derived from the HAPPI dataset, changes in four climate indices for periods with 1.5 and 2.0 ∘C global warming are quantified. These indices include number of days per year with daily mean near-surface apparent temperature of >28 ∘C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-year return period (RI50yr); and the annual consecutive dry days (CDDs). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature-related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 ∘C.

Die wissenschaftlichen Fakten zu notwendigem Klimaschutz und den bereits spürbaren sowie zukünftig zu erwartenden Folgen des Klimawandels liegen so umfassend und eindeutig vor, wie nie zuvor (IPCC, 2014, 2018). Der anthropogene – also menschengemachte – Klimawandel sowie die damit einhergehende globale Erderwärmung sind real und betreffen jeden. Dies ist unter Klimawissenschaftler:innen unumstritten und wird auch im politischen und gesellschaftlichen Diskurs mittlerweile weithin anerkannt. Doch was genau sind die Ursachen für den bereits spürbaren und zukünftig zu erwartenden Klimawandel? Wo genau stehen wir aktuell in Deutschland?

National and international climate services support adaptation to climate change. Climate services should aim to deliver climate service products, which are useable, useful, accurate and suitable for users. Good practices for the design of climate services include transdisciplinary approaches and co-creation of climate service products.The advancement of effective adaptation measures to climate change requires continuous interactions between climate service providers and users of the products. To assess the effectiveness of this co-creation endeavours evaluation is crucial. At present, output and outcome assessments are conducted occasionally in the climate services research field but they are performed ex-post and do not help to adjust the ongoing process of co-creation.Thus, the focus of the presented work is on the formative evaluation of the co-creative development of science-based climate service products. It is done during the run-time of a project with the intention to reflect and readjust the processes. To find the related evaluation criteria, we analysed in detail the process of co-creation of climate service products in the knowledge transfer project ADAPTER (ADAPT tERrestrial systems, https://adapter-projekt.org/). In ADAPTER, simulation-based climate service products have been developed together with key partners and practitioners from the agricultural sector, to support decision-making in the context of climate change adaptation.Starting with the empirical identification of the main characteristics of the product development process, six sub-processes were determined. Then, questions for a formative evaluation were assigned to the different steps and sub-processes. Finally, a literature review on quality criteria in transdisciplinary research delivered additional qualitative aspects and served as theoretical basis. As a result, a scheme of quality criteria and related assessment questions for the different sub-processes in climate service development was created, based on both empirical and theoretical work. Of course, this formative evaluation scheme needs validation and testing but, at least, will be particularly helpful to reflect on and to improve the co-creation processes in climate services and enhance their quality.

Land management practices modify the land surface by changing its biogeophysical and biogeochemical properties, leading to heterogeneous surface and soil conditions. One example for this is irrigation, which is widely used to overcome unsuitable climatic conditions in agriculture to protect plants from water stress. Irrigation leads to a heterogeneous and time-varying soil moisture distribution. Soil moisture conditions are known to feedback with the atmosphere. Previous studies analyzed the soil moisture-precipitation feedback and focused on the development conditions of convection. However, uncertainties in the magnitude and sign of this feedback remain due to the representation of convection in the climate model. Therefore, convection-permitting simulations are crucial to understanding the link between soil moisture and convection.In our study, we investigate this link by taking into account sudden soil moisture changes through irrigation and the resulting heterogeneity of soil moisture distribution. Our aim is to understand how irrigation influences development conditions for convection in the atmosphere. For this purpose, we conduct convection-permitting simulations at high resolution with a focus on the Po valley in Northern Italy. We use the non-hydrostatic version of the regional climate model REMO which in our setup is interactively coupled to its mosaic-based vegetation module iMOVE. For REMO-iMOVE we developed and implemented a new irrigation parameterization based on a fractional approach which makes it suitable for the representation of heterogeneous soil moisture distribution.We will present the effects of irrigation on the development of convection in the Po valley by analyzing selected atmospheric variables from simulations with and without irrigation.

Human-induced climate change is one of the most pressing challenges of our time. The Helmholtz Association is making essential research contributions to mitigate the causes and impacts of climate change and find ways to adapt. The “Net-Zero-2050” project, the Cluster I of the Helmholtz Climate Initiative, scientifically investigates and evaluates strategies and new ways to reduce, extract and permanently store carbon emissions. Two digital knowledge transfer products (DKTPs) were developed to present the complex research results comprehensively: (1) the “Net-Zero-2050 Web-Atlas” provides information on methods and technologies for CO2 reduction and possible reduction paths; (2) the “Soil Carbon App” provides simulated soil carbon data to estimate climate protection potentials through different land management methods. Both formats intend to support users in making informed decisions and developing appropriate climate neutrality strategies. During the two DKTPs development, common main challenges were identified regarding concepts and stakeholder involvement. Along with that, specific approaches to solving the tasks could be distilled for each product. In the still-evolving arena of digital knowledge transfer, no standard methods can be applied. At the same time, communication of climate research results to decision-makers is becoming more and more relevant. This paper extracts the challenges and gives approaches to facilitate a transfer of the gained experience to future similar projects.

The biophysical effects of reforestation and afforestation (herein jointly called re/afforestation) on the diurnal temperature cycle in European summer are investigated by analyzing a regional climate model (RCM) ensemble, established within the Land Use and Climate Across Scales Flagship Pilot Study (LUCAS FPS). With this RCM ensemble, two idealized experiments are performed for Europe, one with a continent with maximized forest cover, and one in which all forests are turned into grassland. First, an in-depth analysis of one ensemble member (“CCLM-VEG3D”) is carried out, to reveal the complex process chain caused by such land use changes (LUCs). From these findings, the whole ensemble is analyzed and principal biophysical effects of re/afforestation are derived. Results show that the diurnal temperature range is reduced at the surface (top of the vegetation) with re/afforestation. Most RCMs simulate colder surface temperatures Tsurf during the day and warmer Tsurf during the night. Thus, for the first time, the principal temperature interrelations found in observation-based studies in the midlatitudes could be reproduced within a model intercomparison study. On the contrary, the diurnal temperature range in the lowest atmospheric model level (Tair) is increased with re/afforestation. This opposing temperature response is mainly caused by the higher surface roughness of forest, enhancing the turbulent heat exchange. Furthermore, these opposing temperature responses demonstrate that the use of the diagnostic 2-m temperature (weighted interpolation between Tsurf and Tair) has a limited potential to assess the effects of re/afforestation. Thus, studies about the biophysical impacts of LUCs should investigate the whole near-surface temperature profile.

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets.

The goal of an interdisciplinary team of scientists at the Climate Service Center Germany (GERICS) was to make the findings of the special report IPCC SR1.5 more accessible to the citizens of Hamburg. Therefore, a flyer was created that is understandable to non-climate scientists, visually attractive and generates interest. It contains up-to-date climate information, readily understandable texts and several graphical visualisations. The team has been working intensively on analysing and processing further the consequences of a 1.5 ∘C global warming for the Hamburg metropolitan region. While the team's natural scientists elaborated the impacts on specific climate indices, other team members focused on the visualisation and communication of the results.

Climate change increasingly affects all parts of society. Different economic sectors such as the agricultural sector have to adapt to climate change. More and more climate services are being developed in order to support this adaptation to climate change with accurate and suitable products. Good practises for the design of climate services include transdisciplinary approaches and co-creation of climate service products. The development of usable and useful climate service products and effective adaptation measures requires constant interactions between climate service providers and users of the products. To assess the effectiveness of these co-creation endeavours, continuous evaluation is crucial. At present, output and outcome assessments are conducted occasionally in this research field. However, these summative evaluations that are preformed ex-post do not help to adjust the ongoing process of co-creation. Therefore, the focus of the presented work is on formative evaluation of the co-creative development of science-based climate service products. A formative evaluation is done during the run-time of a project with the intention to reflect and readjust it. For this purpose, we analysed in detail the process of co-creation of climate service products in the knowledge transfer project ADAPTER (ADAPT tERrestrial systems, https://adapter-projekt.org/) and combine this analysis with a systematic literature review. In ADAPTER, simulation-based climate service products are developed together with key partners and practitioners from the agricultural sector, with the aim of supporting decision making in the context of climate change adaptation.As a first step, main characteristics of the product development process were identified empirically and six sub-processes of product development were determined. Secondly , questions for a formative evaluation were assigned to the different steps and sub-processes. Thirdly, a literature review including fields other than climate services delivered additional qualitative aspects. As a result, a scheme of quality criteria and related assessment questions for the different sub-processes in climate service development was created, based on both empirical and theoretical work. Subsequently, this scheme needs validation and testing. The resulting formative evaluation scheme will be particularly helpful to reflect on and to improve the co-creation processes in climate services and beyond.

Made available in DSpace on 2022-04-28T19:51:16Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-02-01 Stichting Combined Ophthalmic Research Rotterdam Deutsche Kniegesellschaft European Society of Gastrointestinal Endoscopy We describe the first effort within the Coordinated Regional Climate Downscaling Experiment-Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of twenty-first-century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over nine CORDEX domains at ~25-km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, and energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors, and climate service activities. The future of the CORDEX-CORE initiative is also discussed. Abdus Salam International Centre for Theoretical Physics Climate Service Center Germany (GERICS) Oak Ridge National Laboratory University of Innsbruck National Center for Atmospheric Research, Boulder Centro de Investigación Científica y de Educación Superior de Ensenada National Institute of Oceanography and Applied Geophysics (INOGS) Abdus Salam International Centre for Theoretical Physics University of São Paulo Climate Change Research Centre School of Biological Earth and Environmental Science University of New South Wales Institute of Atmospheric Physics Chinese Academy of Sciences Hong Kong University of Science and Technology São Paulo State University Federal University of Itajuba University of Augsburg São Paulo State University

Geoscientific data analysis has to face some challenges regarding seamless data analysis chains, reuse of methods and tools, interdisciplinary approaches and digitalization. Computer science and data science offer concepts to face these challenges. We took the concepts of scientific workflows and component-based software engineering and adapted it to the field of geoscience. In close collaboration of computer and geo-experts, we set up an expedient approach and technology to develop and implement scientific workflows on a conceptual and digital level. We applied the approach in the showcase “Cross-disciplinary Investigation of Flood Events” to introduce and prove the concepts in our geoscientific work environment, and assess how the approach tackles the posed challenges. This is exemplarily demonstrated with the Flood Event Explorer which has been developed in Digital Earth.

Seasonal snow cover plays a major role in the climate system of the Northern Hemisphere via its effect on land surface albedo and fluxes. In climate models the parameterization of interactions between snow and atmosphere remains a source of uncertainty and biases in the representation of local and global climate. Here, we evaluate the ability of an ensemble of regional climate models (RCMs) coupled with different land surface models to simulate snow–atmosphere interactions over Europe in winter and spring. We use a previously defined index, the snow-albedo sensitivity index (SASI), to quantify the radiative forcing associated with snow cover anomalies. By comparing RCM-derived SASI values with SASI calculated from reanalyses and satellite retrievals, we show that an accurate simulation of snow cover is essential for correctly reproducing the observed forcing over middle and high latitudes in Europe. The choice of parameterizations, and primarily the choice of the land surface model, strongly influences the representation of SASI as it affects the ability of climate models to simulate snow cover accurately. The degree of agreement between the datasets differs between the accumulation and ablation periods, with the latter one presenting the greatest challenge for the RCMs. Given the dominant role of land surface processes in the simulation of snow cover during the ablation period, the results suggest that, during this time period, the choice of the land surface model is more critical for the representation of SASI than the atmospheric model.

Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned to adapt to interannual climate variability and extremes. In the ADAPTER project, we collaborate with stakeholders from these groups and generate practically relevant information, tailored climate change indices and usable information products. The challenges of climate change for agriculture are manifold. The genetic traits of crops need to be adapted to a new climatic average, for instance by breeding new sorts of crops that are specialised for warmer and dryer conditions (i.e. maximising average yields). Agricultural practises need to be adapted to changing seasonal weather patterns under changing climate conditions. It is also vital to ensure the resilience to climate extremes by aiming for a low inter-annual yield variability, in order to prevent price shocks or food shortages. In order to adequately determine the optimal balance between specialisation and risk diversification, the agricultural sector hence requires knowledge not only about changes in the mean climate, but also on the variance around the changing mean. In this contribution, we focus on this second aspect by analysing the potential impact of forced changes in climate variability on the stability of crop yields in central Europe. We analyse the changing climate variability in 85 regional climate model projections from Coordinated Downscaling Experiments over Europe (EURO-CORDEX). We first show how the projections indicate a general increase in climate variability during critical development stages of wheat, rapeseed and maize in Europe. Second, we determine several more specific agronomic climate indices that capture events that have previously been shown to be critical for yields, for instance the occurrence of high daily maximum temperature, the seasonal sum of rainfall, the number of dry days, or the occurrence of compound events with simultaneous drought and increased temperatures. Finally, we illustrate how the results can be made accessible to practitioners in the agriculture sector by co-designing interactive browser applications, thus directly supporting the adaptation of the agricultural system to climate change.

Landnutzungs- und Landoberflächenänderungen (LULCC) sind ein wichtiger Antrieb für regionale Klimavariabilität und Klimaänderungen. Bisher wurden LULCC in langjährigen Klimaänderungsprojektionen mit regionalen Klimamodellen (RCMs) nicht standardmäßig berücksichtig. Aus diesem Grund werden im Rahmen der WCRP CORDEX Flagship Pilot Study LUCAS (FPS LUCAS) koordinierte Downscaling-Experimente mit einem Ensemble von RCMs durchgeführt, die Landnutzungsänderungen berücksichtigen. Dafür wurde ein neuer hochaufgelöster LULCC-Datensatz für Europa von 1950-2100 erstellt (LUCAS LUC), der die spezifischen Datenanforderungen von RCMs erfüllt und zum Downscaling von CMIP6-Projektionen dienen soll. Für RCMs wurden bisher noch keine Standards für die Implementierung von jährlichen LULCC-Daten definiert, so dass die LULCC-Abbildung modellspezifisch unterschiedlich ist. Hierbei insbesondere zu nennen sind die unterschiedlichen Landnutzungs- und Landoberflächenklassifikationen der einzelnen Modelle, die eine Konvertierung der LUCAS LUC Daten in Antriebsdaten für RCMs erschweren. Um diese Probleme zu erkennen und mögliche Unsicherheiten bei der Implementierung zu quantifizieren, wurden in der FPS LUCAS zunächst Testsimulationen für die EURO-CORDEX Domain über 2 bis 3 Jahre mit einem Ensemble von RCMs durchgeführt. Als LULCC-Antrieb für diese Simulationen werden LUCAS LUC Daten von zwei unterschiedlichen Epochen verwendet. Der Fokus der Präsentation liegt auf der Analyse der Verteilung einzelner Landnutzungs- und Landoberflächenkategorien und auf dem Einfluss der LULCC-Daten auf biogeophysikalische Größen und bodennahe atmosphärische Variablen. Darauf basierend werden Empfehlungen für die Verwendung von LUCAS LUC Daten in RCM-Experimenten gegeben.

Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect, and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snow melt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. Greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass-snow and forest-snow interactions during the snowmelt season. Pathways to accomplishing this include 1) a more sophisticated representation of forest structure, 2) kilometer scale simulations, and 3) more observational studies on vegetation-snow interactions in Northern Europe.

In the Northern Hemisphere, the seasonal snow cover plays a major role in the climate system via its effect on surface albedo and fluxes. The parameterization of snow-atmosphere interactions in climate models remains a source of uncertainty and biases in the representation of the local and global climate. Here, we evaluate the ability of an ensemble of regional climate models (RCMs) coupled to different land surface models to simulate the snow albedo effect over Europe, in winter and spring. We use a previously defined index, the Snow Albedo Sensitivity Index (SASI), to quantify the radiative forcing due to the snow albedo effect. By comparing RCM-derived SASI values with SASI calculated from reanalyses and satellite retrievals, we show that an accurate simulation of snow cover is essential for correctly reproducing the observed forcing over mid- and high-latitudes in Europe. The choice of parameterizations with first and foremost the choice of the land surface model but also the convection scheme and the planetary boundary layer, strongly influences the representation of SASI as it affects the ability of climate models to simulate snow cover correctly. The agreement between the datasets differs between the accumulation and ablation periods, with the latter one presenting the greatest challenge for the RCMs. Given the dominant role of land surface processes in the simulation of snow cover during the ablation period, the results suggest that the choice of the land surface model is more critical for the representation of SASI than the atmospheric model during this time period.

Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS – "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC Version 1.0 at 0.1° resolution for Europe Hoffmann et al. (2021b,c). The plant functional type distribution for the year 2015 (i.e. LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method based on a cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (submitted). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25° resolution as input for CMIP6 experiments, to derive realistic LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to remotely-sensed PFT time series. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the next generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide.

Assessing multiple climatic and non-climatic variables affecting one region at the same time is a crucial aspect to support climate adaptation action. This publication presents a method to display relevant measures of any three adaptation relevant parameters (or optionally their projected future changes) at once on a map by allocating them to multiple transparency levels of the three primary colors of additive color mixing (red, green, and blue). The overlay of information allows the combined assessment of the regional exposures. The method is demonstrated by two examples based on an ensemble of regional climate projections analyzed for 1.5 °C, 2 °C, and 3 °C global warming periods. The first example shows the increasing number of people at risk for summer climate extremes under 1.5 °C, 2 °C, and 3 °C global warming by combining projected increases in tropical nights and summer intense precipitation days with today’s population density. Under 3 °C global warming, many heavily populated areas across Europe are affected by both heat stress and summer precipitation extremes, whereas under 1.5 °C global warming, heat stress regions are restricted to southern Europe and the large settlements along the Eastern Mediterranean coast. A second example combines daily mean and minimum and maximum summer temperatures and highlights the regional expansion and the increasing robustness of projected mean summer warming with rising global warming levels, as well as the regional day to night differences of the warming signal.

Specific climate adaptation and resilience measures can be efficiently designed and implemented at the regional and local level. Climate and environmental databases are of critical importance to achieving sustainability goals (SDGs) and for the efficient planning and implementation of suitable mitigation measures: Available databases can serve municipalities as a vital starting points to determine requirements, prioritize resources and allocate investments under consideration of commonly tight budget restrictions. High-quality geo, climate and environmental data are now available – data from remote sensing, i.e. Copernicus services will be of crucial importance. Forward-looking approaches exist to using such data to derive forecasts for urban planning process optimization for municipal administrations. On municipal level, however, the existing data have so far only been used to a limited extent, since there are no practical tools for urban planning that can be used to merge and meaningfully combine remote sensing data with local data and to further process and apply in municipal planning processes. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services and procedures, such as the development of practice-oriented technical tools that acquire various remote sensing and in-situ data sets for validation and further processing.

Irrigation is a common land use practice to adapt agriculture to unsuitable climatic conditions. It is highly relevant to ensure food production. Due to the growing population and its food demand in the future, as well as due to climate change, the irrigated areas are expected to increase globally. Therefore, it is important to understand the effects of irrigation on the climate system. Irrigation of cropland alters the biogeophysical properties of the land surface and the soil. Due to the land-atmosphere interactions, these alterations have the potential to affect the atmosphere directly or through feedback processes. Various studies point out that the effects of irrigation, like temperature reduction, are particularly pronounced on local to regional scales where they bear a mitigation potential to regional climate change. This study aims to investigate the effects of irrigation on the regional climate. To model these effects, we developed and implemented a new flexible irrigation parameterization into the regional climate model REMO. In our setup, REMO is interactively coupled to the mosaic-based vegetation module iMOVE, enabling the calculation of irrigation effects and feedbacks on land, vegetation, and atmosphere. Multiple simulations for specific climatic conditions with and without the new irrigation parameterization are conducted on 0.11° resolution for the ”Greater Alpine Region“, which includes some of Europe‘s most intensively irrigated areas like the Po valley in Northern Italy. The differences between these simulations are analyzed to identify and quantify irrigation effects on atmospheric processes. The new irrigation parameterization will be introduced and the analysis of the irrigation effects on the regional climate in the “Greater Alpine Region” will be presented.

Human-induced climate change is one of the most pressing challenges of our time. The Helmholtz Association is making essential contributions to curbing the causes of climate change and finding ways to adapt. With the Helmholtz Climate Initiative, research is concentrated on the two focal points, "reduction of net emissions" and "adaptation to climate impacts". In Net-Zero-2050, Cluster I of the Helmholtz Climate Initiative, strategies and ways to reduce carbon emissions are scientifically investigated and evaluated. Furthermore, two digital communication formats are being developed to comprehensively show the research's complex results. Firstly, the web-based National Net-Zero-2050 Atlas informs the user about different methods and technologies for CO2 reduction and possible reduction paths. Secondly, the Soil Carbon App enables actors of the agricultural sector to assess climate mitigation potentials that arise from using different land management methods. A land surface model is used to simulate future scenarios presented in the app via cloud-based, model-driven workflows.Both formats aim to support users in making decisions and developing strategies. The work on the products follows the principles of comprehensibility, transparency and appropriate information presentation. During the work on the products, we identified challenges such as:How to deal with multi-dimensional data sets and uncertainties? How to present scientific results in a user-friendly manner for varied target groups? How to guide the users of varied target groups through the communication formats? With the aid of a critical internal reflection, approaches to overcome these challenges were developed and applied. For example, the atlas introduces different complexity levels to enable users to gain understanding, despite very diverse backgrounds, prior knowledge and information needs. The app, for its part, offers two main sectors that address the users’ different demands and prior knowledge: (1) it features options to choose from the data and subsequently delivers graphical analyses, and (2) it provides respective interpretation, texts, and web links.The article presents the two dissemination products as well as the challenges and solutions from the development work.

The main goal of Climate Services for eXtremes (which in is an integral part of the ClimXtreme framework) is to advance our understanding of the intensity as well as the spatio-temporal distribution of extreme weather and climate events, but tailored to the needs of stakeholders in the agricultural and forestry sector. The project is designed to optimise the communication between scientists and decision makers and thus to maximise the mutual benefit with regard to climate adaption. The scientists involved learn from the interview partners what climate information is actually required on the ground to facilitate the development of adaptation strategies, whereas the sector experts gain insights into the capabilities and limits of state-of-the-art climate information.In order to increase the efficiency of the knowledge transfer between scientists and stakeholders, we introduce a process-chain based approach: (i) the sector-specific identification of the characteristics of extreme weather conditions in close cooperation with partners from forestry and agriculture, (ii) the analysis of past and future weather and climate extremes with various statistical techniques, (iii) the investigation of the effects of these extremes by means of forest and agricultural case studies, and (iv) the development of possible needs-based adaptation strategies to future climatic conditions and extreme events based on this information.The extended summer drought in Germany during the warm seasons 2018 to 2020 is the perfect testbed for the approach, given the wide-ranging consequences this multi-year event had especially on the forestry sector. The event will be analysed from a probabilistic point of view, i.e. what is the return time and what were the causal factors from an atmospheric dynamic and teleconnection point of view. There is also potential to investigate the role of climate change in terms of altered risks. With this information, we can offer initial guidance for the project partners as to what they have to prepare for. But crucially, the interview feedback will help guide our ultimate research strategy. It will be a function of spatial scale, indices of interest as well as scope and complexity of the data and services our partners require. The new insights will serve as a basis to investigate such extreme drought events under potential future climate conditions.

Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.

Land-use and land cover (LULC) are continuously changing due to environmental changes and anthropogenic activities. Many observational and modeling studies show that LULC changes are important drivers altering land surface feedbacks and land-atmosphere exchange processes that have substantial impact on climate on the regional and local scale. Yet, most long-term regional climate modeling studies do not account for these changes. Therefore, within the WCRP CORDEX Flagship Pilot Study LUCAS (Land Use Change Across Scales) a new workflow was developed to generate high-resolution annual land cover change time series based on past reconstructions and future projections. First, the high-resolution global land cover dataset ESA-CCI LC (~300 m resolution) is aggregated and converted to a 0.1° resolution, fractional plant functional type (PFT) dataset. Second, the land use change information from the land-use harmonized dataset (LUH2), provided at 0.25° resolution as input for CMIP6 experiments, is translated into PFT changes employing a newly developed land use translator (LUT). The new LUT was first applied to the EURO-CORDEX domain. The resulting LULC maps for past and future - the LUCAS LUC dataset - can be applied as land use forcing to the next generation RCM simulations for downscaling CMIP6 by the EURO-CORDEX community and in the framework of FPS LUCAS. The dataset includes land cover and land management practices changes important for the regional and local scale such as urbanization and irrigation. The LUCAS LUC workflow is applied to further CORDEX domains, such as Australasia and North America. The resulting past and future land cover changes will be presented, and challenges regarding the application of the new workflow to different regions will be addressed. In addition, issues related to the implementation of the dataset into different RCMs will be discussed.

Projections of the 21st century potential future climate evolution, especially for precipitation, are associated with high uncertainty and variability. Knowledge of the variability of the projected precipitation and resulting run-offs and the sources of uncertainties form the basis for analysis and assessment of future water-management options as well as potential risks related to droughts and flood events. The variabilities related to climate modelling can only be assessed by using a comparatively large number of climate projections.In our research, we apply a large ensemble of regional climate model projections from the regional climate model REMO, driven by different global climate model simulations, at high temporal (hourly timestep) and high spatial (0.11 degree, or about 12.5 km) resolutions. Although the analysis of such big datasets involves considerable computational and storage capacities, this potentially helps to improve the simulation of future hydrological quantities in river catchments. For the analysis of the behaviour of small river catchments, we apply a semi-distributive hydrological model. Annual and winter average precipitation conditions show a robust and statistically significant increase especially for the RCP8.5 scenario. Precipitation ranges are compared with the ranges of runoff based on hydrological impact model runs driven by a set of simulated parameters from the regional climate model ensemble. The analyses are performed for a sub-catchment of the Lower Elbe system (Krückau catchment), which is a typical small basin (area < 200km2) close to the city of Hamburg in northern Germany. The model runs cover a long simulation period of 150 years (1950-2100) with a temporal resolution of 1 day. Short term model runs with a temporal resolution of 1 hour were carried out for annual and seasonal (summer/winter) maximum runoff derived from the long-term simulations.Average annual runoff shows an increase of 0 to 10 % for the RCP2.6 ensemble and an increase of 0 to 20 % for the RCP8.5 ensemble at the end of the 21st century. Annual and winter average conditions (precipitation sums and average runoff) of the RCP8.5 ensemble show a robust increase across different ensemble simulations. Extreme events however show high variability and no conclusive and robust trend. Analysis shows a good relation between average values of precipitation and average runoff (MQ). Future development of simulated annual maximum runoffs shows only a weak relation with future simulated precipitation extremes. However, summer maximum runoffs tend to show a relation with summer precipitation extremes. The behaviour of winter runoffs might be explained by altered future conditions of snow aggregation and melt in combination with high soil moisture. With increasing average and extreme temperatures, snow fall, snow accumulation and concentrated runoff caused by snow melt in spring are less likely to occur.One of the conclusions drawn is that especially for assessing extreme precipitation and its impacts on small hydrological catchments it is necessary to apply regional climate model projections with high spatial and temporal resolution where further improvement is expected by making use of the upcoming generation of climate simulations on convection permitting scale.

Myocardial infarctions (MI) are a major cause of death worldwide. In addition to well-known individual risk factors, studies have shown that temperature extremes, such as encountered during heat waves, lead to increases in MI. The relationship between health impacts and climate is complex, depending on a multitude of climatic, environmental, sociodemographic and behavioral factors. Machine Learning (ML) is a powerful tool for investigating complex and unknown relationships between extreme environmental conditions and their adverse impacts that has already been applied to other climate extremes, such as in the prediction of flood damages. By combining heterogeneous health, climatic, environmental and socio-economic datasets, this study is a first step in developing an ML model for predicting past and future MI risk due to heat waves.Here, we present first results of our ML approach for modelling heat-related health effects in Augsburg based on the KORA MI and environmental data. The basis of our data-driven approach is the KORA cohort study and the MI Registry in the Augsburg region of Bavaria, Germany, comprising detailed information on MI and underlying health conditions. Additionally, weather and climate data, air pollution data (e.g., PM10, PM2.5, nitrous oxides, and ozone), as well as socio-economic data (household income, education) are used for this study. One of the key challenges is to assemble and integrate heterogeneous data from various sources and prepare them for the appropriate spatial scales. We outline major challenges in combining these data and deriving quantitative models from them.Moreover, we present initial results based on both regression and classification models, discussing model performance for the period between 2000 and 2015, with a focus on two major heat wave events in Germany during 2003 and 2006. Ultimately, this research may be useful in better understanding heat-related MI risks, supporting possible adaptation options in urban areas and in identifying high-risk groups within society.

Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the subsequent dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned with such interannual variability and extremes. Yet, it often remains unclear what long-term adaptation options are most suitable in the context of climate change, mainly because climate projections have uncertainties and are usually not tailored to meet requirements, measures and scales of the individual practicioners. In the ADAPTER project, we explore regional and local change on the weather- and climate-related time scales and together with stakeholders (administration, plant breeders, educators, agricultural advisors), we co-design tailored climate change indices and usable products.In this contribution, we provide a snapshot view of our stakeholders' requirements regarding information about climate change over the next decades. We then focus on the analysis of three groups of indices based on 85 regional climate model simulations from Coordinated Downscaling Experiments over Europe - EURO-CORDEX: (i) changes in daily temperature variability, (ii) occurrence of agricultural droughts in summer, (iii) compound events of combined dryness and elevated temperatures during the same events. We show that these user-oriented, newly constructed indices can capture relevant changes during important phenological development states of typical crops. Finally, we discuss first implications of our findings for different adaptation strategies in Mid-Europe, such as alternating crop rotations, irrigation strategies or plant breeding. The analysis products presented are interactively and publicly available through a product platform (www.adapter-projekt.de) for agricultural stakeholders.