The Paris Agreement of the United Nations Framework Convention on Climate Change aims not only at avoiding +2°C warming (and even limit the temperature increase further to +1.5^oC), but also sets long-term goals to guide mitigation. Therefore, the best available science is required to inform policy makers on the importance of and the adaptation needs in a +1.5^oC warmer world. Seven research institutes from Europe and Turkey integrated their competencies to provide a cross-sectoral assessment of the potential impacts at a pan-European scale. The initial findings of this initiative are presented and key messages communicated. The approach is to select periods based on global warming thresholds rather than the more typical approach of selecting time periods (e.g. end of century). The results indicate that the world is likely to pass the +1.5^oC threshold in the coming decades. Cross-sectoral dimensions are taken into account to show the impacts of global warming that occur in parallel in more than one sector. Also, impacts differ across sectors and regions. Alongside the negative impacts for certain sectors and regions, some positive impacts are projected. Summer tourism in parts of Western Europe may be favoured by climate change; energy demand decreases outweigh increases over most of Europe and catchment yields in hydropower regions will increase. However, such positive findings should be interpreted carefully as we do not take into account exogenous factors that can and will influence Europe such as migration patterns, food production and economic and political instability.

A new ensemble of climate and climate change simulations covering all major inhabited regions with a spatial resolution of about 25 km, from the WCRP CORDEX COmmon Regional Experiment (CORE) Framework, has been established in support of the growing demands for climate services. The main objective of this study is to assess the quality of the simulated climate and its fitness for climate change projections by REMO (REMO2015), a regional climate model of Climate Service Center Germany (GERICS) and one of the RCMs used in the CORDEX-CORE Framework. The CORDEX-CORE REMO2015 simulations were driven by the ECMWF ERA-Interim reanalysis and the simulations were evaluated in terms of biases and skill scores over ten CORDEX Domains against the Climatic Research Unit (CRU) TS version 4.02, from 1981 to 2010, according to the regions defined by the Köppen–Trewartha (K–T) Climate Classification types. The REMO simulations have a relatively low mean annual temperature bias (about $\pm 0.5$ K) with low spatial standard deviation (about $\pm 1.5$ K) in the European, African, North and Central American, and Southeast Asian domains. The relative mean annual precipitation biases of REMO are below $\pm 50$ % in most domains; however, spatial standard deviation varies from $\pm 30$ % to $\pm 200$ %. The REMO results simulated most climate types relatively well with lowest biases and highest skill score found in the boreal, temperate, and subtropical regions. In dry and polar regions, the REMO results simulated a relatively high annual biases of precipitation and temperature and low skill. Biases were traced to: missing or misrepresented processes, observational uncertainty, and uncertainties due to input boundary forcing.

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.

According to the final report of the European Union OMC expert group on strengthening cultural heritage resilience for anthropogenic climate change, the impacts of climate change, particularly extreme weather events, on cultural heritage in Europe have become increasingly evident in recent years and are progressing at an unprecedented speed and scale. Archaeological sites, museum collections, and historical buildings and structures are affected, among others, by rising temperatures or by heavy storms and precipitation events. Deep scientific knowledge about future climate projections is required to develop appropriate preservation strategies and measures to protect and adapt cultural heritage. In this paper we present the first set of results of the KERES project. The project focuses on the impacts of future extreme climate events on the built heritage and historic gardens. An ensemble of climate simulations is used to analyze changes in both climatology and extreme events for several climate variables at two cultural heritage sites in Germany. In this study, a methodology was developed to guide climate scientists on how to better tailor climate information for the needs of stakeholders in the cultural heritage sector. It would help the stakeholders to integrate the results of climate projections into the prevention and emergency management, in particular for the risk assessment of extreme events. The effects of interpolation from a model grid to a location of cultural heritage site and advantages of an ensemble approach have been demonstrated in the study.

To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22^∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.