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Seasonal GCM-based temperature and precipitation projections for the end of the 21st century are presented for five European regions; projections are compared with corresponding estimates given by the PRUDENCE RCMs. For most of the six global GCMs studied, only responses to the SRES A2 and B2 forcing scenarios are available. To formulate projections for the A1FI and B1 forcing scenarios, a super-ensemble pattern-scaling technique has been developed. This method uses linear regression to represent the relationship between the local GCM-simulated response and the global mean temperature change simulated by a simple climate model. The method has several advantages: e.g., the noise caused by internal variability is reduced, and the information provided by GCM runs performed with various forcing scenarios is utilized effectively. The super-ensemble method proved especially useful when only one A2 and one B2 simulation is available for an individual GCM. Next, 95% probability intervals were constructed for regional temperature and precipitation change, separately for the four forcing scenarios, by fitting a normal distribution to the set of projections calculated by the GCMs. For the high-end of the A1FI uncertainty interval, temperature increases close to 10°C could be expected in the southern European summer and northern European winter. Conversely, the low-end warming estimates for the B1 scenario are ~ 1°C. The uncertainty intervals of precipitation change are quite broad, but the mean estimate is one of a marked increase in the north in winter and a drastic reduction in the south in summer. In the RCM simulations driven by a single global model, the spread of the temperature and precipitation projections tends to be smaller than that in the GCM simulations, but it is possible to reduce this disparity by employing several driving models for all RCMs. In the present suite of simulations, the difference between the mean GCM and RCM projections is fairly small, regardless of the number or driving models applied.

The mortality of Scots pine trees in and around Helsinki has been reported in recent years, but the causalities of these deaths have not so far been rigorously examined. Tree-ring analyses have previously shown to effectively reveal historical growth variability and thus hint at the stress factors behind tree mortality. Here, we analyzed the tree rings of pines in two tree classes (living and dead) from an urban park in Helsinki to reveal their growth variations and to examine the obtained chronologies along with climatic data. Guided by tree-ring information, the pine growth over the past century could be divided into four episodes: average growth conditions during the first half of the twentieth century, a suppressed growth period during the 1950s and 1960s, a growth release since the mid-1970s, and a period of recent mortality. The two tree classes became particularly differentiated during the release period in that the growth of surviving pines underwent a more positive and abrupt growth anomaly in comparison to dead pines. The survival of pines could also be linked to their sensitivity to droughts in a long-term context: The growth of still-living pines showed a statistically significant moisture sensitivity over the second half of the century only. The period 2002–2003 (coinciding with drought) was observed as a dendrochronologically dated episode with a 40% mortality. Overall, the results point to the importance of tree competitive strength and climate as predisposing and inciting/contributing factors behind the tree mortality.

We collected relevant observational and measured annual-resolution time series dealing with climate in northern Europe, focusing in Finland.We analysed these series for the reliability of their temperature signal at annual and seasonal resolutions. Importantly, we analysed all of the indicators within the same statistical framework, which allows for their meaningful comparison. In this framework, we employed a cross-validation procedure designed to reduce the adverse effects of estimation bias that may inflate the reliability of various temperature indicators, especially when several indicators are used in a multiple regression model. In our data sets, timing of phenological observations and ice break-up were connected with spring, tree ring characteristics (width, density, carbon isotopic composition) with summer and ice formation with autumn temperatures. Baltic Sea ice extent and the duration of ice cover in different watercourses were good indicators of winter temperatures. Using combinations of various temperature indicator series resulted in reliable temperature signals for each of the four seasons, as well as a reliable annual temperature signal. The results hence demonstrated that we can obtain reliable temperature information over different seasons, using a careful selection of indicators, combining the results with regression analysis, and by determining the reliability of the obtained indicator.

Severe weather can have serious repercussions in the transport sector as a whole by increasing the number of accidents, injuries and other damage, as well as leading to highly increased travel times. This study, a component of the EU FP7 Project EWENT, delineates a Europe-wide climatology of adverse and extreme weather events that can be expected to affect the transport network. We first define and classify the relevant severe weather events by investigating the effects of hazardous conditions on different transportation modes and the infrastructure. Consideration is given to individual phenomena such as snowfall, heavy precipitation, heat waves, cold spells, wind gusts; a combined phenomenon, the blizzard, is also considered. The frequency of severe weather events, together with the changes in their spatial extension and intensity, is analyzed based on the E-OBS dataset (1971–2000) and the ERA-Interim reanalysis dataset (1989–2010). Northern Europe and the Alpine region are the areas most impacted by winter extremes, such as snowfall, cold spells and winter storms, the frequency of heavy snowfall. The frequency of hot days is highest in Southern Europe. Severe winds and blizzards are the most common over the Atlantic and along its shores. Although heavy rainfall may affect the whole continent on an annual basis, extreme precipitation events are relative sparse, affecting particularly the Alps and the Atlantic coastline. A European regionalization covering similar impacts on the transport network is performed.

When thick wet snow covers unfrozen ground at the beginning of winter, herders fear the development of a hard, icy bottom snow layer and the appearance of noxious moulds (microfungi) in semi-domesticated reindeer pastures. Such winter onsets were experienced in 2019 and 2021 in the reindeer herding area of Finland, after which significant reindeer losses, along with collapses in calf production and slaughter animals, were encountered. We studied the development of weather and snow conditions in the late autumn and early winter of 2021–2022 and measured snow conditions in March 2022 in 11 reindeer cooperatives. We also collected samples from reindeer winter forage plants for mycotoxin analysis. We found that the weather and snow conditions during the late autumn and early winter of 2021 caused the formation of a hard, icy bottom snow layer and the development of mycotoxins in pastures. Alternariol (AOH) and alternariol monomethyl ether (AME), produced by Alternaria spp., were found in all 33 samples (104–2562, 61–808 µg/kg DM) and zearalenone (ZEN) by Fusarium spp. in 16 samples (14–206 µg/kg). Certain significant correlations in the concentrations of mycotoxins with snow conditions and ground surface temperatures were found. We assume that besides difficult grazing conditions in the winters of 2019–2020 and 2021–2022, the presence of mycotoxins in pastures has contributed to reindeer losses and reduced the body condition, health, and reproduction of reindeer. As onsets of winters become warmer and rainier, the risk of similar pasture conditions in reindeer herding may increase.

Changes in indices related to frost and snow in Europe by the end of the twenty-first century were analyzed based on experiments performed with seven regional climate models (RCMs). All the RCMs regionalized information from the same general circulation model (GCM), applying the IPCC-SRES A2 radiative forcing scenario. In addition, some simulations used SRES B2 radiative forcing and/or boundary conditions provided by an alternative GCM. Ice cover over the Baltic Sea was examined using a statistical model that related the annual maximum extent of ice to wintertime coastal temperatures. Fewer days with frost and snow, shorter frost seasons, a smaller liquid water equivalent of snow, and milder sea ice conditions were produced by all model simulations, irrespective of the forcing scenario and the driving GCM. The projected changes have implications across a diverse range of human activities. Details of the projections were subject to differences in RCM design, deviations between the boundary conditions of the driving GCMs, uncertainties in future emissions and random effects due to internal climate variability. A larger number of GCMs as drivers of the RCMs would most likely have resulted in somewhat wider ranges in the frost, snow and sea ice estimates than those presented in this paper.

The Climate Change Adaptation Digital Twin (Climate DT), developed as part of the European Commission's Destination Earth (DestinE) initiative, sets up an operational system for producing multi-decadal, multi-model global climate projections and translating climate data into climate impact information to support adaptation efforts. This system delivers data with local granularity at spatial resolutions of 5–10 km and hourly outputs, leading to globally consistent information at scales that matter for decision-making. It also enables the testing of what-if scenarios such as high-resolution storylines, which are physically consistent global simulations of extreme events under different climate conditions and provide contextual insights to support concrete adaptation decisions. They support the generation of more equitable (understood as accessible and relevant across regions) climate information. The Climate DT is built on cutting-edge infrastructure, expert collaboration, and digital innovation. It is designed to support on-demand responses to policy questions, with quantified uncertainty. It will foster interactivity by allowing users to influence simulation design, model output portfolios, and application integration through co-design. AI-based tools, including emulators and chatbots, are being developed in parallel to enhance climate information access. Sector-specific applications are embedded in the system to synchronously translate climate data into tailored climate-impact indicators, with examples provided for energy, water, and forest management. The applications have been co-designed with informed users. A unified, cross-platform workflow defines the orchestration of all components, which is handled by a single workflow manager and relies on containerised components, facilitating automation, portability, maintainability, and traceability. Data management is unified using standard grids (HEALPix), ensuring consistency and easing data usability under a strict governance policy. Streaming enables real-time data use by the data consumers and unlocks access to the unprecedented data wealth produced by the high-resolution simulations. Monitoring tools provide real-time quality control of data and model outputs and enable continuous assessment of the realism of the climate simulations during Climate DT operation. The compute-intensive system is powered by world-class supercomputing capabilities through a strategic partnership with the European High Performance Computing Joint Undertaking (EuroHPC). Despite high computational demands, the Climate DT sets a new benchmark for delivering equitable, credible, and actionable climate information. It complements existing initiatives like CMIP, CORDEX, and national and European climate services, and aligns with global climate science goals to support climate adaptation.

Weather-driven hydrological variability and forest management influence the nutrient export from terrestrial to aquatic systems. We quantified the effect and range of variation in total nitrogen and phosphorus export in Vehka-Kuonanjärvi catchment located in southeastern Finland. A distributed model NutSpaFHy was used with varying weather scenarios (compiled from observed extreme years of dry, wet and wet & mild) and forest management scenarios (including no additional management and intensive clear-cutting of all mature stands in the existing forest structure). Nutrient exports by scenario combinations were compared to modeled baseline export in observed weather. The results showed that the increase in nutrient export by wet & mild weather (over 55%) exceeded the increase caused by the clear-cutting scenario (23 %). Dry weather decreased the exports to tenth of the baseline, which was per hectare 2.22 kg for N, 0.08 kg for P). The results suggest that in future maintaining a good ecological status in aquatic systems can be challenging if extreme wet years with mild winters occur more frequently. Certain catchment characteristics, e.g., deciduous tree percentage, open area percentage and site fertility, influence the export increase induced by the extreme weather. Hotspot analysis enabled identifying areas with currently high nutrient export and areas with high increase induced by the extreme weather. This helps targeting water protection efficiently.

On the basis of fifteen global model simulations of future climate, using the SRES emissions scenarios for greenhouse gases and aerosols, we have constructed national-scale seasonal and annual climate change scenarios for Finland during the 21st century. In approximate terms, the annual mean temperature is projected to rise by 1–3 °C and the annual mean precipitation by 0%–15% by the 2020s, relative to the baseline period 1961–1990. The corresponding increases by the 2050s are 2–5 °C (temperature) and 0%–30% (precipitation), while by the 2080s they are 2–7 °C and 5%–40%, respectively. The projected temperature trends are markedly stronger than that observed during the 20th century. The ranges in the climate change projections reflect the uncertainties arising from differences in model formulation and in emissions scenarios but are, to some extent, affected by the internal variability of climate as well. Seasonally, the projected precipitation changes and their statistical significance are largest in winter and smallest in summer. On the other hand, the projected rather small summertime warming is at least as statistically significant as the larger warming in the other seasons. Based on a literature review, it seems very likely that changes in mean climate are associated with changes in climate extremes as well.

The main subject of this article is to comment on the issue of storminess trends derived from the twentieth century reanalysis (20CR) and from observations in the North Atlantic region written about in Wang et al. (Clim Dyn 40(11–12):2775–2800, 2012 ). The statement that the 20CR estimates would be consistent with storminess derived from pressure-based proxies does not hold for the time prior to 1950.