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This paper studies the impact of land use and land cover change (LULCC) on the climate around 2500 years ago (2.5 ka), a period of rapid transitions across the European landscape. One global climate model was used to force two regional climate models (RCMs). The RCMs used two land cover descriptions. The first was from a dynamical vegetation model representing potential land cover, and the second was from a land cover description reconstructed from pollen data by statistical interpolation. The two different land covers enable us to study the impact of land cover on climate conditions. Since the difference in landscape openness between potential and reconstructed land cover is mostly due to LULCC, this can be taken as a measure of early anthropogenic effects on climate. Since the sensitivity to LULCC is dependent on the choice of climate model, we also use two RCMs. The results show that the simulated 2.5 ka climate was warmer than the simulated pre-industrial (PI, 1850 CE) climate. The largest differences are seen in northern Europe, where the 2.5 ka climate is 2-4 degrees C warmer than the PI period. In summer, the difference between the simulated 2.5 ka and PI climates is smaller (0-3 degrees C), with the smallest differences in southern Europe. Differences in seasonal precipitation are mostly within +/- 10 %. In parts of northern Europe, the 2.5 ka climate is up to 30% wetter in winter than that of the PI climate. In summer there is a tendency for the 2.5 ka climate to be drier than the PI climate in the Mediterranean region. The results also suggest that LULCC at 2.5 ka impacted the climate in parts of Europe. Simulations including reconstructed LULCC (i.e. those using pollen-derived land cover descriptions) give up to 1 degrees C higher temperature in parts of northern Europe in winter and up to 1.5 degrees C warmer in southern Europe in summer than simulations with potential land cover. Although the results are model dependent, the relatively strong response i

This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using "convection-permitting" models to produce reliable climate change information over the Fenno-Scandinavian reg

Based on the Baltic Earth Assessment Reports of this thematic issue in Earth System Dynamics and recent peer-reviewed literature, current knowledge of the effects of global warming on past and future changes in climate of the Baltic Sea region is summarised and assessed. The study is an update of the Second Assessment of Climate Change (BACC II) published in 2015 and focuses on the atmosphere, land, cryosphere, ocean, sediments, and the terrestrial and marine biosphere. Based on the summaries of the recent knowledge gained in palaeo-, historical, and future regional climate research, we find that the main conclusions from earlier assessments still remain valid. However, new long-term, homogenous observational records, for example, for Scandinavian glacier inventories, sea-level-driven saltwater inflows, so-called Major Baltic Inflows, and phytoplankton species distribution, and new scenario simulations with improved models, for example, for glaciers, lake ice, and marine food web, have become available. In many cases, uncertainties can now be better estimated than before because more models were included in the ensembles, especially for the Baltic Sea. With the help of coupled models, feedbacks between several components of the Earth system have been studied, and multiple driver studies were performed, e.g. projections of the food web that include fisheries, eutrophication, and climate change. New datasets and projections have led to a revised understanding of changes in some variables such as salinity. Furthermore, it has become evident that natural variability, in particular for the ocean on multidecadal timescales, is greater than previously estimated, challenging our ability to detect observed and projected changes in climate. In this context, the first palaeoclimate simulations regionalised for the Baltic Sea region are instructive. Hence, estimated uncertainties for the projections of many variables increased. In addition to the well-known influence of the Nor

Coastal environments, in particular heavily populated semi-enclosed marginal seas and coasts like the Baltic Sea region, are strongly affected by human activities. A multitude of human impacts, including climate change, affect the different compartments of the environment, and these effects interact with each other. As part of the Baltic Earth Assessment Reports (BEAR), we present an inventory and discussion of different human-induced factors and processes affecting the environment of the Baltic Sea region, and their interrelations. Some are naturally occurring and modified by human activities (i.e. climate change, coastal processes, hypoxia, acidification, submarine groundwater discharges, marine ecosystems, non-indigenous species, land use and land cover), some are completely human-induced (i.e. agriculture, aquaculture, fisheries, river regulations, offshore wind farms, shipping, chemical contamination, dumped warfare agents, marine litter and microplastics, tourism, and coastal management), and they are all interrelated to different degrees. We present a general description and analysis of the state of knowledge on these interrelations. Our main insight is that climate change has an overarching, integrating impact on all of the other factors and can be interpreted as a background effect, which has different implications for the other factors. Impacts on the environment and the human sphere can be roughly allocated to anthropogenic drivers such as food production, energy production, transport, industry and economy. The findings from this inventory of available information and analysis of the different factors and their interactions in the Baltic Sea region can largely be transferred to other comparable marginal and coastal seas in the world., QC 20220120

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analysed, and the result of a research experiment is presented using model weighting with the participation of six climate model experts and six hydrological model experts. For the experiment, seven climate models are a priori selected from a larger EURO-CORDEX (Coordinated Regional Downscaling Experiment - European Domain) ensemble of climate models, and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual rounds of elicitation of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological-impact modellers in general are more open for assigning weights to different models in a multi-model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models and thereby reduce the uncertaint

It is well established that using kilometer scale grid resolution for simulations of weather systems in weather and climate models enhances their realism. This study explores heavy- and extreme-precipitation characteristics over the Nordic region generated by the regional climate model HARMONIE-Climate (HCLIM). Two model setups of HCLIM are used: ERA-Interim-driven HCLIM12 spanning over Europe at 12 km grid spacing with a convection parameterization scheme and HCLIM3 spanning over the Nordic region with 3 km grid spacing and explicitly resolved deep convection. The HCLIM simulations are evaluated against a unique and comprehensive set of gridded and in situ observation datasets for the warm season from April to September regarding their ability to reproduce sub-daily and daily heavy-precipitation statistics across the Nordic region. Both model setups are able to capture the daily heavy-precipitation characteristics in the analyzed region. At the sub-daily scale, HCLIM3 clearly improves the statistics of occurrence of the most intense heavy-precipitation events and the amplitude and timing of the diurnal cycle of these events compared to its forcing of HCLIM12. Extreme value analysis shows that HCLIM3 provides added value in capturing sub-daily return levels compared to HCLIM12, which fails to produce the most extreme events. The results indicate clear benefits of the convection-permitting model in simulating heavy and extreme precipitation in the present-day climate, therefore, offering a motivating way forward to investigate the climate change impacts in the region.

Climate change is not only about changes in means of climatic variables such as temperature, precipitation and wind, but also their extreme values which are of critical importance to human society and ecosystems. To inspire the Swedish climate research community and to promote assessments of international research on past and future changes in extreme weather events against the global climate change background, the Earth Science Class of the Royal Swedish Academy of Sciences organized a workshop entitled 'Extreme weather events in a warming world' in 2019. This article summarizes and synthesizes the key points from the presentations and discussions of the workshop on changes in floods, droughts, heat waves, as well as on tropical cyclones and extratropical storms. In addition to reviewing past achievements in these research fields and identifying research gaps with a focus on Sweden, future challenges and opportunities for the Swedish climate research community are highlighted.

Atmospheric blocking events are known to locally explain a large part of climate variability. However, despite their relevance, many current climate models still struggle to represent the observed blocking statistics. In this study, simulations of the global climate model EC-Earth are analysed with respect to atmospheric blocking. Seventeen simulations map the uncertainty space defined by the three-model characteristics: atmospheric resolution, physical parameterization and complexity of atmosphere-ocean interaction, namely an atmosphere coupled to an ocean model or forced by surface data. Representation of the real-world statistics is obtained from reanalyses ERA-20C, JRA-55 and ERA-Interim which agree on Northern Hemisphere blocking characteristics. Blocking events are detected on a central blocking latitude which is individually determined for each simulation. The frequency of blocking events tends to be underestimated relative to ERA-Interim over the Atlantic and western Eurasia in winter and overestimated during spring months. However, only few model setups show statistically significant differences compared to ERA-Interim which can be explained by the large inter-annual variability of blocking. Results indicate slightly larger biases relative to ERA-Interim in coupled than in atmosphere-only models but differences between the two are not statistically significant. Although some resolution dependence is present in spring, the signal is weak and only statistically significant if the physical parameterizations of the model are improved simultaneously. Winter blocking is relatively more sensitive to physical parameterizations, and this signal is robust in both atmosphere-only and coupled simulations, although stronger in the latter. Overall, the model can capture blocking frequency well despite biases in representing the mean state of geopotential height over this area. Blocking signatures of geopotential height are represented more similar to ERA-Interim and only, QCR 20190212

Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections.

This study aims to evaluate the direct effects of anthropogenic deforestation on simulated climate at two contrasting periods in the Holocene, similar to 6 and similar to 0.2 k BP in Europe. We apply We apply the Rossby Centre regional climate model RCA3, a regional climate model with 50 km spatial resolution, for both time periods, considering three alternative descriptions of the past vegetation: (i) potential natural vegetation (V) simulated by the dynamic vegetation model LPJ-GUESS, (ii) potential vegetation with anthropogenic land use (deforestation) from the HYDE3.1 (History Database of the Global Environment) scenario (V + H3.1), and (iii) potential vegetation with anthropogenic land use from the KK10 scenario (V + KK10). The climate model results show that the simulated effects of deforestation depend on both local/regional climate and vegetation characteristics. At similar to 6 k BP the extent of simulated deforestation in Europe is generally small, but there are areas where deforestation is large enough to produce significant differences in summer temperatures of 0.5-1 degrees C. At similar to 0.2 k BP, extensive deforestation, particularly according to the KK10 model, leads to significant temperature differences in large parts of Europe in both winter and summer. In winter, deforestation leads to lower temperatures because of the differences in albedo between forested and unforested areas, particularly in the snow-covered regions. In summer, deforestation leads to higher temperatures in central and eastern Europe because evapotranspiration from unforested areas is lower than from forests. Summer evaporation is already limited in the southernmost parts of Europe under potential vegetation conditions and, therefore, cannot become much lower. Accordingly, the albedo effect dominates in southern Europe also in summer, which implies that deforestation causes a decrease in temperatures. Differences in summer temperature due to deforestation range from -1 degree

Assessing climate change impacts on pesticide leaching requires careful consideration of different sources of uncertainty. We investigated the uncertainty related to climate scenario input and its importance relative to parameter uncertainty of the pesticide leaching model. The pesticide fate model MACRO was calibrated against a comprehensive one-year field data set for a well-structured clay soil in southwestern Sweden. We obtained an ensemble of 56 acceptable parameter sets that represented the parameter uncertainty. Nine different climate model projections of the regional climate model RCA3 were available as driven by different combinations of global climate models (GCM), greenhouse gas emission scenarios and initial states of the GCM. The future time series of weather data used to drive the MACRO model were generated by scaling a reference climate data set (1970-1999) for an important agricultural production area in south-western Sweden based on monthly change factors for 2070-2099. 30 yr simulations were performed for different combinations of pesticide properties and application seasons. Our analysis showed that both the magnitude and the direction of predicted change in pesticide leaching from present to future depended strongly on the particular climate scenario. The effect of parameter uncertainty was of major importance for simulating absolute pesticide losses, whereas the climate uncertainty was relatively more important for predictions of changes of pesticide losses from present to future. The climate uncertainty should be accounted for by applying an ensemble of different climate scenarios. The aggregated ensemble prediction based on both acceptable parameterizations and different climate scenarios has the potential to provide robust probabilistic estimates of future pesticide losses.

Most of the last 20 years in Sweden have been mild and wet compared to the 1961–1990 climate reference period. After a few relatively cold years in the mid-1980s, practically all years have been warmer than the preceding 30 years average. During the indicated period, an increase of moisture-related problems (mould growth) was observed in ventilated attics, a moisture sensitive building part. This work investigates hygrothermal performance of ventilated attics in respect to possible climate change. Hygrothermal simulations of attics were performed numerically in Matlab. Four attic constructions are investigated – a conventional attic and three alternative constructions suggested by practitioners. Simulations were done for the period of 1961–2100 using the weather data of RCA3 climate model. Effects of three different emissions scenarios are considered. Hygrothermal conditions in the attic are assessed using a mould growth model. Based on the results the highest risk of mould growth was found on the north roof of the attic in Gothenburg, Sweden. Results point to increment of the moisture problems in attics in future. Different emissions scenarios do not influence the risk of mould growth inside the attic due to compensating changes in different variables. Assessing the future performance of the four attics shows that the safe solution is to ventilate the attic mechanically, though this solution inevitably requires extra use of electrical energy for running the fan. Insulating roofs of the attic can decrease the condensation on roofs, but it cannot decrease the risk of mould growth considerably, on the wooden roof underlay.

Global warming and its effects on climate are of great concern. Climate change can affect buildings in different ways. Increased structural loads from wind and water, changes in energy need and decreased moisture durability of materials are some examples of the consequences. Future climate conditions are simulated by global climate models (GCMs). Downscaling by regional climate models (RCMs) provides weather data with suitable temporal and spatial resolutions for direct use in building simulations. There are two major challenges when the future climate data are used in building simulations. The first is to handle and analyse the huge amount of data. The second challenge is to assess the uncertainties in building simulations as a consequence of uncertainties in the future climate data. In this paper two statistical methods, which have been adopted from climatology, are introduced. Applications of the methods are illustrated by looking into two uncertainty factors of the future climate; operating RCMs at different spatial resolutions and with boundary data from different GCMs. The Ferro hypothesis is introduced as a nonparametric method for comparing data at different spatial resolutions. The method is quick and subtle enough to make the comparison. The parametric method of decomposition of variabilities is described and its application in data assessment is shown by considering RCM data forced by different GCMs. The method enables to study data and its variations in different time scales. It provides a useful summary about data and its variations which makes the comparison between several data sets easier.

A fully coupled atmosphere-ocean general circulation model is used to simulate climate conditions during the last glacial maximum (LGM). Forcing conditions include astronomical parameters, greenhouse gases, ice sheets and vegetation. A 50-yr period of the global simulation is dynamically downscaled to 50 km horizontal resolution over Europe with a regional climate model (RCM). A dynamic vegetation model is used to produce vegetation that is consistent with the climate simulated by the RCM. This vegetation is used in a final simulation with the RCM. The resulting climate is 5-10 degrees C colder than the recent past climate (representative of year 1990) over ice-free parts of Europe as an annual average; over the ice-sheet up to 40 degrees C colder in winter." The average model-proxy error is about the same for summer and winter, for pollen-based proxies. The RCM results are within (outside) the uncertainty limits for winter (summer). Sensitivity studies performed with the RCM indicate that the simulated climate is sensitive to changes in vegetation, whereas the location of the ice sheet only affects the climate around the ice sheet. The RCM-simulated interannual variability in near surface temperature is significantly larger at LGM than in the recent past climate., QC 20110501