Climate change concerns the energy sector to a high degree because the sector is sensitive both to changing conditions for power and heat production, and to changing demand for electricity, heating and cooling. In this study potential consequences of climate change on different parts of the Swedish energy sector were assessed in a series of workshops, where climate and energy scientists, energy systems experts and analysts met with representatives of the energy sector to assess the vulnerability of the sector and consider what climate indicators could be used to assess impacts of relevance.

The impact of climate change depends on the energy type. Hydropower, for which production is naturally linked to weather and climate, is significantly impacted by climate change. For other forms of production, such as nuclear power, other factors such as e.g. policy and technology development are more important. The series of workshops held in this study, where different aspects of climate change and consequences were discussed, proved very successful and has increased our understanding of climate impacts on the energy system.

Despite a growing interest in extreme event attribution, attributing individual weather events remains difficult and uncertain. We have explored extreme event attribution by comparing the method for probabilistic extreme event attribution employed at World Weather Attribution (https://www.worldweatherattribution.org, last access: 22 August 2024) (WWA method) to an approach solely using pre-industrial and current observations (PI method), utilising the extensive and long-running network of meteorological observations available in Sweden. With the long observational records, the PI method is used to calculate the change in probability for two recent extreme events in Sweden without relying on the correlation to the global mean surface temperature (GMST). Our results indicate that the two methods generally agree for an event based on daily maximum temperatures. However, the WWA method results in a weaker indication of attribution compared to the PI method, for which 12 out of 15 stations indicate a stronger attribution than found by the WWA method. On the other hand, for a recent extreme precipitation event, the WWA method results in a stronger indication of attribution compared to the PI method. For this event, only 2 out of 10 stations assessed in the PI method exhibited results similar to the WWA method. Based on the results, we conclude that at least one out of every two of heat waves similar to the summer of 2018 can be attributed to climate change. For the extreme precipitation event in Gävle in 2021, the large variations within and between the two methods make it difficult to draw any conclusions regarding the attribution of the event.

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 implies that anthropogenic land cover changes that had occurred during the Neolithic and Bronze Age could have affected the European climate by 2.5 ka.

Climate change is having a major impact on crop production and food security worldwide, and particularly so for smallholder farmers. As agroforestry is common with smallholder farmers, it is important to not only model the macroclimate, but also the microclimate that crops experience below the canopies. However, there are few highresolution spatiotemporal climate projections for forest understories, because of constraints related to the lack of i) development of models for downscaling global climate projections, ii) high-resolution gridded datasets of environmental factors influencing microclimate, and iii) spatially replicated in-situ microclimate measurements. We focused on a landscape in southwestern Ethiopia where Arabica coffee originated, and, in the present day, is commonly grown as a shade crop. We first examined the relative contribution of in-situ field measurements vs. GIS-derived estimates of vegetation and topographic features in explaining in-situ microclimate. Second, we used a statistical downscaling approach to obtain past and future microclimate maps at 30-meter spatial resolution for the part of the landscape that is covered by trees. Predictive models using in-situ variables performed equal to models with GIS variables, indicating that remote sensing data might substitute for in-situ field measurements. Vegetation and topographic features were both important in explaining microclimatic variation. Our spatiotemporal projections of the microclimate indicate that coffee farming might have to relocate to higher altitudes due to increasing temperatures, that vegetation might buffer the macroclimate at middle altitudes to some extent, and that decreasing trends in relative humidity at the beginning of the wet season might become problematic for coffee production. Taken together, our findings demonstrate that we can rely on remote sensing data to create microclimate maps in landscapes where in-situ field measurements are challenging, and we suggest how these microclimate projections can be used as a tool to promote climate-resilient agriculture at the local and landscape levels.

Multidecadal changes in regional climate can occur as a forced response to changing greenhouse gases and aerosols, as a result of natural internal climate variability, or due to their combination. Internal climate variability is frequently associated with regional changes in large-scale circulation. We investigate how changes in Scandinavian temperature and precipitation conditions during 1961–2020 can be linked to changes in the atmospheric large-scale circulation. The study is based on data from the ERA5 reanalysis and on Swedish average conditions based on observations from the Swedish Meteorological and Hydrological Institute. In general, it is shown that all seasons have become warmer and there is a predominance for more precipitation in the last 30 years. The results also show a clear decrease in daily temperature variability for winter and an increase in summer while there is no similar systematic change for precipitation. Further, we use a circulation type classification technique for identifying ten different circulation types for each calendar month in the 1961–2020 period. Results indicate that changes between the two periods can partly be related to changes in large-scale circulation due to changes in the frequencies of different circulation types. However, it is also clear that the contribution from frequency-related changes to the total change is comparatively low for most months and that changes also within the circulation types are required to explain the total change. The main conclusion of the study is that during the last 30 years it has mostly been warmer than in the preceding 30 years for the same type of weather situation for all months in the year. Consequently, internal climate variability, as represented by changes in the large-scale atmospheric circulation, cannot explain the observed changes in the Scandinavian temperature and precipitation.

Collections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less arbitrary weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs. Also, the method is only useful when inter-simulation variability is limited. This is the case for the average fields that have been studied, but not for the extremes. We have developed analytical expressions for the degree of improvement obtained with the present method, which quantify this conclusion. 

Land-cover changes have a clear impact on local climates via biophysical effects. European land cover has been affected by human activities for at least 6000 years, but possibly longer. It is thus highly probable that humans altered climate before the industrial revolution (AD1750–1850). In this study, climate and vegetation 6000 years (6 ka) ago is investigated using one global climate model, two regional climate models, one dynamical vegetation model, pollen-based reconstruction of past vegetation cover using a model of the pollen-vegetation relationship and a statistical model for spatial interpolation of the reconstructed land cover. This approach enables us to study 6 ka climate with potential natural and reconstructed land cover, and to determine how differences in land cover impact upon simulated climate. The use of two regional climate models enables us to discuss the robustness of the results. This is the first experiment with two regional climate models of simulated palaeo-climate based on regional climate models.

Different estimates of 6 ka vegetation are constructed: simulated potential vegetation and reconstructed vegetation. Potential vegetation is the natural climate-induced vegetation as simulated by a dynamical vegetation model driven by climate conditions from a climate model. Bayesian spatial model interpolated point estimates of pollen-based plant abundances combined with estimates of climate-induced potential un-vegetated land cover were used for reconstructed vegetation. The simulated potential vegetation is heavily dominated by forests: evergreen coniferous forests dominate in northern and eastern Europe, while deciduous broadleaved forests dominate central and western Europe. In contrast, the reconstructed vegetation cover has a large component of open land in most of Europe.

The simulated 6 ka climate using reconstructed vegetation was 0–5 °C warmer than the pre-industrial (PI) climate, depending on season and region. The largest differences are seen in north-eastern Europe in winter with about 4–6 °C, and the smallest differences (close to zero) in southwestern Europe in winter. The simulated 6 ka climate had 10–20% more precipitation than PI climate in northern Europe and 10–20% less precipitation in southern Europe in summer. The results are in reasonable agreement with proxy-based climate reconstructions and previous similar climate modelling studies. As expected, the global model and regional models indicate relatively similar climates albeit with regional differences indicating that, models response to land-cover changes differently.

The results indicate that the anthropogenic land-cover changes, as given by the reconstructed vegetation, in this study are large enough to have a significant impact on climate. It is likely that anthropogenic impact on European climate via land-use change was already taking place at 6 ka. Our results suggest that anthropogenic land-cover changes at 6 ka lead to around 0.5 °C warmer in southern Europe in summer due to biogeophysical forcing.

A natural hazard is a naturally occurring extreme event that has a negative effect on people and society or the environment. Natural hazards may have severe implications for human life and can potentially generate economic losses and damage ecosystems. A better understanding of their major causes, probability of occurrence, and consequences enables society to be better prepared to save human lives as well as to invest in adaptation options. Natural hazards related to climate change are identified as one of the Grand Challenges in the Baltic Sea region. Here, we summarize existing knowledge about extreme events in the Baltic Sea region with a focus on the past 200 years as well as on future climate scenarios. The events considered here are the major hydro-meteorological events in the region and include wind storms, extreme waves, high and low sea levels, ice ridging, heavy precipitation, sea-effect snowfall, river floods, heat waves, ice seasons, and drought. We also address some ecological extremes and the implications of extreme events for society (phytoplankton blooms, forest fires, coastal flooding, offshore infrastructure, and shipping). Significant knowledge gaps are identified, including the response of large-scale atmospheric circulation to climate change and also concerning specific events, for example, the occurrence of marine heat waves and small-scale variability in precipitation. Suggestions for future research include the further development of high-resolution Earth system models and the potential use of methodologies for data analysis (statistical methods and machine learning). With respect to the expected impacts of climate change, changes are expected for sea level, extreme precipitation, heat waves and phytoplankton blooms (increase), and cold spells and severe ice winters (decrease). For some extremes (drying, river flooding, and extreme waves), the change depends on the area and time period studied.

This study investigates present and future European heat wave magnitudes, represented by the Heat Wave Magnitude Index-daily (HWMId), for regional climate models (RCMs) and the driving global climate models (GCMs) over Europe. A subset of the large EURO-CORDEX ensemble is employed to study sources of uncertainties related to the choice of GCMs, RCMs, and their combinations.

We initially compare the evaluation runs of the RCMs driven by ERA-interim reanalysis to E-OBS (observation-based estimates), finding that the RCMs can capture most of the observed spatial and temporal features of HWMId. With their higher resolution compared to GCMs, RCMs can reveal spatial features of HWMId associated with small-scale processes (e.g., orographic effects); moreover, RCMs represent large-scale features of HWMId satisfactorily (e.g., by reproducing the general pattern revealed by E-OBS with high values at western coastal regions and low values at the eastern part). Our results indicate a clear added value of the RCMs compared to the driving GCMs. Forced with the emission scenario RCP8.5, all the GCM and RCM simulations consistently project a rise in HWMId at an exponential rate. However, the climate change signals projected by the GCMs are generally attenuated when downscaled by the RCMs, with the spatial pattern also altered.

The uncertainty in a simulated future change of heat wave magnitudes following global warming can be attributed almost equally to the difference in model physics (as represented by different RCMs) and to the driving data associated with different GCMs. Regarding the uncertainty associated with RCM choice, a major factor is the different representation of the orographic effects. No consistent spatial pattern in the ensemble spread associated with different GCMs is observed between the RCMs, suggesting GCM uncertainties are transformed by RCMs in a complex manner due to the nonlinear nature of model dynamics and physics.

In summary, our results support the use of dynamical downscaling for deriving regional climate realization regarding heat wave magnitudes.

Two ensembles of climate simulations, one global and one regional, are used to investigate model errors and projected climate change in seasonal mean temperature and precipitation over South Asia. The global ensemble includes ten global climate models (GCMs). In the regional ensemble all ten GCMs are downscaled by a regional climate model-RCA4 over South Asia at 50 km resolution. Our focus is on the Indian Summer Monsoon season (June-August) and we show that RCA4 can reproduce, reduce or amplify large-scale GCM biases depending on regions and GCMs. However, the RCA4 bias pattern in precipitation is similar across the simulations, regardless of forcing GCM, indicating a strong RCA4 imprint on the simulated precipitation. For climate change, the results indicate, that RCA4 can change the signal projected by the GCM ensemble and its individual members. There are a few RCA4 simulations with a substantial reduction of projected warming by RCA4 compared to the driving GCMs and with a large regional increase in precipitation absent in the GCMs. We also found that in a number of subregions warm RCA4 biases are related to stronger warming and vice versa, while there is no such dependency in the GCM ensemble. Neither the GCM nor the RCA4 ensemble shows any significant dependency between projected changes and biases for precipitation. Our results implicate that using only RCMs and excluding GCMs, a commonly established approach, can significantly change the message on future regional climate change.