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Climate indices have long been an important tool for the evaluation of climate impacts and are commonly used both in research and practical risk assessment. The term generally refers to advanced statistics derived from daily data, such as the longest spell of consecutive dry days in a year, or the maximal precipitation in a single day in a given month. Initially, these indices were calculated for station data, but with the advent and increased utility of global and regional climate models, they are now commonly calculated for gridded data. The increased spatial resolution, together with the increased length of simulations to hundreds of years, the increased use of ever-larger ensembles of climate simulations, and the interest in a wider selection of possible future climate scenarios renders some established, serial algorithms ineffective. This is compounded by the fact that modern computing architectures derive their growing power no longer from the speedup of single computing units, but rather from the integration of larger numbers of parallel computing units. To fully utilize this potential, it is not enough to implement a straightforward parallelization of existing algorithms. Rather, we need to rethink the computing task from the start in a parallel framework.Here, we present parallel algorithms implemented in the Python framework Climix, that have proven useful in the calculation of climate indices for a large ensemble of climate simulations that provide the basis for the user-oriented climate service of the Swedish Meteorological and Hydrological Institute.Climix is available as open-source software and allows the calculation of a large number of climate indices both from a command-line interface with good support for common HPC schedulers such as SLURM and via a flexible Python API.

Bias adjustment of climate models is today normally performed with quantile mapping methods that account for the whole distribution of the parameter. The bulk of the distribution is well described as long as sufficient data records are used (Berg et al., 2012), however, the extreme tails will always suffer from large uncertainties. These uncertainties stem from both the climate model and the reference data set, which prevents a robust and detailed identification of bias in the extreme tail. Empirical quantile mapping methods are therefore prone to overfitting, and may introduce substantial bias when applied outside the calibration period. Commonly, a constant adjustment is applied for values outside the range of the calibration period, but there is room for improvements of the extrapolation method.While working with a climate service for Sweden, a clear offset was identified between data adjusted within and outside the calibration period for an extreme indicator of daily maximum precipitation. This study explores different extrapolation methods for the extreme tail of the distribution in the spline-based empirical quantile mapping method of the MIdAS bias adjustment method (Berg et al., 2022). By limiting the bias adjustment to the first 95% of the distribution, and thereafter applying a constant or a linear fit to the remaining 5% of data in the tail, the offset is strongly reduced and the adjusted extremes become more robust and plausible.Berg, P., Feldmann, H., & Panitz, H. J. (2012). Bias correction of high resolution regional climate model data. Journal of Hydrology, 448, 80-92.Berg, P., Bosshard, T., Yang, W., & Zimmermann, K. (2022). MIdASv0. 2.1–MultI-scale bias AdjuStment. Geoscientific Model Development, 15(15), 6165-6180.

Doing high quality research involves complex workflows and intermediate datasets. An important part is also sharing of those datasets, software tools and workflows among researchers, and tracking provenance and lineage. It also needs to be stored in a citable permanent repository in order to be referenced in papers and reused subsequently by other researchers. Supporting this research data life cycle properly is a very challenging objective for research infrastructures. This is especially true with rapidly evolving technologies, sustainable funding problems and human expertise. In the climate research infrastructure, many efforts have been made to support end-users and long tail research. There is the basic data distribution, the ESGF data nodes, but this is to support mainly specialized researchers in climate science. This basic infrastructure implements quite strict standards to enable proper data sharing in the research community. This is far from FAIR compliance, but this has proven to be extremely beneficial for collaborative research. Of course, high level components and services can be built on top. This is not an easy task, and a layered approach is always better to hide the underlying complexity and also to prevent technology locking and too complex codes. One example is the IS-ENES C4I 2.0 platform (https://dev.climate4impact.eu/ ), a front-end that eases very much data access, and is acting like a bridge between the data nodes and computing services. The C4I platform provides a very much enhanced Jupyter-la like interface (SWIRRL), with many services to support sharing of data and common workflow for data staging and preprocessing, as well as the development of new analysis methods in a research context. Advanced tools that can calculate end-user products are also made available along with some example notebooks implementing popular workflows. One of these tools is icclim (https://github.com/cerfacs-globc/icclim), a python software package. C4I also includes high-level services such as on-the-fly inter-comparisons between climate simulations with ESMValTool (https://github.com/ESMValGroup/ESMValTool). All this work is also including large efforts to standardize and to become closer to FAIR for data, workflows and software.Another way of helping researchers is to pre-compute end-users products like climate indices. This is extremely useful for users because it can be really complex and time consuming to calculate those products. One example is to provide those users datasets of climate indices pre-computed on CMIP6 simulations would be very valuable for those users. Of course all specific needs cannot be taken into account but the most general ones can be fulfilled. The European Open Science Cloud (EOSC) is providing computing and storage resources through the EGI-ACE project, enabling the possibility to compute several climate indices. In this EGI-ACE Use Case, icclim will be used to compute 49 standard climate indices on a large number of CMIP6 simulations, starting with the most used ones. It could also be extended to ERA5 reanalysis, CORDEX and CMIP5 datasets.This project (IS-ENES3) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°824084.

End users of climate change information are relying on climate services and tools in order to produce meaningful information for specific applications. Data volumes as well as the number of datasets are increasing very rapidly, and the ability to select, process and download all needed data is getting complex, technical and very time-consuming. It is especially true since those datasets are often distributed among several data centres and into a large quantity of files.Several platform are being developed to hide this complexity to users and provide a seamless access to climate data, as well as to provide on-demand data analysis capabilities. We can cite, for example, the Copernicus Data Store (CDS https://cds.climate.copernicus.eu), along with its toolbox to perform online data analysis. Another platform is developed within the H2020 IS-ENES3 project, called climate4impact (C4I 2.0 https://dev.climate4impact.eu ). It is using an enhanced Jupyter-Lab environment called SWIRRL (Software for Interactive Reproducible Research Labs https://gitlab.com/KNMI-OSS/swirrl ) along with a collection of Jupyter notebooks (https://gitlab.com/is-enes-cdi-c4i/notebooks) as useful set of example on how to use the data.Finally, the portal provides interactive pages for the evaluation of climate models (using ESMValTool) to guide users on selecting climate datasets. The notebooks that can be executed in C4I, are developed using a very convenient software library, which is made available via SWIRRL, to calculate climate indices and indicators called icclim (v5.0 https://github.com/cerfacs-globc/icclim ). This library, which is also in the process of being integrated into the C3S, is a flexible python software package to calculate climate indices and indicators. This tool adhere as much as possible to metadata conventions such as the Climate & Forecasting Conventions (CF-1.x) as well as the clix-meta (https://github.com/clix-meta) work that is being done in IS-ENES3. Proper provenance information still needs to be added. The ultimate goal is to be as close as possible to all FAIR aspects. icclim is designed with performance and optimisation in mind, because the goal is to provide on-demand calculations for users. It provides the implementation of most of the international standard climate indices such as ECAD, ETCCDI, ET-SCI, including the correct methodology for calculating percentile indices using the bootstrapping method. It has been validated against R.Climdex as well (https://cran.r-project.org/web/packages/climdex.pcic/index.html). This new 5.x version of icclim is based on functions from the xclim (https://github.com/Ouranosinc/xclim) python library, which was inspired by earlier versions of icclim, but using xarray and dask for data access and processing.In this presentation, the climate4impact 2.0 platform will be described along with the icclim climate indices tool. Important metadata aspects will also be discussed (clix-meta). A few examples using the jupyter notebook collection will be shown. This project (IS-ENES3) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°824084.

Large historical and future ensemble simulations from the Max-Planck Institute and the Canadian Earth System Models and from CMIP5 have been analysed to investigate the uncertainty due to internal variability in multi-decadal temperature and precipitation trends over Europe. Internal variability dominates the uncertainties in temperature and precipitation trends in all seasons at 30-year time scales. Locally, seasonal 30-year temperature trends deviate up to ±3 °C from the ensemble mean trend. Thus, in the entire of Europe, local seasonal temperature changes until year 2050 from below −1 °C up to more than 4 °C are possible according to the model results. Up to 30 of all ensemble members show negative temperature trends until year 2050 in winter, up to 10 of the members in summer. Uncertainties of 30-year precipitation trends due to internal variability exceed the trends almost everywhere in Europe. Only in few European regions more than 75 of the members agree on the sign of the change until year 2050. In southern Sweden, minimum and maximum winter (summer) temperature trends in the next 30 years differ with up to 7 °C (5 °C) between individual members of the large model ensembles. Large positive temperature trends are linked to positive (negative) precipitation trends in winter (summer) in southern Sweden. This variability is attributed to the variability in large scale atmospheric circulation trends, mainly due to internal atmospheric variability. We find only weak linkages between the variability of temperature trends and the dominant decadal to multi-decadal climate modes. This indicates that there is limited potential to predict the multi-decadal variability in climate trends. The main findings from our study are robust across the large ensembles from the different models used in this study but at the local scale, the results depend also on the choice of the model. © Tellus A: 2020. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor Francis Group.

Climate indices play an important role in the practical use of climate and weather data. Their application spans a wide range of topics, from impact assessment in agriculture and urban planning, over indispensable advice in the energy sector, to important evaluation in the climate science community. Several widely used standard sets of indices exist through long-standing efforts of WMO and WCRP Expert Teams (ETCCDI and ET-SCI), as well as European initiatives (ECA&D) and more recently Copernicus C3S activities. They, however, focus on the data themselves, leaving much of the metadata to the individual user. Moreover, these core sets of indices lack a coherent metadata framework that would allow for the consistent inclusion of new indices that continue to be considered every day.In the meantime, the treatment of metadata in the wider community has received much attention. Within the climate community efforts such as the CF convention and the much-expanded scope and detail of metadata in CMIP6 have improved the clarity and long-term usability of many aspects of climate data a great deal.We present a novel approach to metadata for climate indices. Our format describes the existing climate indices consistent with the established standards, adding metadata along the lines of existing metadata specifications. The formulation of these additions in a coherent framework encompassing most of the existing climate index standards allows for its easy extension and inclusion of new climate indices as they are developed.We also present Climix, a new Python software for the calculation of indices based on this description. It can be seen as an example implementation of the proposed standard and features high-performance calculations based on state-of-the-art infrastructure, such as Iris and Dask. This way, it offers shared memory and distributed parallel and out-of-core computations, enabling the efficient treatment of large data volumes as incurred by the high resolution, long time-series of current and future datasets.

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., Tellus B: Chemical and Physical Meteorology, 72 (1), ISSN:0280-6509, ISSN:1600-0889

Europe is one of the world’s largest food producers, and climate change may pose a serious threat to food security in the region. In the present study, we assess the potential impact of climate change on the Colorado Potato Beetle (CPB), Leptinotarsa decemlineata (Say)—a severe pest of potato (Solanum tuberosum, L.). We also investigate the possible impact of climate change on the phenological development of potato. The main focus is on factors that may limit the northward expansion of the CPB, and the number of generations per year in areas where the insect pest is already present. These factors include lack of temperature sum for completed development before winter, and lack of food (i.e. potato) due to mismatches in insect-host plant phenological synchrony. We use a gridded observational dataset and an ensemble of bias corrected regional climate model data for the period of 1981–2099, representing RCP8.5, as input to a potato and CPB phenological model. The results show that in the future, CPB individuals with a low developmental threshold (+10 °C) can complete maturity of two generations per year before potato is harvested in most parts of Europe. A third generation of CPB may not be able to complete maturation due to lack of food in south and central Europe, while temperature becomes a limiting factor further north. In north-eastern Europe, the initiation of a first generation may be delayed due to lack of food in spring. CPBs with a high developmental threshold (+12 °C) will emerge later from winter hibernation, and food availability will therefore not be a problem in spring. However, individuals with a high developmental threshold face a greater risk of regeneration failure caused by harvesting of potato in autumn. The potential lack of food in autumn may also increase the strength of selection towards a low developmental threshold in northern populations. The combined analysis of CPB and potato phenology indicated that climate change can lead to increased pressure from the CPB in most potato growing areas.

In climate change research ensembles of climate simulations are produced in an attempt to cover the uncertainty in future projections. Many climate change impact studies face difficulties using the full number of simulations available, and therefore often only subsets are used. Until now such subsets were chosen based on their representation of temperature change or by accessibility of the simulations. By using more specific information about the needs of the impact study as guidance for the clustering of simulations, the subset fits the purpose of climate change impact research more appropriately. Here, the sensitivity of such a procedure is explored, particularly with regard to the use of different climate variables, seasons, and regions in Europe. While temperature dominates the clustering, the resulting selection is influenced by all variables, leading to the conclusion that different subsets fit different impact studies best. Display Omitted We present a method to reduce ensembles of climate models.We use hierarchical clustering, SVD, Silhouettes and mean distances.To minimise the information loss the selection is fitted to the data application.The method shows strong sensitivity to the choice of variables/information provided.Strong need for a careful and thoughtful selection process is shown.

In the light of the observed and projected regional warming trend in northern Europe, new recommendations are needed for climate-proofing the selection of forest trees seedlings and growth estimates. Drawing on recently developed climate data sets, both observations of the recent past, and scenarios for the future we present and analyse a set of climate indices tailored to be used for operational forestry applications. Data covering Sweden and Finland include two high-resolution gridded observational data sets of daily mean temperature and daily total precipitation for the period 1961-2007, and an ensemble of regional climate model (RCM) projections based on forcing data from six global scenarios representing a medium high emission scenario. The observation-based data sets are used as reference in two ways; as reference for downscaling and bias-adjustment of daily data from the regional scenarios and to highlight recent observed climate changes and how these changes are represented in the RCM ensemble. For seasonal temperature and related climate indices, there is a clear change towards warmer conditions between the two periods 1961-1990 and 1988-2007, most clearly seen in the observational data and less pronounced in the RCM ensemble. For the future period 2035-2065, 'mid-century', the medium high scenario ensemble used in this study shows substantial changes to several key indices relevant to forestry applications. (Less)

Climate change is expected to drive the distribution retraction of northern species. However, particularly in regions with a history of intensive exploitation, changes in habitat management could facilitate distribution expansions counter to expectations under climate change. Here, we test the potential for future forest management to facilitate the southward expansion of an old-forest species from the boreal region into the boreo-nemoral region, contrary to expectations under climate change. We used an ensemble of species distribution models based on citizen science data to project the response of Phellinus ferrugineofuscus, a red-listed old-growth indicator, wood-decaying fungus, to six forest management and climate change scenarios. We projected change in habitat suitability across the boreal and boreo-nemoral regions of Sweden for the period 2020-2100. Scenarios varied in the proportion of forest set aside from production, the level of timber extraction, and the magnitude of climate change. Habitat suitabilities for the study species were projected to show larger relative increases over time in the boreo-nemoral region compared to the boreal region, under all scenarios. By 2100, mean suitabilities in set-aside forest in the boreo-nemoral region were similar to the suitabilities projected for set-aside forest in the boreal region in 2020, suggesting that occurrence in the boreo-nemoral region could be increased. However, across all scenarios, consistently higher projected suitabilities in set-aside forest in the boreal region indicated that the boreal region remained the species stronghold. Furthermore, negative effects of climate change were evident in the boreal region, and projections suggested that climatic changes may eventually counteract the positive effects of forest management in the boreo-nemoral region. Our results suggest that the current rarity of this old-growth indicator species in the boreo-nemoral region may be due to the history of intensive forestry. Forest management therefore has the potential to compensate for the negative effects of climate change. However, increased occurrence at the southern range edge would depend on the dispersal and colonization ability of the species. An increase in the amount of set-aside forest across both the boreal and boreo-nemoral regions is therefore likely to be required to prevent the decline of old-forest species under climate change.

This study addresses the effects of land use change from agricultural to photovoltaic parks (PVPs) on the hydrology of an area. Although many environmental effects have been identified and analyzed, only minor attention has been given to the hydrologic effects of the installation and operation of PVPs. The effects of current PVP installation and operation practices on major hydrologic budget constituents (surface runoff, evapotranspiration and percolation) were identified, conceptualized, quantified and simulated using SWAT model. Vosvozis river basin located in north Greece was selected as a test site. Additionally, long-term effects were simulated using dynamically downscaled climate projections by a Regional Climate Model (RCM) driven by 5 different General Circulation Models (GCMs) for the period 2011–2100. Results indicate that surface runoff and percolation potential are significantly increased at the local scale and have to be considered during PVP siting, especially when sensitive and protected ecosystems are involved.

In this study, we developed models of transfer effects for growth and survival of Scots pine (Pinus sylvestris L.) in Sweden and Finland using a general linear mixed-model approach. For model development, we used 378 provenance and progeny trials with a total of 276 unimproved genetic entries (provenances and stand seed check-lots) distributed over a wide variety of climatic conditions in both countries. In addition, we used 119 progeny trials with 3921 selected genetic entries (open- and control pollinated plus-tree families) for testing model performance. As explanatory variables, both climatic indices derived from high-resolution gridded climate datasets and geographical variables were used. For transfer, latitude (photoperiod) and, for describing the site, temperature sum were found to be main drivers for both survival and growth. In addition, interaction terms (between transfer in latitude and site altitude for survival, and transfer in latitude and temperature sum for growth) entail changed reaction patterns of the models depending on climatic conditions of the growing site. The new models behave in a way that corresponds well to previous studies and recommendations for both countries. The model performance was tested using selected plus-trees from open and control pollinated progeny tests. Results imply that the models are valid for both countries and perform well also for genetically improved material. These models are the first step in developing common deployment recommendations for genetically improved forest regeneration material in both Sweden and Finland.

We describe the process of building a climate service centred on regional climate model results from the Rossby Centre regional climate model RCA4. The climate service has as its central facility a web service provided by the Swedish Meteorological and Hydrological Institute where users can get an idea of various aspects of climate change from a suite of maps, diagrams, explaining texts and user guides. Here we present the contents of the web service and how this has been designed and developed in collaboration with users of the service in a dialogue reaching over more than a decade. We also present the ensemble of climate projections with RCA4 that provides the fundamental climate information presented at the web service. In this context, RCA4 has been used to downscale nine different coupled atmosphere-ocean general circulation models (AOGCMs) from the 5th Coupled Model Intercomparison Project (CMIP5) to 0.44° (c. 50km) horizontal resolution over Europe. Further, we investigate how this ensemble relates to the CMIP5 ensemble.We find that the iterative approach involving the users of the climate service has been successful as the service is widely used and is an important source of information for work on climate adaptation in Sweden. The RCA4 ensemble samples a large degree of the spread in the CMIP5 ensemble implying that it can be used to illustrate uncertainties and robustness in future climate change in Sweden. The results also show that RCA4 changes results compared to the underlying AOGCMs, sometimes in a systematic way.

Assessment of future ecosystem risks should account for the relevant uncertainty sources. This means accounting for the joint effects of climate variables and using modelling techniques that allow proper treatment of uncertainties. We investigate the influence of three of the IPCC's scenarios of greenhouse gas emissions (special report on emission scenarios (SRES)) on projections of the future abundance of a bryophyte model species. We also compare the relative importance of uncertainty sources on the population projections. The whole chain global climate model (GCM)—regional climate model—population dynamics model is addressed. The uncertainty depends on both natural- and model-related sources, in particular on GCM uncertainty. Ignoring the uncertainties gives an unwarranted impression of confidence in the results. The most likely population development of the bryophyte Buxbaumia viridis towards the end of this century is negative: even with a low-emission scenario, there is more than a 65 per cent risk for the population to be halved. The conclusion of a population decline is valid for all SRES scenarios investigated. Uncertainties are no longer an obstacle, but a mandatory aspect to include in the viability analysis of populations.

Tree dehardening and budburst will occur earlier in a warmer climate, and this could lead to an increased risk of frost damage caused by temperature backlashes. By using a spring backlash index and a cold hardiness model, we assessed different aspects of risk for frost damage in Norway spruce forests during the present climate and for one future emission scenario. Uncertainties associated with climate modelling were quantified by using temperature data from three climate data sets: (1) E-Obs gridded observed climate data, (2) an ensemble of data from eight regional climate models (RCM) forced by ERA-40 reanalysis data, (3) an ensemble of regional climate scenarios produced by the regional climate model RCA3 driven at the boundary conditions by seven global climate models (GCM), all representing the SRES A1B emission scenario. The frost risk was analysed for three periods, 1961–1990, 2011–2040 and 2070–2097. The RCA3_GCM ensemble indicated that the risk for spring frost damage may increase in the boreo-nemoral forest zone of southern Scandinavia and the Baltic states/Belarus. This is due to an increased frequency of backlashes, lower freezing temperatures after the onset of the vegetation period and the last spring frost occurring when the trees are closer to budburst. The changes could be transient due to the fine balance between an increased risk of frost damage caused by dehardening during a period when freezing temperatures are common and a decreased risk caused by warmer temperatures. In the nemoral zone, the zone with highest risk for spring backlashes during the reference period (1961–1990), the spring frost severity may increase due to frost events occurring when the trees are closer to budburst. However, the risk in terms of frequency of backlashes and freezing temperature were projected to become lower already in the beginning of this century.

Remote sensing provides spatially and temporally continuous measures of forest reflectance, and vegetation indices calculated from satellite data can be useful for monitoring climate change impacts on forest tree phenology. Monitoring of evergreen coniferous forest is more difficult than monitoring of deciduous forest, as the new buds only account for a small proportion of the green biomass, and the shoot elongation process is relatively slow. In this study, we have analyzed data from 186 coniferous monitoring sites in Sweden covering boreal, southern-boreal, and boreo-nemoral conditions. Our objective was to examine the possibility to track seasonal changes in coniferous forests by time-series of MODIS eight-day vegetation indices, testing the coherence between satellite monitored vegetation indices (VI) and temperature dependent phenology. The relationships between two vegetation indices (NDVI and WDRVI) and four phenological indicators (length of snow season, modeled onset of vegetation period, tree cold hardiness level and timing of budburst) were analyzed. The annual curves produced by two curve fitting methods for smoothening of seasonal changes in NDVI and WDRVI were to a large extent characterized by the occurrence of snow, producing stable seasonal oscillations in the northern part and irregular curves with less pronounced annual amplitude in the southern part of the country. Measures based on threshold values of the VI-curves, commonly used for determining the timing of different phenological phases, were not applicable for Swedish coniferous forests. Evergreen vegetation does not have a sharp increase in greenness during spring, and the melting of snow can influence the vegetation indices at the timing of bud burst in boreal forests. However, the interannual variation in VI-values for specific eight-day periods was correlated with the phenological indicators. This relation can be used for satellite monitoring of potential climate change impacts on northern coniferous spring phenology. (C) 2010 Elsevier Inc. All rights reserved.

Understanding the effects of climate change on boreal forests which hold about 7% of the global terrestrial biomass carbon is a major issue. An important mechanism in boreal tree species is acclimatization to seasonal variations in temperature (cold hardiness) to withstand low temperatures during winter. Temperature drops below the hardiness level may cause frost damage. Increased climate variability under global and regional warming might lead to more severe frost damage events, with consequences for tree individuals, populations and ecosystems. We assessed the potential future impacts of changing frost regimes on Norway spruce ( Picea abies L. Karst.) in Sweden. A cold hardiness and frost damage model were incorporated within a dynamic ecosystem model, LPJ-GUESS. The frost tolerance of Norway spruce was calculated based on daily mean temperature fluctuations, corresponding to time and temperature dependent chemical reactions and cellular adjustments. The severity of frost damage was calculated as a growth-reducing factor when the minimum temperature was below the frost tolerance. The hardiness model was linked to the ecosystem model by reducing needle biomass and thereby growth according to the calculated severity of frost damage. A sensitivity analysis of the hardiness model revealed that the severity of frost events was significantly altered by variations in the hardening rate and dehardening rate during current climate conditions. The modelled occurrence and intensity of frost events was related to observed crown defoliation, indicating that 6–12% of the needle loss could be attributed to frost damage. When driving the combined ecosystem-hardiness model with future climate from a regional climate model (RCM), the results suggest a decreasing number and strength of extreme frost events particularly in northern Sweden and strongly increasing productivity for Norway spruce by the end of the 21st century as a result of longer growing seasons and increasing atmospheric CO 2 concentrations. However, according to the model, frost damage might decrease the potential productivity by as much as 25% early in the century.

The spruce bark beetle Ips typographus is one of the major insect pests of mature Norway spruce forests. In this study, a model describing the temperature-dependent thresholds for swarming activity and temperature requirement for development from egg to adult was driven by transient regional climate scenario data for Sweden, covering the period of 1961-2100 for three future climate change scenarios (SRES A2, A1B and B2). During the 20th century, the weather supported the production of one bark beetle generation per year, except in the north-western mountainous parts of Sweden where the climate conditions were too harsh. A warmer climate may sustain a viable population also in the mountainous part; however, the distributional range of I. typographus may be restricted by the migration speed of Norway spruce. Modelling suggests that an earlier timing of spring swarming and fulfilled development of the first generation will significantly increase the frequency of summer swarming. Model calculations suggest that the spruce bark beetle will be able to initiate a second generation in South Sweden during 50% of the years around the mid century. By the end of the century, when temperatures during the bark beetle activity period are projected to have increased by 2.4-3.8 degrees C, a second generation will be initiated in South Sweden in 63-81% of the years. The corresponding figures are 16-33% for Mid Sweden, and 1-6% for North Sweden. During the next decades, one to two generations per year are predicted in response to temperature, and the northern distribution limit for the second generation will vary. Our study addresses questions applicable to sustainable forest management, suggesting that adequate countermeasures require monitoring of regional differences in timing of swarming and development of I. typographus, and planning of control operations during summer periods with large populations of bark beetles.

Extra-tropical cyclone frequency and intensity are Currently under intense scrutiny because of the destruction recent windstorms have brought to Europe, and because they are a major meridional heat transport mechanism that may respond to differential latitudinal warming trends. Several studies using reanalysis data covering the second half of the 20th century Suggest increasing storm intensity in the northeastern Atlantic and European sector. Fewer analyses cover a longer time period but show different trends or point towards the dominance of interdecadal variability instead of ally clear trends. Hence, it is relevant to analyse cyclone variability over as long a period as possible. In this Study, we analyse interdecadal variability in cyclone activity over northwestern Europe back to AD 1780 by combining information from eight storminess indices applied in all Eulerian framework. These indices, including four new approaches towards gauging cyclone activity, use the series of thrice-daily sea level pressure observations at Lund and Stockholm. We find pronounced interdecadal variability in cyclonic activity but no significant overall consistent long-term trend. The major interdecadal-scale variability common to all indices is in good agreement with geostrophic wind reconstructions for NE Atlantic and NW Europe, and with variations in the North Atlantic oscillation (NAO). Our results show that the reanalysis studies cover a time period chiefly coinciding with a marked, but not exceptional in our 225-year perspective, positive variation in the regional cyclone activity that has more recently reversed. Because of the interdecadal variations, a near-centennial time perspective is needed when analysing variations in extra-tropical cyclone activity and the associated weather conditions over northwestern Europe. Copyright (C) 2009 Royal Meteorological Society

Mass-propagation of the spruce bark beetle, Ips typographus, following windfalls and drought is a serious threat to mature spruce forests. Climate change will affect the population dynamics of I. typographus directly as the swarming activity and development rate are mainly controlled by temperature, and indirectly via changes in availability of brood trees. Today, I. typographus is in general univoltine in southern Sweden. In Denmark, however, the weather conditions usually support the production of a second generation. By modelling the temperature dependent population dynamics, we have evaluated the effect of regional climate change scenarios for the period of 2070-2099 on I. typographus in southern Sweden. Our results indicate that temperature increase will have a step-wise effect on the population dynamics. Earlier spring swarming and a faster development from egg to mature bark beetle increase the probability of a second swarming period during summer. A second swarming will be customary with an annual mean temperature increase of 2-3 degrees C. The thermal requirement for development of a second generation may, however, not be fulfilled every year with an annual mean temperature increase less than 5-6 degrees C. Winter is fatal for immature bark beetles, and the larger the temperature increase, the higher the probability that the second generation will complete development and survive hibernation. The temperature regime during autumn will therefore have a decisive impact on the size of the swarming population next spring. (c) 2007 Elsevier B.V. All rights reserved. (Less)

Probability distributions of daily maximum and minimum temperatures in a suite of ten RCMs are investigated for (1) biases compared to observations in the present day climate and (2) climate change signals compared to the simulated present day climate. The simulated inter-model differences and climate changes are also compared to the observed natural variability as reflected in some very long instrumental records. All models have been forced with driving conditions from the same global model and run for both a control period and a future scenario period following the A2 emission scenario from IPCC. We find that the bias in the fifth percentile of daily minimum temperatures in winter and at the 95th percentile of daily maximum temperature during summer is smaller than 3 (±5°C) when averaged over most (all) European sub-regions. The simulated changes in extreme temperatures both in summer and winter are larger than changes in the median for large areas. Differences between models are larger for the extremes than for mean temperatures. A comparison with historical data shows that the spread in model predicted changes in extreme temperatures is larger than the natural variability during the last centuries.

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This chapter examines past and present studies of variability and changes in atmospheric variables within the North Sea region over the instrumental period; roughly the past 200 years. The variables addressed are large-scale circulation, pressure and wind, surface air temperature, precipitation and radiative properties (clouds, solar radiation, and sunshine duration). Temperature has increased everywhere in the North Sea region, especially in spring and in the north. Precipitation has increased in the north and decreased in the south. There has been a north-eastward shift in storm tracks, which agrees with climate model projections. Due to large internal variability, it is not clear which aspects of the observed changes are due to anthropogenic activities and which are internally forced, and long-term trends are difficult to deduce. The number of deep cyclones seems to have increased (but not the total number of cyclones). The persistence of circulation types seems to have increased over the past century, with ‘more extreme’ extreme events. Changes in extreme weather events, however, are difficult to assess due to changes in instrumentation, station relocations, and problems with digitisation. Without thorough quality control digitised datasets may be useless or even counterproductive. Reanalyses are useful as long as biases introduced by inhomogeneities are properly addressed. It is unclear to what extent circulation over the North Sea region is controlled by distant factors, especially changes in Arctic sea ice.

A new method for optimising threshold values of dry/wet spells is evaluated. A set of indices is used to find the best threshold giving good correspondence between the frequency of dry/wet spells in Hadley Centre regional model (HadRM3) output, reference observations with predetermined thresholds, and area-averaged observations. The analyses focus on selected model gridboxes in 3 different European climate regimes (Sweden, UK, Italy), where station data are available from several locations. In addition, a pan-European analysis using the European Climate Assessment (ECA) dataset is carried out. Generally, there is good agreement between point observations and the corresponding area average using the common thresholds of 0.1 or 1.0 mm with observational data. Applying the optimal thresholds on the model output is important, as it typically results in substantially better agreement between the simulated and observed series of dry/wet days. The fitted optimal pan-European dry/wet threshold is (1) 0.47 or 0.15 mm, depending on model version, for the observed point data threshold of 0.1 mm, and (2) 1.2 or 0.56 mm, depending on model version, for the threshold of 1.0 mm.

The boreal and boreo-nemoral forests in Europe, which occur in northern and northeastern Europe, are dominated by 2 coniferous species, Norway spruce Picea abies (L.) Karst. being economically the most important one. Forestry is of major economic importance in this region. Forestry planning and climate change scenarios are based on similar (long-term) timescales, i.e. between 70 and 120 yr. Within the EU project 'Modelling the Impact of Climate Extremes' (MICE), we have used 'present day' runs (1961-1990) and future scenarios (2070-2100, emission scenarios A2 and B2 from the Special Report on Emissions Scenarios [SRES]) of the HadRM3 regional climate model to study and model direct and indirect effects of changing climate on Norway spruce in Sweden and northern Europe. According to our results, extreme climate events like spring temperature backlashes and summer drought will increase in frequency and duration. In combination with a raised mean temperature, climate extremes will negatively precondition trees (i.e. increase their susceptibility) to secondary damage through pests and pathogens. Decreased forest vitality also makes stands more susceptible to windthrow. Storm damage is discussed based on a 100 yr storm damage record for Sweden. Marginally increased frequencies and windspeeds of storms may cause disproportionate increases in windthrow. Increased economic hazards can be expected from a combination of the increased volumes of wind-thrown timber, and a greater likelihood of additional generations of spruce bark beetle Ips typographus (further encouraged by the increase in fallen timber), as a result of a changing climate with warmer summers. (Less)

In boreal and nemoboreal forests, tree frost hardiness is modified in reaction to cues from day length and temperature. The dehardening processes in Norway spruce, Picea abies, could be estimated to start when the daily mean temperature is above 5 degreesC for 5 days. Bud burst will occur approximately after 120-170 degree-days above 5 degreesC. dependent on genetic differences among provenances. A reduced cold hardiness level during autumn and spring and an advanced onset of bud burst are expected impacts of projected future global wart-ning. The aim of this study was to test if this will increase the risk for frost damage caused by temperature backlashes. This was tested for Sweden by comparing output from the Hadley Centre regional climate model, HadRM3H, for the period 1961-1990 with future IPCC scenario SRES A2 and B2 for 2070-2099. Different indices for calculating the susceptibility to frost damage were used to assess changes in frost damage risk. The indices were based on: (1) the start of dehardening; (2) the severity of the temperature backlash: (3) the timing of bud burst: and (4) the cold hardiness level. The start of dehardening and bud burst were calculated to occur earlier all over the country. which is in line with the overall warming in both climate change scenarios. The frequency of temperature backlashes that may cause frost damage was calculated to increase in the southern part, an effect that became gradually less pronounced towards the north. The different timing of the onset of dehardening mainly caused this systematic latitudinal pattern. In the south, it occurs early in the year when the seasonal temperature progression is slow and large temperature variations occur. In the north, dehardening will occur closer to the spring equinox when the temperature progression is faster. (C) 2004 Elsevier B.V. All rights reserved. (Less)

Severe storm damage has been a recurring problem to the Swedish forestry sector since, at least, the start of the 19th century. This short communication presents a regionally resolved time-series of storm damage in Swedish forests during the last century. Data on storm damage have been gathered from the National Board of Forestry, the Regional Forestry Boards and scientific reports. Storm damage in Swedish forests seems to have increased during the century with a peak around the 1980s. In total 110 million m 3 forest were destroyed by 77 recorded wind storms, with the severe storms in 1954 and 1969 accounting for 49% of the total damage. Reported damage in southern Sweden was normalised against the area of productive forest with trees older than 40 years, for inter annual comparisons. The geostrophic wind was used to describe the regional wind direction during the storm events. Most damage occurred during NNW to SW winds, and by winds from NNE. Apart from a possible shift in storm intensity and frequency, the increase in storm damaged trees can be attributed to changes in regeneration and thinning regimes, variations in storm damage reporting system, increase in forest cover and various damage to root architecture. (Less)

This paper addresses variations in the wind climate in relation to changes in the impact of wind erosion on agricultural lands with Scania, the southernmost province of Sweden, as an example. The literature concerning wind erosion in the region is reviewed and the conclusions are linked to recent analyses of the regional wind climate. Presently, wind erosion occasionally induces problems in light soil areas of the intensively cultivated Scania. It is not a new phenomenon that results from modem farming practice; it was documented already in the 18th century. This was an era of major land use changes because the increasing population required new and more vulnerable areas to be cultivated, which resulted in an ecological crisis due to increased wind erosion. Partly overlapping in time, a series of land amalgamation reforms transformed the land-use resulting in larger fields. Analyses of the frequency of wind directions indicate that variations in the large-scale wind climate occurred at that time. Wind erosion again became a topic of concern in the 20th century. Extensive analyses of several aspects of the wind climate (e.g., storminess and cyclone intensity) of Scandinavia and northwestern Europe show that there have been interdecadal variations but no sign of an overall long-term trend. The recurring periods of intensified wind erosion problems can best be explained as the combined result of a varying wind climate and the lack of appropriate long-term maintenance strategies for protective measures through the intervening periods of benign wind climate. (C) 2003 Elsevier Science B.V. All rights reserved. (Less)

This study compares the performance of different models used to assess the local wind near-surface conditions at an agricultural site in Scania, southern Sweden. The methods are: (a) the WASP model (Wind Analysis and Application Program), (b) separate linear regressions of the two wind vector components, (c) a regression model based on vector correlation, and (d) linear regression of scalar wind. Each method was tested with three different data sets over nine months: wind measurements from the nearby Sturup airport SYNOP station, 10 m surface wind and surface geostrophic wind produced by the operational Swedish Mesoscale Analysis system (Mesan). The wind climate estimations were compared with observed winds at the field site, with respect to mean wind speed, wind direction, wind speed frequency distribution and the relative frequency of winds above 6 m s(-1). All models performed reasonably well with data from Sturup and Mesan surface wind, but gave less reliable results with the Mesan geostrophic data. The estimated frequency of winds above 6 m s(-1) was in general lower than the observed frequency. Overall, best results were obtained with WASP in combination with measurements from Sturup. (Less)

Wind erosion causes severe damage on sandy soils in agricultural areas of north-west Europe. The weather conditions during erosion events are the result of the general atmospheric circulation and are key components in the erosion process. Principal component analysis (PCA) in combination with a non-hierarchical cluster analysis was conducted on de-seasonalized daily mean-sea-level pressure (MSLP) patterns of north-western Europe between 1973 and 1991 to determine the main synoptic types associated with wind erosion on sugar beet fields in Scania, southern Sweden. Cluster analysis of the PCA scores indicated that the observations should be grouped into 14 classes for which average MSLP patterns were computed. To assess the relationship between the patterns and wind erosion, 2 indices were computed. Index of Occurrence (10) is the ratio between the occurrence of the patterns during days with wind erosion and the occurrence during the entire wind erosion season. Index of Erosion Severity (IES) is computed by weighting the 10 with the area damaged by wind erosion associated with each pressure pattern. The IES identifies 2 pressure patterns that have significantly larger values than the other patterns. The most important is a pattern showing easterly flow over Scania, associated with 74 % (912 ha of 1235 ha) of the total damage in the study area. The second most important is a pattern showing westerly flow over Scania, associated with 5 % (63 ha) of the total damage. Both pressure patterns contain strong pressure gradients (in different directions) located over Scania. Depending on the location of the maximum gradient these patterns could be associated with wind erosion in other regions of north-western Europe. (Less)

International audience; Future climate evolution is of primary importance for the societal, economical, political orientations and decision-making. It explains the increasing use of climate projections as input for quantitative impact studies, assessing vulnerability and defining adaptation strategies in different sectors. Here we analyse 17 national and representative use cases so as to identify the diversity of the demand for climate information depending on user profiles as well as the best practices, methods and tools that are needed to answer the different requests. A particular emphasis is put on the workflow that allows to translate climate data into suitable impact data, the way to deal with the different sources of uncertainty and to provide a suited product to users. We identified three complementary tools to close the gap between climate scientists and user needs: an efficient interface between users and providers; an optimized methodology to handle user requests and a portal to facilitate access to data and elaborated products. We detail in the paper how these three tools can limit the intervention of experts, educate users, and lead to the production of useful information. This work provides the basis on which the ENES (European Network for Earth System Modelling) Portal Interface for the Climate Impact Communities is built.

The Late Maunder Minimum (LMM; 1675-1715) delineates a period with marked climate variability within the Little Ice Age in Europe. Gridded monthly mean surface pressure fields were reconstructed for this period for the eastern North Atlantic-European region (25°W-30°E and 35-70°N). These were based on continuous information drawn from proxy and instrumental data taken from several European data sites. The data include indexed temperature and rainfall values, sea ice conditions from northern Iceland and the Western Baltic. In addition, limited instrumental data, such as air temperature from central England (CET) and Paris, reduced mean sea level pressure (SLP) at Paris, and monthly mean wind direction in the Oresund (Denmark) are used. The reconstructions are based on a canonical correlation analysis (CCA), with the standardized station data as predictors and the SLP pressure fields as predictand. The CCA-based model was performed using data from the twentieth century. The period 1901-1960 was used to calibrate the statistical model, and the remaining 30 years (1961-1990) for the validation of the reconstructed monthly pressure fields. Assuming stationarity of the statistical relationships, the calibrated CCA model was then used to predict the monthly LMM SLP fields. The verification results illustrated that the regression equations developed for the majority of grid points contain good predictive skill. Nevertheless, there are seasonal and geographical limitations for which valid spatial SLP patterns can be reconstructed. Backward elimination techniques indicated that Paris station air pressure and temperature, CET, and the wind direction in the Oresund are the most important predictors, together sharing more than 65% of the total variance. The reconstructions are compared with additional data and subjectively reconstructed monthly pressure charts for the years 1675-1704. It is shown that there are differences between the two approaches. However, for extreme years the reconstructions are in good agreement.

The use of vehicle-borne mobile measurement systems to study processes generating micro-scale temperature variations in the landscape requires precise knowledge of factors influencing the measurements. We reported results from field measurements with a sonic anemometer to determine the flow deformation around a vehicle-borne mobile measurement system. These measurements were also used to develop a correction procedure that removes the smoothing induced by the thermal inertia of thermocouples. The procedure is based on Fourier analysis and consists of two components: a low-pass filter that removes noise and aliasing effects above a cut-off frequency and a filter specially designed to remove the effect of the thermal inertia of thermocouple. The correction procedure is applied to two mobile measurement runs acquired during NOPEX CFE1 and compared to sound-virtual temperature recorded at a reference mast. The overall statistics (mean, standard deviation, etc.) are not changed by the correction procedure, but substantial changes occur in the high-frequency part of the observed spectrum that is dominated by turbulent fluctuations during daytime and, during nights, temperature transitions at forest edges and other land-use changes.

The reconstructed surface air pressure series from Lund, southern Sweden, covers the period 1780-1997 and comprises mon than 234000 valid observations (three observations per day), i.e. > 98% of all possible observation occasions. For the Early Instrumental Period (EIP; 1780-1860) data were digitised from the original records. For most of the Modern Instrumental Period (MIP; 1861-) a series was compiled from various databases containing instrument corrected data. During EIP, the series of raw monthly means show several substantial inhomogeneities. With the aid of a detailed reconstruction of the station history, it was possible to remove almost all inhomogeneities during EIP by applying the correct instrument corrections (for barometer temperature, to standard gravity and to mean sea-level pressure) to the series of original observations. In particular, corrections for the temperature and altitude of the barometer eliminated several inhomogeneities. A prerequisite for applying these corrections is the availability of high-resolution data (actual raw observations or daily averages). Further homogenisation was attained by intercomparison of the monthly mean pressure with acknowledged homogeneous series (mainly the UKMO monthly grid, station records from Copenhagen and Edinburgh). Statistical tests of homogeneity showed that no substantial inhomogeneities remain in the final version. The modern part of the final monthly pressure series largely follows that of the southern Baltic Sea region. Furthermore, it shows relatively high pressure during spring (MAM) in the period 1780-1820, which was paralleled by severe wind erosion in southern Scandinavia during this time. Relatively high pressure throughout the year is also notable during a period of precipitation deficit in 1970s. Copyright (C) 1999 Royal Meteorological Society.

Monthly zonal indices (ZI:s) from January 1899 to August 1992 in a North Atlantic area (40° W-5° W) and a north European area (5° E-40° E) are analysed. The ZI:s are calculated as the sea-level pre...

In this study, we quantify the effect of uncertainties in climate projections on an impact model (IPS) that describes the temperature-dependent swarming and development of Ips typographus . Three forcing climate data sets (ensembles) were used: (1) E-Obs gridded observations, (2) ERA-40 reanalysis data downscaled by eight regional climate models (RCMs) and (3) regional scenarios from one RCM forced by seven GCM simulations representing SRES-A1B, for the period of 1961–2097. The IPS_RCM_ERA40 ensemble members, including IPS_RC3_ERA40 , were generally within the IPS_E-Obs confidence limits. The IPS model is however sensitive to the warming during spring and cooling during autumn, and deviations in simulated swarming were related to known climate model biases. The variation between the IPS_RCA3_GCM ensemble members was particularly high in regions where warmer summers (temperature increase from +2 °C to +4 °C) will induce an additional generation per year, for example a shift from one to two generations per year in south Scandinavia, and an increased frequency of three generations per year in central Europe. Impact assessments based on an ensemble of climate data gives more robust decision support than a single climate model approach because it allows a probabilistic assessment of the geographical areas experiencing a transition in biological response.

Several definitions of climate change, climate variation and related terms are reviewed here. It is concluded that incompatible definitions produce considerable differences in the actual usage of the terms depending on context and author. While this, perhaps, is not a problem within the scientific community, it is argued that difficulties may arise when climatological information is interpreted by non-specialists, for example, in a political or socio-economic context. The problem may be caused by inadvertent misinterpretations or even deliberate distortion of the terms by parties with vested interests. Within a scientific context a relative time-scale is usually preferred, while an absolute time-scale is more relevant for practical applications. To promote further discussions, this assumption and some other basic objectives are used to suggest a framework for how climatological terms can be defined and used in a way that is internally consistent, as well as consistent with current usage as far as possible.

In a recent paper, Sansom presents a new representation of subhour rainfall variations as derived from pluviographs from tilting-siphon raingages. This representation is based on breakpoints where the trace changes slope abruptly. He draws on experience from New Zealand, which shows that rainfall intensity normally changes abruptly between approximately steady states. In this comment, some data from Kenya are presented that largely corroborate that observation, but the data also suggest that rainfall intensity sometimes changes in a more gradual way, which is less suitable for a simple breakpoint representation. Problems that may arise if a breakpoint representation is used fog. smooth changes are discussed, and an extension that accommodates both abrupt changes and smooth changes is suggested. For this extension, a digitized point is designated as either being a “true” breakpoint (where the trace changes slope abruptly) or an “ordinary” point, which is needed to represent smooth changes in the t...

This section describes long-term observed climatic changes in atmospheric parameters. The focus is on surface climate conditions, but changes in atmospheric circulation are discussed as they often are behind climatic variability seen on regional and local scales. For a summary introduction on mean atmospheric states and conditions in the Baltic Sea Basin see Annex 1.2 with sections on the general atmospheric circulation (A.1.2.1), surface air temperature (A.1.2.2), precipitation (A.1.2.3), clouds (A.1.2.4), and global radiation (A.1.2.5).

Forestry is of major economical importance in Europe, and recent devastating windstorms have pinpointed the vulnerability of this economic sector to windstorms. Forest damage is an important economic issue at a country level and may become even of larger concern under future conditions following global warming. An underlying question is to what extent the storm damage is due to changes in the wind climate compared to the effect of changes in forest management practices? In this paper, the first part of this rather complex problem is tackled. By using the Canadian Regional Climate Model, CRCM, including a physically based gust parameterisation scheme, NCEPNCAR reanalysis wind fields for the windstorm Anatol, on December 3-4, 1999, were downscaled, into a nested set-up, to 2 km resolution. The aim is to relate the simulated storm wind field to the observed distribution of storm damaged forests in Scania in southern Sweden, as a first methodological step towards analysing the effect of future windstorms in Swedish forests at the highest spatial resolution one can afford nowadays. Our results show that the CRCM produced realistic wind field simulations, compared to station observations, of the windstorm event in 1999. The simulated winds were underestimated at the coasts, but in congruence with inland observations. Most of the damaged forest stands were located on south-westerly (SW) slopes, which indicated a south-westerly wind during the wind throw process. This SW wind direction was evident in the early phase of the simulated storm, but then changed into a westerly flow, at an earlier stage than the true observations specified. Further, most damage occurred in the areas of simulated maximum wind speed greater than 30 m s. To conclude, the CRCM has proven to be a useful tool to realistically simulate a forest damaging storm event. Hence, the model could be used for further study cases, preferably driven by a GCM, in order to reveal a greater understanding about recent storms, which in turn helps us evaluate future climate change driven storm conditions. (c) 2006 Elsevier B.V All rights reserved.

This paper provides an overview of the aims, objectives, research activities under-taken, and a selection of results generated in the European Commission-funded project entitled "Modelling the Impact of Climate Extremes" (MICE) - a pan-European end-to-end assessment, from climate model to impact model, of the potential impacts of climate change on a range of economic sectors important to the region. MICE focussed on changes in temperature, precipitation and wind extremes. The research programme had three main themes - the evaluation of climate model performance, an assessment of the potential future changes in the occurrence of extremes, and an examination of the impacts of changes in extremes on six activity sectors using a blend of quantitative modelling and expert judgement techniques. MICE culminated in a large stakeholder-orientated workshop, the aim of which was not only to disseminate project results but also to develop new stakeholder networks, whose expertise can be drawn on in future projects such as ENSEMBLES. MICE is part of a cluster of three projects, all related to European climate change and its impacts. The other projects in the cluster are PRUDENCE (Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects) and STARDEX (Statistical and Regional Dynamical Downscaling of Extremes for European Regions).