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Urbanized low-lying coastal areas become evidently increasingly vulnerable in the future climate, where higher mean sea level and more powerful storm surges are expected. Safe planning and operation particularly of critical coastal infrastructure, such as nuclear power plants, requires careful evaluation of external events due to extreme weather and sea level conditions. Finland is located in the northeast part of the Baltic Sea, where sea level variations are driven by short-lasting phenomena (storm surges, internal oscillations in the Baltic Sea, tides), long-term mean sea level change (global mean sea level, the post-glacial land uplift and the Baltic Sea water balance), and wind waves. In Finland, there are altogether five nuclear power plant units along the coast and approximately one third of electricity production in the country is grounded on nuclear energy.The Finnish Meteorological Institute has studied weather, climate and sea level hazards potentially posing risks to nuclear power plants since 2007 in several research projects. In this presentation, we will introduce some preliminary results of studies on extreme sea levels on the Finnish coast, which are conducted in the PREDICT project (https://en.ilmatieteenlaitos.fi/predict). Their focus is on the short-term sea level variations, which might be several meters in the Baltic Sea, even if the tides in the region are mostly negligible.In the first study, Bayesian hierarchical modelling is used to estimate return levels of annual sea level maximum on the Finnish coast and non-stationarity in the related sea level extremes. Our model setup enables sharing information on sea level extremes between the neighboring tide gauge stations. Additionally, it accounts temporal variations in the distribution parameters by incorporating climate indices such as North Atlantic oscillation (NAO) in the model. Preliminary results suggest that hierarchical approach reduces the range of uncertainty in the estimated parameters.The second study tackles a question “What is the most severe flooding that could occur in the Baltic Sea coast in the present climate if the weather conditions are optimal?”. In this study, effects of low-pressure intensity, speed, direction and point of origin on the sea level extremes is examined by making simulations with numerical sea level model combined with the synthetic cyclones for the Finnish tide gauge locations. Tentative results indicate that the highest sea level extremes on the Finnish coast are caused by large and slowly propagating wind storms.

A warming climate is associated with increasing hydroclimatic extremes, which are often interconnected through complex processes, prompting their concurrence and/or succession, and causing compound extreme events. It is critical to analyze the risks of compound events, given their disproportionately high adverse impacts. To account for the variability in two or more hydroclimatic variables (e.g., temperature and precipitation) and their dependence, a rising number of publications focuses on multivariate analysis, among which the notion of copula-based probability distribution has attracted tremendous interest. Copula is a mathematical function that expresses the joint cumulative probability distribution of multiple variables. Our focus is to re-emphasize the fundamental requirements and limitations of applying copulas. Confusion about these requirements may lead to misconceptions and pitfalls, which can potentially compromise the robustness of risk analyses for environmental processes and natural hazards. We conducted a systematic literature review of copulas, as a prominent tool in the arsenal of multivariate methods used for compound event analysis, and underpinned them with a hydroclimatic case study in Sweden to illustrate a practical approach to copula-based modeling. Here, we (1) provide end-users with a didactic overview of necessary requirements, statistical assumptions and consequential limitations of copulas, (2) synthesize common perceptions and practices, and (3) offer a user-friendly decision support framework to employ copulas, thereby support researchers and practitioners in addressing hydroclimatic hazards, hence demystify what can be an area of confusion.This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > Methods

For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensemble simulations provided by global (GCMs) and regional climate models (RCMs), which are then downscaled and bias-adjusted for each study site. For bias adjustment, different statistical methods that re-scale climate model outputs have been suggested in the scientific literature. They range from simple univariate methods that adjust each meteorological variable individually to more complex and statistically as well as computationally more demanding multivariate methods that take existing relationships between meteorological variables into consideration. While several attempts have been made over the past decade to evaluate such methods in various regions, there is no guidance for choosing an appropriate bias adjustment method in relation to the study question at hand. In particular, the question whether more complex multivariate methods are worth the effort by resulting in better adjustments of a wide range of univariate, multivariate and temporal features, remains unanswered. We here present an approach to systematically assess the performance of the most commonly used univariate and multivariate bias adjustment methods at different catchment scales in Sweden. Based on a multi-catchment and multi-model approach, we evaluated numerous univariate, multivariate and temporal features of precipitation, temperature and streamflow. Finally, we discuss potential benefits (skills and added value) and trade-offs (complexity and computational demand) of each method, in particular for hydrological climate-change impact studies in high latitudes.

Occurrence probabilities of extreme sea levels required in coastal planning, e.g. for calculating design floods, have been traditionally estimated individually at each tide gauge location. However, these estimates include uncertainties, as sea level observations typically have only a small number of extreme cases such as annual maxima. Moreover, exact information on sea level extremes between the tide gauge locations and incorporation of dependencies between the adjacent stations is often lacking in the analysis. In this study, we use Bayesian hierarchical modeling to estimate return levels of annual maxima of short-term sea level variations related to storm surges in the Finnish coastal region. We use the generalized extreme value (GEV) distribution as the basis and compare three hierarchical model structures of different complexity against tide gauge specific fits. The hierarchical model structures allow to share information on annual maximum sea levels between the neighboring stations and also provide a natural way to estimate uncertainties in the theoretical estimates. The results show that, compared to the tide gauge specific fits, the hierarchical models, which pool information across the stations, provide narrower uncertainty ranges both for the posterior parameter estimates and for the corresponding return levels on most of the tide gauges. The estimated shape parameter of the GEV model is systematically negative for the hierarchical models, which indicates a Weibull-type of behavior for the extremes along the Finnish coast. This also suggests that the hierarchical models can be used to estimate theoretical upper limits of the extremes of short-term sea level variations along the Finnish coast. Depending on the tide gauge and hierarchical model considered, the median value of the theoretical upper limit was 47–73 cm higher than the highest observed sea level.

For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensem-ble simulations provided by global and regional climate models, which are then downscaled and bias-adjusted for eachstudy site. For bias adjustment, different statistical methods that re-scaleclimate model outputs have been suggested inthe scientific literature. They range from simple univariate methods that adjust each meteorological variable individ-ually, to more complex and more demanding multivariate methods that take existing relationships between meteoro-logical variables into consideration. Over the past decade, several attempts have been made to evaluate such methodsin various regions. There is, however, still no guidance for choosing appropriate bias adjustment methods for a study athand. In particular, the question whether the benefits of potentially improved adjustments outweigh the cost of in-creased complexity, remains unanswered.This paper presents a comprehensive evaluation of the performance of two commonly used univariate and two multi-variate bias adjustment methods in reproducing numerous univariate, multivariate and temporal features of precipita-tion and temperature series in different catchments in Sweden. The paper culminates in a discussion on trade-offs between the potential benefits (i.e., skills and added value) and disadvantages (complexity and computational de-mand) of each method to offer plausible, defensible and actionable insights from the standpoint of climate-change im-pact studies in high latitudes.We concluded that all selected bias adjustment methods generally improved the raw climate model simulations, but that not a single method consistently outperformed the other methods. There were, however, differences in the methods' performance for particular statistical features, indicating that other practical aspects such as computationaltime and heavy theoretical requirements should also be taken into consideration when choosing an appropriate biasadjustment method

Coastal cities are going through rapid and challenging changes. Accurate estimates of probabilities of extreme sea levels are a highly valuable asset to face flooding hazards and support safe planning of coastal zones in the future climate. Probabilities of specific extreme events have been traditionally estimated from the observed extremes independently at each tide gauge location. However, this approach has shortcomings. Firstly, sea level observations often cover a relatively short historical time period and thus contain only a small number of extreme cases (e.g. annual maxima). This causes substantial uncertainties when estimating the distribution parameters. Secondly, exact information on sea level extremes between the tide gauge locations and incorporation of dependencies of adjacent stations is often lacking in the analysis.One way to partially tackle these issues is to exploit spatial dependencies exhibited by the sea level extremes. These dependencies emerge from the fact that sea level variations are often driven by the same physical and dynamical factors at the neighboring stations. Bayesian hierarchical modeling offers a way to model these dependencies. The model structure allows to share information on sea level extremes between the neighboring stations and also provides a natural way to represent modeling uncertainties.In this study, we use Bayesian hierarchical modeling to estimate return levels of annual sea level maximum on the Finnish coast, located in the northeast parts of the Baltic Sea. As annual maxima are studied, we use the generalized extreme value (GEV) distribution as the basis of our model. To tailor the model specifically for the target region, spatial dependencies are modeled using covariates, which reflect the distinct geometry of the Baltic Sea. We also account for inter-annual and decadal-scale variations in the distribution parameters by including teleconnection indices such as North Atlantic oscillation (NAO) in our model. Preliminary results show that hierarchical modeling provides added value in comparison to the traditional approach, when applied to the available long-term tidegauge time series in Finland. The work presented here is a part of project PREDICT (Predicting extreme weather and sea level for nuclear power plant safety) that supports nuclear power plant safety in Finland.

Climate models are primary tools to reconstruct past and predict future climates. It is common procedure to use general circulation models (GCMs) for large scale studies and regional climate models (RCMs), for impact studies at a finer spatial resolution. However, climate models face biases compared to observation. To overcome these biases, different statistical methods have been suggested in the scientific literature that employ a transformation algorithm to re-scale (or bias-correct) RCM outputs. Some of these methods (e.g. univariate methods that adjust only one RCM-simulated variable at a time) are comparatively easy to implement while others (e.g., multi-variate correction that guarantees consistency in spatiotemporal fields and different climate variables) that have been introduced lately to the field, are more complex and require advanced statistical knowledge and more computing power. Therefore, the need to further investigate the performance of the latest more complex bias-adjustment methods under different climatic conditions still exists and their added value still needs to be evaluated from different aspects.Thus, we assessed the skill of two commonly used multivariate methods, namely copula based bias adjustment methods and non-parametric n-dimensional multivariate bias correction (MBCn). We further compared them with widely used univariate methods, i.e. the parametric distribution mapping (DS) and the non-parametric quantile delta mapping (QDM), to adjust RCM-simulated temperature and precipitation. We evaluated these methods over 55 Swedish catchments varying in size and climatic features using an ensemble of 10 different RCMs under varying climate conditions to check multiple features that represent both probabilistic and temporal behavior. To evaluate how these methods, perform in nonstationary climate conditions, we performed the assessment over two periods of 22 years each, where the period 1961-1982 is used for calibration and 1983-2004 for validation. The adequacy of each bias adjustment method in reducing the biases varies depending on several factors such as the studied watershed, the applied RCM model, utilized climate variable and the statistical feature that is subjected to adjustment. We further discuss potential issues and trade-offs of each of the applied methods and present an evaluation of each bias-corrected climate variable in terms of its (1) statistical properties, (2) temporal behavior utilizing cross correlation and autocorrelation measures, and (3) dependence structure to the other variable with help of copula-based dependence measures. Finally, we also examined how the four bias-adjustment methods influence the Clausius Clapeyron relation, which serves as an important climatic illustration of the relationship between extreme precipitation and temperature.

For most hydroclimatic applications, precipitation and temperature are of particular interest as they strongly affect the water cycle, can easily be measured and are often readily available from many meteorological stations worldwide. To account for precipitation and temperature variability, their co-dependence and their correlation, several multivariate analysis methods have been adopted in the hydroclimatic literature in recent years. In line with the steadily rising number of publications on this topic, the notion of copula-based probability distribution has also attracted tremendous interest to deal with the complexity of compound events in the multidimensional context. A copula is a function that connects a multivariate distribution to its one-dimensional margins, which allows for a joint distribution of random variables with great flexibility for the marginal distribution. However, there seems to be a lack of comprehensive understanding of the fundamental requirements of the copula concept such as the strength and significance of correlation between variables, autocorrelation effects and the choice of representative copula families, which potentially compromises the robustness of projections of future environmental processes and natural hazards. Therefore, by combining a systematic literature review with a specific hydroclimatic case study in Sweden, we illustrate a practical approach to copula-based modeling, which (1) provides end-users with an overview of necessary requirements, statistical assumptions and consequential limitations of copulas, (2) highlights possible pitfalls and misconceptions, and (3) offers a decision support framework for the application of copulas to support researchers and decision makers in addressing climatological hazards and sustainable development, thereby demystifying what is currently an area of great confusion.

We present the interactive web application GCMeval, available at https://gcmeval.met.no. The tool is a useful resource for climate services by illustrating how model selection affects representation of future climate change. GCMeval was developed in a co-design process engaging users. Based on a thorough analysis of user demands, needs and capabilities, two different user groups were defined: Data users with lots of experience with data processing and Product users with a strong focus on information products. The available data, information, and user interface in GCMeval are tailored to the requirements of the data users.In the tool, the user can select all or a subset of models from the CMIP5 and CMIP6 ensembles and assign weights to different regions, seasons, climate variables, and skill scores. The tool provides visualizations of the spread of future changes in temperature and precipitation which allows the user to study how the sub-ensemble fits in relation to the full multi-model ensemble and to compare climate model results for different regions of the world. A ranking of individual model performance for recent past climate is also provided. The tool can be used to aid in model selection for climate or impact studies, or to illustrate how an already existing selection represents the range of possible future climate outcomes.

We analyze the importance of regional climate models (GCM-RCMs) and model output statistics (MOS) methods as uncertainty sources for future changes of various hydrological variables in Scandinavia. The Hydrological Predictions for the Environment (HYPE) model, driven with daily mean temperature and precipitation, is used to simulate changes in river discharges and other hydrological components from the present-day climate (1980–2009) to mid-21st century conditions (2041–2070). The results show that GCM-RCM differences explain most of the spread in the simulated changes in the annual mean cycle of river discharge. At seasonal level, MOS-method uncertainties are most important during the winter and spring, which is likely explained by the sensitivity of snow processes to the representation of daily variability in the MOS methods. To gain physical insights into the physical processes, the relative importance of changes to temperature or precipitation on changes in surface hydrology are also assessed. In most regions of Scandinavia, changes to temperature explain most of the changes in river discharge volumes and spring peaks. Precipitation changes only have a secondary role in modulating these changes. Again, these results are mostly explained by changes in snow processes in winter and increases in evapotranspiration in summer.

Special Issue: VALUE: Validating and Integrating Downscaling Methods for Climate Change Research: et al., VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process-based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis-driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method-to-method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO-CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value-cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value-cost.eu/validationportal., This work has been performed in the framework of the VALUE COST Action ES1102, under FP7 programme J.M.G. and S.H. acknowledge partial funding from MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R). B.H. and D.R. acknowledge COMPLEX project (FP7-ENV-2012, No: 308601). M.T. was supported by HOPE project (MINECO, CGL2014-52571-R). Participation of M.D., P.S. and R.H. was funded by the Ministry of Education, Youth, and Sports of the Czech Republic contracts LD12029, LD14043 and LD12059, respectively.

The influence of uncertainties in gridded observational reference data on regional climate model (RCM) evaluation is quantified on a pan-European scale. Three different reference data sets are considered: the coarse-resolved E-OBS data set, a compilation of regional high-resolution gridded products (HR) and the European-scale MESAN reanalysis. Five high-resolution ERA-Interim-driven RCM experiments of the EURO-CORDEX initiative are evaluated against each of these references over eight European sub-regions and considering a range of performance metrics for mean daily temperature and daily precipitation. The spatial scale of the evaluation is 0.22°, that is, the grid spacing of the coarsest data set in the exercise (E-OBS). While the three reference grids agree on the overall mean climatology, differences can be pronounced over individual regions. These differences partly translate into RCM evaluation uncertainty. For most cases observational uncertainty is smaller than RCM uncertainty. Nevertheless, for individual sub-regions and performance metrics observational uncertainty can dominate. This is especially true for precipitation and for metrics targeting the wet-day frequency, the pattern correlation and the distributional similarity. In some cases the spatially averaged mean bias can also be considerably affected. An illustrative ranking exercise highlights the overall effect of observational uncertainty on RCM ranking. Over individual sub-domains, the choice of a specific reference can modify RCM ranks by up to four levels (out of five RCMs). For most cases, however, RCM ranks are stable irrespective of the reference. These results provide a twofold picture: model uncertainty dominates for most regions and for most performance metrics considered, and observational uncertainty plays a minor role. For individual cases, however, observational uncertainty can be pronounced and needs to be definitely taken into account. Results can, to some extent, also depend on the treatment of precipitation undercatch in the observational reference., Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters.

We present an interactive tool for selection and evaluation of global climate models. The tool is implemented as a web application using the ”Shiny” R-package and is available at https://gcmeval.met.no . Through this tool, climate models of the CMIP5 and CMIP6 ensembles can be ranked and compared based on their representation of the present climate, with user-determined weights indicating the importance of different regions, seasons, climate variables, and skill scores. The ranking can be used to eliminate the climate models with poorest representation of the present climate. As further guidance, the projected regional mean temperature and precipitation changes for all climate models are displayed in a scatterplot for a chosen season, emission scenario, and time horizon. Ranks and projected changes for a subset of climate models are compared to the whole ensemble. Subsets can be selected interactively and the tool provides on-the-fly visualizations. The combined information of the projected climate change and model ranking can be used as an aid to select subsets of climate models that adequately represent both the present day climate and the range of possible future outcomes. Allowing weighting of different metrics introduces a subjective element in the evaluation process and demonstrates how this can affect the ranking. The tool also illustrates how the range of projected future climate change is sensitive to the choice of models.

In this paper, the ability of two joint bias correction algorithms to adjust biases in daily mean temperature and precipitation is compared against two univariate quantile mapping methods when constructing projections from years 1981–2010 to early (2011–2040) and late (2061–2090) 21st century periods. Using both climate model simulations and the corresponding hydrological model simulations as proxies for the future in a pseudo-reality framework, these methods are inter-compared in a cross-validation manner in order to assess to what extent the more sophisticated methods have added value, particularly from the hydrological modeling perspective. By design, bi-variate bias correction methods improve the inter-variable relationships in the baseline period. Cross-validation results show, however, that both in the early and late 21st century conditions the additional benefit of using bi-variate bias correction methods is not obvious, as univariate methods have a comparable performance. From the evaluated hydrological variables, the added value is most clearly seen in the simulated snow water equivalent. Although not having the best performance in adjusting the temperature and precipitation distributions, quantile mapping applied as a delta change method performs well from the hydrological modeling point of view, particularly in the early 21st century conditions. This suggests that retaining the observed correlation structures of temperature and precipitation might in some cases be sufficient for simulating future hydrological climate change impacts.

Often the meteorological forcing data required for urban hydrological models are unavailable at the required temporal resolution or for the desired period. Although reanalysis data can provide this information, the spatial resolution is often coarse relative to cities, so downscaling is required prior to use as realistic forcing. In this study, WATCH WFDEI reanalysis data are used to force the Surface Urban Energy and Water Balance Scheme (SUEWS). From sensitivity tests in two cities, Vancouver and London with different orography, we conclude precipitation is the most important meteorological variable to be properly downscaled to obtain reliable surface hydrology results, with relative humidity being the second most important. Overestimation of precipitation in reanalysis data at the three sites gives 6-21% higher annual modelled evaporation, 26-39% higher runoff at one site and 4% lower value at one site when compared to modelled values using observed forcing data. Application of a bias correction method to the reanalysis precipitation reduces the model bias compared to using observed forcing data, when evaluated using eddy covariance evaporation measurements. (c) 2017 Elsevier B.V. All rights reserved. Often the meteorological forcing data required for urban hydrological models are unavailable at the required temporal resolution or for the desired period. Although reanalysis data can provide this information, the spatial resolution is often coarse relative to cities, so downscaling is required prior to use as realistic forcing. In this study, WATCH WFDEI reanalysis data are used to force the Surface Urban Energy and Water Balance Scheme (SUEWS). From sensitivity tests in two cities, Vancouver and London with different orography, we conclude precipitation is the most important meteorological variable to be properly downscaled to obtain reliable surface hydrology results, with relative humidity being the second most important. Overestimation of precipitation in reanalysis data at the three sites gives 6-21% higher annual modelled evaporation, 26-39% higher runoff at one site and 4% lower value at one site when compared to modelled values using observed forcing data. Application of a bias correction method to the reanalysis precipitation reduces the model bias compared to using observed forcing data, when evaluated using eddy covariance evaporation measurements. (c) 2017 Elsevier B.V. All rights reserved. Often the meteorological forcing data required for urban hydrological models are unavailable at the required temporal resolution or for the desired period. Although reanalysis data can provide this information, the spatial resolution is often coarse relative to cities, so downscaling is required prior to use as realistic forcing. In this study, WATCH WFDEI reanalysis data are used to force the Surface Urban Energy and Water Balance Scheme (SUEWS). From sensitivity tests in two cities, Vancouver and London with different orography, we conclude precipitation is the most important meteorological variable to be properly downscaled to obtain reliable surface hydrology results, with relative humidity being the second most important. Overestimation of precipitation in reanalysis data at the three sites gives 6-21% higher annual modelled evaporation, 26-39% higher runoff at one site and 4% lower value at one site when compared to modelled values using observed forcing data. Application of a bias correction method to the reanalysis precipitation reduces the model bias compared to using observed forcing data, when evaluated using eddy covariance evaporation measurements. (c) 2017 Elsevier B.V. All rights reserved.

We apply analysis of variance to assess the effect of climate model development on temperature, precipitation and sea level pressure projections using three consecutive climate model generations provided by the Coupled Model Intercomparison Project. The introduction of a new model version mostly randomly affects individual model projections, but models also show some generation-independent characteristics which can potentially serve a basis for model weighting. Mean values of temperature and pressure change differ significantly between the model generations only over limited regions, mainly near the sea-ice edge. If multi-model ensembles are uniformly weighted, the efforts of model development are not completely exploited.

Due to inherent limitations in climate models, their output is biased in relation to observed climate and as such does not provide reliable climate projections. In this study, nine methods used to account for biases in daily precipitation are tested. First, cross-validation tests were made using a set of ENSEMBLES regional model simulations to gain insights in the potential performance of the methods in the future climate. The results show that quantile mapping type methods, being able to modify the shape of the precipitation distribution, often outperform other types of methods. Yet, as the performance depends on time of the year, location and part of the distribution considered, it is not possible to distinguish one universally best performing method. In addition, the improvement relative to the projections that would have been obtained assuming unchanged climate is relatively modest, particularly in the early twentyfirst century conditions. Further tests with different method combinations show that the projections could be potentially improved by using several well performing methods in parallel. In the second part of the study, contributions of method and model differences to the overall variation of precipitation projections are assessed. It is shown that although intermodel differences play an important role, uncertainties related to intermethod differences are substantial, particularly in the tails of the distribution. This suggests that method uncertainty should be taken into account when constructing daily precipitation projections, possibly by using several methods in parallel.

A software package (OZO, Omega-Zwack-Okossi) was developed to diagnose the processes that affect vertical motions and geopotential height tendencies in weather systems simulated by the Weather Research and Forecasting (WRF) model. First, this software solves a generalized omega equation to calculate the vertical motions associated with different physical forcings: vorticity advection, thermal advection, friction, diabatic heating, and an imbalance term between vorticity and temperature tendencies. After this, the corresponding height tendencies are calculated with the Zwack-Okossi tendency equation. The resulting height tendency components thus contain both the direct effect from the forcing itself and the indirect effects (related to the vertical motion induced by the same forcing) of each physical mechanism. This approach has an advantage compared with previous studies with the Zwack-Okossi equation, in which vertical motions were used as an independent forcing but were typically found to compensate the effects of other forcings. The software is tailored to use input from WRF simulations with Cartesian geometry. As an illustration, results for an idealized 10-day baroclinic wave simulation are presented. An excellent agreement is found between OZO and the direct WRF output for both the vertical motion (correlation 0.97 in the midtroposphere) and the height tendency fields (correlation 0.95–0.98 in the whole troposphere). The individual vertical motion and height tendency components demonstrate the importance of both adiabatic and diabatic processes for the simulated cyclone. OZO is an open source tool for both research and education, and the distribution of the software will be supported by the authors.

Because of model biases, projections of future climate need to combine model simulations of recent and future climate with information on observed climate. Here, 10 methods for projecting the distribution of daily mean temperatures are compared, using six regional climate change simulations for Europe. Cross validation between the models is used to assess the potential performance of the methods in projecting future climate. Delta change and bias correction type methods show similar cross-validation performance, with methods based on the quantile mapping approach doing best in both groups due to their apparent ability to reduce the errors in the projected time mean temperature change. However, as no single method performs best under all circumstances, the optimal approach might be to use several well-behaving methods in parallel. When applying the various methods to real-world temperature projection for the late 21st century, the largest intermethod differences are found in the tails of the temperature distribution. Although the intermethod variation of the projections is generally smaller than their intermodel variation, it is not negligible. Therefore, it should be preferably included in uncertainty analysis of temperature projections, particularly in applications where the extremes of the distribution are important.

Increasing temperature is expected to intensify the hydrological cycle. The accompanied changes in (e.g.) snow cover, water availability and river flows impose challenges for several socio-economical sectors, whose operations are affected by these changes. The main question in these sectors is: how to cost-effectively adapt and mitigate to changes in the hydrological cycle? To answer this question, hydrological modeling run with regional climate model (GCM-RCM) simulations as input is typically required. To reduce biases in the GCM-RCM simulations before using them as input in hydrological modeling, adjustments with statistical tools, such as model output statistics (MOS), is required. While MOS methods have been tested comprehensively in the present-day climate, not much has been known about how they perform in transient climatic conditions. This gap is bridged in this thesis by evaluating a set of MOS methods designed for daily mean temperature and precipitation in a changing climate in Europe, using climate model simulations as proxies, i.e., pseudo-realities for the future. The main message from these exercises is that the identification of a single universally well-performing method is practically impossible, as the best performing method varies in time, space and between different distributional aspects. This conclusion is further strengthened when the selected methods are evaluated from the hydrological modeling perspective. Overall, several methods should ideally be used in impact studies. In addition to the pseudo-reality tests, the relative importance of MOS-method differences was compared against GCM-RCM differences as uncertainty sources both in real-world climate projections in Europe and hydrological simulations in Scandinavia. As the second message of this thesis, although climate GCM-RCM differences explain a larger part of the spread in future projections, MOS-method differences are non-negligible, when the high and low extremes are considered. In line with this result, MOS-method uncertainty has the largest contribution to the spread in projected changes of low and high flows. Ilmaston lämpeneminen vaikuttaa veden kiertokulkuun, mikä näkyy muutoksina esimerkiksi lumipeitteessä ja jokivalunnassa. Muutoksilla on yhteiskunnallisia vaikutuksia ja siksi ne täytyy ottaa huomioon mm. vesivoiman ja veden käytön suunnittelussa. Yhteiskunnan näkökulmasta haasteena onkin, miten sopeutua tehokkaasti tulevaisuudessa vallitseviin olosuhteisiin ja miten muutosten haitallisia vaikutuksia voidaan ehkäistä parhaiten. Veden kiertokulun muutoksia voidaan arvioida hydrologisen mallinnuksen avulla. Syötteenä tulevaisuuden hydrologisissa malliajoissa käytetään tyypillisesti alueellisia ilmastomallisimulaatioita. Ilmastomallien tulokset eivät kuitenkaan sellaisenaan vastaa havaittua ilmastoa ja siksi ne täytyy ensin saattaa vertailukelpoisiksi havaintojen kanssa. Tätä varten on kehitetty tilastollisia, ns. harhankorjausmenetelmiä, jotka pyrkivät yhdistämään havainnoista ja ilmastomallisimulaatiosta saatavan tiedon. Menetelmien kirjo on kuitenkin laaja eikä niiden toimivuudesta nykyilmastosta poikkeavissa olosuhteissa ole juurikaan saatavilla tietoa. Tässä työssä tarkasteltiin joukkoa lämpötilalle ja sademäärälle räätälöityjä harhankorjausmenetelmiä muuttuvassa ilmastossa. Tutkimuksen perusteella selvisi, että menetelmien toimivuus vaihtelee ajallisesti ja paikallisesti sekä tarkasteltavan suureen arvojakauman eri osien välillä. Tästä johtuen yksittäisten menetelmien avulla ei saada parasta mahdollista kuvaa siitä, miten lämpötila- ja sademääräjakaumat muuttuvat tulevaisuudessa. Johtopäätökset ovat vastaavanlaisia, kun menetelmiä tarkastellaan hydrologisten mallitulosten näkökulmasta. Tulevien ilmasto-olojen arvioihin liittyy useita epävarmuuslähteitä. Mikäli harhankorjausmenetelmien välisten erojen vaikutus on muihin hajonnan lähteisiin nähden pieni, ei harhankorjausmenetelmän valinnalla ole kokonaisuuden kannalta suurta merkitystä. Tutkimus kuitenkin osoitti, ettei tilastollisten menetelmien välisiä eroja voida jättää huomiotta. Kaiken kaikkiaan ilmastomallisimulaatioiden väliset erot ovat kokonaisuuden kannalta merkittävämpi hajonnan lähde, mutta tilastollisten menetelmien väliset erot selittävät kuitenkin huomattavan osan erityisesti ääriarvojen kokonaishajonnasta. Siksi tulevaisuuden ilmaston arvioissa tulisi hyödyntää samanaikaisesti useita, hyvin toimivia harhankorjausmenetelmiä.