Thordis Thorarinsdottir

• Lutz, Julia; Roksvåg, Thea Julie Thømt; Dyrrdal, Anita Verpe; Lussana, Cristian & Thorarinsdottir, Thordis Linda (2024). Areal reduction factors from gridded data products. Journal of Hydrology. ISSN 0022-1694. 635, p. 1–12. doi: 10.1016/j.jhydrol.2024.131177.
• Scheuerer, Michael; Bahaga, Titike Kassa; Segele, Z.T. & Thorarinsdottir, Thordis Linda (2024). Probabilistic rainy season onset prediction over the greater horn of africa based on long-range multi-model ensemble forecasts. Climate Dynamics. ISSN 0930-7575. doi: 10.1007/s00382-023-07085-y.
• Heinrich-Mertsching, Claudio Constantin; Wahl, Jens Christian; Ordonez, Alba; Stien, Marita; Elvsborg, John & Haug, Ola [Show all 7 contributors for this article] (2023). Assessing present and future risk of water damage using building attributes, meteorology, and topography. The Journal of the Royal Statistical Society, Series C (Applied Statistics). ISSN 0035-9254. 72(4), p. 809–828. doi: 10.1093/jrsssc/qlad043.
• Hellton, Kristoffer Herland; Amdahl, Helga; Thorarinsdottir, Thordis; Alsheikh, Muath K; Aamlid, Trygve S. & Jørgensen, Marit [Show all 8 contributors for this article] (2023). Yield predictions of timothy (Phleum pratense L.) in Norway under future climate scenarios. Agricultural and Food Science. ISSN 1459-6067. 32(2), p. 80–93. doi: 10.23986/afsci.127935. Full text in Research Archive Show summary

  The perennial forage grass timothy (Phleum pratense L.) is the most important forage crop in Norway. Future changes in the climate will affect growing conditions and hence the yield output. We used data from the Norwegian Value for Cultivation and Use testing to find a statistical prediction model for total dry matter yield (DMY) based on agro-climatic variables. The statistical model selection found that the predictors with the highest predictive power were growing degree days (GDD) in July and the number of days with rain (>1mm) in June–July. These predictors together explained 43% of the variability in total DMY. Further, the prediction model was combined with a range of climate ensembles (RCP4.5) to project DMY of timothy for the decades 2050–2059 and 2090–2099 at 8 locations in Norway. Our projections forecast that DMY of today’s timothy varieties may decrease substantially in South-Eastern Norway, but increase in Northern Norway, by the middle of the century, due to increased temperatures and changing precipitation patterns.

• Barna, Danielle Marie; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda & Xu, Chong-Yu (2023). Flexible and consistent Flood–Duration–Frequency modeling: A Bayesian approach. Journal of Hydrology. ISSN 0022-1694. 620. doi: 10.1016/j.jhydrol.2023.129448. Full text in Research Archive Show summary

  Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Many hydrologic applications where flood retention is important, e.g. floodplain management and reservoir design, need design flood values for different durations. Flood–Duration–Frequency (QDF) models extend the standard statistical flood frequency analysis framework to multiple flood durations and are analogous to intensity–duration–frequency models for precipitation. Implementations of QDF models commonly assume simple scaling, where only the magnitude of the index flood is assumed to change with duration, despite empirical analyses showing a more complex dependence structure. We propose a multiscaling extension to existing QDF models where the magnitude of the index flood and the slope of the growth curve may scale independently with duration. In an application to 12 locations in Norway, we assess how three different QDF models capture relationships between floods of different duration. Incorporating duration dependency independently in both the index flood and the growth curve (extended QDF model) improves modeling of both short-duration events and events with long return periods. This model extension further expands the models’ ability to simultaneously model a wide range of durations. As measured by the integrated quadratic distance, the extended QDF model performs better than the original QDF model in 83% of the out of sample subdaily durations studied. Additionally, we find that the choice of durations used to fit QDF models is a highly influential aspect of the modeling process.

• Yuan, Qifen; Thorarinsdottir, Thordis L.; Beldring, Stein; Wong, Wai Kwok & Xu, Chong-Yu (2023). Assessing uncertainty in hydrological projections arising from local-scale internal variability of climate. Journal of Hydrology. ISSN 0022-1694. 620. doi: 10.1016/j.jhydrol.2023.129415. Full text in Research Archive Show summary

  Hydrological impact assessments are increasingly performed at fine spatial and temporal resolutions in order to resolve local-scale changes under a future climate. Apart from the uncertainty represented by different climate models, emission scenarios and post-processing methods, the local-scale internal variability of the climate can be a major source of uncertainty for hydrological projections. To assess the latter at the catchment scale, this paper presents a methodology which is particularly suitable for spatially distributed hydrological models. An ensemble of daily precipitation and daily mean temperature realizations on a high-resolution grid is simulated from stochastic weather generators (WGs) trained on historical data and equipped with climate change information obtained from a regional climate model. Based on the resulting simulated daily runoff data, the significance of changes in the runoff regime is assessed using a statistical hypothesis test, and the variability contributed by the two input variables is quantified using the analysis of variance (ANOVA). As a proof of concept, simulations on a 1-km grid over a period of 19 years are carried out for nine catchments in central Norway. Significant changes in runoff regimes are found, indicating that the trends introduced in the WGs are not overwhelmed by the local-scale internal variability. Variability in the runoff simulations varies substantially throughout the year; it is highest in periods with potential snowmelt and lowest during winter. Temperature is the dominant source of variability in the colder months (November–March) due to its influence on rainfall and snowmelt. High variability in May–June is contributed comparably by both temperature and precipitation. In summer and early autumn the runoff variability is precipitation dominated. The results are in line with findings in the literature where the runoff variability is driven by the large-scale internal climate variability. This indicates that ignoring the local-scale internal variability may yield an underestimation of the overall variability in runoff projections and projected changes.

• Roksvåg, Thea; Lenkoski, Alex; Scheuerer, Michael; Heinrich, Claudio Constantin & Thorarinsdottir, Thordis Linda (2022). Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods. Quarterly Journal of the Royal Meteorological Society. ISSN 0035-9009. doi: 10.1002/qj.4403. Show summary

  Agricultural food production and natural ecological systems depend on a range of seasonal climate indicators that describe seasonal patterns in climatological conditions. This article proposes a probabilistic forecasting framework for predicting the end of the freeze-free season, or the time to a mean daily near-surface air temperature below 0°C (referred to here as hard freeze). The forecasting framework is based on the multimodel seasonal forecast ensemble provided by the Copernicus Climate Data Store and uses techniques from survival analysis for time-to-event data. The original mean daily temperature forecasts are statistically postprocessed and downscaled with a mean and variance correction of each model system before the time-to-event forecast is constructed. In a case study for a region in Fennoscandia covering Norway for the period 1993–2020, the proposed forecasts are found to outperform a climatology forecast from an observation-based data product at locations where the average predicted time to hard freeze is less than 40 days after the initialization date of the forecast on October 1.

• Petropoulos, Fotios; Apiletti, Daniele; Assimakopoulos, Vassilios; Babai, Mohamed Zied; Barrow, Devon K. & Ben Taieb, Souhaib [Show all 80 contributors for this article] (2022). Forecasting: theory and practice. International Journal of Forecasting. ISSN 0169-2070. 38, p. 705–871. doi: 10.1016/j.ijforecast.2021.11.001. Show summary

  Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

• Vandeskog, Silius Mortensønn; Thorarinsdottir, Thordis Linda; Steinsland, Ingelin & Lindgren, Finn (2022). Quantile based modeling of diurnal temperature range with the five-parameter lambda distribution. Environmetrics. ISSN 1180-4009. doi: 10.1002/env.2719. Full text in Research Archive Show summary

  Diurnal temperature range is an important variable in climate science that canprovide information regarding climate variability and climate change. Changesindiurnaltemperaturerangecanhaveimplicationsforhydrology,humanhealthand ecology, among others. Yet, the statistical literature on modeling diurnaltemperature range is lacking. In this article we propose to model the distri-bution of diurnal temperature range using the five-parameter lambda (FPL)distribution. Additionally, in order to model diurnal temperature range withexplanatory variables, we propose a distributional quantile regression modelthat combines quantile regression with marginal modeling using the FPL distri-bution. Inference is performed using the method of quantiles. The models arefitted to 30 years of daily observations of diurnal temperature range from 112weather stations in the southern part of Norway. The flexible FPL distributionshows great promise as a model for diurnal temperature range, and performswell against competing models. The distributional quantile regression model isfitted to diurnal temperature range data using geographic, orographic, and cli-matological explanatory variables. It performs well and captures much of thespatial variation in the distribution of diurnal temperature range in Norway.

• Yuan, Qifen; Thorarinsdottir, Thordis Linda; Beldring, Stein; Wong, Wai Kwok & Xu, Chong-Yu (2021). Bridging the scale gap: obtaining high-resolution stochastic simulations of gridded daily precipitation in a future climate. Hydrology and Earth System Sciences (HESS). ISSN 1027-5606. 25(9), p. 5259–5275. doi: 10.5194/hess-25-5259-2021. Full text in Research Archive Show summary

  Climate change impact assessment related to floods, infrastructure networks, and water resource management applications requires realistic simulations of high-resolution gridded precipitation series under a future climate. This paper proposes to produce such simulations by combining a weather generator for high-resolution gridded daily precipitation, trained on a historical observation-based gridded data product, with coarser-scale climate change information obtained using a regional climate model. The climate change information can be added to various components of the weather generator, related to both the probability of precipitation as well as the amount of precipitation on wet days. The information is added in a transparent manner, allowing for an assessment of the plausibility of the added information. In a case study of nine hydrological catchments in central Norway with the study areas covering 1000–5500 km2, daily simulations are obtained on a 1 km grid for a period of 19 years. The method yields simulations with realistic temporal and spatial structures and outperforms empirical quantile delta mapping in terms of marginal performance.

• Roksvåg, Thea; Lutz, Julia; Grinde, Lars; Dyrrdal, Anita Verpe & Thorarinsdottir, Thordis Linda (2021). Consistent intensity-duration-frequency curves by post-processing of estimated Bayesian posterior quantiles. Journal of Hydrology. ISSN 0022-1694. 603(Part C), p. 1–15. doi: 10.1016/j.jhydrol.2021.127000. Show summary

  As a warming climate leads to more frequent heavy rainfall, the importance of accurate rainfall statistics is increasing. Rainfall statistics are often presented as intensity-duration-frequency (IDF) curves showing the rainfall intensity (return level) that can be expected at a location for a duration, and the frequency of this intensity (return period). IDF curves are commonly constructed by fitting generalized extreme value (GEV) distributions to observed annual maximum rainfall for several target durations. As the estimation is performed independently across durations, the resulting IDF curves may be inconsistent across durations and return periods. This paper proposes to ensure consistency by post-processing the estimated IDF curves. Two post-processing approaches are considered, a quantile selection algorithm that searches for consistent return levels within the posterior quantiles of a Bayesian inference approach, and adjustments based on isotonic regression. The methods are evaluated for simulated data and for Norwegian rainfall data from 83 locations, for hourly and sub-hourly durations. The post-processing yields consistent estimates that are at least as accurate as the unadjusted, inconsistent estimates. We also demonstrate how our approach differs from d-GEV, a method that performs simultaneous estimation across durations. An R implementation for the post-processing methods is available at https://github.com/ClimDesign/fixIDF.

• Thorarinsdottir, Thordis Linda; Sillmann, Jana; Haugen, Marion; Gissibl, Nadine & Sandstad, Marit (2020). Evaluation of CMIP5 and CMIP6 simulations of historical surface air temperature extremes using proper evaluation methods. Environmental Research Letters. ISSN 1748-9326. 15(12). doi: 10.1088/1748-9326/abc778. Full text in Research Archive Show summary

  Reliable projections of extremes by climate models are becoming increasingly important in the context of climate change and associated societal impacts. Extremes are by definition rare events, characterized by a small sample associated with large uncertainties. The evaluation of extreme events in model simulations thus requires performance measures that compare full distributions rather than simple summaries. This paper proposes the use of the integrated quadratic distance (IQD) for this purpose. The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum and minimum near-surface air temperature over Europe and North America against both observation-based data and reanalyses. Several climate models perform well to the extent that these models' performance is competitive with the performance of another data product in simulating the evaluation set. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis. When the model simulations are ranked based on their similarity with the ERA5 reanalysis, more CMIP6 than CMIP5 models appear at the top of the ranking. When evaluated against the HadEX2 data product, the overall performance of the two model ensembles is similar.

• Krüger, Fabian; Lerch, Sebastian; Thorarinsdottir, Thordis & Gneiting, Tilmann (2020). Predictive Inference Based on Markov Chain Monte Carlo Output. International Statistical Review. ISSN 0306-7734. doi: 10.1111/insr.12405. Show summary

  In Bayesian inference, predictive distributions are typically in the form of samples generated via Markov chain Monte Carlo or related algorithms. In this paper, we conduct a systematic analysis of how to make and evaluate probabilistic forecasts from such simulation output. Based on proper scoring rules, we develop a notion of consistency that allows to assess the adequacy of methods for estimating the stationary distribution underlying the simulation output. We then provide asymptotic results that account for the salient features of Bayesian posterior simulators and derive conditions under which choices from the literature satisfy our notion of consistency. Importantly, these conditions depend on the scoring rule being used, such that the choices of approximation method and scoring rule are intertwined. While the logarithmic rule requires fairly stringent conditions, the continuous ranked probability score yields consistent approximations under minimal assumptions. These results are illustrated in a simulation study and an economic data example. Overall, mixture‐of‐parameters approximations that exploit the parametric structure of Bayesian models perform particularly well. Under the continuous ranked probability score, the empirical distribution function is a simple and appealing alternative option.

• Jullum, Martin; Thorarinsdottir, Thordis Linda & Bachl, Fabian E. (2020). Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling. The Journal of the Royal Statistical Society, Series C (Applied Statistics). ISSN 0035-9254. 69(2). doi: 10.1111/rssc.12397. Show summary

  The Greenland Sea is an important breeding ground for harp and hooded seals. Estimates of annual seal pup production are critical factors in the estimation of abundance that is needed for management of the species. These estimates are usually based on counts from aerial photographic surveys. However, only a minor part of the whelping region can be photographed, because of its large extent. To estimate total seal pup production, we propose a Bayesian hierarchical modelling approach motivated by viewing the seal pup appearances as a realization of a log‐Gaussian Cox process by using covariate information from satellite imagery as a proxy for ice thickness. For inference, we utilize the stochastic partial differential equation module of the integrated nested Laplace approximation framework. In a case‐study using survey data from 2012, we compare our results with existing methodology in a comprehensive cross‐validation study. The results of the study indicate that our method improves local estimation performance, and that the increased uncertainty of prediction of our method is required to obtain calibrated count predictions. This suggests that the sampling density of the survey design may not be sufficient to obtain reliable estimates of seal pup production.

• Heinrich, Claudio Constantin; Hellton, Kristoffer Herland; Lenkoski, Alex & Thorarinsdottir, Thordis Linda (2020). Multivariate Postprocessing Methods for High-Dimensional Seasonal Weather Forecasts. Journal of the American Statistical Association. ISSN 0162-1459. doi: 10.1080/01621459.2020.1769634. Show summary

  Seasonal weather forecasts are crucial for long-term planning in many practical situations and skillful forecasts may have substantial economic and humanitarian implications. Current seasonal forecasting models require statistical postprocessing of the output to correct systematic biases and unrealistic uncertainty assessments. We propose a multivariate postprocessing approach using covariance tapering, combined with a dimension reduction step based on principal component analysis for efficient computation. Our proposed technique can correctly and efficiently handle nonstationary, non-isotropic and negatively correlated spatial error patterns, and is applicable on a global scale. Further, a moving average approach to marginal postprocessing is shown to flexibly handle trends in biases caused by global warming, and short training periods. In an application to global sea surface temperature forecasts issued by the Norwegian climate prediction model, our proposed methodology is shown to outperform known reference methods. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

• Haug, Ola; Thorarinsdottir, Thordis Linda; Sørbye, Sigrunn Holbek & Franzke, Christian L.E. (2020). Spatial trend analysis of gridded temperature data at varying spatial scales. Advances in Statistical Climatology, Meteorology and Oceanography (ASCMO). ISSN 2364-3579. 6(1), p. 1–12. doi: 10.5194/ascmo-6-1-2020. Full text in Research Archive Show summary

  Classical assessments of trends in gridded temperature data perform independent evaluations across the grid, thus, ignoring spatial correlations in the trend estimates. In particular, this affects assessments of trend significance as evaluation of the collective significance of individual tests is commonly neglected. In this article we build a space–time hierarchical Bayesian model for temperature anomalies where the trend coefficient is modelled by a latent Gaussian random field. This enables us to calculate simultaneous credible regions for joint significance assessments. In a case study, we assess summer season trends in 65 years of gridded temperature data over Europe. We find that while spatial smoothing generally results in larger regions where the null hypothesis of no trend is rejected, this is not the case for all subregions.

• Schuhen, Nina; Thorarinsdottir, Thordis Linda & Lenkoski, Alex (2019). Rapid adjustment and post-processing of temperature forecast trajectories. Quarterly Journal of the Royal Meteorological Society. ISSN 0035-9009. doi: 10.1002/qj.3718. Show summary

  Modern weather forecasts are commonly issued as consistent multi‐day forecast trajectories with a time resolution of 1–3 hours. Prior to issuing, statistical post‐processing is routinely used to correct systematic errors and misrepresentations of the forecast uncertainty. However, once the forecast has been issued, it is rarely updated before it is replaced in the next forecast cycle of the numerical weather prediction (NWP) model. This paper shows that the error correlation structure within the forecast trajectory can be utilized to substantially improve the forecast between the NWP forecast cycles by applying additional post‐processing steps each time new observations become available. The proposed rapid adjustment is applied to temperature forecast trajectories from the UK Met Office's convective‐scale ensemble MOGREPS‐UK. MOGREPS‐UK is run four times daily and produces hourly forecasts for up to 36 hours ahead. Our results indicate that the rapidly adjusted forecast from the previous NWP forecast cycle outperforms the new forecast for the first few hours of the next cycle, or until the new forecast itself can be rapidly adjusted, suggesting a new strategy for updating the forecast cycle.

• Yuan, Qifen; Thorarinsdottir, Thordis Linda; Beldring, Stein; Wong, Wai Kwok; Huang, Shaochun & Xu, Chong-Yu (2019). New approach for bias correction and stochastic downscaling of future projections for daily mean temperatures to a high-resolution grid. Journal of Applied Meteorology and Climatology. ISSN 1558-8424. 58(12), p. 2617–2632. doi: 10.1175/JAMC-D-19-0086.1. Full text in Research Archive Show summary

  In applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

• Thorarinsdottir, Thordis Linda & Schuhen, Nina (2018). Verification: Assessment of Calibration and Accuracy. In Vannitsem, Stephane; Wilks, Daniel S. & Messner, Jakob W. (Ed.), Statistical Postprocessing of Ensemble Forecasts. Elsevier. ISSN 978-0-12-812372-0. p. 155–186. doi: 10.1016/B978-0-12-812372-0.00006-6. Show summary

  In ensemble forecasting, forecast verification methods are needed to diagnose both the need for statistical postprocessing and the effectiveness of the postprocessing methods in producing calibrated and accurate forecasts. This chapter discusses an array of techniques that can be used in this context, making the distinction between verification tools that are useful for ranking competing forecasters and those that are more appropriate for improving our understanding of the performance of a single method. With a focus on continuous variables, verification methods for both univariate and multivariate forecasts are discussed, including approaches that are specifically tailored to the evaluation of extreme events.

• Thorarinsdottir, Thordis Linda; Hellton, Kristoffer Herland; Steinbakk, Gunnhildur Högnadóttir; Schlichting, Lena & Engeland, Kolbjørn (2018). Bayesian regional flood frequency analysis for large catchments. Water Resources Research. ISSN 0043-1397. 54(9), p. 6929–6947. doi: 10.1029/2017WR022460. Show summary

  Regional flood frequency analysis is commonly applied in situations where there exists insufficient data at a location for a reliable estimation of flood quantiles. We develop a Bayesian hierarchical modeling framework for a regional analysis of data from 203 large catchments in Norway with the generalized extreme value distribution as the underlying model. Generalized linear models on the parameters of the generalized extreme value distribution are able to incorporate location‐specific geographic and meteorological information and thereby accommodate these effects on the flood quantiles. A Bayesian model averaging component additionally assesses model uncertainty in the effect of the proposed covariates. The resulting regional model is seen to give substantially better predictive performance than the regional model currently used in Norway.

• Kobierska-Baffie, Florian Antoine; Engeland, Kolbjørn & Thorarinsdottir, Thordis Linda (2018). Evaluation of design flood estimates - a case study for Norway. Hydrology Research. ISSN 1998-9563. 49(2), p. 450–465. doi: 10.2166/nh.2017.068. Full text in Research Archive Show summary

  The aim of this study was to evaluate the predictive fit of probability distributions to annual maximum flood data, and in particular to evaluate (i) which combination of distribution and estimation method gives the best fit and (ii) whether the answer to (i) depends on record length. These aims were achieved by assessing the sensitivity to record length of the predictive performance of several probability distributions. A bootstrapping approach was used by resampling (with replacement) record lengths of 30 to 90 years (50 resamples for each record length) from the original record and fitting distributions to these subsamples. Subsequently, the fits were evaluated according to several goodness-of-fit measures and to the variability of the predicted flood quantiles. Our initial hypothesis that shorter records favor two-parameter distributions was not clearly supported. The ordinary moments method was the most stable while providing equivalent goodness-of-fit.

• Sillmann, Jana; Thorarinsdottir, Thordis Linda; Keenlyside, Noel; Schaller, Nathalie; Alexander, L. V. & Hegerl, Gabi [Show all 10 contributors for this article] (2017). Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities. Weather and Climate Extremes. ISSN 2212-0947. 18, p. 65–74. doi: 10.1016/j.wace.2017.10.003. Show summary

  Weather and climate extremes are identified as major areas necessitating further progress in climate research and have thus been selected as one of the World Climate Research Programme (WCRP) Grand Challenges. Here, we provide an overview of current challenges and opportunities for scientific progress and cross-community collaboration on the topic of understanding, modeling and predicting extreme events based on an expert workshop organized as part of the implementation of the WCRP Grand Challenge on Weather and Climate Extremes. In general, the development of an extreme event depends on a favorable initial state, the presence of large-scale drivers, and positive local feedbacks, as well as stochastic processes. We, therefore, elaborate on the scientific challenges related to large-scale drivers and local-to-regional feedback processes leading to extreme events. A better understanding of the drivers and processes will improve the prediction of extremes and will support process-based evaluation of the representation of weather and climate extremes in climate model simulations. Further, we discuss how to address these challenges by focusing on short-duration (less than three days) and long-duration (weeks to months) extreme events, their underlying mechanisms and approaches for their evaluation and prediction.

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• Rognli, Odd Arne; Aamlid, Trygve S.; Alsheikh, Muath K; Amdahl, Helga; Dalmannsdottir, Sigridur & Hellton, Kristoffer Herland [Show all 13 contributors for this article] (2023). Securing adaptation of timothy cultivars under climate change and during seed multiplication .
• Rognli, Odd Arne; Reddy Pashapu, Akhil; Kovi, Mallikarjuna Rao; Jørgensen, Marit; Dalmannsdottir, Sigridur & Aamlid, Trygve S. [Show all 13 contributors for this article] (2023). Genetiske endringer i nord-norske timoteisorter over tid og ved oppformering på ulike breddegrader.
• Haug, Ola; Heinrich-Mertsching, Claudio & Thorarinsdottir, Thordis (2023). Assessing risk of water damage to buildings under current and future climates.
• Roksvåg, Thea Julie Thømt; Lutz, Julia; Dyrrdal, Anita Verpe; Lussana, Cristian & Thorarinsdottir, Thordis (2023). Estimating extreme areal precipitation from gridded data products.
• Thorarinsdottir, Thordis (2023). From weather to climate predictions .
• Thorarinsdottir, Thordis (2023). Statistical modelling of environmental extremes.
• Lutz, Julia; Grinde, Lars; Dyrrdal, Anita Verpe; Roksvåg, Thea & Thorarinsdottir, Thordis Linda (2022). Estimating consistent rainfall design values for Norway using Bayesian inference and post-processing of posterior quantiles.
• Thorarinsdottir, Thordis Linda; Solberg, Rune; Lenkoski, Alex & Roksvåg, Thea (2022). Potensialet i data .
• Thorarinsdottir, Thordis Linda; Haugen, Marion & Guttorp, Peter (2022). Extracting robust information from data .
• Thorarinsdottir, Thordis Linda (2022). Climate Futures: Navigating climate risk .
• Thorarinsdottir, Thordis Linda; Roksvåg, Thea; Engeland, Kolbjørn; Barna, Danielle; Xu, Chong-Yu & Lutz, Julia [Show all 8 contributors for this article] (2022). Consistent estimation of extreme precipitation and flooding across multiple durations .
• Thorarinsdottir, Thordis Linda; Barna, Danielle; Roksvåg, Thea; Engeland, Kolbjørn; Xu, Chong-Yu & Lutz, Julia [Show all 8 contributors for this article] (2022). Consistent estimation of extreme precipitation and flooding across multiple durations.
• Thorarinsdottir, Thordis Linda; Heinrich, Claudio Constantin & Guttorp, Peter (2022). Validation of point process predictions with proper scoring rules.
• Thorarinsdottir, Thordis Linda (2022). On the importance of statistics and machine learning in climate research.
• Thorarinsdottir, Thordis Linda; Roksvåg, Thea; Lutz, Julia; Grinde, Lars & Dyrrdal, Anita Verpe (2022). A Bayesian framework to derive consistent intensity-duration-frequency curves from multiple data sources.
• Barna, Danielle; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda & Xu, Chong-Yu (2022). Flood-duration-frequency (QDF) Modeling: Updates and Current Status.
• Barna, Danielle; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda & Xu, Chong-Yu (2022). New Flood-Duration-Frequency Models with a Focus on Estimation of Sub-daily Floods.
• Barna, Danielle; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda & Xu, Chong-Yu (2022). Regional flood-Duration-Frequency (QDF) Models for Norway.
• Roksvåg, Thea; Lenkoski, Alex; Sheuerer, Michael; Heinrich, Claudio Constantin & Thorarinsdottir, Thordis Linda (2022). Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods.
• Roksvåg, Thea; Lenkoski, Alex; Sheuerer, Michael; Heinrich, Claudio Constantin & Thorarinsdottir, Thordis Linda (2022). Probabilistic prediction of the time to hard freeze using seasonal weather forecasts and survival time methods.
• Løland, Anders; Thorarinsdottir, Thordis Linda & Lenkoski, Alex (2022). Episode 16: Climate Futures. Klimaprognoser for 10 dager til 10 år fram / Predicting climate risks 10 days to 10 years ahead. [Internet]. Sannsynligvis VIKTIG (podkast).
• Løland, Anders; Lenkoski, Alex & Thorarinsdottir, Thordis Linda (2022). Hvordan vet vi hvor tørr sommeren i Europa blir? / How to make a seasonal forecast. [Internet]. Sannsynligvis VIKTIG (podkast).
• Roksvåg, Thea; Lenkoski, Alex; Scheuerer, Michael; Heinrich, Claudio Constantin & Thorarinsdottir, Thordis Linda (2022). Prediction of the time to hard freeze using seasonal weather forecasts and survival time methods.
• Barna, Danielle; Engeland, Kolbjørn; Thorarinsdottir, Thordis Linda & Xu, Chong-Yu (2021). A Bayesian approach to Flood-Duration-Analysis.
• Scheuerer, Michael; Thorarinsdottir, Thordis Linda & Lenkoski, Alex (2021). The Climate Futures Center for Research-based Innovation.
• Thorarinsdottir, Thordis Linda & Kolstad, Erik Wilhelm (2021). Om klimarisiko. [Internet]. Energi og Teknologi (podkast).
• Thorarinsdottir, Thordis Linda (2021). Forecast evaluation part III.
• Thorarinsdottir, Thordis Linda (2021). Forecast evaluation part II.
• Thorarinsdottir, Thordis Linda (2021). Forecast evaluation part I.
• Thorarinsdottir, Thordis Linda (2021). On the importance of statistics and machine learning in climate research.
• Thorarinsdottir, Thordis Linda (2021). Machine learning vs statistical methods for climate data analysis.
• Roksvåg, Thea; Lutz, Julia; Grinde, Lars; Dyrrdal, Anita Verpe & Thorarinsdottir, Thordis Linda (2021). New methods for making consistent IDF curves for Norway.
• Roksvåg, Thea; Lutz, Julia; Grinde, Lars; Dyrrdal, Anita Verpe & Thorarinsdottir, Thordis Linda (2021). Consistent Intensity-Duration-Frequency curves by post-processing of estimated Bayesian posterior quantiles.
• Thorarinsdottir, Thordis Linda (2020). From weather to climate predictions.
• Schuhen, Nina; Thorarinsdottir, Thordis Linda & Lenkoski, Alex (2019). Rapid Adjustment of Forecast Trajectories: improving short-term forecast skill through statistical post-processing. Show summary

  The skill of a typical NWP forecast decreases over time, so that forecasts from more recent model runs are generallyconsideredtobemoreskillfulandgivemoreaccuratepredictions.Somepost-processingtechniquesstill make use of older model runs through time-lagging or blending, but with very little relevance, as the newer model runs are preferred. At the same time, technological advances make observations become available in very short time frames and in increasing amounts. We propose a new method, Rapid Adjustment of Forecast Trajectories (RAFT), which works in combination with traditional statistical post-processing techniques and uses short-term observations to improve older forecast runs. As a result, older forecasts match or even surpass the skill of the forecasts from the newest model run. Relying on the inherent correlation structure of the forecast errors between lead times, RAFT updates the tail of a forecast trajectory while the first part verifies. The adaptive regression approach takes into account changesinpredictabilityandlocalpatterns,whilebeingcomputationallyefficient.WewillpresentRAFTversions forhourlysurfacetemperatureand10mwindspeedforecastsfromtheUKMetOffice’sMOGREPS-UKensemble.

• Schuhen, Nina; Thorarinsdottir, Thordis Linda & Lenkoski, Alex (2019). Rapid adjustment of weather forecast trajectories.
• Thorarinsdottir, Thordis Linda (2019). Decision support for climate change adaptation: The importance of uncertainty assessment .
• Thorarinsdottir, Thordis Linda (2019). On developing general and efficient inference algorithms for complicated hierarchical models .
• Thorarinsdottir, Thordis Linda (2019). Statistics in climate research: The importance of stochastic modelling and uncertainty quantification.
• Guttorp, Peter & Thorarinsdottir, Thordis Linda (2019). Local Climate Projections: A Little Money Goes a Long Way. EOS. ISSN 0096-3941. 100. doi: 10.1029/2019EO133113.
• Thorarinsdottir, Thordis Linda; Stefanakos, Christos; Vanem, Erik; Rognebakke, Hanne Therese Wist; Hammer, Hugo Lewi & Øigård, Tor Arne (2019). HDwave: Statistical space-time projections of wave heights.
• Thorarinsdottir, Thordis Linda; Engeland, Kolbjørn & Kobierska, Florian (2019). The effects of uncertainty on design flood estimation.
• Rognebakke, Hanne Therese Wist & Thorarinsdottir, Thordis Linda (2019). Statistical space-time projections of wave heights in the North Atlantic.
• Schuhen, Nina; Thorarinsdottir, Thordis Linda & Lenkoski, Alex (2018). Improving forecasts through rapid updating of temperature trajectories and statistical post-processing.
• Thorarinsdottir, Thordis Linda; Yuan, Qifen; Wong, Wai Kwok; Beldring, Stein; Huang, Shaochun & Xu, Chong-Yu [Show all 7 contributors for this article] (2018). Statistics in climate research: The importance of stochastic modelling and uncertainty quantification.
• Thorarinsdottir, Thordis Linda; Yuan, Qifen; Wong, Wai Kwok; Beldring, Stein; Huang, Shaochun & Xu, Chong-Yu [Show all 7 contributors for this article] (2018). Post-processing climate model output to obtain accurate high-resolution climate projections & why uncertainty matters even if the answer is just a number.
• Thorarinsdottir, Thordis Linda (2018). Spatial hierarchical modelling with a large number of potential covariates.
• Hellton, Kristoffer Herland & Thorarinsdottir, Thordis Linda (2018). Bayesian hierarchical modeling of extreme flood events.
• Thorarinsdottir, Thordis Linda; Heinrich, Claudio Constantin; Lenkoski, Alex; Kolstad, Erik Wilhelm & Paasche, Øyvind (2018). Varsling av vær og klima i maskinlæringens tid. Hvor gode kan sesongvarslene bli?
• Guttorp, Peter; Thorarinsdottir, Thordis Linda & Albert-Green, Alisha (2018). Using nerve fibre data as a statistical laboratory.
• Thorarinsdottir, Thordis Linda (2018). Does Bayes beat squinting? Bayesian modelling of cluster point process models.
• Thorarinsdottir, Thordis Linda (2018). Point processes: Models vs. inference.
• Thorarinsdottir, Thordis Linda (2018). Bayesian modelling of cluster point process models.
• Thorarinsdottir, Thordis Linda; Hellton, Kristoffer Herland; Steinbakk, Gunnhildur Högnadóttir; Schlichting, Lena & Engeland, Kolbjørn (2018). Statistical estimation of extreme floods.
• Thorarinsdottir, Thordis Linda; Lenkoski, Alex; Hellton, Kristoffer Herland; Steinbakk, Gunnhildur Högnadóttir; Dyrrdal, Anita Verpe & Stordal, Frode [Show all 8 contributors for this article] (2018). On developing general and efficient inference algorithms for complicated hierarchical models .
• Guttorp, Peter & Thorarinsdottir, Thordis Linda (2018). How to save Bergen from the sea? Decisions under uncertainty. Significance. ISSN 1740-9705. 15(2), p. 14–18. doi: 10.1111/j.1740-9713.2018.01125.x. Show summary

  Sea level rise poses a threat to the Norwegian coastal city of Bergen and its historic harbour. The threat could be reduced, but greater flood protection comes at greater cost. And, of course, no one knows for certain how far sea level will rise in future. Decision‐makers must therefore decide what to do, and how much to spend, without knowing exactly how bad things could get. Peter Guttorp and Thordis L. Thorarinsdottir explain the problem, and how to deal with the uncertainty

• Lawrence, Deborah; Thorarinsdottir, Thordis Linda; Paquet, Eric R.; Skaugen, Thomas & Engeland, Kolbjørn (2017). FlomQ: Improving flood estimation methods for dam safety in Norway.
• Thorarinsdottir, Thordis Linda (2017). Forecast evaluation II.
• Thorarinsdottir, Thordis Linda (2017). Forecast evaluation I.
• Haug, Ola; Thorarinsdottir, Thordis Linda; Sørbye, Sigrunn Holbek & Franzke, Christian (2017). Spatial trend analysis of gridded temperature data sets at varying spatial scales. Show summary

  In general, reliable trend estimates for temperature data may be challenging to obtain, mainly due to data scarcity. Short data series represent an intrinsic problem, whereas spatial sparsity may, in the case of spatially correlated data, be managed by adding appropriate spatial structure to the model. In this study, we analyse European temperature data over a period of 65 years. We search for trends in seasonal means and investigate the effect of varying the data grid resolution on the significance of the trend estimates obtained. We consider a set of models with different temporal and spatial structures and compare the resulting spatial trends along axes of model complexity and data grid resolution. This is ongoing work and the presentation will sketch the idea and give some preliminary results.

• Jullum, Martin; Thorarinsdottir, Thordis Linda & Bachl, Fabian (2017). Estimating the seal pup abundance in the Greenland Sea with Bayesian hierarchical modeling.
• Wahl, Jens Christian; Heinrich-Mertsching, Claudio; Liu, Izzie Yi; Thorarinsdottir, Thordis & Haug, Ola (2023). Gjensidige Denmark: Water damage risk model and preliminary analysis of storm damages. Norsk Regnesentral.
• Hellton, Kristoffer Herland & Thorarinsdottir, Thordis (2022). Analysis of variety crossings for improved yield in timothy. Norsk Regnesentral Oslo.
• Lenkoski, Alex; Kolstad, Erik Wilhelm & Thorarinsdottir, Thordis Linda (2022). A Benchmarking Dataset for Seasonal Weather Forecasts. Norsk Regnesentral.
• Roksvåg, Thea & Thorarinsdottir, Thordis Linda (2021). Prediksjon av lavvann ved Åbjøra. Norsk Regnesentral.
• Wahl, Jens Christian; Heinrich, Claudio; Thorarinsdottir, Thordis & Haug, Ola (2021). Stedsbasert risiko for vannskader - fase 2: Effekten av bygningsegenskaper, meteorologi og topografi. Norsk Regnesentral.
• Heinrich, Claudio Constantin; Thorarinsdottir, Thordis Linda; Schneider, Max & Guttorp, Peter (2020). Validation of point process predictions with proper scoring rules. Norsk Regnesentral. Show summary

  We introduce a class of proper scoring rules for evaluating spatial point process forecastsbased on summary statistics. These scoring rules rely on Monte-Carlo approximation ofan expectation and can therefore easily be evaluated for any point process model that canbe simulated. In this regard, they are more flexible than the commonly used logarithmicscore; they are also fruitful for evaluating the calibration of a model to specific aspectsof a point process, such as its spatial distribution or tendency towards clustering. Weshow using simulations that our scoring rules are able to discern between competingmodels better than the logarithmic score. An application on growth in Pacific silver firtrees demonstrates the promise of our scores for scientific model selection.

• Thorarinsdottir, Thordis Linda; Schuhen, Nina & Lenkoski, Alex (2020). Trajectory adjustment of lagged seasonal forecast ensembles. Norsk Regnesentral.
• Heinrich, Claudio; Wahl, Jens Christian; Matre, Andreas; Thorarinsdottir, Thordis & Haug, Ola (2020). Risikomodell for vannskader på bygninger og sensitivitet i klimaframskrivninger. Norsk Regnesentral. Full text in Research Archive
• Heinrich, Claudio; Wahl, Jens Christian; Thorarinsdottir, Thordis & Haug, Ola (2020). Risikomodell for vannskader på bygninger. Norsk Regnesentral.
• Wahl, Jens Christian; Heinrich, Claudio; Thorarinsdottir, Thordis; Ordonez, Alba; Trier, Øivind Due & Salberg, Arnt-Børre [Show all 7 contributors for this article] (2020). Stedsbasert risiko for vannskader - fase 1: Vurdering av topografiske indekser. Norsk Regnesentral.
• Skuland, Kristoffer; Heinrich, Claudio Constantin; Lenkoski, Alex & Thorarinsdottir, Thordis Linda (2019). Stratospheric events and long-range Scandinavian winter surface temperature forecasts. Norsk Regnesentral.
• Heinrich, Claudio Constantin; Schneider, Max; Guttorp, Peter & Thorarinsdottir, Thordis Linda (2019). Validation of point process forecasts. Norsk Regnesentral. Show summary

  We introduce a class of proper scoring rules for evaluating spatial point process forecastsbased on summary statistics. These scoring rules rely on Monte-Carlo approximation ofan expectation and can therefore easily be evaluated for any point process model thatcan be simulated. In this regard they are more flexible than the commonly used logar-ithmic score which cannot be evaluated for many point process models, as their densityis only known up to an untractable constant. In simulation studies we demonstrate theusefulness of our scores. Furthermore we consider a scoring rule, the quantile score, thatis commonly used to validate earthquake rate predictions, and show that it lacks propri-ety. As a consequence, several tests that are commonly applied in this context are biasedand systematically favour predictive distributions that are too uniform. We suggest toremedy this issue by replacing the commonly used one-sided by two-sided tests.

• Albert-Green, Alisha; Guttorp, Peter & Thorarinsdottir, Thordis Linda (2018). Does Bayes beat squinting? Estimating unobserved aspects of a spatial cluster process . Norsk Regnesentral. Show summary

  A point process data set on epidermal nerve fiber bundles is used as the basis for a series of experiments in identifying clusters. In this data set we know which secondary points are connected to which primary points.We will pretend that we do not have this information, and using Bayesian tools estimate the information from data. For comparison we also use k-means clustering. We do this both for known cluster centers, and when the cluster centers must be estimated from data.

• Jullum, Martin; Thorarinsdottir, Thordis Linda & Bachl, Fabian (2018). Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling. Norsk Regnesentral. Show summary

  The Greenland Sea is an important breeding ground for harp seals (Pagophilus groenlandicus) and hooded seals (Cystophora cristata). An estimate of the annual seal pup production is a critical factor in the abundance estimation needed for management of the species. Estimates of seal pup production are usually based on counts from aerial photographic surveys. However, due to the large extent of typical whelping regions, only a minor part of the complete area can be photographed. To estimate the total seal pup production, we propose a Bayesian hierarchical modelling approach motivated by viewing the seal pup appearances as a realization of a log-Gaussian Cox process using covariate information from satellite imagery as a proxy for ice-thickness. For inference, we utilize the spatial partial differential equation (SPDE) module of the integrated nested Laplace approximation (INLA) framework. In a case study using survey data from 2012, we compare our results with existing methodology in a comprehensive cross-validation study. The new proposed method improves local estimation performance and more accurately addresses the associated uncertainty.

• Vandeskog, Silius Mortensønn; Haugen, Marion & Thorarinsdottir, Thordis Linda (2018). Evaluation of bias corrected precipitation output from the EURO-CORDEX climate ensemble. Norsk Regnesentral. ISSN 978-82-539-0557-0. Show summary

  Global circulation models (GCMs) are used for projecting climate changes on a global scale. However, when we need information for local climate changes, a dynamical downscaling through a regional climate model (RCM) may be used to gain more precise information. Therefore it is important to make good RCMs that are unbiased when projecting climate changes. This note investigates the skill of precipitation projections from five combinations of global and regional climate models from EURO-CORDEX and four bias correction methods applied to some of these. This is performed by comparing the model outputs with data from the E-OBS and NGCD data products using integrated quadratic distance.

• Thorarinsdottir, Thordis Linda; Engeland, Kolbjørn; Lawrence, Deborah; Pedersen, Øyvind; Tveito, Ole Einar & Hellton, Kristoffer Herland [Show all 27 contributors for this article] (2018). Nytt rammeverk for flomestimering i Norge: Sluttrapport fra forskningsprosjektet FlomQ. Energi Norge. ISSN 978-82-436-1048-4.
• Thorarinsdottir, Thordis Linda & Schuhen, Nina (2017). Verification: assessment of calibration and accuracy. Norsk Regnesentral.

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