Reliable El Niño Southern Oscillation (ENSO) prediction at seasonal-to-interannual lead times would be critical for different stakeholders to conduct suitable management. In recent years, new methods combining climate network analysis with El Niño prediction claim that they can predict El Niño up to 1 year in advance by overcoming the spring barrier problem (SPB). Usually this kind of method develops an index representing the relationship between different nodes in El Niño related basins, and the index crossing a certain threshold is taken as the warning of an El Niño event in the next few months. How well the prediction performs should be measured in order to estimate any improvements. However, the amount of El Niño recordings in the available data is limited, therefore it is difficult to validate whether these methods are truly predictive or their success is merely a result of chance. We propose a benchmarking method by surrogate data for a quantitative forecast validation for small data sets. We apply this method to a naïve prediction of El Niño events based on the Oscillation Niño Index (ONI) time series, where we build a data-based prediction scheme using the index series itself as input. In order to assess the network-based El Niño prediction method, we reproduce two different climate network-based forecasts and apply our method to compare the prediction skill of all these. Our benchmark shows that using the ONI itself as input to the forecast does not work for moderate lead times, while at least one of the two climate network-based methods has predictive skill well above chance at lead times of about one year.

The El Niño Southern Oscillation (ENSO) is one of the most important inter-annual climate phenomena with worldwide impacts. It can influence daily temperature and rainfall, as well as cause extreme weather events and natural disasters. Therefore, early and reliable prediction of the onset and magnitude of ENSO is crucial for different stakeholders. In order to overcome the “spring predictability barrier” in ENSO prediction, recent studies have developed some analysis tools and put forward some forecasting indices based on climate network, claiming they have achieved the long-lead-time (over 6 months) forecasts. However, there are few kinds of research to quantitatively compare the predictive power of these methods. Thus developing a method to measure the quality of these forecasts and compare their predictive power is necessary and meaningful for the improvement of ENSO prediction skills. In these existing researches, in order to set the threshold or estimate the accuracy of the prediction, the standard El Niño indices such as Oceanic Index (ONI), Niño 3.4 Index and etc., are often used to be compared with the invented indices series. In this research, we look into these comparisons and results, and use the receiver operating characteristic curve (ROC) to quantitatively compare these recent analysis tools. Additionally, for demonstrating that the results are not accidental, randomized series obtained by reshuffling the temperature records are analyzed. In this paper, we use the method of surrogate data instead of using shuffle data in the evaluation procedure of the prediction to further improve the evaluation method of the El Nino prediction. (This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.)

We show that the frequency of damaging convective weather events including lightning, hail and severe wind gusts will likely increase over Europe until the end of this century. We apply a set of additive regression models to an ensemble of 14 regional climate simulations and find that convective instability will increase as a result of rising humidity near the earth's surface. Even though a slight decrease in thunderstorm occurrence in southwestern and southeastern Europe is projected, the probability of severe weather will increase throughout Europe, in particular for very large hail. It might be expected that Arctic amplification would lead to a weaker jet stream and, thus lower vertical wind shear, but we find instead that the jet changes little or even increases in situations with convective instability. To cope with the rising hazard probabilities, risk models will need to be adapted, while investment in sturdier structures, like the use of hardened glass in greenhouses or solar panels, will become more cost-effective. Furthermore, the need will grow to advise the public on loss prevention by taking precautionary measures as storms approach.

By 31 December 2018, 39 537 quality-controlled reports of large hail had been submitted to the European Severe Weather Database (ESWD) by volunteers and ESSL. This dataset and the NatCatSERVICE Database of Munich RE jointly allowed us to study the hail hazard and its impacts across Europe over a period spanning multiple decades. We present a spatiotemporal climatology of the ESWD reports, diurnal and annual cycles of large hail, and indicate where and how they may be affected by reporting biases across Europe. We also discuss which hailstorms caused the most injuries and present the only case with hail fatalities in recent times. Additionally, we address our findings on the relation between hail size to the type of impacts that were reported. For instance, the probability of reported hail damage to roofs, windows, and vehicles strongly increases as hail size exceeds 5 cm, while damage to crops, trees, and greenhouses is typically reported with hailstone diameters of 2–3 cm. Injuries to humans are usually reported with hail 4 cm in diameter and larger, and number of injuries increases with increasing hail size. Using the NatCatSERVICE data, we studied economic losses associated with hailstorms occurring in central Europe and looked for long-term changes. The trend in hail losses and the annual number of hail loss days since 1990 to 2018 are compared to that of meteorological conditions favorable for large hail as identified by ESSL’s Additive Regression Convective Hazards model. Both hail loss days and favorable environments show an upward trend, in particular since 2000.

A statistical model for the occurrence of convective hazards was developed and applied to reanalysis data to detect multidecadal trends in hazard frequency. The modeling framework is based on an additive logistic regression for observed hazards that exploits predictors derived from numerical model data. The regression predicts the probability of a severe hazard, which is considered as a product of two components: the probability that a storm occurs and the probability of the severe hazard, given the presence of a storm [P(severe) = P(storm) x P(severe|storm)]. The model was developed using lightning data as an indication of thunderstorm occurrence and hazard reports across central Europe. Although it uses only two predictors per component, it is capable of reproducing the observed spatial distribution of lightning and yields realistic annual cycles of lightning, hail, and wind fairly accurately. The model was applied to ERA-Interim (1979-2016) across Europe to detect any changes in lightning, hail, and wind hazard occurrence. The frequency of conditions favoring lightning, wind, and large hail has increased across large parts of Europe, with the exception of the southwest. The resulting predicted occurrence of 6-hourly periods with lightning, wind, and large hail has increased by 16%, 29%, and 41%, respectively, across western and central Europe and by 23%, 56%, and 86% across Germany and the Alps during the period considered. It is shown that these changes are caused by increased instability in the reanalysis rather than by changes in midtropospheric moisture or wind shear.

The relations between lightning occurrence over Europe from the EUCLID network (2008-2013) and parameters derived from ERA-Interim reanalysis data were studied to increase the understanding of the conditions under which thunderstorms form. The objective was to identify relevant factors beyond instability and convective inhibition, in order to better model thunderstorms using numerical weather prediction or climate model data. It was found that latent instability is only required up to a certain amount of approximately 200-400 J kg(-1) CAPE. For higher values of CAPE (similar to 800-2800 J kg(-1)), the relative frequency of lightning is rather constant. Relative humidity in the low to mid-troposphere has a major influence on storm occurrence with low relative humidity strongly suppressing thunderstorm development. For an average 850-500 hPa relative humidity below 50%, the frequency of lightning decreases to below 15%, even when CIN is negligible and CAPE sufficient. A subtle dependency on wind shear was found in which two regimes of higher frequency of lightning were identified. For very weak and for high shear the probability was higher than for intermediate values of both deep-layer and low-level shear.

The properties of European windstorms under present climate conditions are estimated on the basis of surface wind forecasts from the European Centre for Medium-Range Weather Forecast (ECMWF) Ensemble Prediction System (EPS). While the EPS is designed to provide forecast information of the range of possible weather developments starting from the observed state of weather, we use its archive in a climatological context. It provides a large number of modifications of observed storm events and includes storms that did not occur in reality. Thus it is possible to create a large sample of storm events, which entirely originate from a physically consistent model, whose ensemble spread represents feasible alternative storm realizations of the covered period. This paper shows that the huge amount of identifiable events in the EPS is applicable to reduce uncertainties in a wide range of fields of research focusing on winter storms. Windstorms are identified and tracked in this study over their lifetime using an algorithm based on the local exceedance of the 98th percentile of instantaneous 10 m wind speed, which is associated with a storm severity measure. After removing inhomogeneities in the data set arising from major modifications of the operational system, the distributions of storm severity, storm size, and storm duration are computed. The overall principal properties of the homogenized EPS storm data set are in good agreement with storms from the ERA-Interim data set, making it suitable for climatological investigations of these extreme events. A demonstrated benefit in the climatological context by the EPS is presented. It gives clear evidence of a linear increase of maximum storm intensity and wind field size with storm duration. This relation is not recognizable from a sparse ERA-Interim sample for long-lasting events, as the number of events in the reanalysis is not sufficient to represent these characteristics.

This chapter synthesizes the scientific literature on the detection and attribution of observed changes in natural and human systems in response to observed recent climate change. For policy makers and the public, detection and attribution of observed impacts will be a key element to determine the necessity and degree of mitigation and adaptation efforts. For most natural and essentially all human systems, climate is only one of many drivers that cause change—other factors such as technological innovation, social and demographic changes, and environmental degradation frequently play an important role as well. Careful accounting of the importance of these and other confounding factors is therefore an important part of the analysis.

The western North Pacific (WNP) is the area of the world most frequently affected by tropical cyclones (TCs). However, little is known about the socio-economic impacts of TCs in this region, probably because of the limited relevant loss data. Here, loss data from Munich RE's NatCatSERVICE database is used, a high-quality and widely consulted database of natural disasters. In the country-level loss normalisation technique we apply, the original loss data are normalised to present-day exposure levels by using the respective country's nominal gross domestic product at purchasing power parity as a proxy for wealth. The main focus of our study is on the question of whether the decadal-scale TC variability observed in the Northwest Pacific region in recent decades can be shown to manifest itself economically in an associated variability in losses. It is shown that since 1980 the frequency of TC-related loss events in the WNP exhibited, apart from seasonal and interannual variations, interdecadal variability with a period of about 22 yr – driven primarily by corresponding variations of Northwest Pacific TCs. Compared to the long-term mean, the number of loss events was found to be higher (lower) by 14% (9%) in the positive (negative) phase of the decadal-scale WNP TC frequency variability. This was identified for the period 1980–2008 by applying a wavelet analysis technique. It was also possible to demonstrate the same low-frequency variability in normalised direct economic losses from TCs in the WNP region. The identification of possible physical mechanisms responsible for the observed decadal-scale Northwest Pacific TC variability will be the subject of future research, even if suggestions have already been made in earlier studies.

The science of seasonal predictions has advanced considerably in the last decade. Today, operational predictions are generated by several institutions, especially for variables such as (sea) surface temperatures and precipitation. In contrast, few studies have been conducted on the seasonal predictability of extreme meteorological events such as European windstorms in winter. In this study, the predictive skill of extratropical wintertime windstorms in the North Atlantic/European region is explored in sets of seasonal hindcast ensembles from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) and the ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) projects. The observed temporal and spatial climatological distributions of these windstorms are reasonably well reproduced in the hindcast data. Using hindcasts starting on 1 November, significant predictive skill is found for the December–February windstorm frequency in the period 1980–2001, but also for the January–April storm frequency. Specifically, the model suite run at Météo France shows consistently high skill. Some aspects of the variability of skill are discussed. Predictive skill in the 1980–2001 period is usually higher than for the 1960–2001 period. Furthermore, the level of skill turns out to be related to the storm frequency of a given winter. Generally, winters with high storm frequency are better predicted than winters with medium storm frequency. Physical mechanisms potentially leading to such a variability of skill are discussed.

The influence of sea surface temperature (SST) on the locations of the genesis and of landfall of tropical cyclones in the North Atlantic is analyzed. For that purpose, these locations are calculated from HURDAT and split into two disjoint subsets according to whether SST in the North Atlantic was above or below average in the year the corresponding storms occurred. Landfalls are investigated separately for the groups of cyclones categorized as tropical storms, minor hurricanes, or major hurricanes at the moment of landfall. The locations are considered realizations of inhomogeneous Poisson point processes, and the corresponding density functions are estimated with kernel estimation methods. In this way, any spatial structure inherent in the data is taken into account. These density functions are then compared with Monte Carlo methods from spatial statistics, which allows the detection of areas of statistically significant differences in the two sets with high and low SST, respectively. Results show many such areas, which is of relevance for the insurance industry and mathematical modelling of cyclones, as well as for decision support during the preparation for natural disasters.

In a first step relevant storm events are identified from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis datasets ERA40 (1958-2002) and ERA-INTERIM (1989-2009). Furthermore the identified events are classified using an adequate storm severity index (SSI). For the enlargement of the sample size for statistical analyses, additional potential storm events simulated by the Ensemble Prediction System (EPS) of ECMWF will be identified and classified. This allows to estimate SSI return periods up to several hundred years.

This paper simulates the increase in the average annual loss from tropical cyclones in the North Atlantic for the years 2015 and 2050. The simulation is based on assumptions concerning wealth trends in the regions affected by the storms, considered by the change in material assets (capital stock). Further assumptions are made about the trend in storm intensity resulting from anthropogenic climate change. The simulations use a stochastic model that models the annual storm loss from the number of storms and the loss per storm event. The paper demonstrates that increasing wealth will continue to be the principle loss driver in the future (average annual loss in 2015 32%, in 2050 308%). But climate change will also lead to higher losses (average annual loss in 2015 4%, in 2050 11%). In order to reduce the uncertainties surrounding the assumptions on the trend in capital stock and storm intensity, a sensitivity analysis was carried out, based on the assumptions from current studies on the future costs for tropical storms.