Southeastern South America (SESA)’s precipitation is thought to be influenced by both El Niño Southern Oscillation (ENSO) and Antarctic Oscillation (AAO), especially in austral spring. Previous studies conclude AAO can modulate ENSO’s impact on precipitation over the SESA region without differentiating between El Niño and La Niña events. In the present study, we use composite analysis to further explore the combined impact of AAO and ENSO on austral spring precipitation over Southeastern South America (SESA) to answer this question and explain the dynamic mechanisms. We found that different AAO phases can influence La Niña’s impact on SESA austral spring precipitation considerably, while this does not apply for El Niño events. From our analysis, we found that AAO exerts more impact on austral spring precipitation over SESA compared to ENSO during La Niña years by influencing northerly wind and southward water vapor flux, which contributes most of the moisture into the SESA region, due to the strengthening of South Atlantic subtropical anticyclone and stronger meridional gradient in low-level pressure. Besides, there is an upper-level trough (ridge) over subtropical South America indicating advection of cyclonic (anticyclonic) vorticity inducing anomalous increase (decrease) of precipitation over that region during La Niña/AAO− (La Niña/AAO+). We do not see this opposite difference within El Niño groups combined with different phases of AAO.

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.

Damage caused by floods has generally increased in Europe over the decades, and reducing the flood risk has become a recognised priority throughout the continent. Floods constitute a burden on the economies of European countries and insurance companies, not only because of the damage they cause but also because of the costs of structural flood defences and safety measures in general. The main reasons for the increasing flood risk are: presence of settlements and the growing value of assets in flood-prone areas; climate and environmental (land use and land cover) changes that always happen but have recently been accelerated because of radically different demographic conditions and technological progress. Statistical analyses by Munich Re's NatCatSERVICE database underline the high relevance of floods to European societies. The reasons for the rising trends in flood losses are explained. The flood risk is borne by various stakeholders - individuals, businesses, governments and the insurance sector. This paper discusses the taxonomy of floods, presents flood statistics for Europe and examines the reasons for flood risk increase. It also examines flood risk reduction, focusing on various aspects of flood insurance as a means of risk transfer.

This paper is the primary deliverable of the very first NASA Living With a Star Institute Working Group, Geomagnetically Induced Currents (GIC) Working Group. The paper provides a broad overview of the current status and future challenges pertaining to the science, engineering, and applications of the GIC problem. Science is understood here as the basic space and Earth sciences research that allows improved understanding and physics-based modeling of the physical processes behind GIC. Engineering, in turn, is understood here as the "impact" aspect of GIC. Applications are understood as the models, tools, and activities that can provide actionable information to entities such as power systems operators for mitigating the effects of GIC and government agencies for managing any potential consequences from GIC impact to critical infrastructure. Applications can be considered the ultimate goal of our GIC work. In assessing the status of the field, we quantify the readiness of various applications in the mitigation context. We use the Applications Readiness Level (ARL) concept to carry out the quantification.

Motivated by the needs of the high-voltage power transmission industry, we use data from the high-latitude IMAGE magnetometer array to study characteristics of extreme geoelectric fields at regional scales. We use 10-s resolution data for years 1993–2013, and the fields are characterized using average horizontal geoelectric field amplitudes taken over station groups that span about 500-km distance. We show that geoelectric field structures associated with localized extremes at single stations can be greatly different from structures associated with regionally uniform geoelectric fields, which are well represented by spatial averages over single stations. Visual extrapolation and rigorous extreme value analysis of spatially averaged fields indicate that the expected range for 1-in-100-year extreme events are 3–8 V/km and 3.4–7.1 V/km, respectively. The Quebec reference ground model is used in the calculations.

One of the major challenges pertaining to extreme geomagnetic storms is to understand the basic processes associated with the development of dynamic magnetosphere‐ionosphere currents, which generate large induced surface geoelectric fields. Previous studies point out the existence of localized peak geoelectric field enhancements during extreme storms. We examined induced global geoelectric fields derived from ground‐based magnetometer recordings for 12 extreme geomagnetic storms between the years 1982 and 2005. For the present study two important extreme storms, 29 October 2003 and 13 March 1989, are shown. The primary purpose of this paper is to provide further evidence on the existence of localized peak geoelectric field enhancements and to show that the structure of the geoelectric field during these localized extremes at single sites can differ greatly from globally and regionally averaged fields. Although the physical processes that govern the development of these localized extremes are still not clear, we discuss some possible causes.

Thunderstorm-related normalized economic and insured losses in the United States east of the Rockies from the period 1970-2009 (March-September) exhibit higher peaks and greater variability in the last two decades than in the preceding two decades. To remove the bias from increasingly detected losses over time due to newly built-up locations, only large events that incurred normalized losses of at least $250 million (U.S. dollars) economically ($150 million insured) were selected. These are multistate damage events that are unlikely to have been missed at any time within the analysis period, thus providing for homogeneity of the events covered. Those losses, if aggregated, account for the major proportion (similar to 80%) of all thunderstorm-related losses in the period 1970-2009. This study demonstrates that the pattern of variability in the time series of these losses can be seen as a reflection (fingerprint) of the temporal variability in severe thunderstorm forcing. The meteorological information on forcing is inferred from NCEP-NCAR reanalysis data. No final attribution of the climatic variability identified in thunderstorm forcing and losseseither to natural climate variability or to anthropogenic climate changecan be conclusively arrived at in this study because of the chosen methodology. Nevertheless, the expected impacts of anthropogenic climate change on the forcing of convective storms appear consistent with these findings.

A series of 100‐year extreme geoelectric field and geomagnetically induced current (GIC) scenarios are explored by taking into account the key geophysical factors associated with the geomagnetic induction process. More specifically, we derive explicit geoelectric field temporal profiles as a function of ground conductivity structures and geomagnetic latitudes. We also demonstrate how the extreme geoelectric field scenarios can be mapped into GIC. Generated statistics indicate 20 V/km and 5 V/km 100‐year maximum 10‐s geoelectric field amplitudes at high‐latitude locations with poorly conducting and well‐conducting ground structures, respectively. We show that there is an indication that geoelectric field magnitudes may experience a dramatic drop across a boundary at about 40°–60° of geomagnetic latitude. We identify this as a threshold at about 50° of geomagnetic latitude. The sub‐threshold geoelectric field magnitudes are about an order of magnitude smaller than those at super‐threshold geomagnetic latitudes. Further analyses are required to confirm the existence and location of the possible latitude threshold. The computed extreme GIC scenarios can be used in further engineering analyses that are needed to quantify the geomagnetic storm impact on conductor systems such as high‐voltage power transmission systems. To facilitate further work on the topic, the digital data for generated geoelectric field scenarios are made publicly available.

We review our studies of the statistics of return intervals and extreme events (block maxima) in long-term correlated data sets, characterized by a power-law decaying autocorrelation function with correlation exponent gamma between 0 and 1, for different distributions (Gaussian, exponential, power-law, and log-normal) For the return intervals, the long-term memory leads (i) to a stretched exponential distribution (Weibull distribution), with an exponent equal to gamma, (ii) to long-term correlations among the return intervals themselves, yielding clustering of both small and large return intervals, and (iii) to an anomalous behavior of the mean residual time to the next event that depends on the history and increases with the elapsed time in a counterintuitive way We present an analytical scaling approach and demonstrate that all these features can be seen in long climate records For the extreme events we studied how the long-term correlations in data sets with Gaussian and exponential distribution densities affect the extreme value statistics, i e, the statistics of maxima values within time segments of fixed duration R We found numerically that (i) the integrated distribution function of the maxima converges to a Gumbel distribution for large R similar to uncorrelated signals, (ii) the deviations for finite R depend on both the initial distribution of the records and on their correlation properties, (in) the maxima series exhibit long-term correlations similar to those of the original data, and most notably (iv) the maxima distribution as well as the mean maxima significantly depend on the history, in particular on the previous maximum Finally we evaluate the effect of long-term correlations on the estimation of centennial events, which is an important task in hydrological risk estimation We show that most of the effects revealed in artificial data can also be found in real hydro- and climatological data series

We review recent studies of the statistics of return intervals (i) in long-term correlated monofractal records and (ii) in multifractal records in the absence (or presence) of linear long-term correlations. We show that for the monofractal records which are long-term power-law correlated with exponent γ, the distribution density of the return intervals follows a stretched exponential with the same exponent γ and the return intervals are long-term correlated, again with the same exponent γ. For the multifractal record, significant differences in scaling behavior both in the distribuiton and correlation behavior of return intervals between large events of different magnitudes are demonstrated. In the absence of linear long-term correlations, the nonlinear correlations contribute strongly to the statistics of the return intervals such that the return intervals become long-term correlated even though the original data are linearly uncorrelated (i.e., the autocorrelation function vanishes). The distribution density of the return intervals is mainly described by a power law.

This chapter contains sections titled: Abstract Introduction Statistics of Return Intervals Statistics of Maxima Long-Term Memory in Earthquakes Conclusions Acknowledgment References

. We study the statistics of the return intervals in multifractal data sets with and without linear correlations. In the absence of linear correlations, we find that the nonlinear correlations inherent in multifractal data yield (i) a power-law decay of the autocorrelation function of the return intervals, (ii) a power-law increase of the conditional return period as function of the previous return interval, and (iii) a power-law decay of the probability density function of the return intervals. These features remain unchanged in the presence of linear long-term correlations. Deviations observed in the asymptotic behaviour are probably due to finite size effects. We compare our results with those obtained for uncorrelated and for monofractal long-term correlated data, and demonstrate significant differences. Applications can be found in studying the dynamics of several processes characterised by multifractality, such as turbulence, climate dynamics, heartbeat dynamics, stock market dynamics, and tele-traffic in large networks.

We consider long-term correlated data with several distribution densities (Gaussian, exponential, power law, and log normal) and various correlation exponents gamma (0<gamma<1) , and study the statistics of the return intervals r_{j} between events above some threshold q . We show that irrespective of the distribution, the return intervals are long-term correlated in the same way as the original record, but with additional uncorrelated noise. Due to this noise, the correlations are difficult to observe by the detrended fluctuation analysis (which exhibits a crossover behavior) but show up very clearly in the autocorrelation function. The distribution P_{q}(r) of the return intervals is characterized at large scales by a stretched exponential with exponent gamma , and at short scales by a power law with exponent gamma-1 . We discuss in detail the occurrence of finite-size effects for large threshold values for all considered distributions. We show that finite-size effects are most pronounced in exponentially distributed data sets where they can even mask the stretched exponential behavior in records of up to 10;{6} data points. Finally, in order to quantify the clustering of extreme events due to the long-term correlations in the return intervals, we study the conditional distribution function and the related moments. We find that they show pronounced memory effects, irrespective of the distribution of the original data.

We study the statistics of return intervals between events above a certain threshold in multifractal data sets without linear correlations. We find that nonlinear correlations in the record lead to a power-law (i) decay of the autocorrelation function of the return intervals, (ii) increase in the conditional return period, and (iii) decay in the probability density function of the return intervals. We show explicitly that all the observed quantities depend both on the threshold value and system size, and hence there is no simple scaling observed. We also demonstrate that this type of behavior can be observed in real economic records and can be used to improve considerably risk estimation.

Many natural records exhibit long-term correlations characterized by a power-law decay of the autocorrelation function, C(s) approximately s-gamma, with time lag s and correlation exponent 0<gamma<1. We study how the presence of such correlations affects the statistics of the extreme events, i.e., the maximum values of the signal within time segments of the fixed duration R. We find numerically that (i) the integrated distribution function of the maxima converges to a Gumbel distribution for large R similar to uncorrelated signals, (ii) the deviations for finite R depend on the initial distribution of the records and on their correlation properties, (iii) the maxima series exhibit long-term correlations similar to those of the original data, and most notably (iv) the maxima distribution as well as the mean maxima significantly depend on the history, in particular on the previous maximum. The last item implies that conditional mean maxima and conditional maxima distributions (with the value of the previous maximum as condition) should be considered for an improved extreme event prediction. We provide indications that this dependence of the mean maxima on the previous maximum occurs also in observational long-term correlated records.

We study the statistics of the return intervals between extreme events above a certain threshold in long-term persistent records. We find that the long-term memory leads (i) to a stretched exponential distribution of the return intervals, (ii) to a pronounced clustering of extreme events, and (iii) to an anomalous behavior of the mean residual time to the next event that depends on the history and increases with the elapsed time in a counterintuitive way. We present an analytical scaling approach and demonstrate that all these features can be seen in long climate records. The phenomena should also occur in heartbeat records, Internet traffic, and stock market volatility and have to be taken into account for an efficient risk evaluation.