Representation Of Extreme Events Research Articles

Participatory development of storymaps to visualize the spatiotemporal dynamics and impacts of extreme flood events for disaster preparedness

Floods are one of the costliest natural hazards in Switzerland and worldwide. Therefore, society is confronted with questions about protecting people and assets from flood risks. Key instruments are protective measures, land use regulations, spatial planning, and the interventions by civil protection units if flood magnitudes exceed the protection standards. Both prevention and preparedness require risk awareness from professionals, politicians, and the public. Risk awareness is generally high after an event and low after a period without major events. However, the rarity of extreme flood events limits learning from flood events. The training of intervention forces who should manage flood events with magnitudes beyond hitherto observed flood events requires a comprehensive description and visualization of the flood processes and their impacts. To address this, together with stakeholders and civil protection and intervention planning experts, we co-developed a new way to visualize the spatiotemporal dynamics of extreme flood events and thereby communicate their impacts using dynamical flood storymaps. We selected physically plausible precipitation scenarios from reforecasts to develop storylines of extreme river flood events and their socioeconomic impacts in Switzerland. The co-development process revealed which information is relevant to potential users and how it must be presented. It is shown that storylines of extreme events presented as storymaps are a valuable tool to communicate scientific results in a way that allows practitioners to gain relevant information for their work. Therefore, we built an interactive online tool (www.flooddynamics.ch), enabling the user to analyze the spatiotemporal unfolding of flood events in Switzerland from the start of precipitation to the recession of the flood. The visualization includes maps of inundated areas at hourly timesteps and the related impacts in terms of affected persons, buildings, roads, and infrastructure. Such a temporally explicit (dynamic) representation of extreme events in storymaps, in contrast to static hazard maps, which are commonly used today, is favorable for emergency intervention planning and training and thus for awareness creation and better disaster preparedness.

MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region

Abstract. Regional coupled and Earth system models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the Weather Research and Forecasting (WRF) atmospheric model, the NEMO oceanic model, and the hydrological discharge (HD) model, coupled via the OASIS coupler. The model is implemented at about 1/12∘ of horizontal resolution for the ocean and river routing, while roughly twice coarser for the atmosphere, and it is targeted to long-term investigations. We focus on the evaluation of skill score metrics from several sensitivity experiments devoted to (i) understanding the best vertical physics configuration for NEMO, (ii) identifying the impact of the interactive river runoff, and (iii) choosing the best-performing physics–microphysics suite for WRF in the regional coupled system. The modeling system has been developed for downscaling reanalyses and long-range predictions, as well as coupled data assimilation experiments. We then formulate and show the performance of the system when weakly coupled data assimilation is embedded in the system (variational assimilation in the ocean and spectral nudging in the atmosphere), in particular for the representation of extreme events like intense Mediterranean cyclones (i.e., medicanes). Finally, we outline plans for future extension of the modeling framework.

The role of atmospheric dynamics and large-scale topography in driving heatwaves.

Heatwaves are weather events characterized by extreme near‐surface temperature anomalies that persist for several days, and therefore lead to catastrophic impacts on natural ecosystems, agriculture, human health, and economies. Different physical processes can contribute to the temperature anomaly associated with heatwaves. Previous studies have shown that increased solar radiation and adiabatic heating associated with blocking systems and local land–atmosphere coupling are important drivers of summer heatwaves. Less is known about the fundamental role of atmospheric large‐scale dynamics and topography in generating heatwaves. Here, we perform idealized model simulations where all physical parameterisations are substituted by a simple zonally symmetric temperature relaxation scheme. This allows us to characterize the dynamical processes involved in the life cycle of heatwaves occurring at different latitudes. We find that blocking plays an active role in the life cycle of high‐ and midlatitude heatwaves, while blocking is less relevant for low‐latitude heatwaves. Rossby‐wave packets are the dominant drivers for midlatitude heatwaves, with horizontal advection being the main mechanism leading to heat extremes. Heatwaves exhibit a higher persistence and frequency near the pole and Equator compared with the midlatitudes, but a higher intensity in the midlatitudes compared with higher and lower latitudes. Topography located along the midlatitude jet has the largest impact on the heatwave distribution around the planet, resulting in increased heatwave frequency upstream for moderate topographic forcing and a circumglobal increase for topographic elevations above 6 km. Identifying the most relevant processes driving heatwaves can potentially benefit the prediction and representation of extreme events in operational weather and climate forecast systems.

Non-Hydrostatic Regcm4 (Regcm4-NH): Evaluation of Precipitation Statistics at the Convection-Permitting Scale over Different Domains

Recent studies over different geographical regions of the world have proven that regional climate models at the convection-permitting scale (CPMs) improve the simulation of precipitation in many aspects, such as the diurnal cycle, precipitation frequency, intensity, and extremes at daily—but even more at hourly—time scales. Here, we present an evaluation of climate simulations with the newly developed RegCM4-NH model run at the convection-permitting scale (CP-RegCM4-NH) for a decade-long period, over three domains covering a large European area. The simulations use a horizontal grid spacing of ~3 km and are driven by the ERA-Interim reanalysis through an intermediate driving RegCM4-NH simulation at ~12 km grid spacing with parameterized deep convection. The km-scale simulations are evaluated against a suite of hourly observation datasets with high spatial resolutions and are compared to the coarse-resolution driving simulation in order to assess improvements in precipitation from the seasonal to hourly scale. The results show that CP-RegCM4-NH produces a more realistic representation of precipitation than the coarse-resolution simulation over all domains. The most significant improvements were found for intensity, heavy precipitation, and precipitation frequency, both on daily and hourly time scales in all seasons. In general, CP-RegCM4-NH tends to correctly produce more intense precipitation and to reduce the frequency of events compared to the coarse-resolution one. On the daily scale, improvements in CP simulations are highly region dependent, with the best results over Italy, France, and Germany, and the largest biases over Switzerland, the Carpathians, and Greece, especially during the summer seasons. At the hourly scale, the improvement in CP simulations for precipitation intensity and spatial distribution is clearer than at the daily timescale. In addition, the representation of extreme events is clearly improved by CP-RegCM4-NH, particularly at the hourly time scale, although an overestimation over some subregions can be found. Although biases between the model simulations at the km-scale and observations still exist, this first application of CP-RegCM4-NH at high spatial resolution indicates a clear benefit of convection-permitting simulations and encourages further assessments of the added value of km-scale model configurations for regional climate change projections.

Option pricing of weather derivatives based on a stochastic daily rainfall model with Analogue Year component

In this study, we analyzed option pricing of rainfall derivatives based on stochastic daily rainfall model. We used Markov Chain Analogue Year model (MCAY) in order to describe occurrence process of daily rainfall. We have included the Analogue Year (AY) component in the Markov Chain (MC), which is a new component incorporated in this study and pricing rainfall derivatives. The inclusion of AY in the MC, provides excellent description of the occurrence process of daily rainfall. The amount of daily rainfall on wet days is obtained using Mixed Exponential distribution because it has the advantage of a better representation of extreme events. Combining the occurrence and amount model, we obtained Markov Chain Analogue Year Mixed Exponential model (MCAYMEM). Daily rainfall data from 2005 to 2017 were taken from Ethiopia National Meteorology Agency (ENMA) in order to assess the model performance. Based on the results of the daily rainfall models, we calculated an option price for different months. The price calculated using MCAYMEM gave an excellent result compared to the price calculated using MCMEM. This accuracy is mainly because of AY component included in the MC in the modeling of occurrence process.

Projected Changes in Temperature and Precipitation Extremes over China as Measured by 50-yr Return Values and Periods Based on a CMIP5 Ensemble

Future changes in the 50-yr return level for temperature and precipitation extremes over mainland China are investigated based on a CMIP5 multi-model ensemble for RCP2.6, RCP4.5 and RCP8.5 scenarios. The following indices are analyzed: TXx and TNn (the annual maximum and minimum of daily maximum and minimum surface temperature), RX5day (the annual maximum consecutive 5-day precipitation) and CDD (maximum annual number of consecutive dry days). After first validating the model performance, future changes in the 50-yr return values and return periods for these indices are investigated along with the inter-model spread. Multi-model median changes show an increase in the 50-yr return values of TXx and a decrease for TNn, more specifically, by the end of the 21st century under RCP8.5, the present day 50-yr return period of warm events is reduced to 1.2 yr, while extreme cold events over the country are projected to essentially disappear. A general increase in RX5day 50-yr return values is found in the future. By the end of the 21st century under RCP8.5, events of the present RX5day 50-yr return period are projected to reduce to < 10 yr over most of China. Changes in CDD-50 show a dipole pattern over China, with a decrease in the values and longer return periods in the north, and vice versa in the south. Our study also highlights the need for further improvements in the representation of extreme events in climate models to assess the future risks and engineering design related to large-scale infrastructure in China.

Event-based stochastic point rainfall resampling for statistical replication and climate projection of historical rainfall series

Abstract. Continuous and long rainfall series are a necessity in rural and urban hydrology for analysis and design purposes. Local historical point rainfall series often cover several decades, which makes it possible to estimate rainfall means at different timescales, and to assess return periods of extreme events. Due to climate change, however, these series are most likely not representative of future rainfall. There is therefore a demand for climate-projected long rainfall series, which can represent a specific region and rainfall pattern as well as fulfil requirements of long rainfall series which includes climate changes projected to a specific future period. This paper presents a framework for resampling of historical point rainfall series in order to generate synthetic rainfall series, which has the same statistical properties as an original series. Using a number of key target predictions for the future climate, such as winter and summer precipitation, and representation of extreme events, the resampled historical series are projected to represent rainfall properties in a future climate. Climate-projected rainfall series are simulated by brute force randomization of model parameters, which leads to a large number of projected series. In order to evaluate and select the rainfall series with matching statistical properties as the key target projections, an extensive evaluation procedure is developed.

Quantile-based bias correction and uncertainty quantification of extreme event attribution statements

Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. However, such climate models may have biases in their representation of extreme events. To account for discrepancies in the probabilities of extreme events between observational datasets and model datasets, we demonstrate an appropriate rescaling of the model output based on the quantiles of the datasets to estimate an adjusted risk ratio. Our methodology accounts for various components of uncertainty in estimation of the risk ratio. In particular, we present an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model. In this example, we find that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event.

WRF high resolution dynamical downscaling of ERA-Interim for Portugal

This study proposes a dynamically downscaled climatology of Portugal, produced by a high resolution (9 km) WRF simulation, forced by 20 years of ERA-Interim reanalysis (1989–2008), nested in an intermediate domain with 27 km of resolution. The Portuguese mainland is characterized by large precipitation gradients, with observed mean annual precipitation ranging from about 400 to over 2,200 mm, with a very wet northwest and rather dry southeast, largely explained by orographic processes. Model results are compared with all available stations with continuous records, comprising daily information in 32 stations for temperature and 308 for precipitation, through the computation of mean climatologies, standard statistical errors on daily to seasonally timescales, and distributions of extreme events. Results show that WRF at 9 km outperforms ERA-Interim in all analyzed variables, with good results in the representation of the annual cycles in each region. The biases of minimum and maximum temperature are reduced, with improvement of the description of temperature variability at the extreme range of its distribution. The largest gain of the high resolution simulations is visible in the rainiest regions of Portugal, where orographic enhancement is crucial. These improvements are striking in the high ranking percentiles in all seasons, describing extreme precipitation events. WRF results at 9 km compare favorably with published results supporting its use as a high-resolution regional climate model. This higher resolution allows a better representation of extreme events that are of major importance to develop mitigation/adaptation strategies by policy makers and downstream users of regional climate models in applications such as flash floods or heat waves.

MINIMALISM AND VICTIM TESTIMONY

ABSTRACTThis essay renews a discussion of how historians do, and should, represent atrocity. It argues that the problems of representing extreme violence remain under‐conceptualized; in this context it discusses the strengths and weaknesses of minimalism, a style prevalent both in historiography and in an intellectual culture that values understatement in approaches to violence. The essay traces the general cultural preference for minimalist narratives of suffering, which, it claims, is driven by the widespread conviction that experimental and exuberant narratives convert victims' suffering into kitsch. It then focuses on two works, by Saul Friedländer and by Jan Gross, on the history of Jewish victims during and after the Second World War in order to assess how each uses sophisticated minimalist understatement to represent suffering, but with radically different effects. Finally, it asks historians to reflect upon the representation of extreme events by focusing on narrative style, on questions of ethics, and on the cultural narratives within which their own work on suffering and violence is inevitably embedded—especially given that historians are paying increasing attention to violent events that generate tremendous difficulties in relation to the representation both of victims and perpetrators.

An evaluation of three stochastic rainfall models

Three stochastic rainfall models are evaluated in terms of their ability to reproduce the standard and extreme statistics derived from observed hourly point rainfall data obtained from three different sites in the UK. The models are: a modified version of the Eagleson [Eagleson, P.S., 1972. Dynamics of flood frequency. Water Resources Research 8, 878–898] exponential model (or MEEM), a version of the data-based model of Cameron et al. [Cameron, D.S., Beven, K.J., Tawn, J., Blazkova, S., Naden, P., 1999. Flood frequency estimation for a gauged upland catchment (with uncertainty). Journal of Hydrology 219, 169–187] (or CDFGPDM) and the random parameter Bartlett–Lewis gamma model (or RPBLGM) of Onof and Wheater [Onof, C., Wheater H.S., 1994. Improvements to the modelling of British rainfall using a modified random parameter Bartlett–Lewis rectangular pulse model. Journal of Hydrology 157, 177–195]. The sites are: Elmdon (Birmingham, England), Eskdalemuir (White Esk valley, Scotland), and the Wye (Plynlimon, Wales). For each site, the simulations are conducted on a seasonal (winter and summer) basis, with hourly timestep. For each season, a comparison of the spread of the 1 and 24 h simulated seasonal maximum rainfall totals with those obtained through a statistical analysis of the observed extreme rainfalls is also made. It is shown that the MEEM can effectively reproduce certain observed series standard statistics at each site. It is much poorer in its representation of extreme events, and, for two of the sites, dry periods. The CDFGPDM generally performs well under all the criteria, although it has a tendency to underestimate the observed 1 h seasonal maximum rainfalls for both seasons at Elmdon. The RPBLGM is shown to be reasonable at simulating the standard statistics but is often poorer with respect to the extremes. The implications for flood frequency estimation are highlighted. Possible improvements to each model are considered.