Suchergebnisse

Abstract
While many studies of the effects of global warming on hurricanes predict an increase in various metrics of Atlantic basin-wide activity, it is less clear that this signal will emerge from background noise in measures of hurricane damage, which depend largely on rare, high-intensity landfalling events and are thus highly volatile compared to basin-wide storm metrics. Using a recently developed hurricane synthesizer driven by large-scale meteorological variables derived from global climate models, 1000 artificial 100-yr time series of Atlantic hurricanes that make landfall along the U.S. Gulf and East Coasts are generated for four climate models and for current climate conditions as well as for the warmer climate of 100 yr hence under the Intergovernmental Panel on Climate Change (IPCC) emissions scenario A1b. These synthetic hurricanes damage a portfolio of insured property according to an aggregate wind-damage function; damage from flooding is not considered here. Assuming that the hurricane climate changes linearly with time, a 1000-member ensemble of time series of property damage was created. Three of the four climate models used produce increasing damage with time, with the global warming signal emerging on time scales of 40, 113, and 170 yr, respectively. It is pointed out, however, that probabilities of damage increase significantly well before such emergence time scales and it is shown that probability density distributions of aggregate damage become appreciably separated from those of the control climate on time scales as short as 25 yr. For the fourth climate model, damages decrease with time, but the signal is weak.

The phrase "natural catastrophe" is an oxymoron. Earthquakes, volcanic eruptions, and great storms are all part of nature and on geological time scales are as normal as breathing is to us. The catastrophe is that we insist on building and living on earthquake faults, in floodplains, on the flanks of volcanoes, and in places frequently visited by violent storms. That the ancients did so can hardly be held against them, living in ignorance of the causes and history of such events; but that we do so today speaks volumes about the complex relationship between modern man and nature. It is a relationship that continues to exact an enormous toll in human suffering and which molds the political and physical infrastructure of much of the world today. Continued ignorance of the history and nature of this relationship portends and unending string of "natural" catastrophes. Stuart Schwartz's brilliant and entertaining Sea of Storms casts a welcome light on this fascinating if disturbing blind spot in our relationship to nature, meticulously describing the social effects of hurricanes affecting the greater Caribbean region, from Barbados to the U.S. Gulf and southeast coasts. This is not a blow‐by‐blow account of each and every hurricane known to have affected the region; rather, Schwartz uses individual hurricanes to illustrate the complex interplay between social institutions, notably slavery, political systems such as socialism and capitalism, and natural hazards. Running through this exceptionally well researched book are a number of interesting threads that seem invariant over time. One is the tension between commerce and public safety. As trade expanded across the Caribbean in the 16th Century, Spanish governors concentrated people and wealth in coastal ports to support shipping, thus making island communities increasingly vulnerable to hurricanes. After such ports were smashed, they were simply rebuilt, negating any tendency to adapt to the hazard. Fearing lawsuits from local businesses, New Orleans Mayor Ray Nagin delayed the evacuation of his city as Hurricane Katrina approached in 2005, a decision that probably cost hundreds of lives. The continued reckless development of coastlines in the face of repeated catastrophes testifies to the triumph of money over safety.

Preface -- The myth of natural stability -- Greenhouse physics -- Why the climate problem is difficult -- Determining humanity's influence -- The consequences -- Communicating climate science -- Our options -- The politics surrounding global climate change

Abstract
This paper explores the potential utility of seasonal Atlantic hurricane forecasts to a hypothetical property insurance firm whose insured properties are broadly distributed along the U.S. Gulf and East Coasts. Using a recently developed hurricane synthesizer driven by large-scale meteorological variables derived from global reanalysis datasets, 1000 artificial 100-yr time series are generated containing both active and inactive hurricane seasons. The hurricanes thus produced damage to the property insurer's portfolio of insured property, according to an aggregate wind-damage function. The potential value of seasonal hurricane forecasts is assessed by comparing the overall probability density of the company's profits from a control experiment, in which the insurer purchases the same reinsurance coverage each year, to various test strategies in which the amount of risk retained by the primary insurer, and the corresponding premium paid to the reinsurer, varies according to whether the season is active or quiet, holding the risk of ruin constant.
Under the highly idealized conditions of this experiment, there is a clear advantage to the hypothetical property insurance firm of using seasonal hurricane forecasts to adjust the amount of reinsurance it purchases each year. Under a strategy that optimizes the company's profits by holding the risk of ruin constant, the probability distribution of profit clearly separates from that of the control strategy after less than 10 yr when the seasonal forecasts are perfect. But when a more realistic seasonal forecast skill is assumed, the potential value of forecasts becomes significant only after more than a decade.

The devastating impact by Hurricane Sandy (2012) again showed New York City (NYC) is one of the most vulnerable cities to coastal flooding around the globe. The low‐lying areas in NYC can be flooded by nor'easter storms and North Atlantic hurricanes. The few studies that have estimated potential flood damage for NYC base their damage estimates on only a single, or a few, possible flood events. The objective of this study is to assess the full distribution of hurricane flood risk in NYC. This is done by calculating potential flood damage with a flood damage model that uses many possible storms and surge heights as input. These storms are representative for the low‐probability/high‐impact flood hazard faced by the city. Exceedance probability‐loss curves are constructed under different assumptions about the severity of flood damage. The estimated flood damage to buildings for NYC is between US$59 and 129 millions/year. The damage caused by a 1/100‐year storm surge is within a range of US$2 bn–5 bn, while this is between US$5 bn and 11 bn for a 1/500‐year storm surge. An analysis of flood risk in each of the five boroughs of NYC finds that Brooklyn and Queens are the most vulnerable to flooding. This study examines several uncertainties in the various steps of the risk analysis, which resulted in variations in flood damage estimations. These uncertainties include: the interpolation of flood depths; the use of different flood damage curves; and the influence of the spectra of characteristics of the simulated hurricanes.

Abstract. Storm-surge-induced coastal inundation constitutes a substantial threat to lives and properties along the vast coastline of the Bengal delta. Some of the deadliest cyclones in history made landfall in the Bengal delta region claiming more than half a million lives over the last five decades. Complex hydrodynamics and observational constraints have hindered the understanding of the risk of storm surge flooding of this low-lying (less than 5 m above mean sea level), densely populated (> 150 million) mega-delta. Here, we generated and analysed a storm surge database derived from a large ensemble of 3600 statistically and physically consistent synthetic storm events and a high-resolution storm surge modelling system. The storm surge modelling system is developed based on a custom high-accuracy regional bathymetry enabling us to estimate the surges with high confidence. From the storm surge dataset, we performed a robust probabilistic estimate of the storm surge extremes. Our ensemble estimate shows that there is a diverse range of water level extremes along the coast and the estuaries of the Bengal delta, with well-defined regional patterns. We confirm that the risk of inland storm surge flooding at a given return period is firmly controlled by the presence of coastal embankments and their height. We also conclude that about 10 % of the coastal population is living under the exposure of a 50-year return period inundation under current climate scenarios. In the face of ongoing climate change, which is likely to worsen the future storm surge hazard, we expect our flood maps to provide relevant information for coastal infrastructure engineering, risk zoning, resource allocation, and future research planning.

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Surveys in Geophysics 38 (2017): 1529–1568, doi:10.1007/s10712-017-9428-0. ; Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air–sea interactions and convective organization. ; The EUREC4A project is supported by the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 694768), by the Max Planck Society and by DFG (Deutsche Forschungsgemeinschaft, German Research Foundation) Priority Program SPP 1294.

In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Con- vention on Climate Change (UNFCCC) invited the Inter- governmental Panel on Climate Change (IPCC) to provide a "special report in 2018 on the impacts of global warming of 1.5 â—ŠC above pre-industrial levels and related global green- house gas emission pathwaysâ€. In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we de- scribe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is de- signed to allow for (1) separation of the impacts of histori- cal warming starting from pre-industrial conditions from im- pacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simula- tions); (2) quantification of the impacts of additional warm- ing up to 1.5 â—ŠC, including a potential overshoot and long- term impacts up to 2299, and comparison to higher lev- els of global mean temperature change (based on the low- emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the cli- mate effects based on the same climate scenarios while ac- counting for simultaneous changes in socio-economic con- ditions following the middle-of-the-road Shared Socioeco- nomic Pathway (SSP2, Fricko et al., 2016) and in particu- lar differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0.With the aim of providing the scientific basis for an aggregation of impacts across sectors and anal- ysis of cross-sectoral interactions that may dampen or am- plify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact mod- els across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiver- sity).