scholarly journals Future behavior of wind wave extremes due to climate change

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hector Lobeto ◽  
Melisa Menendez ◽  
Iñigo J. Losada

AbstractExtreme waves will undergo changes in the future when exposed to different climate change scenarios. These changes are evaluated through the analysis of significant wave height (Hs) return values and are also compared with annual mean Hs projections. Hourly time series are analyzed through a seven-member ensemble of wave climate simulations and changes are estimated in Hs for return periods from 5 to 100 years by the end of the century under RCP4.5 and RCP8.5 scenarios. Despite the underlying uncertainty that characterizes extremes, we obtain robust changes in extreme Hs over more than approximately 25% of the ocean surface. The results obtained conclude that increases cover wider areas and are larger in magnitude than decreases for higher return periods. The Southern Ocean is the region where the most robust increase in extreme Hs is projected, showing local increases of over 2 m regardless the analyzed return period under RCP8.5 scenario. On the contrary, the tropical north Pacific shows the most robust decrease in extreme Hs, with local decreases of over 1.5 m. Relevant divergences are found in several ocean regions between the projected behavior of mean and extreme wave conditions. For example, an increase in Hs return values and a decrease in annual mean Hs is found in the SE Indian, NW Atlantic and NE Pacific. Therefore, an extrapolation of the expected change in mean wave conditions to extremes in regions presenting such divergences should be adopted with caution, since it may lead to misinterpretation when used for the design of marine structures or in the evaluation of coastal flooding and erosion.

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alizée Chemison ◽  
Gilles Ramstein ◽  
Adrian M. Tompkins ◽  
Dimitri Defrance ◽  
Guigone Camus ◽  
...  

AbstractStudies about the impact of future climate change on diseases have mostly focused on standard Representative Concentration Pathway climate change scenarios. These scenarios do not account for the non-linear dynamics of the climate system. A rapid ice-sheet melting could occur, impacting climate and consequently societies. Here, we investigate the additional impact of a rapid ice-sheet melting of Greenland on climate and malaria transmission in Africa using several malaria models driven by Institute Pierre Simon Laplace climate simulations. Results reveal that our melting scenario could moderate the simulated increase in malaria risk over East Africa, due to cooling and drying effects, cause a largest decrease in malaria transmission risk over West Africa and drive malaria emergence in southern Africa associated with a significant southward shift of the African rain-belt. We argue that the effect of such ice-sheet melting should be investigated further in future public health and agriculture climate change risk assessments.


2021 ◽  
Author(s):  
luis Augusto sanabria ◽  
Xuerong Qin ◽  
Jin Li ◽  
Robert Peter Cechet

Abstract Most climatic models show that climate change affects natural perils' frequency and severity. Quantifying the impact of future climate conditions on natural hazard is essential for mitigation and adaptation planning. One crucial factor to consider when using climate simulations projections is the inherent systematic differences (bias) of the modelled data compared with observations. This bias can originate from the modelling process, the techniques used for downscaling of results, and the ensembles' intrinsic variability. Analysis of climate simulations has shown that the biases associated with these data types can be significant. Hence, it is often necessary to correct the bias before the data can be reliably used for further analysis. Natural perils are often associated with extreme climatic conditions. Analysing trends in the tail end of distributions are already complicated because noise is much more prominent than that in the mean climate. The bias of the simulations can introduce significant errors in practical applications. In this paper, we present a methodology for bias correction of climate simulated data. The technique corrects the bias in both the body and the tail of the distribution (extreme values). As an illustration, maps of the 50 and 100-year Return Period of climate simulated Forest Fire Danger Index (FFDI) in Australia are presented and compared against the corresponding observation-based maps. The results show that the algorithm can substantially improve the calculation of simulation-based Return Periods. Forthcoming work will focus on the impact of climate change on these Return Periods considering future climate conditions.


2016 ◽  
Author(s):  
R. M. J. Bamunawala ◽  
S. S. L. Hettiarachchi ◽  
S. P. Samarawickrama ◽  
P. N. Wikramanayake ◽  
Roshanka Ranasinghe

2013 ◽  
Vol 4 (3) ◽  
pp. 265-286 ◽  
Author(s):  
Jan Kyselý ◽  
Ladislav Gaál ◽  
Jan Picek ◽  
Martin Schindler

The study deals with estimates of return periods associated with the August 2010 heavy precipitation in northern Bohemia (Czech Republic), which resulted in flooding with enormous material damage and loss of lives, in the present climate and under climate change scenarios. We focus on the record-breaking 1-day and 2-day amounts at lower-elevation stations, exceeding 150 and 250 mm, respectively. The estimates of return periods are based on two methods of regional frequency analysis and they are compared with local (at-site) estimation. The regional methods consistently suggest that the August 2010 event was exceptional in view of past records, but the return levels decline substantially – by a factor of 2–4 – if parameters of the generalized extreme value distribution are allowed to vary in accordance with scenarios based on an ensemble of regional climate model projections for 2070–99. In spite of large uncertainty associated with future climate change scenarios, increased recurrence probability of such heavy precipitation events in the 21st century should be taken into account when designing and implementing flood risk prevention and mitigation measures.


2011 ◽  
pp. 341-348 ◽  
Author(s):  
TOMOYA SHIMURA ◽  
NOBUHITO MORI ◽  
SOTA NAKAJO ◽  
TOMOHRO YASUDA ◽  
HAJIME MASE

2013 ◽  
Vol 165 ◽  
pp. 1921-1926
Author(s):  
Norman Dreier ◽  
Christian Schlamkow ◽  
Peter Fröhle ◽  
Dörte Salecker

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