Southern Hemisphere winter cyclone activity under recent and future climate conditions in multi-model AOGCM simulations

2014 ◽  
Vol 34 (12) ◽  
pp. 3400-3416 ◽  
Author(s):  
J. Grieger ◽  
G.C. Leckebusch ◽  
M.G. Donat ◽  
M. Schuster ◽  
U. Ulbrich
Keyword(s):  
Southern Hemisphere ◽  
Future Climate ◽  
Cyclone Activity ◽  
Climate Conditions ◽  
Hemisphere Winter

scholarly journals Winter precipitation and cyclones in the Mediterranean region: future climate scenarios in a regional simulation

2007 ◽  
Vol 12 ◽  
pp. 153-158 ◽  
Author(s):  
P. Lionello ◽  
F. Giorgi
Keyword(s):  
Mediterranean Region ◽  
Eastern Mediterranean ◽  
Mediterranean Coast ◽  
Future Climate ◽  
Climate Projections ◽  
Northern Coast ◽  
Climate Scenarios ◽  
Cyclone Activity ◽  
Climate Conditions ◽  
The Mediterranean

Abstract. Future climate projections show higher/lower winter (Dec-Jan-Feb) precipitation in the northern/southern Mediterranean region than in present climate conditions. This paper analyzes the results of regional model simulations of the A2 and B2 scenarios, which confirm this opposite precipitation change and link it to the change of cyclone activity. The increase of the winter cyclone activity in future climate scenarios over western Europe is responsible for the larger precipitation at the northern coast of the basin, though the bulk of the change is located outside the Mediterranean region. The reduction of cyclone activity inside the Mediterranean region in future scenarios is responsible for the lower precipitation at the southern and eastern Mediterranean coast.

Design of a rubble mound breakwater under the combined effect of wave heights and water levels, under present and future climate conditions

2021 ◽  
Vol 112 ◽  
pp. 102711
Author(s):  
Soheil Radfar ◽  
Mehdi Shafieefar ◽  
Hassan Akbari ◽  
Panagiota A. Galiatsatou ◽  
Ahmad Rezaee Mazyak
Keyword(s):  
Water Levels ◽  
Future Climate ◽  
Combined Effect ◽  
Climate Conditions ◽  
Wave Heights ◽  
Rubble Mound

Spatial distribution modeling of the wild boar (Sus scrofa) under current and future climate conditions in Iraq

Biologia ◽  
2021 ◽  
Author(s):  
Nabaz R. Khwarahm ◽  
Korsh Ararat ◽  
Barham A. HamadAmin ◽  
Peshawa M. Najmaddin ◽  
Azad Rasul ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Wild Boar ◽  
Sus Scrofa ◽  
Future Climate ◽  
Climate Conditions ◽  
Distribution Modeling

Bias correction of extreme values of high resolution climate simulations for risk analysis.

2021 ◽  
Author(s):  
luis Augusto sanabria ◽  
Xuerong Qin ◽  
Jin Li ◽  
Robert Peter Cechet
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Extreme Values ◽  
The Body ◽  
Future Climate ◽  
Return Periods ◽  
Climate Conditions ◽  
Mitigation And Adaptation ◽  
Climate Simulations ◽  
The Impact

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.

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