Seasonal precipitation variability in regional climate simulations over Northern basins of Tunisia

2013 ◽  
Vol 34 (1) ◽  
pp. 235-248 ◽  
Author(s):  
Zoubeida Bargaoui ◽  
Yves Tramblay ◽  
Emmanuel A. Lawin ◽  
Eric Servat
Keyword(s):  
Regional Climate ◽  
Precipitation Variability ◽  
Seasonal Precipitation ◽  
Climate Simulations

scholarly journals Assessment of localized seasonal precipitation variability in the upper middle catchment of the Olifants River basin

2020 ◽  
Author(s):  
German K. Nkhonjera ◽  
Megersa O. Dinka ◽  
Yali E. Woyessa
Keyword(s):  
River Basin ◽  
Assessment Tool ◽  
Negative Impact ◽  
Swat Model ◽  
Regional Climate ◽  
Precipitation Variability ◽  
Seasonal Precipitation ◽  
Study Results ◽  
Olifants River ◽  
General Reduction

Abstract This study used the Soil and Water Assessment Tool (SWAT) model together with regional climate downscaled (RCD) data from the CORDEX (Africa project), to assess the local seasonal precipitation variability in the upper middle catchment (UMC) of the Olifants River basin. The study results, based on two scenarios (RCP4.5 and RCP8.5), showed a wider monthly and seasonal variability of precipitation. The study also indicated a strong decreasing trend of east-to-west direction of spatial precipitation, with most precipitation concentrated in the eastern part of the study area. Within the western part of the UMC, we also noted another decreasing trend of precipitation from south-to-north with northern areas of the study area receiving the least amount of precipitation. This study has also revealed a considerable general reduction of future seasonal precipitation especially in the mid-term period (2021–2050). The general reduction in future seasonal precipitation, combined with the increasing temperatures in the area, may exacerbate the drought conditions and reduction in streamflow of the main river (Olifants) and its tributaries, consequently having a negative impact on the economic activities in the basin.

Two-Way Integration of WRF and CCSM for Regional Climate Simulations

2013 ◽  
Author(s):  
Wuyin Lin ◽  
Minghua Zhang ◽  
Juanxiong He ◽  
Xiangmin Jiao ◽  
Ying Chen ◽  
...  
Keyword(s):  
Regional Climate ◽  
Climate Simulations

Seasonal precipitation variability modes over South America associated to El Niño‐Southern Oscillation ( ENSO) and non‐ENSO components during the 1951–2016 period

2021 ◽  
Author(s):  
Itamara Parente Souza ◽  
Rita Valéria Andreoli ◽  
Mary Toshie Kayano ◽  
Franci Flores Vargas ◽  
Wilmar L. Cerón ◽  
...  
Keyword(s):  
South America ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Precipitation Variability ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Seasonal Precipitation

Climatological characterization of tropical cyclones detected in the regional climate simulations over the CORDEX‐SEA domain

2021 ◽  
Author(s):  
Jennifer Tibay ◽  
Faye Cruz ◽  
Fredolin Tangang ◽  
Liew Juneng ◽  
Thanh Ngo‐Duc ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Regional Climate ◽  
Climate Simulations

Seasonal precipitation variability in mainland China based on entropy theory

2021 ◽  
Author(s):  
Guocan Wu ◽  
Shun Qin ◽  
Chengcheng Huang ◽  
Zhanshan Ma ◽  
Chunming Shi
Keyword(s):  
Mainland China ◽  
Precipitation Variability ◽  
Seasonal Precipitation ◽  
Entropy Theory

scholarly journals Influence of changed vegetations fields on regional climate simulations in the Barents Sea Region

2007 ◽  
Vol 87 (1-2) ◽  
pp. 35-50 ◽  
Author(s):  
Holger Göttel ◽  
Jörn Alexander ◽  
Elke Keup-Thiel ◽  
Diana Rechid ◽  
Stefan Hagemann ◽  
...  
Keyword(s):  
Barents Sea ◽  
Regional Climate ◽  
The Barents Sea ◽  
Climate Simulations

The first multi-model ensemble of regional climate simulations at kilometer-scale resolution part 2: historical and future simulations of precipitation

2021 ◽  
Author(s):  
Emanuela Pichelli ◽  
Erika Coppola ◽  
Stefan Sobolowski ◽  
Nikolina Ban ◽  
Filippo Giorgi ◽  
...  
Keyword(s):  
Regional Climate ◽  
Model Ensemble ◽  
Climate Simulations

scholarly journals Compound extremes in a changing climate – a Markov chain approach

2016 ◽  
Vol 23 (6) ◽  
pp. 375-390 ◽  
Author(s):  
Katrin Sedlmeier ◽  
Sebastian Mieruch ◽  
Gerd Schädler ◽  
Christoph Kottmeier
Keyword(s):  
Extreme Events ◽  
Climate Models ◽  
Heat Waves ◽  
Dynamic Change ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Northern Germany ◽  
Russian Region ◽  
Climate Simulations ◽  
Potential Impact

Abstract. Studies using climate models and observed trends indicate that extreme weather has changed and may continue to change in the future. The potential impact of extreme events such as heat waves or droughts depends not only on their number of occurrences but also on "how these extremes occur", i.e., the interplay and succession of the events. These quantities are quite unexplored, for past changes as well as for future changes and call for sophisticated methods of analysis. To address this issue, we use Markov chains for the analysis of the dynamics and succession of multivariate or compound extreme events. We apply the method to observational data (1951–2010) and an ensemble of regional climate simulations for central Europe (1971–2000, 2021–2050) for two types of compound extremes, heavy precipitation and cold in winter and hot and dry days in summer. We identify three regions in Europe, which turned out to be likely susceptible to a future change in the succession of heavy precipitation and cold in winter, including a region in southwestern France, northern Germany and in Russia around Moscow. A change in the succession of hot and dry days in summer can be expected for regions in Spain and Bulgaria. The susceptibility to a dynamic change of hot and dry extremes in the Russian region will probably decrease.

scholarly journals Using Empirical Orthogonal Teleconnections to evaluate interannual rainfall variability over China in the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations

2017 ◽  
Author(s):  
Claudia Christine Stephan ◽  
Nicholas P. Klingaman ◽  
Pier Luigi Vidale ◽  
Andrew G. Turner ◽  
Marie-Estelle Demory ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Rainfall Variability ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Unified Model ◽  
Seasonal Precipitation ◽  
Climate Simulations ◽  
Interannual Rainfall Variability ◽  
The Mean

Abstract. Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyze the sensitivity to air-sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ~ 200, 90, and 40 km in the zonal direction at the equator, respectively) are analyzed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China, but improve with finer resolution and coupling. Empirical Orthogonal Teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air-sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal-mean timeseries. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

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