scholarly journals Future snowfall in western and central Europe projected with a high-resolution regional climate model ensemble

2014 ◽  
Vol 41 (12) ◽  
pp. 4294-4299 ◽  
Author(s):  
Hylke de Vries ◽  
Geert Lenderink ◽  
Erik van Meijgaard
2017 ◽  
Vol 5 (3) ◽  
pp. 285-303 ◽  
Author(s):  
Junhong Guo ◽  
Guohe Huang ◽  
Xiuquan Wang ◽  
Yongping Li ◽  
Qianguo Lin

SOLA ◽  
2020 ◽  
Vol 16 (0) ◽  
pp. 132-139
Author(s):  
Sheau Tieh Ngai ◽  
Hidetaka Sasaki ◽  
Akihiko Murata ◽  
Masaya Nosaka ◽  
Jing Xiang Chung ◽  
...  

2021 ◽  
Author(s):  
Florian Ehmele ◽  
Lisa-Ann Kautz ◽  
Hendrik Feldmann ◽  
Yi He ◽  
Martin Kadlec ◽  
...  

<p>Enduring and extensive heavy precipitation associated with widespread river floods are among the main natural hazards affecting Central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to run hydrological models (HMs). To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing 12.000 simulated years. LAERTES-EU is adapted and applied for the use in an HM to calculate discharges for large river catchments in Central Europe, where the Rhine catchment serves as the pilot area for calibration and validation. Quantile mapping with a fixed density function is used to correct the bias in model precipitation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU improves the statistical representativeness also for high return values of precipitation and discharges. While for the Rhine catchment a clear added value is identified, the results are more mixed for other catchments (e.g., the Upper Danube).</p>


2019 ◽  
Vol 101 ◽  
pp. 03004
Author(s):  
Rohit Srivastava ◽  
Ruchita Shah

Global warming is an increase in average global temperature of the earth which lead to climate change. Heterogeneity in the earth-atmosphere system becomes difficult to capture at low resolution (1°x1°) by satellite. Such features may be captured by using high resolution model such as regional climate model (0.5°x 0.5°). This type of study is quite important for a monsoon dominated country like India where Indo-Gangetic Plains (IGP) faces highest heterogeneity due to its geographic location. Present study compares high resolution model features with satellite data over IGP for monsoon season during a normal rainfall year 2010 to understand the actual performance of model. Almost whole IGP simulates relative humidity (RH) with wide range (~50-100%), whereas satellite shows it with narrow range (~60-80%) during September, 2010. Thus model is able to pick the features which were missed by satellite. Hence further model simulation extends over India and adjoining oceanic regions which simulates data of southwest monsoon with high (~70-100%) RH, high (~0.4-0.7) cloud fraction (CF) and low (~80-200 W/m2) outgoing longwave radiation (OLR) over Arabian Sea during June, 2010. Such type of study can be useful to understand heterogeneity at regional scale with the help of high resolution model generated data.


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