scholarly journals Can ensembles of regional climate model simulations improve results from sensitivity studies?

2010 ◽  
Vol 37 (5-6) ◽  
pp. 1111-1118 ◽  
Author(s):  
Travis A. O’Brien ◽  
Lisa C. Sloan ◽  
Mark A. Snyder
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Model Simulations ◽  
Sensitivity Studies ◽  
Climate Model Simulations

scholarly journals Regional climate model simulations of North Atlantic cyclones: frequency and intensity changes

2008 ◽  
Vol 36 ◽  
pp. 1-16 ◽  
Author(s):  
T Semmler ◽  
S Varghese ◽  
R McGrath ◽  
P Nolan ◽  
S Wang ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
North Atlantic ◽  
Climate Model ◽  
Regional Climate ◽  
Model Simulations ◽  
Intensity Changes ◽  
Climate Model Simulations

Adaptation and Application of the large LAERTES-EU RCM Ensemble for Hydrological Modeling

2021 ◽  
Author(s):  
Florian Ehmele ◽  
Lisa-Ann Kautz ◽  
Hendrik Feldmann ◽  
Yi He ◽  
Martin Kadlec ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Central Europe ◽  
Hydrological Modeling ◽  
Climate Model ◽  
Regional Climate ◽  
Added Value ◽  
Data Set ◽  
Model Simulations ◽  
Climate Model Simulations ◽  
Rhine Catchment

<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>

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