scholarly journals GIS-based high-resolution spatial interpolation of precipitation in mountain–plain areas of Upper Pakistan for regional climate change impact studies

2009 ◽  
Vol 99 (3-4) ◽  
pp. 239-253 ◽  
Author(s):  
Muhammad Waseem Ashiq ◽  
Chuanyan Zhao ◽  
Jian Ni ◽  
Muhammad Akhtar
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Change Impact ◽  
Spatial Interpolation ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Impact Studies ◽  
Change Impact

scholarly journals Fine-resolution climate projections enhance regional climate change impact studies

Eos ◽  
2007 ◽  
Vol 88 (47) ◽  
pp. 504-504 ◽  
Author(s):  
Edwin P. Maurer ◽  
Levi Brekke ◽  
Tom Pruitt ◽  
Philip B. Duffy
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Climate Projections ◽  
Impact Studies ◽  
Change Impact ◽  
Fine Resolution

Climate Scenario Development and Applications for Local/Regional Climate Change Impact Assessments: An Overview for the Non-Climate Scientist

2011 ◽  
Vol 5 (6) ◽  
pp. 275-300 ◽  
Author(s):  
Julie A. Winkler ◽  
Galina S. Guentchev ◽  
Perdinan ◽  
Pang-Ning Tan ◽  
Sharon Zhong ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Climate Scenario ◽  
Scenario Development ◽  
Climate Scientist ◽  
Change Impact

scholarly journals Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?

2012 ◽  
Vol 9 (11) ◽  
pp. 12765-12795 ◽  
Author(s):  
C. Teutschbein ◽  
J. Seibert
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Conditions ◽  
Impact Studies ◽  
Split Sample ◽  
Sample Test ◽  
Change Impact

Abstract. In hydrological climate-change impact studies, Regional Climate Models (RCMs) are commonly used to transfer large-scale Global Climate Model (GCM) data to smaller scales and to provide more detailed regional information. However, there are often considerable biases in RCM simulations, which have led to the development of a number of bias correction approaches to provide more realistic climate simulations for impact studies. Bias correction procedures rely on the assumption that RCM biases do not change over time, because correction algorithms and their parameterizations are derived for current climate conditions and assumed to apply also for future climate conditions. This underlying assumption of bias stationarity is the main concern when using bias correction procedures. It is in principle not possible to test whether this assumption is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well bias correction methods perform for conditions different from those used for calibration. For five Swedish catchments, several time series of RCM simulated precipitation and temperature were obtained from the ENSEMBLES data base and different commonly-used bias correction methods were applied. We then performed a differential split-sample test by dividing the data series into cold and warm respective dry and wet years. This enabled us to evaluate the performance of different bias correction procedures under systematically varying climate conditions. The differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation years. More advanced correction methods such as distribution mapping performed relatively well even in the validation period, whereas simpler approaches resulted in the largest deviations and least reliable corrections for changed conditions. Therefore, we question the use of simple bias correction methods such as the widely used delta-change approach and linear scaling for RCM-based climate-change impact studies and recommend using higher-skill bias correction methods.

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