scholarly journals Statistical downscaling of general circulation model outputs to precipitation—part 2: bias‐correction and future projections

2014 ◽  
Vol 34 (11) ◽  
pp. 3282-3303 ◽  
Author(s):  
D. A. Sachindra ◽  
F. Huang ◽  
A. Barton ◽  
B. J. C. Perera
Keyword(s):  
General Circulation Model ◽  
Statistical Downscaling ◽  
Bias Correction ◽  
General Circulation ◽  
Circulation Model ◽  
Future Projections

Statistical downscaling of general circulation model outputs to precipitation, evaporation and temperature using a key station approach

2016 ◽  
Vol 7 (4) ◽  
pp. 683-707
Author(s):  
D. A. Sachindra ◽  
F. Huang ◽  
A. Barton ◽  
B. J. C. Perera
Keyword(s):  
General Circulation Model ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Emission Scenario ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Regression Equations ◽  
Highly Correlated

Using a key station approach, statistical downscaling of monthly general circulation model outputs to monthly precipitation, evaporation, minimum temperature and maximum temperature at 17 observation stations located in Victoria, Australia was performed. Using the observations of each predictand, over the period 1950–2010, correlations among all stations were computed. For each predictand, the station which showed the highest number of correlations above 0.80 with other stations was selected as the first key station. The stations that were highly correlated with that key station were considered as the member stations of the first cluster. By employing this same procedure on the remaining stations, the next key station was found. This procedure was performed until all stations were segregated into clusters. Thereafter, using the observations of each predictand, regression equations (inter-station regression relationships) were developed between the key stations and the member stations for each calendar month. The downscaling models at the key stations were developed using reanalysis data as inputs to them. The outputs of HadCM3 pertaining to A2 emission scenario were introduced to these downscaling models to produce projections of the predictands over the period 2000–2099. Then the outputs of these downscaling models were introduced to the inter-station regression relationships to produce projections of predictands at all member stations.

Statistical downscaling of general circulation model outputs to catchment scale hydroclimatic variables: issues, challenges and possible solutions

2014 ◽  
Vol 5 (4) ◽  
pp. 496-525 ◽  
Author(s):  
D. A. Sachindra ◽  
F. Huang ◽  
A. Barton ◽  
B. J. C. Perera
Keyword(s):  
General Circulation Model ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Circulation Model ◽  
Potential Method ◽  
Mathematical Representation ◽  
Physical Processes ◽  
Catchment Scale ◽  
Scenario Uncertainty ◽  
Hydroclimatic Variables

The aim of this paper is to discuss the issues and challenges associated with statistical downscaling of general circulation model (GCM) outputs to hydroclimatic variables at catchment scale and also to discuss potential solutions to address these issues and challenges. Outputs of GCMs (predictors of statistical downscaling models) suffer a considerable degree of uncertainty, mainly due to the lack of theoretical robustness caused by the limited understanding of various physical processes of the atmosphere and the incomplete mathematical representation of those processes in GCMs. The presence of several future GHG emission scenarios with equal likelihood of occurrence leads to scenario uncertainty. Outputs of a downscaling study are dependent on the quality and the length of the record of field observations, as statistical downscaling models are calibrated and validated against these observations of the hydroclimatic variables (predictands of statistical downscaling models). The downscaled results vary from one statistical downscaling technique to another due to different representations of the predictor–predictand relationships. Also different techniques used in selecting the predictors for statistical downscaling models influence the model outputs. Although statistical downscaling faces these issues, it is still considered as a potential method of predicting the catchment scale hydroclimatology from GCM outputs.

scholarly journals On the bias correction of general circulation model output for Indian summer monsoon

2012 ◽  
Vol 20 (3) ◽  
pp. 349-356 ◽  
Author(s):  
Nachiketa Acharya ◽  
Surajit Chattopadhyay ◽  
U. C. Mohanty ◽  
S. K. Dash ◽  
L. N. Sahoo
Keyword(s):  
General Circulation Model ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Bias Correction ◽  
General Circulation ◽  
Circulation Model ◽  
Model Output ◽  
General Circulation Model Output
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