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Uncertainty resulting from multiple data usage in statistical downscaling

DSpace at IIT Bombay

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Title Uncertainty resulting from multiple data usage in statistical downscaling
 
Creator KANNAN, S
GHOSH, S
MISHRA, V
SALVI, K
 
Subject CLIMATE-CHANGE IMPACTS
DAILY PRECIPITATION
MODELS
INDIA
PROJECTIONS
SCENARIOS
 
Description Statistical downscaling (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalysis data products in model calibration. In the training of SD, for Indian Summer Monsoon Rainfall (ISMR), we use two reanalysis data as predictors and three gridded data products for ISMR from different sources. We observe that the uncertainty resulting from six possible training options is comparable to that resulting from multiple GCMs. Though the original GCM simulations project spatially uniform increasing change of ISMR, at the end of 21st century, the same is not obtained with SD, which projects spatially heterogeneous and mixed changes of ISMR. This is due to the differences in statistical relationship between rainfall and predictors in GCM simulations and observed/reanalysis data, and SD considers the latter.
 
Publisher AMER GEOPHYSICAL UNION
 
Date 2014-12-29T04:37:56Z
2014-12-29T04:37:56Z
2014
 
Type Article
 
Identifier GEOPHYSICAL RESEARCH LETTERS, 41(11)4013-4019
0094-8276
1944-8007
http://dx.doi.org/10.1002/2014GL060089
http://dspace.library.iitb.ac.in/jspui/handle/100/17095
 
Language English