Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138556
Title: Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes
Authors: Vallam, Pramodh
Qin, Xiao Sheng
Keywords: Engineering::Environmental engineering
Issue Date: 2017
Source: Vallam, P., & Qin, X. S. (2018). Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes. Theoretical and Applied Climatology, 134(1-2), 669-688. doi:10.1007/s00704-017-2299-y
Journal: Theoretical and Applied Climatology
Abstract: Anthropogenic-driven climate change would affect the global ecosystem and is becoming a world-wide concern. Numerous studies have been undertaken to determine the future trends of meteorological variables at different scales. Despite these studies, there remains significant uncertainty in the prediction of future climates. To examine the uncertainty arising from using different schemes to downscale the meteorological variables for the future horizons, projections from different statistical downscaling schemes were examined. These schemes included statistical downscaling method (SDSM), change factor incorporated with LARS-WG, and bias corrected disaggregation (BCD) method. Global circulation models (GCMs) based on CMIP3 (HadCM3) and CMIP5 (CanESM2) were utilized to perturb the changes in the future climate. Five study sites (i.e., Alice Springs, Edmonton, Frankfurt, Miami, and Singapore) with diverse climatic conditions were chosen for examining the spatial variability of applying various statistical downscaling schemes. The study results indicated that the regions experiencing heavy precipitation intensities were most likely to demonstrate the divergence between the predictions from various statistical downscaling methods. Also, the variance computed in projecting the weather extremes indicated the uncertainty derived from selection of downscaling tools and climate models. This study could help gain an improved understanding about the features of different downscaling approaches and the overall downscaling uncertainty.
URI: https://hdl.handle.net/10356/138556
ISSN: 0177-798X
DOI: 10.1007/s00704-017-2299-y
Rights: © 2017 Springer-Verlag GmbH Austria. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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