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Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes

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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.

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Acknowledgements

The study was supported by Academic Research Fund Tier 1 (Ref188/14; WBS no.: M4011420.030) from Ministry of Education (MOE), Singapore, and in part by Nanyang Technological University (NTU) Start-Up Grant (WBS: M4081327.030). The authors also appreciate the support from Environmental Process Modeling Centre (EPMC), Nanyang Environment & Water Research Institute (NEWRI), Singapore.

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  1. Environmental Process Modelling Centre (EPMC), Nanyang Environment & Water Research Institute (NEWRI), Nanyang Technological University, Singapore, 639798, Singapore

    P. Vallam & X. S. Qin

  2. School of Civil & Environmental Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    X. S. Qin

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Vallam, P., Qin, X.S. Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes. Theor Appl Climatol 134, 669–688 (2018). https://doi.org/10.1007/s00704-017-2299-y

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