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Downscaling and projection of precipitation from general circulation model predictors in an equatorial climate region by the automated regression-based statistical method

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Abstract

The authors have applied an automated regression-based statistical method, namely, the automated statistical downscaling (ASD) model, to downscale and project the precipitation climatology in an equatorial climate region (Peninsular Malaysia). Five precipitation indices are, principally, downscaled and projected: mean monthly values of precipitation (Mean), standard deviation (STD), 90th percentile of rain day amount, percentage of wet days (Wet-day), and maximum number of consecutive dry days (CDD). The predictors, National Centers for Environmental Prediction (NCEP) products, are taken from the daily series reanalysis data, while the global climate model (GCM) outputs are from the Hadley Centre Coupled Model, version 3 (HadCM3) in A2/B2 emission scenarios and Third-Generation Coupled Global Climate Model (CGCM3) in A2 emission scenario. Meanwhile, the predictand data are taken from the arithmetically averaged rain gauge information and used as a baseline data for the evaluation. The results reveal, from the calibration and validation periods spanning a period of 40 years (1961–2000), the ASD model is capable to downscale the precipitation with reasonable accuracy. Overall, during the validation period, the model simulations with the NCEP predictors produce mean monthly precipitation of 6.18–6.20 mm/day (root mean squared error 0.78 and 0.82 mm/day), interpolated, respectively, on HadCM3 and CGCM3 grids, in contrast to 6.00 mm/day as observation. Nevertheless, the model suffers to perform reasonably well at the time of extreme precipitation and summer time, more specifically to generate the CDD and STD indices. The future projections of precipitation (2011–2099) exhibit that there would be an increase in the precipitation amount and frequency in most of the months. Taking the 1961–2000 timeline as the base period, overall, the annual mean precipitation would indicate a surplus projection by nearly 14~18 % under both GCM output cases (HadCM3 A2/B2 scenarios and CGCM3 A2 scenario). According to the model simulation, the September–November periods might be the more significant months projecting the increment of the precipitation amount around over 50 %, while the precipitation deficit would be seen in March–May periods.

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Acknowledgments

The author(s) would like to acknowledge the Data Access Integration (DAI, see http://quebec.ccsn.ca/DAI/) Team for providing the data and technical support. The DAI data download gateway is made possible through collaboration among the Global Environmental and Climate Change Centre (GEC3), the Adaptation and Impacts Research Division of Environment Canada, and the Drought Research Initiative (DRI). The Ouranos Consortium (in Québec) also provides IT support to the DAI team. This research is funded by the Hydrology and Water Resources Division, DID, Ministry of Water Resources, Malaysia.

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Authors and Affiliations

  1. National Hydraulic Research Institute of Malaysia, Ministry of Natural Resources and Environment, 43300, Seri Kembangan, Selangor, Malaysia

    Mohd Zaki M. Amin

  2. Department of Civil Engineering, University of Bristol, Bristol, UK

    Tanvir Islam

  3. Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    Tanvir Islam

  4. Department of Civil Engineering, Nagasaki University, Nagasaki, Japan

    Tanvir Islam

  5. Hydrology and Water Resources Division, DID, Ministry of Water Resources, Kuala Lumpur, Malaysia

    Asnor M. Ishak

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  1. Mohd Zaki M. Amin
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Correspondence to Tanvir Islam.

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Amin, M.Z.M., Islam, T. & Ishak, A.M. Downscaling and projection of precipitation from general circulation model predictors in an equatorial climate region by the automated regression-based statistical method. Theor Appl Climatol 118, 347–364 (2014). https://doi.org/10.1007/s00704-013-1062-2

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