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Climate 2017, 5(1), 24; doi:10.3390/cli5010024

Future Climate of Colombo Downscaled with SDSM-Neural Network

1
United Nations University, Institute for the Advanced Study of Sustainability (UNU-IAS), 5 Chome-53-70 Jingumae, Shibuya, Tokyo 150-8925, Japan
2
Ministry of Megapolis and Western Development, Battaramulla 10120, Sri Lanka
3
UNU-IAS, IR3S-University of Tokyo, and University of Peradeniya, Tokyo 150-8925, Japan
4
UNU-IAS, Tokyo 150-8925, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Yang Zhang
Received: 5 February 2017 / Revised: 19 March 2017 / Accepted: 20 March 2017 / Published: 22 March 2017
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Abstract

The Global Climate Model (GCM) run at a coarse spatial resolution cannot be directly used for climate impact studies. Downscaling is required to extract the sub-grid and local scale information. This paper investigates if the artificial neural network (ANN) is better than the widely-used regression-based statistical downscaling model (SDSM) for downscaling climate for a site in Colombo, Sri Lanka. Based on seasonal and annual model biases and the root mean squared error (RMSE), the ANN performed better than the SDSM for precipitation. This paper proposes a novel methodology for improving climate predictions by combining SDSM with neural networks. This method will allow a user to apply SDSM with a neural network model for higher skills in downscaling. The study uses the Canadian Earth System Model (CanESM2) of the IPCC Fifth Assessment Report, reanalysis from the National Center for Environmental Prediction (NCEP), and the Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) project data as the observation. SDSM and the focused time-delayed neural network (TDNN) models are used for the downscaling. The projected annual increase for Representative Concentration Pathway (RCP) is 8.5; the average temperature is 2.83 °C (SDSM) and 3.03 °C (TDNN), and rainfall is 33% (SDSM) and 63% (TDNN) for 2080’s. View Full-Text
Keywords: downscaling; climate change; SDSM; neural networks; GCM; Sri Lanka downscaling; climate change; SDSM; neural networks; GCM; Sri Lanka
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Dorji, S.; Herath, S.; Mishra, B.K. Future Climate of Colombo Downscaled with SDSM-Neural Network. Climate 2017, 5, 24.

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