Future Climate of Colombo Downscaled with SDSM-Neural Network

Singay Dorji,Srikantha Herath,Binaya Mishra

doi:10.3390/cli5010024

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.

Highlights

The Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC)concluded that the warming in the climate is ‘unequivocal.’ IPCC AR5 projects an increase of global mean surface temperature for 2081–2100 to 0.3–1.7 ◦ C (RCP2.6) and 2.6–4.8 ◦ C (RCP8.5) [1].The year 2016 was the third consecutive hottest year on record according to the National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA).Globally averaged temperatures in 2016 were 0.99 ◦ C warmer than the mid-20th-century average [2].The Paris Summit participants (Conference of Parties, COP21), in 2015, agreed to limit the rise in global temperature below 2 ◦ C above the pre-industrial level till 2100
The objective of this paper is to investigate if neural networks are better than statistical downscaling model (SDSM) for determining the relationships between Global Climate Model (GCM) predictors and the local climate variables of temperature and rainfall
The model is calibrated with National Center for Environmental Prediction (NCEP) reanalysis from 1961 to 1990 and validated from 1991 to 2005

Summary

Introduction

The Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC)concluded that the warming in the climate is ‘unequivocal.’ IPCC AR5 projects an increase of global mean surface temperature for 2081–2100 to 0.3–1.7 ◦ C (RCP2.6) and 2.6–4.8 ◦ C (RCP8.5) [1].The year 2016 was the third consecutive hottest year on record according to the National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA).Globally averaged temperatures in 2016 were 0.99 ◦ C warmer than the mid-20th-century average [2].The Paris Summit participants (Conference of Parties, COP21), in 2015, agreed to limit the rise in global temperature below 2 ◦ C above the pre-industrial level till 2100. Concluded that the warming in the climate is ‘unequivocal.’ IPCC AR5 projects an increase of global mean surface temperature for 2081–2100 to 0.3–1.7 ◦ C (RCP2.6) and 2.6–4.8 ◦ C (RCP8.5) [1]. Averaged temperatures in 2016 were 0.99 ◦ C warmer than the mid-20th-century average [2]. The global average temperature is already halfway to the target by 2016. Climate change is projected to increase the temperature and intensify the global water cycle, increasing both extreme events and non-rainy days, causing multiple stresses of floods and droughts. It is difficult to predict the future climate due to uncertainties from climate models and various other sources. Prediction of future climate research is important for impact studies and adaptation

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Conclusion