Bias Correction of Climate Modeled Temperature and Precipitation Using Artificial Neural Networks

Abstract

Climate studies and effective environmental management require unbiased climate datasets. This study develops a new bias correction approach using a three-layer feedforward neural network to reduce the biases of climate variables (temperature and precipitation) over northern South America. Air and skin temperature, specific humidity, and net longwave and shortwave radiation are used as inputs to the network for bias correction of 6-hourly temperature. Inputs to the network for bias correction of monthly precipitation are precipitation at lag 0, 1, 2, and 3 months, and also the standard deviation of precipitation from 3 × 3 neighbors around the pixel of interest. The climate model data are provided by the Community Climate System Model, version 3 (CCSM3). Results show that the trained artificial neural network (ANN) can improve the estimation error and correlation of the variables for both calibration and validation periods even when there is a low temporal consistency between the time series of the model data and targets. The developed model is also able to modify the probabilistic structure of the variables although the quantile-based information is not directly considered in the network. The ANN model outperforms linear regression, which is used for comparison purposes. The new method can be used to produce bias-corrected climate variables that can be used as forcing to hydrological and ecological models.