Detection and Attribution of Climate Change Signals in Precipitation in the Chaliyar River Basin, Kerala, India

Scientific studies have yielded evidence to support the common perception that climatic variables and associated natural resources and human systems are being affected by external forcings. Detection and attribution (D&A) of climate change provides a formal tool to decipher the complex causes of climate change. This work aims to statistically detect such climatic change signals, if any, in the monthly precipitation data of the Chaliyar river basin, Kerala, India and to evaluate the factors contributing to it. Data employed for the study includes monthly mean precipitation observations, National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data sets and results of several climate model runs based on natural internal variability and anthropogenic effects. Precipitation data from the GCMs were statistically downscaled to river basin scale using ANN based models and the potential predictors were identified by correlation coefficient analysis. Five widely used General Circulation Models were chosen based on availability of the predictor data in this study. GCM projections for three SRES scenarios, A1B, A2 and B1 were downscaled and the t test at 99% confidence interval was performed on the downscaled monthly precipitation data. Results show that there is consistency in the observed data and anthropogenically forced data. Using a standard fingerprint approach, it was noticed that the observed trends in precipitation over the second half of the 20th century lie outside the range expected from natural internal climate variability alone at 95% confidence level for most of the GCMs. To determine the reason for this climate change signal, attribution study was performed based on correlation coefficient analysis and this yielded a positive relationship between the downscaled A2 scenario precipitation and the observed precipitation. The detection and attribution analysis can be further extended to consider the effects of non climatic localized influences like land use and land cover changes, urbanization etc. (C) 2015 The Authors. Published by Elsevier B.V.