Articles written in Journal of Earth System Science
Volume 119 Issue 4 August 2010 pp 447-460
Changes in seasons and season length are an indicator, as well as an effect, of climate change. Seasonal change profoundly affects the balance of life in ecosystems and impacts essential human activities such as agriculture and irrigation. This study investigates the uncertainty of season length in Karnataka state, India, due to the choice of scenarios, season type and number of seasons. Based on the type of season, the monthly sequences of variables (predictors) were selected from datasets of NCEP and Canadian General Circulation Model (CGCM3). Seasonal stratifications were carried out on the selected predictors using K-means clustering technique. The results of cluster analysis revealed increase in average, wet season length in A2, A1B and B1 scenarios towards the end of 21st century. The increase in season length was higher for A2 scenario whereas it was the least for B1 scenario. COMMIT scenario did not show any change in season length. However, no change in average warm and cold season length was observed across the four scenarios considered. The number of seasons was increased from 2 to 5. The results of the analysis revealed that no distinct cluster could be obtained when the number of seasons was increased beyond three.
Volume 120 Issue 3 June 2011 pp 375-386
Monthly scenarios of relative humidity (𝑅_H) were obtained for the Malaprabha river basin in India using a statistical downscaling technique. Large-scale atmospheric variables (air temperature and specific humidity at 925 mb, surface air temperature and latent heat flux) were chosen as predictors. The predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis dataset for the period 1978–2000, and (2) simulations of the third generation Canadian Coupled Global Climate Model for the period 1978–2100. The objective of this study was to investigate the uncertainties in regional scenarios developed for RH due to the choice of emission scenarios (A1B, A2, B1 and COMMIT) and the predictors selected. Multi-linear regression with stepwise screening is the downscaling technique used in this study. To study the uncertainty in the regional scenarios of 𝑅_H, due to the selected predictors, eight sets of predictors were chosen and a downscaling model was developed for each set. Performance of the downscaling models in the baseline period (1978–2000) was studied using three measures (1) Nash–Sutcliffe error estimate (𝐸_f ), (2) mean absolute error (MAE), and (3) product moment correlation (𝑃). Results show that the performances vary between 0.59 and 0.68, 0.42 and 0.50 and 0.77 and 0.82 for 𝐸_f , MAE and P. Cumulative distribution functions were prepared from the regional scenarios of 𝑅_H developed for combinations of predictors and emission scenarios. Results show a variation of 1 to 6% 𝑅_H in the scenarios developed for combination of predictor sets for baseline period. For a future period (2001–2100), a variation of 6 to 15% 𝑅_H was observed for the combination of emission scenarios and predictors. The variation was highest for A2 scenario and least for COMMIT and B1 scenario.