01-05 December 2025
INCOIS, Hyderabad, India.
| Abstract Submission No. | ABS-04-0180 |
| Title of Abstract | Quantifying Future Air Temperature and Precipitation Changes Over the Indian Ocean With Bias-Corrected CMIP6 Models |
| Authors | Prasanna Kanti Ghoshal*, Apurva P. Joshi, Kunal Chakraborty |
| Organisation | Indian National Centre for Ocean Information Services (INCOIS) |
| Address | Plot No. 81, Ocean Valley, Pragathi Nagar Hyderabad, Telangana, India Pincode: 500090 E-mail: pk.ghoshal-rf@incois.gov.in |
| Country | India |
| Presentation | Oral |
| Abstract | Systematic biases inherent in global Earth System Models (ESMs) significantly constrain their ability to accurately simulate regional-scale ocean-atmosphere processes at regional scale. Consequently, projections of key climate variables such as near-surface air temperature (T2M) and precipitation (PR) from Coupled Model Intercomparison Project Phase 6 (CMIP6) models are associated with considerable uncertainties. This study rigorously evaluates the performance of five widely employed bias-correction techniques against two reanalysis datasets. Among these methods, Quantile Mapping (QM) and Time-varying Delta (TVD) exhibit superior skill, with TVD demonstrating marginally enhanced performance. To further optimize correction, an ensemble technique (ETQM), integrating QM and TVD, is conceptualized and applied. ETQM is utilized to correct biases in T2M and PR from three CMIP6 models over the Indian Ocean domain. During the historical period (1980-2014), ETQM effectively reduces upper T2M extremes by approximately 1.5-4.5% and substantially attenuates the persistent positive bias in PR extremes over the west-central Indian Ocean by 30-40%. In future climate scenarios, the corrected upper T2M extremes decline by 4.0-5.0%, while the variance in T2M anomalies decreases by 4.0-7.0% in the near-term (2015-2040), escalating to 16.0-22.0% by the end of the century (2071-2100). Although the variance in PR anomalies exhibits notable changes following bias-correction, its projected temporal evolution remains largely unaltered. Overall, the bias correction framework suggests a relatively cooler future compared to CMIP6 outputs. These bias-adjusted datasets offer enhanced reliability for climate change studies in the Indian Ocean region. |
| Are you part of IIOE-2 endorsed project | no |
| Keywords | Bias-Correction, CMIP6, Quantile Mapping, Indan Ocean Region |
| For Awards | yes |
| Date Of Birth | 07-04-1995 |
| ECSN Registration Number | IIOE2-ECSN-0135 |