To understand the improvements in the simulations of Indian summer monsoon rainfall (ISMR) by Coupled Model Intercomparison Project 5 (CMIP5) over CMIP3, a comparative study is performed with the original and statistically downscaled outputs of five General Circulation Models (GCMs). We observe that multi-model average of original CMIP5 simulations do not show visible improvements in bias, over CMIP3. We also observe that CMIP5 original simulations have more multi-model uncertainty than those of CMIP3. The statistically downscaled simulations show similar results in terms of bias; however, the uncertainty in CMIP5 downscaled rainfall projections is lower than that of CMIP3.
Keywords:Statistical downscaling Bayesian multimodel average Uncertainty Signal to Noise Ratio (SNR) s u m m a r y Impacts of climate change are typically assessed with fairly coarse resolution General Circulation Models (GCMs), which are unable to resolve local scale features that are critical to precipitation variability. GCM simulations must be downscaled to finer resolutions, through statistical or dynamic modelling for further use in hydrologic analysis. In this study, we use a linear regression based statistical downscaling method for obtaining monthly Indian Summer Monsoon Rainfall (ISMR) projections at multiple spatial resolutions, viz., 0.05°, 0.25°and 0.50°, and compare them. We use 19 GCMs of Coupled Model Intercomparison Project Phase 5 (CMIP5) suite and combine them with multi model averaging and Bayesian model averaging. We find spatially non-uniform changes in projections at all resolutions for both combinations of projections. Our results show that the changes in the mean for future time periods (2020s, 2050s, and 2080s) at different resolutions, viz., 0.05°, 0.25°and 0.5°, obtained with both Multi-Model Average (MMA) and Bayesian Multi-Model Average (BMA) are comparable. We also find that the model uncertainty decreases with projection times into the future for all resolutions. We compute Signal to Noise Ratio (SNR), which represents the climate change signal in simulations with respect to the noise arising from multi-model uncertainty. This appears to be almost similar at different resolutions. The present study highlight that, a mere increase in resolution by a way of computationally more expensive statistical downscaling does not necessarily contribute towards improving the signal strength. Denser data networks and finer resolution GCMs may be essential for producing usable rainfall and hydrologic information at finer resolutions in the context of statistical downscaling.
The projections of plausible changes in future climate using coarse scale General Circulation Models (GCMs) for non‐smooth climate variables, such as precipitation, possess limited skill and are often conflicting. The confidence in precipitation projections is further obscured due to the exclusion of inter‐model and natural internal variability. The present study investigates how far statistical downscaling can provide confidence in future projections while incorporating these major uncertainties under a moderate radiative forcing scenario. Here, we assess the consensus for future changes in Indian Summer Monsoon Rainfall (ISMR) from 20 CMIP5 GCMs and their statistically downscaled counterparts at 0.05° resolution for the 21st century. The Statistical Downscaling (SD) model is skillful in capturing the spatial variability of observed ISMR and shows significant improvement over host GCMs. GCMs show consistent but spatially inhomogeneous increase in future ISMR which intensify towards the end of the century. On the other hand, the downscaled outputs show spatially non‐uniform trends which intensify in their respective directions from near to long term. Both projections show high inter‐model inconsistencies. The multi‐model consensus among GCMs and SD across these in‐congruent models depicts inconclusive and highly uncertain changes in future. The GCMs show significant change with high model inconsistency uniformly across India across time scales. Even though the downscaled outputs show similar results for majority of the Indian landmass, it is highly heterogeneous across time horizons. There is also an emergence of medium evidence for future changes in ISMR for few regions of the country such as the southern Western Ghats, foothills of the Himalaya and central India. The study brings out that even‐improved simulations from downscaling fail to reliably project ISMR due to high inter‐model uncertainty and internal variability. The significant but inconsistent future changes in ISMR projected for a major portion of the Indian subcontinent, in contrast to earlier studies, pose extreme challenges to climate change impact assessments and adaptation/mitigation planning.
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