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Can statistical downscaling improve consensus among CMIP5 models for Indian summer monsoon rainfall projections?
Author(s) -
Madhusoodhanan C. G.,
Shashikanth K.,
Eldho T. I.,
Ghosh Subimal
Publication year - 2018
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.5352
Subject(s) - downscaling , climatology , precipitation , environmental science , general circulation model , coupled model intercomparison project , representative concentration pathways , monsoon , forcing (mathematics) , climate model , climate change , radiative forcing , meteorology , geography , geology , oceanography
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.