DOI: 10.1002/joc.5352
论文题名: Can statistical downscaling improve consensus among CMIP5 models for Indian summer monsoon rainfall projections?
作者: Madhusoodhanan C.G. ; Shashikanth K. ; Eldho T.I. ; Ghosh S.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38, 期: 5 起始页码: 2449
结束页码: 2461
语种: 英语
英文关键词: CMIP5
; consensus
; future projections
; Indian summer monsoon rainfall
; internal variability
; robustness
; statistical downscaling
Scopus关键词: Atmospheric radiation
; Atmospheric thermodynamics
; Climate change
; Rain
; Robustness (control systems)
; Uncertainty analysis
; CMIP5
; Consensus
; Future projections
; Indian summer monsoon rainfall
; Internal variability
; Statistical downscaling
; Climate models
英文摘要: 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. © 2017 Royal Meteorological Society Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/117022
Appears in Collections: 气候减缓与适应
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作者单位: Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India; Department of Civil Engineering, University College of Engineering, Osmania University, Hyderabad, India
Recommended Citation:
Madhusoodhanan C.G.,Shashikanth K.,Eldho T.I.,et al. Can statistical downscaling improve consensus among CMIP5 models for Indian summer monsoon rainfall projections?[J]. International Journal of Climatology,2018-01-01,38(5)