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Understanding the cascade of GCM and downscaling uncertainties in hydro‐climatic projections over India
Author(s) -
Sharma Tarul,
Vittal H.,
Chhabra Surbhi,
Salvi Kaustubh,
Ghosh Subimal,
Karmakar Subhankar
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.5361
Subject(s) - downscaling , climatology , gcm transcription factors , environmental science , general circulation model , climate change , climate model , monsoon , uncertainty analysis , meteorology , precipitation , statistics , mathematics , geography , geology , oceanography
India is a major agrarian country strongly impacted by spatio‐temporal variations in the Indian monsoon. The impact assessment is usually accomplished by implementing projections from general circulation models (GCMs). Unfortunately, these projections cannot capture the dynamicity of the monsoon and require either statistical (SD) or dynamical (DD) downscaling of the GCM projections to a finer resolution. Both downscaling techniques can capture the spatio‐temporal variation in climatic variables but are marred by uncertainty in the projections resulting from the choice of the GCM and downscaling method, which affects climate change adaptations. Here, we assessed uncertainties in the projections of hydro‐climatic variables over India by considering multiple downscaling techniques, multiple GCMs, and their combined effects (referred as the total uncertainty). Multiple hydrological variables were simulated by implementing the variable infiltration capacity model that considered outputs from DD (derived by the coordinated regional climate downscaling experiment, CORDEX) and SD forced with multiple GCM simulations. Our results showed that the SD projections captured the observed spatio‐temporal variability of hydro‐climatic variables more efficiently than the DD projections. Importantly, contribution from the downscaled projections to the total uncertainty was significantly smaller compared to the inter‐GCM uncertainty. We believe uncertainty analysis is an important component of good scientific practice; however, several researchers appear to be rather reluctant to embrace the concept of uncertainty in making projections, predictions, and forecasting. It remains a common practice to show climate change exercises to decision‐makers/stakeholders, without uncertainty bounds. Here, a successful attempt was made to identify the key sources of uncertainty and adequately bracket the uncertainty, indicating a requirement of the code of practice to provide formal guidance, particularly for climate‐change impact assessments. This consequently emphasized the importance of follow‐up research to understand the inter‐GCM uncertainty, which has a significant impact on sustainable agriculture and water resources management in India.