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Large Sensitivity of Simulated Indian Summer Monsoon Rainfall (ISMR) to Global Warming: Implications of ISMR Projections
Author(s) -
Rajesh P. V.,
Goswami B. N.,
Choudhury B. A.,
Zahan Yasmin
Publication year - 2021
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2020jd033511
Subject(s) - environmental science , climatology , scaling , forcing (mathematics) , sensitivity (control systems) , atmospheric sciences , climate sensitivity , monsoon , climate model , climate change , mathematics , physics , geology , geometry , oceanography , electronic engineering , engineering
Reliable projections of Indian Summer Monsoon Rainfall (ISMR) to the next century by climate models is critical for policy toward sustainable development goals but depends on sensitivity of the models simulating ISMR as global mean temperature changes. Here, using observed ISMR and historical global temperature (1850–2005), we find that the mean ISMR scales as (3.95 ± 1.13%)/K, lower than the Clausius‐Clapeyron (CC) scaling (∼7%/K). However, the sensitivity of the intensity of daily rainfall extremes is (29.5 ± 3.4)%/K, four times as large as the CC scaling. We also find that the sensitivities of simulated mean ISMR by 36 CMIP5 and CMIP6 models each at 13.36%/K and 11.82%/K, respectively, are 2–3 times larger than the observed sensitivity and independent of the chosen period. Larger than simulated CC scaling of ISMR is consistent with simulated strengthening of Indian monsoon circulation in contrast to observations. Models' sensitivity appears to be due to their stronger than observed response to forcing over land resulting in stronger north‐south heating gradient and acceleration of monsoon circulation. Projected sensitivity of ISMR in models with increased greenhouse gas forcing weakens compared to the historical period approaching the CC scaling. Required to delineate the contributions of larger than observed intrinsic sensitivity of the models from projected sensitivity, our findings provide a way to correct the bias potentially improving the reliability of ensemble mean projections of ISMR.