Abstract
Isotope-enabled General Circulation Models (GCMs) simulate isotope ratios 18O/16O and D/H (expressed as δ18O and δD) in precipitation. The present study evaluates the skills of seven such GCMs in simulating the monthly average precipitation δ18O and δD values over three Indian regions (west coast India WCI, east coast India ECI, and north India NI) during the summer monsoon season (June–September). Analyses show that models underestimate the mean isotope values over WCI and ECI but have variable responses over NI. The mean bias (Δ = model-observed) in the δ18O values varies from − 0.8‰ (GENESIS) to − 4.1‰ (HadAM3) over WCI and from − 0.2‰ (LMDZ4) to − 6.4‰ (GENESIS) over ECI. Overall, the IsoGSM model simulates isotopes and physical fields better. Observed isotope data show only minor dependence on the rainfall (amount effect) in WCI and ECI, but in contrast, most of the models show a substantial amount effect. The model δ18O value decreases with increasing rainfall at a rate of − 1‰/100 mm/month to − 6‰/100 mm/month. Apart from the local factors (temperature, humidity, and rainfall), isotope biases are also affected by large-scale atmospheric circulation in some models. A decomposition of the isotope biases over WCI resulting from five major processes (vapor formation, uplift, transport, condensation, and raindrop evaporation) is explored. This exercise suggests that the skill of the models depends on how proficiently the models simulate (1) mid-tropospheric vapour isotope values and (2) raindrop evaporation. A strong positive correlation between the biases in isotope values and raindrop evaporation across models shows that an increase of about 7‰ in δD bias of the models occurs for a 10% increase in the evaporation bias.
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source region and the precipitating region (for Kozhikode) for the calculation of isotopic ratios of the vapour and rain
taken from GPCP and ERA5, respectively, for computing these biases. The GNIP dataset at Kozhikode is considered as the observed isotope data. Only significant correlations (p < 0.01) are shown. The other nudged model GISS is not plotted as no significant correlations are found with isotope values
taken from ERA5 for computing these biases. The GNIP dataset at Kozhikode (for IsoGSM-Free) and New Delhi (for IsoGSM-Nudged) are considered as the observed isotope data. Only significant correlations (p < 0.01) are shown
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Data availability
The model outputs are available at the Stable Water Isotope Intercomparison Group, Phase 2 (SWING2; https://data.giss.nasa.gov/swing2/swing2_mirror/). Various gridded meteorological data (temperature, humidity, wind speed, etc.) are used from ERA-5 (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). Cloud microphysical data are obtained from the CLOUDSAT website https://cloudsat.atmos.colostate.edu/data. Vapour isotope data are available from NASA Tropospheric Emission Spectrometer Mission https://tes.jpl.nasa.gov/tes/.
Code availability
Free software Python (https://www.python.org/downloads/) and licensed versions of Microsoft Office and Coreldraw are used for data analyses.
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Acknowledgements
The Indian Institute of Tropical Meteorology, Pune (IITM), is fully supported by the Earth System Science Organization (ESSO) of the Ministry of Earth Sciences, India. This work forms part of the Ph.D. thesis of SSN, who thanks IITM for a fellowship. We thank the reviewers for various suggestions to improve the manuscript. Fruitful discussions with Dr. Kei Yoshimura, Dr. Camille Risi, and Dr. Subrata Kumar Das are acknowledged. We thank Director IITM for his constant encouragement. We also thank all SWING2 project contributors, the IAEA/WMO for the GNIP dataset, the NASA Langley Research Centre, and the Atmospheric Science Data Centre for the TES dataset.
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The Indian Institute of Tropical Meteorology, Pune (IITM) is fully supported by the Earth System Science Organization (ESSO) of the Ministry of Earth Sciences, India.
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Nimya, S.S., Sengupta, S., Parekh, A. et al. Region-specific performances of isotope enabled general circulation models for Indian summer monsoon and the factors controlling isotope biases. Clim Dyn 59, 3599–3619 (2022). https://doi.org/10.1007/s00382-022-06286-1
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DOI: https://doi.org/10.1007/s00382-022-06286-1