Abstract

Raindrop evaporation is an important sub-cloud process that modifies rainfall amount and rainwater isotope values. Earlier studies have shown that various general circulation models (GCMs) do not incorporate this process properly during the simulation of water isotope ratios (oxygen and hydrogen). Our recent study has demonstrated that an inadequate estimation of this process for the Indian Summer Monsoon (ISM) results in significant biases (model-observed values) in the simulation of various GCMs on a monthly scale. However, a quantitative estimation was lacking. The magnitude of raindrop evaporation depends upon ambient humidity and temperature, which vary considerably during the ISM. Consequently, the isotope biases would also vary over various time scales. The present study aims to investigate the magnitude of the monthly scale variation in raindrop evaporation in the simulations and its causal connection with the corresponding variation in isotope biases. Towards this, we compare an 11-year-long (1997–2007) dataset of rain isotope ratios (both oxygen and hydrogen) from an Indian station, Kozhikode (Kerala), obtained under the Global Network of Isotopes in Precipitation (GNIP) programme of the International Atomic Energy Agency (IAEA) with the corresponding outputs of two isotope-enabled nudged GCMs—ISOGSM and LMDZ4. The raindrop evaporation fractions are estimated for 44 ISM months (June–September) of the study period using the Stewart (1975) formalism. Using a simple condensation–accretion model based on equilibrium fractionation from vapour, obtained from two adopted vapour isotope profiles, we estimate the liquid water isotope ratios at the cloud base. Considering this water as the initial rain, the raindrop evaporation fractions are estimated using the observed oxygen and hydrogen isotope ratios of Kozhikode surface rain samples. The estimated fractions show strong positive correlations with the isotope biases (R2 = 0.60 and 0.66). This suggests that lower estimates of raindrop evaporation could be responsible for the rain isotope biases in these two GCMs.

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