CO2 variability over a tropical coastal station in India: Synergy of observation and model

Abstract

Carbon dioxide (CO2), being the prime greenhouse gas, has largest contribution in radiative forcing and global warming due to increased anthropogenic emissions leading to regional and global climate change. We performed CO2 measurements using a continuous monitoring instrument at a tropical coastal station at the southern tip of India (Thumba; 8.54° N, 76.86° E) during 2017–2021. Combining measurements with model simulations and satellite observations, present study aims to understand CO2 variabilities, contribution of various sources and role of dynamics. The observed trend of 2.19 ppm y−1 is well simulated (2.47 ppm y−1) by the chemistry-transport model (MIROC4-ACTM), which is contributed by trends in fossil fuel (4.59 ppm y−1), biosphere (−1.46 ppm y−1) and ocean (−0.66 ppm y−1) tracers of CO2 flux. Mean seasonal cycle with amplitude of about 8 ppm is observed, with a maximum during premonsoon (April) and minimum at the end of the monsoon season (September). The model reproduced the observed seasonal cycle well (r2 = 0.89) and showed that the seasonal peak is predominantly contributed by the land biospheric flux seasonality. Biospheric sink during postmonsoon is counterbalanced by emissions from fossil fuel burning. Biosphere and fossil fuel are major contributors in shaping the observed seasonal pattern with negligible seasonality in oceanic tracer due to its uniform flux patterns in the Indian Ocean. Mean diurnal pattern of CO2 exhibits lower values during daytime (cleaner marine airmass) and higher values during nighttime (continental airmass) with deeper diurnal amplitude in the winter (~50 ppm) than in the summer (~31 ppm). Seasonal pattern in total column CO2 from OCO-2 (Orbiting Carbon Observatory-2) is similar to the surface observations but shows nearly half of the observed seasonal amplitude. Long-term continuous measurements over distinct environments are desirable for further deepening the understanding of CO2 variability, estimation of regional carbon budget and for climate mitigation policies.