An empirical model for estimating daily atmospheric column-averaged CO2 concentration above Sao Paulo state, Brazil

Background: The recent studies of the variations in the atmospheric column-averaged CO2 concentration (X-CO2) above croplands and forests show a negative correlation between X(CO2)and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO2. The remote sensing techniques in this context are very important to observe this relation, however, there is still a time gap in orbital data, since the observation is not daily. Here we analyzed the effects of several variables related to the photosynthetic capacity of vegetation on X-CO2 above Sao Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO2. Results: The data retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) show that Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH) are the most significant factors for predicting the annual X-CO2 cycle. The daily model of X-CO2 estimated from Qg and RH predicts daily X-CO2 with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p <0.01). Conclusion: The obtained results imply that a significant part of daily X-CO2 variations could be explained by meteorological factors and that further research should be done to quantify the effects of the atmospheric transport and anthropogenic emissions.

Automated summary

This study reveals a negative correlation between atmospheric CO2 concentration and photosynthesis and highlights the importance of remote sensing techniques in observing this relationship. By analyzing several variables related to photosynthetic capacity, a daily model is proposed to estimate the natural changes in atmospheric CO2. The results show that meteorological factors significantly impact the daily variations in X-CO2.