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

Luis Miguel da Costa; Gustavo André de Araújo Santos; Alan Rodrigo Panosso; Glauco de Souza Rolim; Newton La Scala

doi:10.1186/s13021-022-00209-7

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

Carbon Balance Manag. 2022 Jun 11;17(1):9. doi: 10.1186/s13021-022-00209-7.

Authors

Luis Miguel da Costa¹, Gustavo André de Araújo Santos^{2

3

4}, Alan Rodrigo Panosso², Glauco de Souza Rolim², Newton La Scala²

Affiliations

¹ Departament of Engineering and Exact Sciences, São Paulo State University, Via de Acesso Prof. Paulo Donato Castellane s/n, Jaboticabal, São Paulo, 14884-900, Brazil. luism.costa00@gmail.com.
² Departament of Engineering and Exact Sciences, São Paulo State University, Via de Acesso Prof. Paulo Donato Castellane s/n, Jaboticabal, São Paulo, 14884-900, Brazil.
³ Campus Avançado Porto Franco, Instituto Federal de Educação, Ciência e Tecnologia do Maranhão - IFMA, Rua Custódio Barbosa, no 09, Centro, Porto Franco, Maranhão, 65970-000, Brazil.
⁴ Center of Agricultural, Natural and Literary Sciences, State University of the Tocantina Region of Maranhão (UEMASUL), Av. Brejo do Pinto, S/N - Brejo do Pinto, Estreito, Maranhão, 65975-000, Brazil.

Abstract

Background: The recent studies of the variations in the atmospheric column-averaged CO₂ concentration ([Formula: see text]) above croplands and forests show a negative correlation between [Formula: see text]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 [Formula: see text] above São 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 [Formula: see text] cycle. The daily model of [Formula: see text] estimated from Qg and RH predicts daily [Formula: see text] 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 [Formula: see text] 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.

Keywords: Carbon cycle; Climate change; Meteorology; OCO-2; Remote sensing; Stepwise regression analysis.