Spatial and temporal distribution and seasonal prediction of satellite measurement of CO2 concentration over Iran

Abstract

ABSTRACT Greenhouse gases play a vital role in the climate system by absorbing the longwave infrared radiation and cause warming of the earth’s atmosphere. Therefore, it is important to be aware of the spatial and temporal distribution of carbon dioxide (CO2) both regionally and globally. In this study, the column-averaged CO2 dry air mole fraction (XCO2) data from Greenhouse Gases Observing Satellite (GOSAT) is used to investigate the spatial and temporal distribution of CO2 over Iran for the period May 2009 to June 2016. Based on the De Martonne climate classification and topography features, six regions were identified for spatial analysis of CO2 concentration. The amount of CO2 concentration over northwest to southwest is relatively higher than other parts of the study area because of natural conditions such as the interaction of topography and prevailing wind direction, and human activities. The CO2 concentration for all regions shows a seasonal cycle such that generally the lowest (highest) level of CO2 occurs in summer (winter) season. The results indicate an upward trend in CO2 concentration over different regions such that the increasing rate of monthly spatial average was 0.18 ppm per month. Moreover, seasonal autoregressive integrated moving average (SARIMA) models were developed to predict the monthly spatial average of CO2 concentration over Iran. The SARIMA models were also constructed for predicting CO2 concentration for the identified six regions. For all fitted SARIMA models, the correlation coefficients between the satellite observations and the predicted CO2 concentration were statistically significant at the 5% significance level such that the Pearson’s correlation coefficient (r) for the spatial average of CO2 concentration was equal to 0.9, 0.88, and 0.65 for the lead times of 1, 2, and 3 months, respectively.