Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023 - Fonseca, Ricardo; Francis, Diana (Frontiers in Environmental Science, 2024)
The Middle East has major sources of anthropogenic carbon dioxide (CO 2 ) emissions, but a dearth of ground-based measurements precludes an investigation of its regional and temporal variability. This is achieved in this work with satellite-derived estimates from the Orbiting Carbon Observatory-2 (OCO-2) and OCO-3 missions from September 2014 to February 2023. The annual maximum and minimum column (XCO 2 ) concentrations are generally reached in spring and autumn, respectively, with a typical seasonal cycle amplitude of 3-8 ± 0.5 ppmv in the Arabian Peninsula rising to 8-10 ± 1 ppmv in the mid-latitudes. A comparison of the seasonal-mean XCO 2 values with the CO 2 emissions estimated using the divergence method stresses the role played by the sources and transport of CO 2 in the spatial distribution of XCO 2 , with anthropogenic emissions prevailing in arid and semi-arid regions that lack persistent vegetation. In the eight-year period 2015-2022, the XCO 2 concentration in the United Arab Emirates (UAE) increased at a rate of about 2.50 ± 0.04 ppmv/year, with the trend empirical orthogonal function technique revealing a hotspot over northeastern UAE and southern Iran in the summer where anthropogenic emissions peak and accumulate aided by low-level wind convergence. A comparison of the satellite-derived XCO 2 with that used to drive climate change models for different emission scenarios in the eight-year period revealed that the XCO 2 used in the latter is overestimated, with maximum differences exceeding 10 ppmv by 2022. This excess in the amount of CO 2 can lead to an over-prediction of the projected increase in temperature in the region, an aspect that needs to be investigated further. This work stresses the need for a ground-based observational network of greenhouse gas concentrations in the Middle East to better understand its spatial and temporal variability and for the evaluation of remote sensing observations as well as climate models.