On the performance of satellite-based observations of <i>X</i>CO<sub>2</sub> in capturing the NOAA Carbon Tracker model and ground-based flask observations over Africa's land mass

Abstract

Abstract. Africa is one of the most data-scarce regions as satellite observation at the Equator is limited by cloud cover and there is a very limited number of ground-based measurements. As a result, the use of simulations from models is mandatory to fill this data gap. A comparison of satellite observation with model and available in situ observations will be useful to estimate the performance of satellites in the region. In this study, GOSAT column-averaged carbon dioxide dry-air mole fraction (XCO2) is compared with the NOAA CT2016 and six flask observations over Africa using 5 years of data covering the period from May 2009 to April 2014. Ditto for OCO-2 XCO2 against NOAA CT16NRT17 and eight flask observations over Africa using 2 years of data covering the period from January 2015 to December 2016. The analysis shows that the XCO2 from GOSAT is higher than XCO2 simulated by CT2016 by 0.28±1.05 ppm, whereas OCO-2 XCO2 is lower than CT16NRT17 by 0.34±0.9 ppm on the African land mass on average. The mean correlations of 0.83±1.12 and 0.60±1.41 and average root mean square deviation (RMSD) of 2.30±1.45 and 2.57±0.89 ppm are found between the model and the respective datasets from GOSAT and OCO-2, implying the existence of a reasonably good agreement between CT and the two satellites over Africa's land region. However, significant variations were observed in some regions. For example, OCO-2 XCO2 are lower than that of CT16NRT17 by up to 3 ppm over some regions in North Africa (e.g. Egypt, Libya, and Mali), whereas it exceeds CT16NRT17 XCO2 by 2 ppm over Equatorial Africa (10∘ S–10∘ N). This regional difference is also noted in the comparison of model simulations and satellite observations with flask observations over the continent. For example, CT shows a better sensitivity in capturing flask observations over sites located in North Africa. In contrast, satellite observations have better sensitivity in capturing flask observations in lower-altitude island sites. CT2016 shows a high spatial mean of seasonal mean RMSD of 1.91 ppm during DJF with respect to GOSAT, while CT16NRT17 shows 1.75 ppm during MAM with respect to OCO-2. On the other hand, low RMSDs of 1.00 and 1.07 ppm during SON in the model XCO2 with respect to GOSAT and OCO-2 are respectively determined, indicating better agreement during autumn. The model simulation and satellite observations exhibit similar seasonal cycles of XCO2 with a small discrepancy over Southern Africa (35–10∘ S) and during wet seasons over all regions.

Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023

“…Around Kuwait, the magnitude of the annual cycle is typically 3-4 ± 0.5 ppmv, Figure 3B, in line with that measured in-situ at Kuwait City from June 1996 to May 2001 (Nasrallah et al, 2003). The seasonal cycle amplitude increases to 8-10 ± 1 ppmv at higher latitudes (Keeling et al, 1996) and in southwestern and southeastern parts of the Arabian Peninsula and tropical Africa (Mengistu and Tsidu, 2020), owing to a more marked seasonality of plant activity on top of anthropogenic emissions in particular in the mid-latitudes (Figure 2A). A similar seasonal cycle amplitude is seen over India where the annual maximum occurs in the drier and hotter pre-monsoon months of April to June, and the minimum in the cooler and wetter summer monsoon months of June to September (Kunchala et al, 2022).…”

Sun-induced fluorescence and near-infrared reflectance of vegetation track the seasonal dynamics of gross primary production over Africa

“…Previous authors have suggested this may result from the inefficiency of satellite measurements in detecting the canopy activities of tropical forest (Tang and Dubayah, 2017), due to limitations in their retrieval due to atmospheric cloud contamination (Frankenberg et al, 2014;Doughty et al, 2019) or to limitations in the EC measurement technique itself (Hayek et al, 2018). Eddy correlation measurements over rainforests are more complicated than over flat vegetation due to the presence of tall uneven canopies (Mercado et al, 2006) as well as stable atmospheric conditions at night (Miller et al, 2004). This comes on top of the uncertainty incurred on the derived GPP, which requires a partitioning of the measured net ecosystem exchange during turbulent conditions (Reichstein et al, 2005).…”