Comparison of CO 2 from NOAA Carbon Tracker reanalysis model and satellites over Africa

Abstract

Abstract. The scarcity of ground-based observations, poor global coverage and resolution of satellite observations necessitate the use of data generated from models to assess spatio-temporal variations of atmospheric CO 2 concentrations in a near continuous manner in a global and regional scale. Africa is one of the most data scarce region as satellite observation at the equator is limited by cloud cover and there are very limited number of ground based measurements. As a result, use of simulations from models are mandatory to fill this data gap. However, the first step in the use of data from models requires assessment of model skill in capturing limited existing observations. Even though, the NOAA Carbon Tracker model is evaluated using TCCON and satellite observations at a global level, its performance should be assessed at a regional scale, specifically in a regions like Africa with a highly varying climatic responses and a growing local source. In this study, NOAA CT2016 CO 2 is compared with the ACOS GOSAT observation over Africa using five years datasets covering the period from April 2009 to June 2014. In addition, NOAA CT2016 CO 2 is compared with OCO-2 observation over Africa using two years data covering the period from January 2015 to December 2016. The results show that the XCO 2 retrieved from GOSAT and OCO-2 are lower than CT2016 model simulation by 0.42 and 0.93 ppm on average respectively, which lie within the range of the errors associated with the GOSAT and OCO-2 XCO 2 retrievals. The mean correlations of 0.73 and 0.6, a regional precisions of 3.49 and 3.77 ppm, and the relative accuracies of 1.22 and 1.95 ppm were found between the model and the two data sets implying the performance of the model in Africa's land regions is reasonably good despite shortage of in-situ observations over the region assimilated in the model. These differences, however, exhibit spatial and seasonal scale variations. Moreover, the model shows some weakness in capturing the whole distribution. For example, the probability of detection ranges from 0.6 to 1 and critical success index ranges from 0.4 to 1 over the continent when the analysis includes data above the 95 th percentile and the whole data respectively. This shows the model misses the higher extreme ends of the CO 2 distribution. Spatially, GOSAT and OCO-2 XCO 2 are lower than that of CT2016 by upto 4 ppm over North Africa (10°–35° N) whereas it exceeds CT2016 XCO 2 by 3 ppm over Equatorial Africa (10° S–10° N). Larger spatial mean biases of 2.11 and 1.8 ppm, 1.25 and 0.73 ppm in CT2016 XCO 2 with respect to that of GOSAT and OCO-2 are observed during winter (DJF) and spring (MAM) while small biases of −0.15 and 0.21 ppm, and 0.2 and −1.14 ppm are observed during summer (JJA) and autumn (SON) respectively. The model simulation has the ability to capture seasonal cycles with a small discrepancy over the North Africa and during winter seasons over all regions. In these cases, the model overestimates the local emissions and underestimate CO 2 loss.