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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 16 May 2018

Research article | 16 May 2018

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This discussion paper is a preprint. It has been under review for the journal Atmospheric Measurement Techniques (AMT). The revised manuscript was not accepted.

Comparison of CO2 from NOAA Carbon Tracker reanalysis model and satellites over Africa

Anteneh Getachew Mengistu1 and Gizaw Mengistu Tsidu1,2 Anteneh Getachew Mengistu and Gizaw Mengistu Tsidu
  • 1Addis Ababa University, Addis Ababa, Ethiopia
  • 2Botswana International University of Science and Technology, Palapye, Botswana

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 CO2 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 CO2 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 CO2 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 XCO2 retrieved from GOSAT and OCO-2 are lower than CT2016 model simulation by 0.42 and 0.93ppm on average respectively, which lie within the range of the errors associated with the GOSAT and OCO-2 XCO2 retrievals. The mean correlations of 0.73 and 0.6, a regional precisions of 3.49 and 3.77ppm, and the relative accuracies of 1.22 and 1.95ppm 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 95th percentile and the whole data respectively. This shows the model misses the higher extreme ends of the CO2 distribution. Spatially, GOSAT and OCO-2 XCO2 are lower than that of CT2016 by upto 4 ppm over North Africa (10°–35°N) whereas it exceeds CT2016 XCO2 by 3ppm over Equatorial Africa (10°S–10°N). Larger spatial mean biases of 2.11 and 1.8ppm, 1.25 and 0.73ppm in CT2016 XCO2 with respect to that of GOSAT and OCO-2 are observed during winter (DJF) and spring (MAM) while small biases of −0.15 and 0.21ppm, and 0.2 and −1.14ppm 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 CO2 loss.

Anteneh Getachew Mengistu and Gizaw Mengistu Tsidu
Anteneh Getachew Mengistu and Gizaw Mengistu Tsidu
Anteneh Getachew Mengistu and Gizaw Mengistu Tsidu
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