Dynamics of Carbon Dioxide Flux Over Africa: Insight from Observations and Model Simulations

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Addis Ababa University


The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric carbon dioxide (CO2). However, large uncertainties surround the impacts of land-use change, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems in Africa’s carbon cycle (savannas and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa’s vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. Satellite retrievals are strongly influenced by land-use changes, cloud cover, and aerosol loading. Moreover, Africa is a continent with wide extremes in surface type (which ranges from desert, rainforest, and Savannah) and aerosol loading. Therefore, the comparison of satellite observations with model and available in-situ observations will be useful to prove the performance of satellites and show how these systematic errors vary geographically over the continent. In this thesis, GOSAT column-averaged dry-air mole fraction of carbon dioxide (XCO2) is compared with the NOAA CT2016 and six flask observations across Africa using five years of data covering the period from May 2009 to April 2014. Besides, XCO2 from OCO-2 is compared with NOAA CT16NRT17 and eight flask observations across Africa using two 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 African landmass on average. The mean correlations of 0.83 _ 0.12 and 0.60 _ 0.41 and an average 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 is 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 Northern 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 from GOSAT, while CT16NRT17 shows RMSD of 1.75 ppm during MAM from OCO-2. On the other hand, the low RMSD of 1.00 and 1.07 ppm during SON in model XCO2 from GOSAT and OCO-2, respectively, indicate 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. Two remotely sensed vegetation products that have been shown to correlate highly with Gross Primary Productivity (GPP): Sun-Induced Fluorescence (SIF) and Near-Infrared Reflectance of vegetation (NIRv) are also analyzed to further understand the dynamics of carbon dioxide flux. A comparison against flux tower observations of daytimepartitioned Net Ecosystem Exchange (NEE) from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of >0.9 (N=12 months, 4 sites) over savannas in the northern and southern hemisphere. These coefficients are slightly higher than for the widely used MPI-BGC GPP products and Enhanced Vegetation Index (EVI). Similar to SIF signals in the neighbouring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture, and is followed by an initial decline during the early dry season that reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R >0.9, N=250+ pixels) with multiyear MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and EVI show saturation relative to peak NIRv and SIF signals during high productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested. Africa’s biome integrated productivity is strongly controlled by the seasonality of soil moisture, with a weak influence of light availability superimposed, indicating that the biome productivity of Africa strongly depends on spatiotemporal drivers. Therefore, an understanding of the spatiotemporal ecosystem dynamics together with its relation to meteorological variables is paramount to quantify the responsiveness of the carbon cycle to climate variability. For that reason, an Empirical Ensemble Mode Decomposition (EEMD) was applied on 17 years monthly time series of natural CO2 flux covering the period from January 2000 to December 2016. The EEMD depicts natural CO2 flux has six periodicities over tropical Africa corresponding to seasonal, interannual, and decadal-scale variabilities which are likely driven by atmospheric and oceanic processes. Seasonal variabilities at quasi-3 months, quasi-6 months, and quasi-12 months contribute about 91.41% of the variability of natural CO2 flux, suggesting that CO2 flux has a strong variability at the seasonal scale. Moreover, high atmospheric CO2 flux was observed during warm and dry conditions. Precipitation is found to be a dominating driver of CO2 flux at the seasonal scale over the west coast of tropical Africa and East Africa. In addition to the six periodicities, the application of EEMD to a monthly time series of CO2 flux indicates the existence of either a nonlinear downward trend or a possible multidecadal periodicity that cannot be captured by the limited length of the current data set. The later is more likely as revealed by a slight reversal at the beginning of 2013. Moreover, analysis of different regions of tropical Africa shows reduced CO2 uptake over most regions since 2000, with exception for tropical North Africa which is found to have increased CO2 uptake most likely due to enhanced vegetation which exceeds deforestation. At the interannual scale, a quasi-2 year and quasi-5 year fluctuations were obtained from the EEMD with a contribution of 6.93% to the total CO2 flux variability. This interannual fluctuation has a significant correlation with Niño 3.4 index, El Niño induced temperature, precipitation, soil moisture, and enhanced vegetation index. A significant positive correlation between a warming temperature and interannual CO2 flux over tropical North Africa and rainforest regions suggests that temperature is the major driver of CO2 fluctuation at the interannual scale over these regions. Conversely, over Western and Tropical East Africa, precipitation was found as the most dominant driver. The anomalously high interannual CO2 flux was found in response to strong El Niño (Niño 3.4 index greater than 1.0) in the years 2009 and 2015/16 over most of Equatorial Africa. During the peak of 2015/16 El Niño, tropical Africa releases 0.2 mol/m2/month CO2 into the atmosphere due to interannual variability. The strongest (0.5 mol/m2/month) contribution was from the tropical rainforest, most likely driven by the rising temperature. Besides, Ethiopian highlands also release 0.4 mol/m2/month CO2 flux due to dry and warm conditions during this strong El Niño event. The CO2 flux mean over 17 years (2000-2016) shows that tropical Africa is a net CO2 sink (-7.02 gC/m2/year). However, during the 2015/16 El Niño years, tropical Africa releases 29.12 gC/m2/year leading to 487.49 TgC/year which is twice the estimated carbon flux of Africa (240 Tg C y􀀀1 ) for the period covering from 2000 to 2005.



Dynamics, Carbon Dioxide, Over Africa, Insight, Observations, Model, Simulations