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In this paper, we quantify the CO2 and N2O emissions from denitrification over the Amazonian wetlands. The study concerns the entire Amazonian wetland ecosystem with a specific focus on three floodplain (FP) locations: the Branco FP, the Madeira FP and the FP alongside the Amazon River. We adapted a simple denitrification model to the case of tropical wetlands and forced it by open water surface extent products from the Soil Moisture and Ocean Salinity (SMOS) satellite. A priori model parameters were provided by in situ observations and gauging stations from the HYBAM Observatory. Our results show that the denitrification and the trace gas emissions present a strong cyclic pattern linked to the inundation processes that can be divided into three distinct phases: activation, stabilization and deactivation. We quantify the average yearly denitrification and associated emissions of CO2 and N2O over the entire watershed at 17.8 kgN ha -1 yr -1 , 0.37 gC- CO2 m -2 yr -1 and 0.18 gN- N2O m -2 yr -1 respectively for the period 2011–2015. When compared to local observations, it was found that the CO2 emissions accounted for 0.01 % of the integrated ecosystem, which emphasizes the fact that minor changes to the land cover may induce strong impacts on the Amazonian carbon budget. Our results are consistent with the state of the art of global nitrogen models with a positive bias of 28 %. When compared to other wetlands in different pedoclimatic environments we found that the Amazonian wetlands have similar emissions of N2O with the Congo tropical wetlands and lower emissions than the temperate and tropical anthropogenic wetlands of the Garonne (France), the Rhine (Europe) and south-eastern Asia rice paddies. In summary our paper shows that a data-model-based approach can be successfully applied to quantify N2O and CO2 fluxes associated with denitrification over the Amazon basin. In the future, the use of higher-resolution remote sensing products from sensor fusion or new sensors like the Surface Water and Ocean Topography (SWOT) mission will permit the transposition of the approach to other large-scale watersheds in tropical environments. [ABSTRACT FROM AUTHOR]

Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2 , i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2 , MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets. [ABSTRACT FROM AUTHOR]

An experiment was conducted to investigate the effect of seasonally asymmetric warming on ecosystem respiration (Re), CH 4 uptake, and N 2 O emissions in alpine grassland of the Tianshan of central Asia, from October 2016 to September 2019. The annual means of Re , CH 4 , and N 2 O fluxes in growing season were 42.83 mg C m -2 h -1 , - 41.57 µ g C m -2 h -1 , and 4.98 µ g N m -2 h -1 , respectively. Furthermore, warming during the non-growing season increased Re and CH 4 uptake by 7.9 % and 10.6 % in the growing season and 10.5 % and 9.2 % in the non-growing season, respectively. However, the increase in N 2 O emission in the growing season was mainly caused by the warming during the growing season (by 29.7 %). The warming throughout the year and warming during the non-growing season increased N 2 O emissions by 101.9 % and 192.3 % in the non-growing season, respectively. The Re , CH 4 uptake, and N 2 O emissions were positively correlated with soil temperature. Our results suggested that Re , CH 4 uptake, and N 2 O emissions were regulated by soil temperature, rather than soil moisture, in the case of seasonally asymmetric warming. In addition, the response rate was defined by the changes in greenhouse gas fluxes driven by warming. In our field experiment, we observed the stimulatory effect of warming during the non-growing season on Re and CH 4 uptake. In contrast, the response rates of Re and N 2 O emissions were gradually attenuated by long-term annual warming, and the response rate of Re was also weakened by warming over the growing season. These findings highlight the importance of warming in the non-growing season in regulating greenhouse gas fluxes, a finding which is crucial for improving our understanding of C and N cycles under the scenarios of global warming. [ABSTRACT FROM AUTHOR]

Satellite retrievals of the column-averaged dry air mole fractions of CO 2 (XCO 2) could help to improve carbon flux estimation due to their good spatial coverage. In this study, in order to assimilate the GOSAT (Greenhouse Gases Observing Satellite) XCO 2 retrievals, the Global Carbon Assimilation System (GCAS) is upgraded with new assimilation algorithms, procedures, a localization scheme, and a higher assimilation parameter resolution. This upgraded system is referred to as GCASv2. Based on this new system, the global terrestrial ecosystem (BIO) and ocean (OCN) carbon fluxes from 1 May 2009 to 31 December 2015 are constrained using the GOSAT ACOS (Atmospheric CO 2 Observations from Space) XCO 2 retrievals (Version 7.3). The posterior carbon fluxes from 2010 to 2015 are independently evaluated using CO 2 observations from 52 surface flask sites. The results show that the posterior carbon fluxes could significantly improve the modeling of atmospheric CO 2 concentrations, with global mean bias decreases from a prior value of 1.6 ± 1.8 ppm to - 0.5 ± 1.8 ppm. The uncertainty reduction (UR) of the global BIO flux is 17 %, and the highest monthly regional UR could reach 51 %. Globally, the mean annual BIO and OCN carbon sinks and their interannual variations inferred in this study are very close to the estimates of CarbonTracker 2017 (CT2017) during the study period, and the inferred mean atmospheric CO 2 growth rate and its interannual changes are also very close to the observations. Regionally, over the northern lands, the strongest carbon sinks are seen in temperate North America, followed by Europe, boreal Asia, and temperate Asia; in the tropics, there are strong sinks in tropical South America and tropical Asia, but a very weak sink in Africa. This pattern is significantly different from the estimates of CT2017, but the estimated carbon sinks for each continent and some key regions like boreal Asia and the Amazon are comparable or within the range of previous bottom-up estimates. The inversion also changes the interannual variations in carbon fluxes in most TransCom land regions, which have a better relationship with the changes in severe drought area (SDA) or leaf area index (LAI), or are more consistent with previous estimates for the impact of drought. These results suggest that the GCASv2 system works well with the GOSAT XCO 2 retrievals and shows good performance with respect to estimating the surface carbon fluxes; meanwhile, our results also indicate that the GOSAT XCO 2 retrievals could help to better understand the interannual variations in regional carbon fluxes. [ABSTRACT FROM AUTHOR]