Intercomparison of global terrestrial carbon fluxes estimated by MODIS and Earth system models

Rain use efficiency gradients across Australian ecosystems

Rain use efficiency (RUE) quantifies the ecosystem's capacity to use precipitation water to assimilate atmospheric CO2. The spatial distribution of RUE and its drivers across the Australian continent is largely unknown. This knowledge gap limits our understanding of the possible contribution of Australian ecosystems to global carbon assimilation. This study investigates the spatial distribution of RUE across diverse terrestrial ecosystems in Australia. The results show that RUE ranges from 0.43 (1st percentile) to 3.10 (99th percentile) g C m-2 mm-1 with a continental mean of 1.19 g C m-2 mm-1. About 68 % of the spatiotemporal variability of RUE can be explained by a multiple linear regression model primarily contributed by climatic predictors. Benchmarked by the model estimation, drainage-diverging/converging landscapes tend to have reduced/increased RUE. The model also revealed the impact of increasing atmospheric CO2 concentration on RUE. The continental mean RUE would increase by between 29.3 and 64.8 % by the end of this century under the SSP5-8.5 scenario in which the CO2 concentration is projected to double from the present level. This increase in projected RUE is attributed to the assumed greening effect of increasing CO2 concentration, which does not consider the saturation of CO2 fertilisation effect and the warming effect on increasing wildfire occurrence. Under the SSP1-2.6 scenario, RUE would decrease by about 7 %. This study provides baseline RUEs of various ecosystems in Australia for investigating the impacts of human interferences and climate change on the capacity of Australian vegetation to assimilate atmospheric CO2 under given precipitation.

Estimating global annual gross primary production based on satellite-derived phenology and maximal carbon uptake capacity

The high uncertainty regarding global gross primary production (GPP) remains unresolved. This study explored the relationships between phenology, physiology, and annual GPP to provide viable alternatives for accurate estimation. A statistical model of integrated phenology and physiology (SMIPP) was developed using GPP data from 145 FLUXNET sites to estimate the annual GPP for various vegetation types. By employing the SMIPP model driven by satellite-derived datasets of the global carbon uptake period (CUP) and maximal carbon uptake capacity (GPPmax), the global annual GPP was estimated for the period from 2001 to 2018. The results demonstrated that the SMIPP model accurately predicted annual GPP, with relative root mean square error values ranging from 11.20 to 19.29% for forest types and 20.49-35.71% for non-forest types. However, wetlands, shrublands, and evergreen forests exhibited relatively low accuracies. The average, trend, and interannual variation of global GPP during 2001-2018 were 132.6 Pg C yr-1, 0.25 Pg C yr-2, and 1.57 Pg C yr-1, respectively. They were within the ranges estimated in other global GPP products. Sensitivity analysis revealed that GPPmax had comparable effects to CUP in high-latitude regions but significantly greater impacts at the global scale, with sensitivity coefficients of 0.85 ± 0.23 for GPPmax and 0.46 ± 0.28 for CUP. This study provides a simple and practical method for estimating global annual GPP and highlights the influence of GPPmax and CUP on global-scale annual GPP.

Projected decline in the strength of vegetation carbon sequestration under climate change in India

Tropical vegetation plays a critical role in terrestrial carbon budget and supply many ecological functions such as carbon sequestration. In recent decades, India has witnessed an increase in net primary productivity (NPP), an important measure of carbon sequestration. However, uncertainties persist regarding the sustainability of these land carbon sinks in the face of climate change. The enhanced NPP is driven by the strong CO2 fertilization effect (CFE), but the temporal patterns of this feedback remain unclear. Using the carbon flux data from the Earth System Models (ESMs), an increasing trend in NPP was observed, with projections of NPP to 2.00 ± 0.12 PgCyr-1 (25 % increase) during 2021-2049, 2.36 ± 0.12 PgCyr-1 (18 % increase) during 2050-2079, and 2.67 ± 0.07 PgCyr-1 (13 % increase) during 2080-2099 in Indian vegetation under SSP585 scenario. This suggests a significant decline in the NPP growth rate. To understand the feedback mechanisms driving NPP, the relative effects of CFE and warming were analyzed. Comparing simulations from the biogeochemically coupled model (BGC) with the fully coupled model, the BGC model projected a 74.7 % increase in NPP, significantly higher than the 55.9 % increase projected by the fully coupled model by the end of the century. This indicates that the consistent increase in NPP was associated with CO2 fertilization. More importantly, results reveal that the decrease in the NPP growth rate was due to the declining contribution of CFE at a rate of -0.62 % per 100 ppm CO2 increase. This decline could be attributed to factors such as nutrient limitations and high temperatures. Additionally, significant shifts in the strength of carbon sinks in offsetting the CO2 emissions were identified, decreasing at a rate of -1.15 % per decade. This decline in the strength of vegetation carbon sequestration may increase the societal dependence on mitigation measures to address climate change.

Global prediction of gross primary productivity under future climate change

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid-high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.

Civil aviation emissions in Argentina

The impact of aviation on climate change is reflected in increasing emissions of CO2 and other pollutants from fuel burning emitted at high altitudes, representing 2.9 % of total Greenhouse gases (GHG) emissions in 2019. However, mitigations options for decarbonization of aviation are difficult to implement given operational safety, technology maturity, energy density and other constraints. One alternative for mitigation is the use of certified sustainable aviation fuel (SAF) with lower carbon intensity than conventional jet fuel (CJF). This research presents an inventory of Argentine civil aviation emissions for its domestic and international flights, and analyzes the possibility of supplying SAF as a mitigation strategy given its abundant biomass production. Argentine aviation activity is presented as a monthly 4D (latitude, longitude, altitude and time) spatial inventory for the interval 2001-2021, based on origin and destination city pairs, aircraft types and airlines. Fuel consumption and pollutant emissions were calculated for landing-and-take-off and cruise phases. Monthly domestic ranged from 67 to 179 kt CO_2eq (2001-2019). Annual peak values occurred in 2019 consuming 560 kt CJF and direct emitting of 1.77 Mt CO_2eq. While Revenue-Passenger-Kilometer (RPK) grew almost 4 times (4.18 × 10⁹ in 2001 to 16.42 × 10⁹ in 2019), the number of flights changed only 1.5 times (from 98,000 in 2002 to 152,000 in 2019). The main efficiency indexes varied from 97 t CJF/RPK, 308 gCO_2eq/RPK to 34 t CJF/RPK, 107 gCO_2eq/RPK between 2001 and 2019, respectively, showing an average annual improvement of 3.5 % due to partial fleet renewal, especially from 2015 onwards. Emissions of other pollutants for 2019 reached total values of CO 14.14 kt; NOx 6.77 kt; PM tot 55.12 kt. For the period 2001-2019, international aviation consumed between 1 Mt - 1.5 Mt CJF, directly emitting between 3.30 and 4.80 Mt of CO_2eq; RPKs went from 6.234 × 10⁹ to 20.524 × 10⁹; the efficiency indices ranged from 529 to 240 gCO_2eq/RPK. The most important changes occurred with an optimization of routes and number of flights and the replacement of the four-engines (B747, A380) by more efficient twin-engines (B777, A330) aircraft. Argentina is not required to any offsetting regulatory program due to its small aviation market (approx. 0.22 % global market in 2019), nor has to date certified SAF production pathways, nevertheless it has potential for SAF availability based on actual biofuels production (ethanol, biodiesel and soybean oil) and biomass feedstock's existences. In this sense this studies proposes that 2019 domestic fuel consumption could be supplied using 79 % exportable amounts of sugarcane ethanol (257 ± 53 kt) (by Ethanol to Jet ETJ) and 34 % of exportable soybean oil (1079 ± 160 kt) (by hydroprocessed esters and fatty acids- HEFA) pathways. For this scenario average GHG emissions reached 1.321 ± 0.115 Mt CO2eq; which would imply a 62 % of the current emission value using CJF (2.17Mt CO2eq), or savings of about 838 kt CO2eq (38 %). At the 2019 level of harvest and biofuel production, up to 1.4 Mt of SAF could be produced from sugarcane ethanol/ETJ and soybean oil/HEFA mitigating up to 1.8 MtCO2eq. A 35 kt CO_2eq annual sectoral national mitigation strategy could be reached by using 14 kt of SAF.

Inventory of China’s Net Biome Productivity since the 21st Century

Net biome productivity (NBP), which takes into account abiotic respiration and metabolic processes such as fire, pests, and harvesting of agricultural and forestry products, may be more scientific than net ecosystem productivity (NEP) in measuring ecosystem carbon sink levels. As one of the largest countries in global carbon emissions, in China, however, the spatial pattern and evolution of its NBP are still unclear. To this end, we estimated the magnitude of NBP in 31 Chinese provinces (except Hong Kong, Macau, and Taiwan) from 2000 to 2018, and clarified its temporal and spatial evolution. The results show that: (1) the total amount of NBP in China was about 0.21 Pg C/yr1. Among them, Yunnan Province had the highest NBP (0.09 Pg C/yr1), accounting for about 43% of China’s total. (2) NBP increased from a rate of 0.19 Tg C/yr1 during the study period. (3) At present, NBP in China’s terrestrial ecosystems is mainly distributed in southwest and south China, while northwest and central China are weak carbon sinks or carbon sources. (4) The relative contribution rates of carbon emission fluxes due to emissions from anthropogenic disturbances (harvest of agricultural and forestry products) and natural disturbances (fires, pests, etc.) were 70% and 9.87%, respectively. This study emphasizes the importance of using NBP to re-estimate the net carbon sink of China’s terrestrial ecosystem, which is beneficial to providing data support for the realization of China’s carbon neutrality goal and global carbon cycle research.