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In this study, high‐resolution CO2 concentration data were generated for East Asia to analyse long‐term changes in atmospheric CO2 concentrations, as East Asia is an important region for understanding the global carbon cycle. Using the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem), atmospheric CO2 concentrations were simulated in East Asia at a resolution of 9 km for a period of 10 years (2009–2018). The generated CO2 concentration data include CO2 concentrations, biogenic CO2 concentrations, anthropogenic CO2 concentrations, oceanic CO2 concentrations, biospheric CO2 uptake, biospheric CO2 release and meteorological variables at 3‐h intervals. The simulated high‐resolution CO2 concentrations, biogenic CO2 concentrations and anthropogenic CO2 concentrations are stored in NetCDF‐4 (Network Common Data Form, version 4) format and are available for download at https://doi.org/10.7910/DVN/PJTBF3. The simulated annual mean surface CO2 concentrations in East Asia were 391.027 ppm in 2009 and 412.949 ppm in 2018, indicating an increase of 21.922 ppm over the 10‐year period with appropriate seasonal variabilities. The monthly mean CO2 concentrations in East Asia were verified using surface CO2 observations and satellite column‐averaged CO2 mole fraction (XCO2) from Orbiting Carbon Observatory 2 (OCO‐2). Based on surface CO2 observations and OCO‐2 XCO2 concentrations, the average root‐mean‐square error (RMSE) of the simulated CO2 concentrations in WRF‐Chem was 2.474 and 0.374 ppm, respectively, which is smaller than the average RMSE of the low‐resolution CarbonTracker 2019B (CT2019B) simulation. Therefore, the simulated high‐resolution atmospheric CO2 concentrations in East Asia in WRF‐Chem over 10 years are reliable data that resemble the observed values and could be highly valuable in understanding the carbon cycle in East Asia. High‐resolution atmospheric CO2 concentration data for East Asia were generated using WRF‐Chem for 2009–2018. Generated data include CO2 concentrations, biogenic and anthropogenic CO2, oceanic CO2 and meteorological variables. Verified with surface CO2 observations and OCO‐2 satellite data, the generated CO2 data exhibits smaller errors compared to low‐resolution models, offering reliable insights into East Asia's carbon cycle.

The analyses of the cumulative sums of the observed yearly averaged atmospheric CO 2 concentrations revealed unambiguously two change points during 0-2021. The first abrupt change that occurred during 1830 delineated the starting epoch of the pre-industrial era, which is marked by a linear increase in concentrations (0.24 ± 0.01 ppm/yr.) as described by the IPCC being driven by economic and population growth. Another notable change occurred during 1943 with the start of a uniform acceleration (0.028 ± 0.000 ppm/yr 2 ) since then. These findings bring not only clarity and precision into the IPCC's vague statement on the topic but also alleviates the bias introduced in estimating the trend (constant velocity) of the atmospheric concentrations, which is three times larger in magnitude (0.78 ± 0.01 ppm/yr.) for the period 1830-2021 if the uniform acceleration since 1943 is not accounted for. If the increased concentrations of CO 2 before 1943 are predominantly caused by the climate system, i.e. of non-anthropogenic origin, then the concentrations will continue to increase with the constant velocity estimated in this study despite the efforts to limit the anthropogenic contributions that are the source of the uniform acceleration since 1943.

Elevated CO2 (eCO2) experiments provide critical information to quantify the effects of rising CO2 on vegetation1–6. Many eCO2 experiments suggest that nutrient limitations modulate the local magnitude of the eCO2 effect on plant biomass1,3,5, but the global extent of these limitations has not been empirically quantified, complicating projections of the capacity of plants to take up CO27,8. Here, we present a data-driven global quantification of the eCO2 effect on biomass based on 138 eCO2 experiments. The strength of CO2 fertilization is primarily driven by nitrogen (N) in ~65% of global vegetation and by phosphorus (P) in ~25% of global vegetation, with N- or P-limitation modulated by mycorrhizal association. Our approach suggests that CO2 levels expected by 2100 can potentially enhance plant biomass by 12 ± 3% above current values, equivalent to 59 ± 13 PgC. The future effect of eCO2 we derive from experiments is geographically consistent with past changes in greenness9, but is considerably lower than the past effect derived from models10. If borne out, our results suggest that the stimulatory effect of CO2 on carbon storage could slow considerably this century. Our research provides an empirical estimate of the biomass sensitivity to eCO2 that may help to constrain climate projections.Elevated CO2 increases plant biomass, providing a negative feedback on global warming. Nutrient availability was found to drive the magnitude of this effect for the majority of vegetation globally, and analyses indicated that CO2 will continue to fertilize plant growth in the next century.

Ecosystem water-use efficiency (WUE) is an important metric linking the global land carbon and water cycles. Eddy covariance-based estimates of WUE in temperate/boreal forests have recently been found to show a strong and unexpected increase over the 1992–2010 period, which has been attributed to the effects of rising atmospheric CO2 concentrations on plant physiology. To test this hypothesis, we forced the observed trend in the process-based land surface model JSBACH by increasing the sensitivity of stomatal conductance (g s) to atmospheric CO2 concentration. We compared the simulated continental discharge, evapotranspiration (ET), and the seasonal CO2 exchange with observations across the extratropical northern hemisphere. The increased simulated WUE led to substantial changes in surface hydrology at the continental scale, including a significant decrease in ET and a significant increase in continental runoff, both of which are inconsistent with large-scale observations. The simulated seasonal amplitude of atmospheric CO2 decreased over time, in contrast to the observed upward trend across ground-based measurement sites. Our results provide strong indications that the recent, large-scale WUE trend is considerably smaller than that estimated for these forest ecosystems. They emphasize the decreasing CO2 sensitivity of WUE with increasing scale, which affects the physiological interpretation of changes in ecosystem WUE.

Increasing CO 2 concentrations are expected to increase plant growth and water efficiency. Tree-ring data covering 150 years from tropical forests show that water-use efficiency has increased with CO 2 concentrations but tree growth has not. The biomass of undisturbed tropical forests has likely increased in the past few decades 1 , 2 , probably as a result of accelerated tree growth. Higher CO 2 levels are expected to raise plant photosynthetic rates 3 and enhance water-use efficiency 4 , that is, the ratio of carbon assimilation through photosynthesis to water loss through transpiration. However, there is no evidence that these physiological responses do indeed stimulate tree growth in tropical forests. Here we present measurements of stable carbon isotopes and growth rings in the wood of 1,100 trees from Bolivia, Cameroon and Thailand. Measurements of carbon isotope fractions in the wood indicate that intrinsic water-use efficiency in both understorey and canopy trees increased by 30–35% over the past 150 years as atmospheric CO 2 concentrations increased. However, we found no evidence for the suggested concurrent acceleration of individual tree growth when analysing the width of growth rings. We conclude that the widespread assumption of a CO 2 -induced stimulation of tropical tree growth may not be valid.

In several biomes, including croplands, wooded savannas, and tropical forests, many small fires occur each year that are well below the detection limit of the current generation of global burned area products derived from moderate resolution surface reflectance imagery. Although these fires often generate thermal anomalies that can be detected by satellites, their contributions to burned area and carbon fluxes have not been systematically quantified across different regions and continents. Here we developed a preliminary method for combining 1‐km thermal anomalies (active fires) and 500 m burned area observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the influence of these fires. In our approach, we calculated the number of active fires inside and outside of 500 m burn scars derived from reflectance data. We estimated small fire burned area by computing the difference normalized burn ratio (dNBR) for these two sets of active fires and then combining these observations with other information. In a final step, we used the Global Fire Emissions Database version 3 (GFED3) biogeochemical model to estimate the impact of these fires on biomass burning emissions. We found that the spatial distribution of active fires and 500 m burned areas were in close agreement in ecosystems that experience large fires, including savannas across southern Africa and Australia and boreal forests in North America and Eurasia. In other areas, however, we observed many active fires outside of burned area perimeters. Fire radiative power was lower for this class of active fires. Small fires substantially increased burned area in several continental‐scale regions, including Equatorial Asia (157%), Central America (143%), and Southeast Asia (90%) during 2001–2010. Globally, accounting for small fires increased total burned area by approximately by 35%, from 345 Mha/yr to 464 Mha/yr. A formal quantification of uncertainties was not possible, but sensitivity analyses of key model parameters caused estimates of global burned area increases from small fires to vary between 24% and 54%. Biomass burning carbon emissions increased by 35% at a global scale when small fires were included in GFED3, from 1.9 Pg C/yr to 2.5 Pg C/yr. The contribution of tropical forest fires to year‐to‐year variability in carbon fluxes increased because small fires amplified emissions from Central America, South America and Southeast Asia—regions where drought stress and burned area varied considerably from year to year in response to El Nino‐Southern Oscillation and other climate modes. Key Points Many fires are below the detection limit of 500 m burned area products Small fires increase global burned area by ~35% Small fires increased global carbon emissions from 1.9 to 2.5 PgC/yr

Interannual variations in the large-scale net ecosystem exchange (NEE) of CO₂ between the terrestrial biosphere and the atmosphere were estimated for 1957–2017 from sustained measurements of atmospheric CO₂ mixing ratios. As the observations are sparse in the early decades, available records were combined into a 'quasi-homogeneous' dataset based on similarity in their signals, to minimize spurious variations from beginning or ending data records. During El Niño events, CO₂ is anomalously released from the tropical band, and a few months later also in the northern extratropical band. This behaviour can approximately be represented by a linear relationship of the NEE anomalies and local air temperature anomalies, with sensitivity coefficients depending on geographical location and season. The apparent climate sensitivity of global total NEE against variations in pan-tropically averaged annual air temperature slowly changed over time during the 1957–2017 period, first increasing (though less strongly than in previous studies) but then decreasing again. However, only part of this change can be attributed to actual changes in local physiological or ecosystem processes, the rest probably arising from shifts in the geographical area of dominating temperature variations. This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

The term Blue Carbon (BC) was first coined a decade ago to describe the disproportionately large contribution of coastal vegetated ecosystems to global carbon sequestration. The role of BC in climate change mitigation and adaptation has now reached international prominence. To help prioritise future research, we assembled leading experts in the field to agree upon the top-ten pending questions in BC science. Understanding how climate change affects carbon accumulation in mature BC ecosystems and during their restoration was a high priority. Controversial questions included the role of carbonate and macroalgae in BC cycling, and the degree to which greenhouse gases are released following disturbance of BC ecosystems. Scientists seek improved precision of the extent of BC ecosystems; techniques to determine BC provenance; understanding of the factors that influence sequestration in BC ecosystems, with the corresponding value of BC; and the management actions that are effective in enhancing this value. Overall this overview provides a comprehensive road map for the coming decades on future research in BC science. The role of Blue Carbon in climate change mitigation and adaptation has now reached international prominence. Here the authors identified the top-ten unresolved questions in the field and find that most questions relate to the precise role blue carbon can play in mitigating climate change and the most effective management actions in maximising this.

Tropical savannas have a ground cover dominated by C4 grasses, with fire and herbivory constraining woody cover below a rainfall-based potential. The savanna biome covers 50% of the African continent, encompassing diverse ecosystems that include densely wooded Miombo woodlands and Serengeti grasslands with scattered trees. African savannas provide water, grazing and browsing, food and fuel for tens of millions of people, and have a unique biodiversity that supports wildlife tourism. However, human impacts are causing widespread and accelerating degradation of savannas. The primary threats are land cover-change and transformation, landscape fragmentation that disrupts herbivore communities and fire regimes, climate change and rising atmospheric CO2. The interactions among these threats are poorly understood, with unknown consequences for ecosystem health and human livelihoods. We argue that the unique combinations of plant functional traits characterizing the major floristic assemblages of African savannas make them differentially susceptible and resilient to anthropogenic drivers of ecosystem change. Research must address how this functional diversity among African savannas differentially influences their vulnerability to global change and elucidate the mechanisms responsible. This knowledge will permit appropriate management strategies to be developed to maintain ecosystem integrity, biodiversity and livelihoods.

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO2 emissions. Quantifying anthropogenic CO2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO2 emissions by an artificial neural network using column-average dry air mole fraction of CO2 (XCO2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO2 anomalies (dXCO2) derived from XCO2 and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO2 emissions especially in the areas with high anthropogenic CO2 emissions. Our results indicate that XCO2 data from satellite observations can be applied in estimating anthropogenic CO2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO2 uptake and emissions, from satellite observations.