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The release of carbon dioxide from the soil to the atmosphere, known as soil respiration, is the second largest terrestrial carbon flux after photosynthesis, but the convergence of the data-driven estimates is unclear. Here we collate all historical data-driven estimates of global soil respiration to analyze convergence and uncertainty in the estimates. Despite the development of a dataset and advanced scaling techniques in the last two decades, we find that inter-model variability has increased. Reducing inter-model variability of global soil respiration is not an easy task, but when the puzzle pieces of the carbon cycle fit together perfectly, climate change prediction will be more reliable.

Halving food wastage at retail and consumer levels by 2030 is a target for Sustainable Development Goal (SDG) 12.3. Although previous studies have indicated that the food wastage extent differs based on the national income level, the relevance of this relationship is debatable owing to the controversial quantification of food wastage, usually performed using two methods based on actual generation or gaps for human calorific requirements. Therefore, in this study, we aimed to investigate this issue by analyzing the correlation between food energy imbalance and per capita income using food wastage generation data for 51 comparable counties. The results revealed possible synergies between SDG 12.3 and the improvement of food security (SDG 2.1) in certain lower-middle-income countries. That is, the per capita food wastage in countries facing a food energy deficit (95 kg/year) is remarkably higher than that in countries that have resolved their food energy deficit (66 kg/year). We presume that prolonging the food shelf-life could be the key factor in linking SDGs 12.3 and 2.1. Furthermore, as the lack of reliable data in lower-middle-income countries hinders the verification of this synergy, we propose 19 lower-middle-income countries for future investigation to verify the synergy between SDGs 12.3 and 2.1.

Soil carbon stocks and their release to the atmosphere are key processes for accurately predicting future climate–terrestrial carbon feedbacks. However, data-driven estimates of these variables exhibit substantial variability across studies. In this work, we compiled all publicly available global maps of soil carbon stock and heterotrophic respiration derived from observational data, converted them into NetCDF format, and standardized them to a 0.5-degree spatial resolution. We calculated the mean, maximum, and minimum values for each grid cell to generate harmonized global maps of both variables. From these harmonized datasets, we further derived estimates of soil carbon turnover time and its temperature sensitivity. The resulting products provide valuable benchmarks for the development, evaluation, and constraint of terrestrial carbon cycle models in Earth system science.

Reducing uncertainty in the response of the Amazon rainforest, a vital component of the Earth system, to future climate change is crucial for refining climate projections. Here we demonstrate an emergent constraint (EC) on the future response of the Amazon carbon cycle to climate change across CMIP6 Earth system models. Models that overestimate past global warming trends, tend to estimate hotter and drier future Amazon conditions, driven by northward shifts of the intertropical convergence zone over the Atlantic Ocean, causing greater Amazon carbon loss. The proposed EC changes the mean CMIP6 Amazon climate-induced carbon loss estimate (excluding CO 2 fertilisation and land-use change impacts) from −0.27 (−0.59–0.05) to −0.16 (−0.42–0.10) GtC year −1 at 4.4 °C warming level, reducing the variance by 34%. This study implies that climate-induced carbon loss in the Amazon rainforest by 2100 is less than thought and that past global temperature trends can be used to refine regional carbon cycle projections. A study shows an emergent constraint on the Amazon carbon cycle response to climate change. The CMIP6 ESMs that overestimate past global temperature trends, tend to project hotter, drier conditions and greater climate-induced Amazon carbon source.

Most of the area contaminated by the Fukushima Daiichi Nuclear Power Plant accident is covered by forest. In this paper, we updated model predictions of temporal changes in the 137 Cs dynamics using the latest observation data and newly provided maps of the predicted 137 Cs activity concentration for wood, which is the most commercially important part of the tree body. Overall, the previous prediction and latest observation data were in very good agreement. However, further validation revealed that the migration from the soil surface organic layer to the mineral soil was overestimated for evergreen needleleaf forests. The new prediction of the 137 Cs inventory showed that although the 137 Cs distribution within forests differed among forest types in the first 5 years, the difference diminished in the later phase. Besides, the prediction of the wood 137 Cs activity concentrations reproduced the different trends of the 137 Cs activity concentrations for cedar, oak, and pine trees. Our simulation suggests that the changes of the wood 137 Cs activity concentration over time will slow down after 5–10 years. Although the model uncertainty should be considered and monitoring and model updating must continue, the study provides helpful information on the 137 Cs dynamics within forest ecosystems and the changes in wood contamination.

Future climate change and increasing atmospheric CO2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510–758 ppm of CO2), vegetation carbon increases by 52–477 Pg C (224 Pg C mean), mainly due to CO2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.

Leaf nitrogen distribution in the plant canopy is an important determinant for canopy photosynthesis. Although the gradient of leaf nitrogen is formed along light gradients in the canopy, its quantitative variations among species and environmental responses remain unknown. Here, we conducted a global meta-analysis of leaf nitrogen distribution in plant canopies. We collected data on the nitrogen distribution and environmental variables from 393 plant canopies (100, 241 and 52 canopies for wheat, other herbaceous and woody species, respectively). The trends were clearly different between wheat and other species; the photosynthetic nitrogen distribution coefficient (Kb) was mainly determined by leaf area index (LAI) in wheat, whereas it was correlated with the light extinction coefficient (KL) and LAI in other species. Some other variables were also found to influence Kb We present the best equations for Kb as a function of environmental variables and canopy characteristics. As a more simple function, Kb = 0·5KL can be used for canopies of species other than wheat. Sensitivity analyses using a terrestrial carbon flux model showed that gross primary production tended to be more sensitive to the Kb value especially when nitrogen content of the uppermost leaf was fixed. Our results reveal that nitrogen distribution is mainly driven by the vertical light gradient but other factors such as LAI also have significant effects. Our equations contribute to an improvement in the projection of plant productivity and cycling of carbon and nitrogen in terrestrial ecosystems.

This study assessed historical changes in emissions of nitrous oxide (N2O), a potent greenhouse gas and stratospheric ozone-depleting substance, from the soils of East Asia to the atmosphere. A process-based terrestrial ecosystem model (VISIT) was used to simulate the nitrogen cycle and associated N2O emissions as a function of climate, land use, atmospheric deposition, and agricultural inputs from 1901 to 2016. The mean regional N2O emission rate in the 2000s was estimated to be 2.03 Tg N2O year−1 (1.29 Tg N year−1; approximately one-third from natural ecosystems and two-thirds from croplands), more than triple the rate in 1901. A sensitivity analysis suggested that the increase of N2O emissions was primarily attributable to the increase of agricultural inputs from fertilizer and manure. The simulated N2O emissions showed a clear seasonal cycle and interannual variability, primarily in response to meteorological conditions and nitrogen inputs. The spatial pattern of the simulated N2O emissions revealed hot spots in agricultural areas of China, South Korea, and Japan. The average N2O emission factor (emission per unit nitrogen input) was estimated to be 1.38%, a value comparable to previous estimates. These biogeochemical modeling results will facilitate identifying ways to mitigate global warming and manage agricultural practices in this region.

Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000-2010 total global GPP estimated from the model ensemble to be 117 ± 13 Pg C yr−1 (mean ± 1 standard deviation), which was higher than MODIS (112 Pg C yr−1), and close to the MTE (120 Pg C yr−1). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However, we observed large uncertainties across the eight ISIMIP2a models, which are probably due to the differences in the formulation of underlying photosynthetic processes. The results of this study are useful in understanding the contributions of different regions to global GPP and its spatiotemporal variability, how the model- and observational-based GPP estimates differ from each other in time and space, and the relative strength of the eight models. Our results also highlight the models' ability to capture the seasonality of GPP that are essential for understanding the inter-annual and seasonal variability of GPP as a major component of the carbon cycle.