East Asia (China, Japan, Koreas and Mongolia) has been the world’s economic engine over at least the past two decades, exhibiting a rapid increase in fossil fuel emissions of greenhouse gases (GHGs) and has expressed the recent ambition to achieve climate neutrality by mid-century. However, the GHG balance of its terrestrial ecosystems remains poorly constrained. Here, we present a synthesis of the three most important long-lived greenhouse gases (CO2, CH4 and N2O) budgets over East Asia during the decades of 2000s and 2010s, following a dual constraint bottom-up and top-down approach. We estimate that terrestrial ecosystems in East Asia is close to neutrality of GHGs, with a magnitude of between 196.9 ± 527.0 Tg CO2eq yr-1 (the top-down approach) and -20.8 ± 205.5 Tg CO2eq yr-1 (the bottom-up approach) during 2000-2019. This net GHG emission includes a large land CO2 sink (-1251.3 ± 456.9 Tg CO2 yr-1 based on the top-down approach and -1356.1 ± 155.6 Tg CO2 yr-1 based on the bottom-up approach), which is being fully offset by biogenic CH4 and N2O emissions, predominantly coming from the agricultural sector. Emerging data sources and modelling capacities have helped achieve agreement between the top-down and bottom-up approaches to within 20% for all three GHGs, but sizeable uncertainties remain in several flux terms. For example, the reported CO2 flux from land use and land cover change varies from a net source of more than 300 Tg CO2 yr-1 to a net sink of ~-700 Tg CO2 yr-1.

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one-third of anthropogenic CO2 emissions during the 1959-2019 period. This sink-estimate is produced by an ensemble of terrestrial biosphere models collectively referred to as the TRENDY ensemble and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well TRENDY models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation-based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference datasets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter-model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.

Gross primary production (GPP) determines the amounts of carbon and energy that enter terrestrial ecosystems. However, the tremendous uncertainty of the GPP still hinders the reliability of the GPP estimates and therefore understanding of the global carbon cycle. In this study, using observations from global eddy covariance (EC) flux towers, we appraised the performance of 22 widely used GPP models and quality of major spatial data layers that drive the models. Results show that the global GPP products generated by the 22 models varied greatly in the means (from 92.7 to 178.9 Pg C yr-1), trends (from -0.25 to 0.84 Pg C yr-1). Model structures (i.e., light use efficiency models, machine learning models, and process-based biophysical models) are an important aspect contributing to the large uncertainty. In addition, various biases in currently available spatial datasets have found (e.g., only 57% of the observed variation in photosynthetically active radiation was explained by the spatial dataset), which contributed greatly affects global GPP estimates. Our analysis indicates that the model development did not converge GPP simulations with the advance of time. Moving forward, research into efficacy of model structures and the precision of input data may be more important than the development of new models for global GPP estimation.

Extreme drought impacts ecosystem function and processes dramatically. However, a comprehensive understanding of how extreme drought affects root biomass at regional scales remains elusive. Here, we investigated the effects across six grasslands with extreme drought treatment replicated across a precipitation gradient in Inner Mongolia, China. We found the root biomass and belowground net primary productivity (BNPP) were significantly positively correlated with precipitation at the reginal scale. Extreme drought decreased the slope of this correlation in 0-10 cm and increased in 10-20 cm. Root biomass and BNPP increased by extreme drought in the four relatively arid sites and decreased in the two relatively mesic sites in 0-10 cm, and the reverse pattern showed in 10-20 cm. These shifts were driven by the response of soil moisture. Our findings suggest that including vertical responses of belowground primary productivity to extreme drought should improve models predictions of plant roots to future climate change.