Jian Zhou

Peatlands store one-third of the global soil carbon (C) on only ∼3% of Earth’s land surface but risk becoming C sources to accelerate climate change due to warming-induced C loss. Elevated CO ₂ (eCO ₂ ) may mitigate this loss by boosting plant C uptake. However, how eCO ₂ and climate warming interact to influence peatland C dynamics remains unclear. Here, we investigated peatland C fate at five warming levels (+0°, +2.25°, +4.5°, +6.75°, and + 9°C), each under ambient CO ₂ (aCO ₂ ) and eCO ₂ (+500 parts per million), by model-guided synthesis of 18 measured variables for each of the 10 treatments from an experiment. Our results showed that the peatland C loss increased with warming at a rate of 62.6 g C m ⁻² year ⁻¹ °C ⁻¹ , reaching 476.5 ± 104.7 g C m ⁻² year ⁻¹ at +9°C under aCO ₂ , primarily because of increased Sphagnum mortality and accelerated decomposition. eCO ₂ reduced the loss rate to 17.3 g C m ⁻² year ⁻¹ °C ⁻¹ , nearly halving the C loss at +9°C (269.4 ± 80.5 g C m ⁻² year ⁻¹ ), mainly due to enhanced tree and shrub growth. Methane emissions increased with warming, similarly under aCO ₂ and eCO ₂ . Our findings suggest that northern peatlands likely become major C sources under climate warming, although rising atmospheric CO ₂ concentration may partially slow down the C loss. Climate-driven shifts in plant community structure, rather than peat decomposition alone, play a critical role in regulating peatland C fate and may threaten the stability of peatland ecosystem functions and services in the future.

Because of global warming, Earth’s ecosystems have been experiencing more frequent and severe heatwaves. Heatwaves are expected to tip terrestrial carbon sequestration by elevating ecosystem respiration and suppressing gross primary productivity (GPP). Here, using the convergent cross-mapping technique, this study detected positive bidirectional causal effects between GPP and respiration in two unprecedented European heatwaves. Heatwaves enhanced the causal effect strength of GPP on respiration rather than respiration on GPP across 40 site-years of observations. Further analyses and global simulations revealed spatial heterogeneity in the heatwave response of the causal link strength between GPP and respiration, which was jointly driven by the local climate and vegetation properties. However, the causal effect strength of GPP on respiration showed considerable uncertainties in CMIP6 models. This study reveals an enhanced causal link strength between GPP and respiration during heatwaves, shedding light on improving projections for terrestrial carbon sink dynamics under future climate extremes.

Significant land greening since the 1980s has been detected through satellite observation, forest inventory, and Earth system modeling. However, whether and to what extent global land greening enhances ecosystem carbon stock remains uncertain. Here, using 40 global models, we first detected a positive correlation between the terrestrial ecosystem carbon stock and leaf area index (LAI) over time. Then, we diagnose the source of uncertainty of simulated the sensitivities of ecosystem carbon stock to LAI based on a traceability analysis. We found that the sensitivity of gross primary productivity (GPP) to LAI is the largest contributor to the model uncertainty in more than 60% of the vegetated grids. Using the ensemble of four long‐term global data sets of GPP and three satellite LAI products from 1982 to 2014, we provided an emergent constraint on the ecosystem carbon stock increase as 0.75 ± 0.46 kg C m⁻² per unit LAI over global land areas. Furthermore, the biome‐based results reveal that the tropical forest regions have the highest inter‐model variation and model bias. Overall, this study identifies the uncertainty source and provides constrained estimates of the greening effect on ecosystem carbon stock at the global scale.

The spatial and temporal variations in terrestrial carbon storage play a pivotal role in regulating future climate change. However, Earth system models (ESMs), which have coupled the terrestrial biosphere and atmosphere, show great uncertainty in simulating the global land carbon storage. Here, based on multiple global datasets and a traceability analysis, we diagnosed the uncertainty source of terrestrial carbon storage in 22 ESMs that participated in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). The modeled global terrestrial carbon storage has converged among ESMs from CMIP5 (1936.9 ± 739.3 PgC) to CMIP6 (1774.4 ± 439.0 PgC) but is persistently lower than the observation-based estimates (2285 ± 669 PgC). By further decomposing terrestrial carbon storage into net primary production (NPP) and ecosystem carbon residence time (τ_E), we found that the decreased intermodel spread in land carbon storage primarily resulted from more accurate simulations on NPP among ESMs from CMIP5 to CMIP6. The persistent underestimation of land carbon storage was caused by the biased τ_E. In CMIP5 and CMIP6, the modeled τ_E was far shorter than the observation-based estimates. The potential reasons for the biased τ_E could be the lack of or incomplete representation of nutrient limitation, vertical soil biogeochemistry, and the permafrost carbon cycle. Moreover, the modeled τ_E became the key driver for the intermodel spread in global land carbon storage in CMIP6. Overall, our study indicates that CMIP6 models have greatly improved the terrestrial carbon cycle, with a decreased model spread in global terrestrial carbon storage and less uncertain productivity. However, more efforts are needed to understand and reduce the persistent data–model disagreement on carbon storage and residence time in the terrestrial biosphere.

Climate warming and extreme hydrological events are threatening the sustainability of wetlands across the globe. However, whether climate warming will amplify or diminish the impact of extreme flooding on wetland ecosystems is unknown. Here, we show that climate warming significantly reduced wetland resistance and resilience to a severe flooding event via a 6-year warming experiment. We first found that warming rapidly altered plant community structure by increasing the dominance of low-canopy species. Then, we showed that warming reduced the resistance and resilience of vegetation productivity to a 72-cm flooding event. Last, we detected slower postflooding carbon processes, such as gross ecosystem productivity, soil respiration, and soil methane emission, under the warming treatment. Our results demonstrate how severe flooding can destabilize wetland vegetation structure and ecosystem function under climate warming. These findings indicate an enhanced footprint of extreme hydrological events in wetland ecosystems in a warmer climate.

Background: An increasing number of ecological processes have been incorporated into Earth system models. However, model evaluations usually lag behind the fast development of models, leading to a pervasive simulation uncertainty in key ecological processes, especially the terrestrial carbon (C) cycle. Traceability analysis provides a theoretical basis for tracking and quantifying the structural uncertainty of simulated C storage in models. Thus, a new tool of model evaluation based on the traceability analysis is urgently needed to efficiently diagnose the sources of inter-model variations on the terrestrial C cycle in Earth system models.Methods: A new cloud-based model evaluation platform, i.e., the online traceability analysis system for model evaluation (TraceME v1.0), was established. The TraceME was applied to analyze the uncertainties of seven models from the Coupled Model Intercomparison Project (CMIP6).Results: The TraceME can effectively diagnose the key sources of different land C dynamics among CMIIP6 models. For example, the analyses based on TraceME showed that the estimation of global land C storage varied about 2.4 folds across the seven CMIP6 models. Among all models, IPSL-CM6A-LR simulated the lowest land C storage, which mainly resulted from its shortest baseline C residence time. Over the historical period of 1850–2014, gross primary productivity and baseline C residence time were the major uncertainty contributors to the inter-model variation in ecosystem C storage in most land grid cells.Conclusion: TraceME can facilitate model evaluation by identifying sources of model uncertainty and provides a new tool for the next generation of model evaluation.