Yiqi Luo

Peatlands cover only ~3 % of Earth’s land surface yet store ~30 % of global soil carbon (C), making them critical components of the terrestrial C cycle and influential regulators of C-climate feedbacks. However, their responses to climate warming and elevated CO₂ remain highly uncertain, in part because Earth system models represent peatland processes with varying levels of complexity and realism, and because peat C pools turn over on centennial to millennial timescales that challenge model evaluation. Here, we present results from the SPRUCE (Spruce and Peatland Responses Under Changing Environments) Model Intercomparison Project (SPRUCE-MIP), which evaluates 15 terrestrial ecosystem models against observations from a long-term whole-ecosystem warming and CO₂-enrichment experiment at an ombrotrophic bog in northern Minnesota, USA. Models were driven by observed meteorology from in situ warming treatments spanning +0 to +9 °C, at either ambient or elevated CO₂ (+500 ppm). Simulated net ecosystem exchange (NEE), net primary productivity (NPP), heterotrophic respiration (HR), and methane (CH₄) fluxes were benchmarked against multi-year observations. Model predictions exhibit a large spread in both baseline C balance, ranging from strong sinks to strong sources under +0 °C warming, and temperature sensitivity, indicating substantial uncertainty in predicting peatland C responses to environmental forcing. Across the ensemble, models fall into four distinct functional response types: (i) models that simulate peatlands as net C sources across all warming and CO₂ conditions; (ii) models that transition from C sinks to sources under warming irrespective of CO₂ level; (iii) models that transition only under ambient CO₂ but remain C sinks under elevated CO₂, reflecting strong CO₂ fertilization effects; and (iv) models that maintain persistent sinks or near-neutral C balance even under extreme warming. While most models predict enhanced NPP under elevated CO₂ across all warming treatments, SPRUCE observations show little or no NPP enhancement under elevated CO₂ at the +0 and +2.25 °C warming levels, with a positive CO₂ fertilization effect emerging only under stronger warming. This discrepancy highlights persistent model biases in representing interactions between warming and CO₂ responses. Process-based analysis further indicates that divergence in modeled responses is associated with differences in vegetation structure (light competition and moss representation), nutrient cycling (nitrogen and phosphorus), and dynamic peat representation. These structural differences systematically influence ecosystem productivity, heterotrophic respiration, and net C balance across the model ensemble. An illustrative parameter sensitivity analysis using the SPRUCE-specific Energy Exascale Earth System Model (E3SM) Land Model (ELM-SPRUCE) further shows that parameter choices can also modulate the magnitude of simulated responses. Together, these results demonstrate that peatland responses to warming and elevated CO₂ are highly sensitive to model structure, parameterization, and process coupling, highlighting the need for improved representation of key peatland processes to reduce uncertainty in Earth system projections. The SPRUCE experimental framework provides a unique benchmark for evaluation and improving process representation and constraining near-term peatland response to environmental change, thereby strengthening confidence in longer-term C-climate projections.

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.

The CO ₂ fertilization effect, quantified as the sensitivity of global photosynthesis to elevated atmospheric CO ₂ concentration ( β _GPP ), is likely overestimated in the current generation of Earth system models. An emerging hypothesis is that coordinated acclimation of leaf structural and physiological traits, represented by specific leaf area (SLA) and area‐based leaf nitrogen content ( N _a ), can reduce photosynthetic sensitivity to elevated CO ₂ , thereby lowering simulated β _GPP in these models. This study integrated experimental data with the Community Atmosphere Biosphere Land Exchange (CABLE) model to investigate how coordinated trait acclimation modifies β _GPP . The global meta‐analysis revealed that elevated CO ₂ decreases the intercept of the relationship between SLA and N _a . Incorporating this relationship into the CABLE model reduced simulated β _GPP by 7.1% globally, with substantial spatial variation. The regional variation was primarily driven by spatial differences in aridity (48.2%), leaf area index (41.4%), and maximum carboxylation rate ( v _cmax , 5.5%), with the coordinated trait effect on gross primary production decreasing along the aridity gradient. Our findings identify coordinated leaf trait acclimation as an important mechanism for refining simulated β _GPP , highlighting the need for trait‐based mechanisms in Earth system models to better predict ecosystem responses to global change.

Peatlands cover only ~3 % of Earth’s land surface yet store ~30 % of global soil carbon (C), making them critical components of the terrestrial C cycle and influential regulators of C-climate feedbacks. However, their responses to climate warming and elevated CO₂ remain highly uncertain, in part because Earth system models represent peatland processes with varying levels of complexity and realism, and because peat C pools turn over on centennial to millennial timescales that challenge model evaluation. Here, we present results from the SPRUCE (Spruce and Peatland Responses Under Changing Environments) Model Intercomparison Project (SPRUCE-MIP), which evaluates 15 terrestrial ecosystem models against observations from a long-term whole-ecosystem warming and CO₂-enrichment experiment at an ombrotrophic bog in northern Minnesota, USA. Models were driven by observed meteorology from in situ warming treatments spanning +0 to +9 °C, at either ambient or elevated CO₂ (+500 ppm). Simulated net ecosystem exchange (NEE), net primary productivity (NPP), heterotrophic respiration (HR), and methane (CH₄) fluxes were benchmarked against multi-year observations. Model predictions exhibit a large spread in both baseline C balance, ranging from strong sinks to strong sources under +0 °C warming, and temperature sensitivity, indicating substantial uncertainty in predicting peatland C responses to environmental forcing. Across the ensemble, models fall into four distinct functional response types: (i) models that simulate peatlands as net C sources across all warming and CO₂ conditions; (ii) models that transition from C sinks to sources under warming irrespective of CO₂ level; (iii) models that transition only under ambient CO₂ but remain C sinks under elevated CO₂, reflecting strong CO₂ fertilization effects; and (iv) models that maintain persistent sinks or near-neutral C balance even under extreme warming. While most models predict enhanced NPP under elevated CO₂ across all warming treatments, SPRUCE observations show little or no NPP enhancement under elevated CO₂ at the +0 and +2.25 °C warming levels, with a positive CO₂ fertilization effect emerging only under stronger warming. This discrepancy highlights persistent model biases in representing interactions between warming and CO₂ responses. Process-based analysis further indicates that divergence in modeled responses is associated with differences in vegetation structure (light competition and moss representation), nutrient cycling (nitrogen and phosphorus), and dynamic peat representation. These structural differences systematically influence ecosystem productivity, heterotrophic respiration, and net C balance across the model ensemble. An illustrative parameter sensitivity analysis using the SPRUCE-specific Energy Exascale Earth System Model (E3SM) Land Model (ELM-SPRUCE) further shows that parameter choices can also modulate the magnitude of simulated responses. Together, these results demonstrate that peatland responses to warming and elevated CO₂ are highly sensitive to model structure, parameterization, and process coupling, highlighting the need for improved representation of key peatland processes to reduce uncertainty in Earth system projections. The SPRUCE experimental framework provides a unique benchmark for evaluation and improving process representation and constraining near-term peatland response to environmental change, thereby strengthening confidence in longer-term C-climate projections.

Soil microbial ecology is data‐rich yet struggles to predict ecosystem functions under ongoing environmental change, including climate warming, altered precipitation patterns, and land‐use intensification. The core challenge lies in linking micron‐scale microbial metabolism to field‐scale outcomes. We argue that progress requires: (i) shifting from taxonomic inventories to functional traits as predictors; (ii) integrating machine learning with ecological theory, rigorous uncertainty quantification, and cross‐validation; (iii) developing hybrid models that merge mechanistic understanding with data‐driven approaches; and (iv) building new theoretical and mathematical frameworks to bridge spatiotemporal scales. We distinguish soil function (ecosystem‐level outcomes) from microbial function (organismal or community‐level activities), as the relationship between them remains poorly understood. We focus on agricultural soils as a primary application domain, given their global significance for food security, climate mitigation, and the urgent need for management‐relevant predictions. Realising predictive microbiology demands standardised data, harmonised metadata, and transdisciplinary collaboration among soil scientists, ecologists, microbiologists, bioinformaticians, and data scientists. This commentary outlines a pathway from descriptive characterisation toward predictive, microbially informed management of multifunctional agricultural soils.

The terrestrial carbon sink has strengthened in recent decades through enhanced maximum CO₂ uptake (MCU) and a longer carbon uptake period (CUP), whereas maximum CO₂ release (MCR) remains less quantified. Using atmospheric inversion estimates and 16 terrestrial biosphere models, we show that MCU and MCR both increased significantly from 1993 to 2022, with MCU rising faster globally (0.18 ± 0.04 versus 0.07 ± 0.01 g C m⁻² yr⁻¹). MCU increases were widespread, whereas MCR increases were strongest at mid- to high latitudes. Enhanced MCU and CUP increased annual net ecosystem productivity by 0.31 ± 0.09 g C m⁻² yr⁻¹, but rising MCR offset 44 ± 20% of this gain, leaving a net increase of 0.18 ± 0.06 g C m⁻² yr⁻¹. Elevated CO₂ dominates long-term increases, while climate variability contributes to spatial heterogeneity. These findings highlight the importance of accounting for carbon release in projections of future terrestrial sink strength.

Peatlands cover only ~3 % of Earth’s land surface yet store ~30 % of global soil carbon (C), making them critical components of the terrestrial C cycle and influential regulators of C-climate feedbacks. However, their responses to climate warming and elevated CO₂ remain highly uncertain, in part because Earth system models represent peatland processes with varying levels of complexity and realism, and because peat C pools turn over on centennial to millennial timescales that challenge model evaluation. Here, we present results from the SPRUCE (Spruce and Peatland Responses Under Changing Environments) Model Intercomparison Project (SPRUCE-MIP), which evaluates 15 terrestrial ecosystem models against observations from a long-term whole-ecosystem warming and CO₂-enrichment experiment at an ombrotrophic bog in northern Minnesota, USA. Models were driven by observed meteorology from in situ warming treatments spanning +0 to +9 °C, at either ambient or elevated CO₂ (+500 ppm). Simulated net ecosystem exchange (NEE), net primary productivity (NPP), heterotrophic respiration (HR), and methane (CH₄) fluxes were benchmarked against multi-year observations. Model predictions exhibit a large spread in both baseline C balance, ranging from strong sinks to strong sources under +0 °C warming, and temperature sensitivity, indicating substantial uncertainty in predicting peatland C responses to environmental forcing. Across the ensemble, models fall into four distinct functional response types: (i) models that simulate peatlands as net C sources across all warming and CO₂ conditions; (ii) models that transition from C sinks to sources under warming irrespective of CO₂ level; (iii) models that transition only under ambient CO₂ but remain C sinks under elevated CO₂, reflecting strong CO₂ fertilization effects; and (iv) models that maintain persistent sinks or near-neutral C balance even under extreme warming. While most models predict enhanced NPP under elevated CO₂ across all warming treatments, SPRUCE observations show little or no NPP enhancement under elevated CO₂ at the +0 and +2.25 °C warming levels, with a positive CO₂ fertilization effect emerging only under stronger warming. This discrepancy highlights persistent model biases in representing interactions between warming and CO₂ responses. Process-based analysis further indicates that divergence in modeled responses is associated with differences in vegetation structure (light competition and moss representation), nutrient cycling (nitrogen and phosphorus), and dynamic peat representation. These structural differences systematically influence ecosystem productivity, heterotrophic respiration, and net C balance across the model ensemble. An illustrative parameter sensitivity analysis using the SPRUCE-specific Energy Exascale Earth System Model (E3SM) Land Model (ELM-SPRUCE) further shows that parameter choices can also modulate the magnitude of simulated responses. Together, these results demonstrate that peatland responses to warming and elevated CO2 are highly sensitive to model structure, parameterization, and process coupling, highlighting the need for improved representation of key peatland processes to reduce uncertainty in Earth system projections. The SPRUCE experimental framework provides a unique benchmark for evaluation and improving process representation and constraining near-term peatland response to environmental change, thereby strengthening confidence in longer-term C-climate projections.

Nitrous oxide (N ₂ O), a potent greenhouse gas and the dominant ozone-depleting substance, is primarily emitted from soils. Understanding atmosphere-to-soil N ₂ O uptake is crucial for harnessing the soil’s natural sink potential, yet its quantification and mechanistic drivers remain poorly constrained. Here, we disentangled gross N ₂ O emission and uptake using the ¹⁵ N ₂ O pool dilution method, revealing key drivers in five cropland soils across China and in labile carbon–amended soils. In particular, the ratio of gross N ₂ O uptake to emission—driven by the soil labile carbon to inorganic nitrogen [dissolved organic carbon/dissolved inorganic nitrogen (DOC/DIN)] ratio—showed pronounced variability (0.28 to 0.79), revealing a powerful but previously underestimated lever for regulating net N ₂ O fluxes. Mechanistically, an elevated DOC/DIN ratio promoted complete denitrification by increasing electron supply, stimulating oxygen-depleting respiration, and enhancing nosZ gene abundance, achieving up to 51% N ₂ O mitigation. Our findings underscore soils as dynamic N ₂ O regulators and propose a carbon-nitrogen–coupled regulation framework, where labile carbon plays a critical role in regulating N ₂ O dynamics.

Plants often exhibit a midday depression in water use (i.e., transpiration), reflecting a constraint on their ability to sustain maximum water transport, which may occur at the cost of reduced photosynthesis. Eddy covariance observations and geostationary satellites cannot quantify this widespread phenomenon globally while resolving fine-scale spatial variability. Using evapotranspiration measurements from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and machine learning, we quantify the global distribution of midday depression in evapotranspiration. Midday depression primarily occurs during peak-growing seasons in temperate zones and dry periods in the tropics, with a morning shift of the peak evapotranspiration time by > 0.5 h. Droughts and heatwaves intensify midday depression, with peak evapotranspiration time shifting earlier, typically by 0.5 to 1.5 h and approaching 2 h during local extremes. By contrast, the coarse resolution upscaled flux products and hourly meteorological reanalysis datasets cannot capture this phenomenon. Midday depression of evapotranspiration detected using ECOSTRESS and machine learning is strongly associated with plant water stress, and helps us better understand how plants use water during drought and heatwaves.