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ABSTRACT Photosynthesis is the largest flux of carbon between the atmosphere and Earth's surface and is driven by enzymes that require nitrogen, namely, ribulose‐1,5‐bisphosphate (RuBisCO). Thus, photosynthesis is a key link between the terrestrial carbon and nitrogen cycle, and the representation of this link is critical for coupled carbon‐nitrogen land surface models. Models and observations suggest that soil nitrogen availability can limit plant productivity increases under elevated CO₂. Plants acclimate to elevated CO₂by downregulating RuBisCO and thus nitrogen in leaves, but this acclimation response is not currently included in land surface models. Acclimation of photosynthesis to CO₂can be simulated by the photosynthetic optimality theory in a way that matches observations. Here, we incorporated this theory into the land surface component of the Energy Exascale Earth System Model (ELM). We simulated land surface carbon and nitrogen processes under future elevated CO₂conditions to 2100 using the RCP8.5 high emission scenario. Our simulations showed that when photosynthetic acclimation is considered, photosynthesis increases under future conditions, but maximum RuBisCO carboxylation and thus photosynthetic nitrogen demand decline. We analyzed two simulations that differed as to whether the saved nitrogen could be used in other parts of the plant. The allocation of saved leaf nitrogen to other parts of the plant led to (1) a direct alleviation of plant nitrogen limitation through reduced leaf nitrogen requirements and (2) an indirect reduction in plant nitrogen limitation through an enhancement of root growth that led to increased plant nitrogen uptake. As a result, reallocation of saved leaf nitrogen increased ecosystem carbon stocks by 50.3% in 2100 as compared to a simulation without reallocation of saved leaf nitrogen. These results suggest that land surface models may overestimate future ecosystem nitrogen limitation if they do not incorporate leaf nitrogen savings resulting from photosynthetic acclimation to elevated CO₂. 

Abstract The dynamics of methane (CH₄) cycling in high-latitude peatlands through different pathways of methanogenesis and methanotrophy are still poorly understood due to the spatiotemporal complexity of microbial activities and biogeochemical processes. Additionally, long-termin situmeasurements within soil columns are limited and associated with large uncertainties in microbial substrates (e.g. dissolved organic carbon, acetate, hydrogen). To better understand CH₄cycling dynamics, we first applied an advanced biogeochemical model,ecosys, to explicitly simulate methanogenesis, methanotrophy, and CH₄transport in a high-latitude fen (within the Stordalen Mire, northern Sweden). Next, to explore the vertical heterogeneity in CH₄cycling, we applied the PCMCI/PCMCI+ causal detection framework with a bootstrap aggregation method to the modeling results, characterizing causal relationships among regulating factors (e.g. temperature, microbial biomass, soil substrate concentrations) through acetoclastic methanogenesis, hydrogenotrophic methanogenesis, and methanotrophy, across three depth intervals (0–10 cm, 10–20 cm, 20–30 cm). Our results indicate that temperature, microbial biomass, and methanogenesis and methanotrophy substrates exhibit significant vertical variations within the soil column. Soil temperature demonstrates strong causal relationships with both biomass and substrate concentrations at the shallower depth (0–10 cm), while these causal relationships decrease significantly at the deeper depth within the two methanogenesis pathways. In contrast, soil substrate concentrations show significantly greater causal relationships with depth, suggesting the substantial influence of substrates on CH₄cycling. CH₄production is found to peak in August, while CH₄oxidation peaks predominantly in October, showing a lag response between production and oxidation. Overall, this research provides important insights into the causal mechanisms modulating CH₄cycling across different depths, which will improve carbon cycling predictions, and guide the future field measurement strategies. 

Abstract Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics. 

Thermoelectric active cooling uses nontraditional thermoelectric materials with high thermal conductivity, high thermoelectric power factor, and relatively low figure of merit (ZT) to transfer large heat flows from a hot object to a cold heat sink. However, prior studies have not considered the influence of external thermal resistances associated with the heat sinks or contacts, making it difficult to design active cooling thermal systems or compare the use of low-ZT and high-ZT materials. Here, we perform a non-dimensionalized analysis of thermoelectric active cooling under forced heat flow boundary conditions, including arbitrary external thermal resistances. We identify the optimal electrical currents to minimize the heat source temperature and find the crossover heat flows at which low-ZT active cooling leads to lower source temperatures than high-ZT and even ZT→+∞ thermoelectric refrigeration. These optimal parameters are insensitive to the thermal resistance between the heat source and thermoelectric materials, but depend strongly on the heat sink thermal resistance. Finally, we map the boundaries where active cooling yields lower source temperatures than thermoelectric refrigeration. For currently considered active cooling materials, active cooling with ZT < 0.1 is advantageous compared to ZT→+∞ refrigeration for dimensionless heat sink thermal conductances larger than 15 and dimensionless source powers between 1 and 100. Thus, our results motivate further investigation of system-level thermoelectric active cooling for applications in electronics thermal management. 

Abstract Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N₂O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N₂O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process‐based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low‐ambition nitrogen regulation policies, EF would increase from 1.18%–1.22% in 2010 to 1.27%–1.34% by 2050, representing a relative increase of 4.4%–11.4% and exceeding the IPCC tier‐1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%–0.35% (relative increase of 11.9%–17%). In contrast, high‐ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying–wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N₂O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N₂O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio‐economic assessments and policy‐making efforts. 

This dataset contains yearly projections of emission factors (EFs) for fertilizer-induced direct nitrous oxide (N2O) emissions across the global agricultural lands with a spatial resolution of 0.5° × 0.5° from 1990 to 2050. Emission factor (EF) is defined as the amount of N2O emitted per unit of nitrogen (N) fertilizer applied, expressed in percentage (%). They are developed from a hybrid modeling framework, Dym-EF (more details can be found in Li et al., 2024). The framework integrates machine learning approaches with an ensemble of eight process-based models from The Global N2O Model Intercomparison Project phase 2 (NMIP2) to learn the relationship between EF dynamics and multiple environmental factors, such as climate, soil properties, nitrogen fertilizer input, and other agricultural management practices. After the hybrid modeling framework was extensively validated, we applied it to develop EF projections under different nitrogen management policies and climate change scenarios, including future climate data from 37 Global Climate Models (GCMs). The annual median and standard deviation (SD) of EF under each scenario represent the projection median and variability derived from climate input data using the 37 GCMs.The dataset filenames follow the structure: 'Scenario'_'N regulation'_'Median/SD', where 'Scenario' corresponds to the different nitrogen management and climate scenarios (e.g., INMS1, INMS2, and INMS3), 'N regulation' corresponds to the different nitrogen management levels (e.g., BAU, LowNRegul, and MedNRegul), and 'Median/SD' indicates whether the file contains the median (Median) or standard deviation (SD) of the projections. All relevant data and further details can be found in the supplementary materials and the cited references.INMS1: Business-as-usual, Land use regulation: Medium, Diet: Meat & dairy-rich, Ambition level: LowINMS2: Low-nitrogen regulation, Land use regulation: Medium, Diet: Medium meat & dairy, Ambition level: LowINMS3: Medium-nitrogen regulation, Land use regulation: Medium, Diet: Medium meat & dairy, Ambition level: ModerateINMS4: High-nitrogen regulation, Land use regulation: Medium, Diet: Medium meat & dairy, Ambition level: HighINMS5: Best-case, Land use regulation: Strong, Diet: Low meat & dairy, Ambition level: HighINMS6: Best-case “Plus”, Land use regulation: Strong, Diet: Ambitious diet shift and food-loss/waste reductions, Ambition level: HighINMS7: Bioenergy, Land use regulation: Strong, Diet: Low meat & dairy, Ambition level: HighWe developed this data using the “ranger” package in R 4.1.1, which is accessible at https://cran.r-project.org/web/packages/ranger/. The optimization of the two hyperparameters (ntree and mtry) was performed using the ‘caret’ package, available at https://topepo.github.io/caret/.This database is developed by Li, L., C. Lu, W. Winiwarter, H. Tian, J. Canadell, A. Ito, A.K. Jain, S. Kou-Giesbrecht, S. Pan, N. Pan, H. Shi, Q. Sun, N. Vuichard, S. Ye., S. Zaehle, Q. Zhu. Enhanced nitrous oxide emission factors due to climate change increase the mitigation challenge in the agricultural sector Global Change Biology (In Press) 

Earth System Models (ESMs) have implemented nitrogen (N) cycles to account for N limitation on terrestrial carbon uptake. However, representing inputs, losses and recycling of N in ESMs is challenging. Here, we use global rates and ratios of key soil N fluxes, including nitrification, denitrification, mineralization, leaching, immobilization and plant uptake (both NH4+ and NO3-), from the literature to evaluate the N cycles in the land model components of two ESMs. The two land models evaluated here, ELMv1-ECA and CLM5.0, originated from a common model but have diverged in their representation of plant/microbe competition for soil N. The models predict similar global rates of gross primary productivity (GPP) but have ~2 to 3-fold differences in their underlying global mineralization, immobilization, plant N uptake, nitrification and denitrification fluxes. Both models dramatically underestimate the immobilization of NO3- by soil bacteria compared to literature values and predict dominance of plant uptake by a single form of mineral nitrogen (NO3- for ELM, with regional exceptions, and NH4+ for CLM5.0). CLM5.0 strongly underestimates the global ratio of gross nitrification:gross mineralization and both models likely substantially underestimate the ratio of nitrification:denitrification. Few experimental data exist to evaluate this last ratio, in part because nitrification and denitrification are quantified with different techniques and because denitrification fluxes are difficult to measure at all. More observational constraints on soil nitrogen fluxes like nitrification and denitrification, as well as greater scrutiny of the functional impact of introducing separate NH4+ and NO3- pools into ESMs, could help improve confidence in present and future simulations of N limitation on the carbon cycle.