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Abstract: Terrestrial evapotranspiration is the second‐largest component of the land water cycle, linking the water, energy, and carbon cycles and influencing the productivity and health of ecosystems. The dynamics of ET across a spectrum of spatiotemporal scales and their controls remain an active focus of research across different science disciplines. Here, we provide an overview of the current state of ET science across in situ measurements, partitioning of ET, and remote sensing, and discuss how different approaches complement one another based on their advantages and shortcomings. We aim to facilitate collaboration among a cross‐disciplinary group of ET scientists to overcome the challenges identified in this paper and ultimately advance our integrated understanding of ET.

Radiative forcing (RF) resulting from changes in surface albedo is increasingly recognized as a significant driver of global climate change but has not been adequately estimated, including by Intergovernmental Panel on Climate Change (IPCC) assessment reports, compared with other warming agents. Here, we first present the physical foundation for modeling albedo-induced RF and the consequent global warming impact (GWIΔα). We then highlight the shortcomings of available current databases and methodologies for calculating GWIΔα at multiple temporal scales. There is a clear lack of comprehensive in situ measurements of albedo due to sparse geographic coverage of ground-based stations, whereas estimates from satellites suffer from biases due to the limited frequency of image collection, and estimates from earth system models (ESMs) suffer from very coarse spatial resolution land cover maps and associated albedo values in pre-determined lookup tables. Field measurements of albedo show large differences by ecosystem type and large diurnal and seasonal changes. As indicated from our findings in southwest Michigan, GWIΔα is substantial, exceeding the RFΔα values of IPCC reports. Inclusion of GWIΔα to landowners and carbon credit markets for specific management practices are needed in future policies. We further identify four pressing research priorities: developing a comprehensive albedo database, pinpointing accurate reference sites within managed landscapes, refining algorithms for remote sensing of albedo by integrating geostationary and other orbital satellites, and integrating the GWIΔα component into future ESMs.

Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM-FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold-induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits.

Tundra and boreal ecosystems encompass the northern circumpolar permafrost region and are experiencing rapid environmental change with important implications for the global carbon (C) budget. We analysed multi-decadal time series containing 302 annual estimates of carbon dioxide (CO2) flux across 70 permafrost and non-permafrost ecosystems, and 672 estimates of summer CO2 flux across 181 ecosystems. We find an increase in the annual CO2 sink across non-permafrost ecosystems but not permafrost ecosystems, despite similar increases in summer uptake. Thus, recent non-growing-season CO2 losses have substantially impacted the CO2 balance of permafrost ecosystems. Furthermore, analysis of interannual variability reveals warmer summers amplify the C cycle (increase productivity and respiration) at putatively nitrogen-limited sites and at sites less reliant on summer precipitation for water use. Our findings suggest that water and nutrient availability will be important predictors of the C-cycle response of these ecosystems to future warming.

Stabilizing climate requires reducing greenhouse gas (GHG) emissions and storing atmospheric carbon dioxide (CO2) in land or ocean systems. Soil management practices can reduce GHG emissions or sequester atmospheric CO2 into inorganic and organic forms. However, whether soil carbon strategies represent a viable and impactful climate mitigation pathway is uncertain. A specific question concerns the role that land-management practices and soil amendments can play in realizing California’s ambition for carbon neutrality by 2045. Here we examine the carbon flux impacts of soil conservation (i.e., compost, reduced tillage, cover crop) and enhanced silicate rock weathering (EW) practices at different areal extents of implementation in cropland, grassland, and savanna in California under two climate change cases. We show that with implementation areas of 15% or 50% of private cultivated land, grassland, and savanna in California, soil conservation practices alone can contribute 1.4 0.7 2.1 % ( − 1.8 − 0.9 − 2.7 Mt CO2eq y−1) and 4.6 2.3 6.9 % ( − 6.0 − 3.0 − 8.9 Mt CO2eq y−1) of the additional emissions reduction needed (beyond previous targets) to meet the 2045 net neutrality goal (−129.3 Mt CO2eq y−1), respectively, on an average annual basis, including climate uncertainty. Including EW in these scenarios increases the total contributions of management practices to 4.1 2.5 5.6 % ( − 5.2 − 3.2 − 7.3 Mt CO2eq y−1) and 13.5 8.2 18.6 % ( − 17.5 − 10.7 − 24.2 Mt CO2eq y−1), respectively, of this reduction. This highlights that the extent of implementation area is a major factor in determining benefits and that EW has the potential to make a real contribution to net reduction targets. Results are similar across climate cases, indicating that contemporary field data can be used to make future projections. With EW there remains mechanistic uncertainties, however, such as rock dissolution rate and environmental controls on weathering products, which require additional field research to improve understanding of the technological efficacy of this approach for California’s 2045 carbon neutrality goal.

Grasslands are one of the most common land-cover types, providing important ecosystem services globally, yet few studies have examined grassland critical-zone functioning throughout hillslopes. This study characterised a coastal grassland over a small hillslope at Point Reyes National Seashore, California, using multidisciplinary techniques, combining remotely-sensed, geophysical, plant, and soil measurements. Clustering techniques delineated the study area into four landscape zones, up-, mid-, and down-slope, and a bordering riparian ecotone, which had distinct environmental properties that varied spatially across the site, with depth, and time. Soil moisture increased with depth and down slope towards a bordering riparian zone, and co-varied with soil CO2 flux rates both spatially and temporally. This highlighted three distinct controls of soil moisture on soil respiration: CO2 fluxes were inhibited by high moisture content in the down-slope during the wet winter months, and converged across landscape positions in the dry summer months, while also displaying post-rain pulses. The normalised difference vegetation index (NDVI) ranged from 0.32 (September)–0.80 (April) and correlated positively with soil moisture and aboveground biomass, moving down slope. Yet, NDVI, aboveground biomass, and soil moisture were not correlated to soil organic carbon (SOC) content (0.4%–4.5%), which was highest in the mid-slope. The SOC content may instead be linked to shifts in dominant grassland species and their rhizosphere properties with landscape position. This multidisciplinary characterisation highlighted significant heterogeneity in grassland properties with landscape position, and demonstrated an approach that could be used to characterise other critical-zone environments on hillslopes.

Contemporary resource management is doubly burdened by high rates of organic material disposal in landfills, generating potent greenhouse gases (GHG), and globally degraded soils, which threaten future food security. Expansion of composting can provide a resilient alternative, by avoiding landfill GHG emissions, returning valuable nutrients to the soil to ensure continued agricultural production, and sequestering carbon while supporting local communities. Recognizing this opportunity, California has set ambitious organics diversion targets in the Short-Lived Climate Pollutant Law (SB1383) which will require significant increases (5 to 8 million tonnes per year) in organic material processing capacity. This paper develops a spatial optimization model to consider how to handle this flow of additional material while achieving myriad social and ecological benefits through compost production. We consider community-based and on-farm facilities alongside centralized, large-scale infrastructure to explore decentralized and diversified alternative futures of composting infrastructure in the state of California. We find using a diversity of facilities would provide opportunity for cost savings while achieving significant emissions reductions of approximately 3.4 ± 1 MMT CO2e and demonstrate that it is possible to incorporate community protection into compost infrastructure planning while meeting economic and environmental objectives.

The sensitivity of soil organic carbon (SOC) decomposition in seasonally frozen soils, such as alpine ecosystems, to climate warming is a major uncertainty in global carbon cycling. Here we measure soil CO2 emission during four years (2018-2021) from the whole-soil warming experiment (4 °C for the top 1 m) in an alpine grassland ecosystem. We find that whole-soil warming stimulates total and SOC-derived CO2 efflux by 26% and 37%, respectively, but has a minor effect on root-derived CO2 efflux. Moreover, experimental warming only promotes total soil CO2 efflux by 7-8% on average in the meta-analysis across all grasslands or alpine grasslands globally (none of these experiments were whole-soil warming). We show that whole-soil warming has a much stronger effect on soil carbon emission in the alpine grassland ecosystem than what was reported in previous warming experiments, most of which only heat surface soils.

The long-standing assumption that aboveground plant litter inputs have a substantial influence on soil organic carbon storage (SOC) and dynamics has been challenged by a new paradigm for SOC formation and persistence. We tested the importance of plant litter chemistry on SOC storage, distribution, composition, and age by comparing two highly contrasting ecosystems: an old-growth coast redwood (Sequoia sempervirens) forest, with highly aromatic litter, and an adjacent coastal prairie, with more easily decomposed litter. We hypothesized that if plant litter chemistry was the primary driver, redwood would store more and older SOC that was less microbially processed than prairie. Total soil carbon stocks to 110 cm depth were higher in prairie (35 kg C m-2) than redwood (28 kg C m-2). Radiocarbon values indicated shorter SOC residence times in redwood than prairie throughout the profile. Higher amounts of pyrogenic carbon and a higher degree of microbial processing of SOC appear to be instrumental for soil carbon storage and persistence in prairie, while differences in fine-root carbon inputs likely contribute to younger SOC in redwood. We conclude that at these sites fire residues, root inputs, and soil properties influence soil carbon dynamics to a greater degree than the properties of aboveground litter.

Over 70% of soil organic carbon (SOC) is stored at a depth greater than 20 cm belowground. A portion of this deep SOC actively cycles on annual to decadal timescales and is sensitive to global change. However, deep SOC responses to global change likely differ from surface SOC responses because biotic controls on SOC cycling become weaker as mineral controls predominate with depth. Here, we synthesize the current information on deep SOC responses to the global change drivers of warming, shifting precipitation, elevated CO2, and land use and land cover change. Most deep SOC responses can only be hypothesized because few global change studies measure deep soils, and even fewer global change experiments manipulate deep soils. We call on scientists to incorporate deep soils into their manipulations, measurements, and models so that the response of deep SOC can be accounted for in projections of nature-based climate solutions and terrestrial feedbacks to climate change.

Organo-mineral and organo-metal associations play an important role in the retention and accumulation of soil organic carbon (SOC). Recent studies have demonstrated a positive correlation between calcium (Ca) and SOC content in a range of soil types. However, most of these studies have focused on soils that contain calcium carbonate (pH > 6). To assess the importance of Ca-SOC associations in lower pH soils, we investigated their physical and chemical interaction in the grassland soils of Point Reyes National Seashore (CA, USA) at a range of spatial scales. Multivariate analyses of our bulk soil characterisation dataset showed a strong correlation between exchangeable Ca (CaExch; 5-8.3 c.molc kg-1) and SOC (0.6-4%) content. Additionally, linear combination fitting (LCF) of bulk Ca K-edge X-ray absorption near-edge structure (XANES) spectra revealed that Ca was predominantly associated with organic carbon across all samples. Scanning transmission X-ray microscopy near-edge X-ray absorption fine structure spectroscopy (STXM C/Ca NEXAFS) showed that Ca had a strong spatial correlation with C at the microscale. The STXM C NEXAFS K-edge spectra indicated that SOC had a higher abundance of aromatic/olefinic and phenolic C functional groups when associated with Ca, relative to C associated with Fe. In regions of high Ca-C association, the STXM C NEXAFS spectra were similar to the spectrum from lignin, with moderate changes in peak intensities and positions that are consistent with oxidative C transformation. Through this association, Ca thus seems to be preferentially associated with plant-like organic matter that has undergone some oxidative transformation, at depth in acidic grassland soils of California. Our study highlights the importance of Ca-SOC complexation in acidic grassland soils and provides a conceptual model of its contribution to SOC preservation, a research area that has previously been unexplored.Supplementary informationThe online version contains supplementary material available at 10.1007/s10533-023-01059-2.

Subsoils contain more than half of soil organic carbon (SOC) and are expected to experience rapid warming in the coming decades. Yet our understanding of the stability of this vast carbon pool under global warming is uncertain. In particular, the fate of complex molecular structures (polymers) remains debated. Here we show that 4.5 years of whole-soil warming (+4 °C) resulted in less polymeric SOC (sum of specific polymers contributing to SOC) in the warmed subsoil (20-90 cm) relative to control, with no detectable change in topsoil. Warming stimulated the subsoil loss of lignin phenols (-17 ± 0%) derived from woody plant biomass, hydrolysable lipids cutin and suberin, derived from leaf and woody plant biomass (-28 ± 3%), and pyrogenic carbon (-37 ± 8%) produced during incomplete combustion. Given that these compounds have been proposed for long-term carbon sequestration, it is notable that they were rapidly lost in warmed soils. We conclude that complex polymeric carbon in subsoil is vulnerable to decomposition and propose that molecular structure alone may not protect compounds from degradation under future warming.

AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux's diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team's quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

Wetland CH₄ emissions are among the most uncertain components of the global CH₄ budget. The complex nature of wetland CH₄ processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH₄ emissions. In this study, we used the flux measurements of CH₄ from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH₄ emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH₄ emissions in all studied wetland types. Ecosystem respiration (CO₂) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH₄ emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH₄ emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH₄ emissions within earth system land models.

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

Arctic shrub expansion alters carbon budgets, albedo, and warming rates in high latitudes but remains challenging to predict due to unclear underlying controls. Observational studies and models typically use relationships between observed shrub presence and current environmental suitability (bioclimate and topography) to predict shrub expansion, while omitting shrub demographic processes and non-stationary response to changing climate. Here, we use high-resolution satellite imagery across Alaska and western Canada to show that observed shrub expansion has not been controlled by environmental suitability during 1984-2014, but can only be explained by considering seed dispersal and fire. These findings provide the impetus for better observations of recruitment and for incorporating currently underrepresented processes of seed dispersal and fire in land models to project shrub expansion and climate feedbacks. Integrating these dynamic processes with projected fire extent and climate, we estimate shrubs will expand into 25% of the non-shrub tundra by 2100, in contrast to 39% predicted based on increasing environmental suitability alone. Thus, using environmental suitability alone likely overestimates and misrepresents shrub expansion pattern and its associated carbon sink.

Soil is the largest terrestrial reservoir of organic carbon and is central for climate change mitigation and carbon-climate feedbacks. Chemical and physical associations of soil carbon with minerals play a critical role in carbon storage, but the amount and global capacity for storage in this form remain unquantified. Here, we produce spatially-resolved global estimates of mineral-associated organic carbon stocks and carbon-storage capacity by analyzing 1144 globally-distributed soil profiles. We show that current stocks total 899 Pg C to a depth of 1 m in non-permafrost mineral soils. Although this constitutes 66% and 70% of soil carbon in surface and deeper layers, respectively, it is only 42% and 21% of the mineralogical capacity. Regions under agricultural management and deeper soil layers show the largest undersaturation of mineral-associated carbon. Critically, the degree of undersaturation indicates sequestration efficiency over years to decades. We show that, across 103 carbon-accrual measurements spanning management interventions globally, soils furthest from their mineralogical capacity are more effective at accruing carbon; sequestration rates average 3-times higher in soils at one tenth of their capacity compared to soils at one half of their capacity. Our findings provide insights into the world's soils, their capacity to store carbon, and priority regions and actions for soil carbon management.

Arctic tundra has the potential to generate significant climate feedbacks, but spatial complexity makes it difficult to quantify the impacts of climate on ecosystem-atmosphere fluxes, particularly in polygonal tundra comprising wetter and drier polygon types on the scale of tens of meters. We measured CO2, CH4, and energy fluxes using eddy covariance for 7 yr (April to November, 2013–2019) in polygonal tundra near Utqiagvik, Alaska. This period saw the earliest snowmelt, latest snow accumulation, and hottest summer on record. To estimate fluxes by polygon type, we combined a polygon classification with a flux-footprint model. Methane fluxes were highest in the summer months but were also large during freeze-up and increased with the warming trend in August–November temperatures. While CO2 respiration had a consistent, exponential relationship with temperature, net ecosystem exchange was more variable among years. CO2 and CH4 exchange (June–September) ranged between −0.83 (Standard error [SE] = 0.03) and −1.32 (SE = 0.04) μmol m−2 s−1 and 13.92 (SE = 0.26)—23.42 (SE = 0.45) nmol m−2 s−1, respectively, and varied interannually (p ≤ 0.05). The maximum-influence method effectively attributed fluxes to polygon types. Areas dominated by low-centered polygons had higher CO2 fluxes except in 2016–2017. Methane fluxes were highest in low-centered polygons 2013–2015 and in flat-centered polygons in subsequent years, possibly due to increasing temperature and precipitation. Sensible and latent heat fluxes also varied significantly among polygon types. Accurate characterization of Arctic fluxes and their climate dependencies requires spatial disaggregation and long term observations.

Subsoils below 20 cm are an important reservoir in the global carbon cycle, but little is known about their vulnerability under climate change. We measured a statistically significant loss of subsoil carbon (-33 ± 11%) in warmed plots of a conifer forest after 4.5 years of whole-soil warming (4°C). The loss of subsoil carbon was primarily from unprotected particulate organic matter. Warming also stimulated a sustained 30 ± 4% increase in soil CO2 efflux due to increased CO2 production through the whole-soil profile. The observed in situ decline in subsoil carbon stocks with warming is now definitive evidence of a positive soil carbon-climate feedback, which could not be concluded based on increases in CO2 effluxes alone. The high sensitivity of subsoil carbon and the different responses of soil organic matter pools suggest that models must represent these heterogeneous soil dynamics to accurately predict future feedbacks to warming.

Increasing global temperatures have the potential to stimulate decomposition and alter the composition of soil organic matter (SOM). However, questions remain about the extent to which SOM quality and quantity along the soil profile may change under future warming. In this study we assessed how +4 °C whole-soil warming affected the quantity and quality of SOM down to 90 cm depth in a mixed-coniferous temperate forest using biomarker analyses. Our findings indicate that 4.5 years of soil warming led to divergent responses in subsoils (>20 cm) as compared to surface soils. Warming enhanced the accumulation of plant-derived n-alkanes over the whole soil profile. In the subsoil, this was at the expense of plant- and microorganism-derived fatty acids, and the relative abundance of SOM molecular components shifted from less microbially transformed to more transformed organic matter. Fine root mass declined by 24.0 ± 7.5% with warming over the whole soil profile, accompanied by reduced plant-derived inputs and accelerated decomposition of aromatic compounds and plant-derived fatty acids in the subsoils. Our study suggests that warming accelerated microbial decomposition of plant-derived inputs, leaving behind more degraded organic matter. The non-uniform, and depth dependent SOM composition and warming response implies that subsoil carbon cycling is as sensitive and complex as in surface soils.

Vegetation composition shifts, and in particular, shrub expansion across the Arctic tundra are some of the most important and widely observed responses of high-latitude ecosystems to rapid climate warming. These changes in vegetation potentially alter ecosystem carbon balances by affecting a complex set of soil-plant-atmosphere interactions. In this review, we synthesize the literature on (a) observed shrub expansion, (b) key climatic and environmental controls and mechanisms that affect shrub expansion, (c) impacts of shrub expansion on ecosystem carbon balance, and (d) research gaps and future directions to improve process representations in land models. A broad range of evidence, including in-situ observations, warming experiments, and remotely sensed vegetation indices have shown increases in growth and abundance of woody plants, particularly tall deciduous shrubs, and advancing shrublines across the circumpolar Arctic. This recent shrub expansion is affected by several interacting factors including climate warming, accelerated nutrient cycling, changing disturbance regimes, and local variation in topography and hydrology. Under warmer conditions, tall deciduous shrubs can be more competitive than other plant functional types in tundra ecosystems because of their taller maximum canopy heights and often dense canopy structure. Competitive abilities of tall deciduous shrubs vs herbaceous plants are also controlled by variation in traits that affect carbon and nutrient investments and retention strategies in leaves, stems, and roots. Overall, shrub expansion may affect tundra carbon balances by enhancing ecosystem carbon uptake and altering ecosystem respiration, and through complex feedback mechanisms that affect snowpack dynamics, permafrost degradation, surface energy balance, and litter inputs. Observed and projected tall deciduous shrub expansion and the subsequent effects on surface energy and carbon balances may alter feedbacks to the climate system. Land models, including those integrated in Earth System Models, need to account for differences in plant traits that control competitive interactions to accurately predict decadal- to centennial-scale tundra vegetation and carbon dynamics.

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.

Wetland methane (CH4) emissions ([Formula: see text]) are important in global carbon budgets and climate change assessments. Currently, [Formula: see text] projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent [Formula: see text] temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that [Formula: see text] are often controlled by factors beyond temperature. Here, we evaluate the relationship between [Formula: see text] and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between [Formula: see text] and temperature, suggesting larger [Formula: see text] sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.

Increasing global temperatures are predicted to stimulate soil microbial respiration. The direct and indirect impacts of warming on soil microbes, nevertheless, remain unclear. This is particularly true for understudied subsoil microbes. Here, we show that 4.5 years of whole-profile soil warming in a temperate mixed forest results in altered microbial community composition and metabolism in surface soils, partly due to carbon limitation. However, microbial communities in the subsoil responded differently to warming than in the surface. Throughout the soil profile-but to a greater extent in the subsoil-physiologic and genomic measurements show that phylogenetically different microbes could utilize complex organic compounds, dampening the effect of altered resource availability induced by warming. We find subsoil microbes had 20% lower carbon use efficiencies and 47% lower growth rates compared to surface soils, which constrain microbial communities. Collectively, our results show that unlike in surface soils, elevated microbial respiration in subsoils may continue without microbial community change in the near-term.

Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20g g€¯S to 20g g€¯N) the spring onset of elevated CH4 emissions starts 3g€¯d earlier, and the CH4 emission season lasts 4g€¯d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at 10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

The microbial community composition in subsoils remains understudied, and it is largely unknown whether subsoil microorganisms show a similar response to global warming as microorganisms at the soil surface do. Since microorganisms are the key drivers of soil organic carbon decomposition, this knowledge gap causes uncertainty in the predictions of future carbon cycling in the subsoil carbon pool (>g50g% of the soil organic carbon stocks are below 30gcm soil depth). In the Blodgett Forest field warming experiment (California, USA) we investigated how +4ggC warming in the whole-soil profile to 100gcm soil depth for 4.5 years has affected the abundance and community structure of microorganisms. We used proxies for bulk microbial biomass carbon (MBC) and functional microbial groups based on lipid biomarkers, such as phospholipid fatty acids (PLFAs) and branched glycerol dialkyl glycerol tetraethers (brGDGTs). With depth, the microbial biomass decreased and the community composition changed. Our results show that the concentration of PLFAs decreased with warming in the subsoil (below 30gcm) by 28g% but was not affected in the topsoil. Phospholipid fatty acid concentrations changed in concert with soil organic carbon. The microbial community response to warming was depth dependent. The relative abundance of Actinobacteria increased in warmed subsoil, and Gram+ bacteria in subsoils adapted their cell membrane structure to warming-induced stress, as indicated by the ratio of anteiso to iso branched PLFAs. Our results show for the first time that subsoil microorganisms can be more affected by warming compared to topsoil microorganisms. These microbial responses could be explained by the observed decrease in subsoil organic carbon concentrations in the warmed plots. A decrease in microbial abundance in warmed subsoils might reduce the magnitude of the respiration response over time. The shift in the subsoil microbial community towards more Actinobacteria might disproportionately enhance the degradation of previously stable subsoil carbon, as this group is able to metabolize complex carbon sources.

Land-atmosphere carbon exchange is known to be extremely heterogeneous in arctic ice-wedge polygonal tundra regions. In this study, a Kalman filter-based method was developed to estimate the spatio-temporal dynamics of daytime average net ecosystem exchange (NEEday) at 0.5-m resolution over a 550 m by 700 m study site. We integrated multi-scale, multi-type datasets, including normalized difference vegetation indices (NDVIs) obtained from a novel automated mobile sensor system (or tram system) and a greenness index map obtained from airborne imagery. We took advantage of the significant correlations between NDVI and NEEday identified based on flux chamber measurements. The weighted average of the estimated NEEday within the flux-tower footprint agreed with the flux tower data in term of its seasonal dynamics. We then evaluated the spatial variability of the growing season average NEEday, as a function of polygon geomorphic classes; i.e., the combination of polygon types—which are known to present different degradation stages associated with permafrost thaw—and microtopographic features (i.e., troughs, centers and rims). Our study suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.

Current knowledge of the mechanisms driving soil organic matter (SOM) turnover and responses to warming is mainly limited to surface soils, although over 50% of global soil carbon is contained in subsoils. Deep soils have different physicochemical properties, nutrient inputs, and microbiomes, which may harbor distinct functional traits and lead to different SOM dynamics and temperature responses. We hypothesized that kinetic and thermal properties of soil exoenzymes, which mediate SOM depolymerization, vary with soil depth, reflecting microbial adaptation to distinct substrate and temperature regimes. We determined the Michaelis-Menten (MM) kinetics of three ubiquitous enzymes involved in carbon (C), nitrogen (N) and phosphorus (P) acquisition at six soil depths down to 90 cm at a temperate forest, and their temperature sensitivity based on Arrhenius/Q 10 and Macromolecular Rate Theory (MMRT) models over six temperatures between 4-50°C. Maximal enzyme velocity (V max) decreased strongly with depth for all enzymes, both on a dry soil mass and a microbial biomass C basis, whereas their affinities increased, indicating adaptation to lower substrate availability. Surprisingly, microbial biomass-specific catalytic efficiencies also decreased with depth, except for the P-acquiring enzyme, indicating distinct nutrient demands at depth relative to microbial abundance. These results suggested that deep soil microbiomes encode enzymes with intrinsically lower turnover and/or produce less enzymes per cell, reflecting distinct life strategies. The relative kinetics between different enzymes also varied with depth, suggesting an increase in relative P demand with depth, or that phosphatases may be involved in C acquisition. V max and catalytic efficiency increased consistently with temperature for all enzymes, leading to overall higher SOM-decomposition potential, but enzyme temperature sensitivity was similar at all depths and between enzymes, based on both Arrhenius/Q 10 and MMRT models. In a few cases, however, temperature affected differently the kinetic properties of distinct enzymes at discrete depths, suggesting that it may alter the relative depolymerization of different compounds. We show that soil exoenzyme kinetics may reflect intrinsic traits of microbiomes adapted to distinct soil depths, although their temperature sensitivity is remarkably uniform. These results improve our understanding of critical mechanisms underlying SOM dynamics and responses to changing temperatures through the soil profile.

Soil organic carbon management has the potential to aid climate change mitigation through drawdown of atmospheric carbon dioxide. To be effective, such management must account for processes influencing carbon storage and re-emission at different space and time scales. Achieving this requires a conceptual advance in our understanding to link carbon dynamics from the scales at which processes occur to the scales at which decisions are made. Here, we propose that soil carbon persistence can be understood through the lens of decomposers as a result of functional complexity derived from the interplay between spatial and temporal variation of molecular diversity and composition. For example, co-location alone can determine whether a molecule is decomposed, with rapid changes in moisture leading to transport of organic matter and constraining the fitness of the microbial community, while greater molecular diversity may increase the metabolic demand of, and thus potentially limit, decomposition. This conceptual shift accounts for emergent behaviour of the microbial community and would enable soil carbon changes to be predicted without invoking recalcitrant carbon forms that have not been observed experimentally. Functional complexity as a driver of soil carbon persistence suggests soil management should be based on constant care rather than one-time action to lock away carbon in soils.

Despite the fundamental importance of soil temperature for Earth's carbon and energy budgets, ecosystem functioning, and agricultural production, studies of climate change impacts on soil processes have mainly relied on air temperatures, assuming they are accurate proxies for soil temperatures. We evaluated changes in soil temperature, moisture, and air temperature predicted over the 21st century from 14 Earth system models. The model ensemble predicted a global mean soil warming of 2.3 ± 0.7 and 4.5 ± 1.1 °C at 100-cm depth by the end of the 21st century for RCPs 4.5 and 8.5, respectively. Soils at 100 cm warmed at almost exactly the same rate as near-surface (~1 cm) soils. Globally, soil warming was slightly slower than air warming above it, and this difference increased over the 21st century. Regionally, soil warming kept pace with air warming in tropical and arid regions but lagged air warming in colder regions. Thus, air warming is not necessarily a good proxy for soil warming in cold regions where snow and ice impede the direct transfer of sensible heat from the atmosphere to soil. Despite this effect, high-latitude soils were still projected to warm faster than elsewhere, albeit at slower rates than surface air above them. When compared with observations, the models were able to capture soil thermal dynamics in most biomes, but some failed to recreate thermal properties in permafrost regions. Particularly in cold regions, using soil warming rather than air warming projections may improve predictions of temperature-sensitive soil processes.

Developing a more mechanistic understanding of soil respiration is hampered by the difficulty in determining the contribution of different organic substrates to respiration and in disentangling autotrophic-versus-heterotrophic and aerobic-versus-anaerobic processes. Here, we use a relatively novel tool for better understanding soil respiration: The apparent respiration quotient (ARQ). The ARQ is the amount of CO2 produced in the soil divided by the amount of O2 consumed, and it changes according to which organic substrates are being consumed and whether oxygen is being used as an electron acceptor. We investigated how the ARQ of soil gas varied seasonally, by soil depth, and by in situ experimental warming (+4 °C) in a coniferousforest whole-soil-profile warming experiment over 2 years. We then compared the patterns in ARQ to those of soil δ13CO2. Our measurements showed strong seasonal variations in ARQ, from ≈ 0.9 during the late spring and summer to 0:7 during the winter. This pattern likely reflected a shift from respiration being fueled by oxidized substrates like sugars and organic acids derived from root and root respiration during the growing season to more reduced substrates such as lipids and proteins derived from microbial necromass during the winter. This interpretation was supported by δ13CO2 values, which were lower, like lipids, in the winter and higher, like sugars, in the summer. Furthermore, experimental warming significantly changed how both ARQ and δ13CO2 responded to soil temperature. Wintertime ARQ and δ13CO2 values were higher in heated than in control plots, probably due to the warming-driven increase in microbial activity that may have utilized oxidized carbon substrates, while growing-season values were lower in heated plots. Experimental warming and phenology change the sources of soil respiration throughout the soil profile. The sensitivity of ARQ to these changes demonstrates its potential as a tool for disentangling the biological sources contributing to soil respiration.

Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO2 and CH4 fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM-Microbe, to examine the microtopographic impacts on CO2 and CH4 fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low-centered polygon (LCP) center, LCP transition, LCP rim, high-centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM-Microbe model against static-chamber measured CO2 and CH4 fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low-elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH4 emissions rates with greater seasonal variations than high-elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO2 + H2) is the most important factor determining CH4 emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH4 emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area-weighted approach before validation against EC-measured CH4 fluxes. The model underestimated the EC-measured CH4 flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH4 flux. The strong microtopographic impacts on CO2 and CH4 fluxes call for a model-data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape.

Developing and testing decadal-scale predictions of soil response to climate change is difficult because there are few long-term warming experiments or other direct observations of temperature response. As a result, spatial variation in temperature is often used to characterize the influence of temperature on soil organic carbon (SOC) stocks under current and warmer temperatures. This approach assumes that the decadal-scale response of SOC to warming is similar to the relationship between temperature and SOC stocks across sites that are at quasi steady state; however, this assumption is poorly tested. We developed four variants of a Reaction-network-based model of soil organic matter and microbes using measured SOC stocks from a 4,000-km latitudinal transect. Each variant reflects different assumptions about the temperature sensitivities of microbial activity and mineral sorption. All four model variants predicted the same response of SOC to temperature at steady state, but different projections of transient warming responses. The relative importance of Qmax, mean annual temperature, and net primary production, assessed using a machine-learning algorithm, changed depending on warming duration. When mineral sorption was temperature sensitive, the predicted average change in SOC after 100 years of 5 °C warming was −18% if warming decreased sorption or +9% if warming increased sorption. When microbial activity was temperature sensitive but mineral sorption was not, average site-level SOC loss was 5%. We conclude that spatial climate gradients of SOC stocks are insufficient to constrain the transient response; measurements that distinguish process controls and/or observations from long-term warming experiments, especially mineral fractions, are needed.

The Arctic is expected to shift from a sink to a source of atmospheric CO2 this century due to climate-induced increases in soil carbon mineralization1. The magnitude of this effect remains uncertain, largely because temperature sensitivities of organic matter decomposition2,3 and the distribution of these temperature sensitivities across soil carbon pools4 are not well understood. Here, a new analytical method with natural abundance radiocarbon was used to evaluate temperature sensitivities across soil carbon pools. With soils from Utqiaġvik (formerly Barrow), Alaska, an incubation experiment was used to evaluate soil carbon age and decomposability, disentangle the effects of temperature and substrate depletion on carbon mineralization, and compare temperature sensitivities of fast-cycling and slow-cycling carbon. Old, historically stable carbon was shown to be vulnerable to decomposition under warming. Using radiocarbon to differentiate between slow-cycling and fast-cycling carbon, temperature sensitivity was found to be invariant among pools, with a Q10 of ~2 irrespective of native decomposition rate. These findings suggest that mechanisms other than chemical recalcitrance mediate the effect of warming on soil carbon mineralization.

The global land surface absorbs about a third of anthropogenic emissions each year, due to the difference between two key processes: ecosystem photosynthesis and respiration. Despite the importance of these two processes, it is not possible to measure either at the ecosystem scale during the daytime. Eddy-covariance measurements are widely used as the closest 'quasi-direct' ecosystem-scale observation from which to estimate ecosystem photosynthesis and respiration. Recent research, however, suggests that current estimates may be biased by up to 25%, due to a previously unaccounted for process: the inhibition of leaf respiration in the light. Yet the extent of inhibition remains debated, and implications for estimates of ecosystem-scale respiration and photosynthesis remain unquantified. Here, we quantify an apparent inhibition of daytime ecosystem respiration across the global FLUXNET eddy-covariance network and identify a pervasive influence that varies by season and ecosystem type. We develop partitioning methods that can detect an apparent ecosystem-scale inhibition of daytime respiration and find that diurnal patterns of ecosystem respiration might be markedly different than previously thought. The results call for the re-evaluation of global terrestrial carbon cycle models and also suggest that current global estimates of photosynthesis and respiration may be biased, some on the order of magnitude of anthropogenic fossil fuel emissions.

There is significant spatial and temporal variability associated with greenhouse gas (GHG) fluxes in high-latitude Arctic tundra environments. The objectives of this study are to investigate temporal variability in CO2 and CH4 fluxes at Barrow, AK and to determine the factors causing this variability using a novel entropy-based classification scheme. In particular, we analyzed which geomorphic, soil, vegetation and climatic properties most explained the variability in GHG fluxes (opaque chamber measurements) during the growing season over three successive years. Results indicate that multi-year variability in CO2 fluxes was primarily associated with soil temperature variability as well as vegetation dynamics during the early and late growing season. Temporal variability in CH4 fluxes was primarily associated with changes in vegetation during the growing season and its interactions with primary controls like seasonal thaw. Polygonal ground features, which are common to Arctic regions, also demonstrated significant multi-year variability in GHG fluxes. Our results can be used to prioritize field sampling strategies, with an emphasis on measurements collected at locations and times that explain the most variability in GHG fluxes. For example, we found that sampling primary environmental controls at the centers of high centered polygons in the month of September (when freeze-back period begins) can provide significant constraints on GHG flux variability - a requirement for accurately predicting future changes to GHG fluxes. Overall, entropy results document the impact of changing environmental conditions (e.g., warming, growing season length) on GHG fluxes, thus providing clues concerning the manner in which ecosystem properties may be shifted regionally in a future climate.

We describe a new coordination activity and initial results for a global synthesis of eddy covariance CH4 flux measurements.

Even though over half of the world's soil organic carbon (SOC) is stored in subsoils (>20 cm deep), and the old ages of subsoil OC indicate its cycling differs from surface SOC, there are few studies examining in situ decomposition processes in deep soils. Here, we added ¹³C-labeled fine roots to 15, 55, and 95 cm depths of a well-characterized coniferous forest Alfisol and monitored the amount of root-derived C remaining over 6, 12, and 30 months. We recovered the root-derived C in microbial phospholipid fatty acids (PLFAs) after 6 months and in coarse (>2 mm) particulate, fine (

Even though over half of the world's soil organic carbon (SOC) is stored in subsoils (>20 cm deep), and the old ages of subsoil OC indicate its cycling differs from surface SOC, there are few studies examining in situ decomposition processes in deep soils. Here, we added 13C-labeled fine roots to 15, 55, and 95 cm depths of a well-characterized coniferous forest Alfisol and monitored the amount of root-derived C remaining over 6, 12, and 30 months. We recovered the root-derived C in microbial phospholipid fatty acids (PLFAs) after 6 months and in coarse (>2 mm) particulate, fine (

In the Arctic, environmental factors governing microbial degradation of soil carbon (C) in active layer and permafrost are poorly understood. Here we determined the functional potential of soil microbiomes horizontally and vertically across a cryoperturbed polygonal landscape in Alaska. With comparative metagenomics, genome binning of novel microbes, and gas flux measurements we show that microbial greenhouse gas (GHG) production is strongly correlated to landscape topography. Active layer and permafrost harbor contrasting microbiomes, with increasing amounts of Actinobacteria correlating with decreasing soil C in permafrost. While microbial functions such as fermentation and methanogenesis were dominant in wetter polygons, in drier polygons genes for C mineralization and CH4 oxidation were abundant. The active layer microbiome was poised to assimilate N and not to release N2O, reflecting low N2O flux measurements. These results provide mechanistic links of microbial metabolism to GHG fluxes that are needed for the refinement of model predictions.

The original PDF version of this Article contained an error in Table 1. On the right-hand side of the third row, the third equation was missing a β as an exponent on the first CB. This has now been corrected in the PDF version of the Article. The HTML version was correct from the time of publication.

Radiocarbon measurements of ecosystem respiration and soil pore space CO2 are useful for determining the sources of ecosystem respiration, identifying environmental controls on soil carbon cycling rates, and parameterizing and evaluating models of the carbon cycle. We measured flux rates and radiocarbon content of ecosystem respiration, as well as radiocarbon in soil profile CO2 in UtqiaAïvik (Barrow), Alaska, during the summers of 2012, 2013, and 2014. We found that radiocarbon in ecosystem respiration (Δ;14CReco) ranged from +60.5 to '160 ‰ with a median value of +23.3 ‰. Ecosystem respiration became more depleted in radiocarbon from summer to autumn, indicating increased decomposition of old soil organic carbon and/or decreased CO2 production from fast-cycling carbon pools. Across permafrost features, ecosystem respiration from high-centered polygons was depleted in radiocarbon relative to other polygon types. Radiocarbon content in soil pore-space CO2 varied between '7.1 and '280 ‰, becoming more negative with depth in individual soil profiles. These pore-space radiocarbon values correspond to CO2 mean ages of 410 to 3350 years, based on a steady-state, one-pool model. Together, these data indicate that deep soil respiration was derived primarily from old, slow-cycling carbon, but that total CO2 fluxes depended largely on autotrophic respiration and heterotrophic decomposition of fast-cycling carbon within the shallowest soil layers. The relative contributions of these different CO2 sources were highly variable across microtopographic features and time in the sampling season. The highly negative Δ14C values in soil pore-space CO2 and autumn ecosystem respiration indicate that when it is not frozen, very old soil carbon is vulnerable to decomposition. Radiocarbon data and associated CO2 flux and temperature data are stored in the data repository of the Next Generation Ecosystem Experiments (NGEE-Arctic) at http://dx.doi.org/10.5440/1364062 and https://doi.org/10.5440/1418853.

Soil organic carbon (SOC) can be defined by measurable chemical and physical pools, such as mineral-associated carbon, carbon physically entrapped in aggregates, dissolved carbon, and fragments of plant detritus. Yet, most soil models use conceptual rather than measurable SOC pools. What would the traditional pool-based soil model look like if it were built today, reflecting the latest understanding of biological, chemical, and physical transformations in soils? We propose a conceptual model—the Millennial model—that defines pools as measurable entities. First, we discuss relevant pool definitions conceptually and in terms of the measurements that can be used to quantify pool size, formation, and destabilization. Then, we develop a numerical model following the Millennial model conceptual framework to evaluate against the Century model, a widely-used standard for estimating SOC stocks across space and through time. The Millennial model predicts qualitatively similar changes in total SOC in response to single factor perturbations when compared to Century, but different responses to multiple factor perturbations. We review important conceptual and behavioral differences between the Millennial and Century modeling approaches, and the field and lab measurements needed to constrain parameter values. We propose the Millennial model as a simple but comprehensive framework to model SOC pools and guide measurements for further model development.

The breakdown and decomposition of plant inputs are critical for nutrient cycling, soil development, and climate-ecosystem feedbacks, but uncertainties persist in how the rates and products of litter decomposition are affected by soil temperature, rhizosphere, and depth of input. We investigated the effects of soil warming (+ 4 °C), rhizosphere, and depth of litter placement on the decomposition of Avena fatua (wild oat grass) root litter in a Mediterranean grassland ecosystem. Field lysimeters were subjected to three environmental treatments (heating, control, and plant removal) and three ¹³C-labeled root litter addition treatments (to A horizon, to B horizon, and no-addition disturbance control) for each of two harvest time points. We buried root litter in February 2014 and measured loss of ¹³C in CO₂ from the soil surface and in leachate as dissolved organic carbon (DOC) over two growing seasons. At the end of each growing season we recovered the ¹³C remaining in the soil. Loss of root litter C occurred almost entirely via heterotrophic respiration, with an estimated