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Peer review is a crucial but time‐consuming and sometimes frustrating part of science. The Editors of JGR: Biogeosciencesare deeply appreciative of, and sincerely thank, the many 2023 reviewers who donated their time and expertise in support of the journal. The editorial team of JGR: Biogeosciencesrecognizes and thanks the researchers who reviewed manuscripts for the journal in 2023. The JGR: BiogeosciencesEditors thank and recognize our 2023 reviewersThese experts donated their time, expertise, and insight to improving the science of their colleagues and community The JGR: BiogeosciencesEditors thank and recognize our 2023 reviewers These experts donated their time, expertise, and insight to improving the science of their colleagues and community

Soil respiration (Rs), the soil‐to‐atmosphere flux of CO2, is a dominant but uncertain part of the carbon cycle, even after decades of study. This review focuses on progress in understanding Rs from laboratory incubations to global estimates. We survey key developments of in situ ecosystem‐scale Rs observations and manipulations, synthesize Rs meta‐analyses and global flux estimates, and discuss the most compelling challenges and opportunities for the future. Increasingly sophisticated lab experiments have yielded insights into the interaction among heterotrophic respiration, substrate supply, and enzymatic kinetics, and extended incubation‐based analyses across space and time. Observational and manipulative field‐based experiments have used improved measurement approaches to deepen our understanding of the integrated effects of environmental change and disturbance on Rs. Freely‐available observational databases have enabled meta‐analyses and studies probing the magnitude of, and constraints on, the global Rs flux. Key challenges for the field include expanding Rs measurements, experiments, and opportunities to under‐represented communities and ecosystems; reconciling independent estimates of global respiration fluxes and trends; testing and leveraging the power of machine learning and process‐based models, both independently and in conjunction with each other; and continuing the field's tradition of using novel experiments to explore diverse mechanisms and ecosystems. “Soil respiration” refers to the flow of carbon dioxide, mostly generated by plant roots and microbes, from the soil to the atmosphere. This flux is large and highly uncertain, and understanding it has significant implications for our ability to predict the effects of land use and climate change. This review summarizes advances, insights, and challenges in soil respiration science. It examines laboratory approaches and findings; documents changes in how researchers measure and understand soil respiration in the natural world; and describes efforts to estimate the global dynamics of soil respiration from openly‐available databases of observations. We conclude by discussing the most compelling challenges and opportunities for the future. Recent decades have seen large advances in measurement methods, experimental approaches, and data availability in soil respiration scienceLab and field‐based experiments have improved our understanding of the integrated effect of environmental change on soil respirationKey challenges include broadening access and continuing to explore diverse mechanisms and ecosystems using novel model‐experiment approaches Recent decades have seen large advances in measurement methods, experimental approaches, and data availability in soil respiration science Lab and field‐based experiments have improved our understanding of the integrated effect of environmental change on soil respiration Key challenges include broadening access and continuing to explore diverse mechanisms and ecosystems using novel model‐experiment approaches

Nuclear magnetic resonance (NMR) spectroscopy is a useful tool for detection and identification of molecular structural information, with increasing applications in environmental sciences. NMR instrument outputs are however heterogeneous and require extensive post‐processing, creating barriers to their use and application by non‐specialists. Here, we report on a new open‐source R package, nmrrr, that processes and visualizes spectral data obtained from one‐dimensional solution‐state and solid‐state NMR experiments; the package also performs relevant calculations commonly applied in natural organic matter communities, such as computing the relative abundance of various functional groups. We document the package's installation, dependencies, and functions; and provide a standard workflow for processing NMR data. This package is currently available on CRAN and GitHub, and community contributions are welcome. Nuclear magnetic resonance (NMR) spectroscopy is used to detect and identify the structure of molecules, and it has become increasingly important in environmental science. The data produced from NMR analyses need a lot of additional processing in order to be useful. This makes it difficult for non‐experts to use and apply this technique. We introduce a new R package “nmrrr” for processing and visualization of NMR data. This is an open‐source package and can be integrated into existing NMR processing workflows. The package can be used to process spectra and peaks data, perform relative abundance calculations, and plot graphs of spectra. The nmrrrpackage is currently available on two platforms: CRAN and GitHub. The nmrrrpackage provides a reproducible workflow for binning and visualizing nuclear magnetic resonance (NMR) dataThe package works for solid and solution‐state experiments, as well as multiple solvents and nucleiThis package can be easily integrated into existing NMR workflows by users The nmrrrpackage provides a reproducible workflow for binning and visualizing nuclear magnetic resonance (NMR) data The package works for solid and solution‐state experiments, as well as multiple solvents and nuclei This package can be easily integrated into existing NMR workflows by users

The high temporal variability of the soil‐to‐atmosphere CO2flux (soil respiration, RS) has been studied at hourly to multiannual time scales but remains less well understood than RSspatial variability. How RSfluxes vary and are autocorrelated at various time lags has practical implications for sampling and more fundamentally for our understanding of its abiotic and biotic underlying mechanisms. We examined the variability, correlation, and sampling requirements of RSover a wide range of temporal scales in a temperate deciduous forest in eastern Maryland, USA, using both automated (temporally continuous, N= 30,036 over 10 months) and survey (spatially diverse, temporally sparse, N= 1,912 over 17 months) data. Data from a global RSdatabase were also used to examine interannual variability in comparable forests. The coefficient of variability of successive measurements generally varied from the minute (median coefficient of variation 16%) to hourly and daily (11–12%) time scales. Successive RSvalues measured at a given collar exhibited a strong hour‐to‐hour correlation (r= 0.931) and a moderate correlation at a 2‐hr lag (0.289); day‐to‐day (i.e., 24 hr lag) hourly observations were uncorrelated. Daily RSmeans were well correlated at a 1‐day lag (r= 0.856) but not at any further time lag. In a linear mixed‐effects model predicting RS, soil temperature and moisture exerted consistently strong effects regardless of time scale, and model coefficient of variability was generally high (>80%). These results provide new opportunities to explore the drivers and variability of RSfluxes, quantify sampling requirements, and improve error propagation. Soils give off carbon dioxide, generated by microbes and plant roots, to the atmosphere. How this “soil respiration” (RS) varies in time, as one measures at minute, hourly, daily, or longer time scales, is related to the processes driving it and has implications for how we estimate this flow of carbon across space and time. We measured RSin a coastal deciduous forest in Maryland, USA, and found that the variability of RS—how much it changed between successive measurements—varied at the different time scales. RSvalues quickly became increasingly random (i.e., uncorrelated with each other) as one measured repeatedly over time. These results help us understand the factors driving this large flow of carbon to the atmosphere and improve our ability to estimate RSat times and places not directly measured. Variability of soil respiration (RS) had different characteristics at subhourly to seasonal time scales in a coastal deciduous forestSuccessive hourly and daily measurements were rarely correlated beyond more than one or two time lagsSoil temperature and moisture consistently explained most of the RSvariability across all time scales

Global soils store at least twice as much carbon as Earth’s atmosphere1,2. The global soil-to-atmosphere (or total soil respiration, RS) carbon dioxide (CO2) flux is increasing3,4, but the degree to which climate change will stimulate carbon losses from soils as a result of heterotrophic respiration (RH) remains highly uncertain5–8. Here we use an updated global soil respiration database9to show that the observed soil surface RH:RSratio increased significantly, from 0.54 to 0.63, between 1990 and 2014 (P= 0.009). Three additional lines of evidence provide support for this finding. By analysing two separate global gross primary production datasets10,11, we find that the ratios of both RHand RSto gross primary production have increased over time. Similarly, significant increases in RHare observed against the longest available solar-induced chlorophyll fluorescence global dataset, as well as gross primary production computed by an ensemble of global land models. We also show that the ratio of night-time net ecosystem exchange to gross primary production is rising across the FLUXNET201512dataset. All trends are robust to sampling variability in ecosystem type, disturbance, methodology, CO2fertilization effects and mean climate. Taken together, our findings provide observational evidence that global RHis rising, probably in response to environmental changes, consistent with meta-analyses13–16and long-term experiments17. This suggests that climate-driven losses of soil carbon are currently occurring across many ecosystems, with a detectable and sustained trend emerging at the global scale. Global soil respiration is rising, probably in response to environmental changes, suggesting that climate-driven losses of soil carbon are occurring worldwide.

Forest soil respiration (Rs) plays an important role in the carbon balance of terrestrial ecosystems. China's forest occupies a large part of the world's forest. However, due to the lack of integrated observation data and appropriate upscaling methodologies, substantial uncertainties exist in the Rs evaluation, which limits our understanding of the carbon balance. Here, we re‐evaluated the total soil carbon effluxes across China by combining field observations from 634 published annual Rs with a machine learning technique (i.e., Random Forest (RF)). Our results revealed that the combination of systematic measurements with the RF model allowed a definite estimate. The average annual Rs was 776.9 g C m−2yr−1, ranging from 411.5 to 1,770.7 g C m−2yr−1. Total forest soil carbon effluxes amounted to 1.17 Pg C yr−1in China. Geographically, annual Rs showed a clear spatially increasing trend from northeast to southwest. Forest type is an important factor in determining the soil respiration rate. Bamboo and Evergreen broadleaf forests were higher than other types of forests. These results provide a unique insight into the magnitudes and mechanisms of soil CO2emissions in China's forest ecosystems. Soil respiration refers to the soil‐to‐atmosphere flux of CO2,primarily generated by root and microbial respiration. Accurate assessment of forest soil respiration in China is a significant part of global‐scale carbon cycles. However, due to the lack of integrated observation data and appropriate upscaling methodologies, substantial uncertainties exist in soil respiration evaluation. Here, we assembled a new database and used a random forest approach to re‐explore the total forest soil respiration and obtained reliable annual forest soil carbon efflux in China. These results contribute to a better understanding and quantification of the national and global carbon cycle. The combination of systematic measurements with the random forest model allowed a definite estimateTotal forest soil carbon effluxes amounted to 1.17 Pg C yr−1in ChinaForest type is an important factor in determining the soil respiration rate The combination of systematic measurements with the random forest model allowed a definite estimate Total forest soil carbon effluxes amounted to 1.17 Pg C yr−1in China Forest type is an important factor in determining the soil respiration rate

We made a commitment to better include underrepresented members of our community in the publication pipeline of JGR: Biogeosciences. This commitment consists of regular updates on our policies and practices, and concrete actions we intend to implement over the next year. So far, our progress to tackle biases and ensure equitable research in the biogeosciences has focused on improving diversity of our associate editor and reviewer pools, increasing awareness of unconscious bias in peer‐review, and promoting inclusion in global collaborations. In this update, we explore manuscript submissions and manuscript decisions by gender, and we present a pilot that aims to promote ethical and equitable global collaborations in resource‐poor settings. We end our editorial by presenting our next set of actions that we plan on completing over the next year, which include a more thorough analysis of reviewer demographics. Here we report on our progress for equitable and inclusive peer‐review and publishing for JGR: BiogeosciencesIn the past year we prioritized a pilot program to ensure equitable global collaborationsOur next set of key priorities to expand and diversify our reviewer pool and publishing community are highlighted Here we report on our progress for equitable and inclusive peer‐review and publishing for JGR: Biogeosciences In the past year we prioritized a pilot program to ensure equitable global collaborations Our next set of key priorities to expand and diversify our reviewer pool and publishing community are highlighted

Fossil fuel combustion and land-use change are the two largest contributors to industrial-era increases in atmospheric CO2concentration. Projections of these are thus fundamental inputs for coupled Earth system models (ESMs) used to estimate the physical and biological consequences of future climate system forcing. While historical data sets are available to inform past and current climate analyses, assessments of future climate change have relied on projections of energy and land use from energy–economic models, constrained by assumptions about future policy, land-use patterns and socio-economic development trajectories. Here we show that the climatic impacts on land ecosystems drive significant feedbacks in energy, agriculture, land use and carbon cycle projections for the twenty-first century. We find that exposure of human-appropriated land ecosystem productivity to biospheric change results in reductions of land area used for crops; increases in managed forest area and carbon stocks; decreases in global crop prices; and reduction in fossil fuel emissions for a low–mid-range forcing scenario. The feedbacks between climate-induced biospheric change and human system forcings to the climate system—demonstrated here—are handled inconsistently, or excluded altogether, in the one-way asynchronous coupling of energy–economic models to ESMs used to date.

The editorial team at JGR: Biogeoscienceswould like to extend thanks to the 2022 reviewers who offered their time and expertise to help make decisions and improve our papers. The editorial team at JGR: Biogeoscienceswould like to extend thanks to the 2022 reviewers who offered their time and expertise to help make decisions and improve our papers. JGR Biogeosciences relies on hundreds of reviewers with wide ranging expertiseThank you to our 2022 reviewers JGR Biogeosciences relies on hundreds of reviewers with wide ranging expertise Thank you to our 2022 reviewers

Earth System Models (ESMs) are increasingly representing agriculture due to its impact on biogeochemical cycles, local and regional climate, and fundamental importance for human society. Realistic large scale simulations may require spatially varying crop parameters that capture crop growth at various scales and among different cultivars, as well as common crop management practices, but their importance is uncertain, and they are often not represented in ESMs. In this study, we examine the impact of using constant versus spatially varying crop parameters using a novel, realistic crop rotation scenario in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2). We implemented crop rotation by using ELMv2's dynamic land unit capability, and then calibrated and validated the model against observations collected at three AmeriFlux sites in the US Midwest with corn soybean rotation. The calibrated model closely captured the magnitude and observed seasonality of carbon and energy fluxes across crops and sites. We performed regional simulations for the US Midwest using the calibrated model and found that spatially varying only a few crop parameters across the region, as opposed to using constant parameters, had a large impact, with the carbon fluxes and energy fluxes both varying by up to 40%. These results imply that large scale ESM simulations using spatially invariant crop parameters may result in biased energy and carbon fluxes estimation from agricultural land, and underline the importance of improving human‐earth systems interactions in ESMs. Crops are increasingly being characterized in global land models because of their impact on local and regional climate. However, there is limited understanding of the impact of crop rotation and of different crop cultivars on carbon and energy fluxes from the land surface. Our study implements crop rotation and spatially varying crop parameters in the Energy Exascale Earth System Model Land Model and finds that doing so improves carbon and energy flux estimation from cropland area. These findings emphasize the importance of capturing agricultural management practices and variability in growth characteristics across different crop cultivars in global land models. This study implements corn soybean rotation and spatially varying crop parameters in the Energy Exascale Earth System Land ModelThe model is calibrated and validated against observations collected at three AmeriFlux sites in the US MidwestWe find that spatially varying crop parameters resulted in improved flux estimation from cropland areas This study implements corn soybean rotation and spatially varying crop parameters in the Energy Exascale Earth System Land Model The model is calibrated and validated against observations collected at three AmeriFlux sites in the US Midwest We find that spatially varying crop parameters resulted in improved flux estimation from cropland areas

Soil respiration—the flow of biologically‐generated CO2from the soil surface to the atmosphere—is a major component of global carbon cycling, but the long‐term response of this flux to altered precipitation regimes remains uncertain, due to different responses of soil respiration in distinct ecosystems with varying degrees of water limitation. We conducted a meta‐analysis to determine the role of soil characteristics (organic carbon stock and clay content), study duration, and ecosystem type (e.g., forest, grassland, etc) in modifying the response of soil respiration to precipitation change. In general, decreased precipitation (N= 128 effect size pairs) decreased soil respiration rates. In contrast, increased precipitation (N= 141) had a more variable and on‐average weak positive effect on this flux; significantly increasing soil respiration only in desert ecosystems. The long‐term response of ecosystems varied, with those that are less constrained by water‐availability (e.g., forests) showing acclimation over time to altered precipitation. Soil organic carbon stock strongly modified the response of soil respiration to decreased precipitation (p< 0.0001), but only weakly modified the response to increased precipitation (p< 0.01). Our results suggest that ecosystems with limited soil water‐holding capacity and strong inherent water‐limitation will show long‐term dynamic change in soil respiration regardless of the direction of precipitation change. Climate change is altering global rainfall patterns, which can affect the global carbon cycle via changes in carbon dioxide release from soil (i.e., soil respiration). It is important to understand how carbon cycling in different ecosystems will respond to increased or decreased precipitation to account for soil feedbacks into atmospheric carbon dioxide concentrations. We combined the results of 80 separate studies to determine the effect of altered rainfall on soil respiration. In addition, we looked at how long the changes lasted, and how different soil properties and the intensity of precipitation changes affected the study results. We found that more precipitation resulted in greater amounts of carbon dioxide leaving the soil, and less precipitation resulted in less. However, the changes weakened over time in ecosystems that typically receive plenty of rainfall (e.g., forests), in contrast to ecosystems that typically receive little rainfall (e.g., deserts) where changes strengthened over time. Changes in the amount of carbon dioxide that left the soil were also affected by the amount of organic carbon in the soil, which impacts how much water soil can hold. Our results suggest that typically dry ecosystems will experience long‐term changes in their carbon cycling whether precipitation increases or decreases. Soil characteristics associated with water‐holding capacity may mitigate the impact of decreased precipitation on soil respirationSoil respiration response to precipitation change shows differential capacity for acclimation in different ecosystemsIncreased precipitation increases soil respiration in all ecosystems in the short‐term, while decreased precipitation has the opposite short‐term effect Soil characteristics associated with water‐holding capacity may mitigate the impact of decreased precipitation on soil respiration Soil respiration response to precipitation change shows differential capacity for acclimation in different ecosystems Increased precipitation increases soil respiration in all ecosystems in the short‐term, while decreased precipitation has the opposite short‐term effect

The effects of changing climate and disturbance on forest water cycling are not well understood. In particular, bryophytes contribute significantly to forest evapotranspiration in poorly drained boreal forests, but few studies have directly measured this flux and how it changes with stand age and soil drainage. We measured bryophyte evaporation (E) in the field (in Canadian Picea marianaforests of varying ages and soil drainages) and under controlled laboratory conditions, and modelled daily Eusing site‐specific meteorological data to drive a Penman–Monteith‐based model. Field measurements of Eaveraged 0·37 mm day−1and ranged from 0·03 (Pleurozium schreberiiin a 77‐year‐old dry stand) to 1·43 mm day−1(Sphagnum ripariumin a 43‐year‐old bog). In the laboratory, moss canopy resistance (which ranged from ∼0 to 1500 s m−1) was constant until a moss water content of ∼6 g g−1and then climbed sharply with further drying; unexpectedly, no difference was observed between the three moss groups (feather mosses, hollow mosses and hummock mosses) tested. Modelled annual Eranged from 0·4 mm day−1, in the well‐drained stands, to ∼1 mm day−1in the 43‐year‐old bog. The Penman–Monteith modelling approach used was relatively insensitive to most parameters but only explained 35% of the variability in field measurements. Bryophyte Ewas greater in bogs than in upland stands, was driven by low‐lying mosses and varied with stand age only in the poorly drained stands; this suggests that bryophytes may provide a buffering effect to fire‐driven changes in tree transpiration. Copyright © 2010 John Wiley & Sons, Ltd.

Forests dominate the global terrestrial carbon budget, but their ability to continue doing so in the face of a changing climate is uncertain. A key uncertainty is how forests will respond to (resistance) and recover from (resilience) rising levels of disturbance of varying intensities. This knowledge gap can optimally be addressed by integrating manipulative field experiments with ecophysiological modeling. We used the Ecosystem Demography‐2.2 (ED‐2.2) model to project carbon fluxes for a northern temperate deciduous forest subjected to a real‐world disturbance severity manipulation experiment. ED‐2.2 was run for 150 years, starting from near bare ground in 1900 (approximating the clear‐cut conditions at the time), and subjected to three disturbance treatments under an ensemble of climate conditions. Both disturbance severity and climate strongly affected carbon fluxes such as gross primary production (GPP), and interacted with one another. We then calculated resistance and resilience, two dimensions of ecosystem stability. Modeled GPP exhibited a two‐fold decrease in mean resistance across disturbance severities of 45%, 65%, and 85% mortality; conversely, resilience increased by a factor of two with increasing disturbance severity. This pattern held for net primary production and net ecosystem production, indicating a trade‐off in which greater initial declines were followed by faster recovery. Notably, however, heterotrophic respiration responded more slowly to disturbance, and it's highly variable response was affected by different drivers. This work provides insight into how future conditions might affect the functional stability of mature forests in this region under ongoing climate change and changing disturbance regimes. Forests play an important role in the carbon cycle. How forests respond and recover to disturbances is largely uncertain. Here, we use a model to investigate the effects of disturbances under different climatic conditions. Climatic conditions affect how forests will respond to and recover from disturbancesDisturbance severity strongly affects carbon fluxes Climatic conditions affect how forests will respond to and recover from disturbances Disturbance severity strongly affects carbon fluxes

Soil heterotrophic respiration (SHR) is important for carbon‐climate feedbacks because of its sensitivity to soil carbon, climatic conditions and nutrient availability. However, available global SHR estimates have either a coarse spatial resolution or rely on simple upscaling formulations. To better quantify the global distribution of SHR and its response to climate variability, we produced a new global SHR data set using Random Forest, up‐scaling 455 point data from the Global Soil Respiration Database (SRDB 4.0) with gridded fields of climatic, edaphic and productivity. We estimated a global total SHR of 46.838.656.3Pg C yr−1over 1985–2013 with a significant increasing trend of 0.03 Pg C yr−2. Among the inputs to generate SHR products, the choice of soil moisture datasets contributes more to the difference among SHR ensemble. Water availability dominates SHR inter‐annual variability (IAV) at the global scale; more precisely, temperature strongly controls the SHR IAV in tropical forests, while water availability dominates in extra‐tropical forest and semi‐arid regions. Our machine‐learning SHR ensemble of data‐driven gridded estimates and outputs from process‐based models (TRENDYv6) shows agreement for a strong association between water variability and SHR IAV at the global scale, but ensemble members exhibit different ecosystem‐level SHR IAV controllers. The important role of water availability in driving SHR suggests both a direct effect limiting decomposition and an indirect effect on litter available from productivity. Considering potential uncertainties remaining in our data‐driven SHR datasets, we call for more scientifically designed SHR observation network and deep‐learning methods making maximum use of observation data. A new data‐driven global soil heterotrophic respiration (SHR) product benefits our understanding of its spatiotemporal dynamicsWater availability plays an important role in driving inter‐annual fluctuation of SHREcosystem‐level drivers for SHR anomalies vary between water‐limiting and non‐water‐limiting regions A new data‐driven global soil heterotrophic respiration (SHR) product benefits our understanding of its spatiotemporal dynamics Water availability plays an important role in driving inter‐annual fluctuation of SHR Ecosystem‐level drivers for SHR anomalies vary between water‐limiting and non‐water‐limiting regions

Methane (CH4) exchange between trees and the atmosphere has recently emerged as an important, but poorly quantified process regulating global climate. The sources (soil and/or tree) and mechanisms driving the increase of CH4in trees and degassing to the atmosphere are inadequately understood, particularly for coastal forests facing increased exposure to seawater. We investigated the eco‐physiological relationship between tree stem wood density, soil and stem oxygen saturation (an indicator of redox state), soil and stem CH4concentrations, soil and stem carbon dioxide (CO2) concentrations, and soil salinity in five forests along the United States coastline. We aim to evaluate the mechanisms underlying greenhouse gas increase in trees and the influence of seawater exposure on stem CH4accumulation. Seawater exposure corresponded with decreased tree survival and increased tree stem methane. Tree stem wood density was significantly correlated with increased stem CH4in seawater exposed gymnosperms, indicating that dying gymnosperm trees may accumulate higher levels of CH4in association with seawater flooding. Further, we found that significant differences in seawater exposed and unexposed gymnosperm tree populations are associated with increased soil and stem CH4and CO2, indicating that seawater exposure significantly impacts soil and stem greenhouse gas abundance. Our results provide new insight into the potential mechanisms driving tree CH4accumulation within gymnosperm coastal forests. Trees emit greenhouse gases such as methane and carbon dioxide to the atmosphere. The origin of these gases includes production in the tree or in the surrounding soils. Disturbances to these systems, such as seawater exposure that increases soil salinity, have an unknown impact on gas production and connectivity between soil and trees. We found that higher soil salinities corresponded to higher soil methane content and increased stem methane. The accumulation of soil and tree methane was lower in sites with no salinity exposure and higher in sites with high salinity. As coastal systems become more vulnerable to changes in seawater exposure, this may have consequences on methane emitted from trees to the atmosphere. Seawater exposure significantly increased internal stem CH4concentrationThere is a significant correlation between stem wood density and stem CH4concentrationLow stem and soil O2were significantly linked to seawater exposure and decreased tree survival Seawater exposure significantly increased internal stem CH4concentration There is a significant correlation between stem wood density and stem CH4concentration Low stem and soil O2were significantly linked to seawater exposure and decreased tree survival

Measuring the soil‐to‐atmosphere carbon dioxide (CO2) flux (soil respiration, RS) is important to understanding terrestrial carbon balance and to forecasting climate change. Such measurements are frequently made using measurement collars permanently inserted into the soil surface. However, differences in measurement duration and frequency, as well as collar properties, may lead to biases in the estimation of annual RS. Using a newly updated global RSdatabase (SRDB‐V5), we investigated the annual RSbias associated with five methodological factors: collar height, collar coverage area, collar insertion depth, measurement duration, and measurement frequency. We found that annual RSwas negatively correlated with collar insertion depth, consistent with the idea that collar insertion cuts roots and thus reduces RS. Annual RSwas also negatively related with collar height and collar coverage area, perhaps because uniform head‐space mixing is difficult to achieve in larger volume chambers; however, these effects were quantitatively small (bias of ~2% to 10% of mean RS). We found no correlation of measurement duration or measurement frequency with annual RS. These findings suggest that variation in RSmethodology generally introduces minimal bias overall. Therefore, compilations of minimally adjusted annual RSmeasurements provide a reliable resource for synthesis studies, global annual RSmodeling, and investigation of how soil carbon responds to climate change. Soil‐to‐atmosphere carbon dioxide (CO2) is the second largest component in the terrestrial carbon cycle; thus, our ability to balance the terrestrial carbon budget and forecast climate change relies upon accurate measurements of this process. Collars permanently installed in the soil are commonly used to measure soil‐to‐atmosphere CO2. However, differences in collar properties, measurement duration, and measurement frequency may lead to biases in the estimation of annual soil‐to‐atmosphere CO2amount. While many studies on the methodology for measuring soil‐to‐atmosphere CO2have been conducted, a comprehensive evaluation of the influence of collar properties and measurement duration on annual soil‐to‐atmosphere CO2variability has not been investigated before. In this study, we use a global data set to analyze soil‐to‐atmosphere CO2measurement bias related to collar properties and measurement duration. We found that annual soil‐to‐atmosphere CO2amount negatively correlated with collar height and insertion depth but showed no significant relationship to measurement duration and measurement frequency. Overall, collar properties and measurement duration contributed minimal bias. The results provide strong support for compiling site‐scale soil‐to‐atmosphere CO2measurements to support synthesis analysis, as well as global soil‐to‐atmosphere CO2modeling. Annual soil respiration (RS) was negatively correlated with collar insertion depth, collar height, and collar coverage areaWe found no correlation of measurement duration or measurement frequency with annual RSOverall, collar properties, measurement duration, and measurement frequency confer minimal bias to annual RSThe results provide strong support for compiling site‐scale RSmeasurements to support synthesis analysis, as well as global RSmodeling

Changes in land and ocean carbon storage in response to elevated atmospheric carbon dioxide concentrations and associated climate change, known as the concentration‐carbon and climate‐carbon feedbacks, are principal controls on the response of the climate system to anthropogenic greenhouse gas emissions. Such feedbacks have typically been quantified in the context of natural ecosystems, but land management activities are also responsive to future atmospheric carbon and climate changes. Here we show that inclusion of such human‐driven responses within an Earth system model shifts both the terrestrial concentration‐carbon and climate‐carbon feedbacks toward increased carbon storage. We introduce a conceptual framework for decomposing these changes into separate concentration‐land cover, climate‐land cover, and land cover‐carbon effects, providing a parsimonious means to diagnose sources of variation across numerical models capable of estimating such feedbacks. Estimating future changes to the Earth's climate requires an understanding of how carbon stored in vegetation and soils will respond to higher carbon dioxide in the atmosphere and changes in climate such as warmer temperatures and changes in precipitation. For instance, if plants and soils release more carbon, this will accelerate human‐driven climate change, which is known as a positive feedback. Because climate change and higher atmospheric carbon dioxide will affect crop and forestry yields, we expect humans to alter their land management activities in the future, leading to greater or lesser storage of carbon in soils and vegetation. Higher crop yields could lead to less crop area globally and greater storage of carbon in forests and other natural vegetation. In this study, we introduce a method for quantifying such human influences on carbon storage, combining a model of land management with a model of atmospheric, land, and ecosystem processes. We find that both higher atmospheric carbon dioxide and climate change tend to reduce the footprint of human agriculture and therefore increase carbon storage on the land. Our method for quantifying such feedbacks provides a simple means to compare across models and identify areas of agreement or disagreement. Changes in atmospheric carbon and climate drive changes in land management that can be characterized as carbon cycle feedbacksLand management changes alter the estimation of both the concentration‐carbon and climate‐carbon feedbacksQuantifying human‐mediated carbon cycle feedbacks provides a framework for diagnosing cross‐model uncertainty