Emissions of hydrogen (H2) gas from human activities are associated with indirect climate warming effects. As the hydrogen economy expands globally (e.g. the use of H2 gas as a fuel), the anthropogenic release of H2 into the atmosphere is expected to rise rapidly as a result of increased leakage. The dominant H2 removal process is uptake into soils; however, removal mechanisms are poorly understood, and the fate and impact of increased H2 emissions remain highly uncertain. Fluxes of H2 within soils are rarely measured, and data to inform global models are based on few studies. This study presents soil H2 fluxes from two field sites in central Scotland, a managed grassland and a planted deciduous woodland, with flux measurements of H2 covering full seasonal cycles. A bespoke flux chamber measurement protocol was developed to deal with the fast decline in headspace concentrations associated with rapid H2 uptake, in which exponential regression models could be fitted to concentration data over a 7 min enclosure time. We estimate annual H2 uptake of -3.1±0.1 and -12.0±0.4 kg H2 ha−1 yr−1 and mean deposition velocities of 0.012±0.002 and 0.088±0.005 cm s−1 for the grassland and woodland sites, respectively. Soil moisture was found to be the primary driver of H2 uptake at the grassland site, where the high silt/clay content of the soil resulted in anaerobic conditions (near zero H2 flux) during wet periods of the year. Uptake of H2 at the forest site was highly variable and did not correlate well with any localised soil properties (soil moisture, temperature, total carbon and nitrogen content). It is likely that the high silt/clay content of the grassland site (55 % silt, 20 % clay) decreased aeration when soils were wet, resulting in poor aeration and low H2 uptake. The well-drained forest site (60 % sand) was not as restricted by exchange of H2 between the atmosphere and the soil, showing instead a large variability in H2 flux that is more likely to be related to heterogeneous factors in the soil that control microbial activity (e.g. labile carbon and microbial densities). The results of this study highlight that there is still much that we do not understand regarding the drivers of H2 uptake in soils and that further field measurements are required to improve global models.

Atmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modelling. Here, atmospheric nitrous oxide (N2O) measurements are used to derive monthly U.K. N2O emissions for 2013-2022 – using the InTEM and RHIME inverse methods – and Swiss N2O emissions for 2017-2022 – using the ELRIS inverse method. We find mean U.K. emissions of 90.5±23.0 and 111.7±32.1 Gg N2O yr-1 for 2013-2022 and corresponding trends of -0.68±0.48 and -2.10±0.72 Gg N2O yr-2, respectively, derived using InTEM and RHIME. The 2013-2022 mean U.K. N2O emissions as reported by the U.K. National Atmospheric Emissions Inventory were relatively constant at 74 Gg N2O yr-1 across this period, which is 14-33% smaller than the U.K. emissions derived from atmospheric data. Top-down Swiss emissions of 10.8±3.8 Gg N2O yr-1 derived using atmospheric measurements were very comparable to those reported in the Swiss National Inventory: 11.5 (8.3 to 14.9) Gg N2O yr-1 over 2017-2021. Pronounced seasonal N2O emissions cycles are inferred in the U.K. and Swiss data with similar seasonal magnitudes observed in both countries. In the U.K., the primary seasonal peak occurs in the spring with a second smaller peak occurring in the late summer for certain years. The springtime peak has a long seasonal decline that contrasts with the sharp rise and fall of N2O emissions estimated from the bottom-up U.K. Emissions Model (UKEM). Similarly, Swiss seasonal N2O emissions peak during the summer with a second smaller peak also occurring in the late summer/early autumn for certain years. Bayesian inference is used to minimize the U.K. seasonal cycle mismatch between the average top-down (atmospheric data-based) and UKEM bottom-up (process model and inventory-based) seasonal emissions at a sub-sector level. Increasing agricultural manure management and decreasing synthetic fertiliser N2O emissions reduces some of the discrepancy between the average U.K. top-down and bottom-up seasonal cycles. Other possibilities could also explain these discrepancies, such as missing emissions from NH3 deposition, but these require further investigation.

Forest ecosystems play an important role in the terrestrial nitrogen (N) cycle, accounting for over a quarter of the land area of the Earth. However, our understanding of nitrogen dynamics in forest systems is limited. The consequences of N deposition to forest ecosystems are often overlooked. In this study, dry deposition of NH3 was replicated over a two-year period in a temperate semi-natural birch forest via a unique custom-built automated NH3 release system to investigate the impact on emissions of the greenhouse gas nitrous oxide (N2O). This study provides evidence that in both natural forest soils (in-situ) and soils under controlled laboratory conditions (ex-situ), the substantial addition of reduced N compounds (NH3/NH4+) had no direct impact on N2O emissions. Emissions of N2O from these soils were dependant on the meeting of several additional thresholds, below which N2O producing activity was constrained. When environmental conditions in-situ were considered warm and wet (soil temperature >12 °C and volumetric water content >20 %), emissions of N2O were an order of magnitude higher than when either of these thresholds was not met, regardless of exposure to NH3 deposition. Ex-situ experiments indicated that microbial activity in the soils was highly constrained by the availability of labile carbon. The addition of glucose to these soils resulted in a considerable increase in N2O emissions after N application. While cumulative NH3 deposition to the in-situ soils was relatively large over the measurement period, there was no accumulation of mineral N observed in the soil, suggesting plant-uptake of N was able to mitigate N loading. The implication of these results is that forest ecosystems may be able to mitigate localised NH3 pollution plumes, in the short-term at least, without incurring an N2O penalty. However, the long-term impacts of N enhancement remain unclear and further long-term field experiments are required to examine the impact of prolonged exposure to high quantities of N deposition to forest soils.

Flux chamber methodologies are used at the global scale to measure the exchange of trace gases between terrestrial surfaces (soils) and the atmosphere. These methods evolved as a simplistic necessity to measure gas fluxes from a time when gas analysers were limited in capability and costs were prohibitively high, since which thousands of studies have deployed a wide variety of chamber methodologies to build vast datasets of soil fluxes. However, analytical limitations of the methods are often overlooked and are poorly understood by the flux community, leading to confusion and misreporting of observations in some cases. In recent years, the number of commercial suppliers of gas analysers claiming to be capable of measuring trace gas fluxes from chambers has drastically increased, with a myriad of analysers (and low-cost sensors) now on offer with a wide variety of capabilities. While chamber designs and the capabilities of analysers vary by orders of magnitude, the rudimentary analytical uncertainties of individual flux measurements can still be standardised for direct comparison of methods. This study aims to serve as a guide to calculate the analytical uncertainty of chamber flux methodologies in a standardised way for direct comparisons. We provide comparisons of a variety of chamber measurement methodologies (closed static and dynamic chamber methods) to highlight the impact of analytical noise, chamber size, enclosure time and number of gas samples. With the associated tools, researchers, commercial suppliers and other stakeholders in the flux community can easily estimate the limitations of a particular methodology to establish and tailor the suitability of particular chambers and instruments to experimental requirements.

Soil organic matter (SOM) is an important component of ecosystem carbon stocks. Generally, SOM found in mineral and organo-mineral soils can be categorised into two fractions: particulate organic matter (POM) and mineral-associated-organic matter (MAOM), both of which contain soil organic carbon (SOC). Understanding the relationship between SOC and SOM fractions provides insight into SOM decomposition and SOC storage potential. Here we show an intriguingly tight relationship between the fraction of SOC in SOM (denoted as f OC $$ {f}_{\mathrm{OC}} $$ ), habitat and soil physical properties, as well as SOC stored in POM and MAOM. This opens up new ways to predict spatial variations in the distribution of POC and MAOC using more widely available f OC $$ {f}_{\mathrm{OC}} $$ data as a covariate. By compiling 14 datasets and 9503 measurements from across Europe and globally we analysed f OC $$ {f}_{\mathrm{OC}} $$ across mineral and organic soils, which fell between 0.38 and 0.58, consistent with variation in carbon of major plant components. f OC $$ {f}_{\mathrm{OC}} $$ followed a habitat gradient with lowest median values in Seagrass sediments (0.36 ± 0.09) and Permafrost habitats, followed by croplands (0.47 ± 0.08) and a maximum in semi-natural habitats (e.g., neutral, acid and calcareous grasslands) (0.56 ± 0.07), with differences between broadleaved (0.50 ± 0.087) and coniferous woodlands (0.53 ± 0.07) which were driven by overall organic matter content. The data show a tight link between vegetation carbon and the contents of SOC and SOM across various habitats, which could be used to inform agricultural soil management, improved land-use planning (e.g., woodlands), and tracking climate-related SOC targets.

U.K. top-down nitrous oxide (N2O) emissions estimates for 2013-2022 Top-down emissions were derived using the InTEM (Manning et al., 2021) and RHIME (Ganesan et al., 2014) inverse models with atmospheric N2O mole fraction measurements from the U.K. DECC network (O'Doherty et al., 2020). This dataset provides monthly top-down emissions estimates for the U.K. (land-only, sea-only, land+sea components) with 68% confidence interval ranges. A priori total monthly N2O emissions created by the U.K. Emissions Model are also provided. This dataset was used in the publication "Combining top-down and bottom-up approaches to evaluate recent trends and seasonal patterns in U.K. N2O emissions" that has been submitted to JGR: Atmospheres by Saboya et al. Further information about the generation of this dataset and its implications can be found in Saboya et al. (2024). Please note InTEM emissions data hold a Crown Copyright.

Carbon monoxide (CO) is an atmospheric pollutant with a positive net warming effect on the climate. The magnitude of CO sources and the fraction of fossil vs biogenic sources are still uncertain and vary across emissions inventories. Measurements of radiocarbon (14C) in CO could potentially be used to investigate the sources of CO on a regional scale because fossil sources lack 14C and reduce the 14C/C ratio (Delta 14C) of atmospheric CO more than biogenic sources. We use regional Lagrangian model simulations to investigate the utility of Delta 14CO measurements for estimating the fossil fraction of CO emissions and evaluating bottom-up emissions estimates (United Kingdom Greenhouse Gas, UKGHG, and TNO Copernicus Atmosphere Monitoring Service, TNO) in London, UK. Due to the high Delta 14CO in atmospheric CO from cosmogenic production, both fossil and biogenic CO emissions cause large reductions in Delta 14CO regionally, with larger reductions for fossil than biogenic CO per ppb added. There is a strong seasonal variation in Delta 14CO in background air and in the sensitivity of Delta 14CO to fossil and biogenic emissions of CO. In the UK, the CO emissions estimate from TNO has a higher fraction from fossil fuels than UKGHG (72% vs 67%). This results in larger simulated decreases in Delta 14C per ppb CO for TNO emissions. The simulated differences between UKGHG and TNO are likely to be easily detectable by current measurement precision, suggesting that Delta 14CO measurements could be an effective tool to understand regional CO sources and assess bottom-up emissions estimates.

Domesticated livestock and their waste streams are considered a significant source of nitrogen (N) and phosphorus (P) pollution at the global scale; however, the waste generated (excreta) by domesticated cats and dogs, whose global numbers are estimated at 700 million and 900 million, respectively, is not included in any global inventories or models of N and P pollution sources. Based on parameters derived from a variety of literature sources, this study estimates the total global N and P excretion from domestic cats and dogs to be 4.32 (1.27-7.38) Tg N yr-1 and 0.76 (0.31-1.21) Tg P yr-1 which are equivalent to 3.3 (1.0-5.7)% of N and 3.3 (1.3-5.3)% of P waste produced by livestock at a global level. These estimates are in line with the combined mass of the animals (the total mass of cats and dogs is equivalent to 3.6% of the total mass of domesticated mammalian livestock). While there is a severe under reporting of waste streams for cat and dog waste deposition in literature, we infer from our estimates that global emissions of N2O and NH3 from cat and dog waste are in the region of 43 (13-74) Gg N2O-N yr-1 and 864 +/- 654 Gg NH3-N, representing an unreported contribution that may exceed 17.7% of the carbon footprint associated with global pet food production (in the form of N2O emissions).

Greenhouse gas stabilisation in the atmosphere is one of the most pressing challenges of this century. Sequestering carbon in the soil by changing land use and management is increasingly proposed as part of climate mitigation strategies, but our understanding of this is limited in quantitative terms. Here we collate a substantial national and regional data set (15 790 soil cores) and analyse it in an advanced statistical modelling framework. This produced new estimates of the effects of land use on soil carbon stock (Sc) in the UK, different in magnitude and ranking order from the previous best estimates. Soil carbon stocks were highest in woodlands, followed by rough grazing, semi-natural grasslands, and improved grasslands, and they were lowest in croplands. Estimates were smaller than the previous estimates, partly because of new data, but mainly because the effect is more reliably characterised using a logarithmic transformation of the data. With the very large data set analysed here, the uncertainty in the differences among land uses was small enough to identify consistent mean effects. However, the variability in these effects was large, and this variability was similar across all surveys. This has important implications for agri-environment schemes seeking to sequester carbon in the soil by altering land use, because the effect of a given intervention is very hard to verify. We examined the validity of the "space-for-time" substitution, and, although the results were not unequivocal, we estimated that the effects are likely to be overestimated by 5 %-33 %, depending upon land use.

We investigate the use of atmospheric oxygen (O2) and carbon dioxide (CO2) measurements for the estimation of the fossil fuel component of atmospheric CO2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O2 and CO2, minimizing the influence of terrestrial biosphere fluxes – is simulated at three sites in the UK, two of which make APO measurements. We present a set of model experiments that estimate the sensitivity of APO simulations to key inputs: fluxes from the ocean, fossil fuel flux magnitude and distribution, the APO baseline, and the exchange ratio of O2 to CO2 fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compare three ocean O2 flux estimates from the NEMO–ERSEM, the ECCO–Darwin ocean model, and the Jena CarboScope (JC) APO inversion. The sensitivity of APO to fossil fuel emission magnitudes and to terrestrial biosphere and fossil fuel exchange ratios is investigated through Monte Carlo sampling within literature uncertainty ranges and by comparing different inventory estimates. We focus our model–data analysis on the year 2015 as ocean fluxes are not available for later years. As APO measurements are only available for one UK site at this time, our analysis focuses on the Weybourne station. Model–data comparisons for two additional UK sites (Heathfield and Ridge Hill) in 2021, using ocean flux climatologies, are presented in the Supplement. Of the factors that could potentially compromise simulated APO-derived fossil fuel CO2 (ffCO2) estimates, we find that the ocean O2 flux estimate has the largest overall influence at the three sites in the UK. At times, this influence is comparable in magnitude to the contribution of simulated fossil fuel CO2 to simulated APO. We find that simulations using different ocean fluxes differ from each other substantially. No single model estimate, or a model estimate that assumed zero ocean flux, provided a significantly closer fit than any other. Furthermore, the uncertainty in the ocean contribution to APO could lead to uncertainty in defining an appropriate regional background from the data. Our findings suggest that the contribution of non-terrestrial sources needs to be better accounted for in model simulations of APO in the UK to reduce the potential influence on inferred fossil fuel CO2 using APO.

Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2 km resolution ("SMUK"). The method is based on a simple linear statistical model, calibrated with 5 years of daily data from cosmic-ray neutron sensors at similar to 40 sites across the country. The model is driven by precipitation, humidity, a remotely sensed "soil water index" satellite product and soil porosity. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from similar to 1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain-Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. The approach reduces uncertainty by integrating multiple data sources, all of which have weaknesses but together act as a better constraint on the true soil moisture. The model explains around 70 % of the variance in the daily observations with a root-mean-square error of 0.05 m3 m-3, better than results from more complex process-based models. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately 1 week behind real time.

Atmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modelling. Here, atmospheric nitrous oxide (NO) measurements made in the United Kingdom (U.K.) and Republic of Ireland are used to derive monthly NO emissions for 2013-2022 using two different inverse methods. We find mean U.K. emissions of 90.5±23.0 (1 σ ) and 111.7±32.1 (1 σ ) Gg NO yr for 2013-2022, and corresponding trends of -0.68±0.48 (1 σ ) Gg NO yr and -2.10±0.72 (1 σ ) Gg NO yr, respectively for the two inverse methods. The U.K. National Atmospheric Emissions Inventory (NAEI) reported mean NO emissions of 73.9 Gg NO yr across this period, which is 14-33% smaller than the emissions derived from atmospheric data. We infer a pronounced seasonal cycle in NO emissions, with a peak occurring in the spring and a second smaller peak in the late summer for certain years. The springtime peak has a long seasonal decline that contrasts with the sharp rise and fall of NO emissions estimated from the bottom-up U.K. Emissions Model (UKEM). Bayesian inference is used to minimize the seasonal cycle mismatch between the average top-down (atmospheric data-based) and bottom-up (process model and inventory-based) seasonal emissions at a sub-sector level. Increasing agricultural manure management and decreasing synthetic fertilizer NO emissions reduces some of the discrepancy between the average top-down and bottom-up seasonal cycles. Other possibilities could also explain these discrepancies, such as missing emissions from NH deposition, but these require further investigation.

Exhaled human breath can contain small, elevated concentrations of methane (CH4) and nitrous oxide (N2O), both of which contribute to global warming. These emissions from humans are not well understood and are rarely quantified in global greenhouse gas inventories. This study investigated emissions of CH4 and N2O in human breath from 104 volunteers in the UK population, to better understand what drives these emissions and to quantify national-scale estimates. A total of 328 breath samples were collected, and age, sex, dietary preference, and smoking habits were recorded for every participant. The percentage of methane producers (MPs) identified in this study was 31%. The percentage of MPs was higher in older age groups with 25% of people under the age of 30 classified as MPs compared to 40% in the 30+ age group. Females (38%) were more likely to be MPs than males (25%), though overall concentrations emitted from both MP groups were similar. All participants were found to emit N2O in breath, though none of the factors investigated explained the differences in emissions. Dietary preference was not found to affect CH4 or N2O emissions from breath in this study. We estimate a total emission of 1.04 (0.86-1.40) Gg of CH4 and 0.069 (0.066-0.072) Gg of N2O in human breath annually in the UK, the equivalent of 53.9 (47.8-60.0) Gg of CO2. In terms of magnitude, these values are approximately 0.05% and 0.1% of the total emissions of CH4 and N2O reported in the UK national greenhouse gas inventories.

The persistence or memory of soil moisture (0) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple U.K. soil moisture products, including satellite-merged (i.e., TCM), in situ (i.e., COSMOS-UK), hydrological model [i.e., Grid-to-Grid (G2G)], statistical model [i.e., Soil Moisture U.K. (SMUK)], and land surface model (LSM) [i.e., Climate Hydrology and Ecology research Support System (CHESS)] data. The drydown decay time scale (T) for all gridded products is computed at an unprecedented resolution of 1-2 km, a scale relevant to weather and climate models. While their range of T differs (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyze the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted T based on COSMOS and LSM are well correlated, with a bias of lower T for COSMOS. Our findings contribute to a growing body of literature to characterize T, with the aim of developing a method to systematically validate model soil moisture products at a range of scales.

The role of greenhouse gases (GHGs) in global climate change is now well recognized and there is a clear need to measure emissions and verify the efficacy of mitigation measures. To this end, reliable estimates are needed of the GHG balance at the national scale and over long time periods, but these estimates are difficult to make accurately. Because measurement techniques are generally restricted to relatively small spatial and temporal scales, there is a fundamental problem in translating these into long‐term estimates on a regional scale. The key challenge lies in spatial and temporal upscaling of short‐term, point observations to estimate large‐scale annual totals, and quantify the uncertainty associated with this upscaling. Here, we review some approaches to this problem and synthesize the work in the recent UK Greenhouse Gas Emissions and Feedbacks Program, which was designed to identify and address these challenges. Approaches to the scaling problem included: instrumentation developments which mean that near‐continuous data sets can be produced with larger spatial coverage; geostatistical methods which address the problem of extrapolating to larger domains, using spatial information in the data; more rigorous statistical methods which characterize the uncertainty in extrapolating to longer time scales; analytical approaches to estimating model aggregation error; enhanced estimates of C flux measurement error; and novel uses of remote sensing data to calibrate process models for generating probabilistic regional C flux estimates.

This review is a summary of the most up-to-date knowledge regarding assessment of atmospheric deposition of reactive nitrogen (N-r) pollutants across complex terrain in the UK. Progress in the understanding of the mechanisms and quantification of N-r deposition in areas of complex topography is slow, as no concerted attempts to measure the components of N-r in complex terrain have been made in the last decade. This is likely due to the inherent complexity of the atmospheric processes and chemical interactions which contribute to deposition in these areas. More than 300 studies have been reviewed, and we have consulted with a panel of international experts which we assembled for that purpose. We report here on key findings and knowledge gaps identified regarding measurement and modelling techniques used to quantify deposition of N-r across complex terrain in the UK, which depending on definition, may represent up to 60% of land coverage across Great Britain. The large body of peer reviewed papers, reports and other items reviewed in this study has highlighted both the strengths and weaknesses in the tools available to scientists, regulators and policy makers. This review highlights that there is no coherent global research effort to constrain the uncertainties in N-r deposition over complex terrain, despite the clearly identified risk of N deposition to ecosystems and water quality. All evidence identified that enhanced N-r deposition across complex terrain occurs, and magnitude of the enhancement is not known; however, there are major uncertainties particularly in the differences between modelled and measured wet deposition in complex terrain and representing accurate surface interactions in models. Using simplified estimates for N-r deposition, based on current understanding of current measurement and model approaches, an enhancement across UK complex terrain in the range of a factor of 1.4-2.5 (i.e. 40-150% larger than current estimates) is likely over complex upland terrain. If at the upper limits of this, then significantly more ecosystems in the UK would be at a direct risk of degradation, and the potential for long-term non-remediable water quality issues increased.

Abstract. We present a method for estimating fossil fuel methane emissions using observations of methane and ethane, accounting for uncertainty in their emission ratio. The ethane:methane emission ratio is incorporated as a variable parameter in a Bayesian model, with its own prior distribution and uncertainty. We find that using an emission ratio distribution mitigates bias from using a fixed, potentially incorrect emission ratio and that uncertainty in this ratio is propagated into posterior estimates of emissions. A synthetic data test is used to show the impact of assuming an incorrect ethane:methane emission ratio and demonstrate how our variable parameter model can better quantify overall uncertainty. We also use this method to estimate UK methane emissions from high-frequency observations of methane and ethane from the UK Deriving Emissions linked to Climate Change (DECC) network. Using the joint methane-ethane inverse model, we estimate annual mean UK methane emissions of approximately 0.27 (95 % uncertainty interval 0.26–0.29) Tg per year from fossil fuel sources and 2.06 (1.99–2.15) Tg per year from non-fossil fuel sources, during the period 2015–2019. Uncertainties in UK fossil fuel emissions estimates are reduced on average by 15 %, and up to 35 %, when incorporating ethane into the inverse model, in comparison to results from the methane-only inversion.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

Global peatlands store more carbon than is naturally present in the atmosphere1,2. However, many peatlands are under pressure from drainage-based agriculture, plantation development and fire, with the equivalent of around 3 per cent of all anthropogenic greenhouse gases emitted from drained peatland3-5. Efforts to curb such emissions are intensifying through the conservation of undrained peatlands and re-wetting of drained systems6. Here we report eddy covariance data for carbon dioxide from 16 locations and static chamber measurements for methane from 41 locations in the UK and Ireland. We combine these with published data from sites across all major peatland biomes. We find that the mean annual effective water table depth (WTDe; that is, the average depth of the aerated peat layer) overrides all other ecosystem- and management-related controls on greenhouse gas fluxes. We estimate that every 10 centimetres of reduction in WTDe could reduce the net warming impact of CO2 and CH4 emissions (100-year global warming potentials) by the equivalent of at least 3 tonnes of CO2 per hectare per year, until WTDe is less than 30 centimetres. Raising water levels further would continue to have a net cooling effect until WTDe is within 10 centimetres of the surface. Our results suggest that greenhouse gas emissions from peatlands drained for agriculture could be greatly reduced without necessarily halting their productive use. Halving WTDe in all drained agricultural peatlands, for example, could reduce emissions by the equivalent of over 1 per cent of global anthropogenic emissions.