Hydrologic Connectivity Regulates Riverine N2O Sources and Dynamics.

Indirect nitrous oxide (N2O) emissions from streams and rivers are a poorly constrained term in the global N2O budget. Current models of riverine N2O emissions place a strong focus on denitrification in groundwater and riverine environments as a dominant source of riverine N2O, but do not explicitly consider direct N2O input from terrestrial ecosystems. Here, we combine N2O isotope measurements and spatial stream network modeling to show that terrestrial-aquatic interactions, driven by changing hydrologic connectivity, control the sources and dynamics of riverine N2O in a mesoscale river network within the U.S. Corn Belt. We find that N2O produced from nitrification constituted a substantial fraction (i.e., >30%) of riverine N2O across the entire river network. The delivery of soil-produced N2O to streams was identified as a key mechanism for the high nitrification contribution and potentially accounted for more than 40% of the total riverine emission. This revealed large terrestrial N2O input implies an important climate-N2O feedback mechanism that may enhance riverine N2O emissions under a wetter and warmer climate. Inadequate representation of hydrologic connectivity in observations and modeling of riverine N2O emissions may result in significant underestimations.

Biases in open-path carbon dioxide flux measurements: Roles of instrument surface heat exchange and analyzer temperature sensitivity.

Eddy covariance (EC) measurements of ecosystem-atmosphere carbon dioxide (CO2) exchange provide the most direct assessment of the terrestrial carbon cycle. Measurement biases for open-path (OP) CO2 concentration and flux measurements have been reported for over 30 years, but their origin and appropriate correction approach remain unresolved. Here, we quantify the impacts of OP biases on carbon and radiative forcing budgets for a sub-boreal wetland. Comparison with a reference closed-path (CP) system indicates that a systematic OP flux bias (0.54 µmol m-2 s-1) persists for all seasons leading to a 110% overestimate of the ecosystem CO2 sink (cumulative error of 78 gC m-2). Two potential OP bias sources are considered: Sensor-path heat exchange (SPHE) and analyzer temperature sensitivity. We examined potential OP correction approaches including: i) Fast temperature measurements within the measurement path and sensor surfaces; ii) Previously published parameterizations; and iii) Optimization algorithms. The measurements revealed year-round average temperature and heat flux gradients of 2.9 °C and 16 W m-2 between the bottom sensor surfaces and atmosphere, indicating SPHE-induced OP bias. However, measured SPHE correlated poorly with the observed differences between OP and CP CO2 fluxes. While previously proposed nominally universal corrections for SPHE reduced the cumulative OP bias, they led to either systematic under-correction (by 38.1 gC m-2) or to systematic over-correction (by 17-37 gC m-2). The resulting budget errors exceeded CP random uncertainty and change the sign of the overall carbon and radiative forcing budgets. Analysis of OP calibration residuals as a function of temperature revealed a sensitivity of 5 µmol m-3 K-1. This temperature sensitivity causes CO2 calibration errors proportional to sample air fluctuations that can offset the observed growing season flux bias by 50%. Consequently, we call for a new OP correction framework that characterizes SPHE- and temperature-induced CO2 measurement errors.

Climate Sensitivity of Peatland Methane Emissions Mediated by Seasonal Hydrologic Dynamics.

Peatlands are among the largest natural sources of atmospheric methane (CH4) worldwide. Peatland emissions are projected to increase under climate change, as rising temperatures and shifting precipitation accelerate microbial metabolic pathways favorable for CH4 production. However, how these changing environmental factors will impact peatland emissions over the long term remains unknown. Here, we investigate a novel data set spanning an exceptionally long 11 years to analyze the influence of soil temperature and water table elevation on peatland CH4 emissions. We show that higher water tables dampen the springtime increases in CH4 emissions as well as their subsequent decreases during late summer to fall. These results imply that any hydroclimatological changes in northern peatlands that shift seasonal water availability from winter to summer will increase annual CH4 emissions, even if temperature remains unchanged. Therefore, advancing hydrological understanding in peatland watersheds will be crucial for improving predictions of CH4 emissions.

Nitrous oxide emissions are enhanced in a warmer and wetter world.

Nitrous oxide (N2O) has a global warming potential that is 300 times that of carbon dioxide on a 100-y timescale, and is of major importance for stratospheric ozone depletion. The climate sensitivity of N2O emissions is poorly known, which makes it difficult to project how changing fertilizer use and climate will impact radiative forcing and the ozone layer. Analysis of 6 y of hourly N2O mixing ratios from a very tall tower within the US Corn Belt-one of the most intensive agricultural regions of the world-combined with inverse modeling, shows large interannual variability in N2O emissions (316 Gg N2O-N⋅y-1 to 585 Gg N2O-N⋅y-1). This implies that the regional emission factor is highly sensitive to climate. In the warmest year and spring (2012) of the observational period, the emission factor was 7.5%, nearly double that of previous reports. Indirect emissions associated with runoff and leaching dominated the interannual variability of total emissions. Under current trends in climate and anthropogenic N use, we project a strong positive feedback to warmer and wetter conditions and unabated growth of regional N2O emissions that will exceed 600 Gg N2O-N⋅y-1, on average, by 2050. This increasing emission trend in the US Corn Belt may represent a harbinger of intensifying N2O emissions from other agricultural regions. Such feedbacks will pose a major challenge to the Paris Agreement, which requires large N2O emission mitigation efforts to achieve its goals.

Error characterization of methane fluxes and budgets derived from a long-term comparison of open- and closed-path eddy covariance systems.

Wetlands represent the dominant natural source of methane (CH4) to the atmosphere. Thus, substantial effort has been spent examining the CH4 budgets of global wetlands via continuous ecosystem-scale measurements using the eddy covariance (EC) technique. Robust error characterization for such measurements, however, remains a major challenge. Here, we quantify systematic, random and gap-filling errors and the resulting uncertainty in CH4 fluxes using a 3.5 year time series of simultaneous open- and closed path CH4 flux measurements over a sub-boreal wetland. After correcting for high- and low frequency flux attenuation, the magnitude of systematic frequency response errors were negligible relative to other uncertainties. Based on three different random flux error estimations, we found that errors of the CH4 flux measurement systems were smaller in magnitude than errors associated with the turbulent transport and flux footprint heterogeneity. Errors on individual half-hourly CH4 fluxes were typically 6%-41%, but not normally distributed (leptokurtic), and thus need to be appropriately characterized when fluxes are compared to chamber-derived or modeled CH4 fluxes. Integrated annual fluxes were only moderately sensitive to gap-filling, based on an evaluation of 4 different methods. Calculated budgets agreed on average to within 7% (≤ 1.5 g - CH4 m-2 yr-1). Marginal distribution sampling using open source code was among the best-performing of all the evaluated gap-filling approaches and it is therefore recommended given its transparency and reproducibility. Overall, estimates of annual CH4 emissions for both EC systems were in excellent agreement (within 0.6 g - CH4 m-2 yr-1) and averaged 18 g - CH4 m-2 yr-1. Total uncertainties on the annual fluxes were larger than the uncertainty of the flux measurement systems and estimated between 7-17%. Identifying trends and differences among sites or site years requires that the observed variability exceeds these uncertainties.

Indirect nitrous oxide emissions from streams within the US Corn Belt scale with stream order.

N2O is an important greenhouse gas and the primary stratospheric ozone depleting substance. Its deleterious effects on the environment have prompted appeals to regulate emissions from agriculture, which represents the primary anthropogenic source in the global N2O budget. Successful implementation of mitigation strategies requires robust bottom-up inventories that are based on emission factors (EFs), simulation models, or a combination of the two. Top-down emission estimates, based on tall-tower and aircraft observations, indicate that bottom-up inventories severely underestimate regional and continental scale N2O emissions, implying that EFs may be biased low. Here, we measured N2O emissions from streams within the US Corn Belt using a chamber-based approach and analyzed the data as a function of Strahler stream order (S). N2O fluxes from headwater streams often exceeded 29 nmol N2O-N m(-2) ⋅ s(-1) and decreased exponentially as a function of S. This relation was used to scale up riverine emissions and to assess the differences between bottom-up and top-down emission inventories at the local to regional scale. We found that the Intergovernmental Panel on Climate Change (IPCC) indirect EF for rivers (EF5r) is underestimated up to ninefold in southern Minnesota, which translates to a total tier 1 agricultural underestimation of N2O emissions by 40%. We show that accounting for zero-order streams as potential N2O hotspots can more than double the agricultural budget. Applying the same analysis to the US Corn Belt demonstrates that the IPCC EF5r underestimation explains the large differences observed between top-down and bottom-up emission estimates.

Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence.

Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.

Feasibility of Recycling Excess Agricultural Nitrate with Electrodialysis.

One of the most serious environmental problems associated with agriculture is excessive nitrate N in waters leaving fields. It is a health hazard in drinking water and a primary cause of hypoxia in ocean waters receiving drainage from agricultural regions. Recent mitigation efforts have focused on techniques that promote denitrification-conversion of excess agricultural nitrate to N. This seems inherently wasteful since industrial production of nitrate fertilizer from N requires a substantial input of energy and is a major source of greenhouse gas emissions. Thus, it is desirable to develop methods to recycle nitrate, keeping it in a form suitable for reuse as fertilizer. One possibility is electrodialysis, in which direct current is passed through alternating cation- and anion-permeable membranes, creating separate streams of dilute and concentrated water. We tested the concept under controlled conditions in a greenhouse and in a field setting on a contaminated trout stream with nitrate N concentrations consistently above 20 mg L. The solar-powered field system removed 42% of the nitrate from water passing through it and concentrated it in a tank for subsequent application as fertilizer. The upper limit of concentration was approximately 520 mg L, above which precipitation of calcite limited operation. Economic analysis indicates that in comparison to denitrification methods such as bioreactors, electrodialysis is likely to be more expensive per unit of nitrate removed. The approach will be most feasible for situations in which nitrate concentrations are well above environmental standards for extended periods, to maximize operating time and nitrate removal rate.

Impact of Kura Clover Living Mulch on Nitrous Oxide Emissions in a Corn-Soybean System.

Nitrous oxide (NO), produced primarily in agricultural soils, is a potent greenhouse gas and is the dominant ozone-depleting substance. Efforts to reduce NO emissions are underway, but mitigation results have been inconsistent. The leguminous perennial kura clover ( M. Bieb.) (KC) can grow side-by-side with cash crops in rotational corn ( L.)-soybean ( L.) systems. With biological nitrogen fixation, KC provides land managers an opportunity to reduce external fertilizer inputs, which may diminish problematic NO emissions. To investigate the effect of a KC living mulch on NO emissions, automated soil chambers coupled to a NO analyzer were used to measure hourly fluxes from April through October in a 2-yr corn-soybean (CS) rotation. Emissions from the KC treatment were significantly greater than those from the conventional CS treatment despite the fact that the KC treatment received substantially less inorganic nitrogen fertilizer. A seasonal tradeoff was observed with the KC treatment wherein emissions before strip-tillage were reduced but were surpassed by high losses after strip-tillage and postanthesis. These results represent the first reported measurements of NO emissions from a KC-based living mulch. The findings cast doubt on the efficacy of KC for mitigating NO loss in CS systems. However, if KC reduces nitrate leaching losses, as has been reported elsewhere, it may result in lower indirect (offsite) NO emissions.

Quantifying nitrous oxide fluxes on multiple spatial scales in the Upper Midwest, USA.

This study seeks to quantify the roles of soybean and corn plants and the cropland ecosystem in the regional N2O budget of the Upper Midwest, USA. The N2O flux was measured at three scales (plant, the soil-plant ecosystem, and region) using newly designed steady-state flow-through plant chambers, a flux-gradient micrometeorological tower, and continuous tall-tower observatories. Results indicate that the following. (1) N2O fluxes from unfertilized soybean (0.03 ± 0.05 nmol m(-2) s(-1)) and fertilized corn plants (-0.01 ± 0.04 nmol m(-2) s(-1)) were about one magnitude lower than N2O emissions from the soil-plant ecosystem (0.26 nmol m(-2) s(-1) for soybean and 0.95 nmol m(-2) s(-1) for corn), confirming that cropland N2O emissions were mainly from the soil. (2) Fertilization increased the corn plant flux for a short period (about 20 days), and late-season fertilization dramatically increased the soybean plant emissions. (3) The direct N2O emission from cropland accounted for less than 20 % of the regional flux, suggesting a significant influence by other sources and indirect emissions, in the regional N2O budget.

The influence of plants on atmospheric methane in an agriculture-dominated landscape.

The primary objective of this study was to clarify the influence of crop plants on atmospheric methane (CH4) in an agriculture-dominated landscape in the Upper Midwest of the United States. Measurements were carried out at two contrasting scales. At the plant scale, CH4 fluxes from soybean and corn plants were measured with a laser-based plant chamber system. At the landscape scale, the land surface flux was estimated with a modified Bowen ratio technique using measurements made on a tall tower. The chamber data revealed a diurnal pattern for the plant CH4 flux: it was positive (an emission rate of 0.4±0.1 nmol m(-2) s(-1), average of soybean and corn, in reference to the unit ground area) during the day, and negative (an uptake rate of -0.8±0.8 nmol m(-2) s(-1)) during the night. At the landscape scale, the flux was estimated to be 14.8 nmol m(-2) s(-1) at night and highly uncertain during the day, but the available references and the flux estimates from the equilibrium methods suggested that the CH4 flux during the entire observation period was similar to the estimated nighttime flux. Thus, soybean and corn plants have a negligible role in the landscape-scale CH4 budget.

Multiple-model based simulation of urban atmospheric methane concentration and the attributions to its seasonal variations: A case study in Hangzhou megacity, China.

Cities are treated as global methane (CH4) emission hotspots and the monitoring of atmospheric CH4 concentration in cities is necessary to evaluate anthropogenic CH4 emissions. However, the continuous and in-situ observation sites within cities are still sparsely distributed in the largest CH4 emitter as of China, and although obvious seasonal variations of atmospheric CH4 concentrations have been observed in cities worldwide, questions regarding the drivers for their temporal variations still have not been well addressed. Therefore, to quantify the contributions to seasonal variations of atmospheric CH4 concentrations, year-round CH4 concentration observations from 1st December 2020 to 30th November 2021 were conducted in Hangzhou megacity, China, and three models were chosen to simulate urban atmospheric CH4 concentration and partition its drivers including machine learning based Random Forest (RF) model, atmospheric transport processes based numerical model (WRF-STILT), and regression analysis based Multiple Linear Regression (MLR) model. The findings are as follows: (1) the atmospheric CH4 concentration showed obvious seasonal variations and were different with previous observations in other cities, the seasonality were 5.8 ppb, 21.1 ppb, and 50.1 ppb between spring-winter, summer-winter and autumn-winter, respectively, where the CH4 background contributed by -8.1 ppb, -44.6 ppb, and -1.0 ppb, respectively, and the CH4 enhancements contributed by 13.9 ppb, 65.7 ppb, and 51.1 ppb. (2) The RF model showed the highest accuracy in simulating CH4 concentrations, followed by MLR model and WRF-STILT model. (3) We further partition contributions from different factors, results showed the largest contribution was from temperature-induced increase in microbial process based CH4 emissions including waste treatment and wetland, which ranged from 38.1 to 76.3 ppb when comparing different seasons with winter. The second largest contribution was from seasonal boundary layer height (BLH) variations, which ranged from -13.4 to -6.3 ppb. And the temperature induced seasonal CH4 emission and enhancement variations were overwhelming BLH changes and other meteorological parameters.

Automated, low-power chamber system for measuring nitrous oxide emissions.

Continuous measurement of soil NO emissions is needed to constrain NO budget and emission factors. Here, we describe the performance of a low-power Teledyne NO analyzer and automated chamber system, powered by wind and solar, that can continuously measure soil NO emissions. Laboratory testing of the analyzer revealed significant temperature sensitivity, causing zero drift of -10.6 nmol mol °C. However, temperature-induced span drift was negligible, so the associated error in flux measurement for a typical chamber sampling period was on the order of 0.016 nmol m s. The 1-Hz precision of the analyzer over a 10-min averaging interval, after wavelet decomposition, was 1.5 nmol mol, equal to that of a tunable diode laser NO analyzer. The solar/wind hybrid power system performed well during summer, but system failures increased in frequency in spring and fall, usually at night. Although increased battery storage capacity would decrease down time, supplemental power from additional sources may be needed to continuously run the system during spring and fall. The hourly flux data were numerically subsampled at weekly intervals to assess the accuracy of integrated estimates derived from manually sampling static chambers. Weekly sampling was simulated for each of the five weekdays and for various times during each day. For each weekday, the cumulative N emissions estimate using only morning measurements was similar (within 15%) to the estimate using only afternoon measurements. Often, weekly sampling partially or completely missed large episodic NO emissions that continuous automated chamber measurements captured, causing weekly measurements to underestimate cumulative N emissions for 9 of the 10 sampling scenarios.

Anthropogenic CO2 emission reduction during the COVID-19 pandemic in Nanchang City, China.

China is the largest CO2 emitting country on Earth. During the COVID-19 pandemic, China implemented strict government control measures on both outdoor activity and industrial production. These control measures, therefore, were expected to significantly reduce anthropogenic CO2 emissions. However, large discrepancies still exist in the estimated anthropogenic CO2 emission reduction rate caused by COVID-19 restrictions, with values ranging from 10% to 40% among different approaches. Here, we selected Nanchang city, located in eastern China, to examine the impact of COVID-19 on CO2 emissions. Continuous atmospheric CO2 and ground-level CO observations from January 1st to April 30th, 2019 to 2021 were used with the WRF-STILT atmospheric transport model and a priori emissions. And a multiplicative scaling factor and Bayesian inversion method were applied to constrain anthropogenic CO2 emissions before, during, and after the COVID-19 pandemic. We found a 37.1-40.2% emission reduction when compared to the COVID-19 pandemic in 2020 with the same period in 2019. Carbon dioxide emissions from the power industry and manufacturing industry decreased by 54.5% and 18.9% during the pandemic period. The power industry accounted for 73.9% of total CO2 reductions during COVID-19. Further, emissions in 2021 were 14.3-14.9% larger than in 2019, indicating that economic activity quickly recovered to pre-pandemic conditions.

Response of nitrous oxide emissions to individual rain events and future changes in precipitation.

Changing precipitation has the potential to alter nitrous oxide (N2 O) emissions from agricultural regions. In this study, we applied the Coupled Model Intercomparison Project Phase 5 end-of-century RCP 8.5 (business as usual) precipitation projections for the U.S. Upper Midwest and examined the effects of mean precipitation changes, characterized by increased early-season rainfall and decreased mid- to late-season rainfall, on N2 O emissions from a conventionally managed corn (Zea mays L.) cropping system grown in an indoor mesocosm facility over four growing seasons. We also assessed the response of N2 O emissions to over 1,000 individual rain events. Nitrous oxide emissions were most strongly correlated with water-filled pore space (WFPS) and soil nitrogen (N) status. After rain events, the change in N2 O emissions, relative to pre-rain emissions, was more likely to be positive when soil NO3 - was >40 mg N kg-1 soil and soil NH4 + was >10 mg N kg-1 soil and was more likely to be negative when soil NO3 - was >40 mg N kg-1 soil and soil NH4 + was <10 mg N kg-1 soil. Similarly, hourly N2 O emissions remained <5 nmol m- 2 s-1 when combined NH4 + + NO3 - was <20 mg N kg-1 soil or NH4 + and NO3 - were <5 and 20 mg N kg-1 soil, respectively. Rain event magnitude did not substantially affect the change in N2 O flux. Finally, growing-season N2 O emissions, soil moisture, and inorganic N content were not affected by the future precipitation pattern. Near-optimal soil WFPS combined with soil N concentrations above the identified thresholds favor higher N2 O emissions.

Identification and correction of spectral contamination in 2H/1H and 18O/16O measured in leaf, stem, and soil water.

Plant water extracts typically contain organic materials that may cause spectral interference when using isotope ratio infrared spectroscopy (IRIS), resulting in errors in the measured isotope ratios. Manufacturers of IRIS instruments have developed post-processing software to identify the degree of contamination in water samples, and potentially correct the isotope ratios of water with known contaminants. Here, the correction method proposed by an IRIS manufacturer, Los Gatos Research, Inc., was employed and the results were compared with those obtained from isotope ratio mass spectrometry (IRMS). Deionized water was spiked with methanol and ethanol to create correction curves for δ(18)O and δ(2)H. The contamination effects of different sample types (leaf, stem, soil) and different species from agricultural fields, grasslands, and forests were compared. The average corrections in leaf samples ranged from 0.35 to 15.73‰ for δ(2)H and 0.28 to 9.27‰ for δ(18)O. The average corrections in stem samples ranged from 1.17 to 13.70‰ for δ(2)H and 0.47 to 7.97‰ for δ(18)O. There was no contamination observed in soil water. Cleaning plant samples with activated charcoal had minimal effects on the degree of spectral contamination, reducing the corrections, by on average, 0.44‰ for δ(2)H and 0.25‰ for δ(18)O. The correction method eliminated the discrepancies between IRMS and IRIS for δ(18)O, and greatly reduced the discrepancies for δ(2)H. The mean differences in isotope ratios between IRMS and the corrected IRIS method were 0.18‰ for δ(18)O, and -3.39‰ for δ(2)H. The inability to create an ethanol correction curve for δ(2)H probably caused the larger discrepancies. We conclude that ethanol and methanol are the primary compounds causing interference in IRIS analyzers, and that each individual analyzer will probably require customized correction curves.

Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions.

We apply airborne measurements across three seasons (summer, winter and spring 2017-2018) in a multi-inversion framework to quantify methane emissions from the US Corn Belt and Upper Midwest, a key agricultural and wetland source region. Combing our seasonal results with prior fall values we find that wetlands are the largest regional methane source (32 %, 20 [16-23] Gg/d), while livestock (enteric/manure; 25 %, 15 [14-17] Gg/d) are the largest anthropogenic source. Natural gas/petroleum, waste/landfills, and coal mines collectively make up the remainder. Optimized fluxes improve model agreement with independent datasets within and beyond the study timeframe. Inversions reveal coherent and seasonally dependent spatial errors in the WetCHARTs ensemble mean wetland emissions, with an underestimate for the Prairie Pothole region but an overestimate for Great Lakes coastal wetlands. Wetland extent and emission temperature dependence have the largest influence on prediction accuracy; better representation of coupled soil temperature-hydrology effects is therefore needed. Our optimized regional livestock emissions agree well with the Gridded EPA estimates during spring (to within 7 %) but are ∼25 % higher during summer and winter. Spatial analysis further shows good top-down and bottom-up agreement for beef facilities (with mainly enteric emissions) but larger (∼30 %) seasonal discrepancies for dairies and hog farms (with >40 % manure emissions). Findings thus support bottom-up enteric emission estimates but suggest errors for manure; we propose that the latter reflects inadequate treatment of management factors including field application. Overall, our results confirm the importance of intensive animal agriculture for regional methane emissions, implying substantial mitigation opportunities through improved management.

Top-Down Constraints on Methane Point Source Emissions From Animal Agriculture and Waste Based on New Airborne Measurements in the U.S. Upper Midwest.

Agriculture and waste are thought to account for half or more of the U.S. anthropogenic methane source. However, current bottom-up inventories contain inherent uncertainties from extrapolating limited in situ measurements to larger scales. Here, we employ new airborne methane measurements over the U.S. Corn Belt and Upper Midwest, among the most intensive agricultural regions in the world, to quantify emissions from an array of key agriculture and waste point sources. Nine of the largest concentrated animal feeding operations in the region and two sugar processing plants were measured, with multiple revisits during summer (August 2017), winter (January 2018), and spring (May-June 2018). We compare the top-down fluxes with state-of-science bottom-up estimates informed by U.S. Environmental Protection Agency methodology and site-level animal population and management practices. Top-down point source emissions are consistent with bottom-up estimates for beef concentrated animal feeding operations but moderately lower for dairies (by 37% on average) and significantly lower for sugar plants (by 80% on average). Swine facility results are more variable. The assumed bottom-up seasonality for manure methane emissions is not apparent in the aircraft measurements, which may be due to on-site management factors that are difficult to capture accurately in national-scale inventories. If not properly accounted for, such seasonal disparities could lead to source misattribution in top-down assessments of methane fluxes.

A global database of water vapor isotopes measured with high temporal resolution infrared laser spectroscopy.

The isotopic composition of water vapour provides integrated perspectives on the hydrological histories of air masses and has been widely used for tracing physical processes in hydrological and climatic studies. Over the last two decades, the infrared laser spectroscopy technique has been used to measure the isotopic composition of water vapour near the Earth's surface. Here, we have assembled a global database of high temporal resolution stable water vapour isotope ratios (δ18O and δD) observed using this measurement technique. As of March 2018, the database includes data collected at 35 sites in 15 Köppen climate zones from the years 2004 to 2017. The key variables in each dataset are hourly values of δ18O and δD in atmospheric water vapour. To support interpretation of the isotopologue data, synchronized time series of standard meteorological variables from in situ observations and ERA5 reanalyses are also provided. This database is intended to serve as a centralized platform allowing researchers to share their vapour isotope datasets, thus facilitating investigations that transcend disciplinary and geographic boundaries.