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Spatio-temporal patterns of methane (CH4) and carbon dioxide (CO2) release from natural sources needs to be better understood across the Arctic region. Climate change in the Arctic is occurring at a pace that may be 2 to 4 times the global average, and existing measurements derive from a limited number of field sites, and most originate during the growing season although important studies show that release continues during winter. The Mackenzie River Delta in the western Canadian Arctic holds thin and destabilizing permafrost, high organic content soils, a high proportion of wetlands, and vast natural gas occurrences at depth, all of which create high methane potential. In the present study, we conducted atmospheric CH4 and CO2 measurements using a mobile laboratory equipped with a greenhouse gas analyzer during the summer and winter. We also visited known aquatic and terrestrial CH4 flux hotspots, including pingos, lakes, river channels and wetlands, where we measured concentration transects and stable carbon isotope (13C-CH4) values to characterize CH4source and spatial pattern. Source stable carbon isotope (δ13C-CH4) signatures at hotspots ranged from -42 to -88 ‰ δ13C-CH4. Active surface microbial production was responsible for at least 4 of the 8 hotspots investigated, indicating that microbial production may be responsible for a greater number of CH4 hotspots than is indicated by previous studies in the region. Mobile surveys showed that shrubland, grassland and deep water were the most important ecosystems for CH4 and CO2 production during the wintertime and that low lying areas of the Delta had the highest atmospheric mixing ratios of CH4.

Anthropogenic methane emissions are generated in several economic sectors, including agriculture, waste management, oil and gas production, and others. Canada is one of the world’s largest oil and gas producers, ranks in the top-25 for agricultural production, and is the world’s largest waste producer per capita. As a result, the methane emission potential is high in parts of Canada where all these activities co-occur. To quantify emissions from multiple co-located sectors, we conducted a case study in Grande Prairie, a small city in Canada’s west dominated by oil and gas production and agriculture. Our goal in this study was to produce a gridded dataset of emissions for the Grande Prairie region. In November 2021, we measured atmospheric mixing ratios of methane using a high-precision gas analyzer mounted in a truck, and estimated emission rates using an inverse Gaussian plume model. During our campaigns, we passed downwind of roughly 220 oil and gas sites and 20 farms with grazing cattle or bison present. We detected emissions at about one-quarter of the oil and gas sites and one-third of the farms, and we also observed emissions from waste management and power generation facilities. Methane emissions from oil and gas production sites were relatively low compared to others we have measured in Canada, but despite this we still found that oil and gas was the dominant methane-emitting sector in the Grande Prairie region. The results of this study feed into a long-term methane monitoring study, focused on multiple economic sectors, methane source types, and detection approaches.

Soil respiration (i.e. from soils and roots) provides one of the largest global fluxes of carbon dioxide (CO2) to the atmosphere and is likely to increase with warming, yet the magnitude of soil respiration from rapidly thawing Arctic-boreal regions is not well understood. To address this knowledge gap, we first compiled a new CO2 flux database for permafrost-affected tundra and boreal ecosystems in Alaska and Northwest Canada. We then used the CO2 database, multi-sensor satellite imagery, and random forest models to assess the regional magnitude of soil respiration. The flux database includes a new Soil Respiration Station network of chamber-based fluxes, and fluxes from eddy covariance towers. Our site-level data, spanning September 2016 to August 2017, revealed that the largest soil respiration emissions occurred during the summer (June–August) and that summer fluxes were higher in boreal sites (1.87 ± 0.67 g CO2–C m−2 d−1) relative to tundra (0.94 ± 0.4 g CO2–C m−2 d−1). We also observed considerable emissions (boreal: 0.24 ± 0.2 g CO2–C m−2 d−1; tundra: 0.18 ± 0.16 g CO2–C m−2 d−1) from soils during the winter (November–March) despite frozen surface conditions. Our model estimates indicated an annual region-wide loss from soil respiration of 591 ± 120 Tg CO2–C during the 2016–2017 period. Summer months contributed to 58% of the regional soil respiration, winter months contributed to 15%, and the shoulder months contributed to 27%. In total, soil respiration offset 54% of annual gross primary productivity (GPP) across the study domain. We also found that in tundra environments, transitional tundra/boreal ecotones, and in landscapes recently affected by fire, soil respiration often exceeded GPP, resulting in a net annual source of CO2 to the atmosphere. As this region continues to warm, soil respiration may increasingly offset GPP, further amplifying global climate change.

An acceleration of model-data synthesis activities has leveraged many terrestrial carbon datasets, but utilization of soil respiration (RS) data has not kept pace. We identify three major challenges in interpreting RS data, and opportunities to utilize it more extensively and creatively: (1) When RS is compared to ecosystem respiration (RECO) measured from EC towers, it is not uncommon to find RS > RECO. We argue this is most likely due to difficulties in calculating RECO, which provides an opportunity to utilize RS for EC quality control. (2) RS integrates belowground heterotrophic and autotrophic activity, but many models include only an explicit heterotrophic output. Opportunities exist to use the total RS flux for data assimilation and model benchmarking methods rather than less-certain partitioned fluxes. (3) RS is generally measured at a very different resolution than that needed for comparison to EC or ecosystem- to global-scale models. Downscaling EC fluxes to match the scale of RS, and improvement of RS upscaling techniques will improve resolution challenges. RS data can bring a range of benefits to model development, particularly with larger databases and improved data sharing protocols to make RS data more robust and broadly available to the research community.

Carbon sequestration and methane flux in wetlands in Nova Scotia are measured. The social benefits associated with carbon storage are estimated using the net sequestration rate and estimates of the social cost of carbon from the Dynamic Integrated model of Climate and the Economy (DICE model). The net benefits of restoring wetlands in agricultural cropland are estimated based on these values and costs of restoration from the literature. The aim is to put a value on wetlands in Nova Scotia using original data rather than benefit transfers from other regions, thereby informing policy aimed at wetlands management in the region. Based on the results of this study, wetlands in Nova Scotia sequester 6.45 tCO2eha−1 yr−1 on average, and release 1.46 tCO2e ha−1 yr−1 as methane. The total benefits of carbon sequestration in wetlands in Nova Scotia are roughly $124–$373 ha−1 yr−1, and range from $5105 to $39,795 ha−1 in total. The social benefit of wetlands in terms of carbon sequestration is as high as $9.66 billion in Nova Scotia. Results indicate that protection of existing wetlands can be warranted on economic grounds. On average, it is not optimal to create wetlands for carbon sequestration, although it may be economically viable to target wetlands that are particularly productive in terms of storing CO2. It may also be viable to restore wetlands if ecosystem services are considered along with carbon sequestration.

Radiocarbon is an exceptionally useful tool for studying soil-respired CO2, providing information about soil carbon turnover rates, depths of production, and the biological sources of production through partitioning. Unfortunately, little work has been done to thoroughly investigate the possibility of inherent biases present in current measurement techniques, like those present in δ13CO2 methodologies, caused by disturbances to the soil's natural diffusive regime. This study investigates the degree of bias present in four 14C sampling chamber methods using a three-dimensional numerical soil-atmosphere CO2 diffusion model. The four chambers were tested in an idealized, surrogate reality by assessing measurement bias with varying Δ14C and δ13C signatures of production, collar lengths, soil biological productivity rates, and soil diffusivities. The static and Iso-FD chambers showed almost no isotopic measurement bias, significantly outperforming dynamic chambers, which demonstrated biases up to 200‰ in some modeled scenarios. The study also showed that 13C and 14C diffusive fractionation are not a constant multiple of one another, but that the δ13C correction still works in diffusive scenarios because the change in fractionation is not large enough to impact measured Δ14C values during chamber equilibration.

While radiocarbon ( 14 C) abundances in standing stocks of soil carbon have been used to evaluate rates of soil carbon turnover on timescales of several years to centuries, soil-respired 14 CO 2 measurements are an important tool for identifying more immediate responses to disturbance and climate change. Soil Δ 14 CO 2 data, however, are often temporally sparse and could be interpreted better with more context for typical seasonal ranges and trends. We report on a semi-high-frequency sampling campaign to distinguish physical and biological drivers of soil Δ 14 CO 2 at a temperate forest site in northern Wisconsin, USA. We sampled 14 CO 2 profiles every three weeks during snow-free months through 2012 in three intact plots and one trenched plot that excluded roots. Respired Δ 14 CO 2 declined through the summer in intact plots, shifting from an older C composition that contained more bomb 14 C to a younger composition more closely resembling present 14 C levels in the atmosphere. In the trenched plot, respired Δ 14 CO 2 was variable but remained comparatively higher than in intact plots, reflecting older bomb-enriched 14 C sources. Although respired Δ 14 CO 2 from intact plots correlated with soil moisture, related analyses did not support a clear cause-and-effect relationship with moisture. The initial decrease in Δ 14 CO 2 from spring to midsummer could be explained by increases in 14 C-deplete root respiration; however, Δ 14 CO 2 continued to decline in late summer after root activity decreased. We also investigated whether soil moisture impacted vertical partitioning of CO 2 production, but found this had little effect on respired Δ 14 CO 2 because CO 2 contained modern bomb C at depth, even in the trenched plot. This surprising result contrasted with decades to centuries-old pre-bomb CO 2 produced in lab incubations of the same soils. Our results suggest that root-derived C and other recent C sources had dominant impacts on respired Δ 14 CO 2 in situ, even at depth. We propose that Δ 14 CO 2 may have declined through late summer in intact plots because of continued microbial turnover of root-derived C, following declines in root respiration. Our results agree with other studies showing declines in the 14 C content of soil respiration over the growing season, and suggest inputs of new photosynthates through roots are an important driver.

Stable carbon isotopes have become a critical and often used tool in understanding ecological and physical processes affecting gas production and emissions in soil. While the insights gained using chamber based flux methods have been significant, it is known now that many of these chamber methods have an inherent bias that complicates the interpretation of their measurements. Here we present a new chamber method that uses diffusive membranes to control CO2 flow into and out of the chamber, and can measure the isotopic composition of soil flux without inducing a bias. We present numerical modeling, followed by laboratory calibration and field measurements using this new method coupled to a Cavity Ring Down Spectrometer (CRDS). Simulations, as well was lab and field results showed that the method is both robust over a range of environmental conditions and can be unbiased, unlike other chamber approaches. Finally, we discuss possibilities for future improvements and variations on the measurement approaches we used.

Summary 1. A better understanding of temporal and spatial variability of soil CO2 fluxes is essential to improve model predictions of soil effluxes. To accomplish that goal, high-frequency and long-term data sets for model development and validation are needed. However, the cost and high maintenance associated with the current technology make high-frequency measurements for small or large spatially distributed grids difficult to achieve. Here, we describe a new observational infrastructure for monitoring soil CO2 efflux, which is attractive because of its low cost and low power consumption compared to traditional methods. 2. Three observational stations equipped with forced diffusion (FD) chambers were deployed in the summer of 2010 across a 1000-km transect in Atlantic Canada. At half-hourly resolution, each observational station recorded soil carbon dioxide (CO2) efflux from two flux chambers and from a suite of meteorological sensors and peripherals. Each station was equipped with telemetry, and data were continuously downloaded for c. 1 year. 3. The average power consumption for each station was roughly a third of a LI-COR LI-8100 system. The FD chambers were approximately four times more affordable than conventional equipment and were also reliable with

a b s t r a c t Soil respiration represents a source of CO2 that greatly exceeds that of fossil fuel use and requires close monitoring in order to account for its contribution to net carbon exchange. While technological innovations continue to improve the quality of respiration monitoring, considerably less effort has been invested in the conceptual framework for monitoring networks. A network architecture and planning process is needed that allows us to anticipate the evolution of significant changes in this carbon pool, to ensure that expertise and resources are available at the appropriate times. In this study we outline a Monte Carlo network design process for large-area soil CO2 respiration monitoring using ground-based soil efflux equipment. Our effort is designed to accommodate many different possible network parameters, such as various temporal evolutions of soil respiration, deployment schedules and the accessibility of remote areas. Sensitivity tests are used to show the dependence of an optimized network to each factor in order to inform the decision making process about the scope and distribution of required monitoring. We also evaluate whether inexpensive and already existing proxy sensors can augment the network performance. The soil region under consideration was predicted to cumulatively respire approximately 200 Pg of carbon over the next 50 years, with results suggesting the need for soil flux observational nodes in several parts of the North American Arctic, but especially in northwestern Canada, Alaska, and at points in the eastern Canadian Arctic Archipelago. The ideal network locations proved most sensitive to moisture and temperature changes and the associated response of the soil. Correspondingly, the sensitivity analysis showed that accurate temperature, moisture and respiration data is crucial to efficient network placement.

Measurement of the delta(13)C value of soil-respired CO(2) (delta(r)) has become a commonplace method through which ecosystem function and C dynamics can be better understood. Despite its proven utility there is currently no consensus on the most robust method with which to measure delta(r). Static and dynamic chamber systems are both commonly used for this purpose; however, the literature on these methods provides evidence suggesting that measurements of delta(r) made with these chamber systems are neither repeatable (self-consistent) nor comparable across methodologies. Here we use a three-dimensional (3-D) numerical soil-atmosphere-chamber model to test these chamber systems in a 'surrogate reality'. Our simulations show that each chamber methodology is inherently biased and that no chamber methodology can accurately predict the true delta(r) signature under field conditions. If researchers intend to use delta(r) to study in situ ecosystem processes, the issues with these chamber systems need to be corrected either by using diffusive theory or by designing a new, unbiased delta(r) measurement system.

Measurement of the delta(13)C value of soil-respired CO(2) (delta(r)) has become a commonplace method through which ecosystem function and C dynamics can be better understood. Despite its proven utility there is currently no consensus on the most robust method with which to measure delta(r). Static and dynamic chamber systems are both commonly used for this purpose; however, the literature on these methods provides evidence suggesting that measurements of delta(r) made with these chamber systems are neither repeatable (self-consistent) nor comparable across methodologies. Here we use a three-dimensional (3-D) numerical soil-atmosphere-chamber model to test these chamber systems in a 'surrogate reality'. Our simulations show that each chamber methodology is inherently biased and that no chamber methodology can accurately predict the true delta(r) signature under field conditions. If researchers intend to use delta(r) to study in situ ecosystem processes, the issues with these chamber systems need to be corrected either by using diffusive theory or by designing a new, unbiased delta(r) measurement system.

[1] End-member mixing models, such as the Keeling and Miller-Tans plots, are frequently used to interpret isotopic data collected from environments where mass transfer occurs due to diffusive processes, however, researchers do not commonly consider the effect of diffusive kinetic fractionation on assumptions of linearity in these mixing models. Risk and Kellman (2008) recently showed the potential for non-linearity in the Keeling plot approach, but their simplified model offers only a first order approximation of this effect in complex systems such as soils. Here we use 3-D numerical simulations and measurements of soil δ13C-CO2 flux accumulating in a static head space chamber to conclusively show that in diffusive environments Keeling plots are non-linear, violating key assumptions of the technique and potentially creating a large source of error in data analysis and interpretation.

[1] Measurement of the isotopic composition of soil and soil-respired CO2 (δ13CO2) has become an invaluable tool in understanding ecosystem carbon-cycling processes. While steady state work has been indispensable in understanding the effects of diffusive transport on soil CO2 isotopic composition, it is crucial that researchers studying temporally dependent processes, such as soil CO2 efflux, realize that these systems are rarely at steady state. Non-steady-state effects could result in misinterpretation of isotopic data, but have not been addressed in the literature, despite their fundamental importance to researchers who use isotopes in diffusive, non-steady-state environments. Here, we use an isotopologue-based model to study dynamic fractionation, which we propose is a byproduct of transient changes in environmental variables. Time varying soil characteristics and processes such as biological production rate, soil pore space, diffusivity and atmospheric concentration were all found to induce non-steady-state gas transport conditions in the soil leading to transient changes in the isotopic composition of soil CO2 flux. The main driving force behind this transport related fractionation of CO2 is the rate of the change in 12CO2 gradient compared to that of 13CO2. These numerical simulations show that dynamic fractionation exists under non-steady-state diffusive conditions and suggest that isotopic data collected in non-steady-state, natural environments, cannot be properly interpreted without considering dynamic fractionation effects.