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Abstract. Landscapes are often assumed to be homogeneous when interpreting eddy covariance fluxes, which can lead to biases when gap-filling and scaling up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence net ecosystem exchange (NEE) of carbon dioxide (CO2) and methane (CH4) fluxes. With warming temperatures, Arctic ecosystems are changing from a net sink to a net source of carbon to the atmosphere in some locations, but the Arctic's carbon balance remains highly uncertain. In this study we report results from growing season NEE and CH4 fluxes from an eddy covariance tower in the Yukon–Kuskokwim Delta in Alaska. We used footprint models and Bayesian Markov chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent land-cover fluxes based on high-resolution land-cover maps of the region. We compared three types of footprint models and used two land-cover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. We used artificially created gaps of withheld observations to compare gap-filling performance using our derived land-cover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. We also compared resulting regional carbon budgets when scaling up observations using heterogeneous and homogeneous approaches. Traditional gap-filling methods performed worse at predicting artificially withheld gaps in NEE than those that accounted for heterogeneous landscapes, while there were only slight differences between footprint models and land-cover maps. We identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). We resolved distinct seasonality in tundra growing season NEE fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO2 sink by a factor of 2 and underestimated CH4 emissions by a factor of 2 when compared to scaling with any method that accounts for landscape heterogeneity. We show how Bayesian MCMC, analytical footprint models, and high-resolution land-cover maps can be leveraged to derive detailed land-cover carbon fluxes from eddy covariance time series. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic. 

Abstract In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO₂and CH₄fluxes across the YK Delta of south-west AK. We found that waterbody size and contour were strong predictors for aquatic CO₂emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km²) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001–0.1 km²) contributed the majority of carbon emissions from waterbodies. Waterbody CH₄emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to our machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO₂and CH₄emissions by 26%–79% and 8%–53% respectively. This study demonstrates the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling C emissions in the arctic. 

Abstract The Arctic–Boreal Zone is rapidly warming, impacting its large soil carbon stocks. Here we use a new compilation of terrestrial ecosystem CO₂fluxes, geospatial datasets and random forest models to show that although the Arctic–Boreal Zone was overall an increasing terrestrial CO₂sink from 2001 to 2020 (mean ± standard deviation in net ecosystem exchange, −548 ± 140 Tg C yr⁻¹; trend, −14 Tg C yr⁻¹;P < 0.001), more than 30% of the region was a net CO₂source. Tundra regions may have already started to function on average as CO₂sources, demonstrating a shift in carbon dynamics. When fire emissions are factored in, the increasing Arctic–Boreal Zone sink is no longer statistically significant (budget, −319 ± 140 Tg C yr⁻¹; trend, −9 Tg C yr⁻¹), and the permafrost region becomes CO₂neutral (budget, −24 ± 123 Tg C yr⁻¹; trend, −3 Tg C yr⁻¹), underscoring the importance of fire in this region. 

Volatile chemical products (VCPs) and other non-combustion-related sourceshave become important for urban air quality, and bottom-up calculationsreport emissions of a variety of functionalized compounds that remainunderstudied and uncertain in emissions estimates. Using a new instrumentalconfiguration, we present online measurements of oxygenated organiccompounds in a US megacity over a 10 d wintertime sampling period, whenbiogenic sources and photochemistry were less active. Measurements wereconducted at a rooftop observatory in upper Manhattan, New York City, USAusing a Vocus chemical ionization time-of-flight mass spectrometer, withammonium (NH4+) as the reagent ion operating at 1 Hz. The range ofobservations spanned volatile, intermediate-volatility, and semi-volatileorganic compounds, with targeted analyses of ∼150 ions, whoselikely assignments included a range of functionalized compound classes suchas glycols, glycol ethers, acetates, acids, alcohols, acrylates, esters,ethanolamines, and ketones that are found in various consumer, commercial,and industrial products. Their concentrations varied as a function of winddirection, with enhancements over the highly populated areas of the Bronx,Manhattan, and parts of New Jersey, and included abundant concentrations ofacetates, acrylates, ethylene glycol, and other commonly used oxygenatedcompounds. The results provide top-down constraints on wintertime emissionsof these oxygenated and functionalized compounds, with ratios to commonanthropogenic marker compounds and comparisons of their relative abundancesto two regionally resolved emissions inventories used in urban air qualitymodels. 

Abstract. Understanding the sources and sinks of methane (CH4)is critical to both predicting and mitigating future climate change. Thereare large uncertainties in the global budget of atmospheric CH4, butnatural emissions are estimated to be of a similar magnitude toanthropogenic emissions. To understand CH4 flux from biogenic sourcesin the United States (US) of America, a multi-scale CH4 observationnetwork focused on CH4 flux rates, processes, and scaling methods isrequired. This can be achieved with a network of ground-based observationsthat are distributed based on climatic regions and land cover. To determinethe gaps in physical infrastructure for developing this network, we need tounderstand the landscape representativeness of the current infrastructure.We focus here on eddy covariance (EC) flux towers because they are essentialfor a bottom-up framework that bridges the gap between point-based chambermeasurements and airborne or satellite platforms that inform policydecisions and global climate agreements. Using dissimilarity,multidimensional scaling, and cluster analysis, the US was divided into 10clusters distributed across temperature and precipitation gradients. Weevaluated dissimilarity within each cluster for research sites with activeCH4 EC towers to identify gaps in existing infrastructure that limitour ability to constrain the contribution of US biogenic CH4 emissionsto the global budget. Through our analysis using climate, land cover, andlocation variables, we identified priority areas for research infrastructureto provide a more complete understanding of the CH4 flux potential ofecosystem types across the US. Clusters corresponding to Alaska and theRocky Mountains, which are inherently difficult to capture, are the mostpoorly represented, and all clusters require a greater representation ofvegetation types. 

Rapid Arctic environmental change affects the entire Earth system as thawing permafrost ecosystems release greenhouse gases to the atmosphere. Understanding how much permafrost carbon will be released, over what time frame, and what the relative emissions of carbon dioxide and methane will be is key for understanding the impact on global climate. In addition, the response of vegetation in a warming climate has the potential to offset at least some of the accelerating feedback to the climate from permafrost carbon. Temperature, organic carbon, and ground ice are key regulators for determining the impact of permafrost ecosystems on the global carbon cycle. Together, these encompass services of permafrost relevant to global society as well as to the people living in the region and help to determine the landscape-level response of this region to a changing climate. 

Abstract Where does the carbon released by burning fossil fuels go? Currently, ocean and land systems remove about half of the CO 2 emitted by human activities; the remainder stays in the atmosphere. These removal processes are sensitive to feedbacks in the energy, carbon, and water cycles that will change in the future. Observing how much carbon is taken up on land through photosynthesis is complicated because carbon is simultaneously respired by plants, animals, and microbes. Global observations from satellites and air samples suggest that natural ecosystems take up about as much CO 2 as they emit. To match the data, our land models generate imaginary Earths where carbon uptake and respiration are roughly balanced, but the absolute quantities of carbon being exchanged vary widely. Getting the magnitude of the flux is essential to make sure our models are capturing the right pattern for the right reasons. Combining two cutting-edge tools, carbonyl sulfide (OCS) and solar-induced fluorescence (SIF), will help develop an independent answer of how much carbon is being taken up by global ecosystems. Photosynthesis requires CO 2 , light, and water. OCS provides a spatially and temporally integrated picture of the “front door” of photosynthesis, proportional to CO 2 uptake and water loss through plant stomata. SIF provides a high-resolution snapshot of the “side door,” scaling with the light captured by leaves. These two independent pieces of information help us understand plant water and carbon exchange. A coordinated effort to generate SIF and OCS data through satellite, airborne, and ground observations will improve our process-based models to predict how these cycles will change in the future. 

Abstract Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO 2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO 2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO 2 later in the season.