Methane (CH₄) fluxes from Alaska and other arctic regions may be sensitive to thawing permafrost and future climate change, but estimates of both current and future fluxes from the region are uncertain. This study estimates CH₄ fluxes across Alaska for 2012–2014 using aircraft observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and a geostatistical inverse model (GIM). We find that a simple flux model based on a daily soil temperature map and a static map of wetland extent reproduces the atmospheric CH₄ observations at the statewide, multiyear scale more effectively than global-scale process-based models. This result points to a simple and effective way of representing CH₄ fluxes across Alaska. It further suggests that process-based models can improve their representation of key processes and that more complex processes included in these models cannot be evaluated given the information content of available atmospheric CH₄ observations. In addition, we find that CH₄ emissions from the North Slope of Alaska account for 24% of the total statewide flux of 1.74 ± 0.26 Tg CH₄ (for May–October). Global-scale process models only attribute an average of 3% of the total flux to this region. This mismatch occurs for two reasons: process models likely underestimate wetland extent in regions without visible surface water, and these models prematurely shut down CH₄ fluxes at soil temperatures near 0°C. Lastly, we find that the seasonality of CH₄ fluxes varied during 2012–2014 but that total emissions did not differ significantly among years, despite substantial differences in soil temperature and precipitation.

Wetlands are the largest global natural methane (CH₄) source, and emissions between 50 and 70° N latitude contribute 10–30 % to this source. Predictive capability of land models for northern wetland CH₄ emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH₄ emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with site- to regional-scale observations. A comparison of the CH₄ fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH₄ emissions when compared with CarbonTracker CH₄ predictions. CLM4.5 CH₄ emission predictions agree well with growing season (May–September) CarbonTracker Alaskan regional-level CH₄ predictions and site-level observations. However, CLM4.5 underestimated CH₄ emissions in the cold season (October–April). The monthly atmospheric CH₄ mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH₄ mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH₄ emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH₄ cycle are from the wetland extent, cold-season CH₄ production and CH₄ transport processes. We recommend more cold-season experimental studies in high-latitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH₄ emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements.

Molecular hydrogen (H₂) is an atmospheric trace gas with a large microbe-mediated soil sink, yet cycling of this compound throughout ecosystems is poorly understood. Measurements of the sources and sinks of H₂ in various ecosystems are sparse, resulting in large uncertainties in the global H₂ budget. Constraining the H₂ cycle is critical to understanding its role in atmospheric chemistry and climate. We measured H₂ fluxes at high frequency in a temperate mixed deciduous forest for 15 months using a tower-based flux-gradient approach to determine both the soil-atmosphere and the net ecosystem flux of H₂. We found that Harvard Forest is a net H₂ sink (−1.4 ± 1.1 kg H₂ ha⁻¹) with soils as the dominant H₂ sink (−2.0 ± 1.0 kg H₂ ha⁻¹) and aboveground canopy emissions as the dominant H₂ source (+0.6 ± 0.8 kg H₂ ha⁻¹). Aboveground emissions of H₂ were an unexpected and substantial component of the ecosystem H₂ flux, reducing net ecosystem uptake by 30% of that calculated from soil uptake alone. Soil uptake was highly seasonal (July maximum, February minimum), positively correlated with soil temperature and negatively correlated with environmental variables relevant to diffusion into soils (i.e., soil moisture, snow depth, snow density). Soil microbial H₂ uptake was correlated with rhizosphere respiration rates (r = 0.8, P < 0.001), and H₂ metabolism yielded up to 2% of the energy gleaned by microbes from carbon substrate respiration. Here, we elucidate key processes controlling the biosphere–atmosphere exchange of H₂ and raise new questions regarding the role of aboveground biomass as a source of atmospheric H₂ and mechanisms linking soil H₂ and carbon cycling. Results from this study should be incorporated into modeling efforts to predict the response of the H₂ soil sink to changes in anthropogenic H₂ emissions and shifting soil conditions with climate and land-use change.

We demonstrate the use of compact solar-tracking Fourier transform spectrometers (Bruker EM27/SUN) for differential measurements of the column-averaged dry-air mole fractions of CH₄ and CO₂ within urban areas. Using Allan variance analysis, we show that the differential column measurement has a precision of 0.01 % for X_CO2 and X_CH4 with an optimum integration time of 10 min, corresponding to Allan deviations of 0.04 ppm and 0.2 ppb, respectively. The sensor system is very stable over time and after relocation across the continent. We report tests of the differential column measurement, and its sensitivity to emission sources, by measuring the downwind-minus-upwind column difference ΔX_CH4 across dairy farms in the Chino area, California, and using the data to verify emissions reported in the literature. Ratios of spatial column differences ΔX_CH4∕ΔX_CO2 were observed across Pasadena within the Los Angeles basin, indicating values consistent with regional emission ratios from the literature. Our precise, rapid measurements allow us to determine significant short-term variations (5–10 min) of X_CO2 and X_CH4 and to show that they represent atmospheric phenomena.

Overall, this study helps establish a range of new applications for compact solar-viewing Fourier transform spectrometers. By accurately measuring the small differences in integrated column amounts across local and regional sources, we directly observe the mass loading of the atmosphere due to the influence of emissions in the intervening locale. The inference of the source strength is much more direct than inversion modeling using only surface concentrations and less subject to errors associated with small-scale transport phenomena.

With rapid changes in climate and the seasonal amplitude of carbon dioxide (CO₂) in the Arctic, it is critical that we detect and quantify the underlying processes controlling the changing amplitude of CO₂ to better predict carbon cycle feedbacks in the Arctic climate system. We use satellite and airborne observations of atmospheric CO₂ with climatically forced CO₂ flux simulations to assess the detectability of Alaskan carbon cycle signals as future warming evolves. We find that current satellite remote sensing technologies can detect changing uptake accurately during the growing season but lack sufficient cold season coverage and near-surface sensitivity to constrain annual carbon balance changes at regional scale. Airborne strategies that target regular vertical profile measurements within continental interiors are more sensitive to regional flux deeper into the cold season but currently lack sufficient spatial coverage throughout the entire cold season. Thus, the current CO₂ observing network is unlikely to detect potentially large CO₂ sources associated with deep permafrost thaw and cold season respiration expected over the next 50 y. Although continuity of current observations is vital, strategies and technologies focused on cold season measurements (active remote sensing, aircraft, and tall towers) and systematic sampling of vertical profiles across continental interiors over the full annual cycle are required to detect the onset of carbon release from thawing permafrost.

Northern high-latitude carbon sources and sinks, including those resulting from degrading permafrost, are thought to be sensitive to the rapidly warming climate. Because the near-surface atmosphere integrates surface fluxes over large ( ∼  500–1000 km) scales, atmospheric monitoring of carbon dioxide (CO₂) and methane (CH₄) mole fractions in the daytime mixed layer is a promising method for detecting change in the carbon cycle throughout boreal Alaska. Here we use CO₂ and CH₄ measurements from a NOAA tower 17 km north of Fairbanks, AK, established as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE), to investigate regional fluxes of CO₂ and CH₄ for 2012–2014. CARVE was designed to use aircraft and surface observations to better understand and quantify the sensitivity of Alaskan carbon fluxes to climate variability. We use high-resolution meteorological fields from the Polar Weather Research and Forecasting (WRF) model coupled with the Stochastic Time-Inverted Lagrangian Transport model (hereafter, WRF-STILT), along with the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM), to investigate fluxes of CO₂ in boreal Alaska using the tower observations, which are sensitive to large areas of central Alaska. We show that simulated PolarVPRM–WRF-STILT CO₂ mole fractions show remarkably good agreement with tower observations, suggesting that the WRF-STILT model represents the meteorology of the region quite well, and that the PolarVPRM flux magnitudes and spatial distribution are generally consistent with CO₂ mole fractions observed at the CARVE tower. One exception to this good agreement is that during the fall of all 3 years, PolarVPRM cannot reproduce the observed CO₂ respiration. Using the WRF-STILT model, we find that average CH₄ fluxes in boreal Alaska are somewhat lower than flux estimates by Chang et al. (2014) over all of Alaska for May–September 2012; we also find that enhancements appear to persist during some wintertime periods, augmenting those observed during the summer and fall. The possibility of significant fall and winter CO₂ and CH₄ fluxes underscores the need for year-round in situ observations to quantify changes in boreal Alaskan annual carbon balance.

Existing estimates of methane (CH₄) fluxes from North American wetlands vary widely in both magnitude and distribution. In light of these differences, this study uses atmospheric CH₄ observations from the US and Canada to analyze seven different bottom-up, wetland CH₄ estimates reported in a recent model comparison project. We first use synthetic data to explore whether wetland CH₄ fluxes are detectable at atmospheric observation sites. We find that the observation network can detect aggregate wetland fluxes from both eastern and western Canada but generally not from the US. Based upon these results, we then use real data and inverse modeling results to analyze the magnitude, seasonality, and spatial distribution of each model estimate. The magnitude of Canadian fluxes in many models is larger than indicated by atmospheric observations. Many models predict a seasonality that is narrower than implied by inverse modeling results, possibly indicating an oversensitivity to air or soil temperatures. The LPJ-Bern and SDGVM models have a geographic distribution that is most consistent with atmospheric observations, depending upon the region and season. These models utilize land cover maps or dynamic modeling to estimate wetland coverage while most other models rely primarily on remote sensing inundation data.

Arctic terrestrial ecosystems are major global sources of methane (CH₄); hence, it is important to understand the seasonal and climatic controls on CH₄ emissions from these systems. Here, we report year-round CH₄ emissions from Alaskan Arctic tundra eddy flux sites and regional fluxes derived from aircraft data. We find that emissions during the cold season (September to May) account for ≥50% of the annual CH₄ flux, with the highest emissions from noninundated upland tundra. A major fraction of cold season emissions occur during the “zero curtain” period, when subsurface soil temperatures are poised near 0 °C. The zero curtain may persist longer than the growing season, and CH₄ emissions are enhanced when the duration is extended by a deep thawed layer as can occur with thick snow cover. Regional scale fluxes of CH₄ derived from aircraft data demonstrate the large spatial extent of late season CH₄ emissions. Scaled to the circumpolar Arctic, cold season fluxes from tundra total 12 ± 5 (95% confidence interval) Tg CH₄ y⁻¹, ∼25% of global emissions from extratropical wetlands, or ∼6% of total global wetland methane emissions. The dominance of late-season emissions, sensitivity to soil environmental conditions, and importance of dry tundra are not currently simulated in most global climate models. Because Arctic warming disproportionally impacts the cold season, our results suggest that higher cold-season CH₄ emissions will result from observed and predicted increases in snow thickness, active layer depth, and soil temperature, representing important positive feedbacks on climate warming.