Description: Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).

Description: Recent warming in the Arctic, which has been amplified during the winter1,2,3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO2)4. However, the amount of CO2 released in winter is not known and has not been well represented by ecosystem models or empirically based estimates5,6. Here we synthesize regional in situ observations of CO2 flux from Arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1,662 TgC per year from the permafrost region during the winter season (October–April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (−1,032 TgC per year). Extending model predictions to warmer conditions up to 2100 indicates that winter CO2 emissions will increase 17% under a moderate mitigation scenario—Representative Concentration Pathway 4.5—and 41% under business-as-usual emissions scenario—Representative Concentration Pathway 8.5. Our results provide a baseline for winter CO2 emissions from northern terrestrial regions and indicate that enhanced soil CO2 loss due to winter warming may offset growing season carbon uptake under future climatic conditions.

Description: This dataset provides a synthesis of winter ( September-April) in situ soil CO2 flux measurement data from locations across pan-Arctic and Boreal permafrost regions. The in situ data were compiled from 66 published and 21 unpublished studies conducted from 1989-2017. The data sources (publication references) are provided. Sampling sites spanned pan-Arctic Boreal and tundra regions (〉53 Deg N) in continuous, discontinuous, and isolated/sporadic permafrost zones. The CO2 flux measurements were aggregated at the monthly level, or seasonally when monthly data were not available, and are reported as the daily average (g C m-2 day-1) over the interval. Soil moisture and temperature data plus environmental and ecological model driver data (e.g., vegetation type and productivity, soil substrate availability) are also included based on gridded satellite remote sensing and reanalysis sources.

Description: 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.

Description: Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

Description: In its latest assessment report the IPCC stresses the need for carbon dioxide removal (CDR) to counterbalance residual emissions to achieve net zero carbon dioxide or greenhouse gas emissions. There are currently a wide variety of CDR measures available. Their potential and feasibility, however, depends on context specific conditions, as among others biophysical site characteristics, or availability of infrastructure and resources. In our study, we selected 13 CDR concepts which we present in the form of exemplary CDR units described in dedicated fact sheets. They cover technical CO2 removal (two concepts of direct air carbon capture), hybrid solutions (six bioenergy with carbon capture technologies) and five options for natural sink enhancement. Our estimates for their CO2 removal potentials in 2050 range from 0.06 to 30 million tons of CO2, depending on the option. Ten of the 13 CDR concepts provide technical removal potentials higher than 1 million tons of CO2 per year. To better understand the potential contribution of analyzed CDR options to reaching net-zero CO2 emissions, we compare our results with the current CO2 emissions and potential residual CO2 emissions in 2050 in Germany. To complement the necessary information on technology-based and hybrid options, we also provide an overview on possible solutions for CO2 storage for Germany. Taking biophysical conditions and infrastructure into account, northern Germany seems a preferable area for deployment of many concepts. However, for their successful implementation further socio-economic analysis, clear regulations, and policy incentives are necessary.

Description: Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models. This dataset describes the data generated in a literature synthesis, covering 358 study sites on vegetation or glacier (〉=60°N latitude), which contained surface energy budget observations. The literature synthesis comprised 148 publications searched on the ISI Web of Science Core Collection.

Description: Arctic tundra exhibits large landscape heterogeneity in microtopography, hydrology, and active layer depth. While many carbon flux measurements and experiments are done at or below the mesoscale (≤1 km), modern ecosystem carbon modeling is often done at scales of 0.25°–1.0° latitude, creating a mismatch between processes, process input data, and verification data. Here we arrange the naturally complex terrain into mesoscale landscape types of varying microtopography and moisture status to evaluate how landscape types differ in terms of CO2 and CH4 balances and their combined warming potential, expressed as CO2 equivalents (CO2-eq). Using a continuous 4 year dataset of CO2 and CH4 fluxes obtained from three eddy covariance (EC) towers, we investigate the integrated dynamics of landscape type, vegetation community, moisture regime, and season on net CO2 and CH4 fluxes. EC towers were situated across a moisture gradient including a moist upland tundra, a heterogeneous polygon tundra, and an inundated drained lake basin. We show that seasonal shifts in carbon emissions buffer annual carbon budget differences caused by site variability. Of note, high growing season gross primary productivity leads to higher fall zero-curtain CO2 emissions, reducing both variability in annual budgets and carbon sink strength of more productive sites. Alternatively, fall zero-curtain CH4 emissions are equal across landscape types, indicating site variation has little effect on CH4 emissions during the fall despite large differences during the growing season. We find that the polygon site has the largest mean warming potential (107 ± 8.63 g C–CO2-eq m−2 yr−1) followed by the drained lake basin site (82.12 ± 9.85 g C–CO2-eq m−2 yr−1) and the upland site (77.19 ± 21.8 g C–CO2-eq m−2 yr−1), albeit differences were not significant. The highest temperature sensitivities are also at the polygon site with mixed results between CO2 and CH4 at the other sites. Results show a similar mean annual net warming effect across dominant landscape types but that these landscape types vary significantly in the amounts and timing of CO2 and CH4 fluxes.

Description: Peatlands are areas with naturally accumulated thick layers of dead organic materials. While peatlands cover about 3% of the world’s land area, their carbon storage is estimated equivalent to ~30% of all soil carbon, ~75% of all atmospheric carbon, and as much carbon as all terrestrial biomass. Drained peatlands due to past human uses can emit carbon and be a key source of greenhouse gases while rewetted peatlands usually have significantly reduced CO2 emissions and can even become a carbon sink. However, quantifying the potential and limitations of reducing emissions by peatland rewetting is challenging. Carbon fluxes on peatlands are both spatially complex and temporally dynamic owing to their microtopography, changing water levels and associated vegetation status. Here we demonstrated the generation of temporally consistent ~biweekly 5-m images over 8 years (2013-2020) at visible and near infrared bands (VNIR) to track the temporal trajectories of vegetation and surface water and estimate cover-specific carbon fluxes at a rewetted peatland site in northeastern Germany (Figure 1). To ensure temporally-consistent multispectral images for the subsequent analyses of vegetation/water covers, we set up a two-stage normalization procedure that normalized the images from RapidEye (SmallSats) and PlanetScope (CubeSats) to rigorously calibrated multispectral sensors onboard large satellites (Landsat-7/8 and Sentinel-2). The two-stage normalization procedure produced two levels of image normalization that allows for downstream applications to balance between the quality and the quantity of available normalized CubeSat images in a time series. A quantitative evaluation approach using daily MODIS images as bridging benchmark data revealed that the temporal consistency in CubeSat images was comparable to that in Landsat and Sentinel-2 images, which confirmed the efficacy of the normalization procedure. The temporal information in the stack of normalized 5-m images helped us estimate the vegetation types and the changes in vegetation/surface water covers throughout the 8 years. The within-year time series of CubeSat images at the three visible and one near-infrared bands showed discernible differences among vegetation types at this peatland sites, which promises systemic mapping of vegetation compositions in peatlands using very-high-resolution CubeSat imagery time series over heterogeneous peatlands. We aggregated vegetation and surface water covers within each year into three condition categories at the peatland site, always emergent vegetation, always surface water, and alternating between vegetation and water. The estimated areas of the three condition categories closely covary with the measured water table depths at the site (Figure 2). The substantial areal expansion of always emergent vegetation at the site, that are captured by the CubeSat imagery time series, aligns well with the timing of three drought events (2016, 2018 and 2019) in this region. These surface covers and conditions at both high temporal and spatial resolutions from CubeSat images allow us to disaggregate ecosystem-scale measurements of CO2 and CH4 fluxes by the eddy covariance (EC) tower at the site into cover-specific fluxes. We attribute CO¬2 and CH4 fluxes measured by EC over 8 years to the three surface condition categories through a nonparametric approach to flux decomposition using annual maps of surface condition categories and half-hourly EC-measurement footprints. The disaggregated carbon fluxes improve our upscaled estimates of carbon emission/sequestration over rewetted peatland sites. Such spatial-temporally-resolved carbon fluxes in dynamic and heterogeneous peatlands will contribute to better informed restoration and protection of peatlands.