Strong geologic methane emissions from discontinuous terrestrial permafrost in the Mackenzie Delta, Canada

Arctic permafrost caps vast amounts of old, geologic methane (CH4) in subsurface reservoirs. Thawing permafrost opens pathways for this CH4 to migrate to the surface. However, the occurrence of geologic emissions and their contribution to the CH4 budget in addition to recent, biogenic CH4 is uncertain. Here we present a high-resolution (100 m × 100 m) regional (10,000 km²) CH4 flux map of the Mackenzie Delta, Canada, based on airborne CH4 flux data from July 2012 and 2013. We identify strong, likely geologic emissions solely where the permafrost is discontinuous. These peaks are 13 times larger than typical biogenic emissions. Whereas microbial CH4 production largely depends on recent air and soil temperature, geologic CH4 was produced over millions of years and can be released year-round provided open pathways exist. Therefore, even though they only occur on about 1% of the area, geologic hotspots contribute 17% to the annual CH4 emission estimate of our study area. We suggest that this share may increase if ongoing permafrost thaw opens new pathways. We conclude that, due to permafrost thaw, hydrocarbon-rich areas, prevalent in the Arctic, may see increased emission of geologic CH4 in the future, in addition to enhanced microbial CH4 production

Spatial variability of aircraft-measured surface energy fluxes in permafrost landscapes

Arctic ecosystems are undergoing a very rapid change due to global warming and their response to climate change has important implications for the global energy budget. Therefore, it is crucial to understand how energy fluxes in the Arctic will respond to any changes in climate related parameters. However, attribution of these responses is challenging because measured fluxes are the sum of multiple processes that respond differently to environmental factors. Here, we present the potential of environmental response functions for quantitatively linking energy flux observations over high latitude permafrost wetlands to environmental drivers in the flux footprints. We used the research aircraft POLAR 5 equipped with a turbulence probe and fast temperature and humidity sensors to measure turbulent energy fluxes along flight tracks across the Alaskan North Slope with the aim to extrapolate the airborne eddy covariance flux measurements from their specific footprint to the entire North Slope. After thorough data pre-processing, wavelet transforms are used to improve spatial discretization of flux observations in order to relate them to biophysically relevant surface properties in the flux footprint. Boosted regression trees are then employed to extract and quantify the functional relationships between the energy fluxes and environmental drivers. Finally, the resulting environmental response functions are used to extrapolate the sensible heat and water vapor exchange over spatio-temporally explicit grids of the Alaskan North Slope. Additionally, simulations from the Weather Research and Forecasting (WRF) model were used to explore the dynamics of the atmospheric boundary layer and to examine results of our extrapolation

Space‐Scale Resolved Surface Fluxes Across a Heterogeneous, Mid‐Latitude Forested Landscape

The Earth\u27s surface is heterogeneous at multiple scales owing to spatial variability in various properties. The atmospheric responses to these heterogeneities through fluxes of energy, water, carbon, and other scalars are scale-dependent and nonlinear. Although these exchanges can be measured using the eddy covariance technique, widely used tower-based measurement approaches suffer from spectral losses in lower frequencies when using typical averaging times. However, spatially resolved measurements such as airborne eddy covariance measurements can detect such larger scale (meso-β, meso-γ) transport. To evaluate the prevalence and magnitude of these flux contributions, we applied wavelet analysis to airborne flux measurements over a heterogeneous mid-latitude forested landscape, interspersed with open water bodies and wetlands. The measurements were made during the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors intensive field campaign. We ask, how do spatial scales of surface-atmosphere fluxes vary over heterogeneous surfaces across the day and across seasons? Measured fluxes were separated into smaller-scale turbulent and larger-scale mesoscale contributions. We found significant mesoscale contributions to sensible and latent heat fluxes through summer to autumn which would not be resolved in single-point tower measurements through traditional time-domain half-hourly Reynolds decomposition. We report scale-resolved flux transitions associated with seasonal and diurnal changes of the heterogeneous study domain. This study adds to our understanding of surface-atmospheric interactions over unstructured heterogeneities and can help inform multi-scale model-data integration of weather and climate models at a sub-grid scale

The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany

Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m3. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m3). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm

Airborne LiDAR and stereo-photogrammetric characterization of permafrost landscapes and thaw subsidence

Rapid climate change in the northern high latitudes has a strong impact on permafrost stability, apparent as coastal erosion, subsidence, or lake dynamics with potentially severe consequences for local communities and ecology. In a rapidly warming Arctic, the monitoring of these processes is essential to understand and predict permafrost dynamics over the upcoming decades. These landscape dynamics are highly diverse, localized, but widely distributed and require datasets with very high spatial resolution, which are barely achieved by satellite data alone. Repeat observations over several years allow for unprecedented insights into highly critical landscape dynamics and the potential integration with and validation of more coarse resolution satellite data. AWI’s research aircraft (Polar-5 and Polar-6) were equipped with airborne LiDAR (full-waveform, multi-echo) as well with experimental modular sensors such as the DLR-developed multi-spectral optical Modular Airborne Camera System (MACS) with a spatial resolution of few cm, stereo capabilities and a very broad radiometric range. The incoming data stream of acquired laser return point cloud data as well as hundreds of thousands of high-resolution images for individual campaigns poses new challenges of handling and processing large data volumes. Here we present an overview about past and upcoming flight campaigns in Alaska and northwestern Canada. Furthermore, we will show applications of the acquired datasets, such as assessments of subsidence, coastal erosion or infrastructure development

Regional scale assessment of methane emission from Arctic permafrost for improving process-based models

Wetlands are the dominant natural source of methane release on a global scale. Estimates about the contribution of Arctic permafrost wetlands to the emission are still uncertain and need further assessment. A reason for that variability is the heterogeneity of the Arctic permafrost landscapes. They extend over large areas and are characterized by varying environmental properties like land cover, surface temperature or soil water content. With chamber and tower measurements, exchange processes of matter fluxes have been measured for decades and have contributed to our understanding of the underlying processes. These results give an idea about possible changes in the future related to changing climatic conditions. For conclusions on a regional scale, however, these measurements cannot represent the true spatial variability of these fluxes, due to their local quality. Regional information about the fluxes, especially carbon fluxes, is indispensable for assessing and predicting the climatic importance of the Arctic permafrost regions. Therefore, regional flux information can help to develop large-scale prediction models for the Arctic. To overcome this spatial limitation we use airborne measurements. During the Airborne Measurements of Methane Fluxes (AIRMETH) campaigns we conducted low level flights across the North Slope of Alaska and the Mackenzie Delta in Canada in the summers of 2012 and 2013. A combination of mechanistic and data-driven analysis tools allows us to relate the measured methane fluxes to spatio-temporally resolved surface properties and basic meteorological information. The aim is to develop a predictive model that produces maps of methane emissions, based on the spatial variation of the land surface and meteorological states. Here we will show first results from the campaigns conducted in the Mackenzie Delta in 2012 and 2013

The Airborne Measurements of Methane Fluxes (AirMeth) Arctic Campaign

One of the most pressing questions with regard to climate feedback processes in a warming Arctic is the regional-scale methane release from Arctic permafrost areas. The Airborne Measurements of Methane Fluxes (AIRMETH) campaign is designed to quantitatively and spatially explicitly address this question. Ground-based eddy covariance (EC) measurements provide continuous in-situ observations of the surface-atmosphere exchange of methane. However, these observations are rare in the Arctic permafrost zone and site selection is bound by logistical constraints among others. Consequently, these observations cover only small areas that are not necessarily representative of the region of interest. Airborne measurements can overcome this limitation by covering distances of hundreds of kilometers over time periods of a few hours. Here, we present the potential of environmental response functions (ERFs) for quantitatively linking methane flux observations in the atmospheric surface layer to meteorological and biophysical drivers in the flux footprints. For this purpose thousands of kilometers of AIRMETH data across the Alaskan North Slope are utilized, with the aim to extrapolate the airborne EC methane flux observations to the entire North Slope. The data were collected aboard the research aircraft POLAR 5, using its turbulence nose boom and fast response methane and meteorological sensors. After thorough data pre-processing, Reynolds averaging is used to derive spatially integrated fluxes. To increase spatial resolution and to derive ERFs, we then use wavelet transforms of the original high-frequency data. This enables much improved spatial discretization of the flux observations, and the quantification of continuous and biophysically relevant land cover properties in the flux footprint of each observation. A machine learning technique is then employed to extract and quantify the functional relationships between the methane flux observations and the meteorological and biophysical drivers in the flux footprints. Lastly, the resulting ERFs are used to extrapolate the methane release over spatio-temporally explicit grids of the Alaskan North Slope. Metzger et al. (2013) have demonstrated the efficacy of this technique for regionalizing airborne EC heat flux observations to within an accuracy of ≤18% and a precision of ≤5%. Here, we show for the first time results from applying the ERF procedure to airborne methane EC measurements, and report its potential for spatio-temporally explicit inventories of the regional-scale methane exchange. References: Metzger, S., Junkermann, W., Mauder, M., Butterbach-Bahl, K., Trancón y Widemann, B., Neidl, F., Schäfer, K., Wieneke, S., Zheng, X. H., Schmid, H. P., and Foken, T.: Spatially explicit regionalization of airborne flux measurements using environmental response functions, Biogeosciences, 10, 2193-2217, doi:10.5194/bg-10-2193-2013, 2013. Cite as: Author(s) (2013), Title, Abstract B43G-07 presented at 2013 Fall Meeting, AGU, San Francisco, Calif., 9-13 Dec

Regional Scaling of Airborne Eddy Covariance Flux Observation

The earth’s surface is tightly coupled to the global climate system by the vertical exchange of energy and matter. Thus, to better understand and potentially predict changes to our climate system, it is critical to quantify the surface-atmosphere exchange of heat, water vapor, and greenhouse gases on climate-relevant spatial and temporal scales. Currently, most flux observations consist of ground-based, continuous but local measurements. These provide a good basis for temporal integration, but may not be representative of the larger regional context. This is particularly true for the Arctic, where site selection is additionally bound by logistical constraints, among others. Airborne measurements can overcome this limitation by covering distances of hundreds of kilometers over time periods of a few hours. The Airborne Measurements of Methane Fluxes (AIRMETH) campaigns are designed to quantitatively and spatially explicitly address this issue: The research aircraft POLAR 5 is used to acquire thousands of kilometers of eddy-covariance flux data. During the AIRMETH-2012 and AIRMETH-2013 campaigns we measured the turbulent exchange of energy, methane, and (in 2013) carbon dioxide over the North Slope of Alaska, USA, and the Mackenzie Delta, Canada. Here, we present the potential of environmental response functions (ERFs) for quantitatively linking flux observations to meteorological and biophysical drivers in the flux footprints. We use wavelet transforms of the original high-frequency data to improve spatial discretization of the flux observations. This also enables the quantification of continuous and biophysically relevant land cover properties in the flux footprint of each observation. A machine learning technique is then employed to extract and quantify the functional relationships between flux observations and the meteorological and biophysical drivers. The resulting ERFs are used to extrapolate fluxes over spatio-temporally explicit grids of the study area. The presentation will focus on 2012 sensible and latent heat fluxes observed over the North Slope of Alaska and the scaling performance of the ERF approach

Low permafrost methane emissions from regional airborne flux measurements

Large uncertainties still exist in the global methane budget with clear disagreements between bottom-up and top-down estimates, limiting confidence in climate projections. This is particularly true in the Arctic, which is warming rapidly while storing vast amounts of organic carbon that could potentially be released as carbon dioxide and methane, adding a new greenhouse gas source of unknown magnitude. Regional scale methane emission estimates and functional relationships between potential drivers and methane fluxes are currently unavailable. The Airborne Measurements of Methane Fluxes (AIRMETH) campaigns are designed to quantitatively and spatially explicitly address this question. While ground-based eddy covariance (EC) measurements provide continuous in-situ observations of the surface-atmosphere exchange of energy and matter, they are rare in the Arctic permafrost zone and site selection is bound by logistical constraints among others. Consequently, these observations cover only small areas that are not necessarily representative of the region of interest. Airborne measurements can overcome this limitation by covering distances of hundreds of kilometers over time periods of a few hours. During the AIRMETH-2012 campaign aboard the research aircraft POLAR 5 we measured turbulent exchange fluxes of energy and methane along thousands of kilometers covering the North Slope of Alaska. Time-frequency (wavelet) analysis, footprint modeling, and machine learning techniques are used to extract spatially resolved turbulence statistics and fluxes, spatially resolved contributions of land cover and biophysical surface properties to each flux observation, as well as regionally valid functional relationships between environmental drivers and observed fluxes that can explain spatial flux patterns and – if available in temporal resolution – allow for spatio-temporal scaling of the observations. Here we present a 100 m resolution gridded methane flux map for the North Slope of Alaska, covering about 90.000 km2. We show that surface properties like elevation, temperature, and NDVI along with meteorological drivers such as shortwave radiation, water vapor mixing ratio, and horizontal wind speed are sufficient to explain and project the measured fluxes. The median methane flux for the campaign period (end of June/beginning of July) was 19.4 mg m−2 d−1 after excluding all values with � 30 % standard error. The largest fluxes were observed along the coast and in the Arctic coastal plain, decreasing towards the Brooks Range