Man-made and natural CH4 and VOC emissions from marine and terrestrial sources in the North Sea region


Importance and motivation

Although the atmospheric abundance of methane (CH4) is about 200 times smaller compared to that of carbon dioxide (CO2), it is a far more potent warming agent as its greenhouse warming potential (GWP) is 25-30 times larger (depending on the considered time frame) than that of CO2 (IPCC 2013). Anthropogenic and natural fluxes from the terrestrial and ocean environment towards the atmosphere are balanced by the oxidation of CH4 in the atmosphere. This oxidation is initiated by OH radicals and contributes to tropospheric ozone production (e.g. Seinfeld and Pandis 2006), thus linking local-scale emissions of methane to regional-and global-scale formation of ozone and other photo-oxidants affecting air quality. A recent estimate of the atmospheric CH4 lifetime is in the order of about 9 to 10 years (IPCC 2013) leading to the well-mixed global atmospheric distribution of CH4.

Today anthropogenic sources of agriculture/waste and biomass burning are the dominating fluxes into the atmosphere, followed by natural sources e.g. from wetlands in tropical and Arctic regions or other geological sources (e.g. Kirschke et al. 2013). However, also leakage of methane and volatile organic compounds (VOCs) from oil, gas and coal exploration on land and at sea leads to in detail yet not quantified emissions which manifests in huge discrepancies in top-down and bottom-up estimates of the methane emissions (IPCC 2013). To overcome this discrepancy and to distinguish natural from anthropogenic sources, an evaluation of individual, local sources (e.g. leaking oil and gas wells) by detailed field studies accompanied by high resolution modelling is needed to assess the impact of local CH4 and VOC sources on atmospheric concentrations.

As an example for strong local gas emissions, Show Case A we focus on the North Sea region as it shows specific anthropogenic (active and abandoned gas wells; Vielstädte et al. 2016) and natural sources and sinks. During Digital Earth existing marine data as well as those from data bases (e.g. MEMENTO) will be collected and complemented by additionally acquired data during marine cruise and air plane campaigns (GEOMAR, UFZ, DLR, GFZ; -> MOSES & CoMET campaigns). In general, natural gas release from seep sites in the North Sea is a widespread phenomenon studied for several decades (e.g. Hovland & Judd 1988, Schneider v. Deimling et al. 2011, Urban et al. 2016, Römer et al. 2017), focussing on dissolved and free gas fluxes alike. Ebullition depends on internal and external properties and forces; these are e.g. sediment permeability/geological pathways that connect biogenic methane gas pockets in the sub-seafloor with the seafloor surface or tides and wave activity that modify the hydrostatic pressure on the gas source. Once bubbles are released, transport, dissolution and dispersion depend on oceanic parameters as temperature and salinity (McGinnis et al., 2006), as well as the seasonal establishment/break-down of pycnoclines and tide-dependently changing current directions. In contrast, the amount of dissolved methane released from the seafloor is driven by aerobic and anaerobic biochemical processes close to the sediment-water interface (Boetius et al., 2000; Sommer et al., 2006) which similarly effect gas concentrations in the water column. At the sea surface, dissolved gas is equilibrating with the atmosphere depending on temperature, salinity, the concentration gradient between water and atmosphere with the gas transfer velocity being also depending on wind and wave activity (e.g. Wanninkhof 1992, Wanninkhof et al. 2009).

Although the study region appears small on a global scale and its estimated contribution to the global methane budget has been estimated as small as well, the region is very well suited as a Show Case in Digital Earth. This is because it allows to investigate the fate of CH4 from the geosphere across the interfaces seafloor/ocean and ocean/atmosphere into the atmosphere in an exemplary manner. Making a consolidate effort in data integration and better correlation and interpolation of data, will help to understand and reduce discrepancies in top-down and bottom-up estimates of methane emissions (and other gases) in general. Furthermore, this regional approach allows to study the related impact on the tropospheric ozone burden in this densely populated area of Europe. In this respect, this Show Case is a truly compartment integrating study following a holistic approach of Earth System studies.

Challenges and approach

The scientific challenge of Show Case A is to assess all impacts of the localized methane sources, including their varying internal and external forcing on seasonal to daily time scales and their strong spatial heterogenic distribution. Methane flux estimates need to be combined across interfaces from different data and modelling platforms and consistent flux budgets (accounting for scale differences) will be used to evaluate impacts on air quality diagnostics. For this, an explicit interaction between Earth and Data Scientists with respect to the upscaling of methane fluxes (bottom-up approach) is required. The upscaling of meaningful source data (e.g. sea surface fluxes-data) in a bottom-up approach is challenging, if not impossible, unless it can be linked to long-term measurements. This would facilitate a verified assessment in time, allowing correlations with spatially available data products, thus providing spatially and temporally ‘correct’ flux budgets. Our scientific approaches to tackle this challenge are explained in more detail further below.

Part of these data will be collected in already planned field campaigns. The methodologies developed in WP1 and WP2 will guide a statistically meaningful sampling, define regions and specific seep locations to be investigated (Task 1.1) and plan the sampling layout in accordance to the environmental conditions at the time of sampling (Task 1.2, Task 2.2). Methodologies for closing data gaps and refining data acquisition for covering different temporal and spatial scales will be applied in Task 1.3. A first hurdle will be the integration of different data from various sources as literature, data from data bases as PANGAEA, MEMENTO the Global Carbon Project, but also unpublished data from colleagues; this will also be tackled by Task 2.2. Next to machine learning methods (Task 2.4), visual data exploration in a 4D environment will make ‘old’ data explorable, while new data can be visualized in (near) real-time and model-data can be fit by clever parametrisation. The usefulness and value of the developed Data Science approaches will be tested prior and during scheduled field campaigns.

Once the bottom-up fluxes are determined, they can be compared with top-down measured or model-derived fluxes with their characteristic spatial resolution of about 100 –1200 km2. Overcoming the problem of different data set-resolutions (meters to 100m for marine seafloor sources; 100m to few km for sea surface fluxes) will require means that consider uncertainties and which allow testing different parameterisations and algorithms in an interactive visual and computational environment (Task 2.3, 2.4). Thus, enabling seamless data exchange and scientific discipline integrating easy data exploration/analyses workflows and tools as well as streamlined data assimilation into models build the requirements of Show Case A towards WP1 and WP2 but also towards Frontier Simulations in ESM.

Data and Methods

Show Case A bases on ongoing studies by GEOMAR, AWI and HZG in the North Sea region (e.g. European Project STEMM CCS) which will support existing, series of monitoring data (e.g. COSYNA; Helgoland MarGate) and recently acquired data (cruise POS518 in Oct. 2017). Upcoming ship campaigns to the North Sea (cruises POS526 and POS527 Jul.-Sep. 2018) as well as a HALO air plane campaign by DLR (CoMET) will support with data acquired at the same time (summer 2018). Furthermore, GFZ-led air plane campaigns using Eddy Covariance techniques for flux measurements are in the planning for 2019. Continuous observations of the atmospheric chemical composition by the research infrastructures IAGOS will guarantee additional data of regular vertical profiles of chemical species like ozone, water vapour, CO and NOx over central European airports like Paris, Frankfurt and Amsterdam (south of the source region) and Helsinki (east of the source region). They will support Digital Earth with data used as boundary conditions for ICON-ART model studies (see below) on regional-scale air quality effects driven by methane emission from the North Sea. We further anticipate to acquire terrestrial data using mobile FTIR technology (UFZ) during campaigns in 2019 at specific locations in North Germany (natural and anthropogenic sources); these locations will be selected after a thorough investigation of e.g. the Station Database of the Umweltbundesamt (UBA;

The atmospheric model ICON-ART is co-developed (together with DWD and MPI for Meteorology) at KIT and will be utilized to correlate measured gas fluxes with model runs to validate sensitivities and investigate the impact of such directly measured small scaled fluxes on air quality. The model will be used for up scaling of local flux measurements and predict/assess their sub-grid variability. Respective data assimilation and model runs will be undertaken in close cooperation and with support from ESM Frontier Simulations (the ESM simulations are done by Digital Earth partners). In this respect HZG will also provide access to the regional scale atmospheric chemistry transport model (CMAQ) and its emission model SMOKE. CMAQ treats atmospheric photochemistry, in particular ozone chemistry and atmospheric secondary particle formation. SMOKE delivers 4D high resolution emission data of all main anthropogenic sources that can be directly used in CMAQ to study chemical interactions of methane with other chemical components. In a first step, methane will be treated as an inert tracer and its dispersion will be studied on the regional scale and the local scale with varying grid resolutions that go down to 1 x 1 km². Atmospheric methane oxidation can later be included in the model to study the effects on the atmospheric composition.

HZG will provide current fields and field predictions for the southern North Sea that are presently available at seven km resolution with eleven vertical layers. These data are routinely updated every hour based on radar measurements in the German Bight that are assimilated into the ocean circulation model (GETM). Surface current fields at 1 km horizontal resolution are also available. 3D versions of these high resolution maps and predictive fields will be made available for this Show Case. The GETM model output will be used to simulate Lagrangian pathways of methane bubbles from the seep sites through the water column to the atmosphere. The turbulent eddy processes that is not resolved within the GETM model will be parameterized using Marcov models (Griffa et al. 1996; Berloff and McWilliams et al. 2002; Koszalka et al. 2013a, b) and accounting for the positive buoyancy and gas exchange processes across the bubble interface (McGinnis et al. 2006). The Lagrangian model parameters will be tuned by 1) comparing the predicted bubble pathways to the methane concentration and bubble detection observations acquired during ship campaigns (past: Urban et al. 2016, Vielstadte et al. 2016, Römer et al. 2017, Mau et al 2015; planned: summer 2018), and 2) comparing the maps of the emission locations at the ocean surface derived from Lagrangian pathways to the measured gas fluxes (CoMET, IAGOS) and to the results of the atmospheric (ICON-ART) model runs (i.e., matching the ocean-based and atmospheric-based results at the air-sea interface). The Lagrangian solutions will in turn contribute to optimizing future observations and to developing methane emission parameterizations in the regional (ICON-ART) as well as in large-scale ESM Frontier Simulations.

Using machine learning techniques (Task 2.4), we will develop workflows for automatic knowledge extraction from these heterogeneous input data (observed and modelled; integrating various spatio-temporal scales; including uncertainty analysis) to support the fine-tuning of the Lagrangian model parameters. Lagrangian simulations are an excellent tool of visual exploration: the individual methane bubble trajectories from the seafloor to the atmosphere and the derived pathways statistics' will support effective visualization of the aggregated observational data and model results (Task 2.3.) within Show Case A. We anticipate, that the Lagrangian solutions thus will strongly contribute to optimize future observations (-> SMART Monitoring) and to strengthen the methane emission parameterizations in the regional (ICON-ART) as well as large-scale ESM Frontier Simulations.