Accurate measurements of atmospheric carbon dioxide and methane mole fractions at the Siberian coastal site Ambarchik

Sparse data coverage in the Arctic hampers our understanding of its carbon cycle dynamics and our predictions of the fate of its vast carbon reservoirs in a changing climate. In this paper, we present accurate measurements of atmospheric carbon dioxide (CO2) and methane (CH4) dry air mole fractions at the new atmospheric carbon observation station Ambarchik, which closes a large gap in the atmospheric trace gas monitoring network in northeastern Siberia. The site, which has been operational since August 2014, is located near the delta of the Kolyma River at the coast of the Arctic Ocean. Data quality control of CO2 and CH4 measurements includes frequent calibrations traced to World Meteorological Organization (WMO) scales, employment of a novel water vapor correction, an algorithm to detect the influence of local polluters, and meteorological measurements that enable data selection. The available CO2 and CH4 record was characterized in comparison with in situ data from Barrow, Alaska. A footprint analysis reveals that the station is sensitive to signals from the East Siberian Sea, as well as the northeast Siberian tundra and taiga regions. This makes data from Ambarchik highly valuable for inverse modeling studies aimed at constraining carbon budgets within the pan-Arctic domain, as well as for regional studies focusing on Siberia and the adjacent shelf areas of the Arctic Ocean.Sparse data coverage in the Arctic hampers our understanding of its carbon cycle dynamics and our predictions of the fate of its vast carbon reservoirs in a changing climate. In this paper, we present accurate measurements of atmospheric carbon dioxide (CO2) and methane (CH4) dry air mole fractions at the new atmospheric carbon observation station Ambarchik, which closes a large gap in the atmospheric trace gas monitoring network in northeastern Siberia. The site, which has been operational since August 2014, is located near the delta of the Kolyma River at the coast of the Arctic Ocean. Data quality control of CO2 and CH4 measurements includes frequent calibrations traced to World Meteorological Organization (WMO) scales, employment of a novel water vapor correction, an algorithm to detect the influence of local polluters, and meteorological measurements that enable data selection. The available CO2 and CH4 record was characterized in comparison with in situ data from Barrow, Alaska. A footprint analysis reveals that the station is sensitive to signals from the East Siberian Sea, as well as the northeast Siberian tundra and taiga regions. This makes data from Ambarchik highly valuable for inverse modeling studies aimed at constraining carbon budgets within the pan-Arctic domain, as well as for regional studies focusing on Siberia and the adjacent shelf areas of the Arctic Ocean.Peer reviewe

Ground-based station network in Arctic and Subarctic Eurasia : an overview

The international Pan-Eurasian Experiment (PEEX) program addresses the full spectrum of problems related to climate change in Eurasian Northern latitudes. All PEEX activities rely on the bulk of high-quality observational data provided by the ground and marine stations, remote sensing and satellite tools. So far, no coordinated station network has ever existed in Eurasia, moreover, the current scope of relevant research remains largely unknown as no prior assessment has been done to date. This paper makes the first attempt to overview the existing ground station pool in the Arctic-Boreal region with the focus on Russia. The geographical, climatic and ecosystem representativeness of the current stations is discussed, the gaps are identified and tentative station network developments are proposed.Peer reviewe

Estimating Local CH4 Emissions in the Upper Silesian Coal Basin Using Inverse Modelling

Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG) with respect to radiative forcing. Since pre-industrial times, the globally averaged CH4 concentration in the atmosphere has risen by a factor of 2.5. A large fraction of global anthropogenic CH4 emissions originates from point sources, e.g. coal mine ventilation shafts. International treaties foresee GHG emission reductions, entailing independent monitoring and verification support capacities. Considering the spatially widespread distribution of point sources, remote sensing approaches are favorable, in order to enable rapid survey of larger areas. In this respect, active remote sensing by airborne lidar is promising, such as provided by the integrated-path differential-absorption lidar CHARM-F operated by DLR. Installed onboard the German research aircraft HALO, CHARM-F serves as a demonstrator for the future satellite mission MERLIN. CHARM-F measures weighted vertical column mixing ratios of CO2 and CH4 below the aircraft. In spring 2018, measurements were taken in the Upper Silesian Coal Basin (USCB) in Poland. The USCB is considered to be a European hotspot of CH4 emissions, covering an area of approximately 50 km × 50 km. Due to the high number of coal mines and density of ventilation shafts in the USCB, individual CH4 exhaust plumes can overlap. This makes simple mass balance approaches to determine the emission rates of single shafts in a direct manner using for instance the cross-sectional flux method, difficult. Therefore, we apply inverse modelling to obtain an estimate of the individual emission rates. Specifically, we employ the Weather Research and Forecast Model (WRF) coupled to the CarbonTracker Data Assimilation Shell (CTDAS), an Ensemble Kalman Filter. CTDAS-WRF propagates an ensemble realization of the a priori CH4 emissions forward in space and time, samples the simulated CH4 concentrations along the measurement’s flight path, and scales the a priori emission rates to optimally fit the measured values, while remaining tied to the prior. Hereby, we obtain a regularized a posteriori best emission estimate for the individual ventilation shafts. Here, we report on the results of this inverse modelling approach, including individual and aggregated emission estimates and their uncertainties

Estimating local CH4 emissions in the Upper Silesian Coal Basin using inverse modelling

Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG) with respect to radiative forcing. Since pre-industrial times, the globally averaged CH4 concentration in the atmosphere has risen by a factor of 2.5. A large fraction of global anthropogenic CH4 emissions originates from localized point sources, e.g. coal mine ventilation shafts. International treaties foresee GHG emission reductions, entailing independent monitoring and verification support capacities. Considering the spatially widespread distribution of point sources, remote sensing approaches are favourable, in order to enable rapid survey of larger areas. In this respect, active remote sensing by airborne lidar is promising, such as provided by the integrated-path differential-absorption lidar CHARM-F operated by DLR. Installed onboard the German research aircraft HALO, CHARM-F serves as a demonstrator for future satellite missions, e.g. MERLIN. CHARM-F simultaneously measures weighted vertical column mixing ratios of CO2 and CH4 below the aircraft. In spring 2018, during the CoMet field campaign, measurements were taken in the Upper Silesian Coal Basin (USCB) in Poland. The USCB is considered to be a European hotspot of CH4 emissions, covering an area of approximately 50 km × 50 km. Due to the high number of coal mines and density of ventilation shafts in the USCB, individual CH4 exhaust plumes can overlap. This makes simple approaches to determine the emission rates of single shafts, i.e. the cross-sectional flux method, difficult. Therefore, we use an inverse modelling approach to obtain an estimate of the individual emission rates. Specifically, we employ the Weather Research and Forecast Model (WRF) coupled to the CarbonTracker Data Assimilation Shell (CTDAS), an Ensemble Kalman Filter. CTDAS-WRF propagates an ensemble realization of the a priori CH4 emissions forward in space and time, samples the simulated CH4 concentrations along the measurement’s flight path, and scales the a priori emission rates to optimally fit the measured values, while remaining tied to the prior. Hereby, we obtain a regularized a posteriori best emission estimate for the individual ventilation shafts. Here, we report on the results of this inverse modelling approach, including individual and aggregated emission estimates, their uncertainties, and to which extent the data are able to constrain individual emitters independently

Quantifying localized carbon dioxide emissions from space: the CO2Image mission

Space-based observations of carbon dioxide (CO2) are the backbone of the global and national-scale carbon monitoring systems that are currently being developed to support and verify greenhouse gas emission reduction measures. Current and planned public satellite missions, such as GOSAT 1+2, OCO 1-3 and the European Union's Anthropogenic Carbon Dioxide Monitoring mission CO2M, aim at constraining national and regional-scale emissions down to scales of urban agglomerations and large point sources with emissions in excess of ~10 MtCO2/year. We report on the DLR demonstrator mission CO2Image, which is planned for launch in 2026. The mission will complement the suite of planned CO2 sensors by zooming in on facility-scale emissions, detecting and quantifying emissions from point sources as small as 1 MtCO2/year. A fleet of CO2Image sensors would be able to monitor roughly 90% of the CO2 emissions from coal-fired power plants worldwide. The key feature of the mission is a target region approach, measuring approximately 75 tiles of size ~50 x 50 km2 per day at a resolution of 50 x 50 m2. Thus, CO2Image will be able to resolve plumes from individual localized sources, essentially providing super-resolution nests for survey missions such as CO2M. In addition, the choice of the spectral window will allow the detection of point sources of methane as small as 100 kg CH4/hr will also be possible. We present the instrument concept, a spaceborne push-broom imaging grating spectrometer developed and built by DLR. It will measure spectra of reflected solar radiation in the short wave infrared spectral band around 2000 nm. It relies on a comparatively compact design with a single spectral window and a spectral resolution of approximately ~1 nm. This spectral resolution has been optimized for greenhouse gas retrieval and should provide improved precision and accuracy compared to hyperspectral sensors with comparable spatial resolution. We will further discuss the overall mission concept in terms of the sampling strategy, outlining how target scenes will be selected. As a publicly-funded mission, CO2Image will provide public, transparent information about anthropogenic greenhouse gas emissions from space