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In order to assess how the last sea level rise affected the Aegean archipelago, we quantified the magnitude and rate of geographic change for the Aegean islands during the last sea-level-rise episode (21 kyr BP-present) with a spatially explicit geophysical model. An island-specific Area-Distance-Change (ADC) typology was constructed, with higher ADC values representing a higher degree of change. The highest fragmentation rates of the Aegean archipelago occurred in tandem with the largest rates of sea-level-rise occurring between 17 kyr and 7 kyr ago. Sea-level rise resulted in an area loss for the Aegean archipelago of approximately 70%. Spatiotemporal differences in sea-level changes across the Aegean Sea and irregular bathymetry produced a variety of island surface-area loss responses, with area losses ranging from 20% to N90% per island. In addition, sea-level rise led to increased island isolation, increasing distances of islands to continents to N200% for some islands. We discuss how rates of area contractions and distance increases may have affected biotas, their evolutionary history and genetics. Five testable hypotheses are proposed to guide future research. We hypothesize that islands with higher ADC-values will exhibit higher degrees of community hyper-saturation, more local extinctions, larger genetic bottlenecks, higher genetic diversity within species pools, more endemics and shared species on continental fragments and higher z-values of the power-law species-area relationship. The developed typology and the quantified geographic response to sea-level rise of continental islands, as in the Aegean Sea, present an ideal research framework to test biogeographic and evolutionary hypotheses assessing the role of rates of area and distance change affecting biota.

Summary An important step in the processing of seismic data that are recorded at the free surface is the isolation of the primary incident wavefield from the total recorded wavefield (which is contaminated with the immediate reflections off the free surface). We present a 3-D wavefield reconstruction technique, based on numerical wavefield injection along a closed boundary, that allows us to isolate this primary wavefield from measurements at the free surface. The technique consists of injecting only the three-component particle velocity recordings acquired at the free surface into a numerical wavefield simulation, and additionally requires information about the medium properties. The result of our proposed procedure is the separation of elastic waves into their first-order incident and reflected constituents, even when the recording or injection surface has sharp corners. With the use of synthetic data it is shown that the method achieves close to numerically exact wavefield separation, provided that the true elastic model in the interior is used. In practice, the parameters for a homogeneous elastic model can be determined efficiently from the surface data itself using an optimisation scheme. Finally, the wavefield separation technique is successfully applied to experimental data, with particle velocity recordings acquired along five faces of a cubic granite rock volume. In addition to characterising materials in laboratories, the presented technique has applications in numerical modelling and in so-called immersive experimentation, where the incident field is required to immerse an elastic object in an arbitrary larger, virtual elastic environment.

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Summary We present a method to position point-sources at arbitrary locations on finite-difference (FD) grids. We show that implementing point-sources on single nodes can cause considerable errors when modeling with the FD method. In contrast, we propose to create a spatially distributed source (over multiple nodes) that nonetheless creates the desired point-source response. The spatial point-source is formulated in the wavenumber domain and is based on the FD coefficients used for the wave propagation. Using this ‘FD-consistent source’ on 1-D and 2-D examples, we show that we can obtain superior fits to analytical solutions compared to single-node or sinc-function source implementations, and we show that sources can be offset to arbitrary locations from ‘on’ the grid to ‘off’ the grid, while resulting in solutions that are identical to within machine precision

Since the Paris Agreement was adopted in 2015, the role of space-based observations for monitoring anthropogenic greenhouse gas (GHG) emissions has increased. To meet the requirements for monitoring carbon dioxide (CO2) emissions, the European Copernicus programme is preparing a dedicated CO2 Monitoring (CO2M) satellite constellation that will provide CO2 and nitrogen dioxide (NO2) observations at 4 km2 resolution along a 250 km wide swath. In this paper, we adapt the recently developed divergence method to derive both CO2 and nitrogen oxide (NOx) emissions of cities and power plants from a CO2M satellite constellation by using synthetic observations from the COSMO-GHG model. Due to its long lifetime, the large CO2 atmospheric background needs to be removed to highlight the anthropogenic enhancements before calculating the divergence. Since the CO2 noise levels are large compared to the anthropogenic enhancements, we apply different denoising methods and compare the effect on the CO2 emission estimates. The annual NOx and CO2 emissions estimated from the divergence maps using the peak fitting approach are in agreement with the expected values, although with larger uncertainties for CO2. We also consider the possibility to use co-emitted NOx emission estimates for quantifying the CO2 emissions, by using source-specific NOx-to-CO2 emission ratios derived directly from satellite observations. In general, we find that the divergence method provides a promising tool for estimating CO2 emissions, alternative to typical methods based on inverse modeling or on the analysis of individual CO2 plumes.

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Abstract. Global anthropogenic CO2 sources are dominated by power plants and large industrial facilities. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modelling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in-situ and remote sensing observations collected during the CoMet measurement campaign down-wind of the coal-fired power plants at Bełchatów in Poland and Jänschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models. The models include three Large-Eddy-Simulation (LES) models, two mesoscale numerical weather prediction (NWP) models, and one Lagrangian particle dispersion model (LPDM) and cover a wide range of model resolutions from 200 m to 2 km horizontal grid spacing. At the time of the aircraft measurements between late morning and early afternoon, the simulated plumes were slightly (at Jänschwalde) to highly (at Bełchatów) turbulent, consistent with the observations, and extended over the whole depth of the atmospheric boundary layer (ABL, up to 1800 m a.s.l. in the case of Bełchatów). The stochastic nature of turbulent plumes puts fundamental limitations to a point-by-point comparison between simulations and observations. Therefore, the evaluation focused on statistical properties such as plume amplitude and width as a function of distance from the source. LES and NWP models showed similar performance and sometimes remarkable agreement with the observations when operated at comparable resolution. A resolution of 1 km or better, however, appears to be necessary to realistically capture turbulent plume structures. At coarser resolution, the plumes disperse too quickly especially in the near field (0–8 km from the source) and turbulent structures are increasingly smoothed out. Total vertical columns are easier to simulate accurately than the vertical distribution of CO2, since the latter is critically affected by profiles of vertical stability, especially near the top of the ABL. Cross-sectional flux and integrated mass enhancement methods applied to synthetic CO2M data generated from the model simulations with a random noise of 0.5 ppm–1.0 ppm suggest that emissions from a power plant like Bełchatów can be estimated with an accuracy of about 20 % from single overpasses. Estimates of the effective wind speed are a critical input for these methods. Wind speeds in the middle of the ABL appear to be a good approximation for plumes in a well-mixed ABL as encountered during CoMet.