ALBERT

Description: Deepwater spills pose a unique challenge for reliable predictions of oil transport and fate, since live oil spewing under very high hydrostatic pressure has characteristics remarkably distinct from oil spilling in shallow water. It is thus important to describe in detail the complex thermodynamic processes occurring in the near-field, meters above the wellhead, and the hydrodynamic processes in the far-field, up to kilometers away. However, these processes are typically modeled separately since they occur at different scales. Here we directly couple two oil prediction applications developed during the Deepwater Horizon blowout operating at different scales: the near-field Texas A&M Oilspill Calculator (TAMOC) and the far-field oil application of the Connectivity Modeling System (oil-CMS). To achieve this coupling, new oil-CMS modules were developed to read TAMOC output, which consists of the description of distinct oil droplet “types,” each of specific size and pseudo-component mixture that enters at a given mass flow rate, time, and position into the far field. These variables are transformed for use in the individual-based framework of CMS, where each droplet type fits into a droplet size distribution (DSD). Here we used 19 pseudo-components representing a large range of hydrocarbon compounds and their respective thermodynamic properties. Simulation results show that the dispersion pathway of the different droplet types varies significantly. Indeed, some droplet types remain suspended in the subsea over months, while others accumulate in the surface layers. In addition, the decay rate of oil pseudo-components significantly alters the dispersion, denoting the importance of more biodegradation and dissolution studies of chemically and naturally dispersed live oil at high pressure. This new modeling tool shows the potential for improved accuracy in predictions of oil partition in the water column and of advancing impact assessment and response during a deepwater spill.

Description: Petroleum is one of the most complex naturally occurring organic mixtures. The physical and chemical properties of petroleum in a reservoir depend on its molecular composition and the reservoir conditions (temperature, pressure). The composition of petroleum varies greatly, ranging from the simplest gas (methane), condensates, conventional crude oil to heavy oil and oil sands bitumen with complex molecules having molecular weights in excess of 1000 daltons (Da). The distribution of petroleum constituents in a reservoir largely depends on source facies (original organic material buried), age (evolution of organisms), depositional environment (dysoxic versus anoxic), maturity of the source rock (kerogen) at time of expulsion, primary/secondary migration, and in-reservoir alteration such as biodegradation, gas washing, water washing, segregation, and/or mixing from different oil charges. These geochemical aspects define the physical characteristics of a petroleum in the reservoir, including its density and viscosity. When the petroleum is released from the reservoir through an oil exploration accident like in the case of the Deepwater Horizon event, several processes are affecting the physical and chemical properties of the petroleum from the well head into the deep sea. A better understanding of these properties is crucial for the development of near-field oil spill models, oil droplet and gas bubble calculations, and partitioning behavior of oil components in the water. Section 3.1 introduces general aspects of the origin of petroleum, the impact of geochemical processes on the composition of a petroleum, and some molecular compositional and physicochemical background information of the Macondo well oil. Section 3.2 gives an overview over experimental determination of all relevant physicochemical properties of petroleum, especially of petroleum under reservoir conditions. Based on the phase equilibrium modeling using equations of state (EOS), a number of these properties can be predicted which is presented in Sect. 3.3 along with a comparison to experimental data obtained with methods described in Sect. 3.2.

Description: Nitrogen fixers, or diazotrophs, play a key role in the carbon and nitrogen cycle of the world oceans, but the controlling mechanisms are not comprehensively understood yet. The present study compares two paradigms on the ecological niche of diazotrophs in an Earth System Model (ESM). In our standard model configuration, which is representative for most of the state-of-the-art pelagic ecosystem models, diazotrophs take advantage of zooplankton featuring a lower food preference for diazotrophs than for ordinary phytoplankton. We compare this paradigm with the idea that diazotrophs are more competitive under oligotrophic conditions, characterized by low (dissolved, particulate, organic and inorganic) phosphorous availability. Both paradigms are supported by observational evidence and lead to a similar good agreement to the most recent and advanced observation-based nitrogen fixation estimate in our ESM framework. Further, we illustrate that the similarity between the two paradigms breaks in a RCP 8.5 anthropogenic emission scenario. We conclude that a more advanced understanding of the ecological niche of diazotrophs is mandatory for assessing the cycling of essential nutrients, especially under changing environmental conditions. Our results call for more in-situ measurements of cyanobacteria biomass if major controls of nitrogen fixation in the oceans are to be dissected.

Description: Landslide is one of the dangerous types of natural hazards. This phenomenon causes damages in many countries every year. A detailed landslide hazard assessment is necessary to reduce these damages. This research aims to map the landslide susceptibility zoning (LSZ) using the fuzzy logic method and GIS in the Sorkhab basin as a part of the Zagros fold and thrust belt (FTB), northwestern Iran. All slide types were recorded in fieldwork as landslide inventory. Based on the results, four types, i.e., debris slide, earth slide, and rock fall and complex of landslides, was identified in the region. Then, the effect of each landslide contributing factor including topographical elevation heights, slope classes, aspect classes, geological units, proximity to faults, land covers, rainfall classes, and proximity to streams was constructed in GIS and subsequently normalized using fuzzy membership functions. Finally, by combining all standardized layers using the fuzzy gamma operator, a final map of LSZ was produced. The results showed that a 0.9 fuzzy gamma operator has a high accuracy for the LSZ map in the study area. Besides, the accuracy of the LSZ map revealed a strong relationship (R2) between susceptibility classes, and landslide inventory was calculated using a scatter plot equal to 0.79. Hence, the method represented an appropriate accuracy in predicting the landslide susceptibility in the study area.

Description: Ice flow models of the Antarctic ice sheet are commonly used to simulate its future evolution in response to different climate scenarios and assess the mass loss that would contribute to future sea level rise. However, there is currently no consensus on estimates of the future mass balance of the ice sheet, primarily because of differences in the representation of physical processes, forcings employed and initial states of ice sheet models. This study presents results from ice flow model simulations from 13 international groups focusing on the evolution of the Antarctic ice sheet during the period 2015–2100 as part of the Ice Sheet Model Intercomparison for CMIP6 (ISMIP6). They are forced with outputs from a subset of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), representative of the spread in climate model results. Simulations of the Antarctic ice sheet contribution to sea level rise in response to increased warming during this period varies between −7.8 and 30.0 cm of sea level equivalent (SLE) under Representative Concentration Pathway (RCP) 8.5 scenario forcing. These numbers are relative to a control experiment with constant climate conditions and should therefore be added to the mass loss contribution under climate conditions similar to present-day conditions over the same period. The simulated evolution of the West Antarctic ice sheet varies widely among models, with an overall mass loss, up to 18.0 cm SLE, in response to changes in oceanic conditions. East Antarctica mass change varies between −6.1 and 8.3 cm SLE in the simulations, with a significant increase in surface mass balance outweighing the increased ice discharge under most RCP 8.5 scenario forcings. The inclusion of ice shelf collapse, here assumed to be caused by large amounts of liquid water ponding at the surface of ice shelves, yields an additional simulated mass loss of 28 mm compared to simulations without ice shelf collapse. The largest sources of uncertainty come from the climate forcing, the ocean-induced melt rates, the calibration of these melt rates based on oceanic conditions taken outside of ice shelf cavities and the ice sheet dynamic response to these oceanic changes. Results under RCP 2.6 scenario based on two CMIP5 climate models show an additional mass loss of 0 and 3 cm of SLE on average compared to simulations done under present-day conditions for the two CMIP5 forcings used and display limited mass gain in East Antarctica.

Description: Landslide deposits offshore many volcanic islands provide evidence of catastrophic lateral collapses. These deposits span a larger volume range than their continental equivalents, and can generate devastating tsunamis. All historical volcanic-island lateral collapses have occurred in arc settings, and have been characterised by rapid failure and efficient tsunami generation. The varied morphology of their deposits is influenced both by lithological properties and the nature of the substrate. Many deposits show evidence of extensive seafloor erosion and transformation into debris flows, and the propagation of frontally-confined sediment deformation beyond and beneath the primary deposit. Mobilised volumes can far exceed that of the initial failure, and accurate deposit interpretation requires internal geophysical imaging and sampling. Around intraplate ocean-island volcanoes, multi-unit turbidites suggest that lateral collapses may occur in discrete stages; although this would reduce their overall tsunamigenic potential, the volumes of individual stages of collapse remain very large. Numerical models of both landslide and tsunami processes in ocean-island settings are difficult to test, and the smaller collapses that typify island arcs are an important focus of research due to their higher global frequency, availability of direct failure and tsunami observations, and a need to better understand the signals of incipient collapse to develop approaches for tsunami hazard mitigation.

Description: Since the sunlight only penetrates a few hundred meters into the ocean, deep-diving robots have to bring their own light sources for imaging the deep sea, e.g., to inspect hydrothermal vent fields. Such co-moving light sources mounted not very far from a camera introduce uneven illumination and dynamic patterns on seafloor structures but also illuminate particles in the water column and create scattered light in the illuminated volume in front of the camera. In this scenario, a key challenge for forward-looking robots inspecting vertical structures in complex terrain is to identify free space (water) for navigation. At the same time, visual SLAM and 3D reconstruction algorithms should only map rigid structures, but not get distracted by apparent patterns in the water, which often resulted in very noisy maps or 3D models with many artefacts. Both challenges, free space detection, and clean mapping could benefit from pre-segmenting the images before maneuvering or 3D reconstruction. We derive a training scheme that exploits depth maps of a reconstructed 3D model of a black smoker field in 1400 m water depth, resulting in a carefully selected, ground-truthed data set of 1000 images. Using this set, we compare the advantages and drawbacks of a classical Markov Random Field-based segmentation solution (graph cut) and a deep learning-based scheme (U-Net) to finding free space in forward-looking cameras in the deep ocean.

Description: Nowadays underwater vision systems are being widely applied in ocean research. However, the largest portion of the ocean - the deep sea - still remains mostly unexplored. Only relatively few image sets have been taken from the deep sea due to the physical limitations caused by technical challenges and enormous costs. Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community. The shortage of deep sea images and the corresponding ground truth data for evaluation and training is becoming a bottleneck for the development of underwater computer vision methods. Thus, this paper presents a physical model-based image simulation solution, which uses an in-air texture and depth information as inputs, to generate underwater image sequences taken by robots in deep ocean scenarios. Different from shallow water conditions, artificial illumination plays a vital role in deep sea image formation as it strongly affects the scene appearance. Our radiometric image formation model considers both attenuation and scattering effects with co-moving spotlights in the dark. By detailed analysis and evaluation of the underwater image formation model, we propose a 3D lookup table structure in combination with a novel rendering strategy to improve simulation performance. This enables us to integrate an interactive deep sea robotic vision simulation in the Unmanned Underwater Vehicles simulator. To inspire further deep sea vision research by the community, we release the source code of our deep sea image converter to the public (https://www.geomar.de/en/omv-research/robotic-imaging-simulator).

Description: In deep water conditions, vision systems mounted on underwater robotic platforms require artificial light sources to illuminate the scene. The particular lighting configurations significantly influence the quality of the captured underwater images and can make their analysis much harder or easier. Nowadays, classical monolithic Xenon flashes are gradually being replaced by more flexible setups of multiple powerful LEDs. However, this raises the question of how to arrange these light sources, given different types of seawater and-depending-on different flying altitudes of the capture platforms. Hence, this paper presents a rendering based coarse-to-fine approach to optimize recent multi-light setups for underwater vehicles. It uses physical underwater light transport models and target ocean and mission parameters to simulate the underwater images as would be observed by a camera system with particular lighting setups. This paper proposes to systematically vary certain design parameters such as each LED’s orientation and analyses the rendered image properties (such as illuminated image area and light uniformity) to find optimal light configurations. We report first results on a real, ongoing AUV light design process for deep sea mission conditions.

Description: Remobilization of soil carbon as a result of permafrost degradation in the drainage basin of the major Siberian rivers combined with higher precipitation in a warming climate potentially increase the flux of terrestrial derived dissolved organic matter (tDOM) into the Arctic Ocean. The Laptev (LS) and East Siberian Seas (ESS) receive enormous amounts of tDOM-rich river water, which undergoes at least one freeze-melt cycle in the Siberian Arctic shelf seas. To better understand how freezing and melting affect the tDOM dynamics in the LS and ESS, we sampled sea ice, river and seawater for their dissolved organic carbon (DOC) concentration and the colored fraction of dissolved organic matter. The sampling took place in different seasons over a period of 9 years (2010–2019). Our results suggest that the main factor regulating the tDOM distribution in the LS and ESS is the mixing of marine waters with freshwater sources carrying different tDOM concentrations. Of particular importance in this context are the 211 km3 of meltwater from land-fast ice from the LS, containing ~ 0.3 Tg DOC, which in spring mixes with 245 km3 of river water from the peak spring discharge of the Lena River, carrying ~ 2.4 Tg DOC into the LS. During the ice-free season, tDOM transport on the shelves takes place in the surface mixed layer, with the direction of transport depending on the prevailing wind direction. In winter, about 1.2 Tg of brine-related DOC, which was expelled from the growing land-fast ice in the LS, is transported in the near-surface water layer into the Transpolar Drift Stream that flows from the Siberian Shelf toward Greenland. The actual water depth in which the tDOM-rich brines are transported, depends mainly on the density stratification of the LS and ESS in the preceding summer and the amount of ice produced in winter. We suspect that climate change in the Arctic will fundamentally alter the dynamics of tDOM transport in the Arctic marginal seas, which will also have consequences for the Arctic carbon cycle.

Description: The Last Glacial Maximum (LGM, ~ 21,000 years ago) has been a major focus for evaluating how well state-of-the-art climate models simulate climate changes as large as those expected in the future using paleoclimate reconstructions. A new generation of climate models have been used to generate LGM simulations as part of the Palaeoclimate Modelling Intercomparison Project (PMIP) contribution to the Coupled Model Intercomparison Project (CMIP). Here we provide a preliminary analysis and evaluation of the results of these LGM experiments (PMIP4-CMIP6) and compare them with the previous generation of simulations (PMIP3-CMIP5). We show that the PMIP4-CMIP6 are globally less cold and less dry than the PMIP3-CMIP5 simulations, most probably because of the use of a more realistic specification of the northern hemisphere ice sheets in the latest simulations although changes in model configuration may also contribute to this. There are important differences in both atmospheric and ocean circulation between the two sets of experiments, with the northern and southern jet streams being more poleward and the changes in the Atlantic Meridional Overturning Circulation being less pronounced in the PMIP4-CMIP6 simulations than in the PMIP3-CMIP5 simulations. Changes in simulated precipitation patterns are influenced by both temperature and circulation changes. Differences in simulated climate between individual models remain large so, although there are differences in the average behaviour across the two ensembles, the new simulation results are not fundamentally different from the PMIP3-CMIP5 results. Evaluation of large-scale climate features, such as land-sea contrast and polar amplification, confirms that the models capture these well and within the uncertainty of the palaeoclimate reconstructions. Nevertheless, regional climate changes are less well simulated: the models underestimate extratropical cooling, particularly in winter, and precipitation changes. The spatial patterns of increased precipitation associated with changes in the jet streams are also poorly captured. However, changes in the tropics are more realistic, particularly the changes in tropical temperatures over the oceans. Although these results are preliminary in nature, because of the limited number of LGM simulations currently available, they nevertheless point to the utility of using paleoclimate simulations to understand the mechanisms of climate change and evaluate model performance.

Description: The COVID-19 pandemic necessitates a change in conference formats for 2020. This shift offers a unique opportunity to address long-standing inequities in access and issues of sustainability associated with traditional conference formats, through testing online platforms. However, moving online is not a panacea for all of these concerns, particularly those arising from uneven distribution of access to the Internet and other technology. With conferences and events being forced to move online, this is a critical juncture to examine how online formats can be used to best effect and to reduce the inequities of in-person meetings. In this article, we highlight that a thoughtful and equitable move to online formats could vastly strengthen the global socio-ecological research community and foster cohesive and effective collaborations, with ecology and society being the ultimate beneficiaries.

Description: This paper presents a novel data set of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. The data set provides a unique and physically consistent data set, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100 × 10-year simulations and 25 × 10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. The changes in four climate indices for temperature targets of 1.5 °C and 2.0 °C global warming are quantified: number of days per year with daily mean near-surface apparent temperature of 〉 28 °C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-yr return period (RI50yr); and the annual Consecutive Dry Days (CDD). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at a policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 °C.

Description: The Bokanjac–Poličnik system, as a complex set of mutually interrelated Dinaric karst catchments and sub-catchments, is a highly vulnerable and limited groundwater source for the wider Zadar area in northern Dalmatia, Croatia. Based on hydrogeological, hydrochemical, and hydrological research, including the prediction of groundwater discharge by the end of the twenty-first century, a complex study was performed with the following main aims: (1) groundwater protection in the present state, (2) assessment of future groundwater protection, and (3) prediction of drinking water availability and quality under the predicted climate change conditions. Long-term prediction of changes in groundwater quantity, as well as investigations of trends in groundwater quality, will allow us to protect this essential natural resource with respect to possible negative trends. The results showed that a significant decrease in the quantity of available groundwater is possible and that extraction will have to be well planned because any decrease in the groundwater pressure in this area will cause a further decrease in quality, especially regarding the possibility of seawater intrusions into the aquifer. The results of this study were incorporated in sanitary protection zones.

Description: The Greenland Ice Sheet (GrIS) mass loss has been accelerating at a rate of about 20 ± 10 Gt/yr2 since the end of the 1990's, with around 60 % of this mass loss directly attributed to enhanced surface meltwater runoff. However, in the climate and glaciology communities, different approaches exist on how to model the different surface mass balance (SMB) components using: (1) complex physically-based climate models which are computationally expensive; (2) intermediate complexity energy balance models; (3) simple and fast positive degree day models which base their inferences on statistical principles and are computationally highly efficient. Additionally, many of these models compute the SMB components based on different spatial and temporal resolutions, with different forcing fields as well as different ice sheet topographies and extents, making inter-comparison difficult. In the GrIS SMB model intercomparison project (GrSMBMIP) we address these issues by forcing each model with the same data (i.e., the ERA-Interim reanalysis) except for two global models for which this forcing is limited to the oceanic conditions, and at the same time by interpolating all modelled results onto a common ice sheet mask at 1 km horizontal resolution for the common period 1980–2012. The SMB outputs from 13 models are then compared over the GrIS to (1) SMB estimates using a combination of gravimetric remote sensing data from GRACE and measured ice discharge, (2) ice cores, snow pits, in-situ SMB observations, and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Our results reveal that the mean GrIS SMB of all 13 models has been positive between 1980 and 2012 with an average of 340 ± Gt/yr, but has decreased at an average rate of −7.3 Gt/yr2 (with a significance of 96 %), mainly driven by an increase of 8.0 Gt/yr2 (with a significance of 98 %) in meltwater runoff. Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting the need for accurate representation of the GrIS ablation zone extent and processes driving the surface melt. In addition, a higher density of in-situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 mWE/yr due to large discrepancies in modelled snowfall accumulation. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of same order than RCMs with observations and remain then useful tools for long-term simulations. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present day SMB relative to observations, suggesting that biases are not systematic among models.

Description: It is virtually certain that the mean surface temperature of the Earth will continue to increase under realistic emission scenarios, yet comparatively little is known about future changes in climate variability. This study explores changes in climate variability over the large range of climates simulated by the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5/6) and the Paleoclimate Modeling Intercomparison Project Phase 3 (PMIP3), including time slices of the Last Glacial Maximum, the mid-Holocene, and idealized experiments (1 % CO2 and abrupt4×CO2). These states encompass climates within a range of 12 ∘C in global mean temperature change. We examine climate variability from the perspectives of local interannual change, coherent climate modes, and through compositing extremes. The change in the interannual variability of precipitation is strongly dependent upon the local change in the total amount of precipitation. At the global scale, temperature variability is inversely related to mean temperature change on intra-seasonal to multidecadal timescales. This decrease is stronger over the oceans, while there is increased temperature variability over subtropical land areas (40∘ S–40∘ N) in warmer simulations. We systematically investigate changes in the standard deviation of modes of climate variability, including the North Atlantic Oscillation, the El Niño–Southern Oscillation, and the Southern Annular Mode, with global mean temperature change. While several climate modes do show consistent relationships (most notably the Atlantic Zonal Mode), no generalizable pattern emerges. By compositing extreme precipitation years across the ensemble, we demonstrate that the same large-scale modes influencing rainfall variability in Mediterranean climates persist throughout paleoclimate and future simulations. The robust nature of the response of climate variability, between cold and warm climates as well as across multiple timescales, suggests that observations and proxy reconstructions could provide a meaningful constraint on climate variability in future projections.

Description: European heat waves have increased during the two recent decades. Particularly 2015 and 2018 were characterized by a widespread area of cold North Atlantic sea surface temperatures (SSTs) in early summer as well as positive surface temperature anomalies across large parts of the European continent during later summer. The European heat wave of 2018 is further suggested to be induced by a quasi-stationary and high-amplified Rossby wave pattern associated with the so-called quasi-resonant amplification (QRA) mechanism. In this study, we evaluate the North Atlantic SST anomalies and the QRA theory as potential drivers for European heat waves for the first time in combination by using the ERA-5 reanalysis product. A composite and correlation study reveals that cold North Atlantic SST anomalies in early summer favour a more undulating jet stream and a preferred trough-ridge pattern in the North Atlantic–European sector. Further we found that cold North Atlantic SSTs promote a stronger double jet occurrence in this sector. Thus, favorite conditions for a QRA signature are evident together with a necessary preconditioning of a double jet. However, our wave analysis covering two-dimensional probability density distributions of phase speed and amplitude does not confirm a relationship between cold North Atlantic SSTs and the QRA theory, compositing cold SSTs, high double jet indices (DJIs) or both together. Instead, we can show that cold North Atlantic SST events enhance the dominance of transient waves. In the presence of a trough during cold North Atlantic events, we obtain a slow-down of the transient waves, but not necessarily an amplification or stationarity. The deceleration of the transient waves result in a longer duration of a trough over the North Atlantic accompanied by a ridge downstream over Europe, triggering European heat episodes. Although a given DJI preconditioning may also be subject to the onset of certain QRA events, our study found no general relation between cold North Atlantic SST events and the QRA diagnostics. Our study highlights the relevance of cold North Atlantic SSTs for the onset of high European temperatures by affecting travelling jet stream undulations (but without involving QRA in general). Further attention should be drawn not only to the influence of North Atlantic SST year-to-year variability, but also to the effect of the North Atlantic warming hole as a negative SST anomaly in the long term, which is projected to evolve through climate change.

Description: We examined small-scale distribution and feeding ecology of a non-native fish species, round goby (Neogobius melanostomus (Pallas, 1814)), in different habitats of a coastal lagoon situated in the south-western Baltic Sea. First observations of round goby in this lagoon were reported in 2011, 3 years before the current study was conducted, and information on this species’ basic ecology in different habitats is limited. We found that mainly juvenile round gobies are non-randomly distributed between habitats and that abundances potentially correlate positively with vegetation density and thus structural complexity of the environment. Abundances were highest in shallower, more densely vegetated habitats indicating that these areas might act as a refuge for small round gobies by possibly offering decreased predation risk and better feeding resources. Round goby diet composition was distinct for several length classes suggesting an ontogenetic diet shift concerning crustacean prey taxa between small (≤ 50 mm total length, feeding mainly on zooplankton) and medium individuals (51–100 mm, feeding mainly on benthic crustaceans) and another diet shift of increasing molluscivory with increasing body size across all length classes. Differences in round goby diet between habitats within the smallest length class might potentially be related to prey availability in the environment, which would point to an opportunistic feeding strategy. Here, we offer new insights into the basic ecology of round goby in littoral habitats, providing a better understanding of the ecological role of this invasive species in its non-native range, which might help to assess potential consequences for native fauna and ecosystems.

Description: The sea level contribution of the Antarctic ice sheet constitutes a large uncertainty in future sea level projections. Here we apply a linear response theory approach to 16 state-of-the-art ice sheet models to estimate the Antarctic ice sheet contribution from basal ice shelf melting within the 21st century. The purpose of this computation is to estimate the uncertainty of Antarctica's future contribution to global sea level rise that arises from large uncertainty in the oceanic forcing and the associated ice shelf melting. Ice shelf melting is considered to be a major if not the largest perturbation of the ice sheet's flow into the ocean. However, by computing only the sea level contribution in response to ice shelf melting, our study is neglecting a number of processes such as surface-mass-balance-related contributions. In assuming linear response theory, we are able to capture complex temporal responses of the ice sheets, but we neglect any self-dampening or self-amplifying processes. This is particularly relevant in situations in which an instability is dominating the ice loss. The results obtained here are thus relevant, in particular wherever the ice loss is dominated by the forcing as opposed to an internal instability, for example in strong ocean warming scenarios. In order to allow for comparison the methodology was chosen to be exactly the same as in an earlier study (Levermann et al., 2014) but with 16 instead of 5 ice sheet models. We include uncertainty in the atmospheric warming response to carbon emissions (full range of CMIP5 climate model sensitivities), uncertainty in the oceanic transport to the Southern Ocean (obtained from the time-delayed and scaled oceanic subsurface warming in CMIP5 models in relation to the global mean surface warming), and the observed range of responses of basal ice shelf melting to oceanic warming outside the ice shelf cavity. This uncertainty in basal ice shelf melting is then convoluted with the linear response functions of each of the 16 ice sheet models to obtain the ice flow response to the individual global warming path. The model median for the observational period from 1992 to 2017 of the ice loss due to basal ice shelf melting is 10.2 mm, with a likely range between 5.2 and 21.3 mm. For the same period the Antarctic ice sheet lost mass equivalent to 7.4 mm of global sea level rise, with a standard deviation of 3.7 mm (Shepherd et al., 2018) including all processes, especially surface-mass-balance changes. For the unabated warming path, Representative Concentration Pathway 8.5 (RCP8.5), we obtain a median contribution of the Antarctic ice sheet to global mean sea level rise from basal ice shelf melting within the 21st century of 17 cm, with a likely range (66th percentile around the mean) between 9 and 36 cm and a very likely range (90th percentile around the mean) between 6 and 58 cm. For the RCP2.6 warming path, which will keep the global mean temperature below 2 ∘C of global warming and is thus consistent with the Paris Climate Agreement, the procedure yields a median of 13 cm of global mean sea level contribution. The likely range for the RCP2.6 scenario is between 7 and 24 cm, and the very likely range is between 4 and 37 cm. The structural uncertainties in the method do not allow for an interpretation of any higher uncertainty percentiles. We provide projections for the five Antarctic regions and for each model and each scenario separately. The rate of sea level contribution is highest under the RCP8.5 scenario. The maximum within the 21st century of the median value is 4 cm per decade, with a likely range between 2 and 9 cm per decade and a very likely range between 1 and 14 cm per decade.

Description: Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4×105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN's ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76 % of all rainy and 97 % of all nonrainy periods. From all periods with a reference rain rate larger than 0.6 mm h−1, more than 90 % was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51 %. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany.

Description: Seagrass meadows deliver important ecosystem services such as nutrient cycling, enhanced biodiversity, and contribution to climate change mitigation and adaption through carbon sequestration and coastal protection. Seagrasses, however, are facing the impacts of ocean warming and marine heatwaves, which are altering their ecological structure and function. Shifts in species composition, mass mortality events, and loss of ecosystem complexity after sudden extreme climate events are increasingly common, weakening the ecosystem services they provide. In the west coast of Australia, Shark Bay holds between 0.7 and 2.4% of global seagrass extent (〉4300 km2), but in the austral summer of 2010/2011, the Ningaloo El Niño marine heatwave resulted in the collapse of ~1300 km2 of seagrass ecosystem extent. The loss of the seagrass canopy resulted in the erosion and the likely remineralization of ancient carbon stocks into 2–4 Tg CO2-eq over 6 years following seagrass loss, increasing emissions from land-use change in Australia by 4–8% per annum. Seagrass collapse at Shark Bay also impacted marine food webs, including dugongs, dolphins, cormorants, fish communities, and invertebrates. With increasing recurrence and intensity of marine heatwaves, seagrass resilience is being compromised, underlining the need to implement conservation strategies. Such strategies must precede irreversible climate change-driven tipping points in ecosystem functioning and collapse and result from synchronized efforts involving science, policy, and stakeholders. Management should aim to maintain or enhance the resilience of seagrasses, and using propagation material from heatwave-resistant meadows to restore impacted regions arises as a challenging but promising solution against climate change threats. Although scientific evidence points to severe impacts of extreme climate events on seagrass ecosystems, the occurrence of seagrass assemblages across the planet and the capacity of humans to modify the environment sheds some light on the capability of seagrasses to adapt to changing ecological niches.

Description: Rainfall is one of the most important environmental variables. However, it is a challenge to measure it accurately over space and time. During the last decade, commercial microwave links (CMLs), operated by mobile network providers, have proven to be an additional source of rainfall information to complement traditional rainfall measurements. In this study, we present the processing and evaluation of a German-wide data set of CMLs. This data set was acquired from around 4000 CMLs distributed across Germany with a temporal resolution of 1 min. The analysis period of 1 year spans from September 2017 to August 2018. We compare and adjust existing processing schemes on this large CML data set. For the crucial step of detecting rain events in the raw attenuation time series, we are able to reduce the amount of misclassification. This was achieved by using a new approach to determine the threshold, which separates a rolling window standard deviation of the CMLs' signal into wet and dry periods. For the compensation for wet antenna attenuation, we compare a time-dependent model with a rain-rate-dependent model and show that the rain-rate-dependent model performs better for our data set. We use RADOLAN-RW, a gridded gauge-adjusted hourly radar product from the German Meteorological Service (DWD) as a precipitation reference, from which we derive the path-averaged rain rates along each CML path. Our data processing is able to handle CML data across different landscapes and seasons very well. For hourly, monthly, and seasonal rainfall sums, we found good agreement between CML-derived rainfall and the reference, except for the winter season due to non-liquid precipitation. We discuss performance measures for different subset criteria, and we show that CML-derived rainfall maps are comparable to the reference. This analysis shows that opportunistic sensing with CMLs yields rainfall information with good agreement with gauge-adjusted radar data during periods without non-liquid precipitation.

Description: Mass loss from the Antarctic Ice Sheet constitutes the largest uncertainty in projections of future sea level rise. Ocean-driven melting underneath the floating ice shelves and subsequent acceleration of the inland ice streams are the major reasons for currently observed mass loss from Antarctica and are expected to become more important in the future. Here we show that for projections of future mass loss from the Antarctic Ice Sheet, it is essential (1) to better constrain the sensitivity of sub-shelf melt rates to ocean warming and (2) to include the historic trajectory of the ice sheet. In particular, we find that while the ice sheet response in simulations using the Parallel Ice Sheet Model is comparable to the median response of models in three Antarctic Ice Sheet Intercomparison projects – initMIP, LARMIP-2 and ISMIP6 – conducted with a range of ice sheet models, the projected 21st century sea level contribution differs significantly depending on these two factors. For the highest emission scenario RCP8.5, this leads to projected ice loss ranging from 1.4 to 4.0 cm of sea level equivalent in simulations in which ISMIP6 ocean forcing drives the PICO ocean box model where parameter tuning leads to a comparably low sub-shelf melt sensitivity and in which no surface forcing is applied. This is opposed to a likely range of 9.1 to 35.8 cm using the exact same initial setup, but emulated from the LARMIP-2 experiments with a higher melt sensitivity, even though both projects use forcing from climate models and melt rates are calibrated with previous oceanographic studies. Furthermore, using two initial states, one with a previous historic simulation from 1850 to 2014 and one starting from a steady state, we show that while differences between the ice sheet configurations in 2015 seem marginal at first sight, the historic simulation increases the susceptibility of the ice sheet to ocean warming, thereby increasing mass loss from 2015 to 2100 by 5 % to 50 %. Hindcasting past ice sheet changes with numerical models would thus provide valuable tools to better constrain projections. Our results emphasize that the uncertainty that arises from the forcing is of the same order of magnitude as the ice dynamic response for future sea level projections.

Description: Summary of Ilse Seibold's vita Ilse Seibold, née Usbeck, was born May 8, 1925 in Breslau, Silesia, and went to school in Halle/Saale during WW2. She started her studies of geology and paleontology at the University of Halle and at the Humboldt University in Berlin, and later at the University of Tübingen, where she received her doctorate as micropaleontologist in 1951 with Otto Schindewolf as her supervisor. She remained active as productive scientist over many decades. In 1952, she married Dr. Eugen Seibold, who in 1958 became professor at Kiel University, founded one of Europe's most important institutes for marine geology, and later became president of the German Science Foundation (DFG), and subsequently of the European Science Foundation (ESF). Being a scientist herself Ilse Seibold soon evolved to a deeply reflective insider of geological sciences. She followed her husband during his scientific career from his appointments in Tübingen, Bonn, Karlsruhe, Kiel, to Bonn and Strasbourg/Freiburg i.Br. She accompanied Eugen on his sabbatical leave at Scripps Institution of Oceanography in La Jolla, CA. She participated in countless international scientific meetings. Together with Eugen she published many papers that document her independence and autonomy as scientist. She gained deep insights into the origins of the geosciences and their historical evolution, up to the ideas of fine arts. We are happy that she documented in her publications a broad range of her scientific and distinguished-humane impressions.

Description: We describe and test a new model of biological marine silicate cycling, implemented in the Kiel Marine Biogeochemical Model version 3 (KMBM3), embedded in the University of Victoria Earth System Climate Model (UVic ESCM) version 2.9. This new model adds diatoms, which are a key component of the biological carbon pump, to an existing ecosystem model. This new model combines previously published parameterizations of a diatom functional type, opal production and export with a novel, temperature-dependent dissolution scheme. Modelled steady-state biogeochemical rates, carbon and nutrient distributions are similar to those found in previous model versions. The new model performs well against independent ocean biogeochemical indicators and captures the large-scale features of the marine silica cycle to a degree comparable to similar Earth system models. Furthermore, it is computationally efficient, allowing both fully coupled, long-timescale transient simulations and “offline” transport matrix spinups. We assess the fully coupled model against modern ocean observations, the historical record starting from 1960 and a business-as-usual atmospheric CO2 forcing to the year 2300. The model simulates a global decline in net primary production (NPP) of 1.4 % having occurred since the 1960s, with the strongest declines in the tropics, northern midlatitudes and Southern Ocean. The simulated global decline in NPP reverses after the year 2100 (forced by the extended RCP8.5 CO2 concentration scenario), and NPP returns to 98 % of the pre-industrial rate by 2300. This recovery is dominated by increasing primary production in the Southern Ocean, mostly by calcifying phytoplankton. Large increases in calcifying phytoplankton in the Southern Ocean offset a decline in the low latitudes, producing a global net calcite export in 2300 that varies only slightly from pre-industrial rates. Diatom distribution moves southward in our simulations, following the receding Antarctic ice front, but diatoms are outcompeted by calcifiers across most of their pre-industrial Southern Ocean habitat. Global opal export production thus drops to 75 % of its pre-industrial value by 2300. Model nutrients such as phosphate, silicate and nitrate build up along the Southern Ocean particle export pathway, but dissolved iron (for which ocean sources are held constant) increases in the upper ocean. This different behaviour of iron is attributed to a reduction of low-latitude NPP (and consequently, a reduction in both uptake and export and particle, including calcite scavenging), an increase in seawater temperatures (raising the solubility of particulate iron) and stratification that “traps” the iron near the surface. These results are meant to serve as a baseline for sensitivity assessments to be undertaken with this model in the future.