Advances in modelling subglacial lakes and their interaction with the Antarctic ice sheet

Subglacial lakes have long been considered hydraulically isolated water bodies underneath ice sheets. This view changed radically with the advent of repeat-pass satellite altimetry and the discovery of multiple lake discharges and water infill, associated with water transfer over distances of more than 200 km. The presence of subglacial lakes also influences ice dynamics, leading to glacier acceleration. Furthermore, subglacial melting under the Antarctic ice sheet is more widespread than previously thought, and subglacial melt rates may explain the availability for water storage in subglacial lakes and water transport. Modelling of subglacial water discharge in subglacial lakes essentially follows hydraulics of subglacial channels on a hard bed, where ice sheet surface slope is a major control on triggering subglacial lake discharge. Recent evidence also points to the development of channels in deformable sediment in West Antarctica, with significant water exchanges between till and ice. Most active lakes drain over short time scales and respond rapidly to upstream variations. Several Antarctic subglacial lakes exhibit complex interactions with the ice sheet due to water circulation. Subglacial lakes can therefore—from a modelling point of view—be seen as confined small oceans underneath an imbedded ice shelf.</jats:p

The role of ice streams in a coupled ice flow-ocean modeling approach at the Filchner-Ronne Ice Shelf, Antarctica

The ice flow at the margins of the Antarctic Ice Sheet (AIS) is moderated by large ice shelves. Their buttressing effect substantially controls the mass balance of the AIS and thus its contribution to sea level rise. Recent results of ocean circulation models indicate that warm circumpolar water of the Southern Ocean may override the continental slope front and boost basal ice shelf melting. In particular, simulations demonstrate the redirection of a warm coastal current into the Filchner Trough and underneath the Filchner-Ronne Ice Shelf (FRIS) within the next decades. The increase of water temperature in the sub-shelf cavity is estimated to dramatically raise the basal shelf melting. Coupled simulations with a finite elements ocean model and a three-dimensional thermomechanical ice flow model reveal that the consequent thinning of the FRIS would lead to an extensive grounding line retreat associated with a vast mass loss of the AIS. In this subsequent study, we aim for an enhanced understanding of the complex feedbacks between ocean circulation and ice dynamics of the grounded AIS. Therefor, we focus on the ice streams which are draining into the FRIS and dominating the mass transport from grounded to floating ice. For a better representation of these fast-flowing ice streams we expand the above ice flow model by the incorporation of local processes at the ice base. There, sediment deformation and lubrication by subglacial hydrology locally allow high basal sliding rates and thus create the precondition for the development of ice streams. Based on satellite-observed ice surface velocity patterns we identify such areas with low basal drag and parametrize the ice flow model accordingly. As a result, the modeled ice flow patterns will depict velocity and locations of observed ice streams in the catchment of the FRIS more realistically. We present first results of this advanced ice-flow modeling approach, anticipating an even larger response of the AIS on increased sub-shelf melting rates in future coupled simulations

Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets

This article proposes a method to design network-aware flexibility requests for local flexibility markets. These markets are becoming increasingly important for distribution system operators (DSOs) to ensure grid safety while minimizing costs and public opposition to new network investments. Despite extended recent literature on local flexibility markets, little attention has been paid to quantifying the flexibility required at each location, considering physical network constraints (e.g. line and voltage limits). The method introduced uses a chance-constrained optimization model and a LinDistFlow approximation to consider both physical network constraints and uncertainty caused by renewable production or demand fluctuations. Unlike other methods, it avoids sharing sensitive grid data with the market operator. We compare our approach against a stochastic market-clearing mechanism which serves as a benchmark, and we derive analytical conditions for the performance of our method to determine flexibility requests. We show on two case studies that our method outperforms the stochastic market-clearing benchmark in terms of computation time while achieving comparable social welfare and costs for the DSOs. One of the case studies is conducted on an actual German distribution grid, showing that the proposed method can scale well to real-sized networks.</p

Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets

Local flexibility markets will become a central tool to procure flexibility for distribution system operators (DSOs), who need to ensure a safe grid operation against increased costs and public opposition towards new network investments. Despite extended recent literature on local flexibility markets, little attention has been paid on how to determine the amount of flexibility required at each location, considering the constraints that the network introduces (e.g. line and voltage limits). Addressing an open question for several DSOs, this paper introduces a method to design network-aware flexibility requests from a DSO perspective. In that, we also consider uncertainty, which could be the result of fluctuating renewable production or demand. We compare our approach against a stochastic market clearing mechanism, which serves as a benchmark; and we derive analytical conditions for the performance of our method to determine flexibility requests. We demonstrate our methods on a real German distribution grid.Comment: 10 pages, 7 figure

Decadal hindcasts initialized using observed surface wind stress: Evaluation and prediction out to 2024

We use surface air temperature to evaluate the decadal forecast skill of the fully coupled Max Planck Institut Earth System Model (MPI-ESM) initialized using only surface wind stress applied to the ocean component of the model (Modini: Model initialization by partially coupled spin-up). Our analysis shows that the greenhouse gas forcing alone results in a significant forecast skill on the 2–5 and 6–9 year range even for uninitialized hindcasts. For the first forecast year, the forecast skill of Modini is generally comparable with previous initialization procedures applied to MPI-ESM. But only Modini is able to generate a significant skill (correlation) in the tropical Pacific for a 2–5 year (and to a lesser extent for a 6–9 year) hindcast. Modini is also better able to capture the observed hiatus in global warming in hindcast mode than the other methods. Finally, we present forecasts for 2015 and the average of years 2016–2019 and 2020–2024, predicting an end to the hiatus

MOSAiC goes O2A - Arctic Expedition Data Flow from Observations to Archives

During the largest polar expedition in history starting in September 2019, the German research icebreaker Polarstern spends a whole year drifting with the ice through the Arctic Ocean. The MOSAiC expedition takes the closest look ever at the Arctic even throughout the polar winter to gain fundamental insights and most unique on-site data for a better understanding of global climate change. Hundreds of researchers from 20 countries are involved. Scientists will use the in situ gathered data instantaneously in near-real time modus as well as long afterwards all around the globe taking climate research to a completely new level. Hence, proper data management, sampling strategies beforehand, and monitoring actual data flow as well as processing, analysis and sharing of data during and long after the MOSAiC expedition are the most essential tools for scientific gain and progress. To prepare for that challenge we adapted and integrated the research data management framework O2A “Data flow from Observations to Archives” to the needs of the MOSAiC expedition on board Polarstern as well as on land for data storage and access at the Alfred Wegener Institute Computing and Data Center in Bremerhaven, Germany. Our O2A-framework assembles a modular research infrastructure comprising a collection of tools and services. These components allow researchers to register all necessary sensor metadata beforehand linked to automatized data ingestion and to ensure and monitor data flow as well as to process, analyze, and publish data to turn the most valuable and uniquely gained arctic data into scientific outcomes. The framework further allows for the integration of data obtained with discrete sampling devices into the data flow. These requirements have led us to adapt the generic and cost-effective framework O2A to enable, control, and access the flow of sensor observations to archives in a cloud-like infrastructure on board Polarstern and later on to land based repositories for international availability. Major roadblocks of the MOSAiC-O2A data flow framework are (i) the increasing number and complexity of research platforms, devices, and sensors, (ii) the heterogeneous interdisciplinary driven requirements towards, e. g., satellite data, sensor monitoring, in situ sample collection, quality assessment and control, processing, analysis and visualization, and (iii) the demand for near real time analyses on board as well as on land with limited satellite bandwidth. The key modules of O2A's digital research infrastructure established by AWI are implementing the FAIR principles: SENSORWeb, to register sensor applications and sampling devices and capture controlled meta data before and alongside any measurements in the field Data ingest, allowing researchers to feed data into storage systems and processing pipelines in a prepared and documented way, at best in controlled near real-time data streams Dashboards allowing researchers to find and access data and share and collaborate among partners Workspace enabling researchers to access and use data with research software utilizing a cloud-based virtualized infrastructure that allows researchers to analyze massive amounts of data on the spot Archiving and publishing data via repositories and Digital Object Identifiers (DOI