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  • 1
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    Sea ice targeted geoengineering simulation with the AWI Climate Model (2019)
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven | Supplement to: Zampieri, Lorenzo; Goessling, Helge (2019): Sea ice targeted geoengineering can delay Arctic sea ice decline but not global warming. Earth's Future, 7, https://doi.org/10.1029/2019EF001230
    Details
    Publication Date: 2023-03-16
    Description: To counteract global warming, a geoengineering approach that aims at intervening in the Arctic ice-albedo feedback has been proposed (Desch et al., (2017)). A large number of wind-driven pumps shall spread seawater on the surface in winter to enhance ice growth, allowing more ice to survive the summer melt. We test this idea with a coupled climate model by modifying the surface exchange processes such that the physical effect of the pumps is simulated. This database contains a selection of fields from CMIP5 type RCP 8.5 ensemble climate projections with the AWI Climate Model (AWI-CM). The data are stored as netCDF and include the following variables: Monthly averaged time series of pan-Arctic sea ice extent and volume from 1850 to 2100. These are divided into a "Historical" simulation (1850 to 1999; 1 ensemble member), a "Control" simulation (2000 to 2100; 4 ensemble members) and a "Geoengineering" simulation (2020 to 2100; 4 ensemble members). Monthly averaged 2D fields of 2m temperature, total cloud cover, net solar radiation energy flux and total precipitation for the "Control", "Geoengineering" and "Extreme Geoengineering" simulations. The data are averaged over two periods: 2021 to 2060 and 2061 to 2100. Further details about the data can be found in the publication associated with the database. For practical reasons, the full climate model output is stored at DKRZ and will be made available only upon request to the authors. This dataset has been created with the financial support of the Federal Ministry of Education and Research of Germany in the framework of the research group Seamless Sea Ice Prediction (SSIP; grant 01LN1701A).
    Keywords: Arctic ice management; Climate modelling; File content; File format; File name; File size; Geoengineering; Sea ice; sea ice modelling; Seamless Sea Ice Prediction; SSIP; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 60 data points
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    DATA PANGAEA
  • 2
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    Linear Kinematic Features (leads & pressure ridges) detected and tracked in RADARSAT Geophysical Processor System (RGPS) sea-ice deformation data from 1997 to 2008 (2019)
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Details
    Publication Date: 2023-09-14
    Description: Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-set includes LKFs that were detected and tracked in sea ice deformation data for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). The data-set spans the winter month (November to May) from 1997 to 2008. A detailed description of the data-set and of the algorithms deriving it is provided in Hutter et al. (2019).
    Keywords: Arctic_LKFs_1997-2008; Arctic Ocean; SAT; Satellite remote sensing
    Type: Dataset
    Format: application/zip, 80.3 MBytes
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    DATA PANGAEA
  • 3
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    Sea‐Ice Forecasts With an Upgraded AWI Coupled Prediction System (2022)
    Mu, Longjiang ; Nerger, Lars ; Streffing, Jan ; [et al.]
    Details
    Publication Date: 2024-02-05
    Description: A new version of the AWI Coupled Prediction System is developed based on the Alfred Wegener Institute Climate Model v3.0. Both the ocean and the atmosphere models are upgraded or replaced, reducing the computation time by a factor of 5 at a given resolution. This allowed us to increase the ensemble size from 12 to 30, maintaining a similar resolution in both model components. The online coupled data assimilation scheme now additionally utilizes sea‐surface salinity and sea‐level anomaly as well as temperature and salinity profile observations. Results from the data assimilation demonstrate that the sea‐ice and ocean states are reasonably constrained. In particular, the temperature and salinity profile assimilation has mitigated systematic errors in the deeper ocean, although issues remain over polar regions where strong atmosphere‐ocean‐ice interaction occurs. One‐year‐long sea‐ice forecasts initialized on 1 January, 1 April, 1 July and 1 October from 2003 to 2019 are described. To correct systematic forecast errors, sea‐ice concentration from 2011 to 2019 is calibrated by trend‐adjusted quantile mapping using the preceding forecasts from 2003 to 2010. The sea‐ice edge raw forecast skill is within the range of operational global subseasonal‐to‐seasonal forecast systems, outperforming a climatological benchmark for about 2 weeks in the Arctic and about 3 weeks in the Antarctic. The calibration is much more effective in the Arctic: Calibrated sea‐ice edge forecasts outperform climatology for about 45 days in the Arctic but only 27 days in the Antarctic. Both the raw and the calibrated forecast skill exhibit strong seasonal variations.
    Description: Plain Language Summary: Ocean data sparseness and systematic model errors pose problems for the initialization of coupled seasonal forecasts, especially in polar regions. Our global forecast system follows a seamless approach with refined ocean resolution in the Arctic. The new version presented here features higher computational efficiency and utilizes more ocean and sea‐ice observations. Ice‐edge forecasts outperform a climatological benchmark for about 1 month, comparable to established systems.
    Description: Key Points: We describe an upgrade of the AWI Coupled Prediction System with new ocean and atmosphere models and more observations assimilated. Independent evaluations show advances in the new version on the analysis of the sea‐ice and ocean states against the old one. Calibrated sea‐ice edge forecasts outperform a climatological benchmark for around 1 month in both hemispheres.
    Description: National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809
    Description: Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347
    Description: Deutsche Forschungsgemeinschaft
    Description: https://doi.org/10.5281/zenodo.6335383
    Description: https://github.com/FESOM/fesom2/releases/tag/AWI-CM3_v3.0
    Description: https://doi.org/10.5281/zenodo.6335498
    Description: https://oasis.cerfacs.fr/en/
    Description: https://doi.org/10.5281/zenodo.4905653
    Description: http://forge.ipsl.jussieu.fr/ioserver
    Description: https://doi.org/10.5281/zenodo.6335474
    Description: http://pdaf.awi.de/
    Description: https://doi.org/10.5281/zenodo.6481116
    Keywords: ddc:551.6 ; seamless sea ice forecast ; multivariate data assimilation ; forecast calibration ; spatial probability score
    Language: English
    Type: doc-type:article
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    CITATION GEO-LEO
  • 4
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    AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model (2022)
    Copernicus Publications (EGU)
    Details
    Publication Date: 2024-02-07
    Description: We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3), which has higher skills in representing the observed climatology and better computational efficiency than its predecessors. Its ocean component FESOM2 (Finite-volumE Sea ice-Ocean Model) has the multi-resolution functionality typical of unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of the latest developments in the numerical-weather-prediction community in climate sciences. In this paper we describe the coupling of the model components and evaluate the model performance on a variable-resolution (25-125 km) ocean mesh and a 61 km atmosphere grid, which serves as a reference and starting point for other ongoing research activities with AWI-CM3. This includes the exploration of high and variable resolution and the development of a full Earth system model as well as the creation of a new sea ice prediction system. At this early development stage and with the given coarse to medium resolutions, the model already features above-CMIP6-average skills (where CMIP6 denotes Coupled Model Intercomparison Project phase 6) in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model.
    Type: Article , PeerReviewed
    Format: text
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 5
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    Sea-ice prediction across timescales and the role of model complexity (2021)
    Universität Bremen
    In:  EPIC3Universität Bremen, 155 p.
    Details
    Publication Date: 2021-02-15
    Description: In addition to observations and lab experiments, the scientific investigation of the Arctic and Antarctic sea ice is conducted through the employment of geophysical models. These models describe in a numerical framework the physical behavior of sea ice and its interactions with the atmosphere, ocean, and polar biogeochemical systems. Sea-ice models find application in the quantification of the past, present, and future sea-ice evolution, which becomes particularly relevant in the context of a warming climate system that causes the reduction of the Arctic sea ice cover. Because of the sea-ice decline, the navigation in the Arctic ocean increased substantially in the recent past, a trend that is expected to continue in the next decades and that requires the formulation of reliable sea-ice predictions at various timescales. Sea-ice predictions can be delivered by modern forecast systems that feature dynamical sea-ice models. The simulation of sea ice is at the center of this thesis: A coupled climate model with a simple sea-ice component is used to quantify potential impacts of a geoengineering approach termed "Arctic Ice Management"; the skill of current operational subseasonal-to-seasonal sea-ice forecasts, based on global models with a varying degree of sea-ice model complexity, is evaluated; and, lastly, an unstructured-grid ocean model is equipped with state-of-the-art sea-ice thermodynamics to study the impact of sea-ice model complexity on model performance. In chapter 2, I examine the potential of a geoengineering strategy to restore the Arctic sea ice and to mitigate the warming of the Arctic and global climate throughout the 21st century. The results, obtained with a fully coupled climate model, indicate that it is theoretically possible to delay the melting of the Arctic sea ice by ~60 years, but that this does not reduce global warming. In chapters 3 and 4, I assess the skill of global operational ensemble prediction systems in forecasting the evolution of the Arctic and Antarctic sea-ice edge position at subseasonal timescales. I find that some systems produce skillful forecasts more than 1.5 months ahead, but I also find evidence of substantial model biases and issues concerning data assimilation and model formulation. Chapter 5 deals with the impact of sea-ice model complexity on model performance. I present a new formulation of the FESOM2 sea-ice/ocean model with a revised description of the sea-ice thermodynamics, including various parameterizations of physical processes at the subgrid-scale. The model formulation grants substantial modularity in terms of sea-ice physics and resolution. The new system is used for assessing the impact of the sea-ice model complexity on the FESOM2 performance in different atmosphere-forced setups with a specific parameter-tuning approach and a special focus on sea-ice related variables. The results evidence that a more sophisticated model formulation is beneficial for the model representation of the sea-ice concentration and snow thickness, while less relevant for sea-ice thickness and drift. I also highlight a dependence of the model performance on the atmospheric forcing product used as boundary conditions. In the final part of this thesis, I formulate recommendations for future developments in the field of sea-ice modeling, with particular emphasis on FESOM2 and, more generally, on the modeling infrastructure under development at the Alfred Wegener Institute.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Thesis , notRev , info:eu-repo/semantics/other
    Format: application/pdf
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  • 6
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    Sea Ice Targeted Geoengineering Can Delay Arctic Sea Ice Decline but not Global Warming (2019)
    American Geophysical Union
    In:  EPIC3Earth's Future, American Geophysical Union, 7(12), pp. 1296-1306, ISSN: 2328-4277
    Details
    Publication Date: 2021-02-15
    Description: To counteract global warming, a geoengineering approach that aims at intervening in the Arctic ice‐albedo feedback has been proposed. A large number of wind‐driven pumps shall spread seawater on the surface in winter to enhance ice growth, allowing more ice to survive the summer melt. We test this idea with a coupled climate model by modifying the surface exchange processes such that the physical effect of the pumps is simulated. Based on experiments with RCP 8.5 scenario forcing, we find that it is possible to keep the late‐summer sea ice cover at the current extent for the next ∼60 years. The increased ice extent is accompanied by significant Arctic late‐summer cooling by ∼1.3 K on average north of the polar circle (2021–2060). However, this cooling is not conveyed to lower latitudes. Moreover, the Arctic experiences substantial winter warming in regions with active pumps. The global annual‐mean near‐surface air temperature is reduced by only 0.02 K (2021–2060). Our results cast doubt on the potential of sea ice targeted geoengineering to mitigate climate change.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 7
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    Prediction of Arctic sea ice on subseasonal to seasonal time scales (2017)
    In:  EPIC3ECMWF Seminar
    Details
    Publication Date: 2017-12-19
    Description: Sea ice forecasts are becoming a demanding need since human activities in the Arctic are constantly increasing and this trend is expected to continue. In this context, the recent availability of the Subseasonal to Seasonal Prediction Project (S2S) Dataset has a particularly good timing and provides a solid base to make an initial assessment of the predictive skills of probabilistic forecast systems with dynamical sea ice. In this study, we employ different verification metrics to compare the S2S sea ice forecasts with satellite observations and the models’ own analyses. In particular, the focus is on the sea ice spatial distribution in the Arctic, which is relevant information for potential final users. The verification metrics, specifically chosen to quantify the quality of the forecasted sea ice edge position, are the Integrated Ice Edge Error (IIEE), the Spatial Probability Score (SPS) and the Modified Hausdorff Distance (MHD). Despite the early development stage of Arctic sea ice predictions on the seasonal time scale, and the fact that the main focus of the S2S systems is mostly not on sea ice per se, our findings reveal that some of the S2S models are promising, exhibiting better predictive skills than the observation-based climatology and persistence. However, the results also point to critical aspects concerning the data assimilation procedure and the tuning of the models, which can strongly affect the forecasts quality. The comparison of different versions of the ECMWF forecast system shows the benefits brought by a coupled dynamical description of the sea ice instead of its prescription based on persistence and climatological records. Moreover, the systematic application of the verification metrics to such a broad pool of forecasts provides useful indications about strengths and limitation of the verification metrics themselves. Given the increasing availability of new and better sea ice observations and the possible improvements to coupled seasonal forecast systems, the formulation of reliable Arctic sea ice predictions for the subseasonal to seasonal time scales appears to be a realistic target for the scientific community.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
    Format: application/pdf
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  • 8
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    Developing a dataset of Linear Kinematic Features (LKFs) for the evaluation of small-scale sea ice deformation (2017)
    In:  EPIC3FAMOS Workshop, Woods Hole, USA, 2017-10-24-2017-10-27
    Details
    Publication Date: 2017-11-06
    Description: The Arctic sea ice deforms constantly due to stresses imposed by winds, ocean currents and interaction with coastlines. The most dominant features produced by this deformation in the ice cover are leads and pressure ridges that are often referred to as Linear Kinematic Features (LKFs). With increasing resolution of classical (viscous-plastic) sea ice models, or using new rheological frameworks (e.g. Maxwell elasto-brittle), sea-ice models start to resolve this small-scale deformation. So far, scaling properties of sea-ice deformation are commonly used to evaluate the modelled LKFs, besides other measures like lead area density. These metrics evade the problem of detecting individual LKFs by taking statistics over continuous fields like sea ice deformation or concentration. This way, they can provide specific information, but lack a comprehensive description of LKFs. We detect individual LKFs in sea ice deformation fields from satellite observations with an object detection algorithm. Combining this information with the sea ice drift fields used to derive the deformation fields, the LKFs are tracked in time. In doing so, the spatial characteristics (density, length, orientation, intersection angle, curvature) as well as the temporal evolution can be extracted from the same data-set. This algorithm can be applied to modelled sea-ice deformation and drift to enable a consistent comparison and thorough evaluation of simulated sea-ice deformation. We present preliminary results of LKFs detected in the RGPS data set and give examples of possible applications.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 9
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    A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation (2018)
    In:  EPIC3POLAR 2018, Davos, Swiss, 2018-06-19-2018-06-23
    Details
    Publication Date: 2018-08-06
    Description: The Arctic sea ice deforms continuously due to stresses imposed by winds, ocean currents and interaction with coastlines. The most dominant features produced by this deformation in the ice cover are leads and pressure ridges that are often referred to as Linear Kinematic Features (LKFs). With increasing resolution of classical (viscous-plastic) sea ice models, or using new rheological frameworks (e.g. Maxwell elasto-brittle), sea-ice models start to resolve this small-scale deformation. Typical measures for evaluating the modelled LKFs include scaling properties of sea-ice deformation or lead area density. These metrics avoid the problem of detecting individual LKFs by applying statistics over continuous fields such as sea ice deformation or concentration. In this way, these statistical metrics can provide specific information, but lack a comprehensive description of LKFs. We detect individual LKFs in sea ice deformation fields from satellite observations with an object detection algorithm. Combining this information with the sea ice drift fields used to derive the deformation fields, the LKFs are tracked in time. In doing so, the spatial characteristics (density, length, orientation, intersection angle, curvature) and the temporal evolution can be extracted from the same data-set. Our algorithm can be applied to both observed and modelled sea-ice deformation and drift making possible a consistent comparison and thorough evaluation.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 10
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    Bright Prospects for Arctic Sea Ice Prediction on Subseasonal Time Scales (2018)
    Wiley
    In:  EPIC3Geophysical Research Letters, Wiley, ISSN: 0094-8276
    Details
    Publication Date: 2018-10-02
    Description: With retreating sea ice and increasing human activities in the Arctic come a growing need for reliable sea ice forecasts up to months ahead. We exploit the subseasonal‐to‐seasonal prediction database and provide the first thorough assessment of the skill of operational forecast systems in predicting the location of the Arctic sea ice edge on these time scales. We find large differences in skill between the systems, with some showing a lack of predictive skill even at short weather time scales and the best producing skillful forecasts more than 1.5 months ahead. This highlights that the area of subseasonal prediction in the Arctic is in an early stage but also that the prospects are bright, especially for late summer forecasts. To fully exploit this potential, it is argued that it will be imperative to reduce systematic model errors and develop advanced data assimilation capacity.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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