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  • 1
    Online Resource
    Online Resource
    Ocean Kinetic Energy Backscatter Parametrization on Unstructured Grids: Impact on Global Eddy‐Permitting Simulations
    American Geophysical Union (AGU) ; 2020
    In:  Journal of Advances in Modeling Earth Systems Vol. 12, No. 1 ( 2020-01)
    Details
    In: Journal of Advances in Modeling Earth Systems, American Geophysical Union (AGU), Vol. 12, No. 1 ( 2020-01)
    Abstract: We present a stable ocean kinetic energy backscatter scheme in a global ocean model setup The scheme substantially improves sea surface height variability comparable to much higher resolution Biases in mean sea surface height, temperature, and salinity are also improved
    Type of Medium: Online Resource
    ISSN: 1942-2466 , 1942-2466
    URL: Article
    DOI: 10.1029/2019MS001855
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2020
    detail.hit.zdb_id: 2462132-8
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  • 2
    Online Resource
    Online Resource
    Stochastic representations of model uncertainties at ECMWF: state of the art and future vision
    Wiley ; 2017
    In:  Quarterly Journal of the Royal Meteorological Society Vol. 143, No. 707 ( 2017-07), p. 2315-2339
    Details
    In: Quarterly Journal of the Royal Meteorological Society, Wiley, Vol. 143, No. 707 ( 2017-07), p. 2315-2339
    Abstract: Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5–10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.
    Type of Medium: Online Resource
    ISSN: 0035-9009 , 1477-870X
    URL: Issue
    URL: Article
    DOI: 10.1002/qj.2017.143.issue-707
    DOI: 10.1002/qj.3094
    RVK:
    UA 1000
    RVK:
    UA 7650
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 3142-2
    detail.hit.zdb_id: 2089168-4
    SSG: 14
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  • 3
    Online Resource
    Online Resource
    Effects of Stochastic Ice Strength Perturbation on Arctic Finite Element Sea Ice Modeling
    American Meteorological Society ; 2013
    In:  Journal of Climate Vol. 26, No. 11 ( 2013-06-01), p. 3785-3802
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 26, No. 11 ( 2013-06-01), p. 3785-3802
    Abstract: The ice strength parameter P* is a key parameter in dynamic/thermodynamic sea ice models that cannot be measured directly. Stochastically perturbing P* in the Finite Element Sea Ice–Ocean Model (FESOM) of the Alfred Wegener Institute aims at investigating the effect of uncertainty pertaining to this parameterization. Three different approaches using symmetric perturbations have been applied: 1) reassignment of uncorrelated noise fields to perturb P* at every grid point, 2) a Markov chain time correlation, and 3) a Markov chain time correlation with some spatial correlation between nodes. Despite symmetric perturbations, results show an increase of Arctic sea ice volume and a decrease of Arctic sea ice area for all three approaches. In particular, the introduction of spatial correlation leads to a substantial increase in sea ice volume and mean thickness. The strongest response can be seen for multiyear ice north of the Greenland coast. An ensemble of eight perturbed simulations generates a spread in the multiyear ice comparable to the interannual variability of the model. Results cannot be reproduced by a simple constant global modification of P*.
    Type of Medium: Online Resource
    ISSN: 0894-8755 , 1520-0442
    URL: Article
    DOI: 10.1175/JCLI-D-12-00388.1
    RVK:
    UA 4548
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2013
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
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  • 4
    Online Resource
    Online Resource
    A Stochastic Representation of Subgrid Uncertainty for Dynamical Core Development
    American Meteorological Society ; 2019
    In:  Bulletin of the American Meteorological Society Vol. 100, No. 6 ( 2019-06), p. 1091-1101
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 100, No. 6 ( 2019-06), p. 1091-1101
    Abstract: Numerical weather prediction and climate models comprise a) a dynamical core describing resolved parts of the climate system and b) parameterizations describing unresolved components. Development of new subgrid-scale parameterizations is particularly uncertain compared to representing resolved scales in the dynamical core. This uncertainty is currently represented by stochastic approaches in several operational weather models, which will inevitably percolate into the dynamical core. Hence, implementing dynamical cores with excessive numerical accuracy will not bring forecast gains, may even hinder them since valuable computer resources will be tied up doing insignificant computation, and therefore cannot be deployed for more useful gains, such as increasing model resolution or ensemble sizes. Here we describe a low-cost stochastic scheme that can be implemented in any existing deterministic dynamical core as an additive noise term. This scheme could be used to adjust accuracy in future dynamical core development work. We propose that such an additive stochastic noise test case should become a part of the routine testing and development of dynamical cores in a stochastic framework. The overall key point of the study is that we should not develop dynamical cores that are more precise than the level of uncertainty provided by our stochastic scheme. In this way, we present a new paradigm for dynamical core development work, ensuring that weather and climate models become more computationally efficient. We show some results based on tests done with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) dynamical core.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-17-0040.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2019
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 5
    Online Resource
    Online Resource
    Stochastic Parameterization: Toward a New View of Weather and Climate Models
    American Meteorological Society ; 2017
    In:  Bulletin of the American Meteorological Society Vol. 98, No. 3 ( 2017-03-01), p. 565-588
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 98, No. 3 ( 2017-03-01), p. 565-588
    Abstract: The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-15-00268.1
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2017
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 6
    Online Resource
    Online Resource
    On the Relation between Fourier and Walsh--Rademacher Spectra for Isotropic Random Fields
    Elsevier BV ; 2022
    In:  SSRN Electronic Journal
    Details
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    URL: Article
    DOI: 10.2139/ssrn.4207464
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
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  • 7
    Online Resource
    Online Resource
    “Wissenschaft fürs Wohnzimmer” – 2 years of weekly interactive, scientific livestreams on YouTube
    Copernicus GmbH ; 2023
    In:  Polarforschung Vol. 91 ( 2023-08-14), p. 31-43
    Details
    In: Polarforschung, Copernicus GmbH, Vol. 91 ( 2023-08-14), p. 31-43
    Abstract: Abstract. Science communication is becoming increasingly important to connect academia and society and to counteract misinformation. Online video platforms, such as YouTube, allow easily accessible communication of scientific knowledge to audiences made up of the general public. In April 2020, a diverse group of researchers from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, launched the YouTube channel called “Wissenschaft fürs Wohnzimmer” (translated to “Living Room Science”) to stream scientific talks about climate change and biodiversity every Thursday evening, with the aim to reach a broad range of members of the public with a general interest in science and climate. Here we report on the numbers and diversity of content, viewers, and presenters from 2 years and 100 episodes of weekly livestreams. Presented topics encompass all areas of polar research, the scientific and societal aspects of climate change and biodiversity loss, and new technologies to deal with the changing world and climate of the future. We show that constant engagement by a group of co-hosts and presenters representing all topics, career stages, and genders enables the continuous growth of views and subscriptions, i.e. a measurable impact. After 783 d, the channel gained 30 251 views and 828 subscribers and hosted well-known scientists, while enabling especially early-career researchers to foster their outreach and media skills. We show that interactive and science-related videos, both live and on-demand, within a pleasant atmosphere, can be produced alongside the main research activity by scientists, while also maintaining high quality. We further discuss the challenges and possible improvements for the future. Our experiences will help other researchers conduct meaningful scientific outreach and push the boundaries of existing formats towards a better understanding of climate change and our planet.
    Type of Medium: Online Resource
    ISSN: 2190-1090
    URL: Article
    DOI: 10.5194/polf-91-31-2023
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2023
    detail.hit.zdb_id: 123536-9
    detail.hit.zdb_id: 2382035-4
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  • 8
    Online Resource
    Online Resource
    Potential sea ice predictability and the role of stochastic sea ice strength perturbations
    American Geophysical Union (AGU) ; 2014
    In:  Geophysical Research Letters Vol. 41, No. 23 ( 2014-12-16), p. 8396-8403
    Details
    In: Geophysical Research Letters, American Geophysical Union (AGU), Vol. 41, No. 23 ( 2014-12-16), p. 8396-8403
    Abstract: Sea ice model uncertainty estimates increase spread for subseasonal predictions Seasonal prediction estimates not affected by sea ice model uncertainties
    Type of Medium: Online Resource
    ISSN: 0094-8276 , 1944-8007
    URL: Issue
    URL: Article
    DOI: 10.1002/grl.v41.23
    DOI: 10.1002/2014GL062081
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2014
    detail.hit.zdb_id: 2021599-X
    detail.hit.zdb_id: 7403-2
    SSG: 16,13
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  • 9
    Online Resource
    Online Resource
    AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model
    Copernicus GmbH ; 2022
    In:  Geoscientific Model Development Vol. 15, No. 16 ( 2022-08-29), p. 6399-6427
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 15, No. 16 ( 2022-08-29), p. 6399-6427
    Abstract: Abstract. 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 of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    DOI: 10.5194/gmd-15-6399-2022
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2022
    detail.hit.zdb_id: 2456725-5
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  • 10
    Online Resource
    Online Resource
    Influence of stochastic sea ice parametrization on climate and the role of atmosphere–sea ice–ocean interaction
    The Royal Society ; 2014
    In:  Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences Vol. 372, No. 2018 ( 2014-06-28), p. 20130283-
    Details
    In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, The Royal Society, Vol. 372, No. 2018 ( 2014-06-28), p. 20130283-
    Abstract: The influence of a stochastic sea ice strength parametrization on the mean climate is investigated in a coupled atmosphere–sea ice–ocean model. The results are compared with an uncoupled simulation with a prescribed atmosphere. It is found that the stochastic sea ice parametrization causes an effective weakening of the sea ice. In the uncoupled model this leads to an Arctic sea ice volume increase of about 10–20% after an accumulation period of approximately 20–30 years. In the coupled model, no such increase is found. Rather, the stochastic perturbations lead to a spatial redistribution of the Arctic sea ice thickness field. A mechanism involving a slightly negative atmospheric feedback is proposed that can explain the different responses in the coupled and uncoupled system. Changes in integrated Antarctic sea ice quantities caused by the stochastic parametrization are generally small, as memory is lost during the melting season because of an almost complete loss of sea ice. However, stochastic sea ice perturbations affect regional sea ice characteristics in the Southern Hemisphere, both in the uncoupled and coupled model. Remote impacts of the stochastic sea ice parametrization on the mean climate of non-polar regions were found to be small.
    Type of Medium: Online Resource
    ISSN: 1364-503X , 1471-2962
    URL: Article
    DOI: 10.1098/rsta.2013.0283
    RVK:
    AX 30011
    Language: English
    Publisher: The Royal Society
    Publication Date: 2014
    detail.hit.zdb_id: 208381-4
    detail.hit.zdb_id: 1462626-3
    SSG: 11
    SSG: 5,1
    SSG: 5,21
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