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  • 1
    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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  • 2
    Electronic Resource
    Electronic Resource
    [S.l.] : American Institute of Physics (AIP)
    Journal of Applied Physics 71 (1992), S. 1229-1236 
    ISSN: 1089-7550
    Source: AIP Digital Archive
    Topics: Physics
    Notes: The selective bismuth segregation and the microprocess of fracture for the three [101] tilt copper bicrystals Σ9(2¯1¯2) 38.94°, Σ11(3¯2¯3) 50.48°, and Σ33(5¯4¯5) 58.99° have been studied by a molecular dynamics technique. The results show that the Bi segregation and the fracture behavior of the Cu-Bi bicrystals are strongly dependent on the grain boundary (GB) structure. The Bi segregation is strongly related to the polyhedra constructing the GB cores and the stress fields of the GB dislocations (GBDs), and the GB embrittlement of copper induced by the Bi segregation is determined by the segregated concentration and the distribution of Bi atoms. With the increase of the relative number of pentagonal bipyramids and the localization of the stress fields of the GBDs in the GBs, the bicrystals Σ9, Σ11, and Σ33 show a decreasing propensity for the Bi segregation and subsequent different fracture behaviors. The severe intergranular brittle fracture that happens in the Σ9 bicrystal is mainly caused by the breaking of weakened Cu-Cu bonds, which is related to the highly concentrated Bi segregation at the GB core. In the case of the Σ11 bicrystal, the segregation of Bi atoms at the GB shows an inhomogeneous distribution characteristic, so that the fracture is intergranular but with a large amount of shear deformation. The transgranular fraction that appears in the Σ33 bicrystal is related to the low concentration of the Bi segregation and the dispersive distribution of the Bi atoms along the GB and in the grains.
    Type of Medium: Electronic Resource
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  • 3
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    In:  EPIC37th International Symposium on Data Assimilation, Kobe, Japan, January 21-24, 2019
    Publication Date: 2020-02-26
    Description: We discuss a strategy to build a highly scalable and flexible data assimilation system on the basis of the Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de) using the example of the coupled climate model AWI-CM (Sidorenko et al., Climate Dynamics, 44 (2015) 757-780). AWI-CM consists of the finite-element sea ice-ocean model FESOM, which uses an unstructured model grid, and the model ECHAM6 for the atmosphere. The model compartments are coupled using OASIS3-MCT. The model system consists of two separate executable programs for the ocean and atmosphere. The assimilation system is generated by online-coupling of AWI-CM and PDAF. This modifies AWI-CM to perform ensemble forecasting and data assimilation and allows to fully keep the ensemble information in memory avoiding costly file operations and model restarts. The resulting assimilation system supports to apply the assimilation both in-compartment (i.e. weakly-coupled) as well as cross-compartment (i.e. strongly-coupled). Discussed are the structure and computational performance of the assimilation system as well as results from the assimilation of sea surface temperature and ocean profile data sets into a realistic configuration of AWI-CM.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 4
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    In:  EPIC34th OceanPredict Data Assimilation Task Team Meeting, CERFACS, Toulouse, France, January 20-22, 2020
    Publication Date: 2020-02-26
    Description: The coupled atmosphere-ocean model AWI-CM has been augmented for ensemble data assimilation using the parallel data assimilation framework (PDAF). AWI-CM consists of the atmosphere model ECHAM6 and the unstructured grid finite element ocean model FESOM. PDAF provides the environment for ensemble forecasts and the ensemble filters for the assimilation. The work aims at strongly-coupled data assimilation, hence using cross-covariances between the atmosphere and ocean in the analysis step of the data assimilation process. As a first step oceanic observations are assimilated into the coupled model system in a setup of weakly coupled data assimilation and the effect one the coupled model state is assessed. We discuss the setup of the system, which is generic and hence also applicable for other coupled, but also uncoupled models. Further, challenges of the assimilation into the coupled system and initial results from strongly-coupled assimilation are discussed.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 5
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    In:  EPIC3Seminar at School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China, November 5, 2019
    Publication Date: 2020-02-29
    Description: Data assimilation combines observational information with numerical models taking into account the errors in both the observations and the model. In ensemble data assimilation the errors in the model state are dynamically estimated using an ensemble of model states. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. The coupled models simulate different compartments of the Earth system as well as their interactions. For example coupled atmosphere-ocean models like the AWI Climate Model (AWI-CM), simulate the physics in both compartments and fluxes in between then. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. Ensemble data assimilation methods can be applied with these model systems, but have a high high computing cost. To allow us to efficiently perform the data assimilation, the parallel data assimilation framework (PDAF) has been developed. I will discuss the application and challenges of coupled ensemble data assimilation on the examples of the data assimilative model system of AWI-CM coupled to PDAF and a coupled ocean-biogeochemical model consistent of the ocean circulation model MITgcm and the ecosystem model REcoM2.
    Repository Name: EPIC Alfred Wegener Institut
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  • 6
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    In:  EPIC3Fifth Workshop on Coupling Technologies for Earth System Models, September 21 - 24, 2020, online
    Publication Date: 2020-10-27
    Description: We discuss how to build an ensemble data assimilation system using a direct connection between a coupled Earth system model (ESM) and the ensemble data assimilation software PDAF (Parallel Data Assimilation Framework, http://pdaf.awi.de). The direct connection results in a data assimilation program with high flexibility, efficiency, and parallel scalability. For this we augment the source code of the coupled model by data assimilation routines and hence create an online-coupled assimilative model. This first modifies the coupled model to be able to simulate an ensemble. Using a combination of in-memory access and parallel communication with the Message Passing Interface (MPI) standard we can further add the analysis step of ensemble-based assimilation methods. Thus the assimilation of observations is computed without the need to stop and restart the whole coupled model system. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler that couples the different model compartments. This strategy to build the assimilation system allows us to perform both weakly coupled (in-compartment) and strongly coupled (cross-compartment) assimilation. The assimilation frequency can be kept flexible, so that the assimilation of observations from different compartments of the ESM can be performed at different intervals. Further, the reading and writing of disk files is minimized. The resulting assimilative model can be run in the same way as the regular ESM, but with additional parameters controlling the assimilation and with a higher number of processors to simulate the ensemble. Using the example of the coupled climate model AWI-CM that contains the FESOM model for the ocean and sea ice and ECHAM6 for the atmosphere, both coupled through the OASIS-MCT coupler, we discuss the features of the online assimilation coupling strategy and the performance of the resulting assimilative model.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 7
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    COPERNICUS GESELLSCHAFT MBH
    In:  EPIC3Geoscientific Model Development, COPERNICUS GESELLSCHAFT MBH, 13(9), pp. 4305-4321, ISSN: 1991-959X
    Publication Date: 2020-09-18
    Description: Data assimilation integrates information from observational measurements with numerical models. When used with coupled models of Earth system compartments, e.g., the atmosphere and the ocean, consistent joint states can be estimated. A common approach for data assimilation is ensemble-based methods which utilize an ensemble of state realizations to estimate the state and its uncertainty. These methods are far more costly to compute than a single coupled model because of the required integration of the ensemble. However, with uncoupled models, the ensemble methods also have been shown to exhibit a particularly good scaling behavior. This study discusses an approach to augment a coupled model with data assimilation functionality provided by the Parallel Data Assimilation Framework (PDAF). Using only minimal changes in the codes of the different compartment models, a particularly efficient data assimilation system is generated that utilizes parallelization and in-memory data transfers between the models and the data assimilation functions and hence avoids most of the file reading and writing, as well as model restarts during the data assimilation process. This study explains the required modifications to the programs with the example of the coupled atmosphere–sea-ice–ocean model AWI-CM (AWI Climate Model). Using the case of the assimilation of oceanic observations shows that the data assimilation leads only to small overheads in computing time of about 15 % compared to the model without data assimilation and a very good parallel scalability. The model-agnostic structure of the assimilation software ensures a separation of concerns in which the development of data assimilation methods can be separated from the model application.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 8
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    In:  EPIC3Seminar at Collaborative Research Center 1294 'Data Assimilation', Potsdam, Germany, September 13, 2019
    Publication Date: 2020-02-29
    Description: Coupled models simulate different compartments of the Earth system as well as their interactions. For example coupled ocean-biogoechemical models simulate ocean circulation, biogeochemical processes and the carbon cycle. Coupled atmosphere-ocean models like the AWI Climate Model (AWI-CM), simulate the physics in both compartments and fluxes in between then. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. Ensemble data assimilation methods can be applied with these model systems, however the need to compute an ensemble of model integrations strongly increases the already high computing cost of the models. To allow us to perform the data assimilation in supercomputers, the parallel data assimilation framework (PDAF) has been developed. I will discuss the application and challenges of coupled ensemble data assimilation with PDAF on the example of two different coupled model systems: the ocean-biogeochemical model MITgcm-REcoM and the atmosphere-ocean model AWI-CM.
    Repository Name: EPIC Alfred Wegener Institut
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  • 9
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    In:  EPIC3Seminar at Institute for Geodesy and Geoinformation, University of Bonn, Germany, September 27, 2019
    Publication Date: 2020-02-29
    Description: Coupled models simulate different compartments of the Earth system as well as their interactions. For example coupled atmosphere-ocean models like the AWI Climate Model (AWI-CM), simulate the physics in both compartments and fluxes in between then. Data assimilation is used with coupled models to generate model fields to initialize model predictions, for computing a model state over time as a reanalysis, to optimize model parameters, and to assess model deficiencies. Ensemble data assimilation methods can be applied with these model systems, but have a high high computing cost. To allow us to efficiently perform the data assimilation, the parallel data assimilation framework (PDAF) has been developed. I will discuss the application and challenges of coupled ensemble data assimilation on the example of the data assimilative model system AWI-CM coupled to PDAF.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 10
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    In:  EPIC3Seminar at Data Assimilation Research Center, University of Reading, UK, June 13, 2018
    Publication Date: 2019-01-29
    Description: Efficient ensemble data assimilation with coupled models poses particular challenges due to the comp lexity of the model system and due to its high computational cost. On the methodological side, one h as to account for different time scales, but also distinct correlation lengths, of different model c ompartments like the ocean and the atmosphere. Computationally, one often has to deal with multiple program executables, a coupler software, observation handling for different model compartments, and a large number of processors required to compute a complex coupled model. I will focus on the computational aspects and discuss the steps required to build a highly scalable and flexible data assimilation system can be built on the basis of the Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de) using the example of the coupled climate model AWI-CM (Sidorenko et al., Climate Dynamics, 44 (2015) 757-780). AWI-CM consists of the finite-element sea ice-ocean model FESOM, which uses an unstructured model grid, and the model ECHAM6 for the atmosphere. The model coupling is implemented with OASIS-MCT and the model system consists of two separate executable programs for the ocean and atmosphere. Next to the implementation steps, the scalability of the assimilation system is discussed with a realistic configuration of AWI-CM. The high scalability is obtained by an online-connection strategy for the data assimilation system. First, the parallelization of the coupled model system is modified so that the coupled model can perform ensemble forecasts. Second, the analysis (solver) step is directly inserted into the time-stepping loops of each model compartment. Augmenting the coupled model in this online way, the ensemble information is kept in memory and transferred by parallel communication when necessary. Thus, one avoids the need to repeatedly write an ensemble of model fields into files and read them again for the analysis step. Further, the coupled model is only started once and there is no need to stop and restart the whole coupled model to compute the analysis step. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler. These modifications of the model are supported by the framework structure of PDAF. In addition to the parallel online connection for the data assimilation system, the analysis step has to be parallelized. Here, the different model compartments are treated like parallel subdomains of the model. In this way, one can one can use the data assimilation algorithms provided by PDAF and can implement and perform the analysis step in analogy to uncoupled models. However, one has to take into account the different model grids and possible distinct ways in which the model compartments store their model fields. This results in a data assimilation system that can perform the assimilations both in-compartment (for weakly coupled assimilation) and cross-compartment (for strongly coupled assimilation).
    Repository Name: EPIC Alfred Wegener Institut
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