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  • Copernicus Publications  (4)
  • PANGAEA  (4)
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  • 1
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    Eddy-permitting and eddy-resolving simulations of the Fram Strait ocean dynamics with the Finite-Element Sea-Ice Ocean Model (FESOM), links to NetCDF files (2017)
    PANGAEA
    In:  Supplement to: Wekerle, Claudia; Wang, Qiang; von Appen, Wilken-Jon; Danilov, Sergey; Schourup-Kristensen, Vibe; Jung, Thomas (2017): Eddy-Resolving Simulation of the Atlantic Water Circulation in the Fram Strait With Focus on the Seasonal Cycle. Journal of Geophysical Research: Oceans, 122(11), 8385-8405, https://doi.org/10.1002/2017JC012974
    Details
    Publication Date: 2023-03-16
    Description: Eddy driven recirculation of Atlantic Water (AW) in the Fram Strait modifies the amount of heat that reaches the Arctic Ocean, but is difficult to constrain in ocean models due to very small Rossby radius there. In this study we explore the effect of resolved eddies on the AW circulation in a locally eddy-resolving simulation of the global Finite-Element-Sea ice-Ocean-Model (FESOM) integrated for the years 2000-2009, by focusing on the seasonal cycle. An eddy-permitting simulation serves as a control run. Our results suggest that resolving local eddy dynamics is critical to realistically simulate ocean dynamics in the Fram Strait. Strong eddy activity simulated by the eddy-resolving model, with peak in winter and lower values in summer, is comparable in magnitude and seasonal cycle to observations from a long-term mooring array, whereas the eddy-permitting simulation underestimates the observed magnitude. Furthermore, a strong cold bias in the central Fram Strait present in the eddy-permitting simulation is reduced due to resolved eddy dynamics, and AW transport into the Arctic Ocean is increased with possible implications for the Arctic Ocean heat budget. Given the good agreement between the eddy-resolving model and measurements, it can help filling gaps that point-wise observations inevitably leave. For example, the path of the West Spitsbergen Current offshore branch, measured in the winter months by the mooring array, is shown to continue cyclonically around the Molloy Deep in the model, representing the major AW recirculation branch in this season.
    Keywords: AWI_PhyOce; File content; File format; File name; File size; FRAM; Fram Strait; Fram-Strait; FRontiers in Arctic marine Monitoring; Physical Oceanography @ AWI; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 100 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 2
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    FESOM Arctic Ocean sea ice concentration and thickness 1995-2014, links to movies in mp4 format (2016)
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven | Supplement to: Wang, Qiang; Danilov, Sergey; Jung, Thomas; Kaleschke, Lars; Wernecke, Andreas (2016): Sea ice leads in the Arctic Ocean: Model assessment, interannual variability and trends. Geophysical Research Letters, 43(13), 7019-7027, https://doi.org/10.1002/2016GL068696
    Details
    Publication Date: 2023-03-16
    Description: Northern Hemisphere sea ice from a Finite-Element Sea-Ice Ocean Model (FESOM) 4.5 km resolution simulation carried out by researchers from Alfred Wegener Institute (AWI), Germany. Concentration is shown with color; thickness is shown with shading. A global 1 degree mesh is used, with the "Arctic Ocean" locally refined to 4.5 km. South of CAA and Fram Strait the resolution is not refined in this simulation. The animation indicates that the 4.5 km model resolution helps to represent the small scale sea ice features, although much higher resolution is required to fully resolve the ice leads. The animation is created by Michael Böttinger from DKRZ (https://www.dkrz.de).
    Keywords: Arctic; DATE/TIME; File content; File format; File size; pan-Arctic; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 3
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    Results of a Finite-Element Sea-Ice Ocean Model (FESOM) set-up to study the interannual to decadal variability in the deep-water formation rates (2013)
    PANGAEA
    In:  Supplement to: Scholz, Patrick; Lohmann, Gerrit; Wang, Qiang; Danilov, Sergey (2013): Evaluation of a Finite-Element Sea-Ice Ocean Model (FESOM) set-up to study the interannual to decadal variability in the deep-water formation rates. Ocean Dynamics, 63(4), 347-370, https://doi.org/10.1007/s10236-012-0590-0
    Details
    Publication Date: 2023-01-13
    Description: The characteristics of a global set-up of the Finite-Element Sea-Ice Ocean Model under forcing of the period 1958-2004 are presented. The model set-up is designed to study the variability in the deep-water mass formation areas and was therefore regionally better resolved in the deep-water formation areas in the Labrador Sea, Greenland Sea, Weddell Sea and Ross Sea. The sea-ice model reproduces realistic sea-ice distributions and variabilities in the sea-ice extent of both hemispheres as well as sea-ice transport that compares well with observational data. Based on a comparison between model and ocean weather ship data in the North Atlantic, we observe that the vertical structure is well captured in areas with a high resolution. In our model set-up, we are able to simulate decadal ocean variability including several salinity anomaly events and corresponding fingerprint in the vertical hydrography. The ocean state of the model set-up features pronounced variability in the Atlantic Meridional Overturning Circulation as well as the associated mixed layer depth pattern in the North Atlantic deep-water formation areas.
    Keywords: File format; File name; File size; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 32 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 4
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    Eddy kinetic energy in the Arctic Ocean from moored velocity observations (2022)
    PANGAEA
    Details
    Publication Date: 2024-04-30
    Description: Mesoscale eddies are important for many aspects of the dynamics of the Arctic Ocean. These include the maintenance of the halocline and the Atlantic Water boundary current through lateral eddy fluxes, shelf-basin exchanges, transport of biological material and sea ice, and the modification of the sea-ice distribution. Here we review what is known about the mesoscale variability and its impacts in the Arctic Ocean in the context of an Arctic Ocean responding rapidly to climate change. In addition, we present the first quantification of eddy kinetic energy (EKE) from moored observations across the entire Arctic Ocean, which we compare to output from an eddy resolving numerical model. We show that EKE is largest in the northern Nordic Seas/Fram Strait and it is also elevated along the shelfbreak of the Arctic Circumpolar Boundary Current, especially in the Beaufort Sea. In the central basins it is 100-1000 times lower. Except for the region affected by southward sea-ice export south of Fram Strait, EKE is stronger when sea-ice concentration is low compared to dense ice cover. Areas where conditions typical in the Atlantic and Pacific prevail will increase. Hence, we conclude that the future Arctic Ocean will feature more energetic mesoscale variability. This table provides (eddy) kinetic energy in the Arctic Ocean calculated from moorings and a numerical model across the entire record and averaged over certain conditions (seasons, ice concentration). The calculations are explained in the manuscript (Eddies and the distribution of eddy kinetic energy in the Arctic Ocean). The used mooring data was compiled from six different sources as listed below and identified in the table based on the Source ID.
    Keywords: 250_MOOR; 293-S1_MOOR; 293-X1_MOOR; 293-X2_MOOR; 293-X3_MOOR; 295-S2_MOOR; A01_MOOR; AK1-1_MOOR; AK2-1_MOOR; AK3-1_MOOR; AK4-1_MOOR; AK5-1_MOOR; AK6-1_MOOR; AK7-1_MOOR; Akademik Tryoshnikov; AM1-91_MOOR; AM2-91_MOOR; AO1-92_MOOR; Arctic Ocean; ARK-XIV/2; ARK-XVIII/1; ARK-XXIX/3; ARK-XXX/1.2; ARK-XXX/2, GN05; ARK-XXXI/4; ATWAIN200_MOOR; AWI_PhyOce; AWI401-1_MOOR; AWI402-1_MOOR; AWI403-1_MOOR; AWI403-2_MOOR; AWI404-1_MOOR; AWI406-1_MOOR; AWI410-2_MOOR; AWI411-2_MOOR; AWI412-4_MOOR; AWI413-4_MOOR; AWI415-1_MOOR; AWI416-1_MOOR; AWI417-1_MOOR; AWI418-1_MOOR; BaffinBay_2_MOOR; BaffinBay_MOOR; BarrowSt_81_MOOR; BarrowSt_C_MOOR; BarrowSt_N_MOOR; BarrowSt_S_MOOR; BarrowSt_SC_MOOR; BarrowSt_Ss_MOOR; BG_a_MOOR; BG_b_MOOR; BG_c_MOOR; BG_d_MOOR; BI3_MOOR; BR1_MOOR; BR2_MOOR; BR3_MOOR; BRA_MOOR; BRB_MOOR; BRG_MOOR; BRK_MOOR; BS2_MOOR; BS3_MOOR; BS4_MOOR; BS5_MOOR; BS6_MOOR; BSO1_MOOR; BSO2_MOOR; BSO3_MOOR; BSO4_MOOR; BSO5_MOOR; C1_MOOR; C2_MOOR; C3_MOOR; C4_MOOR; C5_MOOR; C6_MOOR; CA04_MOOR; CA05_MOOR; CA06_MOOR; CA07_MOOR; CA08_MOOR; CA10_MOOR; CA11_MOOR; CA12_MOOR; CA13_MOOR; CA15_MOOR; CA16_MOOR; CA20_MOOR; CM-1_MOOR; CM-2_MOOR; CS1_MOOR; CS-1A_MOOR; CS2_MOOR; CS-2A_MOOR; CS3_MOOR; CS-3A_MOOR; CS4_MOOR; CS5_MOOR; Depth, bottom/max; Depth, top/min; DEPTH, water; DS_TUBE8_MOOR; Duration; EA1_MOOR; EA2_MOOR; EA3_MOOR; EA4_MOOR; EBC_MOOR; eddies; eddy kinetic energy; Eddy kinetic energy, 2000-2010; Eddy kinetic energy, 2010-2020; Eddy kinetic energy, at depth; Eddy kinetic energy, autumn; Eddy kinetic energy, ice; Eddy kinetic energy, mean; Eddy kinetic energy, model bandpass; Eddy kinetic energy, model online; Eddy kinetic energy, no ice; Eddy kinetic energy, some ice; Eddy kinetic energy, spring; Eddy kinetic energy, summer; Eddy kinetic energy, winter; EGN-1; EGS-1; EGS1-2; EGS2-1; EGS4-1; ELEVATION; F10-1; F1-1; F11_MOOR; F11-2; F12_MOOR; F12-1; F13_MOOR; F13-1; F14_MOOR; F14-1; F15-1; F16-1; F17_MOOR; F2-1; F3-1; F4-1; F5-1; F6-1; F7-1; F8-1; F9-1; FB2b_MOOR; FB6_MOOR; First year of observation; FRAM; FRontiers in Arctic marine Monitoring; FRS782_MOOR; FSC1_MOOR; FSC2_MOOR; FSC3_MOOR; FSC4_MOOR; GS-3_2_MOOR; HG-IV-S-1; High-frequency kinetic energy; HSNE60_MOOR; HudsonBay_MOOR; HudsonStrait_MOOR; I1_MOOR; I2_MOOR; I3_MOOR; IdF1-1; IdF2-1; IdF3-1; IdF4-1; ISWRIG_MOOR; Karasik-2015; KS02_MOOR; KS04_MOOR; KS06_MOOR; KS08_MOOR; KS10_MOOR; KS12_MOOR; KS14_MOOR; L97; LA97/2; Lance; Last year of observation; LATITUDE; LM3_MOOR; LONGITUDE; Low-frequency kinetic energy; M11_MOOR; M12_MOOR; M13_MOOR; M14_MOOR; M15_MOOR; M16_MOOR; M3_MOOR; M5_MOOR; M6_MOOR; M9a_MOOR; MA2B_MOOR; MB1B_MOOR; MB2B_MOOR; MB4B_MOOR; Mean kinetic energy; MOOR; Mooring; Mooring (long time); MOORY; N198_2_MOOR; N198_MOOR; N525_MOOR; N541_MOOR; NABOS_2015_AK1-1, NABOS_2018_AK1-1; NABOS_2015_AK2-1, NABOS_2018_AK2-1; NABOS_2015_AK3-1, NABOS_2018_AK3-1; NABOS_2015_AK4-1, NABOS_2018_AK4-1; NABOS_2015_AK5-1, NABOS_2018_AK5-1; NABOS_2015_AK6-1,NABOS_2018_AK6-1; NABOS_2015_AK7-1, NABOS_2018_AK7-1; NABOS, AT2015; NABOS 2015; Nansen-2015; North Greenland Sea; NPEO_MOOR; NWNA_MOOR; NWNB_MOOR; NWNC_MOOR; NWND_MOOR; NWNE_MOOR; NWNF_MOOR; NWNG_MOOR; NWSB_MOOR; NWSD_MOOR; NWSE_2_MOOR; NWSE_MOOR; OLIK-1_MOOR; OSL2a_MOOR; OSL2f_MOOR; Physical Oceanography @ AWI; Polarstern; PS100; PS100/039-2, PS114_25-1,ARKR02-01; PS100/045-1, PS114_29-2; PS100/047-1, PS114_40-2; PS100/053-1, PS114_36-1; PS100/073-1, PS109_20-1; PS100/106-1, PS114_23-2; PS100/142-1, PS109_139-1; PS100/180-2, PS109_111-1; PS100/181-1, PS109_112-1; PS100/182-1, PS109_113-1; PS100/183-1, PS109_114-1; PS109; PS109_133-1, PS114_52-1; PS109_138-2, PS114_53-1; PS109_148-1, PS114_60-2; PS114; PS52; PS62; PS94; PS99/070-1, PS107_3-1; PS99.2; R071_MOOR; R1-1; R2-1; R290_MOOR; R3-1; R333_MOOR; R356_MOOR; R4-1; R5-1; Reference/source; SS-5_MOOR; StA_MOOR; Station label; Stor_MOOR; Total kinetic energy; V-319_MOOR; Velocity, east; Velocity, north; Vilk_MOOR; WBC_MOOR; WG1_MOOR; WG15_MOOR; WG4_MOOR; Wunsch-NN1_MOOR; Wunsch-NN2_MOOR; Y1_MOOR; Y2_MOOR; YP_MOOR
    Type: Dataset
    Format: text/tab-separated-values, 4806 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 5
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    Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations with AWI-CM1.0 (2019)
    Copernicus Publications
    In:  EPIC3Geoscientific Model Development, Copernicus Publications, 12(7), pp. 2635-2656, ISSN: 1991-9603
    Details
    Publication Date: 2019-08-19
    Description: Models from phase 5 of the Coupled Model Inter-comparison Project (CMIP5) show substantial biases in the deep ocean that are larger than the level of natural variability and the response to enhanced greenhouse gas concentrations. Here, we analyze the influence of horizontal resolution in a hierarchy of five multi-resolution simulations with the AWI Climate Model (AWI-CM), the climate model used at the Al-fred Wegener Institute, Helmholtz Centre for Polar and Ma-rine Research, which employs a sea ice–ocean model com-ponent formulated on unstructured meshes. The ocean grid sizes considered range from a nominal resolution of ∼ 1◦ (CMIP5 type) up to locally eddy resolving. We show that increasing ocean resolution locally to resolve ocean eddies leads to reductions in deep ocean biases, although these im-provements are not strictly monotonic for the five different ocean grids. A detailed diagnosis of the simulations allows to identify the origins of the biases. We find that two key re-gions at the surface are responsible for the development of the deep bias in the Atlantic Ocean: the northeastern North Atlantic and the region adjacent to the Strait of Gibraltar. Furthermore, the Southern Ocean density structure is equally improved with locally explicitly resolved eddies compared to parameterized eddies. Part of the bias reduction can be traced back towards improved surface biases over outcrop-ping regions, which are in contact with deeper ocean layers along isopycnal surfaces. Our prototype simulations provide guidance for the optimal choice of ocean grids for AWI-CM to be used in the final runs for phase 6 of CMIP (CMIP6) and for the related flagship simulations in the High Resolution Model Intercomparison Project (HighResMIP). Quite remarkably, retaining resolution only in areas of high eddy activity along with excellent scalability characteristics of the unstructured-mesh sea ice–ocean model enables us to per-form the multi-centennial climate simulations needed in a CMIP context at (locally) eddy-resolving resolution with a throughput of 5–6 simulated years per day.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev , info:eu-repo/semantics/article
    Format: application/pdf
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  • 6
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    The Finite Element Sea Ice-Ocean Model (FESOM) v.1.4: formulation of an ocean general circulation model (2014)
    Copernicus Publications
    In:  EPIC3Geoscientific Model Development, Copernicus Publications, 7(2), pp. 663-693, ISSN: 1991-959X
    Details
    Publication Date: 2014-05-26
    Description: The Finite Element Sea Ice-Ocean Model (FESOM) is the first global ocean general circulation model based on unstructured-mesh methods that has been developed for the purpose of climate research. The advantage of unstructured-mesh models is their flexible multi-resolution modelling functionality. In this study, an overview of the main features of FESOM will be given; based on sensitivity experiments a number of specific parameter choices will be explained; and directions of future developments will be outlined. It is argued that FESOM is sufficiently mature to explore the benefits of multi-resolution climate modelling and that its applications will provide information useful for the advancement of climate modelling on unstructured meshes.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 7
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    Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso (2023)
    Copernicus Publications
    In:  EPIC3Geoscientific Model Development, Copernicus Publications, 16(17), pp. 5153-5178, ISSN: 1991-959X
    Details
    Publication Date: 2023-09-19
    Description: Numerical simulations employing prognostic sta- ble water isotopes can not only facilitate our understanding of hydrological processes and climate change but also al- low for a direct comparison between isotope signals obtained from models and various archives. In the current work, we describe the performance and explore the potential of a new version of the Earth system model AWI-ESM (Alfred We- gener Institute Earth System Model), labeled AWI-ESM-2.1- wiso, in which we incorporated three isotope tracers into all relevant components of the water cycle. We present here the results of pre-industrial (PI) and mid-Holocene (MH) simula- tions. The model reproduces the observed PI isotope compo- sitions in both precipitation and seawater well and captures their major differences from the MH conditions. The sim- ulated relationship between the isotope composition in precipitation (d18Op) and surface air temperature is very similar between the PI and MH conditions, and it is largely consis- tent with modern observations despite some regional model biases. The ratio of the MH–PI difference in δ18Op to the MH–PI difference in surface air temperature is comparable to proxy records over Greenland and Antarctica only when summertime air temperature is considered. An amount effect is evident over the North African monsoon domain, where a negative correlation between δ18Op and the amount of pre- cipitation is simulated. As an example of model applications, we studied the onset and withdrawal date of the MH West African summer monsoon (WASM) using daily variables. We find that defining the WASM onset based on precipitation alone may yield erroneous results due to the substantial daily variations in precipitation, which may obscure the dis- tinction between pre-monsoon and monsoon seasons. Com- bining precipitation and isotope indicators, we suggest in this work a novel method for identifying the commencement of the WASM. Moreover, we do not find an obvious difference between the MH and PI periods in terms of the mean onset of the WASM. However, an advancement in the WASM with- drawal is found in the MH compared to the PI period due to an earlier decline in insolation over the northern location of Intertropical Convergence Zone (ITCZ).
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
    Location Call Number Limitation Availability
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  • 8
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    Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso (2023)
    Copernicus Publications
    Details
    Publication Date: 2024-02-07
    Description: Numerical simulations employing prognostic stable water isotopes can not only facilitate our understanding of hydrological processes and climate change but also allow for a direct comparison between isotope signals obtained from models and various archives. In the current work, we describe the performance and explore the potential of a new version of the Earth system model AWI-ESM (Alfred Wegener Institute Earth System Model), labeled AWI-ESM-2.1-wiso, in which we incorporated three isotope tracers into all relevant components of the water cycle. We present here the results of pre-industrial (PI) and mid-Holocene (MH) simulations. The model reproduces the observed PI isotope compositions in both precipitation and seawater well and captures their major differences from the MH conditions. The simulated relationship between the isotope composition in precipitation (δ18Op) and surface air temperature is very similar between the PI and MH conditions, and it is largely consistent with modern observations despite some regional model biases. The ratio of the MH–PI difference in δ18Op to the MH–PI difference in surface air temperature is comparable to proxy records over Greenland and Antarctica only when summertime air temperature is considered. An amount effect is evident over the North African monsoon domain, where a negative correlation between δ18Op and the amount of precipitation is simulated. As an example of model applications, we studied the onset and withdrawal date of the MH West African summer monsoon (WASM) using daily variables. We find that defining the WASM onset based on precipitation alone may yield erroneous results due to the substantial daily variations in precipitation, which may obscure the distinction between pre-monsoon and monsoon seasons. Combining precipitation and isotope indicators, we suggest in this work a novel method for identifying the commencement of the WASM. Moreover, we do not find an obvious difference between the MH and PI periods in terms of the mean onset of the WASM. However, an advancement in the WASM withdrawal is found in the MH compared to the PI period due to an earlier decline in insolation over the northern location of Intertropical Convergence Zone (ITCZ).
    Type: Article , PeerReviewed
    Format: text
    Format: text
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    OceanRep: Article in a Scientific Journal - peer-reviewed
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