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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)

Wekerle, Claudia ; Wang, Qiang ; von Appen, Wilken-Jon ; [weitere]

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

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Publikationsdatum: 2023-03-16

Beschreibung: 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.

Schlagwort(e): 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

Materialart: Dataset

Format: text/tab-separated-values, 100 data points

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FESOM Arctic Ocean sea ice concentration and thickness 1995-2014, links to movies in mp4 format (2016)

Wang, Qiang ; Böttinger, Michael ; Danilov, Sergey ; [weitere]

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

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Publikationsdatum: 2023-03-16

Beschreibung: 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).

Schlagwort(e): Arctic; DATE/TIME; File content; File format; File size; pan-Arctic; Uniform resource locator/link to file

Materialart: Dataset

Format: text/tab-separated-values, 8 data points

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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)

Scholz, Patrick ; Lohmann, Gerrit ; Wang, Qiang ; [weitere]

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

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Publikationsdatum: 2023-01-13

Beschreibung: 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.

Schlagwort(e): File format; File name; File size; Uniform resource locator/link to file

Materialart: Dataset

Format: text/tab-separated-values, 32 data points

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Eddy kinetic energy in the Arctic Ocean from moored velocity observations (2022)

von Appen, Wilken-Jon ; Baumann, Till ; Janout, Markus A ; [weitere]

PANGAEA

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Publikationsdatum: 2024-07-01

Beschreibung: 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.

Schlagwort(e): 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

Materialart: Dataset

Format: text/tab-separated-values, 4806 data points

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Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations with AWI-CM1.0 (2019)

Rackow, Thomas ; Sein, Dmitry V. ; Semmler, Tido ; [weitere]

Copernicus Publications

In: EPIC3Geoscientific Model Development, Copernicus Publications, 12(7), pp. 2635-2656, ISSN: 1991-9603

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Publikationsdatum: 2019-08-19

Beschreibung: 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

Materialart: Article , isiRev , info:eu-repo/semantics/article

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Properties and dynamics of mesoscale eddies in Fram Strait from a comparison between two high-resolution ocean–sea ice models (2020)

Wekerle, Claudia ; Hattermann, Tore ; Wang, Qiang ; [weitere]

COPERNICUS GESELLSCHAFT MBH

In: EPIC3Ocean Science, COPERNICUS GESELLSCHAFT MBH, 16, pp. 1225-1246, ISSN: 1812-0784

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Publikationsdatum: 2021-07-01

Repository-Name: EPIC Alfred Wegener Institut

Materialart: Article , isiRev

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Impact of horizontal resolution on global ocean–sea ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2) (2020)

Chassignet, Eric P. ; Yeager, Stephen G. ; Fox-Kemper, Baylor ; [weitere]

COPERNICUS GESELLSCHAFT MBH

In: EPIC3Geoscientific Model Development, COPERNICUS GESELLSCHAFT MBH, 13(9), pp. 4595-4637, ISSN: 1991-9603

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Publikationsdatum: 2020-10-05

Beschreibung: This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958–2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (∼ 1∘) to eddy-resolving (∼ 0.1∘) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar, and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and the Antarctic Circumpolar Current – are significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve” most of these features, the improvements in temperature and salinity are inconsistent among the different model families, and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.

Repository-Name: EPIC Alfred Wegener Institut

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Assessment of the Finite-volumE Sea ice-Ocean Model (FESOM2.0) - Part 1: Description of selected key model elements and comparison to its predecessor version (2019)

Scholz, Patrick ; Sidorenko, Dmitry ; Gurses, Ozgur ; [weitere]

COPERNICUS GESELLSCHAFT MBH

In: EPIC3Geoscientific Model Development, COPERNICUS GESELLSCHAFT MBH, 12(11), pp. 4875-4899, ISSN: 1991-9603

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Publikationsdatum: 2020-05-15

Beschreibung: The evaluation and model element description of the second version of the unstructured-mesh Finite-volumE Sea ice-Ocean Model (FESOM2.0) are presented. The new version of the model takes advantage of the finite-volume approach, whereas its predecessor version, FESOM1.4 was based on the finite-element approach. The model sensitivity to arbitrary Lagrangian–Eulerian (ALE) linear and nonlinear free-surface formulation, Gent–McWilliams eddy parameterization, isoneutral Redi diffusion and different vertical mixing schemes is documented. The hydrographic biases, large-scale circulation, numerical performance and scalability of FESOM2.0 are compared with its predecessor, FESOM1.4. FESOM2.0 shows biases with a magnitude comparable to FESOM1.4 and simulates a more realistic Atlantic meridional overturning circulation (AMOC). Compared to its predecessor, FESOM2.0 provides clearly defined fluxes and a 3 times higher throughput in terms of simulated years per day (SYPD). It is thus the first mature global unstructured-mesh ocean model with computational efficiency comparable to state-of-the-art structured-mesh ocean models. Other key elements of the model and new development will be described in follow-up papers.

Repository-Name: EPIC Alfred Wegener Institut

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Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2) (2020)

Tsujino, Hiroyuki ; Urakawa, L. Shogo ; Griffies, Stephen M. ; [weitere]

COPERNICUS GESELLSCHAFT MBH

In: EPIC3Geoscientific Model Development, COPERNICUS GESELLSCHAFT MBH, 13(8), pp. 3643-3708, ISSN: 1991-9603

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Publikationsdatum: 2020-10-19

Beschreibung: We present a new framework for global ocean–sea-ice model simulations based on phase 2 of the Ocean Model Intercomparison Project (OMIP-2), making use of the surface dataset based on the Japanese 55-year atmospheric reanalysis for driving ocean–sea-ice models (JRA55-do). We motivate the use of OMIP-2 over the framework for the first phase of OMIP (OMIP-1), previously referred to as the Coordinated Ocean–ice Reference Experiments (COREs), via the evaluation of OMIP-1 and OMIP-2 simulations from 11 state-of-the-science global ocean–sea-ice models. In the present evaluation, multi-model ensemble means and spreads are calculated separately for the OMIP-1 and OMIP-2 simulations and overall performance is assessed considering metrics commonly used by ocean modelers. Both OMIP-1 and OMIP-2 multi-model ensemble ranges capture observations in more than 80 % of the time and region for most metrics, with the multi-model ensemble spread greatly exceeding the difference between the means of the two datasets. Many features, including some climatologically relevant ocean circulation indices, are very similar between OMIP-1 and OMIP-2 simulations, and yet we could also identify key qualitative improvements in transitioning from OMIP-1 to OMIP-2. For example, the sea surface temperatures of the OMIP-2 simulations reproduce the observed global warming during the 1980s and 1990s, as well as the warming slowdown in the 2000s and the more recent accelerated warming, which were absent in OMIP-1, noting that the last feature is part of the design of OMIP-2 because OMIP-1 forcing stopped in 2009. A negative bias in the sea-ice concentration in summer of both hemispheres in OMIP-1 is significantly reduced in OMIP-2. The overall reproducibility of both seasonal and interannual variations in sea surface temperature and sea surface height (dynamic sea level) is improved in OMIP-2. These improvements represent a new capability of the OMIP-2 framework for evaluating process-level responses using simulation results. Regarding the sensitivity of individual models to the change in forcing, the models show well-ordered responses for the metrics that are directly forced, while they show less organized responses for those that require complex model adjustments. Many of the remaining common model biases may be attributed either to errors in representing important processes in ocean–sea-ice models, some of which are expected to be reduced by using finer horizontal and/or vertical resolutions, or to shared biases and limitations in the atmospheric forcing. In particular, further efforts are warranted to resolve remaining issues in OMIP-2 such as the warm bias in the upper layer, the mismatch between the observed and simulated variability of heat content and thermosteric sea level before 1990s, and the erroneous representation of deep and bottom water formations and circulations. We suggest that such problems can be resolved through collaboration between those developing models (including parameterizations) and forcing datasets. Overall, the present assessment justifies our recommendation that future model development and analysis studies use the OMIP-2 framework.

Repository-Name: EPIC Alfred Wegener Institut

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