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  • American Chemical Society (ACS)  (30)
  • Elsevier  (10)
  • 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
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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
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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
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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
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    DATA PANGAEA
  • 5
    Electronic Resource
    Electronic Resource
    Application of satellite imagery to monitoring of sea ice motion in North China Sea
    Amsterdam : Elsevier
    Advances in Space Research 12 (1992), S. 163-167 
    Details
    ISSN: 0273-1177
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Type of Medium: Electronic Resource
    URL: http://linkinghub.elsevier.com/retrieve/pii/0273-1177(92)90212-G
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    Paper (German National Licenses)
    Fulltext
  • 6
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    Simulating the Agulhas system in global ocean models – nesting vs. multi-resolution unstructured meshes (2018)
    Elsevier
    In:  Ocean Modelling, 121 . pp. 117-131.
    Details
    Publication Date: 2021-02-08
    Description: Highlights: • Comparison of global NEMO and FESOM configurations with emphasis on the Agulhas system. • Both models simulate a reasonable and comparable large-scale circulation. • Both models have individual strengths and weaknesses to match the observations of the WBC system. • The numerical cost of FESOM is twice the one of NEMO. Abstract: Many questions in ocean and climate modelling require the combined use of high resolution, global coverage and multi-decadal integration length. For this combination, even modern resources limit the use of traditional structured-mesh grids. Here we compare two approaches: A high-resolution grid nested into a global model at coarser resolution (NEMO with AGRIF) and an unstructured-mesh grid (FESOM) which allows to variably enhance resolution where desired. The Agulhas system around South Africa is used as a testcase, providing an energetic interplay of a strong western boundary current and mesoscale dynamics. Its open setting into the horizontal and global overturning circulations also requires global coverage. Both model configurations simulate a reasonable large-scale circulation. Distribution and temporal variability of the wind-driven circulation are quite comparable due to the same atmospheric forcing. However, the overturning circulation differs, owing each model's ability to represent formation and spreading of deep water masses. In terms of regional, high-resolution dynamics, all elements of the Agulhas system are well represented. Owing to the strong nonlinearity in the system, Agulhas Current transports of both configurations and in comparison with observations differ in strength and temporal variability. Similar decadal trends in Agulhas Current transport and Agulhas leakage are linked to the trends in wind forcing. Although the number of 3D wet grid points used in FESOM is similar to that in the nested NEMO, FESOM uses about two times the number of CPUs to obtain the same model throughput (in terms of simulated model years per day). This is feasible due to the high scalability of the FESOM code.
    Type: Article , PeerReviewed
    Format: text
    Format: text
    DOI: 10.1016/j.ocemod.2017.12.002
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 7
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    An assessment of Antarctic Circumpolar Current and Southern Ocean meridional overturning circulation during 1958–2007 in a suite of interannual CORE-II simulations (2015)
    Elsevier
    In:  Ocean Modelling, 93 . pp. 84-120.
    Details
    Publication Date: 2019-09-23
    Description: Highlights: • We focus on ACC and Southern Ocean MOC during 1958–2007 in 17 CORE-II forced models. • Most CORE-II simulations are close to eddy saturation. • Most CORE-II simulations are far from showing signs of eddy compensation. • Constant in time or space k results in poor representation of mesoscale eddy effects. • MOC has larger sensitivity than ACC transport even in eddy saturated state. Abstract: In the framework of the second phase of the Coordinated Ocean-ice Reference Experiments (CORE-II), we present an analysis of the representation of the Antarctic Circumpolar Current (ACC) and Southern Ocean meridional overturning circulation (MOC) in a suite of seventeen global ocean–sea ice models. We focus on the mean, variability and trends of both the ACC and MOC over the 1958–2007 period, and discuss their relationship with the surface forcing. We aim to quantify the degree of eddy saturation and eddy compensation in the models participating in CORE-II, and compare our results with available observations, previous fine-resolution numerical studies and theoretical constraints. Most models show weak ACC transport sensitivity to changes in forcing during the past five decades, and they can be considered to be in an eddy saturated regime. Larger contrasts arise when considering MOC trends, with a majority of models exhibiting significant strengthening of the MOC during the late 20th and early 21st century. Only a few models show a relatively small sensitivity to forcing changes, responding with an intensified eddy-induced circulation that provides some degree of eddy compensation, while still showing considerable decadal trends. Both ACC and MOC interannual variabilities are largely controlled by the Southern Annular Mode (SAM). Based on these results, models are clustered into two groups. Models with constant or two-dimensional (horizontal) specification of the eddy-induced advection coefficient κ show larger ocean interior decadal trends, larger ACC transport decadal trends and no eddy compensation in the MOC. Eddy-permitting models or models with a three-dimensional time varying κ show smaller changes in isopycnal slopes and associated ACC trends, and partial eddy compensation. As previously argued, a constant in time or space κ is responsible for a poor representation of mesoscale eddy effects and cannot properly simulate the sensitivity of the ACC and MOC to changing surface forcing. Evidence is given for a larger sensitivity of the MOC as compared to the ACC transport, even when approaching eddy saturation. Future process studies designed for disentangling the role of momentum and buoyancy forcing in driving the ACC and MOC are proposed.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1016/j.ocemod.2015.07.009
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 8
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    An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part III: Hydrography and fluxes (2016)
    Elsevier
    In:  Ocean Modelling, 100 . pp. 141-161.
    Details
    Publication Date: 2019-06-28
    Description: Highlights: • We compare the simulated Arctic Ocean in 15 global ocean–sea ice models. • There is a large spread in temperature bias in the Arctic Ocean between the models. • Warm bias models have a strong temperature anomaly of inflow of Atlantic Water. • Dense outflows formed on Arctic shelves are not captured accurately in the models. In this paper we compare the simulated Arctic Ocean in 15 global ocean-sea ice models in the framework of the Coordinated Ocean-ice Reference Experiments, phase II (CORE-II). Most of these models are the ocean and sea-ice components of the coupled climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments. We mainly focus on the hydrography of the Arctic interior, the state of Atlantic Water layer and heat and volume transports at the gateways of the Davis Strait, the Bering Strait, the Fram Strait and the Barents Sea Opening. We found that there is a large spread in temperature in the Arctic Ocean between the models, and generally large differences compared to the observed temperature at intermediate depths. Warm bias models have a strong temperature anomaly of inflow of the Atlantic Water entering the Arctic Ocean through the Fram Strait. Another process that is not represented accurately in the CORE-II models is the formation of cold and dense water, originating on the eastern shelves. In the cold bias models, excessive cold water forms in the Barents Sea and spreads into the Arctic Ocean through the St. Anna Through. There is a large spread in the simulated mean heat and volume transports through the Fram Strait and the Barents Sea Opening. The models agree more on the decadal variability, to a large degree dictated by the common atmospheric forcing. We conclude that the CORE-II model study helps us to understand the crucial biases in the Arctic Ocean. The current coarse resolution state-of-the-art ocean models need to be improved in accurate representation of the Atlantic Water inflow into the Arctic and density currents coming from the shelves.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
    DOI: 10.1016/j.ocemod.2016.02.004
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 9
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    North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part I: Mean states (2014)
    Elsevier
    In:  Ocean Modelling, 73 . pp. 76-107.
    Details
    Publication Date: 2019-09-23
    Description: Highlights: • Phase II of the Coordinated Ocean-ice Reference Experiments (CORE-II) is introduced. • Solutions from CORE-II simulations from eighteen participating models are presented. • Mean states in the North Atlantic with a focus on AMOC are examined. • The North Atlantic solutions differ substantially among the models. • Many factors, including parameterization choices, contribute to these differences. Simulation characteristics from eighteen global ocean–sea-ice coupled models are presented with a focus on the mean Atlantic meridional overturning circulation (AMOC) and other related fields in the North Atlantic. These experiments use inter-annually varying atmospheric forcing data sets for the 60-year period from 1948 to 2007 and are performed as contributions to the second phase of the Coordinated Ocean-ice Reference Experiments (CORE-II). The protocol for conducting such CORE-II experiments is summarized. Despite using the same atmospheric forcing, the solutions show significant differences. As most models also differ from available observations, biases in the Labrador Sea region in upper-ocean potential temperature and salinity distributions, mixed layer depths, and sea-ice cover are identified as contributors to differences in AMOC. These differences in the solutions do not suggest an obvious grouping of the models based on their ocean model lineage, their vertical coordinate representations, or surface salinity restoring strengths. Thus, the solution differences among the models are attributed primarily to use of different subgrid scale parameterizations and parameter choices as well as to differences in vertical and horizontal grid resolutions in the ocean models. Use of a wide variety of sea-ice models with diverse snow and sea-ice albedo treatments also contributes to these differences. Based on the diagnostics considered, the majority of the models appear suitable for use in studies involving the North Atlantic, but some models require dedicated development effort.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1016/j.ocemod.2013.10.005
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 10
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    An assessment of global and regional sea level for years 1993–2007 in a suite of interannual CORE-II simulations (2014)
    Elsevier
    In:  Ocean Modelling, 78 . pp. 35-89.
    Details
    Publication Date: 2019-09-23
    Description: Highlights: • Global mean sea level simulated in interannual CORE simulations. • Regional sea level patterns simulated in interannual CORE simulations. • Theoretical foundation for analysis of global mean sea level and regional patterns. Abstract: We provide an assessment of sea level simulated in a suite of global ocean-sea ice models using the interannual CORE atmospheric state to determine surface ocean boundary buoyancy and momentum fluxes. These CORE-II simulations are compared amongst themselves as well as to observation-based estimates. We focus on the final 15 years of the simulations (1993–2007), as this is a period where the CORE-II atmospheric state is well sampled, and it allows us to compare sea level related fields to both satellite and in situ analyses. The ensemble mean of the CORE-II simulations broadly agree with various global and regional observation-based analyses during this period, though with the global mean thermosteric sea level rise biased low relative to observation-based analyses. The simulations reveal a positive trend in dynamic sea level in the west Pacific and negative trend in the east, with this trend arising from wind shifts and regional changes in upper 700 m ocean heat content. The models also exhibit a thermosteric sea level rise in the subpolar North Atlantic associated with a transition around 1995/1996 of the North Atlantic Oscillation to its negative phase, and the advection of warm subtropical waters into the subpolar gyre. Sea level trends are predominantly associated with steric trends, with thermosteric effects generally far larger than halosteric effects, except in the Arctic and North Atlantic. There is a general anti-correlation between thermosteric and halosteric effects for much of the World Ocean, associated with density compensated changes.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
    DOI: 10.1016/j.ocemod.2014.03.004
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    OceanRep: Article in a Scientific Journal - peer-reviewed
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