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  • 1
    Online Resource
    Online Resource
    Barents Sea upstream events impact the properties of Atlantic water inflow into the Arctic Ocean : evidence from 2005 to 2006 downstream observations (2009)
    Associated volumes
    Details Availability
    In: Deep-sea research / 1, Amsterdam [u.a.] : Elsevier Science, 1993, 56(2009), 4, Seite 513-527, 1879-0119
    In: volume:56
    In: year:2009
    In: number:4
    In: pages:513-527
    Description / Table of Contents: Inflow of Atlantic water (AW) from Fram Strait and the Barents Sea into the Arctic Ocean conditions the intermediate (100-1000 m) waters of the Arctic Ocean Eurasian margins. While over the Siberian margin the Fram Strait AW branch (FSBW) has exhibited continuous dramatic warming beginning in 2004, the tendency of the Barents Sea AW branch (BSBW) has remained poorly known. Here we document the contrary cooling tendency of the BSBW through the analysis of observational data collected from the icebreaker Kapitan Dranitsyn over the continental slope of the Eurasian Basin in 2005 and 2006. The CTD data from the R.V. Polarstern cruise in 1995 were used as a reference point for evaluating external atmospheric and sea-ice forcing and oxygen isotope analysis. Our data show that in 2006 the BSBW core was saltier (by ~0.037), cooler (~0.41 ʿC), denser (by ~0.04 kg/m3), deeper (by 150-200 m), and relatively better ventilated (by 78 mymol/kg of dissolved oxygen, or by 1.11.7% of saturation) compared with 2005. We hypothesize that the shift of the meridional wind from off-shore to on-shore direction during the BSBW translation through the Barents and northern Kara seas results in longer surface residence time for the BSBW sampled in 2006 compared with samples from 2005. The cooler, more saline, and better-ventilated BSBW sampled in 2006 may result from longer upstream translation through the Barents and northern Kara seas where the BSBW was modified by sea-ice formation and interaction with atmosphere. The data for stable oxygen isotopes from 1995 and 2006 reveals amplified brine modification of the BSBW core sampled downstream in 2006, which supports the assumption of an increased upstream residence time as indicated by wind patterns and dissolved oxygen values.
    Type of Medium: Online Resource
    Pages: graph. Darst
    ISSN: 1879-0119
    DOI: 10.1016/j.dsr.2008.11.005
    Language: English
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  • 2
    Online Resource
    Online Resource
    Seasonal modification of the Arctic Ocean intermediate water layer off the eastern Laptev Sea continental shelf break (2009)
    Associated volumes
    Details Availability
    In: Journal of geophysical research. C, Oceans, Hoboken, NJ : Wiley, 1978, 114(2009), 6, 2169-9291
    In: volume:114
    In: year:2009
    In: number:6
    In: extent:19
    Description / Table of Contents: Through the analysis of observational mooring data collected at the northeastern Laptev Sea continental slope in 2004-2007, we document a hydrographic seasonal signal in the intermediate Atlantic Water (AW) layer, with generally higher temperature and salinity from December-January to May-July and lower values from May-July to December-January. At the mooring position, this seasonal signal dominates, contributing up to 75% of the total variance. Our data suggest that the entire AW layer down to at least 840 m is affected by seasonal cycling, although the strength of the seasonal signal in temperature and salinity reduces from 260 m (±0.25ʿC and ±0.025 psu) to 840 m (±0.05ʿC and ±0.005 psu). The seasonal velocity signal is substantially weaker, strongly masked by high-frequency variability, and lags the thermohaline cycle by 45-75 days. We hypothesize that our mooring record shows a time history of the along-margin propagation of the AW seasonal signal carried downstream by the AW boundary current. Our analysis suggests that the seasonal signal in the Fram Strait Branch of AW (FSBW) at 260 m is predominantly translated from Fram Strait, while the seasonality in the Barents Sea branch of AW (BSBW) domain (at 840 m) is attributed instead to the seasonal signal input from the Barents Sea. However, the characteristic signature of the BSBW seasonal dynamics observed through the entire AW layer leads us to speculate that BSBW also plays a role in seasonally modifying the properties of the FSBW.
    Type of Medium: Online Resource
    Pages: 19 , graph. Darst
    ISSN: 2169-9291
    URL: http://onlinelibrary.wiley.com/enhanced/doi/10.1029/2008JC005229/
    DOI: 10.1029/2008JC005229
    Language: English
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  • 3
    Online Resource
    Online Resource
    Variability of Arctic sea ice (2010)
    Hamburg : Max-Planck-Inst. für Meteorologie
    Details Availability
    Keywords: Hochschulschrift ; Forschungsbericht ; Arktis ; Meereis ; Ausdehnung ; Temperaturschwankung ; Klimavariation
    Type of Medium: Online Resource
    Pages: Online-Ressource (143 S., 25,5 MB) , graph. Darst., Kt
    Series Statement: Berichte zur Erdsystemforschung 79
    URL: http://www.mpimet.mpg.de/fileadmin/publikationen/Reports/WEB_BzE_79.pdf
    URL: https://edocs.tib.eu/files/e01fn13/636408760.pdf
    URL: http://epub.sub.uni-hamburg.de/epub/volltexte/2015/41832/
    URL: https://edocs.tib.eu/files/e01fn13/636408760l.pdf
    Language: English
    Note: Zugl.: Hamburg, Univ., Diss., 2010 , Systemvoraussetzungen: Acrobat reader.
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  • 4
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    Sea Ice Rheology Experiment (SIREx): 2. Evaluating Linear Kinematic Features in High‐Resolution Sea Ice Simulations (2022)
    Details
    Publication Date: 2022-09-22
    Description: Simulating sea ice drift and deformation in the Arctic Ocean is still a challenge because of the multiscale interaction of sea ice floes that compose the Arctic Sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). In SIREx, skill metrics are designed to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) to provide guidance for modeling small‐scale deformation. These LKFs are narrow bands of localized deformation that can be observed in satellite images and also form in high resolution sea ice simulations. In this contribution, spatial and temporal properties of LKFs are assessed in 36 simulations of state‐of‐the‐art sea ice models and compared to deformation features derived from the RADARSAT Geophysical Processor System. All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs and LKF lifetimes pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric boundary condition affect simulated LKFs as much as the model's sea ice rheology and numerics. Only in very high resolution simulations (≤2 km) the concentration and thickness anomalies along LKFs are large enough to affect air‐ice‐ocean interaction processes.
    Description: Plain Language Summary: Winds and ocean currents continuously move and deform the sea ice cover of the Arctic Ocean. The deformation eventually breaks an initially closed ice cover into many individual floes, piles up floes, and creates open water. The distribution of ice floes and open water between them is important for climate research, because ice reflects more light and energy back to the atmosphere than open water, so that less ice and more open water leads to warmer oceans. Current climate models cannot simulate sea ice as individual floes. Instead, a variety of methods is used to represent the movement and deformation of the sea ice cover. The Sea Ice Rheology Experiment (SIREx) compares these different methods and assesses the deformation of sea ice in 36 numerical simulations. We identify and track deformation features in the ice cover, which are distinct narrow areas where the ice is breaking or piling up. Comparing specific spatial and temporal properties of these features, for example, the different amounts of fractured ice in specific regions, or the duration of individual deformation events, to satellite observations provides information about the realism of the simulations. From this comparison, we can learn how to improve sea ice models for more realistic simulations of sea ice deformation.
    Description: Key Points: All models simulate linear kinematic features (LKFs), but none accurately reproduces all LKF statistics. Resolved LKFs are affected strongest by spatial and temporal resolution of model grid and atmospheric forcing and rheology. Accurate scaling of deformation rates is a proxy only for realistic LKF numbers but not for any other LKF static.
    Description: DOE
    Description: HYCOM NOPP
    Description: Innovation Fund Denmark and the Horizon 2020 Framework Programme of the European Union
    Description: National centre for Climate Research, SALIENSEAS, ERA4CS
    Description: German Helmholtz Climate Initiative REKLIM (Regional Climate Change)
    Description: Gouvernement du Canada, Natural Sciences and Engineering Research Council of Canada (NSERC) http://dx.doi.org/10.13039/501100000038
    Description: Environment and Climate Change Canada Grants & Contributions program
    Description: Office of Naval Research Arctic and Global Prediction program
    Description: U.S. Department of Energy Regional and Global Model Analysis program
    Description: National Science Foundation Arctic System Science program
    Description: Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659
    Description: https://zenodo.org/communities/sirex
    Keywords: ddc:550.285
    Language: English
    Type: doc-type:article
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    CITATION GEO-LEO
  • 5
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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
  • 6
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    Cyclone Activity in the Arctic From an Ensemble of Regional Climate Models (Arctic CORDEX) (2018)
    AGU (American Geophysical Union) | Wiley
    In:  Journal of Geophysical Research: Atmospheres, 123 (5). pp. 2537-2554.
    Details
    Publication Date: 2021-01-08
    Description: The ability of state‐of‐the‐art regional climate models to simulate cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic‐CORDEX initiative. Some models employ large‐scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble are compared with the results forced by four reanalyses (ERA‐Interim, National Centers for Environmental Prediction‐Climate Forecast System Reanalysis, National Aeronautics and Space Administration‐Modern‐Era Retrospective analysis for Research and Applications Version 2, and Japan Meteorological Agency‐Japanese 55‐year reanalysis) in winter and summer for 1981–2010 period. In addition, we compare cyclone statistics between ERA‐Interim and the Arctic System Reanalysis reanalyses for 2000–2010. Biases in cyclone frequency, intensity, and size over the Arctic are also quantified. Variations in cyclone frequency across the models are partly attributed to the differences in cyclone frequency over land. The variations across the models are largest for small and shallow cyclones for both seasons. A connection between biases in the zonal wind at 200 hPa and cyclone characteristics is found for both seasons. Most models underestimate zonal wind speed in both seasons, which likely leads to underestimation of cyclone mean depth and deep cyclone frequency in the Arctic. In general, the regional climate models are able to represent the spatial distribution of cyclone characteristics in the Arctic but models that employ large‐scale spectral nudging show a better agreement with ERA‐Interim reanalysis than the rest of the models. Trends also exhibit the benefits of nudging. Models with spectral nudging are able to reproduce the cyclone trends, whereas most of the nonnudged models fail to do so. However, the cyclone characteristics and trends are sensitive to the choice of nudged variables.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1002/2017JD027703
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 7
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    Barents Sea upstream events impact the properties of Atlantic water inflow into the Arctic Ocean: evidence from 2005-2006 downstream observations (2009)
    Elsevier
    In:  Deep Sea Research Part I: Oceanographic Research Papers, 56 (4). pp. 513-527.
    Details
    Publication Date: 2019-12-31
    Description: Inflow of Atlantic water (AW) from Fram Strait and the Barents Sea into the Arctic Ocean conditions the intermediate (100–1000 m) waters of the Arctic Ocean Eurasian margins. While over the Siberian margin the Fram Strait AW branch (FSBW) has exhibited continuous dramatic warming beginning in 2004, the tendency of the Barents Sea AW branch (BSBW) has remained poorly known. Here we document the contrary cooling tendency of the BSBW through the analysis of observational data collected from the icebreaker Kapitan Dranitsyn over the continental slope of the Eurasian Basin in 2005 and 2006. The CTD data from the R.V. Polarstern cruise in 1995 were used as a reference point for evaluating external atmospheric and sea-ice forcing and oxygen isotope analysis. Our data show that in 2006 the BSBW core was saltier (by ∼0.037), cooler (by ∼0.41 °C), denser (by ∼0.04 kg/m3), deeper (by 150–200 m), and relatively better ventilated (by 7–8 μmol/kg of dissolved oxygen, or by 1.1–1.7% of saturation) compared with 2005. We hypothesize that the shift of the meridional wind from off-shore to on-shore direction during the BSBW translation through the Barents and northern Kara seas results in longer surface residence time for the BSBW sampled in 2006 compared with samples from 2005. The cooler, more saline, and better-ventilated BSBW sampled in 2006 may result from longer upstream translation through the Barents and northern Kara seas where the BSBW was modified by sea-ice formation and interaction with atmosphere. The data for stable oxygen isotopes from 1995 and 2006 reveals amplified brine modification of the BSBW core sampled downstream in 2006, which supports the assumption of an increased upstream residence time as indicated by wind patterns and dissolved oxygen values.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1016/j.dsr.2008.11.005
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 8
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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)
    Copernicus Publications (EGU)
    Details
    Publication Date: 2023-02-08
    Description: 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 processlevel 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.
    Type: Article , PeerReviewed
    Format: text
    Format: text
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 9
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    The Atlantic Meridional Overturning Circulation in High‐Resolution Models (2020)
    AGU (American Geophysical Union)
    Details
    Publication Date: 2023-02-08
    Description: The Atlantic meridional overturning circulation (AMOC) represents the zonally integrated stream function of meridional volume transport in the Atlantic Basin. The AMOC plays an important role in transporting heat meridionally in the climate system. Observations suggest a heat transport by the AMOC of 1.3 PW at 26°N—a latitude which is close to where the Atlantic northward heat transport is thought to reach its maximum. This shapes the climate of the North Atlantic region as we know it today. In recent years there has been significant progress both in our ability to observe the AMOC in nature and to simulate it in numerical models. Most previous modeling investigations of the AMOC and its impact on climate have relied on models with horizontal resolution that does not resolve ocean mesoscale eddies and the dynamics of the Gulf Stream/North Atlantic Current system. As a result of recent increases in computing power, models are now being run that are able to represent mesoscale ocean dynamics and the circulation features that rely on them. The aim of this review is to describe new insights into the AMOC provided by high-resolution models. Furthermore, we will describe how high-resolution model simulations can help resolve outstanding challenges in our understanding of the AMOC.
    Type: Article , PeerReviewed
    Format: text
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 10
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    Future projections of cyclone activity in the Arctic for the 21st century from regional climate models (Arctic-CORDEX) (2019)
    Elsevier
    In:  Global and Planetary Change, 182 (Article number 103005).
    Details
    Publication Date: 2021-01-08
    Description: Changes in the characteristics of cyclone activity (frequency, depth and size) in the Arctic are analyzed based on simulations with state-of-the-art regional climate models (RCMs) from the Arctic-CORDEX initiative and global climate models (GCMs) from CMIP5 under the Representative Concentration Pathway (RCP) 8.5 scenario. Most of RCMs show an increase of cyclone frequency in winter (DJF) and a decrease in summer (JJA) to the end of the 21st century. However, in one half of the RCMs, cyclones become weaker and substantially smaller in winter and deeper and larger in summer. RCMs as well as GCMs show an increase of cyclone frequency over the Baffin Bay, Barents Sea, north of Greenland, Canadian Archipelago, and a decrease over the Nordic Seas, Kara and Beaufort Seas and over the sub-arctic continental regions in winter. In summer, the models simulate an increase of cyclone frequency over the Central Arctic and Greenland Sea and a decrease over the Norwegian and Kara Seas by the end of the 21st century. The decrease is also found over the high-latitude continental areas, in particular, over east Siberia and Alaska. The sensitivity of the RCMs' projections to the boundary conditions and model physics is estimated. In general, different lateral boundary conditions from the GCMs have larger effects on the simulated RCM projections than the differences in RCMs' setup and/or physics.
    Type: Article , PeerReviewed
    DOI: 10.1016/j.gloplacha.2019.103005
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    OceanRep: Article in a Scientific Journal - peer-reviewed
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