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  • 1
    Publication Date: 2023-01-30
    Description: These data are outputs from climate simulations carried out using HadGEM3-GC3.1 described in the associated journal article. The three simulations that constitute the STANDARD ensemble described in the article are u-as371, u-as372, u-as373, and the three simulations that comprise the GREASE ensemble in the article are labelled here as u-bj941, u-bn121 and u-bn122. Outputs from the sea ice and ocean components of the model are archived here separately (labelled '_ice' and '_ocean'). These data were produced by University of Otago, New Zealand, in collaboration with the UK Met Office for a project funded by the New Zealand Deep South National Science Challenge using the Monsoon system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, a strategic partnership between the Met Office and the Natural Environment Research Council.
    Keywords: climate modeling; File format; File name; File size; HadGEM3-GC3.1; Polar; Sea ice; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 48 data points
    Location Call Number Limitation Availability
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  • 2
    Publication Date: 2019-10-17
    Description: For ice concentrations less than 85%, internal ice stresses in the sea ice pack are small andsea ice is said to be in free drift. The sea ice drift is then the result of a balance between Coriolisacceleration and stresses from the ocean and atmosphere. We investigate sea ice drift using data fromindividual drifting buoys as well as Arctic-wide gridded fields of wind, sea ice, and ocean velocity. Weperform probabilistic inverse modeling of the momentum balance of free-drifting sea ice, implemented toretrieve the Nansen number, scaled Rossby number, and stress turning angles. Since this problem involvesa nonlinear, underconstrained system, we used a Monte Carlo guided search scheme—the NeighborhoodAlgorithm—to seek optimal parameter values for multiple observation points. We retrieve optimal dragcoefficients ofCA=1.2×10−3andCO=2.4×10−3from 10-day averaged Arctic-wide data from July 2014that agree with the AIDJEX standard, with clear temporal and spatial variations. Inverting daily averagedbuoy data give parameters that, while more accurately resolved, suggest that the forward model oversimplifies the physical system at these spatial and temporal scales. Our results show the importance of the correct representation of geostrophic currents. Both atmospheric and oceanic drag coefficients are found to decrease with shorter temporal averaging period, informing the selection of drag coefficient for short timescale climate models.
    Description: Published
    Description: 6388–6413
    Description: 5A. Ricerche polari e paleoclima
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Limitation Availability
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