Evaluating ice shelf basal melt rates in the Southern Ocean high-resolution model (SOhi) against satellite remote sensing
Data files
Sep 12, 2026 version files 6.87 GB
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README.md
1.36 KB
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SHTfwFlx.nc
6.87 GB
Abstract
Ice shelf melting driven by a warm ocean has a critical impact on ice sheet mass balance in Antarctica and the rates of sea level rise. The Southern Ocean high-resolution (SOhi) model is a 1/24 degrees simulation developed to study ocean processes on the margin of Antarctica, containing sea ice, oceanic tides, and all ice shelves. Here, we provide melt rates from the Southern Ocean high-resolution (SOhi) simulation used in the evaluation of the model against two satellite-derived estimates. The dataset is provided as 6-hour averages in units of kg/m2/s, covering the period between March 1, 2007 and December 31, 2007. The outputs provide a comprehensive, detailed mapping of ice shelf melt rates around Antarctica, capturing areas of higher melt near grounding lines and ice fronts, and areas of freezing versus high meltwater production. Discrepancies between model and satellite estimates provide guidelines for model improvements, requirements for additional observations of ice shelf cavities, and the assembly of calibration/validation datasets to constrain the models.
Dataset DOI: 10.5061/dryad.931zcrk32
Basal melt rates from the Southern Ocean high-resolution simulation as 6-hour averages from March 01, 2007 to December 31, 2007. The domain of the output are -85.5°S to 0°S and 0°E to 360°E. The data is stored in netCDF format containing the following variables:
Dimensions
date: Iteration since 2005-01-01 00:00:00 with 1 iteration = 60 seconds
lat: Latitude at cell's center in degrees North
lon: Longitude at cell's center in degrees East
Variables
SHTfwFlx (date, lat, lon): Basal melt rates from SOhi in units of kg/m2/s
aRAC (lat, lon): Area of each grid cell in units of m2
Data stored in the netCDF can be accessed via Python using:
import netCDF4 as nc
dataset = nc.Dataset('path/SHTfwFlx.nc', mode='r')
var_data = dataset['var_name']
Iterations can be converted to Python datetime using:
from datetime import datetime, timedelta
import netCDF4 as nc
import numpy as npdataset = nc.Dataset('path/SHTfwFlx.nc', mode='r')
iterations = np.asarray(dataset['date'])
dates = [datetime(2005, 1, 1, 0) + timedelta(seconds = int(i)*60.0) for i in iterations]
