Published October 28, 2025 | Version v1

Sea Ice Area Ensemble for Uncertainty Quantification

  • 1. ROR icon Universität Hamburg

Description

Sea Ice Area (SIA) is the sum of Sea Ice Concentration (SIC) times grid cell area, here based in the OSI SAF Climate Data Record 'OSI450a'. Noise representing errors in SIC is generated with temporal and spatial error correlations similar to https://doi.org/10.5194/tc-18-2473-2024. The code used for this dataset is available on Github Each ensemble member represents one possible realization of the true SIA, consistent with OSI SAF observations. Ensemble spread indicates the random (non-systematic) uncertainty of the time series. This does not include biasses as we see between different SIA products which should be handled separately.

Each CSV file contains daily data with the first column giving the date ('YYYY-MM-DD') and 50 ensemble members as indicated by the first row ('batch001iXXX' with XXX zero padded counter from 0 to 49). each cell contains the SIA of that date/ensemble member in km2. 

Separate files are given for both hemisphere and individual regions, according to the NSIDC sea ice region masks https://doi.org/10.5067/CYW3O8ZUNIWC 

Naming convention:

'SIA_XH.csv' or 'SIA_XH_reg_regid.csv' where XH is NH for northern hemisphere or SH for southern hemisphere and regid is the region identifier ('Sea_ID') as in the NSIDC mask, repeated here for convenience:

NH:
Region  Sea_ID
Central_Arctic     1.0
Beaufort     2.0
Chukchi-NA     3.0
Chukchi-Asia     3.0
E_Siberian     4.0
Laptev     5.0
Kara     6.0
Barents     7.0
E_Greenland     8.0
Baffin     9.0
St_Lawr    10.0
Hudson    11.0
Can_Arch    12.0
Bering-NA    13.0
Bering-Asia    13.0
Okhotsk    14.0
Japan    15.0
Bohai    16.0
Baltic    17.0
Gulf_Alaska    18.0


SH:
Region  Sea_ID
Weddell     1.0
Indian     2.0
Pacific     3.0
RossEast     4.0
RossWest     4.0
BellAmund     5.0

 

Files

SIA_NH.csv

Files (195.5 MB)

Name Size
md5:e754807c30fe8423b69368864121902e
8.1 MB Preview Download
md5:f75bc034606b3a960d001b1f05eaa45b
7.7 MB Preview Download
md5:8221524bee3b726e2cf7bca78d99aa1a
8.7 MB Preview Download
md5:d50edde84589e9317828ac194cc1ff16
7.6 MB Preview Download
md5:619ce7bb88567e8d732647259275a319
7.3 MB Preview Download
md5:29d9f05b6a46d729920dbe93822c7b08
7.6 MB Preview Download
md5:987dea3217813d58ec3a722e89a79d30
8.3 MB Preview Download
md5:75beba2dd191d64c69c39d7ca665b795
8.5 MB Preview Download
md5:d779da66b17a6490d0fa98b62b3a0192
9.6 MB Preview Download
md5:e78b4cb3c6d8e4000f84f966a21b9f4b
7.9 MB Preview Download
md5:8f0c17f9da634b6063f1fa3ae3b99841
8.5 MB Preview Download
md5:3c4eedeac56157b7585c6900f54680bd
7.4 MB Preview Download
md5:e7b0a0511d73a88b9e194e903944ec7f
7.4 MB Preview Download
md5:f56fcd5883c08805a5c4e2c17c23bb7a
7.4 MB Preview Download
md5:c128930aaf4f16921b4c400724242026
7.5 MB Preview Download
md5:11f0f5e9a747360b36f5dc009dba301a
7.4 MB Preview Download
md5:65e8d129987c5463e33e65787151f195
7.5 MB Preview Download
md5:4676880a7acb98431df4079f73748a0c
7.5 MB Preview Download
md5:b07faa3d017893775da25f13c675273b
7.5 MB Preview Download
md5:8756b875e82a02ca8682d81ae356f5db
8.1 MB Preview Download
md5:299c092237953cc249e140b8099e593e
7.7 MB Preview Download
md5:a726e0f7d7c5b97db7c42a2380c97303
7.6 MB Preview Download
md5:142277ae4a192708f08c0b36fc0957f9
7.6 MB Preview Download
md5:caa20b861820676b2b08731ff243247b
7.7 MB Preview Download
md5:77d14f3f1accf0dffb9dc07e704621f6
7.6 MB Preview Download

Additional details

Related works

Is described by
Journal article: 10.5194/tc-18-2473-2024 (DOI)
Journal article: 10.5194/tc-20-3783-2026 (DOI)
Is variant form of
Dataset: 10.5281/zenodo.21793108 (DOI)

Funding

Deutsche Forschungsgemeinschaft
522421911