AI Model Affirms ENSO's Boost to Subseasonal Predictability of Antarctic Sea Ice: supplemental data ...

• Main Author: Wang, Yunhe
• Format: Dataset
• Language: unknown
• Published: figshare 2023
• Subjects: Physical oceanography; Antarctic; Antarc*; Sea ice
• Online Access: https://dx.doi.org/10.6084/m9.figshare.24572866.v1; https://figshare.com/articles/dataset/AI_Model_Affirms_ENSO_s_Boost_to_Subseasonal_Predictability_of_Antarctic_Sea_Ice_supplemental_data/24572866/1

cor_spatial38c.hdf contains SIPNet skill information, comprising four subsets: 'mid', 'nino', 'nina', and 'all', representing model skill under neutral conditions, El Niño, La Niña, and the entire time range, respectively.acc_per_spatial38.hdf is similar to cor_spatial38c but represents the model skill for anomaly persistence.cor_spatial38_linear.hdf, like cor_spatial38c, represents the model skill but specifically for the linear SIPNet model.sic_stddev.hdf contains sea ice variability information with three subsets: 'nino', 'mid', and 'nina', denoting sea ice variability under El Niño, neutral conditions, and La Niña, respectively.t2m_composite38.hdf: Surface air temperature composite dataset, containing composites for four seasons.sst_composite38.hdf: Similar to t2m_composite38, but for sea surface temperature.mslp_composite38.hdf: Similar to t2m_composite38, but for sea level pressure.obser_sic_composite.hdf: Similar to t2m_composite38, but for observed sea ice concentration.predi_sic_composite.hdf: ...