Numerical data analysed to produce Figure 3a of Nature Climate Change submission "Five challenges for subseasonal to decadal prediction research " by Merryfield et al.

• Main Author: William Merryfield
• Format: Other/Unknown Material
• Language: unknown
• Published: Zenodo 2022
• Subjects: sea ice; seasonal forecast; Sea ice
• Online Access: https://doi.org/10.5281/zenodo.6805523

NetCDF4-formatted files containing daily sea ice concentration data from Environment and Climate Change Canada's CanSIPSv2seasonal forecasting system described in Lin et al. (2020)https://doi.org/10.1175/WAF-D-19-0259.1 2 models, CanCM4i and GEM-NEMO 10 ensemble members foreach model, each in separate files as indicated by suffixes _1 to _10 initialized May 1, 1980 to 2021 840 files total (42 predicted years x 10 ensemble members x 2 models) model outputs interpolated to common 1-degree grid The calibrated probabilistic forecast map shown in Figure 3a is based onthenonhomogeneous censored Gaussian regression (NCGR) method described in Dirkson et al, (2021)https://doi.org/10.1175/WAF-D-20-0066.1 and produced using scripts available athttps://github.com/adirkson/sea-ice-timing The procedureuses as inputs freeze-up dates calculated from the provided model outputs as described in Sigmond et al. (2016)https://doi.org/10.1002/2016GL071396 NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3: https://nsidc.org/data/G02202/versions/3