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Arctic sea ice; Data assimilation; Observational uncertainties; Seasonal climate prediction; Atmospheric temperature; Climatology; Forecasting; NASA; Sea ice; Surface properties
Arctic sea ice; Bootstrap algorithms; Data assimilation; Max Planck Institute; Observational uncertainties; Sea ice concentration; Seasonal climate forecast; Seasonal climate prediction
Abstract:
We investigate how observational uncertainty in satellite-retrieved sea ice concentrations affects seasonal climate predictions. To do so, we initialize hindcast simulations with the Max Planck Institute Earth System Model every 1 May and 1 November from 1981 to 2011 with two different sea ice concentration data sets, one based on the NASA Team and one on the Bootstrap algorithm. For hindcasts started in November, initial differences in Arctic sea ice area and surface temperature decrease rapidly throughout the freezing period. For hindcasts started in May, initial differences in sea ice area increase over time. By the end of the melting period, this causes significant differences in 2 meter air temperature of regionally more than 3°C. Hindcast skill for surface temperatures over Europe and North America is higher with Bootstrap initialization during summer and with NASA Team initialization during winter. This implies that the observational uncertainty also affects forecasts of teleconnections that depend on northern hemispheric climate indices. ©2016. American Geophysical Union.
