Details:

Journal Title:
Journal of Climate
Personal Author:
Bushuk, Mitchell; Yang, Xiaosong; Winton, Michael; Msadek, Rym; Harrison, Matthew; ... More +
Bushuk, Mitchell; Yang, Xiaosong; Winton, Michael; Msadek, Rym; Harrison, Matthew; Rosati, Anthony; Gudgel, Rich; Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research); GFDL (Geophysical Fluid Dynamics Laboratory); CPO (Climate Program Office);
OAR (Oceanic and Atmospheric Research); GFDL (Geophysical Fluid Dynamics Laboratory); CPO (Climate Program Office); Less -
Description:
Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of observing system experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea ice predictions in the Barents Sea. We find notable skill improvements due to the inclusion of both sea surface temperature (SST) satellite observations and subsurface conductivity–temperature–depth (CTD) measurements. The SST data are found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal time scales.
Keywords:
[+]
Numerical Weather Forecasting
Sea Ice

Source:
Journal of Climate, 32(20), 7017-7035, 2019
DOI:
https://doi.org/10.1175/JCLI-D-19-0179.1
Document Type:
Journal Article;
Place as Subject:
Barents Sea;
Rights Information:
Other
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:fed65bde25b7628d788e4856c5591f8575c0280d5ce32f2d9928acd9d0b137ed
File Type:

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