TY - JOUR
T1 - CGCM and AGCM seasonal climate predictions
T2 - A study in CCSM4
AU - Infanti, Johnna M.
AU - Kirtman, Ben P.
N1 - Funding Information:
The authors would like to thank Dughong Min and the Center for Ocean- Land-Atmosphere studies for creating and maintaining the initial states for CCSM4 climate prediction studies. The free software NCAR Command Language was used to create the plots and analyze data. The authors would also like to thank the NMME program partners. NMME data, including CCSM4 fully coupled hindcasts, can be found at http://www.cpc.ncep.noaa.gov/products/ NMME/. CCSM4 model code is available from NCAR at http://www.cesm.ucar. edu/models/ccsm4.0/. The prescribed SST CAM4 simulation data are archived at the Center for Computational Science at the University of Miami and are available upon request. We would like to thank two anonymous reviewers whose comments greatly improved this manuscript. The authors acknowledge support from NOAA NA10OAR4320143, NA15OAR4320064, NA16OAR4310149, and NA16OAR4310141.
PY - 2017
Y1 - 2017
N2 - Seasonal climate predictions are formulated from known present conditions and simulate the near-term climate for approximately a year in the future. Recent efforts in seasonal climate prediction include coupled general circulation model (CGCM) ensemble predictions, but other efforts have included atmospheric general circulation model (AGCM) ensemble predictions that are forced by time-varying sea surface temperatures (SSTs). CGCMs and AGCMs have differences in the way surface energy fluxes are simulated, which may lead to differences in skill and predictability. Concerning model biases, forecasted SSTs have errors compared to observed SSTs, which may also affect skill and predictability. This manuscript focuses on the role of the ocean in climate predictions and includes the influences of ocean-atmosphere coupling and SST errors on skill and predictability. We perform a series of prediction experiments comparing coupled and uncoupled Community Climate System Model version 4.0 (CCSM4) predictions and forecasted versus observed SSTs to determine which is the leading cause for differences in skill and predictability. Overall, prediction skill and predictability are only weakly influenced by ocean-atmosphere coupling, with the exception of the western Pacific, while errors in forecasted SSTs significantly impact skill and predictability. Comparatively, SST errors lead to more significant and robust differences in prediction skill and predictability versus inconsistencies in ocean-atmosphere coupling.
AB - Seasonal climate predictions are formulated from known present conditions and simulate the near-term climate for approximately a year in the future. Recent efforts in seasonal climate prediction include coupled general circulation model (CGCM) ensemble predictions, but other efforts have included atmospheric general circulation model (AGCM) ensemble predictions that are forced by time-varying sea surface temperatures (SSTs). CGCMs and AGCMs have differences in the way surface energy fluxes are simulated, which may lead to differences in skill and predictability. Concerning model biases, forecasted SSTs have errors compared to observed SSTs, which may also affect skill and predictability. This manuscript focuses on the role of the ocean in climate predictions and includes the influences of ocean-atmosphere coupling and SST errors on skill and predictability. We perform a series of prediction experiments comparing coupled and uncoupled Community Climate System Model version 4.0 (CCSM4) predictions and forecasted versus observed SSTs to determine which is the leading cause for differences in skill and predictability. Overall, prediction skill and predictability are only weakly influenced by ocean-atmosphere coupling, with the exception of the western Pacific, while errors in forecasted SSTs significantly impact skill and predictability. Comparatively, SST errors lead to more significant and robust differences in prediction skill and predictability versus inconsistencies in ocean-atmosphere coupling.
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U2 - 10.1002/2016JD026391
DO - 10.1002/2016JD026391
M3 - Article
AN - SCOPUS:85026422553
VL - 122
SP - 7416
EP - 7432
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
SN - 2169-897X
IS - 14
ER -