CGCM and AGCM Seasonal Climate Predictions -- A study in CCSM4

Infanti, Johnna M.; Kirtman, Ben P.

doi:10.1002/2016JD026391

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

i

CGCM and AGCM Seasonal Climate Predictions -- A study in CCSM4

2017
By Infanti, Johnna M. ; Kirtman, Ben P.
Source: J. Geophys. Res. Atmos., 122, 7416– 7432

[PDF-2.75 MB]

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Journal Title:

Journal of Geophysical Research: Atmospheres
Personal Author:

Infanti, Johnna M. ; Kirtman, Ben P.

Infanti, Johnna M. ; Kirtman, Ben P. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies)

OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) ; CIMAS (Cooperative Institute for Marine and Atmospheric Studies) Less -
Description:

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.
Keywords:

[+]

Climatology Forecasting Precipitation Temperature AGCM CGCM Climate Prediction Predictability
Source:

J. Geophys. Res. Atmos., 122, 7416– 7432
DOI:

https://doi.org/10.1002/2016JD026391
Document Type:

Journal Article
Funding:

Grant no. NA16OAR4310149 ; Grant no. NA16OAR4310141 ; Grant no. NA15OAR4320064 ; Grant no. NA10OAR4320143

Grant no. NA16OAR4310149 ; Grant no. NA16OAR4310141 ; Grant no. NA15OAR4320064 ; Grant no. NA10OAR4320143 Less -
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urn:sha256:81bc89ffb01a14da900db119e91921d30edcaa954effa8f4aef98a4039d09e6c
Download URL:

https://repository.library.noaa.gov/view/noaa/28420/noaa_28420_DS1.pdf
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CGCM and AGCM Seasonal Climate Predictions -- A study in CCSM4

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