Decadal Predictability without ocean dynamics

Srivastava, Abhishekh; DelSole, Timothy

doi:10.1073/pnas.1614085114

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Decadal Predictability without ocean dynamics

2017
By Srivastava, Abhishekh ; DelSole, Timothy
Source: PNAS February 28, 2017 114 (9) 2177-2182

[PDF-967.45 KB]

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Journal Title:

Proceedings of the National Academy of Sciences
Personal Author:

Srivastava, Abhishekh ; DelSole, Timothy

Srivastava, Abhishekh ; DelSole, Timothy Less -
Corporate Authors:

United States. National Oceanic and Atmospheric Administration. Office of Oceanic and Atmospheric Research ; Modeling, Analysis, Predictions, and Projections Program (U.S.)

United States. National Oceanic and Atmospheric Administration. Office of Oceanic and Atmospheric Research ; Modeling, Analysis, Predictions, and Projections Program (U.S.) Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office)

OAR (Oceanic and Atmospheric Research) ; CPO (Climate Program Office) Less -
Description:

This paper shows that the most predictable components of internal variability in coupled atmosphere–ocean models are remarkably similar to the most predictable components of climate models without interactive ocean dynamics (i.e., models whose ocean is represented by a 50-m-deep slab ocean mixed layer with no interactive currents). Furthermore, a linear regression model derived solely from dynamical model output can skillfully predict observed anomalies in these components at least a year or two in advance, indicating that these model-derived components and associated linear dynamics are realistic. These results suggest that interactive ocean circulation is not essential for the existence of multiyear predictability previously identified in coupled models and observations.
Keywords:

[+]

Forecasting Average Predictability Time CMIP Decadal Predictability Decadal Prediction
Source:

PNAS February 28, 2017 114 (9) 2177-2182
DOI:

https://doi.org/10.1073/pnas.1614085114
Pubmed ID:

28193900
Pubmed Central ID:

PMC5338527
Document Type:

Journal Article
Funding:

Grant no. NA14OAR4310160
Rights Information:

Other
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:defbac32ae9b02911dc9c8220898f9170a4ba406fa194f97762e5b56d4a076da
Download URL:

https://repository.library.noaa.gov/view/noaa/28385/noaa_28385_DS1.pdf
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Decadal Predictability without ocean dynamics

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