Title: Enhancing Skill of Initialized Decadal Predictions Using a Dynamic Model of Drift

Abstract

Since near-term predictions of present-day climate are controlled by both initial condition predictability and boundary condition predictability, initialized prediction experiments aim to augment the external-forcing-related predictability realized in uninitialized projections with initial-condition-related predictability by appropriate observation-based initialization. However, and notwithstanding the considerable effort expended in finding such “good” initial states, a striking feature of current, state-of-the-art, initialized decadal hindcasts is their tendency to quickly drift away from the initialized state, with attendant loss of skill. We derive a dynamical model for such drift, and after validating it we show that including a recalibrated version of the model in a postprocessing framework is able to significantly augment the skill of initialized predictions beyond that achieved by a use of current techniques of postprocessing alone. We also show that the new methodology provides further crucial insights into issues related to initialized predictions.

Authors:

Nadiga, Balasubramanya T.  ^[1];

• Search DOE PAGES for author "Nadiga, Balasubramanya T."
• Search DOE PAGES for ORCID "0000-0001-8100-4692"

Verma, Tarun  ^[1];

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• Search DOE PAGES for ORCID "0000-0001-7730-1483"

Weijer, Wilbert  ^[1];

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• Search DOE PAGES for ORCID "0000-0002-5654-562X"

Urban, Nathan Mark  ^[1]

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• Search DOE PAGES for ORCID "0000-0002-2264-3512"

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+ Show Author Affiliations

1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Publication Date:

Fri Aug 16 00:00:00 EDT 2019

Research Org.:

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE Office of Science (SC). Advanced Scientific Computing Research (ASCR) (SC-21); USDOE National Nuclear Security Administration (NNSA); Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division

OSTI Identifier:

1597343

Report Number(s):

LA-UR-19-23774
Journal ID: ISSN 0094-8276

Grant/Contract Number:  

89233218CNA000001

Resource Type:

Accepted Manuscript

Journal Name:

Geophysical Research Letters

Additional Journal Information:

Journal Volume: 46; Journal Issue: 16; Journal ID: ISSN 0094-8276

Publisher:

American Geophysical Union

Country of Publication:

United States

Language:

English

Subject:

58 GEOSCIENCES; Earth Sciences