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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:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
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); USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23). Climate and Environmental Sciences 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

Citation Formats

Nadiga, Balasubramanya T., Verma, Tarun, Weijer, Wilbert, and Urban, Nathan Mark. Enhancing Skill of Initialized Decadal Predictions Using a Dynamic Model of Drift. United States: N. p., 2019. Web. doi:10.1029/2019GL084223.
Nadiga, Balasubramanya T., Verma, Tarun, Weijer, Wilbert, & Urban, Nathan Mark. Enhancing Skill of Initialized Decadal Predictions Using a Dynamic Model of Drift. United States. doi:10.1029/2019GL084223.
Nadiga, Balasubramanya T., Verma, Tarun, Weijer, Wilbert, and Urban, Nathan Mark. Fri . "Enhancing Skill of Initialized Decadal Predictions Using a Dynamic Model of Drift". United States. doi:10.1029/2019GL084223. https://www.osti.gov/servlets/purl/1597343.
@article{osti_1597343,
title = {Enhancing Skill of Initialized Decadal Predictions Using a Dynamic Model of Drift},
author = {Nadiga, Balasubramanya T. and Verma, Tarun and Weijer, Wilbert and Urban, Nathan Mark},
abstractNote = {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.},
doi = {10.1029/2019GL084223},
journal = {Geophysical Research Letters},
number = 16,
volume = 46,
place = {United States},
year = {2019},
month = {8}
}

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