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Arctic Sea Ice Predictability and Pr...

Yang, Chao-Yuan.

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Arctic Sea Ice Predictability and Prediction.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Arctic Sea Ice Predictability and Prediction./
Author:	Yang, Chao-Yuan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	173 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-11, Section: B.
Contained By:	Dissertations Abstracts International80-11B.
Subject:	Climate Change. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13864504
ISBN:	9781392137338

Arctic Sea Ice Predictability and Prediction.
Yang, Chao-Yuan.

Arctic Sea Ice Predictability and Prediction. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 173 p.

Source: Dissertations Abstracts International, Volume: 80-11, Section: B.

Thesis (Ph.D.)--State University of New York at Albany, 2019.

This item must not be sold to any third party vendors.

Arctic sea ice has experienced dramatic changes for the past few decades, which has profound global climatic effects, or feedbacks. The drastic changes and their associated impacts have led to increasing demand for sea ice predictions from a wide scope of stakeholders across seasonal to decadal timescales. Thus, it is important to improve our understanding of sea ice predictability on different timescales and our ability to predict Arctic sea ice.Previous studies mainly focus on sea ice predictability on seasonal to interannual time scales. Relatively little attention has been paid to assessing the predictive skill of sea ice at decadal timescales. In this thesis, the assessment of CMIP5 decadal hindcasts was conducted to examine the skill of CGCMs in predicting sea ice at longer timescales. Results show that for most models, the areas showing significant predictive skill of sea ice concentration become broader associated with increasing lead times. This increasing skill is mainly due to the capability of CMIP5 models to predict the observed Arctic sea ice decreasing trend. Sea ice in the Atlantic side has lower predictability than that of the Pacific side, particularly at a lead-time of 3-7 years, but the Atlantic side shows reemerging predictive skill at a lead-time of 6-8 years. The analysis also suggested that initialized decadal hindcasts show improved predictive skill compared to uninitialized simulations. In contrast to the Arctic, all CMIP5 models do not show any predictive skill for Antarctic sea ice due to the lack of skill predicting observed Antarctic sea ice trends.To improve our capability to predict Arctic sea ice, we have developed a new fully-coupled modeling system configured for the Arctic region. Specifically, the Los Alamos Sea Ice Model is coupled with the Coupled-Ocean-Atmosphere-Wave-Sediment Transport modeling system. A series of sensitivity experiments with different physics options (i.e., cloud microphysics, radiation transfer, ocean advection, ice rheology, ice thermodynamics) has been performed to determine the 'optimal' physics configuration that could simulate reasonable sea ice variations, serving as the baseline simulation for operational predictions. It is well known that dynamic models used to predict Arctic sea ice at seasonal time scales strongly depend on model initial conditions. Thus, a data assimilation that assimilates sea ice observations to generate skillful model initialization is needed to improve Arctic sea ice prediction. Parallel Data Assimilation Framework (PDAF) was implemented into the new modeling system. SSMIS sea ice concentration, CyroSat-2 and SMOS sea ice thickness were assimilated with the localized error subspace transform ensemble Kalman filter (LESTKF), which improves initial sea ice extent/concentration and thickness. We then conducted Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice states show reasonable sea ice evolution and small biases in simulated minimum sea ice extent, although predictions also show delayed ice re-freezing. A series of sensitivity experiments with different ice thickness initialization approaches suggests the initialization of sea ice thickness is critical for skillful sea ice prediction.

ISBN: 9781392137338Subjects--Topical Terms:

894284
Climate Change.

Arctic Sea Ice Predictability and Prediction.
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500 $a Publisher info.: Dissertation/Thesis.
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506 $a This item must not be sold to any third party vendors.
520 $a Arctic sea ice has experienced dramatic changes for the past few decades, which has profound global climatic effects, or feedbacks. The drastic changes and their associated impacts have led to increasing demand for sea ice predictions from a wide scope of stakeholders across seasonal to decadal timescales. Thus, it is important to improve our understanding of sea ice predictability on different timescales and our ability to predict Arctic sea ice.Previous studies mainly focus on sea ice predictability on seasonal to interannual time scales. Relatively little attention has been paid to assessing the predictive skill of sea ice at decadal timescales. In this thesis, the assessment of CMIP5 decadal hindcasts was conducted to examine the skill of CGCMs in predicting sea ice at longer timescales. Results show that for most models, the areas showing significant predictive skill of sea ice concentration become broader associated with increasing lead times. This increasing skill is mainly due to the capability of CMIP5 models to predict the observed Arctic sea ice decreasing trend. Sea ice in the Atlantic side has lower predictability than that of the Pacific side, particularly at a lead-time of 3-7 years, but the Atlantic side shows reemerging predictive skill at a lead-time of 6-8 years. The analysis also suggested that initialized decadal hindcasts show improved predictive skill compared to uninitialized simulations. In contrast to the Arctic, all CMIP5 models do not show any predictive skill for Antarctic sea ice due to the lack of skill predicting observed Antarctic sea ice trends.To improve our capability to predict Arctic sea ice, we have developed a new fully-coupled modeling system configured for the Arctic region. Specifically, the Los Alamos Sea Ice Model is coupled with the Coupled-Ocean-Atmosphere-Wave-Sediment Transport modeling system. A series of sensitivity experiments with different physics options (i.e., cloud microphysics, radiation transfer, ocean advection, ice rheology, ice thermodynamics) has been performed to determine the 'optimal' physics configuration that could simulate reasonable sea ice variations, serving as the baseline simulation for operational predictions. It is well known that dynamic models used to predict Arctic sea ice at seasonal time scales strongly depend on model initial conditions. Thus, a data assimilation that assimilates sea ice observations to generate skillful model initialization is needed to improve Arctic sea ice prediction. Parallel Data Assimilation Framework (PDAF) was implemented into the new modeling system. SSMIS sea ice concentration, CyroSat-2 and SMOS sea ice thickness were assimilated with the localized error subspace transform ensemble Kalman filter (LESTKF), which improves initial sea ice extent/concentration and thickness. We then conducted Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice states show reasonable sea ice evolution and small biases in simulated minimum sea ice extent, although predictions also show delayed ice re-freezing. A series of sensitivity experiments with different ice thickness initialization approaches suggests the initialization of sea ice thickness is critical for skillful sea ice prediction.
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