Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature
Abstract
A framework for analyzing and bench marking climate model outputs is built upon δ-MAPS, a recently developed complex network analysis method. The framework allows for the possibility of highlighting quantifiable topological differences across data sets, capturing the magnitude of interactions including lagged relationships and quantifying the modeled internal variability, changes in domains properties and in their connections over space and time. A set of four metrics is proposed to assess and compare the modeled domains shapes, strengths, and connectivity patterns. δ-MAPS is applied to investigate the topological properties of sea surface temperature from observational data sets and in asubset of the Community Earth System Model (CESM) Large Ensemble focusing on the past 35 years and over the 20th and 21st centuries. Model ensemble members are mapped in a reduced metric space to quantify internal variability and average model error. It is found that network properties are on average robust whenever individual member or ensemble trends are removed. The assessment identifies biases in the CESM representation of the connectivity patterns that stem from too strong autocorrelations of domains signals and from the overestimation of the El Niño–Southern Oscillation amplitude and its thermodynamic feedback onto the tropical band in most members.
- Authors:
-
[1];
[2];
[3];
- School of Earth &, Atmospheric SciencesGeorgia Institute of Technology Atlanta GA USA
- School of Earth &, Atmospheric SciencesGeorgia Institute of Technology Atlanta GA USA, Institute of Geosciences and Earth ResourcesNational Research Council of Italy Pisa Italy
- School of Earth &, Atmospheric SciencesGeorgia Institute of Technology Atlanta GA USA, ENACÉcole Polytechnique Fédérale de Lausanne Lausanne Switzerland, Institute of Chemical Engineering SciencesFoundation for Research and Technology Hellas Patras Greece, Institute for Environmental Research and Sustainable DevelopmentNational Observatory of Athens P. Penteli Greece
- College of ComputingGeorgia Institute of Technology Atlanta GA USA
- Publication Date:
- Research Org.:
- Georgia Institute of Technology, Atlanta, GA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1560159
- Alternate Identifier(s):
- OSTI ID: 1560162; OSTI ID: 1610943
- Grant/Contract Number:
- SC0007145; NNX13AP63G
- Resource Type:
- Published Article
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Additional Journal Information:
- Journal Name: Journal of Advances in Modeling Earth Systems Journal Volume: 11 Journal Issue: 6; Journal ID: ISSN 1942-2466
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; climate variability; network analysis; model validation; model comparison; ENSO; future projections
Citation Formats
Falasca, Fabrizio, Bracco, Annalisa, Nenes, Athanasios, and Fountalis, Ilias. Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature. United States: N. p., 2019.
Web. doi:10.1029/2019MS001654.
Falasca, Fabrizio, Bracco, Annalisa, Nenes, Athanasios, & Fountalis, Ilias. Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature. United States. doi:10.1029/2019MS001654.
Falasca, Fabrizio, Bracco, Annalisa, Nenes, Athanasios, and Fountalis, Ilias. Tue .
"Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature". United States. doi:10.1029/2019MS001654.
@article{osti_1560159,
title = {Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature},
author = {Falasca, Fabrizio and Bracco, Annalisa and Nenes, Athanasios and Fountalis, Ilias},
abstractNote = {A framework for analyzing and bench marking climate model outputs is built upon δ-MAPS, a recently developed complex network analysis method. The framework allows for the possibility of highlighting quantifiable topological differences across data sets, capturing the magnitude of interactions including lagged relationships and quantifying the modeled internal variability, changes in domains properties and in their connections over space and time. A set of four metrics is proposed to assess and compare the modeled domains shapes, strengths, and connectivity patterns. δ-MAPS is applied to investigate the topological properties of sea surface temperature from observational data sets and in asubset of the Community Earth System Model (CESM) Large Ensemble focusing on the past 35 years and over the 20th and 21st centuries. Model ensemble members are mapped in a reduced metric space to quantify internal variability and average model error. It is found that network properties are on average robust whenever individual member or ensemble trends are removed. The assessment identifies biases in the CESM representation of the connectivity patterns that stem from too strong autocorrelations of domains signals and from the overestimation of the El Niño–Southern Oscillation amplitude and its thermodynamic feedback onto the tropical band in most members.},
doi = {10.1029/2019MS001654},
journal = {Journal of Advances in Modeling Earth Systems},
number = 6,
volume = 11,
place = {United States},
year = {2019},
month = {6}
}
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DOI: 10.1029/2019MS001654
DOI: 10.1029/2019MS001654
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