An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation
Abstract Constructing suitable tree ring width (TRW) proxy system models (PSMs) is an emerging research focus in paleoclimate data assimilation (PDA). Currently, however, it is unknown as to which TRW PSMs are optimal for practical PDA applications. This study proposes an artificial neural networks...
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2019-04-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2018MS001525 |
| _version_ | 1818665221930090496 |
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| author | Miao Fang Xin Li |
| author_facet | Miao Fang Xin Li |
| author_sort | Miao Fang |
| collection | DOAJ |
| description | Abstract Constructing suitable tree ring width (TRW) proxy system models (PSMs) is an emerging research focus in paleoclimate data assimilation (PDA). Currently, however, it is unknown as to which TRW PSMs are optimal for practical PDA applications. This study proposes an artificial neural networks (ANN)‐based TRW PSM and compares its performance with those of existing TRW PSMs, including linear univariate model, linear multivariate model, and physically based VS‐Lite model. The results show that ANN‐based TRW PSM is more suitable for practical PDA applications than other three TRW PSMs in terms of performance and universality. Overall, the performances of the four TRW PSMs in PDA can be ranked as follows (from best to worst): ANN, linear multivariate model, linear univariate model, and physically based VS‐Lite model. In addition, the results of our study not only indicate that the ANN model is a really effective tool for constructing TRW PSM in practical PDA applications but also imply that the ANN model has the potential to provide new insights into the construction of other types of PSMs (e.g., speleothem δ18O PSM) when physics of the climate‐proxy relationships cannot be described fully in advance. |
| first_indexed | 2024-12-17T05:45:12Z |
| format | Article |
| id | doaj.art-c2241af511ba475687a87b964058f04d |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-12-17T05:45:12Z |
| publishDate | 2019-04-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-c2241af511ba475687a87b964058f04d2022-12-21T22:01:20ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662019-04-0111489290410.1029/2018MS001525An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data AssimilationMiao Fang0Xin Li1Northwest Institute of Eco‐Environment and Resource Chinese Academy of Sciences Lanzhou ChinaInstitute of Tibetan Plateau Research Chinese Academy of Sciences Beijing ChinaAbstract Constructing suitable tree ring width (TRW) proxy system models (PSMs) is an emerging research focus in paleoclimate data assimilation (PDA). Currently, however, it is unknown as to which TRW PSMs are optimal for practical PDA applications. This study proposes an artificial neural networks (ANN)‐based TRW PSM and compares its performance with those of existing TRW PSMs, including linear univariate model, linear multivariate model, and physically based VS‐Lite model. The results show that ANN‐based TRW PSM is more suitable for practical PDA applications than other three TRW PSMs in terms of performance and universality. Overall, the performances of the four TRW PSMs in PDA can be ranked as follows (from best to worst): ANN, linear multivariate model, linear univariate model, and physically based VS‐Lite model. In addition, the results of our study not only indicate that the ANN model is a really effective tool for constructing TRW PSM in practical PDA applications but also imply that the ANN model has the potential to provide new insights into the construction of other types of PSMs (e.g., speleothem δ18O PSM) when physics of the climate‐proxy relationships cannot be described fully in advance.https://doi.org/10.1029/2018MS001525paleoclimate data assimilationproxy system model of tree ring widthartificial neural netwokclimate reconstruction |
| spellingShingle | Miao Fang Xin Li An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation Journal of Advances in Modeling Earth Systems paleoclimate data assimilation proxy system model of tree ring width artificial neural netwok climate reconstruction |
| title | An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation |
| title_full | An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation |
| title_fullStr | An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation |
| title_full_unstemmed | An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation |
| title_short | An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation |
| title_sort | artificial neural networks based tree ring width proxy system model for paleoclimate data assimilation |
| topic | paleoclimate data assimilation proxy system model of tree ring width artificial neural netwok climate reconstruction |
| url | https://doi.org/10.1029/2018MS001525 |
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