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Title: Predictability of tropical vegetation greenness using sea surface temperatures

Abstract

Much research has examined the sensitivity of tropical terrestrial ecosystems to various environmental drivers. The predictability of tropical vegetation greenness based on sea surface temperatures (SSTs), however, has not been well explored. This study employed fine spatial resolution remotely-sensed Enhanced Vegetation Index (EVI) and SST indices from tropical ocean basins to investigate the predictability of tropical vegetation greenness in response to SSTs and established empirical models with optimal parameters for hindcast predictions. Three evaluation metrics were used to assess the model performance, i.e., correlations between historical observed and predicted values, percentage of correctly predicted signs of EVI anomalies, and percentage of correct signs for extreme EVI anomalies. Our findings reveal that the pan-tropical EVI was tightly connected to the SSTs over tropical ocean basins. The strongest impacts of SSTs on EVI were identified mainly over the arid or semi-arid tropical regions. The spatially-averaged correlation between historical observed and predicted EVI time series was 0.30 with its maximum value reaching up to 0.84. Vegetated areas across South America (25.76%), Africa (33.13%), and Southeast Asia (39.94%) were diagnosed to be associated with significant SST-EVI correlations (p < 0.01). In general, statistical models correctly predicted the sign of EVI anomalies, with their predictabilitymore » increasing from ~60% to nearly 100% when EVI was abnormal (anomalies exceeding one standard deviation). These results provide a basis for the prediction of changes in greenness of tropical terrestrial ecosystems at seasonal to intra-seasonal scales. Furthermore, the statistics-based observational relationships have the potential to facilitate the benchmarking of Earth System Models regarding their ability to capture the responses of tropical vegetation growth to long-term signals of oceanic forcings.« less

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2];  [3];  [4]; ORCiD logo [5];  [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]
  1. Univ. of Texas at Austin, Austin, TX (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Univ. of Wisconsin-Madison, Madison, WI (United States)
  4. Chinese Academy of Sciences (CAS), Beijing (China)
  5. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1545226
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Environmental Research Communications
Additional Journal Information:
Journal Volume: 1; Journal Issue: 3; Journal ID: ISSN 2515-7620
Publisher:
IOP Science
Country of Publication:
United States
Language:
English
Subject:
predictability; tropical vegetation greenness; sea surface temperatures

Citation Formats

Yan, Binyan, Mao, Jiafu, Shi, Xiaoying, Hoffman, Forrest M., Notaro, Michael, Zhou, Tianjun, Mcdowell, Nate, Dickinson, Robert E., Xu, Min, Gu, Lianhong, and Ricciuto, Daniel M. Predictability of tropical vegetation greenness using sea surface temperatures. United States: N. p., 2019. Web. doi:10.1088/2515-7620/ab178a.
Yan, Binyan, Mao, Jiafu, Shi, Xiaoying, Hoffman, Forrest M., Notaro, Michael, Zhou, Tianjun, Mcdowell, Nate, Dickinson, Robert E., Xu, Min, Gu, Lianhong, & Ricciuto, Daniel M. Predictability of tropical vegetation greenness using sea surface temperatures. United States. doi:10.1088/2515-7620/ab178a.
Yan, Binyan, Mao, Jiafu, Shi, Xiaoying, Hoffman, Forrest M., Notaro, Michael, Zhou, Tianjun, Mcdowell, Nate, Dickinson, Robert E., Xu, Min, Gu, Lianhong, and Ricciuto, Daniel M. Tue . "Predictability of tropical vegetation greenness using sea surface temperatures". United States. doi:10.1088/2515-7620/ab178a. https://www.osti.gov/servlets/purl/1545226.
@article{osti_1545226,
title = {Predictability of tropical vegetation greenness using sea surface temperatures},
author = {Yan, Binyan and Mao, Jiafu and Shi, Xiaoying and Hoffman, Forrest M. and Notaro, Michael and Zhou, Tianjun and Mcdowell, Nate and Dickinson, Robert E. and Xu, Min and Gu, Lianhong and Ricciuto, Daniel M.},
abstractNote = {Much research has examined the sensitivity of tropical terrestrial ecosystems to various environmental drivers. The predictability of tropical vegetation greenness based on sea surface temperatures (SSTs), however, has not been well explored. This study employed fine spatial resolution remotely-sensed Enhanced Vegetation Index (EVI) and SST indices from tropical ocean basins to investigate the predictability of tropical vegetation greenness in response to SSTs and established empirical models with optimal parameters for hindcast predictions. Three evaluation metrics were used to assess the model performance, i.e., correlations between historical observed and predicted values, percentage of correctly predicted signs of EVI anomalies, and percentage of correct signs for extreme EVI anomalies. Our findings reveal that the pan-tropical EVI was tightly connected to the SSTs over tropical ocean basins. The strongest impacts of SSTs on EVI were identified mainly over the arid or semi-arid tropical regions. The spatially-averaged correlation between historical observed and predicted EVI time series was 0.30 with its maximum value reaching up to 0.84. Vegetated areas across South America (25.76%), Africa (33.13%), and Southeast Asia (39.94%) were diagnosed to be associated with significant SST-EVI correlations (p < 0.01). In general, statistical models correctly predicted the sign of EVI anomalies, with their predictability increasing from ~60% to nearly 100% when EVI was abnormal (anomalies exceeding one standard deviation). These results provide a basis for the prediction of changes in greenness of tropical terrestrial ecosystems at seasonal to intra-seasonal scales. Furthermore, the statistics-based observational relationships have the potential to facilitate the benchmarking of Earth System Models regarding their ability to capture the responses of tropical vegetation growth to long-term signals of oceanic forcings.},
doi = {10.1088/2515-7620/ab178a},
journal = {Environmental Research Communications},
number = 3,
volume = 1,
place = {United States},
year = {2019},
month = {4}
}

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