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 predictability increasing frommore » ~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. Moreover, 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:

Yan, Binyan ^[1]; Mao, Jiafu  ^[2];

• Search DOE PAGES for author "Mao, Jiafu"
• Search DOE PAGES for ORCID "0000-0002-2050-7373"

Shi, Xiaoying  ^[2];

• Search DOE PAGES for author "Shi, Xiaoying"
• Search DOE PAGES for ORCID "0000-0001-8994-5032"

Hoffman, Forrest M.  ^[2];

• Search DOE PAGES for author "Hoffman, Forrest M."
• Search DOE PAGES for ORCID "0000-0001-5802-4134"

Notaro, Michael ^[3]; Zhou, Tianjun ^[4]; Mcdowell, Nate  ^[5];

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• Search DOE PAGES for ORCID "0000-0002-2178-2254"

Dickinson, Robert E. ^[1]; Xu, Min  ^[2];

• Search DOE PAGES for author "Xu, Min"
• Search DOE PAGES for ORCID "0000-0003-3443-0300"

Gu, Lianhong  ^[2];

• Search DOE PAGES for author "Gu, Lianhong"
• Search DOE PAGES for ORCID "0000-0001-5756-8738"

Ricciuto, Daniel M.  ^[2]

• Search DOE PAGES for author "Ricciuto, Daniel M."
• Search DOE PAGES for ORCID "0000-0002-3668-3021"

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+ Show Author Affiliations

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:

Tue Apr 30 00:00:00 EDT 2019

Research Org.:

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER)

OSTI Identifier:

1545226

Alternate Identifier(s):

OSTI ID: 1633540

Report Number(s):

PNNL-SA-142768
Journal ID: ISSN 2515-7620

Grant/Contract Number:  

AC05-00OR22725; AC05-76RL01830

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:

54 ENVIRONMENTAL SCIENCES; predictability; tropical vegetation greenness; sea surface temperatures