Predictability of Tropical Vegetation Greenness Using Sea Surface Temperatures

TitlePredictability of Tropical Vegetation Greenness Using Sea Surface Temperatures
Publication TypeJournal Article
Year of Publication2019
AuthorsYan, 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.
JournalEnvironmental Research Communications
Volume1
Number3
Pages031003
Date Published06/2019
Abstract / Summary

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 the 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.

URLhttp://dx.doi.org/10.1088/2515-7620/ab178a
DOI10.1088/2515-7620/ab178a
Journal: Environmental Research Communications
Year of Publication: 2019
Volume: 1
Number: 3
Pages: 031003
Date Published: 06/2019

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 the 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.

DOI: 10.1088/2515-7620/ab178a
Citation:
Yan, B, J Mao, X Shi, FM Hoffman, M Notaro, T Zhou, N McDowell, et al.  2019.  "Predictability of Tropical Vegetation Greenness Using Sea Surface Temperatures."  Environmental Research Communications 1(3): 031003.  https://doi.org/10.1088/2515-7620/ab178a.