Document Type

Article

Publication Date

12-1-2024

Abstract

Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, Seasonal Extraction in autogressive integrated moving average (ARIMA) Time Series (SEATS), and the seasonal and trend decomposition using local regression (Loess) (STL) approach. The long-term trends in LST data are driven by climate change rather than seasonal fluctuations. We employed neural network autoregression (NNAR), bagged error, trend, and seasonal (BaggedETS), exponential smoothing models, and the STL method to project future LST values. We also explored advanced forecasting models like dynamic harmonic regression; trigonometric seasonality, Box–Cox transformation, autoregressive moving average (ARMA) errors, and trend and seasonal components (TBATS); and seasonal autoregressive integrated moving average (SARIMA) for comparative performance analysis. Extended warm periods were identified for Abu Ali Island between 2017 and 2018 through several decomposition methods, likely linked to the 2015/16 El Niño event. We also conducted a marine heatwave (MHW) analysis from 2010 to 2020, establishing a pronounced impact of the 2015/16 El Niño on the Arabian Gulf. In nesting beach environments with high LST, marine heatwaves could have a significant impact on sea turtle populations without human intervention such as artificially cooling the nest temperature. The SARIMA model showed higher forecasting precision for in situ weather data, while the NNAR model demonstrated superior performance with remotely sensed data.

Comments

This article was originally published in Journal of Applied Meteorology and Climatology, volume 63, in 2024. https://doi.org/10.1175/JAMC-D-24-0092.1.

Peer Reviewed

1

Copyright

© Copyright 2024 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy).

Available for download on Wednesday, March 19, 2025

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