Reconstructing Ocean Heat Content for Revisiting Global Ocean Warming from Remote Sensing Perspectives
| Autor: | An Wang, Wenfang Lu, Tian Qin, Hua Su |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
geography
geography.geographical_feature_category Effects of global warming on oceans Science long short-term memory (LSTM) Global warming Physics::Geophysics remote sensing data time-series reconstruction Approximation error Spatial ecology General Earth and Planetary Sciences Environmental science Gradient boosting Ocean heat content Oceanic basin ocean heat content (OHC) Argo Physics::Atmospheric and Oceanic Physics OPEN-LSTM dataset Remote sensing |
| Zdroj: | Remote Sensing Volume 13 Issue 19 Pages: 3799 Remote Sensing, Vol 13, Iss 3799, p 3799 (2021) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs13193799 |
| Popis: | Global ocean heat content (OHC) is generally estimated using gridded, model and reanalysis data; its change is crucial to understanding climate anomalies and ocean warming phenomena. However, Argo gridded data have short temporal coverage (from 2005 to the present), inhibiting understanding of long-term OHC variabilities at decadal to multidecadal scales. In this study, we utilized multisource remote sensing and Argo gridded data based on the long short-term memory (LSTM) neural network method, which considers long temporal dependence to reconstruct a new long time-series OHC dataset (1993–2020) and fill the pre-Argo data gaps. Moreover, we adopted a new machine learning method, i.e., the Light Gradient Boosting Machine (LightGBM), and applied the well-known Random Forests (RFs) method for comparison. The model performance was measured using determination coefficients (R2) and root-mean-square error (RMSE). The results showed that LSTM can effectively improve the OHC prediction accuracy compared with the LightGBM and RFs methods, especially in long-term and deep-sea predictions. The LSTM-estimated result also outperformed the Ocean Projection and Extension neural Network (OPEN) dataset, with an R2 of 0.9590 and an RMSE of 4.45 × 1019 in general in the upper 2000 m for 28 years (1993–2020). The new reconstructed dataset (named OPEN-LSTM) correlated reasonably well with other validated products, showing consistency with similar time-series trends and spatial patterns. The spatiotemporal error distribution between the OPEN-LSTM and IAP datasets was smaller on the global scale, especially in the Atlantic, Southern and Pacific Oceans. The relative error for OPEN-LSTM was the smallest for all ocean basins compared with Argo gridded data. The average global warming trends are 3.26 × 108 J/m2/decade for the pre-Argo (1993–2004) period and 2.67 × 108 J/m2/decade for the time-series (1993–2020) period. This study demonstrates the advantages of LSTM in the time-series reconstruction of OHC, and provides a new dataset for a deeper understanding of ocean and climate events. |
| Databáze: | OpenAIRE |
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