Name
Title | ||
|---|---|---|
Reconstructing Ocean Heat Content for Revisiting Global Ocean Warming from Remote Sensing Perspectives |
Abstract | ||
|---|---|---|
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 (R-2) 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 R-2 of 0.9590 and an RMSE of 4.45 x 10(19) 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 x 10(8) J/m(2)/decade for the pre-Argo (1993-2004) period and 2.67 x 10(8) J/m(2)/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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13193799 | REMOTE SENSING |
Keywords | DocType | Volume |
ocean heat content (OHC), long short-term memory (LSTM), OPEN-LSTM dataset, remote sensing data, time-series reconstruction | Journal | 13 |
Issue | Citations | PageRank |
19 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Su | 1 | 0 | 2.03 |
| Tian Qin | 2 | 0 | 0.34 |
| An Wang | 3 | 0 | 0.68 |
| Wenfang Lu | 4 | 0 | 0.34 |