OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data

Su, Hua; Zhang, Haojie; Geng, Xupu; Qin, Tian; Lu, Wenfang; Yan, Xiao‐Hai

doi:10.3390/rs12142294

Cited by 31 publications

(25 citation statements)

References 47 publications

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“…Nevertheless, further fine-tuning of SOM with more modes could be the future work for a better operational forecast system. More applications of SOM-HMM and other data-driven methods (Su et al, 2020) in other problems (Lu et al, 2019) can help facilitate those data-driven approaches. In this aspect, deep learning approaches such as the Convolutional Long-Short Term Memory (Shi et al, 2015;Zhang et al, 2020) can provide powerful tools for the forecasting problems of two-dimensional features.…”

Section: Discussionmentioning

confidence: 99%

Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes

Wang

Jiang

et al. 2022

JGR Oceans

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Numerical models are of fundamental usage for estuarine and coastal sciences. Although numerical simulations are widely applied, analyzing and improving them are often challenging tasks given their large volume and huge parameter space. In this study, a novel data‐driven framework is introduced to study the Minjiang River Plume (MJRP). The framework combines Self‐Organizing Map (SOM) clustering with a Hidden Markov Model (HMM). A three‐dimensional Regional Ocean Model System for MJRP is first configurated with realistic atmospheric, oceanic, and riverine forcings. By applying SOM clustering to the modeled sea surface salinity (SSS) with ∼2,000 2‐day averaged records from 2010 to 2020, we identify six major patterns of MJRP. Each pattern exhibits distinct circulation and plume structures. These MJRP patterns contain not only seasonal signals, but also rich short‐term variabilities driven by the riverine inputs and oceanic dynamics. Then, the SOM‐HMM method was applied to predict the future of the hidden state (i.e., patterns of MJRP) from the observable states (wind and river runoff). With a hypothetic SSS product from a geostationary satellite as the ground truth, we show that the SOM‐HMM method can predict MJRP patterns considerably high prediction accuracy and computational efficiency. Further, these patterns were translated back to SSS with high forecast skills. Combining a conventional numerical model with a data‐driven method, this approach can be promisingly applied in the short‐term marine forecast to support the utilization and management of other estuaries.

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Section: Discussionmentioning

confidence: 99%

Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes

Wang

Jiang

et al. 2022

JGR Oceans

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“… Future coordinated intercomparisons are necessary to evaluate the performance of mapping methods, including other methods not examined in this study (Barth et al 2014;Cheng et al 2017;Kuusela and Stein 2018;Su et al 2021;Su et al 2020), based on synthetic profiles from Argo and/or altimeter observations as well as from de-drifted model simulations that conserve tracer properties (Allison et al 2019;Cheng and Zhu2014;Cheng et al 2017;Garry et al 2019;Good 2017;Palmer et al 2019).…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Quantifying Spread in Spatiotemporal Changes of Upper-Ocean Heat Content Estimates: An Internationally Coordinated Comparison

et al. 2022

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The Earth system is accumulating energy due to human-induced activities. More than 90% of this energy has been stored in the ocean as heat since 1970, with ~64% of that in the upper 700 m. Differences in upper ocean heat content anomaly (OHCA) estimates, however, exist. Here, we use a dataset protocol for 1970–2008 – with six instrumental bias adjustments applied to expendable bathythermograph (XBT) data, and mapped by six research groups – to evaluate the spatio-temporal spread in upper OHCA estimates arising from two choices: firstly, those arising from instrumental bias adjustments; and secondly those arising from mathematical (i.e. mapping) techniques to interpolate and extrapolate data in space and time. We also examined the effect of a common ocean mask, which reveals that exclusion of shallow seas can reduce global OHCA estimates up to 13%. Spread due to mapping method is largest in the Indian Ocean and in the eddy-rich and frontal regions of all basins. Spread due to XBT bias adjustment is largest in the Pacific Ocean within 30°N–30°S. In both mapping and XBT cases, spread is higher for 1990–2004. Statistically different trends among mapping methods are not only found in the poorly-observed Southern Ocean but also on the well-observed Northwest Atlantic. Our results cannot determine the best mapping or bias adjustment schemes but they identify where important sensitivities exist, and thus where further understanding will help to refine OHCA estimates. These results highlight the need for further coordinated OHCA studies to evaluate the performance of existing mapping methods along with comprehensive assessment of uncertainty estimates.

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“…The multisource satellite and Argo gridded data used in this study are as follows (Table 1 Based on previous studies, this study used seven independent variables to estimate OHC, including remote sensing data and space-time parameters (i.e., SST, SSH, USSW, VSSW [u and v components of SSW], LON, LAT, and DOY). It is likely not necessary to consider sea surface salinity in large-scale and time series variations [40,46]. The spatial resolution of sea surface data was unified to 1 • × 1 • by the nearest neighbor interpolation method, and the temporal resolution was unified monthly.…”

Section: Study Area and Datamentioning

confidence: 99%

“…Irrgang et al [39] estimated global OHC from 1990-2015 from tidal magnetic satellite observations using an ANN algorithm. Su et al [40] used an ANN to construct a long-term OHC dataset, known as the Ocean Projection and Extension neural Network (OPEN), which shows a comparative advantage with the IAP dataset. However, these methods lack temporal dependence considerations.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Ocean Heat Content for Revisiting Global Ocean Warming from Remote Sensing Perspectives

Qin

Wang

et al. 2021

Remote Sensing

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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.

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OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data

Cited by 31 publications

References 47 publications

Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes

Data‐Driven Method With Numerical Model: A Combining Framework for Predicting Subtropical River Plumes

Quantifying Spread in Spatiotemporal Changes of Upper-Ocean Heat Content Estimates: An Internationally Coordinated Comparison

Reconstructing Ocean Heat Content for Revisiting Global Ocean Warming from Remote Sensing Perspectives

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