Acceleration in the Global Mean Sea Level Rise: 2005–2015

Yi, Shuang; Heki, Kosuke; An, Qian

doi:10.1002/2017gl076129

Cited by 37 publications

(22 citation statements)

References 44 publications

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“…There has been an unprecedented rise in global sea levels, with devastating physical and socioeconomical effects on the coastal communities (Nicholls and Cazenave 2010;Nidhinarangkoon et al 2020;Ranjbar et al 2020;Schaefer et al 2020;Shi et al 2020;Stammer et al 2013). Since the start of the 20th century, the Global Mean Sea Level (GMSL) has risen by about 16-21cm, with more than 7 cm of this occurring since 1993 (Global Change Research Program 2019;Jevrejeva et al 2016;Yi et al 2017). Sea level rise (SLR) is a global environmental issue and a threat to coastal communities that are usually characterized with high population and infrastructure densification.…”

Section: Introductionmentioning

confidence: 99%

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Balogun

Adebisi

2021

Geomatics, Natural Hazards and Risk

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This study aims to integrate a broad spectrum of ocean-atmospheric variables to predict sea level variation along West Peninsular Malaysia coastline using machine learning and deep learning techniques. 4 scenarios of different combinations of variables such as sea surface temperature, sea surface salinity, sea surface density, surface atmospheric pressure, wind speed, total cloud cover, precipitation and sea level data were used to train ARIMA, SVR and LSTM neural network models. Results show that atmospheric processes have more influence on prediction accuracy than ocean processes. Combining ocean and atmospheric variables improves the model prediction at all stations by 1-9% for both SVR and LSTM. The means of R accuracy of optimal performing LSTM, SVR and ARIMA models at all stations are 0.853, 0.748 and 0.710, respectively. Comparison of model performance shows that the LSTM model trained with ocean and atmospheric variables is optimal for predicting sea level variation at all stations except Pulua Langkawi where ARIMA model trained without ocean-atmospheric variables performed best due to the dominating tide influence. This suggests that performance and suitability of prediction models vary across regions and selecting an optimal prediction model depends on the dominant physical processes governing sea level variability in the area of investigation.

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Section: Introductionmentioning

confidence: 99%

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Balogun

Adebisi

2021

Geomatics, Natural Hazards and Risk

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“…To find out the importance of the ocean-to-land leakage, we compare the results by three methods: M2: the uniform ocean layer is used, so it is the same as previously used (Chen et al 2013;Yi et al 2017). M3: the non-uniform ocean layer is used, which is the method proposed in this study.…”

Section: R E S U Lt S O F T H E O C E a N -T O -L A N D L E A K A G Ementioning

confidence: 99%

“…Fig. 1 illustrates this problem with a profile across the equator of a simplified landocean model (Yi et al 2017). Considering global conservation of water mass, a decrease of water mass storage on land will result in an increase of the ocean mass by the same amount.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous oceanic mass distribution in GRACE observations and its leakage effect

Heki

2020

Geophysical Journal International

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SUMMARY Signal leakage between the land and ocean is a challenge in using Gravity Recovery and Climate Experiment (GRACE) observation data to study global mass redistributions. Although the leakage occurs in both directions, more attention has been paid to the land-to-ocean leakage and less to the ocean-to-land leakage. Here, we show that the ocean-to-land leakage is non-uniform and non-negligible and propose a new forward modelling method to fully consider bi-directional leakages with the help of the global Ocean ReAnalysis System ORAS5. This observation-driven model could significantly reduce the variations in ocean grids and thus decrease the ocean-to-land leakage. The results with different treatment of the ocean signal leakage are compared. We find that failing to consider the ocean-to-land leakage will cause an underestimation of ∼20 per cent in the seasonal variation and will introduce a bias of several giga-tons in the secular trend. Although the uniform and non-uniform model have similar results in the global average of seasonal mass variations, the non-uniform ocean model is necessary in most places, especially near the Arctic Ocean, the Sea of Japan and the Gulf of Carpentaria. Despite these achievements, we also point out that there is still much room for improvement in ocean mass models, particularly in long-term trends. Our results indicate the importance of the ocean-to-land leakage correction in the mass estimation in coastal land areas using the GRACE data.

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“…Following the improvement of some key technologies in the 1990s, such as satellite altimetry, satellite gravity, and steric sea level Argo arrays, more comprehensive measurements have been available to study GMSL change. The GMSL has been shown to have increased rapidly and an accelerating trend (Cazenave et al, 2018; Nerem et al, 2018; Yi et al, 2017). The rate of GMSL rise has increased to 3.0–3.5 mm•yr − 1 since 2005 and has risen sharply to approximately 4.4 mm•yr − 1 since 2010 (Chambers et al, 2017; Chen et al, 2018; Dieng et al, 2017; Legeais et al, 2018; Yi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The Balance and Abnormal Increase of Global Ocean Mass Change From Land Using GRACE

Jin

Shum

et al. 2020

Earth and Space Science

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Global ocean mass (GOM) change, which is mainly influenced by water outflow from land, has been identified as one of the important contributors to present-day sea level rise. Here, we delineated GOM change by dividing the land into five parts, which are exorheic river basins, endorheic basins, polar ice sheets, and land glaciers and ice caps, by the Gravity Recovery and Climate Experiment mass concentration solutions, 2002-2017. We estimated their total contribution to GOM change to be 568.92 ± 21.43 GT•yr −1 , which is statistically congruent with the Gravity Recovery and Climate Experiment estimated GOM change (579.46 ± 46.62 GT•yr −1 ). The melting of Earth's ice reservoirs is the dominant contribution in the whole period. However, an abnormal increase is found in the trend of GOM change with an amplitude of approximately 100% since August 2008. Further finding reveals that melting of Antarctica Ice Sheets are increasing fast and significantly dominates among other geophysical contributions to the abnormal increase at 34.28%, following with the water storage change of exorheic river basins at 28.71% and the melting of Greenland Ice Sheets at 22.76%, which is postulated to be resulting from intensified ice wastages and precipitation during the last decade.

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Acceleration in the Global Mean Sea Level Rise: 2005–2015

Cited by 37 publications

References 44 publications

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Heterogeneous oceanic mass distribution in GRACE observations and its leakage effect

The Balance and Abnormal Increase of Global Ocean Mass Change From Land Using GRACE

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