A Numerical Simulation of Long-Term Sea Level Change in the East Asian Marginal Seas

2018 ◽  
Vol 85 ◽  
pp. 546-550
Author(s):  
Cheol-Ho Kim ◽  
Chan Joo Jang ◽  
Min-Woo Kim
Keyword(s):  
Numerical Simulation ◽  
Sea Level ◽  
East Asian ◽  
Sea Level Change ◽  
Marginal Seas ◽  
Level Change ◽  
East Asian Marginal Seas

Sea level in the Global Geodetic Observing System

2020 ◽  
Author(s):  
Elizabeth Bradshaw ◽  
Andy Matthews ◽  
Kathy Gordon ◽  
Angela Hibbert ◽  
Sveta Jevrejeva ◽  
...  
Keyword(s):  
Sea Level ◽  
Working Group ◽  
Sea Level Change ◽  
Tide Gauges ◽  
Controlled Vocabularies ◽  
Mean Sea Level ◽  
Level Change ◽  
Level Data ◽  
Metadata Standards

<p>The Permanent Service for Mean Sea Level (PSMSL) is the global databank for long-term mean sea level data and is a member of the Global Geodetic Observing System (GGOS) Bureau of Networks and Observations. As well as curating long-term sea level change information from tide gauges, PSMSL is also involved in developing other products and services including the automatic quality control of near real-time sea level data, distributing Global Navigation Satellite System (GNSS) sea level data and advising on sea level metadata development.<br>At the GGOS Days meeting in November 2019, the GGOS Focus Area 3 on Sea Level Change, Variability and Forecasting was wrapped up, but there is still a requirement in 2020 for GGOS to integrate and support tide gauges and we will discuss how we will interact in the future. A recent paper (Ponte et al., 2019) identified that only “29% of the GLOSS [Global Sea Level Observing System] GNSS-co-located tide gauges have a geodetic tie available at SONEL [Système d'Observation du Niveau des Eaux Littorales]” and we as a community still need to improve the ties between the GNSS sensor and tide gauges. This may progress as new GNSS Interferometric Reflectometry (GNSS-IR) sensors are installed to provide an alternative method to observe sea level. As well as recording the sea level, these sensors will also provide vertical land movement information from one location. PSMSL are currently developing an online portal of uplift/subsidence land data and GNSS-IR sea level observation data. To distribute the data, we are creating/populating controlled vocabularies and generating discovery metadata.<br>We are working towards FAIR data management principles (data are findable, accessible, interoperable and reusable) which will improve the flow of quality controlled sea level data and in 2020 we will issue the PSMSL dataset with a Digital Object Identifier. We have been working on improving our discovery and descriptive metadata including creating a use case for the Research Data Alliance Persistent (RDA) Identification of Instruments Working Group to help improve the description of a time series where the sensor and platform may change and move many times. Representatives from PSMSL will sit on the GGOS DOIs for Data Working Group and would like to contribute help with controlled vocabularies, identifying metadata standards etc. We will also contribute to the next GGOS implementation plan.<br>Ponte, Rui M., et al. (2019) "Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level." <em>Frontiers in Marine Science</em> 6(437).</p>

scholarly journals Long-term regional trend and variability of mean sea level during the satellite altimetry era

2019 ◽  
Vol 83 (2) ◽  
pp. 111
Author(s):  
Quang-Hung Luu ◽  
Qing Wu ◽  
Pavel Tkalich ◽  
Ge Chen
Keyword(s):  
Sea Level ◽  
Climatic Factors ◽  
Sea Level Change ◽  
Climate System ◽  
Regional Trend ◽  
Mean Sea Level ◽  
Level Change ◽  
The Pacific ◽  
Change In Mean

The rise and fall of mean sea level are non-uniform around the global oceans. Their long-term regional trend and variability are intimately linked to the fluctuations and changes in the climate system. In this study, geographical patterns of sea level change derived from altimetric data over the period 1993-2015 were partitioned into large-scale oscillations allied with prevailing climatic factors after an empirical orthogonal function analysis. Taking into account the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillations (PDO), the sea level change deduced from the multiple regression showed a better estimate than the simple linear regression thanks to significantly larger coefficients of determination and narrower confidence intervals. Regional patterns associated with climatic factors varied greatly in different basins, notably in the eastern and western regions of the Pacific Ocean. The PDO exhibited a stronger impact on long-term spatial change in mean sea level than the ENSO in various parts of the Indian and Pacific Oceans, as well as of the subtropics and along the equator. Further improvements in the signal decomposition technique and physical understanding of the climate system are needed to better attain the signature of climatic factors on regional mean sea level.

Coastal Sea Level Change from in the North Eastern Atlantic

2020 ◽  
Author(s):  
Luciana Fenoglio-Marc ◽  
Bernd Uebbing ◽  
Jürgen Kusche ◽  
Salvatore Dinardo
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Sea Level Change ◽  
Altimeter Data ◽  
Level Change ◽  
The North ◽  
North Eastern ◽  
Coastal Sea

<p>A significant part of the World population lives in the coastal zone, which is affected by coastal sea level rise and extreme events. Our hypothesis is that the most accurate sea level height measurements are derived from the Synthetic Aperture Altimetry (SAR) mode. This study analyses the output of dedicated processing and assesses their impacts on the sea level change of the North-Eastern Atlantic. </p><p>It will be shown that SAR altimetry reduces the minimum usable distance from five to three kilometres when the dedicated coastal retrackers SAMOSA+ and SAMOSA++ are applied to data processed in SAR mode. A similar performance is achieved with altimeter data processed in pseudo low resolution mode (PLRM) when the Spatio-Temporal Altimeter sub-waveform Retracker (STAR) is used. Instead the Adaptive Leading Edge Sub-waveform retracker (TALES) applied to PLRM is less performant. SAR processed altimetry can recover the sea level heights with 4 cm accuracy up to 3-4 km distance to coast. Thanks to the low noise of SAR mode data, the instantaneous SAR and in-situ data have the highest agreement, with the smallest standard deviation of differences and the highest correlation. A co-location of the altimeter data near the tide gauge is the best choice for merging in-situ and altimeter data. The r.m.s. (root mean squared) differences between altimetry and in-situ heights remain large in estuaries and in coastal zone with high tidal regimes, which are still challenging regions. The geophysical parameters derived from CryoSat-2 and Sentinel-3A measurements have similar accuracy, but the different repeat cycle of the two missions locally affects the constructed time-series.</p><p>The impact of these new SAR observations in climate change studies is assessed by evaluating regional and local time series of sea level. At distances to coast smaller than 10 Kilometers the sea level change derived from SAR and LRM data is in good agreement. The long-term sea level variability derived from monthly time-series of LRM altimetry and of land motion-corrected tide gauges agrees within 1 mm/yr for half of in-situ German stations. The long-term sea level variability derived from SAR data show a similar behaviour with increasing length of the time series.</p><p> </p>

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