Diagnostic models for estimating mean sea level change using hybrid model of exponential smoothing and neural network

2015 ◽  
Author(s):  
Dadan Kusnandar ◽  
Muhlasah Novitasari Mara ◽  
Naomi Nessyana Debataraja
Keyword(s):  
Neural Network ◽  
Sea Level ◽  
Hybrid Model ◽  
Sea Level Change ◽  
Exponential Smoothing ◽  
Mean Sea Level ◽  
Level Change ◽  
Diagnostic Models

An EEMD and BP neural network hybrid approach for modeling regional sea level change

2018 ◽  
Vol 121 ◽  
pp. 139-146
Author(s):  
Lei He ◽  
◽  
Jilong Chen ◽  
Yue Zhang ◽  
Tengjiao Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Sea Level ◽  
Bp Neural Network ◽  
Hybrid Approach ◽  
Sea Level Change ◽  
Level Change ◽  
Regional Sea Level ◽  
Regional Sea Level Change

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 The land-ice contribution to 21st century dynamic sea-level rise

2014 ◽  
Vol 11 (1) ◽  
pp. 123-169 ◽  
Author(s):  
T. Howard ◽  
J. Ridley ◽  
A. K. Pardaens ◽  
R. T. W. L. Hurkmans ◽  
A. J. Payne ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
21St Century ◽  
Sea Level Change ◽  
Sea Level Changes ◽  
Mean Sea Level ◽  
Ice Melt ◽  
Level Change ◽  
Global Mean ◽  
Dynamic Sea Level

Abstract. Climate change has the potential to locally influence mean sea level through a number of processes including (but not limited to) thermal expansion of the oceans and enhanced land ice melt. These lead to departures from the global mean sea level change, due to spatial variations in the change of water density and transport, which are termed dynamic sea level changes. In this study we present regional patterns of sea-level change projected by a global coupled atmosphere–ocean climate model forced by projected ice-melt fluxes from three sources: the Antarctic ice sheet, the Greenland ice sheet and small glaciers and ice caps. The largest ice melt flux we consider is equivalent to almost 0.7 m of global sea level rise over the 21st century. Since the ice melt is not constant, the evolution of the dynamic sea level changes is analysed. We find that the dynamic sea level change associated with the ice melt is small, with the largest changes, occurring in the North Atlantic, contributing of order 3 cm above the global mean rise. Furthermore, the dynamic sea level change associated with the ice melt is similar regardless of whether the simulated ice fluxes are applied to a simulation with fixed or changing atmospheric CO2.

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.

scholarly journals STUDY ON SEA LEVEL RISE IN THE WESTERN INDONESIA

2018 ◽  
Vol 29 ◽  
pp. 31-40
Author(s):  
Hadikusumah
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Air Temperature ◽  
Sea Level Change ◽  
Mean Sea Level ◽  
Level Change ◽  
Weather Stations ◽  
Level Analysis

Study on mean sea level (MSL) rise has been done on tide data at some locations in the Western Indonesia. To account the effect of climate change, air temperature analyses from some weather stations are also performed. The results showed that air temperature has changed between 0.0 to 0.44°C per ten years. The sea level analysis showed that mean sea level at Western Indonesia rise between 3.10 to 9.27 mm per year. Based on the results, the prediction on mean sea level change in the years of 2000, 2030, 2050 and 2100 for Cirebon location are 17 cm, 39 cm, 55 cm, and 92 cm, respectively.

Global Mean Sea Level Change: Correction

Science ◽  
1997 ◽  
Vol 275 (5303) ◽  
pp. 1049i-1053 ◽  
Author(s):  
R. S. Nerem
Keyword(s):  
Sea Level ◽  
Sea Level Change ◽  
Mean Sea Level ◽  
Level Change ◽  
Global Mean Sea Level ◽  
Global Mean

scholarly journals Variations in global mean sea level associated with the 1997-1998 ENSO event: Implications for measuring long term sea level change

1999 ◽  
Vol 26 (19) ◽  
pp. 3005-3008 ◽  
Author(s):  
R. S. Nerem ◽  
D. P. Chambers ◽  
E. W. Leuliette ◽  
G. T. Mitchum ◽  
B. S. Giese
Keyword(s):  
Sea Level ◽  
Sea Level Change ◽  
Enso Event ◽  
Mean Sea Level ◽  
Level Change ◽  
Global Mean Sea Level ◽  
Global Mean

scholarly journals Future storm surge impacts on insurable losses for the North Sea region

2011 ◽  
Vol 11 (4) ◽  
pp. 1205-1216 ◽  
Author(s):  
L. Gaslikova ◽  
A. Schwerzmann ◽  
C. C. Raible ◽  
T. F. Stocker
Keyword(s):  
Sea Level ◽  
Storm Surge ◽  
North Sea ◽  
Sea Level Change ◽  
Coastal Protection ◽  
Sea Level Changes ◽  
Mean Sea Level ◽  
Level Change ◽  
The North ◽  
The North Sea

Abstract. The influence of climate change on storm surges including increased mean sea level change and the associated insurable losses are assessed for the North Sea basin. In doing so, the newly developed approach couples a dynamical storm surge model with a loss model. The key element of the approach is the generation of a probabilistic storm surge event set. Together with parametrizations of the inland propagation and the coastal protection failure probability this enables the estimation of annual expected losses. The sensitivity to the parametrizations is rather weak except when the assumption of high level of increased mean sea level change is made. Applying this approach to future scenarios shows a substantial increase of insurable losses with respect to the present day. Superimposing different mean sea level changes shows a nonlinear behavior at the country level, as the future storm surge changes are higher for Germany and Denmark. Thus, the study exhibits the necessity to assess the socio-economic impacts of coastal floods by combining the expected sea level rise with storm surge projections.

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