The application of satellite altimetry in establishing regional mean sea level

2011 ◽  
Author(s):  
Xinghua Zhou ◽  
Cuiyu Sun ◽  
Ning Lei ◽  
Huayi Zhang
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Mean Sea Level

A 25-Year Satellite Altimetry-Based Global Mean Sea Level Record

2017 ◽  
pp. 187-210 ◽  
Author(s):  
R. Steven Nerem ◽  
Michaël Ablain ◽  
Anny Cazenave ◽  
John Church ◽  
Eric Leuliette
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Mean Sea Level ◽  
Global Mean Sea Level ◽  
Global Mean

scholarly journals Recent Sea Level Change in the Black Sea from Satellite Altimetry and Tide Gauge Observations

2020 ◽  
Vol 9 (3) ◽  
pp. 185 ◽  
Author(s):  
Nevin Avşar ◽  
Şenol Kutoğlu
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Black Sea ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
The Black Sea ◽  
Sea Level Changes ◽  
Early 19Th Century ◽  
Mean Sea Level ◽  
The Mean

Global mean sea level has been rising at an increasing rate, especially since the early 19th century in response to ocean thermal expansion and ice sheet melting. The possible consequences of sea level rise pose a significant threat to coastal cities, inhabitants, infrastructure, wetlands, ecosystems, and beaches. Sea level changes are not geographically uniform. This study focuses on present-day sea level changes in the Black Sea using satellite altimetry and tide gauge data. The multi-mission gridded satellite altimetry data from January 1993 to May 2017 indicated a mean rate of sea level rise of 2.5 ± 0.5 mm/year over the entire Black Sea. However, when considering the dominant cycles of the Black Sea level time series, an apparent (significant) variation was seen until 2014, and the rise in the mean sea level has been estimated at about 3.2 ± 0.6 mm/year. Coastal sea level, which was assessed using the available data from 12 tide gauge stations, has generally risen (except for the Bourgas Station). For instance, from the western coast to the southern coast of the Black Sea, in Constantza, Sevastopol, Tuapse, Batumi, Trabzon, Amasra, Sile, and Igneada, the relative rise was 3.02, 1.56, 2.92, 3.52, 2.33, 3.43, 5.03, and 6.94 mm/year, respectively, for varying periods over 1922–2014. The highest and lowest rises in the mean level of the Black Sea were in Poti (7.01 mm/year) and in Varna (1.53 mm/year), respectively. Measurements from six Global Navigation Satellite System (GNSS) stations, which are very close to the tide gauges, also suggest that there were significant vertical land movements at some tide gauge locations. This study confirmed that according to the obtained average annual phase value of sea level observations, seasonal sea level variations in the Black Sea reach their maximum annual amplitude in May–June.

scholarly journals Quantifying the impact of basin dynamics on the regional sea level rise in the Black Sea

Ocean Science ◽  
2017 ◽  
Vol 13 (3) ◽  
pp. 443-452 ◽  
Author(s):  
Arseny A. Kubryakov ◽  
Sergey V. Stanichny ◽  
Denis L. Volkov
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Black Sea ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
The Black Sea ◽  
Southeastern Part ◽  
Mean Sea Level ◽  
The Impact ◽  
Dynamic Sea Level

Abstract. Satellite altimetry measurements show that the magnitude of the Black Sea sea level trends is spatially uneven. While the basin-mean sea level rise from 1993 to 2014 was about 3.15 mm yr−1, the local rates of sea level rise varied from 1.5–2.5 mm yr−1 in the central part to 3.5–3.8 mm yr−1 at the basin periphery and over the northwestern shelf and to 5 mm yr−1 in the southeastern part of the sea. We show that the observed spatial differences in the dynamic sea level (anomaly relative to the basin-mean) are caused by changes in the large- and mesoscale dynamics of the Black Sea. First, a long-term intensification of the cyclonic wind curl over the Black Sea, observed in 1993–2014, strengthened divergence in the center of the basin and led to the rise of the sea level in coastal and shelf areas and a lowering in the basin's interior. Second, an extension of the Batumi anticyclone to the west resulted in  ∼  1.2 mm yr−1 higher rates of sea level rise in the southeastern part of the sea. Further, we demonstrate that the large-scale dynamic sea level variability in the Black Sea can be successfully reconstructed using the wind curl obtained from an atmospheric reanalysis. This allows for the correction of historical tide gauge records for dynamic effects in order to derive more accurate estimates of the basin-mean sea level change in the past, prior to the satellite altimetry era.

Effect of the processing methodology on satellite altimetry-based global mean sea level rise over the Jason-1 operating period

2013 ◽  
Vol 88 (4) ◽  
pp. 351-361 ◽  
Author(s):  
Olivier Henry ◽  
Michael Ablain ◽  
Benoit Meyssignac ◽  
Anny Cazenave ◽  
Dallas Masters ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Operating Period ◽  
Mean Sea Level ◽  
Global Mean Sea Level ◽  
Global Mean

scholarly journals Sea Level Rise of Sumatera Waters based on Multi-Satellite Altimetry Data

2018 ◽  
Vol 50 (2) ◽  
pp. 162
Author(s):  
Isna Uswatun Khasanah
Keyword(s):  
Linear Regression ◽  
Sea Level Rise ◽  
Sea Level ◽  
Coastal Area ◽  
Satellite Altimetry ◽  
Altimetry Data ◽  
Global Test ◽  
Mean Sea Level ◽  
West Sumatra ◽  
Satellite Altimetry Data

The information of sea level rise was needed in the Indonesia as archipelago country to management risk and development coastal area. This research study took in West Sumatra waters, because the majority people have lived in coastal area and some areas is located below 100 m above Mean Sea Level (MSL). The sea level data was taken from multi-satellite altimetry, they are Topex/Poseidon, Jason-1, and Jason-2. The period of data started from 1993 until 2015.Preliminary data processing of satellite altimetry was done by global test and post-processing of satellite altimetry data. The sea level rise analysis done by linear regression methods. Linear regression formula of sea level rise in West Sumatra Waters during the period was  y = 1.586 + 0.0000113x. The change of sea level during period 1993 until 2015 was 3.394 cm with mean sea level rise value was 1.35 mm/year

A New Estimation of Mean Sea Level in the Arctic Ocean from Satellite Altimetry

2012 ◽  
Vol 35 (sup1) ◽  
pp. 61-81 ◽  
Author(s):  
P. Prandi ◽  
M. Ablain ◽  
A. Cazenave ◽  
N. Picot
Keyword(s):  
Arctic Ocean ◽  
Sea Level ◽  
Satellite Altimetry ◽  
The Arctic ◽  
Mean Sea Level ◽  
The Arctic Ocean

scholarly journals On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series

2017 ◽  
Vol 122 (11) ◽  
pp. 8371-8384 ◽  
Author(s):  
B. D. Beckley ◽  
P. S. Callahan ◽  
D. W. Hancock ◽  
G. T. Mitchum ◽  
R. D. Ray
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Mean Sea Level ◽  
Global Mean Sea Level ◽  
Global Mean

scholarly journals Mean sea level determination from satellite altimetry

1979 ◽  
Vol 2 (2) ◽  
pp. 127-144 ◽  
Author(s):  
W. D. Kahn ◽  
B. B. Agrawal ◽  
R. D. Brown
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Mean Sea Level

scholarly journals Year by year closure adjustment of global mean sea level budget, inclusive of lumped snow, water vapor, and permafrost mass components

2020 ◽  
Vol 10 (1) ◽  
pp. 83-90
Author(s):  
H. Bâki Iz ◽  
C. K. Shum
Keyword(s):  
Water Vapor ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Linear Trend ◽  
The Other ◽  
Mean Sea Level ◽  
Lump Sum ◽  
Snow Water ◽  
Global Mean Sea Level ◽  
Global Mean

AbstractGlobal mean sea level budget is rigorously adjusted during the period 2005–2015 with an emphasis on closing the budget on a year by year basis as opposed to using linear trends of global mean sea level components. The adjustment also accounts for the effect of snow, water vapor, and permafrost mass components as a lump sum. The approach provides better resolution for evaluating individual contribution of each budget component year by year in tandem with the other components. Year by year budget misclosures and the confidence intervals of the year by year adjusted budget components are suggestive of an increasing non-linearity in satellite altimetry derived global mean sea level measurements starting in 2012, which are not present in the other components. The solution also generates time series iteratively for the lumped snow, water vapor, and permafrost mass components as well as an estimate for its linear trend, 0.06±0.59 mm/yr. Nonetheless, its standard error is markedly large because of the un-modeled variability in satellite altimetry observed yearly averaged global mean sea level anomalies.

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