scholarly journals Assessment of Altimetric Range and Geophysical Corrections and Mean Sea Surface Models—Impacts on Sea Level Variability around the Indonesian Seas

2017 ◽  
Vol 9 (2) ◽  
pp. 102 ◽  
Author(s):  
Eko Handoko ◽  
Maria Fernandes ◽  
Clara Lázaro
Keyword(s):  
Sea Level ◽  
Sea Surface ◽  
Sea Level Variability ◽  
Indonesian Seas ◽  
Surface Models ◽  
Mean Sea Surface

Contributions of Wind Forcing and Surface Heating to Interannual Sea Level Variations in the Atlantic Ocean

2006 ◽  
Vol 36 (9) ◽  
pp. 1739-1750 ◽  
Author(s):  
Cécile Cabanes ◽  
Thierry Huck ◽  
Alain Colin de Verdière
Keyword(s):  
Atlantic Ocean ◽  
Sea Level ◽  
Wind Stress ◽  
Large Scale ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Altimeter Data ◽  
Local Response ◽  
Sea Level Variability ◽  
Sea Level Variations

Abstract Interannual sea surface height variations in the Atlantic Ocean are examined from 10 years of high-precision altimeter data in light of simple mechanisms that describe the ocean response to atmospheric forcing: 1) local steric changes due to surface buoyancy forcing and a local response to wind stress via Ekman pumping and 2) baroclinic and barotropic oceanic adjustment via propagating Rossby waves and quasi-steady Sverdrup balance, respectively. The relevance of these simple mechanisms in explaining interannual sea level variability in the whole Atlantic Ocean is investigated. It is shown that, in various regions, a large part of the interannual sea level variability is related to local response to heat flux changes (more than 50% in the eastern North Atlantic). Except in a few places, a local response to wind stress forcing is less successful in explaining sea surface height observations. In this case, it is necessary to consider large-scale oceanic adjustments: the first baroclinic mode forced by wind stress explains about 70% of interannual sea level variations in the latitude band 18°–20°N. A quasi-steady barotropic Sverdrup response is observed between 40° and 50°N.

Regional Mean Sea Surface Model over the Eastern Mediterranean Sea

2020 ◽  
Author(s):  
Milaa Murshan ◽  
Balaji Devaraju ◽  
Nagarajan Balasubramanium ◽  
Onkar Dikshit
Keyword(s):  
Time Series ◽  
Mediterranean Sea ◽  
Sea Level ◽  
Tide Gauge ◽  
Eastern Mediterranean ◽  
Sea Surface ◽  
Surface Model ◽  
Eastern Mediterranean Sea ◽  
Mean Sea Level ◽  
Mean Sea Surface

<p>The Mean Sea Level is not an equipotential surface because it is subject to several variations, e.g., the tides, currents, winds, etc. Mean Sea Level can be measured either by tide gauges near to coastlines relative to local datum or by satellite altimeter above the reference ellipsoid. From this observable quantity, one can derive a non-observable quantity at which the potential is constant called geoid and differs from mean sea surface by amount of ±1 m. This separation is called Sea Surface Topography. In this research, the data of nine altimetric Exact Repeat Missions (Envisat, ERS_1 of 35 days (phase C and G), ERS_2, GFO, Jason_1, Jason_2, Jason_3, Topex/Poseidon and SARAL) were used for computing the regional mean sea surface model over the eastern Mediterranean Sea. The data of all missions together span approximately 25 years from September -1992 to January-2017 and referenced to Topex ellipsoid.  Which is later transformed to WGS84 ellipsoid, as it is chosen to be a unified datum in this study. Prior to computing the altimetric MSS,  altimetric sea surface height measurements were validated  by comparing  time series of altimetric-MSL with mean sea level time series calculated from three in-situ tide gauge measurements.  The sea surface heights values of the derived MSS model is between 15.6 and 26.7 m. And the linear trend slope is between -3.02 to 6.53 mm/year.</p><p>Keywords: Mean Sea Level, Satellite Altimetry, Tide Gauge, Exact Repeat Missions</p>

scholarly journals Deep learning of dynamic sea-level variability to investigate the relationship with the floods in Gothenburg

2021 ◽  
Author(s):  
Omid Memarian Sorkhabi
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Sea Level Rise ◽  
Sea Level ◽  
Sea Surface ◽  
Sea Level Variability ◽  
Annual Increase ◽  
The Relationship ◽  
Dynamic Sea Level ◽  
And Sea Level

Abstract It is important to study the relationship between floods and sea-level rise due to climate change. In this research, dynamic sea-level variability with deep learning has been investigated. In this research sea surface temperature (SST) from MODIS, wind speed, precipitation and sea-level rise from satellite altimetry investigated for dynamic sea-level variability. An annual increase of 0.1 ° C SST is observed around the Gutenberg coast. Also in the middle of the North Sea, an annual increase of about 0.2 ° C is evident. The annual sea surface height (SSH) trend is 3 mm on the Gothenburg coast. We have a strong positive spatial correlation of SST and SSH near the Gothenburg coast. In the next step dynamic sea-level variability is predicted with long short time memory. Root mean square error of wind speed, precipitation, and mean sea-level forecasts are 0.84 m/s, 48 mm and 2.4 mm. The annual trends resulting from 5-year periods, show a significant increase from 28 mm to 46 mm per year in the last 5 year periods. The rate of increase has doubled. The wavelet can be useful for detecting dynamic sea-level variability.

scholarly journals A global mean sea surface temperature dataset for the Last Interglacial (129–116 ka) and contribution of thermal expansion to sea level change

2020 ◽  
Vol 12 (4) ◽  
pp. 3341-3356
Author(s):  
Chris S. M. Turney ◽  
Richard T. Jones ◽  
Nicholas P. McKay ◽  
Erik van Sebille ◽  
Zoë A. Thomas ◽  
...  
Keyword(s):  
Sea Level ◽  
Sea Surface ◽  
Global Network ◽  
Future Climate Change ◽  
Last Interglacial ◽  
Maximum Peak ◽  
Surface Temperatures ◽  
Mean Sea Surface ◽  
Global Sea Level ◽  
Sst Anomalies

Abstract. A valuable analogue for assessing Earth's sensitivity to warming is the Last Interglacial (LIG; 129–116 ka), when global temperatures (0 to +2 ∘C) and mean sea level (+6 to 11 m) were higher than today. The direct contribution of warmer conditions to global sea level (thermosteric) is uncertain. We report here a global network of LIG sea surface temperatures (SST) obtained from various published temperature proxies (e.g. faunal and floral plankton assemblages, Mg ∕ Ca ratios of calcareous organisms, and alkenone U37K′). We summarize the current limitations of SST reconstructions for the LIG and the spatial temperature features of a naturally warmer world. Because of local δ18O seawater changes, uncertainty in the age models of marine cores, and differences in sampling resolution and/or sedimentation rates, the reconstructions are restricted to mean conditions. To avoid bias towards individual LIG SSTs based on only a single (and potentially erroneous) measurement or a single interpolated data point, here we report average values across the entire LIG. Each site reconstruction is given as an anomaly relative to 1981–2010, corrected for ocean drift, and where available seasonal estimates are provided (189 annual, 99 December–February, and 92 June–August records). To investigate the sensitivity of the reconstruction to high temperatures, we also report maximum values during the first 5 millennia of the LIG (129–124 ka). We find mean global annual SST anomalies of 0.2 ± 0.1 ∘C averaged across the LIG and an early maximum peak of 0.9 ± 0.1 ∘C, respectively. The global dataset provides a remarkably coherent pattern of higher SST increases at polar latitudes than in the tropics (demonstrating the polar amplification of surface temperatures during the LIG), with comparable estimates between different proxies. Polewards of 45∘ latitude, we observe annual SST anomalies averaged across the full LIG of > 0.8 ± 0.3 ∘C in both hemispheres with an early maximum peak of > 2.1 ± 0.3 ∘C. Using the reconstructed SSTs suggests a mean LIG global thermosteric sea level rise of 0.08 ± 0.1 m and a peak contribution of 0.39 ± 0.1 m, respectively (assuming warming penetrated to 2000 m depth). The data provide an important natural baseline for a warmer world, constraining the contributions of Greenland and Antarctic ice sheets to global sea level during a geographically widespread expression of high sea level, and can be used to test the next inter-comparison of models for projecting future climate change. The dataset described in this paper, including summary temperature and thermosteric sea level reconstructions, is available at https://doi.org/10.1594/PANGAEA.904381 (Turney et al., 2019).

scholarly journals Kinematics of global mean thermosteric sea level during 1993–2019

2021 ◽  
Vol 11 (1) ◽  
pp. 75-82
Author(s):  
H. Bâki İz
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Temperature Gradients ◽  
Sea Surface ◽  
Sea Level Changes ◽  
Mean Sea Level ◽  
Mean Sea Surface ◽  
Global Mean Sea Level ◽  
Global Sea Level ◽  
Global Mean

Abstract Because oceans cover 71% of Earth’s surface, ocean warming, consequential for thermal expansion of sea water, has been the largest contributor to the global mean sea level rise averaged over the 20 th and the early 21 st century. This study first generates quasi-observed monthly globally averaged thermosteric sea level time series by removing the contributions of global mean sea level budget components, namely, Glaciers, Greenland, Antarctica, and Terrestrial Water Storage from satellite altimetry measured global sea level changes during 1993–2019. A baseline kinematic model with global mean thermosteric sea level trend and a uniform acceleration is solved to evaluate the performance of a rigorous mixed kinematic model. The model also includes coefficients of monthly lagged 60 yearlong cumulative global mean sea surface temperature gradients and control variables of lunisolar origins and representations for first order autoregressive disturbances. The mixed kinematic model explains 94% (Adjusted R 2)1 of the total variability in quasi-observed monthly and globally averaged thermosteric time series compared to the 46% of the baseline kinematic model’s Adjusted R 2. The estimated trend, 1.19±0.03 mm/yr., is attributed to the long-term ocean warming. Whereas eleven statistically significant (α = 0.05) monthly lagged cumulative global mean sea surface temperature gradients each having a memory of 60 years explain the remainder transient global mean thermosteric sea level changes due to the episodic ocean surface warming and cooling during this period. The series also exhibit signatures of a statistically significant contingent uniform global sea level acceleration and periodic lunisolar forcings.

scholarly journals Gauging the Improvement of Recent Mean Sea Surface Models: A New Approach for Identifying and Quantifying Their Errors

2018 ◽  
Vol 123 (8) ◽  
pp. 5889-5911 ◽  
Author(s):  
Marie‐Isabelle Pujol ◽  
Philippe Schaeffer ◽  
Yannice Faugère ◽  
Matthias Raynal ◽  
Gerald Dibarboure ◽  
...  
Keyword(s):  
Sea Surface ◽  
New Approach ◽  
Surface Models ◽  
Mean Sea Surface

scholarly journals SPATIO-TEMPORAL CHARACTERISTICS OF SEA LEVEL ANOMALY IN THE INDONESIAN WATER

GEOMATIKA ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 103
Author(s):  
Dina Anggreni Sarsito ◽  
Dudy Darmawan Wijaya ◽  
Nur Fajar Trihantoro ◽  
Muhammad Syahrullah Fathulhuda ◽  
Dhota Pradipta
Keyword(s):  
Sea Level ◽  
Geographical Location ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Sea Level Anomaly ◽  
Level Data ◽  
Mean Sea Surface ◽  
Spatio Temporal ◽  
East Asia Region ◽  
The Impact

<p>Indonesia is an archipelago state lies between Indian and Pacific Oceans at the South East Asia region. Its unique geomorphological and geographical setting affect variabilities of instantaneous sea surface height (ISSH) concering to one of the sea reference surface i.e mean sea surface height (MSSH). The differences between both heights, known as sea level anomaly (SLA), can be recognized as one of the parameter that describes the dynamic phenomena of the ocean. We investigated the Spatiotemporal characteristics of long-term SLA in this research based on 30 years of sea-level data derived from the multi-mission of satellite Altimetry (Topex/Poseidon, Jason-1, Jason-2 and Jason-3). The Spatiotemporal of SLA characteristics in Indonesian waters indicate substantial variations due to the influences of geographical location, bathymetric depth, and seasonal patterns. The SLA rate in the Indonesian region provides values that vary between 3.4 mm/yr to 5.3 mm/yr that higher than 3.2 mm/yr global SLA rate. The impact caused by the phenomenon needs to be taken into account given the vulnerability and disaster that could endanger the islands and coastal area in Indonesia. <strong></strong></p>

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