Absolute sea level variability of Arctic Ocean in 1993–2018 from satellite altimetry and tide gauge observations

2021 ◽  
Vol 40 (10) ◽  
pp. 76-83
Author(s):  
Yanguang Fu ◽  
Yikai Feng ◽  
Dongxu Zhou ◽  
Xinghua Zhou
Keyword(s):  
Arctic Ocean ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Sea Level Variability

Validation of the ALES Coastal Altimetry Dataset against the Norwegian Tide Gauges

2021 ◽  
Author(s):  
Fabio Mangini ◽  
Antonio Bonaduce ◽  
Léon Chafik ◽  
Laurent Bertino
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Linear Trend ◽  
Tide Gauges ◽  
Sea Level Variability ◽  
Preliminary Results ◽  
In Situ Data ◽  
Coastal Sea

<p>Satellite altimetry measurements, complemented by in-situ records, have made a fundamental contribution to the understanding of global sea level variability for almost 30 years. Due to land contamination, it performs best over the open ocean. However, over the years, there has been a significant effort to improve the altimetry products in coastal regions. Indeed, altimetry observations could be fruitfully used in the coastal zone to complement the existing tide gauge network which, despite its relevance, does not represent the entire coast. Given the important role of coastal altimetry in oceanography, we have recently decided to check the quality of a new coastal altimetry dataset, ALES, along the coast of Norway. The Norwegian coast is well covered by tide gauges and, therefore, particularly suitable to validate a coastal altimetry dataset. Preliminary results show a good agreement between in-situ and remote sensing sea-level signals in terms of linear trend, seasonal cycle and inter-annual variability. For example, the linear correlation coefficient between the inter-annual sea level variability from altimetry and tide gauges exceeds 0.8. Likewise, the root mean square difference between the two is less than 2 cm at most tide gauge locations. A comparison with Breili et al. (2017) shows that ALES performs better than the standard satellite altimetry products at estimating sea level trends along the coast of Norway. Notably, in the Lofoten region, the difference between the sea level trends computed using ALES and the tide gauges range between 0.0 to 0.7 mm/year, compared to circa 1 to 3 mm/year found by Breili et al. (2017). These preliminary results go in the direction of obtaining an accurate characterization of coastal sea-level at the high latitudes based on coastal altimetry records, which can represent a valuable source of information to reconstruct coastal sea-level signals in areas where in-situ data are missing or inaccurate.</p>

scholarly journals Satellite Altimetry and Tide Gauge Observed Teleconnections between Long-Term Sea Level Variability in the U.S. East Coast and the North Atlantic Ocean

2019 ◽  
Vol 11 (23) ◽  
pp. 2816
Author(s):  
Qing Xu ◽  
Kai Tu ◽  
Yongcun Cheng ◽  
Weiping Wang ◽  
Yongjun Jia ◽  
...  
Keyword(s):  
Sea Level ◽  
North Atlantic ◽  
Satellite Altimetry ◽  
North Atlantic Ocean ◽  
Tide Gauge ◽  
Atlantic Multidecadal Oscillation ◽  
Meridional Overturning Circulation ◽  
Sea Level Variability ◽  
Tropical North Atlantic ◽  
Cape Hatteras

Rising sea levels amplify the threat and magnitude of storm surges in coastal areas. The U.S. east coast region north of Cape Hatteras has shown a significant sea level rise acceleration and is believed to be a “hot-spot” for accelerating tidal flooding. To better understand the forcing mechanism of long-term regional sea level change, in order to more efficiently implement local sea level rise adaptation and mitigation measures, this work investigated the teleconnections between low-frequency sea level variability in the coastal region north of Cape Hatteras and the subpolar/tropical North Atlantic Ocean by using tide gauge measurements, satellite altimetry data and a sea level reconstruction dataset. The correlation analysis demonstrates that the tide-gauge measured sea level variability in the area north of Cape Hatteras is highly and positively correlated with that observed by satellite altimetry in the subpolar and tropical North Atlantic between 1993 and 2002. Over the following decade (2003–2012), the phase of the teleconnection in the subpolar region was reversed and the spatio-temporal correlation in the tropical North Atlantic was enhanced. Furthermore, the positive correlation in the region north of Cape Hatteras’s near shore area is strengthened, while the negative correlation in the Gulf Stream front region is weakened. The North Atlantic Oscillation and Atlantic Multidecadal Oscillation, which affect variations of the Atlantic Meridional Overturning Circulation and Gulf Stream, were shown to have significant impacts on the decadal changes of the teleconnections. Coherent with satellite altimetry data, the reconstructed sea level dataset in the 20th century exhibits similar spatial correlation patterns with the Atlantic Meridional Overturning Circulation, North Atlantic Oscillation and Atlantic Multidecadal Oscillation indices.

scholarly journals Sea level variability in the Arctic Ocean observed by satellite altimetry

2012 ◽  
Vol 9 (4) ◽  
pp. 2375-2401 ◽  
Author(s):  
P. Prandi ◽  
M. Ablain ◽  
A. Cazenave ◽  
N. Picot
Keyword(s):  
Arctic Ocean ◽  
Sea Level ◽  
Satellite Altimetry ◽  
The Arctic ◽  
Altimetry Data ◽  
Tide Gauges ◽  
Sea Level Variability ◽  
Ocean Mass ◽  
The Arctic Ocean ◽  
Satellite Altimetry Data

Abstract. We investigate sea level variability in the Arctic Ocean from observations. Variability estimates are derived both at the basin scale and on smaller local spatial scales. The periods of the signals studied vary from high frequency (intra-annual) to long term trends. We also investigate the mechanisms responsible for the observed variability. Different data types are used, the main one being a recent reprocessing of satellite altimetry data in the Arctic Ocean. Satellite altimetry data is compared to tide gauges measurements, steric sea level derived from temperature and salinity fields and GRACE ocean mass estimates. We establish a consistent regional sea level budget over the GRACE availability era (2003–2009) showing that the sea level drop observed by altimetry over this period is driven by ocean mass loss rather than steric effects. The comparison of altimetry and tide gauges time series show that the two techniques are in good agreement regarding sea level trends. Coastal areas of high variability in the altimetry record are also consistent with tide gauges records. An EOF analysis of September mean altimetry fields allows identifying two regions of wind driven variability in the Arctic Ocean: the Beaufort Gyre region and the coastal European and Russian Arctic. Such patterns are related to atmospheric regimes through the Arctic Oscillation and Dipole Anomaly.

scholarly journals Studying Sea Level Variability in Indonesia Sea Based on Satellite Altimetry

2020 ◽  
Vol 618 ◽  
pp. 012040
Author(s):  
Dina A Sarsito ◽  
Muhammad Syahrullah ◽  
Dudy D Wijaya ◽  
Dhota Pradipta ◽  
Heri Andreas
Keyword(s):  
Sea Level ◽  
Satellite Altimetry ◽  
Sea Level Variability

Impact of Satellite Altimetry on Simulations of Sea Level Variability by an Indian Ocean Model

2001 ◽  
Vol 24 (1) ◽  
pp. 53-63 ◽  
Author(s):  
S. K. Singh ◽  
Sujit Basu ◽  
Raj Kumar ◽  
Vijay K. Agarwal
Keyword(s):  
Indian Ocean ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Ocean Model ◽  
Sea Level Variability

Validation of sea surface heights from satellite altimetry along the Indian coast

2021 ◽  
Author(s):  
Milaa Murshan ◽  
Balaji Devaraju ◽  
Nagarajan Balasubramanian ◽  
Onkar Dikshit
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Atmospheric Correction ◽  
North Indian Ocean ◽  
Sea Surface ◽  
Indian Coast ◽  
Vertical Land Motion ◽  
And Performance

<p>Satellite altimetry provides measurements of sea surface height of centimeter-level accuracy over open oceans. However, its accuracy reduces when approaching the coastal areas and over land regions. Despite this downside, altimetric measurements are still applied successfully in these areas through altimeter retracking processes. This study aims to calibrate and validate retracted sea level data of Envisat, ERS-2, Topex/Poseidon, Jason-1, 2, SARAL/AltiKa, Cryosat-2 altimetric missions near the Indian coastline. We assessed the reliability, quality, and performance of these missions by comparing eight tide gauge (TG) stations along the Indian coast. These are Okha, Mumbai, Karwar, and Cochin stations in the Arabian Sea, and Nagapattinam, Chennai, Visakhapatnam, and Paradip in the Bay of Bengal. To compare the satellite altimetry and TG sea level time series, both datasets are transformed to the same reference datum. Before the calculation of the bias between the altimetry and TG sea level time series, TG data are corrected for Inverted Barometer (IB) and Dynamic Atmospheric Correction (DAC). Since there are no prior VLM measurements in our study area, VLM is calculated from TG records using the same procedure as in the Technical Report NOS organization CO-OPS 065. </p><p>Keywords— Tide gauge, Sea level, North Indian ocean, satellite altimetry, Vertical land motion</p>

Sea level variations in the coastal region from altimetry – Using optimal interpolation in the Baltic Sea for 3-day mean sea level from altimetry, tide gauge and model data

2021 ◽  
Author(s):  
Ida Margrethe Ringgaard ◽  
Jacob L. Høyer ◽  
Kristine S. Madsen ◽  
Adili Abulaitijiang ◽  
Ole B. Andersen
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
Temporal Resolution ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Coastal Region ◽  
Optimal Interpolation ◽  
The Baltic Sea ◽  
Spatial Coverage ◽  
The Baltic

<p>The rise and fall of the sea surface in the coastal region is observed closely by two different sources: tide gauges measure the relative sea level anomaly at the coast at high temporal resolution (minutes or hours) and satellite altimeters measure the absolute sea surface height of the open ocean along tracks multiple times a day. However, these daily tracks are scattered across the Baltic Sea with each track being repeated at a lower temporal resolution (days). Due to the inverse relationship between spatial and temporal coverage of the satellite altimetry data, gridded satellite altimetry products often prioritize spatial coverage over temporal resolution, thus filtering out the high sea level variability. In other words, the satellite data, and especially averaged products, often miss the daily sea level variability, such as storm surges, which is most important for all societies in the coastal region. To compensate for the sparse spatial coverage from satellite altimetry, we here present an experimental product developed as part of the ESA project Baltic+SEAL:  on a 3-day scale, the DMI Optimal Interpolation (DMI-OI) method is combined with error statistics from a storm surge model as well as 3-day averages from both tide gauge observations and satellite altimetry tracks to generate a gridded sea level anomaly product for the Baltic Sea for year 2017. The product captures the overall temporal evolution of the sea level changes well for most areas with an average RMSE wrt. tide gauge observations of 17.2 cm and a maximum of 34.2 cm. Thus, the 3-day mean gridded product shows potential as an alternative to monthly altimetry products, although further work is needed.</p>

scholarly journals Atmospheric circulation changes and their impact on extreme sea levels around Australia

2019 ◽  
Vol 19 (5) ◽  
pp. 1067-1086 ◽  
Author(s):  
Frank Colberg ◽  
Kathleen L. McInnes ◽  
Julian O'Grady ◽  
Ron Hoeke
Keyword(s):  
Sea Level ◽  
Large Scale ◽  
Climate Models ◽  
Tide Gauge ◽  
Austral Summer ◽  
Sea Level Changes ◽  
Sea Level Variability ◽  
Sea Levels ◽  
Extreme Sea Levels ◽  
Coastal Sea

Abstract. Projections of sea level rise (SLR) will lead to increasing coastal impacts during extreme sea level events globally; however, there is significant uncertainty around short-term coastal sea level variability and the attendant frequency and severity of extreme sea level events. In this study, we investigate drivers of coastal sea level variability (including extremes) around Australia by means of historical conditions as well as future changes under a high greenhouse gas emissions scenario (RCP 8.5). To do this, a multi-decade hindcast simulation is validated against tide gauge data. The role of tide–surge interaction is assessed and found to have negligible effects on storm surge characteristic heights over most of the coastline. For future projections, 20-year-long simulations are carried out over the time periods 1981–1999 and 2081–2099 using atmospheric forcing from four CMIP5 climate models. Changes in extreme sea levels are apparent, but there are large inter-model differences. On the southern mainland coast all models simulated a southward movement of the subtropical ridge which led to a small reduction in sea level extremes in the hydrodynamic simulations. Sea level changes over the Gulf of Carpentaria in the north are largest and positive during austral summer in two out of the four models. In these models, changes to the northwest monsoon appear to be the cause of the sea level response. These simulations highlight a sensitivity of this semi-enclosed gulf to changes in large-scale dynamics in this region and indicate that further assessment of the potential changes to the northwest monsoon in a larger multi-model ensemble should be investigated, together with the northwest monsoon's effect on extreme sea levels.

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