scholarly journals Emulating ocean dynamic sea level by two‐layer pattern scaling

2021 ◽  
Author(s):  
Jiacan Yuan ◽  
Robert E. Kopp
Keyword(s):  
Sea Level ◽  
Ocean Dynamic ◽  
Dynamic Sea Level

scholarly journals Ocean-only FAFMIP: Understanding Regional Patterns of Ocean Heat Content and Dynamic Sea Level Change.

2020 ◽  
Author(s):  
Alexander Todd ◽  
Laure Zanna ◽  
Matthew Couldrey ◽  
Jonathan M. Gregory ◽  
Quran Wu ◽  
...  
Keyword(s):  
Sea Level ◽  
Heat Content ◽  
Sea Level Change ◽  
Ocean Heat Content ◽  
Regional Patterns ◽  
Level Change ◽  
Dynamic Sea Level

scholarly journals Dynamic Sea Level Variation from GNSS: 2020 Shumagin Earthquake Tsunami Resonance and Hurricane Laura

2020 ◽  
Author(s):  
Kristine M. Larson ◽  
Thorne Lay ◽  
Yoshiki Yamazaki ◽  
Kwok Fai Cheung ◽  
Lingling Ye ◽  
...  
Keyword(s):  
Sea Level ◽  
Level Variation ◽  
Sea Level Variation ◽  
Dynamic Sea Level

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.

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 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 What causes the spread of model projections of ocean dynamic sea-level change in response to greenhouse gas forcing?

2020 ◽  
Author(s):  
Matthew P. Couldrey ◽  
Jonathan M. Gregory ◽  
Fabio Boeira Dias ◽  
Peter Dobrohotoff ◽  
Catia M. Domingues ◽  
...  
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Sea Level ◽  
North Atlantic ◽  
Ocean Dynamics ◽  
The Arctic ◽  
Large Temperature ◽  
Flux Change ◽  
Heat Uptake ◽  
Dynamic Sea Level

Abstract Sea levels of different atmosphere–ocean general circulation models (AOGCMs) respond to climate change forcing in different ways, representing a crucial uncertainty in climate change research. We isolate the role of the ocean dynamics in setting the spatial pattern of dynamic sea-level (ζ) change by forcing several AOGCMs with prescribed identical heat, momentum (wind) and freshwater flux perturbations. This method produces a ζ projection spread comparable in magnitude to the spread that results from greenhouse gas forcing, indicating that the differences in ocean model formulation are the cause, rather than diversity in surface flux change. The heat flux change drives most of the global pattern of ζ change, while the momentum and water flux changes cause locally confined features. North Atlantic heat uptake causes large temperature and salinity driven density changes, altering local ocean transport and ζ. The spread between AOGCMs here is caused largely by differences in their regional transport adjustment, which redistributes heat that was already in the ocean prior to perturbation. The geographic details of the ζ change in the North Atlantic are diverse across models, but the underlying dynamic change is similar. In contrast, the heat absorbed by the Southern Ocean does not strongly alter the vertically coherent circulation. The Arctic ζ change is dissimilar across models, owing to differences in passive heat uptake and circulation change. Only the Arctic is strongly affected by nonlinear interactions between the three air-sea flux changes, and these are model specific.

scholarly journals On the Drivers and Predictability of Seasonal-to-Interannual Variations in Regional Sea Level

2016 ◽  
Vol 29 (21) ◽  
pp. 7565-7585 ◽  
Author(s):  
C. D. Roberts ◽  
D. Calvert ◽  
N. Dunstone ◽  
L. Hermanson ◽  
M. D. Palmer ◽  
...  
Keyword(s):  
Climate Variability ◽  
Sea Level ◽  
Time Scales ◽  
Large Scale ◽  
Meridional Overturning Circulation ◽  
Wind Forcing ◽  
Thermocline Depth ◽  
Sea Level Variability ◽  
Modes Of Climate Variability ◽  
Dynamic Sea Level

Abstract Observations and eddy-permitting ocean model simulations are used to evaluate the drivers of sea level variability associated with 15 modes of climate variability covering the Atlantic, Pacific, Indian, and Southern Oceans. Sea level signals are decomposed into barotropic, steric, and inverted barometer contributions. Forcings are decomposed into surface winds, buoyancy fluxes, and Ekman pumping. Seasonal-to-interannual sea level variability in the low latitudes is governed almost entirely by the thermosteric response to wind forcing associated with tropical modes of climate variability. In the extratropics, changes to dynamic sea level associated with atmospheric modes of variability include a substantial barotropic response to wind forcing, particularly over the continental shelf seas. However, wind-driven steric changes are also important in some locations. On interannual time scales, wind-forced steric changes dominate, although heat and freshwater fluxes are important in the northwest Atlantic, where low-frequency sea level variations are associated with changes in the Atlantic meridional overturning circulation. Using the version 3 of the Met Office Decadal Prediction System (DePreSys3), the predictability of large-scale dynamic sea level anomalies on seasonal-to-interannual time scales is evaluated. For the first year of the hindcast simulations, DePreSys3 exhibits skill exceeding persistence over large regions of the Pacific, Atlantic, and Indian Oceans. Skill is particularly high in the tropical Indo-Pacific because of the accurate initialization and propagation of thermocline depth anomalies associated with baroclinic adjustments to remote wind forcing. Skill in the extratropics is hindered by the limited predictability of wind anomalies associated with modes of atmospheric variability that dominate local and/or barotropic responses.

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