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

Author(s):  
Jiacan Yuan ◽  
Robert E. Kopp
2020 ◽  
Author(s):  
Alexander Todd ◽  
Laure Zanna ◽  
Matthew Couldrey ◽  
Jonathan M. Gregory ◽  
Quran Wu ◽  
...  

Author(s):  
Kristine M. Larson ◽  
Thorne Lay ◽  
Yoshiki Yamazaki ◽  
Kwok Fai Cheung ◽  
Lingling Ye ◽  
...  

Ocean Science ◽  
2017 ◽  
Vol 13 (3) ◽  
pp. 443-452 ◽  
Author(s):  
Arseny A. Kubryakov ◽  
Sergey V. Stanichny ◽  
Denis L. Volkov

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.


2021 ◽  
Author(s):  
Omid Memarian Sorkhabi

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.


2014 ◽  
Vol 11 (1) ◽  
pp. 123-169 ◽  
Author(s):  
T. Howard ◽  
J. Ridley ◽  
A. K. Pardaens ◽  
R. T. W. L. Hurkmans ◽  
A. J. Payne ◽  
...  

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.


2020 ◽  
Author(s):  
Matthew P. Couldrey ◽  
Jonathan M. Gregory ◽  
Fabio Boeira Dias ◽  
Peter Dobrohotoff ◽  
Catia M. Domingues ◽  
...  

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.


2016 ◽  
Vol 29 (21) ◽  
pp. 7565-7585 ◽  
Author(s):  
C. D. Roberts ◽  
D. Calvert ◽  
N. Dunstone ◽  
L. Hermanson ◽  
M. D. Palmer ◽  
...  

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.


Sign in / Sign up

Export Citation Format

Share Document