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.

On Sea Level Variability and Trends in United States Coastal Waters and Relationships with Climate Factors

2015 ◽  
Vol 07 (01n02) ◽  
pp. 1550005 ◽  
Author(s):  
Leonard J. Pietrafesa ◽  
Shaowu Bao ◽  
Tingzhuang Yan ◽  
Michael Slattery ◽  
Paul T. Gayes
Keyword(s):  
United States ◽  
Sea Level Rise ◽  
Sea Level ◽  
Gulf Stream ◽  
The United States ◽  
The Arctic ◽  
Climate Factors ◽  
Sea Level Variability ◽  
Mean Sea Level ◽  
And Sea Level

Significant portions of the United States (U.S.) property, commerce and ecosystem assets are located at or near the coast, making them vulnerable to sea level variability and change, especially relative rises. Although global mean sea level (MSL) and sea level rise (SLR) are fundamental considerations, regional mean sea level (RSL) variability along the boundaries of U.S. along the two ocean basins are critical, particularly if the amplitudes of seasonal to annual to inter-annual variability is high. Of interest is that the conventional wisdom of the U.S. agencies, the National Aeronautics and Space Administration (NASA) and the National Oceanic and Atmospheric Administration (NOAA) which both contend that the sources of sea level rise are related principally to heat absorption and release by the ocean(s) to the atmosphere and vice versa, and by Polar glacier melting and freshwater input into the ocean(s). While these phenomena are of great importance to SLR and sea level variability (SLV), we assess a suite of climate factors and the Gulf Stream, for evidence of correlations and thus possible influences; though causality is beyond the scope of this study. In this study, climate factors related to oceanic and atmospheric heat purveyors and reservoirs are analyzed and assessed for possible correlations with sea level variability and overall trends on actionable scales (localized as opposed to global scale). The results confirm that oceanic and atmospheric temperature variability and the disposition of heat accumulation or the lack thereof, are important players in sea level variability and rise, but also that the Atlantic Multi-Decadal Oscillation, the El Niño-Southern Oscillation, the Pacific Decadal Oscillation, the Arctic Oscillation, the Quasi-Biennial Oscillation, the North Atlantic Oscillation, Solar Irradiance, the Western Boundary Current-Gulf Stream, and other climate factors, can have strong correlative and perhaps even causal, modulating effects on the monthly to seasonal to annual to inter-annual to decadal to multi-decadal sea level variability at the community level.

scholarly journals Indian Ocean Dynamic Sea Level, Variability And Projections In CMIP6 Models

2022 ◽  
Author(s):  
Abhisek Chatterjee ◽  
Sajidh C K
Keyword(s):  
High Resolution ◽  
Indian Ocean ◽  
Sea Level Rise ◽  
Sea Level ◽  
Sea Level Variability ◽  
Regional Sea Level ◽  
The Indian Ocean ◽  
High Resolution Models ◽  
Mean State ◽  
Dynamic Sea Level

Abstract The regional sea level variability and its projection amidst the global sea level rise is one of the major concerns for coastal communities. The dynamic sea level plays a major role in the observed spatial deviations in regional sea level rise from the global mean. The present study evaluates 27 climate model simulations from the sixth phase of the coupled model intercomparison project (CMIP6) for their representation of the historical mean states, variability and future projections for the Indian Ocean. Most models reproduce the observed mean state of the dynamic sea level realistically, however consistent positive bias is evident across the latitudinal range of the Indian Ocean. The strongest sea level bias is seen along the Antarctic Circumpolar Current (ACC) regime owing to the stronger than observed south Indian Ocean westerlies and its equatorward bias. Further, this equatorward shift of the wind field resulted in stronger positive windstress curl across the southeasterly trade winds in the southern tropical basin and easterly wind bias along the equatorial waveguide. These anomalous easterly equatorial winds cause upwelling in the eastern part of the basin and keeps the thermocline shallower in the model than observed, resulted in enhanced variability for the dipole zonal mode or Indian Ocean dipole in the tropics. In the north Indian Ocean, the summer monsoon winds are weak in the model causing weaker upwelling and positive sea level bias along the western Arabian Sea. The high-resolution models compare better in simulating the sea level variability, particularly in the eddy dominated regions like the ACC regime in interannual timescale. However, these improved variabilities do not necessarily produce a better mean state likely due to the enhanced mixing driven by parametrizations set in these high-resolution models. Finally, the overall pattern of the projected dynamic sea level rise is found to be similar for the mid (SSP2-4.5) and high-end (SSP5-8.5) scenarios, except that the magnitude is higher under the high emission situation. Notably, the projected dynamic sea level change is found to be milder when only the best performing models are used compared to the full ensemble.

Sign in / Sign up

Export Citation Format

Share Document