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

Author(s):  
Abhisek Chatterjee ◽  
Sajidh C K

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.

2019 ◽  
Vol 53 (9-10) ◽  
pp. 5653-5673
Author(s):  
A. G. Nidheesh ◽  
Matthieu Lengaigne ◽  
Jérôme Vialard ◽  
Takeshi Izumo ◽  
A. S. Unnikrishnan ◽  
...  

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.


2017 ◽  
Vol 30 (14) ◽  
pp. 5585-5595 ◽  
Author(s):  
Yoshi N. Sasaki ◽  
Ryosuke Washizu ◽  
Tamaki Yasuda ◽  
Shoshiro Minobe

Sea level variability around Japan from 1906 to 2010 is examined using a regional ocean model, along with observational data and the CMIP5 historical simulations. The regional model reproduces observed interdecadal sea level variability, for example, high sea level around 1950, low sea level in the 1970s, and sea level rise during the most recent three decades, along the Japanese coast. Sensitivity runs reveal that the high sea level around 1950 was induced by the wind stress curl changes over the North Pacific, characterized by a weakening of the Aleutian low. In contrast, the recent sea level rise is primarily caused by heat and freshwater flux forcings. That the wind-induced sea level rise along the Japanese coast around 1950 is as large as the recent sea level rise highlights the importance of natural variability in understanding regional sea level change on interdecadal time scales.


2015 ◽  
Vol 28 (23) ◽  
pp. 9143-9165 ◽  
Author(s):  
Yuanlong Li ◽  
Weiqing Han

Abstract In this study decadal (≥10 yr) sea level variations in the Indian Ocean (IO) during 1950–2012 are investigated using the Hybrid Coordinate Ocean Model (HYCOM). The solution of the main run agrees well with observations in the western-to-central IO. Results of HYCOM experiments reveal large spatial variations in the mechanisms of decadal sea level variability. Within the tropical IO (north of 20°S), decadal sea level variations achieve maximum amplitude in the south IO thermocline ridge region. They are predominantly forced by decadal fluctuations of surface wind stress associated with climate variability modes, while the impact of other processes is much smaller. The Somali coast and the western Bay of Bengal are two exceptional regions, where ocean internal (unforced) variability has large contribution. Between 28° and 20°S in the subtropical south IO, surface heat flux and ocean internal variability are the major drivers of decadal sea level variability. Heat budget analysis for the upper 300 m of this region suggests that surface heat flux affects regional thermosteric sea level through both local surface heating and heat transport by ocean circulation. In the southwestern IO south of 30°S, where stochastic winds are strong, stochastic wind forcing and its interaction with ocean internal variability generate pronounced decadal variations in sea level. The comprehensive investigation of decadal sea level variability over the IO from an oceanic perspective will contribute to decadal sea level prediction research, which has a high societal demand.


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