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

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.

Deep learning of dynamic sea-level variability to investigate the relationship with the floods in Gothenburg

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.

Role of Regional Ocean Dynamics in Dynamic Sea Level Projections by the end of the 21st Century over Southeast Asia

<p>Southeast Asia is especially vulnerable to the impacts of sea-level rise due to the presence of many low-lying small islands and highly populated coastal cities. However, our current understanding of sea-level projections and changes in upper-ocean dynamics over this region currently rely on relatively coarse resolution (~100 km) global climate model (GCM) simulations and is therefore limited over the coastal regions. Here using GCM simulations from the High-Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to (1) examine the improvement of mean-state biases in the tropical Pacific and dynamic sea-level (DSL) over Southeast Asia; (2) generate projection on DSL over Southeast Asia under shared socioeconomic pathways phase-5 (SSP5-585); and (3) diagnose the role of changes in regional ocean dynamics under SSP5-585. We select HighResMIP models that included a historical period and shared socioeconomic pathways (SSP) 5-8.5 future scenario for the same ensemble and estimate the projected changes relative to the 1993-2014 period. Drift corrected DSL time series is estimated before examining the projected changes. Due to improved simulation of heat, salt, and mass distribution in the ocean, HighResMIP models not only reduce mean state biases in the tropical Pacific (such as cold-tongue sea surface temperature bias), but also near Southeast Asia including DSL. Despite intermodel diversity, there is an overall agreement of increasing sea-level over Southeast Asia. The multimodel ensemble of HighResMIP models suggests a rise of 0.2 m sea level in dynamic sea level (combined with thermosteric component) over Southeast Asia by 2070. Sea-level rises further up to 0.5 m by the end of the 21<sup>st</sup> century. Further, we found regional heat and mass transport changes have a major role in the projected sea-level pattern over Southeast Asia. For example, heat convergence to the east of Vietnam can account for most of the sea-level rise in the region. Our study can provide better insight into the contribution of regional ocean dynamics to DSL projections and useful to suggest for further ocean modelling studies.</p>

The sources of sea-level rise in the Mediterranean Sea since 1960

<p>Sea-level change is geographically non-uniform, with regional departures that can reach several times the global average. Characterizing this spatial variability and understanding its causes is crucial to the design of adaptation strategies for sea-level rise. This, as it turns out, is no easy feat, primarily due to the sparseness of the observational sea-level record in time and space. Long tide gauge records are restricted to a few locations along the coast. Satellite altimetry offers a better spatial coverage but only since 1992. In the Mediterranean Sea, the tide gauge network is heavily biased towards the European shorelines, with only one record with at least 35 years of data on the African coasts. Past studies have attempted to address the difficulties related to this data sparseness in the Mediterranean Sea by combining the available tide gauge records with satellite altimetry observations. The vast majority of such studies represent sea level through a combination of altimetry-derived empirical orthogonal functions whose temporal amplitudes are then inferred from the tide gauge data. Such methods, however, have tremendous difficulty in separating trends and variability, make no distinction between relative and geocentric sea level, and tell us nothing about the causes of sea level changes. Here, we combine observational data from tide gauges and altimetry with sea-level fingerprints of land-mass changes through a Bayesian hierarchical model to quanify the sources of sea-level rise since 1960 at any arbitrary location in the Mediterranean Sea. We find that Mediterranean sea level rose at a relatively low rate from 1960 to 1990, primarily due to dynamic sea-level changes in the nearby Atlantic, at which point it started rising significantly faster with comparable contributions from dynamic sea level and land-mass changes.</p>