Annual Mean Sea Level Research Articles

Digitizing the Williamstown, Australia Tide‐Gauge Record Back to 1872: Insights Into Changing Extremes

AbstractFor Williamstown tide gauge, at the northern‐most point of Port Phillip Bay (PPB), Melbourne, Victoria, tide registers from 1872 to 1966 and marigrams from 1950 to 1966, were digitized to extend sea‐level records back almost 100 years. Despite some vertical datum issues in the early part of the record, the data set is suitable for extreme sea‐level trend analysis after removal of the annual mean sea level. The newly digitized data was combined with the digital record to produce a combined record from 1872 to 2020. Analysis of this record revealed known problems of siltation of the tide gauge stilling well and associated reduction in tidal range at times during 1880–1895 and 1910–1940. A positive trend in tidal amplitude of 0.41 ± 0.01 mm yr−1 was found over 1966–2020, likely due to reduced hydraulic friction at the narrow entrance to PPB. Extreme sea‐level trends were examined over 1872–2020 for storm tides (the combination of storm surge and tide) after removal of the annual mean, and residuals after subtraction of the predicted tides. A non‐stationary Gumbel distribution with a time‐varying location parameter revealed statistically significant declining trends in the residuals of −0.73 ± 0.02 mm yr−1, consistent with the observed poleward movement of storm surge‐producing mid‐latitude weather systems. For storm tides a smaller declining trend of −0.40 ± 0.01 mm yr−1 was found. These trends are approximately an order of magnitude smaller than the current positive rates of mean sea level rise, meaning that storm tide hazard will continue to increase in the future. This information is relevant for future adaptation planning.

Climate change and coastal erosion hotspots in West Africa: The case of Togo

Global warming is a worldwide phenomenon with disastrous implications ranging from ocean warming and glacier melting to sea level rise and coastal damage. This research assesses climate change trends, investigates coastal erosion hotspots and forecasts shoreline dynamics in West Africa. It also includes information on coastal erosion impacts and current coastal defence measures. The data set includes annual mean sea level anomaly, eddy kinetic energy, wave energy data and monthly climate data, such as precipitation, temperature, wind speed, and sunshine, from 1988 to 2023. Additionally, field survey data and Sentinel-2 satellite images were employed from 2015 to 2023 for coastal erosion monitoring. The Mann-Kendall test and Sen's slope test have been employed for climate trend analysis, while the DSAS tools have been used to identify coastal erosion hotspots and forecast future trends. The results revealed a significant increasing trend in annual sea level rise anomaly, annual wave energy, annual eddy kinetic energy, monthly temperature, and monthly sunshine. According to the shoreline dynamics investigation on Togolese coastal area, the Aflao-Port transect had significant accretion rates (up to 15 m/years), whereas the Port-Aneho transect experienced coastal erosion (up to −12 m/years). Climate change may increase wave energy intensity in certain regions, while coastal development can alter littoral transport and shoreline changes. Although there are soft and hard stabilisation solutions to counteract coastal erosion, such as groynes, sandbags, reforestation, and a series of nozzles in Togolese coast, the phenomenon of coastal erosion has evolved in recent years. Future projections in 2033 show that coastal erosion could move from 50 to 250 m inland. It is recommended that climate trend indicators, shoreline dynamics, and coastal stabilisation solutions be incorporated into all coastal and marine policies for sustainable management, with a view to ensuring minimal disruption to the ecosystem services provided by the sea and coast to people.

Changes in Sea Level along the South China Sea Coast Based on the Homogenized Tide Gauge Data

To estimate the changes in the annual mean sea level (MSL) and extreme sea levels (ESLs), the largest collection of tide gauge records from 10 tidal stations along the northern coast of the South China Sea (SCS) were analyzed in this study. Here, all the tide gauge records had been homogenized by a two-step process involving the detection of inhomogeneities, that is, breakpoints caused by non-climatic changes and the application of the adjustment. The study’s conclusions, based on the homogenized tide gauge records, can be summarized as follows: The instrument change and station relocation are the main causes for the identified inhomogeneities. From 1989 to 2018, the sea level along the SCS was at an average rate of 4.0 mm per year, as measured by the homogenized tide gauges. The ESLs from the nine tidal stations rose notably with interannual fluctuations, except for the XSA station. Additionally, the ESLs exhibited substantial decadal variations. The ESLs rose along the northern coast of the SCS and were accelerated at most stations throughout the whole study period, especially after the 1980s. There were significant positive correlations between the ESL and the annual MSL at most tide gauges. The MSL’s changes, especially long-term changes, play an important role in the change in ESLs.

The sea level time series of Trieste, Molo Sartorio, Italy (1869–2021)

Abstract. The sea level observations carried out at Trieste, Molo Sartorio, from 1869 to 2021 have been revised and updated. Information on the tide gauges and on the geodetic benchmarks on Molo Sartorio during that period have been collected. Basic quality checks have been applied. The hourly data for the 1917–1938 period, digitized from the original charts, have allowed us to build a time series of hourly sea level heights from 1905 to 2021. Gaps of up to 24 h have been filled by interpolation. The errors affecting the monthly and annual mean sea levels have been estimated. The availability of monthly and annual means prior to 1904 allowed us to build a mean sea level time series spanning 153 years, characterized by linear trends of an observed sea level of 1.36 ± 0.17 mm yr−1 and of an inverse-barometer-corrected sea level of 1.45 ± 0.13 mm yr−1. A significant acceleration of 0.008 ± 0.004 mm yr−2 was estimated from the inverse-barometer-corrected sea level time series. This data set represents the most up-to-date data set of sea level observations and ancillary information relative to the tide-gauge station of Trieste, Molo Sartorio. The data are available through SEANOE (https://doi.org/10.17882/62758, Raicich, 2022).

Multivariate analysis of multiscale periodicity in North American annual mean sea level based on wavelet coherence and multiple wavelet coherence

Abstract The relationships between sea levels and single climate indexes have been widely explored. However, sea level is controlled by multiple climate factors simultaneously with differences among places and time scales. Despite this, few studies have addressed the relationships between sea levels and multiple climate indexes. Here, the interrelations between the annual mean sea level (AMSL) and individual climate indexes and combinations of climate indexes were characterized by wavelet coherence (WTC) and multiple wavelet coherence (MWC). The results showed that six climate indexes had a significant correlation with AMSL, among which the Atlantic Multidecadal Oscillation (AMO) had the least significant correlation, but Niño3.4 had the significant correlation. The average significant coherence area (PASC) value for Niño3.4 was 14.03% and its average wavelet coherence (AWC) value was 0.79. By combining climate indexes, the average values of PASC and AWC can be significantly increased. The average PASC and AWC values of the five climate index combinations were the largest, 95.87% and 0.965, respectively, followed by four, three, and two index combinations. A single climate index is not sufficient to explain sea level change in North America. Sea level changes in North America require between three and five climate indexes to explain, depending on the region. By calculating PASC and AWC values, this study provides the possibility to understand the impact of the combined effects of multiple climate indexes on AMSL, is of great help to screen the best predictor of sea level, and provides a new method to reveal the complex mechanism of sea level change.

A locally relevant framework for assessing the risk of sea level rise under changing temperature conditions: Application in New Caledonia, Pacific Ocean

Sea level rise is a key feature in a warmer world and its impact can be seen globally. Assessing climate change-induced sea level rise, therefore, is urgently needed particularly in small island nations, where the threats of sea level rise are immediate, but the level of preparedness is low. Here, we propose a stochastic simulator to link changes in Mean Annual Temperature (MAT) to Mean Annual Sea Level (MASEL) at the local scale. This is through what-if scenarios that are developed based on the association between local temperature and sea level. The model can provide a basis for a bottom-up impact assessment by addressing limitations of applying large-scale projections in small islands and facilitating the accessibility of the impact assessment to stakeholders. For this purpose, we decompose the MAT and MASEL signals into their linear trend and autocorrelation components as well as independent and identically distributed residual terms. We further explore the association between trend and residual terms of MAT and MASEL. If such dependencies exist, scenarios of sea level can be synthesized based on the trend and residual terms of temperature. We use linear regression to link trends of MAT and MASEL, and copulas to formulate dependencies between residuals. This allows stochastic sampling of MASEL conditioned to trend and random variability in MAT. This framework is used for retrospective and prospective simulations of MASEL in Nouméa, the capital city of New Caledonia, the Pacific. We set up six different model configurations for developing the stochastic sampler, each including various parametric options. By selecting the best setup from each configuration, we provide a multi-model stochastic projection of MASEL, assuming the persistence in current long-term trend in MAT and MASEL. We demonstrate how such simulations can be used for a risk-based impact assessments and discuss sources of uncertainty in future projections.

Determination of Long-Term (1993–2019) Sea Level Rise Trends Around the Korean Peninsula Using Ocean Tide-Corrected, Multi-Mission Satellite Altimetry Data

It is vital to improve estimations of long-term trends in global and regional sea level rise to help mitigate and adapt to climate change. Satellite altimetry data have been widely used for this purpose; however, data collected in regions with strong tidalmotions often suffer from significant aliasing effects unless they are sufficiently corrected using accurate ocean tide models.Long-term trends estimated from altimetry data are often also considerably affected by regional circulation changes, and by artificial effects arising from inconsistencies between different satellite missions. Here, we focused on two regions with high (>5 mm⋅yr–1) rates of long-term linear trend in sea level rise (LTSLR) around the Korean Peninsula (KP). We addressed the impacts of tidal correction and mission inconsistency in satellite altimetry data, and discussed the potential impacts of circulation changes on LTSLR. Because the LTSLR estimation is affected by the aliasing effects of altimetry data when the tidal motions are not corrected sufficiently, yet the correction depends on the performance of ocean tide models, we employed eight ocean tide models to correct altimetry data for comparison and validated the results against observations from 13 tide gauge (TG) stations around the KP. We also estimated LTSLR from 1993 to 2019 using annual mean sea level anomalies (SLAs) from two satellite (two-sat) and all 21 satellite (all-sat) missions, with corrections for ocean tides. The TPXO9 model showed the most reasonable spatial LTSLR rate pattern (∼3 mm⋅yr–1), with the smallest difference from TG observations. It performed best near the west coast where the tidal range was the largest and when using two-sat data, because of inconsistencies in all-sat altimetry data. In contrast, off the east coast, where the impact of tidal correction is negligible, the high (∼7 mm⋅yr–1) LTSLR rates were robust regardless of ocean tide models and altimetry missions, potentially driven by long-term changes in regional circulation. Our results highlight the importance of tidal correction and mission inconsistency for improving LTSLR estimations around the KP. They also have significant implications for determining regional sea level rise under changing circulation patterns, within and beyond the region.

Effect of Summer Sea Level Rise on Storm Surge Analysis

Typhoons occur intensively between July and October, and the sea level is the highest during this time. In particular, the mean sea level in summer in Korea is higher than the annual mean sea level about 14.5cm in the west coast, 9.0 to 14.5cm in the south coast, and about 9.0 cm in the east coast. When the rising the sea level and a large typhoon overlap in summer, it can cause surges and flooding in low-lying coastal areas. Therefore, accurate calculation of the surge height is essential when designing coastal structures and assessing stability in order to reduce coastal hazards on the lowlands. In this study, the typhoon surge heights considering the summer mean sea level rise (SH_m) was calculated, and the validity of the analysis of abnormal phenomena was reviewed by comparing it with the existing surge height considering the annual mean sea level (SH_a). As a result of the re-analyzed study of typhoon surge heights for BOLAVEN (SANBA), which influenced in August and September during the summer sea level rise periods, yielded the differences of surge heights (cm) between SH_a and SH_m 7.8~24.5 (23.6~34.5) for the directly affected zone of south-west (south-east) coasts, while for the indirect south-east (south-west) coasts showed -1.0~0.0 (8.3~12.2), respectively. Whilst the differences between SH_a and SH_m of typhoons CHABA (KONG-REY) occurred in October showed remarkably lessened values as 5.2~ 14.2 (19.8~21.6) for the directly affected south-east coasts and 3.2~6.3 (-3.2~3.7) for the indirectly influenced west coast, respectively. The results show the SH_a does not take into account the increased summer mean sea level, so it is evaluated that it is overestimated compared to the surge height that occurs during an actual typhoon. Therefore, it is judged that it is necessary to re-discuss the feasibility of the surge height standard design based on the existing annual mean sea level, along with the accurate establishment of the concept of surge height.

An Investigation into Sea Level Rise in Sri Lankan Waters with Satellite AltimetryCorrected version of this article has been publshed under the section "Corrigendum" in the same issue on 15th October

Most of the socio-economic activities of Sri Lanka are centred in the coastal regions, which can be described according to its unique geographical and geophysical backgrounds. It is estimated that Sri Lanka has a shore line about 1,700 km in length. Therefore, better knowledge and understanding of the characteristics of sea level is very essential. The sea level has traditionally been measured mainly at tide gauges set up along the coast line. However, the number of tide gauge stations is few and sparse in most countries. But, with the advancements of satellite altimetry technology, it is now accepted as an alternative technique to study regional and global sea level changes. The basic concept in altimetry is the range measurements to the water surface measured from the altimeter antenna. The surface of the sea, or sea level, is continuously changing for many reasons. Here, some changes are fast whereas others occur gradually. Among them, some factors are natural causes, while others occur due to man-made activities such as the greenhouse effect. These changes can be regional or extend at a global scale. The main aim of this research was to examine the trends in sea level rise around the Sri Lankan coastline using two decades of satellite altimetry data from 1993 to 2012. The computed sea level rise trends around Sri Lanka is around 2.5 to 3 mm per year, with a variance of +/- 0.6 mm accuracy. The obtained trend of sea level rise from the altimetry data was validated with tide gauge data in Colombo, and found to be highly correlated, with a correlation coefficient (R2) of 0.93 for annual mean sea level. This study confirms that satellite altimetry can be successfully used to investigate the long term sea level trend studies, particularly in the absence of long term tide data.

North SEAL: a new dataset of sea level changes in the North Sea from satellite altimetry

Abstract. Information on sea level and its temporal and spatial variability is of great importance for various scientific, societal, and economic issues. This article reports about a new sea level dataset for the North Sea (named North SEAL) of monthly sea level anomalies (SLAs), absolute sea level trends, and amplitudes of the mean annual sea level cycle over the period 1995–2019. Uncertainties and quality flags are provided together with the data. The dataset has been created from multi-mission cross-calibrated altimetry data preprocessed with coastal dedicated approaches and gridded with an innovative least-squares procedure including an advanced outlier detection to a 6–8 km wide triangular mesh. The comparison of SLAs and tide gauge time series shows good consistency, with average correlations of 0.85 and maximum correlations of 0.93. The improvement with respect to existing global gridded altimetry solutions amounts to 8 %–10 %, and it is most pronounced in complicated coastal environments such as river mouths or regions sheltered by islands. The differences in trends at tide gauge locations depend on the vertical land motion model used to correct relative sea level trends. The best consistency with a median difference of 0.04±1.15 mm yr−1 is reached by applying a recent glacial isostatic adjustment (GIA) model. With the presented sea level dataset, for the first time, a regionally optimized product for the entire North Sea is made available. It will enable further investigations of ocean processes, sea level projections, and studies on coastal adaptation measures. The North SEAL data are available at https://doi.org/10.17882/79673 (Müller et al., 2021).

Effects of sea ice change on the Arctic climate: insights from experiments with a polar atmospheric regional climate model

Abstract To get insights into the effects of sea ice change on the Arctic climate, a polar atmospheric regional climate model was used to perform two groups of numerical experiments with prescribed sea ice cover of typical mild and severe sea ice. In experiments within the same group, the lateral boundary conditions and initial values were kept the same. The prescribed sea ice concentration (SIC) and other fields for the lower boundary conditions were changed every six hours. 10-year integration was completed, and monthly mean results were saved for analysis in each experiment. It is shown that the changes in annual mean surface air temperature have close connections with that in SIC, and the maximum change of temperature surpasses 15 K. The effects of SIC changes on 850 hPa air temperature is also evident, with more significant changes in the group with reduced sea ice. The higher the height, the weaker the response in air temperature to SIC change. The annual mean SIC change creates the pattern of differences in annual mean sea level pressure. The degree of significance in pressure change is modulated by atmospheric stratification stability. In response to reduction/increase of sea ice, the intensity of polar vortex weakens/strengthens.

Внутри- и межгодовая изменчивость сгонно-нагонных колебаний уровня моря в Северном Каспии

The article presents the results of hydrodynamic modeling of sea level fluctuations in the Caspian Sea using the ADCIRC model for the period from 1979 to 2017. Surges are simulated using an irregular triangulation computational grid with a changing cell size, that easily adapts to changes in shoreline and depth and, therefore, more accurately describes coastal zones. It is found that the seasonal variability of surges is characterized by a maximum in winter and spring and by a minimum in summer. On the western coast of the North Caspian Sea, the maximum sea-level fluctuations are observed in December-February, and on the northern and eastern coasts – in February and March. The areas of inundation of the coastal territory of the Russian sector of the sea are identified. For the surge on March 12–16, 1995, that was one of the most catastrophic ones in terms of its consequences, the inundation area made up 53% of the possible flooded area, and the inundation area for the surge registered on March 27–April 1, 2015 made up 71 %. The more severe consequences of the surge in 1995 are associated with the fact that the mean annual sea level, that determines the possible flooded area, in 1995 was much higher than in 2015. Keywords: ADCIRC, Caspian Sea, unstructured grid, storm surge, sea level, numerical modeling Fig. 11. Ref. 15.

Drivers of Interannual Sea Level Variability on the Northwestern European Shelf

AbstractSea level on the northwestern European shelf (NWES) varies substantially from year to year. Removing explained parts of interannual sea level variability from observations helps to improve estimates of long‐term sea level trends. To this end, the contributions of different drivers to interannual sea level variability need to be understood and quantified. We quantified these contributions for the entire NWES by performing sensitivity experiments with a high‐resolution configuration of the Regional Ocean Modeling System (ROMS). The lateral and atmospheric boundary conditions were derived from reanalyses. We compared our model results with satellite altimetry data and used our sensitivity experiments to show that nonlinear feedbacks cause only minor interannual sea level variability on the shelf. This indicates that our experiments can be used to separate the effects of different drivers. We find that wind dominates the variability of annual mean sea level in the southern and eastern North Sea (up to 4.7‐cm standard deviation), whereas the inverse barometer effect dominates elsewhere on the NWES (up to 1.7‐cm standard deviation). In contrast, forcing at the lateral ocean boundaries results in small and coherent variability on the shelf (0.5‐cm standard deviation). Variability driven by buoyancy fluxes ranges from 0.5‐ to 1.3‐cm standard deviation. The results of our sensitivity experiments explain the (anti)correlation between interannual sea level variability at different locations on the NWES and can be used to estimate sea level rise from observations in this region with higher accuracy.

Seasonal reconstructions coupling ice core data and an isotope-enabled climate model – methodological implications of seasonality, climate modes and selection of proxy data

Abstract. The research area of climate field reconstructions has developed strongly during the past 20 years, motivated by the need to understand the complex dynamics of the earth system in a changing climate. Climate field reconstructions aim to build a consistent gridded climate reconstruction of different variables, often from a range of climate proxies, using either statistical tools or a climate model to fill the gaps between the locations of the proxy data. Commonly, large-scale climate field reconstructions covering more than 500 years are of annual resolution. In this method study, we investigate the potential of seasonally resolved climate field reconstructions based on oxygen isotope records from Greenland ice cores and an isotope-enabled climate model. Our analogue-type method matches modeled isotope patterns in Greenland precipitation to the patterns of ice core data from up to 14 ice core sites. In a second step, the climate variables of the best-matching model years are extracted, with the mean of the best-matching years comprising the reconstruction. We test a range of climate reconstructions, varying the definition of the seasons and the number of ice cores used. Our findings show that the optimal definition of the seasons depends on the variability in the target season. For winter, the vigorous variability is best captured when defining the season as December–February due to the dominance of large-scale patterns. For summer, which has weaker variability, albeit more persistent in time, the variability is better captured using a longer season of May–October. Motivated by the scarcity of seasonal data, we also test the use of annual data where the year is divided during summer, that is, not following the calendar year. This means that the winter variability is not split and that the annual data then can be used to reconstruct the winter variability. In particularly when reconstructing the sea level pressure and the corresponding main modes of variability, it is important to take seasonality into account, because of changes in the spatial patterns of the modes throughout the year. Targeting the annual mean sea level pressure for the reconstruction lowers the skill simply due to the seasonal geographical shift of the circulation modes. Our reconstructions based on ice core data also show skill for the North Atlantic sea surface temperatures, in particularly during winter for latitudes higher than 50∘ N. In addition, the main modes of the sea surface temperature variability are qualitatively captured by the reconstructions. When testing the skill of the reconstructions using 19 ice cores compared to the ones using eight ice cores, we do not find a clear advantage of using a larger data set. This could be due to a more even spatial distribution of the eight ice cores. However, including European tree-ring data to further constrain the summer temperature reconstruction clearly improves the skill for this season, which otherwise is more difficult to capture than the winter season.

Impacts of Atmospheric Pressure on the Annual Maximum of Monthly Sea-Levels in the Northeast Asian Marginal Seas

Monthly mean sea-levels have annual maxima in August in the northeast Asian marginal seas (NEAMS). Based on satellite altimetry data, the rising rate of the August NEAMS sea-level (ANS, 4.2 mm∙yr−1) is greater than those of the NEAMS (3.6 mm∙yr−1) and global (3.4 mm∙yr−1) annual mean sea-levels. Thus, the interannual variations of ANS are classified as relatively high (period H) and low (period L) years and have been analysed because of the high risk of sea-level fluctuation to the coastal regions in August. In period H, there are large atmospheric pressure gradients between the high pressure zone in the Kuroshio Extension (KE) and the low pressure zone in the west of Taiwan (WT). In period L, the atmospheric pressure gradients are small between the above-mentioned zones. Large atmospheric pressure gradients induce strong west-northwestward wind stresses and more Ekman transport from the northwest Pacific Ocean into the NEAMS. The correlation coefficient between August NEAMS sea-level index (ANSI), which is the difference of atmospheric pressure anomalies between the KE and the WT, and the August NEAMS sea-level anomaly (ANSA) is 0.73. Although there is a significant correlation (coefficient: 0.64) between ANSA and the East Asian summer monsoon index (EASMI), ANSI might be more useful in estimating the variability of ANSA.

Assessment of weather variability over Safaga harbour, Egypt

Meteorological conditions are key elements in the management of any harbour. The present study aims to present and analyse the meteorological conditions over Safaga Harbour of Egypt. The work is based on meteorological data recorded on hourly basis from January 2007 to December 2017 from the automated weather station in the Harbour. Results revealed a general trend of slight increase in the mean annual air temperature anomaly over the study period with a rate of 0.01 °C/year. The mean annual sea level pressure anomaly also had an increasing trend with a rate of 0.11 hPa/year. The dominant wind directions over the Harbour were N-NW-WNW during the study period. The year 2015 was the year of the lowest annual mean wind speed (9.74 kt), while 2008 was the year of the highest one (13.40 kt). There was a trend of decrease in the mean annual wind speed anomaly with a rate of −0.3 kt/year. Results also revealed a general trend of decrease in the mean annual relative humidity anomaly with a rate of −0.37%/year. Six, seven, thirteen and seventeen extreme values were detected for the air temperature, sea level pressure, wind speed and relative humidity, respectively.

KAJIAN KENAIKAN MUKA AIR LAUT DAN TINGGI GENANGAN (ROB) PADA TAHUN 2023, 2028, DAN 2033 DI KOTA SAUMLAKI, KABUPATEN MALUKU TENGGARA BARAT

Kota Saumlaki berlokasi di Kabupaten Maluku Tenggara Barat tepatnya pada Pulau Yamdena. Kota ini terletak pada wilayah pesisir yang menjadikannya bersentuhan langsung dengan kondisi eksternal seperti pasang surut. Adapun subduksi lempeng Indo-Australia dan Eurasia yang terletak di sebelah Barat Laut Saumlaki menjadikannya semakin rentan terhadap perubahan muka air laut. Penelitian ini dilakukan dengan tujuan untuk mengetahui besar kenaikan muka air laut berdasarkan analisis data pasang surut tahun 2014-2018 dan memprediksi tinggi genangan yang akan menutupi pantai pada 2023, 2028 dan 2033. Metode yang digunakan dalam penelitian ini adalah metode kuantitatif bersifat studi kasus. Penelitian ini menggunakan data pasang surut wilayah Saumlaki yang didapat dari Badan Informasi Geospasial (BIG) yang kemudian diolah untuk mendapatkan nilai MSL (Mean Sea Level) bulanan dan tahunan. Berdasarkan penelitian yang dilakukan diketahui laju kenaikan muka air laut Kota Saumlaki, Kabupaten Maluku Tenggara Barat adalah 3,75cm/tahun dan termasuk ke dalam tipe pasang surut campuran condong ke harian ganda (Mixed Semi-Diurnal) dengan bilangan Formzahl pada 2018 = 0,602, nilai MSL = 383cm; nilai LLWL = 226,4cm; nilai HHWL = 541cm; nilai ZO = 314cm. Prediksi tinggi genangan di Kota Saumlaki, Maluku Tenggara Barat pada 2023 = 175,75cm; pada 2028 = 194,5cm; dan pada 2033 = 213,25cm.

Heterogeneous sea-level rises along coastal zones and small islands

Coastal zones and many small islands are highly susceptible to sea-level rise (SLR). Coastal zones have a large exposed population and integrated high-value assets, and islands provide diverse ecosystem services to millions of people worldwide. The coastal zones and small islands affected by SLR are likely to suffer from submergence, flooding and erosion in the future. However, very few studies have addressed the heterogeneity in SLR changes and the potential risk to coastal zones and small islands. Here we used the mean sea level (MSL) derived from satellite altimetry data to analyse the trends and accelerations of SLRs along global coastal zones and small islands. We found that except for the Antarctic coastal zone, the annual MSL within 50 km of the coasts presented an increasing trend of 3.09 ± 0.13 mm a−1 but a decreasing acceleration of −0.02 ± 0.02 mm a−2 from 1993 to 2017. The highest coastal MSL trend of 3.85 ± 0.60 mm a−1 appeared in Oceania, and the lowest trend of 2.32 ± 0.37 mm a−1 occured in North America. Africa, North America and South America showed acceleration trends, and Eurasia, Australia and Oceania had deceleration trends. Further, MSLs around global small islands reflected an increasing trend with a rate of 3.01 ± 0.16 mm a−1 but a negative acceleration of −0.02 ± 0.02 mm a−2. Regional heterogeneity in the trends and accelerations of MSLs along the coasts and small islands suggests that stakeholders should take discriminating precautions to cope with future disadvantageous impacts of the SLR.

A Statistical Model for Frequency of Coastal Flooding in Honolulu, Hawaii, During the 21st Century

AbstractThe state of Hawaii and city of Honolulu experienced an unprecedented number of minor flooding episodes during 2017 due to the combination of seasonal high tides and record‐high mean sea levels. To quantify the impact of sea level rise on the tendency for flooding events to cluster in future years, we developed a hierarchical statistical model describing the number of days per year for which sea level exceeds a prescribed threshold in Honolulu as a function of annual mean sea level and the amplitude of the highest tides. Based on this model, we generate probabilistic projections of exceedance days per year for the 21st century, which show pronounced inflections in the frequency of exceedance days due to the interaction between sea level rise and long‐period (18.6 year) modulation of tidal amplitude. Analysis of the projections demonstrates how planning for the “typical” future year can substantially underestimate flooding impacts during inevitable severe years that experience many more exceedance days than expected in a probabilistic sense. The projections also show the potential for rapid, subdecadal transitions from occasional to chronic threshold exceedance during the second half of the century, suggesting that implementation of adaptation and mitigation strategies may need to begin prior to the emergence of occasional minor impacts in affected areas.

Multi-decadal variability in seasonal mean sea level along the North Sea coast

Abstract. Seasonal deviations from annual-mean sea level in the North Sea region show a large low-frequency component with substantial variability at decadal and multi-decadal timescales. In this study, we quantify low-frequency variability in seasonal deviations from annual-mean sea level and look for drivers of this variability. The amplitude, as well as the temporal evolution of this multi-decadal variability shows substantial variations over the North Sea region, and this spatial pattern is similar to the well-known pattern of the influence of winds and pressure changes on sea level at higher frequencies. The largest low-frequency signals are found in the German Bight and along the Norwegian coast. We find that the variability is much stronger in winter and autumn than in other seasons and that this winter and autumn variability is predominantly driven by wind and sea-level pressure anomalies which are related to large-scale atmospheric patterns. For the spring and summer seasons, this atmospheric forcing explains a smaller fraction of the observed variability. Large-scale atmospheric patterns have been derived from a principal component analysis of sea-level pressure. The first principal component of sea-level pressure over the North Atlantic Ocean, which is linked to the North Atlantic Oscillation (NAO), explains the largest fraction of winter-mean variability for most stations, while for some stations, the variability consists of a combination of multiple principal components. The low-frequency variability in season-mean sea level can manifest itself as trends in short records of seasonal sea level. For multiple stations around the North Sea, running-mean 40-year trends for autumn and winter sea level often exceed the long-term trends in annual mean sea level, while for spring and summer, the seasonal trends have a similar order of magnitude as the annual-mean trends. Removing the variability explained by atmospheric variability vastly reduces the seasonal trends, especially in winter and autumn.