Internal Variability Role on Estimating Sea Level Acceleration in Fremantle Tide Gauge Station

Low frequency internal signals bring challenges to signify the role of anthropogenic factors in sea level rise and to attain a certain accuracy in trend and acceleration estimations. Due to both spatially and temporally poor coverage of the relevant data sets, identification of internal variability patterns is not straightforward. In this study, the identification and the role of low frequency internal variability (decadal and multidecadal) in sea level change of Fremantle tide gauge station is analyzed using two climate indices, Pacific Decadal Oscillation (PDO) and Tripole Interdecadal Pacific Oscillation (TPI). It is shown that the multidecadal sea level variability is anticorrelated with corresponding components of climate indices in the Pacific Ocean, with correlation coefficients of −0.9 and −0.76 for TPI and PDO, respectively. The correlations are comparatively low on decadal time scale, −0.5 for both indices. This shows that internal variability on decadal and multidecadal scales affects the sea level variation in Fremantle unequally and thus, separate terms are required in trajectory models. To estimate trend and acceleration in Fremantle, three trajectory models are tested. The first model is a simple second-degree polynomial comprising trend and acceleration terms. Low passed PDO, representing decadal and interdecadal variabilities in Pacific Ocean, added to the first model to form the second model. For the third model, decomposed signals of decadal and multidecadal variability of TPI are added to the first model. In overall, TPI represents the low frequency internal variability slightly better than PDO for sea level variation in Fremantle. Although the estimated trends do not change significantly, the estimated accelerations varies for the three models. The accelerations estimated from the first and second models are statistically insignificant, 0.006 ± 0.012 mm yr−2 and 0.01 ± 0.01 mm yr−2, respectively, while this figure for the third model is 0.018 ± 0.011 mm yr−2. The outcome exemplifies the importance of modelling low frequency internal variability in acceleration estimations for sea level rise in regional scale

Multidecadal Sea Level Variability in the Baltic Sea and Its Impact on Acceleration Estimations

Multidecadal sea level variation in the Baltic Sea is investigated from 1900 to 2020 deploying satellite and in situ datasets. As a part of this investigation, nearly 30 years of satellite altimetry data are used to compare with tide gauge data in terms of linear trend. This, in turn, leads to validation of the regional uplift model developed for the Fennoscandia. The role of North Atlantic Oscillation (NAO) in multidecadal variations of the Baltic Sea is also analyzed. Although NAO impacts the Baltic Sea level on seasonal to decadal time scales according to previous studies, it is not a pronounced factor in the multidecadal variations. The acceleration in the sea level rise of the basin is reported as statistically insignificant in recent studies or even decelerating in an investigation of the early 1990s. It is shown that the reason for these results relates to the global warming hiatus in the 1950s−1970s, which can be seen in all eight tide gauges used for this study. To account for the slowdown period, the acceleration in the basin is investigated by fitting linear trends to time spans of six to seven decades, which include the hiatus. These results imply that the sea level rise is accelerated in the Baltic Sea during the period 1900–2020

Multidecadal Sea Level Variability in the Baltic Sea and Its Impact on Acceleration Estimations

Multidecadal sea level variation in the Baltic Sea is investigated from 1900 to 2020 deploying satellite and in situ datasets. As a part of this investigation, nearly 30 years of satellite altimetry data are used to compare with tide gauge data in terms of linear trend. This, in turn, leads to validation of the regional uplift model developed for the Fennoscandia. The role of North Atlantic Oscillation (NAO) in multidecadal variations of the Baltic Sea is also analyzed. Although NAO impacts the Baltic Sea level on seasonal to decadal time scales according to previous studies, it is not a pronounced factor in the multidecadal variations. The acceleration in the sea level rise of the basin is reported as statistically insignificant in recent studies or even decelerating in an investigation of the early 1990s. It is shown that the reason for these results relates to the global warming hiatus in the 1950s−1970s, which can be seen in all eight tide gauges used for this study. To account for the slowdown period, the acceleration in the basin is investigated by fitting linear trends to time spans of six to seven decades, which include the hiatus. These results imply that the sea level rise is accelerated in the Baltic Sea during the period 1900–2020

Barystatic and steric sea level variations in the Baltic Sea and implications of water exchange with the North Sea in the satellite era

Satellite altimetry, satellite gravimetry, and in-situ subsurface salinity and temperature profiles are used to investigate the total, barystatic, and steric sea level variations in the Baltic Sea, respectively. To estimate the steric sea level, the density variations are weighted in deeper layers to prevent overestimation of their contribution. We show that the sum of barystatic and steric components exhibits excellent cross correlation (0.9) with satellite altimetry sea level variations and also explains up to 84% of total signal variability from 2002 to 2019. Considering the dominance of barystatic sea level variations in the basin and the limitation of satellite gravimetry in resolving the mass change in water-land transition zones (known as the leakage problem), the mismatch is likely attributed to the inadequate accuracy of the barystatic datasets. The total sea level and its contributors are further decomposed into seasonal, interannual, and decadal temporal components. It is shown that despite its insignificant contributions to seasonal and interannual changes, the steric sea level plays an important role in decadal variations. Additionally, we show that the interannual variations of the barystatic sea level are governed by the North Atlantic Oscillation in the basin. The sea level variation in the North Sea is also examined to deduce the water exchange patterns on different time scales. A drop in the North Sea level can be seen from 2005 to 2011 which is followed by the Baltic Sea level with a ~3-year lag, implying the outflow from the Baltic Sea to the North Sea

Estimating sea level rise around Australia using a new approach to account for low frequency climate signals

Regional sea level studies help to identify the vulnerable areas to the sea level rise. This study investigates the impact of climate modes on sea level variations and trends around Australia using altimetry data, climate indices, and sea level records from tide gauge stations. Here, we show that the sea level variations are negatively correlated with climate indices to the north and west of Australia. The spectral analyses of the climate indices and tide gauge data suggest that a low frequency signal with a period of 11 years emerges during the mid 1980s. Since the 25-year length of the satellite altimetry record is yet too short to detect low frequency signals, their effect on the estimation of regional sea level trend is unknown. Therefore, we estimate the sea level trend with consideration of this signal and using a two-step method. All signals with periods shorter than 7.5 years are first removed from sea level time series and then the trend is estimated using the parametric model that includes the 11-year signal. The skill of the parametric model in explaining the variations in sea level anomaly validates the presence of the 11-year signal detected in the spectrograms of the tide gauge data and climate indices. The average sea level trend for the study area is estimated as 3.85 ± 0.15 mm/year

Mean sea surface and mean dynamic topography determination from Cryosat-2 data around Australia

In this study, we use seven years of Cryosat-2 data to improve Mean Sea Surface (MSS) and also to estimate Mean Dynamic Topography (MDT) around Australia. Sea Level Anomaly (SLA) map, obtained from Cryosat-2 data, shows substantial spatial striping effects in the areas where annual signal has considerable amplitudes. This signal causes shifts among the SLAs acquired from adjacent tracks since they have collected at different times of the year. In order to mitigate these effects, we used Topex/Poseidon and follow on missions to estimate the seasonal signals in the Cryosat-2 data points. MSSC2 is then estimated by (1) removing these signals from SLAs, (2) averaging in a 0.1° × 0.1° grid cells, and (3) finally adding them to the DTUMSS13. The resultant surface shows good agreement with the MSS estimated by Jason-1 and Jason-2 data in altimetry nominal points. MDTC2 is also estimated in the study area using the MSSC2 and the geoid. It is in good agreement with two widely used global MDT models although showing higher values due to the effect of sea-level rise. When compared to the estimated MDT in tide gauge stations using geodetic data, MDTC2 statistically performs better than the global models

Sea Level Variation around Australia and Its Relation to Climate Indices

This study aims at investigating the intradecadal and decadal signals of the sea level using 25 years of altimetry data around Australia. We have used the multivariable spectral analysis to extract six periodic signals at the 95% confidence level from altimetry-derived sea-level time series in the study area. They are signals with periods of 1, 1.5, 3, 4.3, 5.7 and 11.17 years, which can also be detected in the estimated power spectra from climate indices of the Interdecadal Pacific Oscillation, Multivariate ENSO Index, and Pacific Decadal Oscillation. A parametric model including the detected periodic signals is used to estimate sea-level trends. The determined trends in the area are in a good agreement with recent studies that consider effects of climate indices through a multivariate regression model. The advantage of our model is to present more descriptive explanation of the sea level signals around Australia in terms of periodicity and spatial variability