Monthly Mean Sea Level Research Articles

An assessment of the atmospheric centers of action in the northern hemisphere winter

In the northern hemisphere, there are six permanent or semi-permanent atmospheric activity centers, namely the Icelandic Low, Aleutian Low, India Low, Mongolia High, North Pacific High, and North Atlantic High. The first four are semi-permanent action centers and the last two are permanent circulation systems. The India Low exists only during the summer. By using 160 years (1850–2009) of monthly mean sea level pressure data from the Hadley Centre in the UK, we conduct a comprehensive study of the five boreal winter atmospheric centers of action (ACAs). Based on a unified definition and a method determined in previous studies, we calculate the indices of areal coverage (S), intensity (P), and position of action center (λ c, φ c) for each of these five ACAs. Through an in-depth analysis of these indices and their relationships with climate variables, we evaluate the indices by describing and explaining areal climate anomalies, particularly precipitation and temperature anomalies in China. We show that (1) ACAs significantly influence the climate anomalies of surrounding areas (2) the influences of oceanic ACAs are larger and the intensity anomalies of ACAs have a greater impact than their location displacement, and (3) ACAs exert more control on temperature than they do on precipitation. For the two ACAs over the north Atlantic, the impacts of their intensities on the anomalies of temperature and precipitation are similar. For the two ACAs over the north Pacific, their influences are almost the opposite. The most influential ACA for climate anomalies in China during the boreal winter is HMO. When HMO is stronger, China has a colder winter and it is wetter in the north. With stronger ACAs in the upstream, i.e., the Icelandic Low and North Atlantic High, northern China has a warmer winter. The ACAs over the north Pacific exert little influence on climate anomalies in China during winter. The analyses presented in this paper provide a set of useful indices for defining and describing ACAs, and they suggest insightful applications for these indices.

Causes of seasonal sea level anomalies in the coastal region of the East China Sea

Based on the analysis of sea level, air temperature, sea surface temperature (SST), air pressure and wind data during 1980–2013, the causes of seasonal sea level anomalies in the coastal region of the East China Sea (ECS) are investigated. The research results show: (1) sea level along the coastal region of the ECS takes on strong seasonal variation. The annual range is 30–45 cm, larger in the north than in the south. From north to south, the phase of sea level changes from 140° to 231°, with a difference of nearly 3 months. (2) Monthly mean sea level (MSL) anomalies often occur from August to next February along the coast region of the ECS. The number of sea level anomalies is at most from January to February and from August to October, showing a growing trend in recent years. (3) Anomalous wind field is an important factor to affect the sea level variation in the coastal region of the ECS. Monthly MSL anomaly is closely related to wind field anomaly and air pressure field anomaly. Wind-driven current is essentially consistent with sea surface height. In August 2012, the sea surface heights at the coastal stations driven by wind field have contributed 50%–80% of MSL anomalies. (4) The annual variations for sea level, SST and air temperature along the coastal region of the ECS are mainly caused by solar radiation with a period of 12 months. But the correlation coefficients of sea level anomalies with SST anomalies and air temperature anomalies are all less than 0.1. (5) Seasonal sea level variations contain the long-term trends and all kinds of periodic changes. Sea level oscillations vary in different seasons in the coastal region of the ECS. In winter and spring, the oscillation of 4–7 a related to El Nino is stronger and its amplitude exceeds 2 cm. In summer and autumn, the oscillations of 2–3 a and quasi 9 a are most significant, and their amplitudes also exceed 2 cm. The height of sea level is lifted up when the different oscillations superposed. On the other hand, the height of sea level is fallen down.

A new atmospheric proxy for sea level variability in the southeastern North Sea: observations and future ensemble projections

Atmosphere–ocean interactions are known to dominate seasonal to decadal sea level variability in the southeastern North Sea. In this study an atmospheric proxy for the observed sea level variability in the German Bight is introduced. Monthly mean sea level (MSL) time series from 13 tide gauges located in the German Bight and one virtual station record are evaluated in comparison to sea level pressure fields over the North Atlantic and Europe. A quasi-linear relationship between MSL in the German Bight and sea level pressure over Scandinavia and the Iberian Peninsula is found. This relationship is used (1) to evaluate the atmospheric contribution to MSL variability in hindcast experiments over the period from 1871–2008 with data from the twentieth century reanalysis v2 (20CRv2), (2) to isolate the high frequency meteorological variability of MSL from longer-term changes, (3) to derive ensemble projections of the atmospheric contribution to MSL until 2100 with eight different coupled global atmosphere–ocean models (AOGCM’s) under the A1B emission scenario and (4) two additional projections for one AOGCM (ECHAM5/MPI-OM) under the B1 and A2 emission scenarios. The hindcast produces a reasonable good reconstruction explaining approximately 80 % of the observed MSL variability over the period from 1871 to 2008. Observational features such as the divergent seasonal trend development in the second half of the twentieth century, i.e. larger trends from January to March compared to the rest of the year, and regional variations along the German North Sea coastline in trends and variability are well described. For the period from 1961 to 1990 the Kolmogorov-Smirnow test is used to evaluate the ability of the eight AOGCMs to reproduce the observed statistical properties of MSL variations. All models are able to reproduce the statistical distribution of atmospheric MSL. For the target year 2100 the models point to a slight increase in the atmospheric component of MSL with generally larger changes during winter months (October–March). Largest MSL changes in the order of ~5–6 cm are found for the high emission scenario A2, whereas the moderate B1 and intermediate A1B scenarios lead to moderate changes in the order of ~3 cm. All models point to an increasing atmospheric contribution to MSL in the German Bight, but the uncertainties are considerable, i.e. model and scenario uncertainties are in the same order of magnitude.

A score‐based method for assessing the performance of GCMs: A case study of southeastern Australia

AbstractA multi‐criteria score‐based method is developed to assess General Circulation Model (GCM) performance at the regional scale. Application of the method assessing 25 GCM simulations of monthly mean sea level pressure (MSLP) and air temperature, and monthly and annual rainfall over the southeastern Australia region for 1960/1961–1999/2000 indicate that GCMs usually simulate monthly temperature better than monthly rainfall and mean sea level pressure. For example, the mean observed annual temperature for the study region is 16.7°C while the median and mean values of 25 GCMs are 16.8 and 16.9°C, respectively, and 24 GCMs (except BCC:CM1) can reproduce the annual cycle of temperature accurately, with a minimum correlation coefficient of 0.99. In contrast, the mean observed annual rainfall for the study region is 502 mm, whereas the GCM values vary from 195 to 807 mm, and 12 out of 25 GCMs produce a negative correlation coefficient of monthly rainfall annual cycle. However, GCMs overestimate trend magnitude for temperature, but underestimate for rainfall. The observed annual temperature trend is +0.007°C/yr, while both the median and mean GCM values are +0.013°C/yr, which is almost double the observed magnitude. The observed annual rainfall trend is +0.62mm/yr, while the median and mean values of 25 GCMs are 0.21and 0.36mm/yr, respectively. This demonstrates the advantages of using multi‐criteria to assess GCMs performance. The method developed in this study can easily be extended to different study regions and results can be used for better informed regional climate change impact analysis.

Temperature extremes in Europe: overview of their driving atmospheric patterns

Abstract. As temperature extremes have a deep impact on environment, hydrology, agriculture, society and economy, the analysis of the mechanisms underlying their occurrence, including their relationships with the large-scale atmospheric circulation, is particularly pertinent and is discussed here for Europe and in the period 1961–2010 (50 yr). For this aim, a canonical correlation analysis, coupled with a principal component analysis (BPCCA), is applied between the monthly mean sea level pressure fields, defined within a large Euro-Atlantic sector, and the monthly occurrences of two temperature extreme indices (TN10p – cold nights and TX90p – warm days) in Europe. Each co-variability mode represents a large-scale forcing on the occurrence of temperature extremes. North Atlantic Oscillation-like patterns and strong anomalies in the atmospheric flow westwards of the British Isles are leading couplings between large-scale atmospheric circulation and winter, spring and autumn occurrences of both cold nights and warm days in Europe. Although summer couplings depict lower coherence between warm and cold events, important atmospheric anomalies are key driving mechanisms. For a better characterization of the extremes, the main features of the statistical distributions of the absolute minima (TNN) and maxima (TXX) are also examined for each season. Furthermore, statistically significant downward (upward) trends are detected in the cold night (warm day) occurrences over the period 1961–2010 throughout Europe, particularly in summer, which is in clear agreement with the overall warming.

Mean Sea Level Variability and Influence of the North Atlantic Oscillation on Long-Term Trends in the German Bight

Changes in the seasonal cycle of mean sea level (MSL) may affect the heights of storm surges and thereby flood risk in coastal areas. This study investigates the intra- and inter-annual variability of monthly MSL and its link to the North Atlantic Oscillation using records from 13 tide gauges located in the German Bight. The amplitudes of the seasonal MSL cycle are not regionally uniform and vary between 20 and 29 cm. Generally, the amplitudes are smaller at the southwestern stations, increasing as one travels to the northeastern part. The amplitudes, as well as the phase of the seasonal cycle, are characterized by a large inter-annual and inter-decadal variability, but no long-term trend could be detected. Nevertheless, in the last two decades annual maximum peaks more frequently occurred in January and February, whereas beforehand an accumulation was detected for the November and December period. These changes in phase in the various sea level time series are consistent with a shift in the annual cycle, which is, however, not significant. The changes are associated with strongly increasing trends in monthly MSL of the winter season (J–M), which are considerably higher compared to the remaining seasons. For the same season, the MSL and North Atlantic Oscillation (NAO) indices show strong similarities, resulting in statistically significant correlations (r ~ 0.7). Hence, these changes are linked with changing pressure conditions over the North Atlantic, which lead to a strong phase of positive values in the NAO index between the 1960’s and 1990’s.

Smooth Piecewise Algebraic Approximation as Applied to Large-Scale 2D Scattered Geodetic Data Fitting

We have developed an efficient method, Smooth Piecewise Algebraic Approximation (hereafter SPAA), to automatically compute a smooth approximation of large-scale functional scattered 2D observation points and tilt between them. The area of study is divided into patches and piecewise algebraic surfaces are fitted to the data. When the surfaces are approximated, a set of constraints is imposed in such a way that the resulting function is continuous only in the zero and first derivatives everywhere in the region, which results in a very short computation time. In other word, the surfaces are fitted simultaneously, using the constraints as set-conditions which the parameters of the surfaces must also satisfy. This method does not require a triangulation or quadrangulation of the data points and as such, it is very well suited for extremely large datasets.This method has been successfully applied to the monthly mean sea level and re-levelling data in Canada to thereby compile a map of Vertical Crustal Movements (VCM) in the region. The VCM model obtained using this method accommodates different kinds of scattered geodetic data, while yielding the optimum approximation to them. Enforcing the continuity and smoothness throughout the surfaces, the VCM model of Canada highlights the long wavelength temporal variations of the crust in the region, mainly due to Post Glacial Rebound (PGR). As a result, using the method of SPAA, a more physically meaningful VCM is modelled.

Sea level trends and NAO influences: The Bristol Channel/Severn Estuary

Fifteen years, 1993 (earliest available) to 2007 inclusive of monthly mean and extreme (maximum and minimum) sea level data were assessed for four tide gauges located in the Bristol Channel (Mumbles and Ilfracombe) and Severn Estuary (Newport and Hinkley Point). Results showed decreasing maximum sea level trends and increasing minimum sea level trends, resulting in convergence. However, maximum extreme sea levels on the Welsh shoreline (Mumbles and Newport) were higher than corresponding locations on the English coast (Ilfracombe and Hinkley Point). Analysis showed that from 1995 to 1998 inclusive, maximum extreme sea levels were significantly higher at Mumbles (t = 2.342; df = 10; p < 0.05), Newport (t = 5.034; df = 13; p < 0.01) and Hinkley Point (t = 3.570; df = 13; p < 0.01) and were correlated to increased storm frequencies during these years. However, Ilfracombe (t = 1.472; df = 12; p > 0.05) did not demonstrate similar significance, possibly due to tide gauge location and coastal aspect, while tidal influences became more dominant as the tidal prism moved up the estuary. Actual mean sea levels (MSL) at Newport (t = 2.880; df = 14; p < 0.05) and Hinkley Point (t = 5.282; df = 14; p < 0.01) were significantly higher than predicted; at Mumbles (t = 2.673; df = 11; p < 0.05) they were significantly lower than predicted; while Ilfracombe (t = 1.989; df = 13; p > 0.05) once again showed no significant difference. Mumbles is the only location with off-shore sand waves and analysis suggested these as the cause of opposite trends. Sea level variation was strongly correlated to the North Atlantic Oscillation (NAO) Index, especially for maximum extreme sea levels during positive phases (R 2 = 86%), and higher positive or negative NAO Index values resulted in larger sea level ranges. Further analysis showed a rising Bristol Channel and Severn Estuary MSL trend of 2.4 mm yr − 1 and a 2050 MSL of 0.370 m is projected to inform future management. However, continuous updating and refinement of the sea level datasets will be needed.

Reconsideration of the True versus Apparent Arctic Oscillation

Abstract The physical reality of the Arctic Oscillation (AO; or northern annular mode) is considered. The data used are mainly the monthly mean sea level pressure (SLP). A schematic figure is first presented to illustrate the relationship between the North Atlantic Oscillation (NAO)–Pacific–North American Oscillation (PNA) system and the AO–negative correlation mode between the Atlantic and the Pacific (AO–NCM) system. Although the NAO–PNA (apparent AO–NCM) and true AO–NCM systems give rise to the same EOFs, the probability density functions for the time coefficients of the two leading modes are different. Therefore, the discrimination of the two systems is possible. Several pieces of evidence indicate that, in the real world, the NAO–PNA and the AO–NCM are located on almost the same plane in phase space. This means that the NAO–PNA and AO–NCM systems have the same variations on the plane in common, implying that when the NAO–PNA system is real, the AO–NCM is unlikely to be real. Simple independent component analysis is carried out to distinguish between the true and apparent AO–NCM systems, indicating that the NAO and PNA are independent oscillations, that is, true ones. The analysis is extended to the winter mean SLP field, for which the EOF shows the NAO–PNA but not the AO–NCM. This may be due to the fact that the winter mean NAO and PNA patterns have little spatial correlation. Calculations using randomly selected samples also indicate that when the NAO and PNA patterns have little spatial correlation, the AO never appears as EOF1. All the preceding results show that almost all characteristics of the AO–NCM can be explained from those of the NAO–PNA. Hence it is concluded that the AO, which is extracted by EOF analysis from the temporarily independent but spatially overlapping variations of the NAO and PNA, is almost apparent.

North Atlantic climate variability from a self‐organizing map perspective

North Atlantic variability in general, and the North Atlantic Oscillation (NAO) in particular, is a long‐studied, very important but still not well‐understood problem in climatology. The recent trend to a higher wintertime NAO index was accompanied by an additional increase in the Azores High not coupled to changes in the Icelandic Low, as shown by a self‐organizing maps (SOMs) analysis of monthly mean DJF mean sea level pressure data from 1957 to 2002. SOMs are a nonlinear tool to optimally extract a user‐specified number of patterns or icons from an input data set and to uniquely relate any input data field to an icon, allowing analyses of occurrence frequencies and transitions complementary to principal component analysis (PCA). SOMs analysis of ERA‐40 data finds a North Atlantic “monopole” roughly colocated with the mean position of the Azores High, as well as the well‐known NAO dipole involving the Icelandic Low and the subtropical high. Little trend is shown in December, but the Azores High increased along with the NAO in January and February over the study interval, with implications for storminess in northwestern Europe. In short, our SOM‐based analyses of winter MSLP have both confirmed prior knowledge and expanded it through the relative ease of use and power with nonlinear systems of the SOM‐based approach to climatological analysis.

Arctic or North Atlantic Oscillation? Arguments based on the principal component analysis methodology

The Arctic Oscillation (AO) appears as the leading unrotated mode of principal component analysis (PCA) of monthly mean sea level pressure anomalies, whereas the North Atlantic Oscillation (NAO) results from rotated PCA, regardless of the number of PCs rotated. Three criteria are employed to decide whether the interpretation in terms of the NAO or AO should be preferred: the degree of simple structure, the similarity between the PC loadings and correlation/covariance maps, and the sensitivity to spatial subsampling. All these criteria favour, to a different extent, the interpretation in terms of the NAO. This is further supported by more general arguments. Therefore, the statistical arguments suggest that in interpreting the Northern Hemisphere circulation variability, the sectorial view, i.e. the NAO, should be preferred to the hemispheric view, i.e. the AO. Our analysis supports the idea expressed in other studies that the AO is rather a statistical artifact.

Glacier mass balance in southern norway modelled by circulation indices and spring‐summer temperatures ad 1781–2000

By stepwise regression analysis the accumulation, ablation, and equilibrium line altitude (ELA) were modelled by circulation indices and spring‐summer temperature on six Norwegian glaciers (Ålfotbreen, Nigardsbreen, Rembesdalsskåka, Storbreen, Hellstugubreen and Gråsubreen). The circulation indices were derived from a gridded monthly mean sea level pressure (MSLP) data set, whereas temperature series were derived from instrumental and proxy data.Analyses showed that accumulation on the western glaciers was strongly related to western airflow perpendicular to the main mountain range releasing precipitation on the glaciers. No other airflow variable significantly improved the regression. For the continental glaciers, circulating air in connection with low pressure systems was also found to be important. This may explain the lack of synchronicity in the glaciers' development in southern Norway during the Holocene. Accumulation was better modelled using the MSLP data set than by using the North Atlantic Oscillation (NAO) index as predictor.The decadal variations of accumulation, ablation, and ELA were analysed by a Gaussian low pass filter. The well‐known abundant accumulation on Norwegian glaciers during the early 1990s turned out to be unprecedented during the entire series (since 1781), whereas the accumulation of the 1960s has not been lower since the early 19th century according to model results. Ablation increased significantly from the mid‐19th century to the 1930s. The ablation maximum during the 1930s has not yet been exceeded. Also the 1930s show extremely high ELA values.

Predicting the Onset of Australian Winter Rainfall by Nonlinear Classification

Abstract A method for predicting the timing of winter rains is presented, making no assumptions about the functional form of any relationships that may exist. Ideas built on classification and regression trees and machine learning are used to develop robust predictive rules. These methods are applied in a case study to predict the timing of winter rain in five farming towns in the southwest of Western Australia. The variables used to construct the model are mean monthly sea surface temperatures (SSTs) over a 72-cell grid in the Indian Ocean, Perth monthly mean sea level pressure (MSLP), and monthly values of the Southern Oscillation index (SOI). A predictive model is constructed from data over the period 1949–99. This model correctly classifies the onset of the winter rains approximately 80% of the time with SST variables proving to be the most important in deriving the predictions. Further analysis indicates a change point in the mid-1970s, a well-known phenomenon in the region. The prediction rates are significantly worse after 1975. Furthermore, the important region of the Indian Ocean, in terms of SSTs for prediction, moves from the Tropics down toward the Southern Ocean after this date.

A Simplified Linear Framework for Interpreting Patterns of Northern Hemisphere Wintertime Climate Variability

Abstract The principal patterns of variability of the extratropical Northern Hemisphere (NH) wintertime circulation are examined, based on 42 yr of data from the NCAR–NCEP reanalyses. The two-dimensional phase space defined by the two leading PCs of the monthly mean sea level pressure (SLP) field poleward of 20°N is used as a basis for surveying the structure of the geopotential height and surface air temperature (SAT) fields. Together these two patterns account for roughly half the variance of SLP on interannual time scales and longer, and virtually all the planetary-scale SLP trends over the 42-yr period of record. The leading EOF corresponds to the NH annular mode (NAM), and the second EOF resembles the Pacific–North America (PNA) pattern. The leading EOF of the monthly mean geopotential height field at various levels throughout the troposphere and lower stratosphere is well represented by linear combinations of these two SLP patterns, as are the intraseasonal and interannual SLP fields, the NAM, the N...

Winter surface pressure anomalies over Eurasia and Indian summer monsoon

In this study, the spatial relationships between the monthly mean sea level pressure (MSLP) anomalies and Indian summer monsoon rainfall (ISMR) have been examined using the gridded MSLP data (GMSLP2) for 46 years (1949–1994). Winter MSLP anomalies over NW Europe (south Europe) are negatively (positively) correlated with ISMR. During the strong (deficient) monsoon years, there is enhanced (weakened) poleward pressure gradient over Eurasia. The Europe pressure gradient index, which is an indicator of the winter poleward pressure gradient, is significantly and positively correlated with ISMR. This relationship also suggests a link between the North Atlantic Oscillation and ISMR on inter‐annual time scale. The winter pressure gradient over Eurasia has physical linkage with Indian summer monsoon rainfall, through winter snow cover anomalies and springtime mid latitude activities like upper tropospheric blocking highs and stationary wave anomalies.

Influence of long-term regional and large-scale atmospheric circulation on the Baltic sea level

The connection between variations in the North Atlantic Oscillation (NAO) index and theBaltic sea level has been investigated for the period 1825—1997. The association between theNAO and the strength of the zonal geostrophic wind stress over the Northwest Atlantic suggestsan NAO impact on Baltic sea level variations, because the monthly mean sea level mainly isdetermined by externally driven variations caused by wind conditions over the North Sea.Several period bands were found to have high correlation between oscillations in the winter(JFM) NAO index and the Baltic Sea winter mean sea level. The correlation was, however, higher in the 20th century than in the 19th. During the last two decades, the correlation betweenthe NAO index and the sea level has been exceptionally high. The winter mean of a regionalatmospheric circulation index had a correlation with the Kattegat winter mean sea level of 0.93.With the Baltic sea level the correlation was 0.91, compared with the NAO index correlationfor the same period of 0.74. The regional index also showed a high correlation with the meansummer and mean autumn sea levels, when the corresponding seasonal NAO indices showeda weak connection. The temporal variation of the connection with the NAO index implies aregional atmospheric circulation occasionally differing from the large-scale circulation associatedwith the NAO. Seasonal means of the sea level in Stockholm do, however, reflect theregional wind climate to a large extent, and the Baltic sea level is a useful proxy for identificationsof climatic dependencies in the region.

LONG-TERM FLUCTUATION CHARACTERISTICS OF SOUTHERN OSCILLATION

Southern Oscillation (SO) is an atmospheric see-saw phenomenon in tropical Pacific sea level pressure between the eastern and western hemispheres associated with the El Nino and La Nina oceanographic features. The oscillation can be characterized by Southern Oscillation Index (SOI) which is derived from values of the monthly mean sea level pressure difference between Tahiti and Darwin, Australia. The impacts of SO on climate are widespread and extend far beyond the tropical Pacific, a phenomenon known as teleconnection.In this study, long-term fluctuation characteristics of SO are presented as guidance when using SOI for other analyses. The results show SO intensity (i. e., cross-correlation of Tahiti and Darwin pres sure deviations) is increasing almost constantly since around 1900. The reasons of increase of SO intensity are also investigated statistically in detail.

STATISTICAL CHARACTERISTICS OF SOUTHERN OSCILLATION INDEX AND ITS BAROMETRIC PRESSURE DATA

The impacts of Southern Oscillation (SO) on climate are widespread and extend far beyond the tropical Pacific, a phenomenon known as teleconnection. The oscillation can be characterized by Southern Oscillation Index (SOI) which is derived from values of the monthly mean sea level pressure difference between Tahiti and Darwin, Australia. Its best-known extreme is the El Nifio event.In this study, general statistical characteristics of SOI and the data from which it is derived (i.e. mean sea level pressure data at Tahiti and Darwin) are presented as guidance when using SOI for other analyses.The characteristics include the availability of the barometric pressure data, statistics of monthly pressure data, correlation of SO intensity, frequency analysis of SOI by magnitude and by month (January- December), duration properties of SOI by run analysis.

An appraisal of NCEP/NCAR reanalysis MSLP data viability for climate studies in the South Pacific

Fifty years of monthly mean sea level pressure (MSLP) data from the NCEP/NCAR reanalysis (hereinafter NNR) are validated against station observations in the extratropical South Pacific and West Antarctica. The period for which the NNR may be considered reliable differs markedly between regions. It is only realistic from ∼1970 across West Antarctica. A lack of surface observations entering the model causes the NNR to be poorly constrained prior to the availability of GTS‐based data in 1967: the latter coincides with a significant improvement in the NNR MSLP across the eastern South Pacific. Substantial spurious negative trends occur within the NNR MSLP at high latitudes; although the decrease is greatest prior to the advent of satellite sounder data it continues into the 1990s south of 60°S. In addition, widely used tropical and extra‐tropical circulation indices are poorly represented in the NNR data prior to the 1960s.

Interannual Sea Level Variations and Annual Tides in the Northwestern Pacific

Specific properties of the interannual sea level variations and annual tides in the Northwestern Pacific were studied. Several tide stations were monitored. The monthly mean sea level for the year of 1995 was analyzed at each tide station. A seismic event in 1995, some tectonic activity around the subject area, and the Kuroshio (the oceanic western boundary current) may possibly contaminate results which would have occurred from the astronomical annual tide alone.