dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009; Levitus et al. , 2012). In coastal areas the spatial and temporal variability of the sea level at seasonal scales is useful to characterize the circulation, monitor shorelines, detect extremes and trends in sea level, and better understand dynamics of estuaries. In addition, most of these processes may have a significant impact on marine life. In this work we compare the annual variability of sea level anomaly (SLA) computed from the AVISO weekly gridded multi-mission altimeter data and from tide gauges. Because the use of satellite altimetry is particularly useful in regions that lack long-term, high-quality records, such as most of the coasts of Asia, South America and Africa, we propose to use monthly climatologies constructed with ancillary data. As a case study we focus only in one of those regions, the Southwestern Atlantic continental shelf (SWACS) and we show that the annual component of the AVISO data correspond to the in-situ annual signal with a RMSD lower than 2.
1 cm. The paper is organized as follows: in Sections 2 and 3 we describe the datasets used and the methodology, respectively. Then in Section 4 results and discussion are presented in three sections: Section 4. 1 deals with the worldwide comparisons; Section 4. 2 compares results obtained at the TGs located over islands with those obtained over continents; Section 4. 3 improves the spatial coverage of TGs by incorporating stations that are not concomitant with the altimetry data while Section 4. 4 presents the results from the Southwestern Atlantic. Finally, Section 5 presents a summary of conclusions.
Tide gauges Tide gauges data are used to validate the seasonal cycle derived from altimetry data. We downloaded and extracted monthly SSH time series for the locations indicated in Fig. 1  from the Permanent Service for Mean Sea Level (PSMSL)  (Holgate et al. , 2013; PSMSL, 2013). Wherever possible, PSMSL used available datum information to tie different records at a location to produce Revised Local Reference (RLR) tide gauge (TG) records. We selected all TGs located south of 72°N and not being flagged as suspicious (http://www. psmsl. org/data/obtaining/notes. php). The northern limit is chosen to avoid contamination by seasonal sea-ice. We then separated the database into two subsets, named A and B. Subset A includes all data from the TGs stations having at least 10 years of data concomitant with the AVISO period  (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) , a distance up to 30 km between TG and the nearest grid points, and that presented less than 20% of missing data.
With the selection criteria mentioned above, we selected 478 TG stations of 1291 initially available (Fig. 1 ). The 478 TG stations were then classified as "continental" and "island" based on their geographical location. Subset B includes all TGs with more than 10 years of data located between 20-54°S and 70-42°W in the SWACS (Fig. 1 ). In this subset only 4 TGs out of 15 have more than 10 years of data concomitant with the altimeter time period. Furthermore, in a large portion of the coast, between 28. 5°S and 34. 5°S, there is only one TG (Porto do Rio Grande) that is catalogued as Metric data in the PSMSL database (i. e. no datum control, low quality compared with RLR). This TG includes 23 years of data between 1981 and 2003. However, despite of the extended record length, this TG record contains 79% of missing values.
Methodology To compare satellite and TG monthly time series, both datasets were corrected for atmospheric pressure effect using the inverted barometer (IB) approach. The IB is a reasonable assumption at seasonal and longer scales considering a pure isostatic response of the sea level to atmospheric pressure variations  (Han et al. , 1993). Altimetry data was first de-corrected from atmospheric pressure and wind effects by adding-up the DAC (available at http://www. aviso. oceanobs. com/). Hereafter, we refer to the altimetry and TG time series as SLAsat and SLAtg, respectively. The IB correction was computed using sea level pressure (SLP) from the National Centers for Environmental Prediction (NCEP) reanalysis data  (Kalnay et al. , 1996)  provided by National Oceanic and Atmospheric Administration (NOAA).
The SLP database spatial resolution is 2. 5°Â 2. 5°. To estimate the IB effect, monthly SLP means were extracted at the NCEP grid point closest to each TG location. We selected the NCEP database since it covers the whole TG time period that starts in 1948, in contrast with the ERA-Interim ECMWF database that starts in 1979. Note that we applied the IB correction and not the DAC to both TG and satellite data for two reasons; (i) the high frequencies of the wind are filtered out at time scales longer than a month; (ii) DAC is available since 1992 while subset B defined in the previous section includes TGs not concomitant with the altimeter time period. The SLAsat and SLAtg annual cycle was extracted from the SLAsat and SLAtg climatologies by harmonic analysis using the least-squares method. Both SLAtg and SLAsat were linearly detrended to avoid biases when computing the respective annual cycles.
The harmonic analysis is an estimate of the time series represented by a sum of cosines with different amplitudes and phases: ∑ π θ = ¯+ - + y t y C f t y t ( ) cos (2 ) ( ) (1) q M q q q r where C q , f q and θ q are the amplitude, frequency and phase of the qth constituent, respectively. ȳ is the mean SLA and y r is the residual. The aim of the least-squares method is to minimize the squared difference between the original data and the data fitted by Eq. (  1 ). Root mean squares of the differences (RMSD) between each TG and the closest SLAsat gridded data point were estimated as = ∑ - X X N RMSD ( ) (2) 1 2 2 where X 1 and X 2 are the annual harmonics of the in-situ and satellite data, respectively, and N is the length of the times series. It is important to note that the above formula considers both the amplitude and phase differences between the two dataset.
In order to quantify how well the annual fit adjusts to the climatologies, we defined the goodness of the fit (Gof) as the percentage of the observed variance explained by the annual harmonic. The average of the Gof for all the TGs considered and for the corresponding SLAsat time series are of 84. 51% and 87. 85%, respectively, suggesting that indeed the annual signal is the dominant signal in the climatologies constructed. We also considered the vertical crustal motions due to post-glacial rebound as a possible source of error in the comparison between the satellite and in-situ data. To correct the TG data for vertical displacements of land we used the Glacial Isostatic Adjustment (GIA) data from the ICE-5G model  (Peltier, 2004). This model provides the present-day rate of change of relative sea level respect to the solid Earth.
Worldwide comparison Fig. 2  shows the annual amplitudes of SLAsat and SLAtg. We observed that both data set presented a similar spatial pattern, dominated by values between 4 and 10 cm. Maximum amplitudes were found in the coast of India, west and northeast of Australia, China and Japan, with values within the 12 and 33 cm range. In some of these regions the annual amplitude of SLAsat underestimated the amplitude derived from the TGs. The spatial distribution of the annual phases of SLAsat and SLAtg (Fig. 3 ) was also similar, showing the expected difference between hemispheres. In general, the annual cycle peaks between the end of February and early June in the Southern Hemisphere, while SLA peaks between August and November in the Northern Hemisphere. In order to quantify the amplitudes and phases differences, we calculated the RMSD (Eq.
(  2 )). Results showed that the mean of the RMSD computed at 478 worldwide-sites was lower than 1. 6 cm (Table  1 ). 88. 5% of the estimated RMSD were lower than 3 cm and 76. 4% were equal or lower than 2 cm (Fig. 4 ). Only 34 TGs presented RMSD higher than 4 cm (Fig. 4 ). These results clearly indicate that the annual cycle captured by the TGs is well represented by the large majority of the nearby gridded altimetry SLA. The spatial distribution of the RMSD of the annual cycle reveals the regions where this magnitude is relatively large (Fig. 1 ). In what follows we consider the regions where RMSD are higher than 4 cm and show that different phenomena make significant and distinct contributions to the SLA climatologies depending on whether TG or altimetry data are used.
Narrow coastal currents Along the west coast of North America, between 42°N and 55°N (Fig. 1 ) RMSDs were higher than 4. 3 cm. Strong and narrow coastal currents are seasonally present in response to coastal wind-forced upwelling/downwelling (e. g. Strub and James, 2000). Those strong, narrow coastal currents are indeed the cause of the difference between the gridded AVISO altimetry product and TGs in this region  (Saraceno et al. , 2008). Evidently, the gridded altimetry product captures the open ocean conditions and is not able to solve the narrow coastal current that makes a large contribution to the SLA measured by the TGs. Saraceno et al. (2008)  show that the correct seasonal SLA fields close to the coast can be reproduced by interpolating the off-shore AVISO data with the coastal TGs.
River outflows Large TG annual amplitudes ( $25 cm) were observed at two coastal stations located in the northern Bay of Bengal (21. 8°N-89. 83°W). In contrast, an amplitude of only $ 15 cm was estimated from SLAsat at the closest grid point. Hence a RMSD larger than 6 cm was obtained (Fig. 1 ). Vinogradov and Ponte (2010)  arrived at similar results comparing TOPEX/POSEIDON along-track altimetry data with TGs at seasonal time scales. They argued that large monsoonal-driven variability of the Ganges River is responsible for the observed differences. We argue that the altimeter grid point used to compare with the TG data mostly reflects the changes in open ocean circulation and not the coastal circulation affected by the river discharge. This is probably due to the fact that the gridding procedure ignores most of the data close to the coast.
A similar scenario occurs in the Gulf of Carpenteria (14°S-139°E), north of Australia. At annual scale the mean sea level variation is due to monsoon variability, to changes in SLP and to the steric-effect  (Forbes and Church, 1983). All these forcings generate a large annual SLA amplitude in the southeast part of the gulf  (Forbes and Church, 1983). The annual amplitudes of the SLAtg in the southeast of this region were about 33 cm while the annual amplitudes of the SLAsat were 23 cm. Both time series were in phase and the differences in the amplitudes were reflected in the RMSD (Fig. 1 ). Since the TGs are located in the Norman River and Embley River, we argue that the altimeter data reflect the open ocean sea level variations while the SLAtg varies mostly in response to river height variability.
Interpolation aliasing In the Gulf of Thailand (10. 58°N-101. 17°W) there are 3 TGs (Ko Mattaphon, Ko Sichang and Cendering) where the SLAsat underestimated the annual amplitude observed with SLAtg, giving origin to a RMSD of 6 cm. These large discrepancies could be due to the large distance (4 100 km) existing between the TGs and the TO-PEX/Jason along-track data  (Trisirisatayawong et al. , 2011). ERS/ Envisat altimetry tracks pass closer to the TGs, but their lower repeat-orbit periodicity (35 days) compared to the TOPEX/Jason periodicity (10 days) makes the distance to the latter a key factor to explain the large RMSD observed in the region  (Trisirisatayawong et al. , 2011). As the gridded altimetry product interpolates the two along-track datasets  (Ducet et al. , 2000) , the distance to the region with the largest density of coastal data is most likely a key issue leading to the observed SLA discrepancies.
Islands vs. continental TGs The mean RMSD considering only the 73 TGs located over islands was 0. 64 cm (Table  1 ), while the 405 continental TGs leaded to a mean RMSD of 1. 76 cm (Table  1 ). This difference in RMSD may be due to the fact that there are more continental TGs than over islands. To account for this difference we randomly selected 73 TGs among the 405 continental TGs and computed the average of the RMSD. We repeated this procedure 100 times. The median of the 100 means of RMSDs was 1. 76 cm, which is larger than that based on the 73 island TGs (0. 64 cm). The correlations between the annual amplitudes and annual phases were higher in the island TG subset than in the continental subset (Table  1 ). These results clearly indicate that the satellite altimeter data provides a more robust representation of the annual cycle in open ocean, where SLAsat estimates are not affected by large continental masses.
Satellite altimetry data are affected by the proximity of land at distances shorter than 50 km, since the radiometer footprints encounter the coastline and corrupt the raw along-track signal (e. g. Strub, 2001). Although this can also occur over islands, land contamination and aliasing due to high frequencies is generally smaller around islands than approaching continents  (Ponte and Lyard, 2002; Mitchum, 1994). Also, the wet tropospheric correction is probably more homogeneous in the proximity of islands than close to continents. On the other hand, as shown in Fig. 1 , very few TGs are located along large portions of the coasts of Central and South America, Africa and the Arabian Peninsula. Including TGs from the UHSLC (University of Hawaii Sea Level Center, http://uhslc. soest. hawaii. edu) database and applying the data selection criteria described above does not modify the geographical coverage of TGs shown in Fig.
Extending the number of TG considered To improve the spatial coverage of the TGs, we considered all the TGs that have more than 10 years of data but are not necessarily concomitant with the altimetry era. Our assumption is that monthly climatologies do not change over the time of the TG records. To validate this hypothesis we separated the TGs that have data since 1970 from the 478 TGs selected in Section 2. 2 and constructed two climatologies using: (i) ten years of data, before the satellite altimetry era (between January 1975 and December 1985) and (ii) ten years of data during the altimetry era (between January 1995 and December 2005). We then extracted the annual harmonic of those climatologies and compared them. We present the results obtained in Japan where there is one of the densest TG networks.
RMSD between the annual cycles of the two periods considered were lower than 2 cm in the 63 stations around Japan (Fig. 6 ), with an average value of 0. 61 cm (Table  2 ). The annual amplitudes of the two periods were well correlated with a correlation coefficient of 0. 99 (95% confidence level, CL) (Table  2 ). The phases were also significantly correlated (Table  2 ). These results suggest that, at least for the coast of Japan, the annual cycles have not changed in the time periods considered. At global scale, results also showed a good agreement between the two climatologies: 88. 45% of the RMSDs were lower than 2 cm. The annual amplitudes and phases were also well correlated (0. 92 and 0. 87, respectively, 95% CL). In the following section, we assume that the annual cycle in the SWACS does not change in time and therefore we used TG data collected prior to the altimeter era to improve the spatial coverage of TG data.
SWACS analysis As a case study to analyse the annual cycle in a region with few TGs, we computed RMSDs between SLAsat and SLAtg constructing TGs climatologies with all TGs that have more than 10 years of data but are not necessarily concomitant with the satellite altimetry era in the SWACS (Fig. 7 ). Only three of the 15 TGs that have data for more than 10 years since October 1992 (stations 6, 9 and 15, Fig. 7 , Table  3 ) were included in the previous analysis. A monthly and an inter-annual analysis of the annual component of the SLAsat are presented as well for this region.
SWACS: monthly time series We first compared the SLAsat and SLAtg monthly time series for the period 1993-2010 at Mar del Plata (station 9 in Fig. 7 ). Both time series displayed a very similar pattern of variation, with a marked seasonal cycle during the entire record length (Fig. 8 ). The largest differences were observed during the first half of 1994 and during the second half of 2003. Along-shore wind stress could cause upwelling or downwelling close to the coast. However, wind stress time series (not shown) did not explain the observed Fig. 6. Location of the tide gauges available at the PSMSL database considered for the validation of the altimetry data on the coast of Japan. Colours indicate the root mean square difference (cm) of SLA between two periods of time  (1975-1985 and 1995-2005).
SWACS: climatologies Fig. 9  shows the SLAtg and SLAsat climatologies from 4 sites: Rio de Janeiro, La Paloma, Mar del Plata and Quequén (stations 1, 4, 9 and 10 in Fig. 7 ). At each station both climatologies were in good agreement. Error bars, computed as 71 standard deviation, overlapped in both datasets in the 15 locations, suggesting that the seasonal cycle computed from TGs and altimetry data did not present significant differences. We then computed the annual harmonic of the climatologies for the 15 time series in the SWACS as we did for the global data set (see methodology in Section 3). Gof values, that are the percentage of the variance explained by the annual harmonic, express the level of agreement between the data and the fit independently of the data sources and locations (Table  3 ).
Gof were larger than 84% for the satellite data and larger than 73% for the TG data, except at Colonia, where the Gof in the TG was $52% (Table  3 ). The low Gof at Colonia is due to the fact that the semiannual component explains 33% of the variance (Fig. 10 ). Colonia is located on the northern margin of the Rio de la Plata estuary. The origin of the semiannual component might be associated with variability of the river discharge  (Meccia et al. , 2009). In the SWACS the RMSD between the annual harmonics of SLAsat and SLAtg climatologies ranged between 0. 22 and 2. 09 cm (Table  4 , Fig. 7 ). As discussed in Sections 4. 1, 76. 4% of the 478 worldwide TG considered presented a RMSD lower than 2 cm. Thus our results suggest a very good agreement between TGs and gridded altimetry data in the SWACS at the annual scale.
, 2014). Therefore, the phase lag is due to the fact that the SLAsat mainly captures the radiative solar forcing and the SLAtg also captures the wind stress forcing. At Palermo, located on the south margin of the Rio de la Plata, the distance to the nearest TOPEX/Jason along track is 150 km. Thus, as in the Gulf of Thailand, the distance to the region with the largest density of data to construct the SLAsat might affect the comparison with the TG. Furthermore, as noted above, the annual variability of the Rio de la Plata river discharge might cause the semiannual variability in the TG (Fig. 10a ), which is not apparent in the SLAsat, leading to the observed phase differences. Finally, we considered the TG at Porto do Rio Grande (Fig. 7 ). As noted in Section 2. 2, this is the only TG available between 28.
5 °S and 34. 5 °S i. e. along more than 850 km of coast. This TG was not included in the PSMSL dataset analysed in this work because it did not fit our selection criteria. However, using the best subset of those data (monthly average of two years, 1989 and 2003) the RMSD with the closest AVISO data was 1. 82 cm. This result suggests that the annual component of the altimetry data is also in good agreement with in-situ data in this region. Extensive coastal regions without TGs in the SWACS are also present south of 48°S (Fig. 7 ). Unfortunately, even considering time series that do not match our selection criteria, the length of those time series is too short to extract the annual component.
Summary and conclusions The aim of this work was to compare the annual component of SLA as retrieved from gridded altimetry data with that retrieved from 478 tide gauges distributed worldwide. The comparison was performed through the analysis of the annual harmonic of the seasonal cycle fitted to climatologies of both data sets. Results obtained at global scale show RMSDs lower than 2 cm, which is the nominal accuracy of open ocean satellite altimetry  (Fu et al. , 1994; Le Traon et al. , 1998) , for 76. 4% of the cases. The largest RMSD values are associated with near-coastal process such as narrow currents (e. g. California Current), nearby river discharges (e. g. Ganges River, Jiulong River) and seasonal sea ice (e. g. Kara Sea, the Gulf of Bothnia and the Gulf of Finland, Gulf of Carpentaria).
Our results are consistent with studies based on along track data instead of gridded altimetry data  (Vinogradov and Ponte, 2010). In other cases, RMSD higher than 4 cm are associated with the presence of a semiannual signal and complex coastline (e. g. southern Baltic Sea). Moreover, at other locations the large RMSD might also be partly due to the larger distance between the TG and the closest region with the  dense TOPEX/Jason along-track data, such as the Gulf of Thailand and Panamá bay. As shown by our results, the annual cycle constructed from 10year of in-situ data before altimetry era is consistent with the annual cycle constructed during the altimetry era. The high coherence between both data sets (RMSD lower than 2 cm) suggests that the annual cycle of SLA is stationary during several decades.
A more careful analysis should be done before further exploiting the satellite altimetry data in regions where the RMSD between altimeter and TG data is higher than 4 cm. Custom corrections to along-track altimetry data are probably necessary in those areas. Ultimately, interpolation of those improved alongtrack altimetry data will improve the accuracy of SLA close to the coast and over continental shelves, better resolving shorter time scales than those analysed in this study. In this sense data provided by the future SWOT (Surface Water Ocean Topography) mission will make a great contribution since it will incorporate two SAR antennae providing a 120 km wide ground track (http:// smsc. cnes. fr/SWOT/).
Acknowledgements This study is a contribution to  EUMETSAT/CNES DSP/OT/12-2118 ,  ANPCyT   PICT 2012-0467 ,  CONICET PIP 112-20110100176 ,  PIO 133-20130100242 ,  CONAE-001  and  UBACyT 20020100100840 Projects. ARP  and OOM acknowledge the support of Grant  3070  from the  Inter-American Institute for Global Change Research (National Science Foundation  Grant  GEO-1128040 ). The altimeter products were produced by SSALTO/DUACS and distributed by Aviso with support from  CNES. Dynamic atmospheric Corrections are produced by  CLS Space Oceanography Division  using the Mog2D model from Legos and distributed by Aviso, with support from  CNES  (http://www. aviso. oceanobs. com/). Comments by two anonymous reviewers help improve the paper.
If only melting occurs at the interface, with no water accumulation and no refreezing (as, for example at the NGRIP site in Greenland), can we then rely on the paleoclimatic information gathered in the basal layers? The EPICA (European Project for Ice Coring in Antarctica) Dome C ice core potentially provides us with an opportunity to investigate that specific case. In this paper, we are using a multiparametric approach, combining new and existing low-resolution (50 cm) data for the bottom 60 m of ice from the EDC (EPICA dome C) ice core with a new high-resolution (1. 5 to 8 cm) chemical data set in order to better understand the processes at work and evaluate how these might have altered the environmental archive.