dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods: Data Set and Natural Variability In this study we analyzed the longest available monthly tide gauge records (>60 years of Revised Local Reference (RLR) data collected in the 1888-2008 time span) at nine tide gauge stations located in the Mediterranean Sea, belonging to the Permanent Service for Mean Sea Level (www. psmsl. org). The location of the stations, the time span of the records and the analyzed time series are shown in Figures  1  and 2. The choice of data sets with length exceeding 60 years aims to avoid the effect of inter-decadal variations affecting tidal recordings shorter than ~50 years  [35, 36]. In addition, to avoid the introduction of artificial signals in the records, interpolations on data gaps were not applied. The identification of sea-level trends and large timescales variability from sea-level records is a debated argument and the best methods to discriminate between acceleration and intrinsic climate variability remains an open issue.
Indeed, since in tide gauge data the long-term trend is embedded in fluctuations at many different time scales, from daily tides to the seasonal cycle as well as interannual and decadal variations, the filtering method used to remove these variations may impact the accuracy of the calculated trend. Different approaches to this problem have been proposed (for a detailed review see  [37] ). Among these approaches, the EMD has been used to characterize the intrinsic fluctuations and the long-term trend in sea-level data  [38] [39] [40] [41] [42] [43] [44] [45]. This technique has been developed to analyze non-stationary and non-linear data, such as tide gauge records, by decomposing the signal in modes that provide a description of the intrinsic timescales present in the time series. Being "adaptive"" the modes are empirically obtained from the original time series according to its local characteristics.
We remark that, as pointed out by  [45] , the results of single IMFs from tide gauge data need of a careful interpretation. However, a discussion about the physical meaning of the single IMF is out of the scope of this paper since we use the EMD as a low-pass filter by adding up several IMFs. In this case, the EMD has shown to be a useful tool to reduce the impact of high-frequency variability and noise  [45]. The contribution L LT , for the analyzed tide gauges, is shown in Figure  3. All records show a long-term variability in a well-defined range of timescales of about 20-30 and, for the longer records, >45 years, in the typical ranges where internal modes of natural variability are detected  [47]. Observational datasets and simulations of the typical scales of Atlantic internal modes of natural variability show variations at decadal timescales that are linked to the ocean-atmosphere coupling  [47] [48] [49] [50].
To check the robustness of our approach in estimating future sea levels and the accuracy of the SLNV evaluation, we carried out a simple test using data from Marseille, the station with the longest and almost complete time series. Firstly, we applied the EMD analysis on sea-level data collected in the interval 1888-1957 (70 years). Two IMFs with τ j > 15 years (τ 9 = 33. 4 ± 5. 4 years and τ 10 = 58. 1 ± 7. 6 years) were found. Then, sea levels for the next 50 years  were evaluated through the model, by using the linear trend from the fit to Equation (  4 ) and were compared to the real complete data set. Figure  6  shows real data, both reduced at the 1 year of resolution (red dashed line) and smoothed by a 15-years sliding window (red full line), and the model curve (black line) with the 90% confidence interval (enclosed by black dashed lines).
Conclusions Sea levels for 2050 and 2100 at nine locations in the Central-Northern Mediterranean coasts were estimated from tide gauge data by including the contribution of both the vertical land motion and the natural variability. The VLM was inferred at a set of locations nearby tide gauges through continuous GPS measurements. The SLNV was estimated by the EMD analysis on the tide gauge data. The EMD approach revealed modes at characteristic time scales in the ranges 20-30 and >45 years, associated with the long-term natural variability, resulting from the combined effect of teleconnection patterns. Expected sea levels in 2050 and 2100 were then estimated by using IPCC projection, for the time dependent sea-level rate, the measured VLM rate, and a superposition of periodic functions, whose properties were obtained from the EMD results, to model the contribution of the long-term variability.
To understand these issues of climate change, a number of different Earth Observation (EO) datasets are brought together: sea-ice area, concentration, drift, thickness as well as the topography of the ocean at global and regional scales. In climate change studies based on satellite data, it is a major challenge to construct homogeneous time series from consecutive satellite sensors, which are required for the detection of changes over several decades. This is the main goal of the Climate Change Initiative from European Space Agency (ESA), with projects both on the Sea Level  [12, 13]  and on Sea Ice  [14]. For climate studies, the sea level needs to be measured on a regional basis (using a grid mesh of size 50-100 km) to within 1 cm, with a stability of the measurement able to resolve any long-term trend to within 1 mm yr -1  [15].
Relevant Altimeter Datasets Altimeters provide nadir measurements of range, with a fine along-track spacing but with no swath coverage. This means that with a single altimeter, adequate spatial coverage is only achieved upon collating a pattern of different tracks spanning tens of days; therefore, the production of self-consistent maps of the sea level on a monthly basis requires an analysis and homogenization of the data from multiple altimeters (see Figure  1  of Quartly et al. [12] ). For Arctic analyses, there is the complication that whilst there is a dense coverage at the satellite's turning latitude (defined by its orbital inclination), there is no coverage beyond that. Since the chosen orbit for a satellite is a compromise with other mission demands, only a few altimeters have provided data north of 72 • N (the limit for Geosat and Geosat Follow On).
The algorithms are used to assign the received radar echoes based on pattern recognition, by analysing similarities and differences among the waveform data, without being limited to a certain satellite altimetry mission or specific surface conditions. The statistical approaches are divided into (i) supervised classification algorithms, which require already classified training datasets (e. g. , NN) in order to link received waveforms on the basis of predefined characteristics for specific surface types, and (ii) unsupervised classification algorithms (e. g. , partitional clustering), which perform a waveform assignment without pre-classified radar echoes. The present paper provides one example for each of these approaches. Müller et al. [37]  produced a clustering algorithm that used six waveform features in an unsupervised classification (i.
e. , a system in which there no predefined target groupings). For this, they needed a reference dataset comprising the majority of all possible scatter types. The partitioning of data is achieved using the K-medoids clustering algorithm, which categorizes waveforms into a predefined number of K classes. Figure  5  shows the separation of more than 300,000 Envisat waveforms into 30 classes. These clusters are then assigned to different surface conditions in order to condense them to "ocean", "ice floe" and "lead/polynya" returns. This is achieved by analysing the mean feature values of each cluster and comparing the selected exemplary waveforms with the SAR data (see Section 2. 3. 2). For example, clusters displaying very narrow and clear single-peaked waveforms (e. g. , cluster numbers 2, 10, 11, 12, 20 and 26) are assigned to the lead/polynya clusters.
Again the input data is not the full set of waveform bins but a small set of characteristics describing the shape and amplitude of the waveform, which include PP, TES and LEW, as well as the existence or not of extra peaks in the trailing edge. Neural network classifiers directly model discriminant functions (functions enabling the prediction of the group membership of a sample based on the values of the input predictive variables) if the output values are defined in an appropriate way. It requires a large training dataset with enough representative examples of each of the predefined classes. The purpose is to classify the different geometrical shapes of the echoes (see Figure  6 ) and not the different surfaces, even if some links can be made between them. It is important to define the classes not only for all echo shapes of interest but also for all other waveforms numerous enough to impact the classifier.
Statistical Techniques There are now also some attempts at applying statistical techniques to the characteristics derived from the SAR waveform or RIP. The K-medoids approach of Müller et al. [37]  has been extended to work successfully with these data  [39] , and Shen et al. [44]  have implemented the machine-learning approach known as "random forest". Challenges still remain in the discrimination between leads and melt ponds, i. e. , calm water patches lying on top of the ice during the melt season. Finally, it has to be stressed that, in defining absolute thresholds for SAR altimetry classifiers, developers must be aware that changes in the Level 0 to Level 1 data processing (e. g. , application of Hamming weights in the Fourier analysis) can influence the performances of the method. Thus, conclusions about the most appropriate waveform classification and, especially, on the relevant thresholds are likely to be specific to the ground processing applied before the user sees the data.
Given the suite of different SAR instruments from different providers and that power constraints limit the duty cycle (i. e. , the proportion of time for which the instrument operates), developing a large matchup database of SAR and altimeter measurements with appropriate sea-ice drift corrections is a large time-consuming task. Müller et al. [37]  used short-wave C-Band SAR data from Sentinel-1A and Radarsat-2 and long-wave L-Band images from ALOS with image coordinates shifted according to the daily ice motion vectors from NSIDC (National Snow and Ice Data Center). Figure  8  illustrates the processing steps they used. First, a median filter is applied to the original image to reduce speckle noise, and then, a minimum filter is used to emphasise the dark areas (probable leads). The minimum filtering effectively expands the area ascribed as "leads" and is applied to overcome some of the uncertainties in the sea-ice motion.
Validation Through Dedicated Aircraft Campaigns A convenient way to acquire coincident altimeter and image data is to carry both instruments on the same aircraft platform. Such data could be used for the validation of surface classification, since coincident measurements would eliminate the need to compensate for ice drift between acquisitions. To date, there have been two large airborne campaigns flying suitable instruments: NASA's Operation IceBridge (OIB) and ESA's CryoSat Validation Experiment (CryoVEx). However, the aim of the missions has been to provide freeboard and snow and sea ice thickness estimates, so the data from imaging sensors have been used as an integral part of surface classification rather than for validation. Among the many airborne instruments often used during OIB are the Airborne Topographic Mapper (ATM, a conically-scanning laser altimeter), a Ku-band radar altimeter and optical systems such as CAMBOT and Digital Mapping System (DMS)  [57].
This process, which is most relevant for very sharp leading edges, avoids the loss of information that otherwise occurs upon squaring the voltages  [76]  and is fully consistent with the process to estimate the range path delay within the instrument during the internal calibration (CAL1) or transponder calibration: In this case, the signal is always highly oversampled in order to estimate the path delay to within a few mm. Dinardo et al. [79]  have also suggested that the processing scheme could be configured to forego the multi-look capability of SAR altimetry and to use a single (nadir) look in these circumstances, as this will be sufficient to fit the peaky specular echo. Such an approach would have the benefit of a faster computational speed. Further, it is important to point out that, when retracking SAR altimetry data over sea-ice, different Level1b processing baselines can be implemented in order to identify that which is most appropriate for the procedure to retrieve sea-ice freeboard.
This new retracker, referred to as SAMOSA+, can discriminate between return waveforms from diffusive and specular scattering surfaces, enabling an appropriate retracking to be carried out. It has been applied successfully to retrieve sea-ice freeboard from CryoSat-2 SAR data. The other approach by Kurtz et al. [62]  is the development of a physical model to retrack SAR and SAR in echoes from both sea-ice and lead reflections. The ability of the model to adapt for both sea-ice and lead echoes is based on the variation of two parameters: one modelling the efficiency of backscattering from a surface as a function of the incidence angle and another modelling the standard deviation of the heights illuminated by the footprint. The validation against OIB data showed a significant gain in consistency in freeboard and thickness retrievals when compared with the results obtained with a threshold retracker.
Ensuring Consistency in Space and Time When compiling a long-term dataset spanning multiple instruments and various retrackers, it is essential to minimise the biases between constituent parts. Although a mean bias between instruments may be determined on a global basis or via dedicated calibration sites, it is important that these offsets are also evaluated in the Arctic context. This is critical because some studies have used different retrackers for waveforms from floes, leads and open ocean, whilst others have adopted one retracker, such as TFMRA, but noted that the very different waveform shapes essentially mean that they have different retrack points  [77]. Ideally, the utilisation of a unified model (see Section 3. 3) should avoid any artificial change in the sea level associated with the ice edge (as the sea level is derived from open ocean waveforms on one side and predominantly from leads on the other).
Further challenges exist in the context of multi-mission datasets spanning several decades, as there have been changes in technology (LRM and SAR) and differences in processing methodology between missions (e. g. , the use or not of Hamming weighting). Of particular note is that AltiKa operates at a different radar frequency to all other missions (Table  1 ); thus, volume scattering from snow is more significant at this frequency, leading to changes in the retrack point over ice floes  [92]. A critical issue in the area of consistency is the quality and reliability of the geophysical corrections (see Section 4), since some missions enable more reliable corrections than others, e. g. , due to the presence of a second radar frequency or having an on-board microwave radiometer. Finally, we note that the changes in a sea-ice cover on both seasonal and interannual scales necessitate extra care when analysing for long-term changes in sea level.
The Sentinel-3 adds crucial information about the MSS north of Canada. However, the WTC correction based on MWR data must be avoided to prevent highly unrealistic values north of the Canadian Arctic Archipelago (see Figure  15 ). Several MSS products exist based on different satellite altimetry input data over varying periods. Differences and properties of these products are assessed in Skourup et al. [96]. The state-of-the-art MSS fields covering the whole Arctic Ocean are the UCL13 and DTU15 MSS. (The coverage of the CNES/CLS15 MSS is incomplete, and it is therefore not generally applicable for pan-Arctic studies. ) The UCL13 MSS is the one provided to the user within the ESA CryoSat-2 baseline-C data products and has been specially tuned to improve sea-ice freeboard retrieval. Outside of the Arctic, the CryoSat products revert to the CLS 2011 MSS to enable global applicability.
The choice of MSS should also reflect the period of data to be analysed, as the mean will change (by definition) with time. For example, the DTU MSS surface is referenced to the 1993-2012 period and, in the Arctic, is derived from ERS-1, ERS-2, Envisat and almost six years (2010-2015) of CryoSat-2 baseline-B data. North of 88 • N, this MSS is tapered towards EGM2008 GGM  [97] , which is a representation of the geoid. 4. 1. 3. Amplitude and Scale Length of Corrections Table  3  and Figure  16  show a simple analysis of the magnitude and length scales of the various corrections for Sentinel-3A. (The majority of the corrections are from models, and thus, similar values would be obtained for extracts from models along other altimeter tracks; the exceptions are WTC derived from the MWR and the ionospheric correction derived from the dual-frequency measurements.
) The data analysed are from Sentinel-3A cycle 035 (September 2018) over the Arctic Ocean north of 72 • N. Note that S. D. gives the variation in the correction; it is not a measure of its error. For MSS, the ocean tide and the loading tide, there are two alternatives in the standard data stream, only one of which should be applied. Table  3  shows that MSS and DTC are the largest corrections, but the latter has little spatial variation. MSS solution 1 is not appropriate for pan-Arctic studies on account of no values being supplied for 12. 8% of locations, and the WTC based on MWR measurements is also invalid for many locations (as is expected when the instrument footprint is dominated by sea ice). The ionospheric correction derived from dual-frequency measurements is too noisy and frequently unavailable, as the underlying range measurements at Ku-and C-band are highly variable.
Freeboard to Thickness Conversions In this subsection, we assume that the mean altimetric return is from the surface of the sea ice, as expected by Beaven et al. [101] ; in this case, the overlying snow layer produces a path length correction due to the reduced speed of radar propagation in snow. The issue of whether the reflection is always from the ice/snow interface is addressed in Section 4. 2. 3. The estimation of sea-ice thickness, t i , from the altimeter-derived sea-ice freeboard is based on Archimedes Principle, assuming that the sea ice is freely floating (see Figure  17 ). The application of this is common to all studies using altimeter data over sea ice in the scientific literature. Variations to a certain degree do exist in the parameterization of the conversion, which requires the densities of sea ice, ρ i , sea water, ρ w and the snow load given by snow depth, t s and density ρ s as input parameters: t i = t s • ρ s + f • ρ w ρ w -ρ i ( 2 ) where f is the freeboard determined from the altimetry.
However, Lawrence et al. [100]  claim some success using a combination of Ku-band radar (Envisat) and laser (ICESat); although the operation of ICESAT was intermittent (operating for only a few months in the year), this could still contribute to a better snow-depth climatology for the 21st century. Field observations over large areas that study the relationship of freeboard and draft  [108]  are still required to reduce uncertainties in the freeboard to thickness conversion. In the future, the snow depth from reanalysis products may be provided as an auxiliary data source to improve the ice thickness retrieval from radar altimetry.
Kwok  [71]  simulated the impact of the snow layer on the tracking point for determining surface elevations. He also examined airborne snow and Ku-band airborne radar data from OIB and concluded that scattering at the snow surface and within the snow layer is non-negligible and causes an alteration of the CryoSat-2 waveform tracking point that is evaluated to derive surface elevations. Similar findings have been reported by Ricker et al. [112] , who compared snow and ice thickness measurements from buoys with coincident CryoSat-2 overflights. They estimated that the assumption that the main scattering horizon is represented by the snow-ice interface led to a mean ice thickness bias of 1. 4 m for MYI after the strong snowfall events during 2013/2014. Armitage and Ridout  [81]  derived a freeboard from CryoSat-2 Ku-band and AltiKa Ka-band radar altimeters.
Freeboard and Sea-Ice Thickness There are several different methods to evaluate the satellite-derived sea-ice freeboard and thickness. Through the years of satellite altimetry, in situ observations, airborne campaigns, submarines, and drifting and moored buoys have all been invaluable in measuring the sea-ice and snow properties. However, as it is difficult and expensive to operate in the harsh environment with cold temperatures and darkness during the Arctic winter, such observations are still sparsely distributed in space and time (see Figure  19 ). The various evaluation data sets have their own pros and cons with respect to spatial and temporal resolution. Airborne and submarine surveys cover larger areas and usually represent short temporal scales (days or months), whereas the moored and drifting buoys represent point measurements over longer time scales and provide information about seasonal variations.
Freeboard There are two reasonably extensive datasets for a direct satellite sea-ice freeboard evaluation. One is from NASA's Operation IceBridge, with the sea-ice freeboard given by the total freeboard (snow + sea ice) measured by a laser altimeter minus the snow depth measured by the snow radar. The other is from the airborne campaigns carried out as part of ESA's CryoSat Validation Experiment (CryoVEx), where ASIRAS (an airborne version of the CryoSat-2 SIRAL altimeter) provides coincident Ku-band radar freeboard data. More recent campaigns (2016 and 2017) have included an airborne Ka-band radar altimeter (KAREN) to evaluate SARAL/AltiKa and to exploit the potential of a dual-frequency concept for future satellite missions. The processing of the KAREN data is still ongoing, so results can not be included here.
The measurements obtained by the OIB snow radar also provide a valuable validation of snow depth. The airborne OIB and CryoVEx data are primarily used to evaluate the satellite radar freeboard, to investigate penetration depths of both Ku-and Ka-band radars and the potential for snow-depth retrieval  [60, 81, 92, 100, 129]  and to examine the sensitivity to the choice of using different retrackers  [56, 62]. These studies consistently conclude that the predominant signal from Ku-band satellite radars such as CryoSat-2 and Envisat corresponds to the reflections from close to the snow-ice boundary. Other studies of CryoSat-2  [59, 112]  used Ice Mass Balance (IMB) buoys and a combination of airborne and in situ measurements of sea-ice and snow properties to show that the influence of snow cover on Ku-band penetration is not negligible in specific regions and/or snow conditions (see Section 4.
Sea-Ice Thickness (SIT) In the literature the most commonly used observations to evaluate satellite-derived SIT are values derived from the NASA operation IceBridge (OIB), total (sea ice + snow) thickness by airborne electromagnetic (AEM) sensors and sea-ice draft. The latter is measured by upward-looking sonars (ULS) either from submarine cruises or moored buoys (for example, during the Beaufort Gyre Experiment; BGEP). A more detailed description of these data sets is found in Lindsay and Schweiger  [131]  and Kwok and Cunningham  [72]. As none of the abovementioned observations measure the SIT directly, the satellite-derived estimates need to be changed into either draft or total thickness before a comparison, with a priori assumptions of snow depth and/or densities of snow and sea ice. In addition, SIT obtained by OIB are derived by the measured total freeboard and snow depth according to Equation (2).