dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis To gain a qualitative understanding of the structure of the water governance networks, we first visualized them using NetDraw v2. 121 software  (Borgatti 2002). We then used UCInet v6. 411 software  (Borgatti et al. 2002)  to analyze network metrics based on graph theory. More specifically, we analyzed degree centrality  (Hanneman and Riddle 2005)  to identify (1) central actors with many connections that could play an important role in enhancing collaboration  (Bodin and Crona 2009) , and (2) bridging actors that have the capacity to link different types of actors or spatial scales in the networks  (Vignola et al. 2013). We also identified weak or lack of connections, which could represent possible barriers for collaboration and adaption decision making. The findings of the network analysis were complemented with interview data to elaborate further on the role of agency and to identify possible mechanisms that could improve interaction in the networks  (Carlsson and Berkes 2005).
ACKNOWLEDGMENTS This study was conducted as part of the  EcoAdapt  project " Ecosystem-based Strategies and Innovations in Water Governance Networks for Adaptation to Climate Change in Latin American Landscapes. "EcoAdapt is a four-year action-research project funded under the  EU Framework Programme  ( ENV. 2011. 4. 2. 3-1 ENV ). Project information can be found at http://www. ecoadapt. eu/. We thank the coordinators and teams of field researchers and assistants in Bolivia, Chile, and Argentina that collaborated with the data collection and provided critical support and feedback for this study. We also thank the anonymous reviewers that helped greatly to improve the manuscript.
Detailed understanding of submesoscale processes and their role in global ocean circulation is constrained, in part, by the lack of global observational datasets of sufficiently high resolution. Here, the potential of thermosalinograph (TSG) and Visible Infrared Imager Radiometer Suite (VIIRS) data is evaluated, to characterize the submesoscale structure of the near-surface temperature fields in the Gulf Stream and Sargasso Sea. In addition to spectral density, the structure function is considered, a statistical measure less susceptible to data gaps, which are common in the satellite-derived fields. The structure function is found to be an unreliable estimator, especially for steep spectral slopes, nominally between 2 and 3, typical of the Gulf Stream and Sargasso regions. A quality-control threshold is developed based on the number and size of gaps to ensure reliable spectral density estimates.
2011 ). To date, most studies of submesoscale processes have been numerical owing to the paucity of observational datasets at these scales. A number of multiplatform experiments have been undertaken at these scales [e. g. , Scalable Lateral Mixing and Coherent Turbulence (LatMix) 1 ; Ocean Surface Mixing, Ocean Submesoscale Interaction Study (OSMOSIS) 2  ], but these experiments are expensive and very limited in space and time. On the other hand, until recently, routine sampling of the upper ocean, undertaken either over large spatial scales and/or large temporal scales, has been relatively coarse: the highest-resolution satelliteborne observations of the ocean surface have sampled at approximately 1 km, but the noise associated with these samples rendered them of little use at scales smaller than O(10 km)  (Deschamps et al.
1981 (Deschamps et al. , 1984)). Gliders have been employed to sample along repeat tracks (e. g. ,  Todd et al. 2011; Cole and Rudnick 2012 ); however, these sections are still relatively isolated and coverage is far from global. Furthermore, with the exception of a small number of datasets, the spatial scale at which long-term, repeated ship surveys sample tends to be O(5 km) and coarser. The latter is determined by the temporal sampling rate and ship speed. Since these observations tend to be taken from commercial vessels, there is little control over the ship speed and the sampling rate is constrained by the instrument, ship-to-shore transmission rates, and/ or onboard storage capacity. Sections from research vessels, repeated as the ships go to and from their home port, may be of interest for the study of coastal processes, but the repeated portion of these sections tends to be short and there is little regularity in their frequency.
Unfortunately, infrared radiometers are susceptible to cloud contamination so retrieved SST fields tend to be spatially patchy. A second objective of this project, therefore, is to explore the role of data gaps and their characteristics to better understand differences between in situ and satellitederived SST datasets. Different methodologies for deriving the statistical measures are compared for both observational and synthetic datasets. Specifically, we compare temperature spectra and structure functions obtained from VIIRS with those obtained from an acoustic Doppler current profiler (ADCP) and a TSG, both mounted on the Motor Vessel (MV) Oleander. Temperatures acquired as part of the ADCP system, although coarser in spatial resolution (3-5 km) than the TSG data (;75 m), are included here as a reference dataset in that their spectra have been published in recent studies of submesoscale processes in the region (e.
g. ,  Wang et al. 2010; Callies and Ferrari 2013). The Oleander route transects several distinct dynamical regimes; our analysis focuses on the Sargasso Sea and the Gulf Stream (Fig. 1 ). To test the reliability of the different methods in each of these regions, we also apply them to synthetic datasets with known properties. The datasets used are introduced in detail in the next section. In section 3 we provide an overview of the different spectral methods used. Analysis of the TSG and VIIRS data is provided in section 4. We close with a summary and discussion in section 5. In three appendixes we discuss a simple model for the ADCP sensor response and apply the methods used to synthetic data with known properties and provide a list of acronyms.
5 s. Because temperature can be altered while water is flowing through the intake pipes (;4. 5 m before the SBE 45 sample is taken), the thermosalinograph was equipped with an additional SBE 38 remote temperature sensor (TEX; accuracy: 0. 0001 K, resolution: 0. 00025 K, and response time: 0. 5 s) from September 2007 to fall 2013. The SBE 38 measures temperature directly at the intake, that is, the temperature ''external'' to the Oleander. In our study we analyze the temperature data from September 2007 to June 2013 from the SBE 45 (TSG) and SBE 38 (TEX) sensors. Analyzing both time series provides the opportunity to explore whether the quality of the ''interior'' measurements is sufficient to provide reliable estimates of spectral slopes. The data were downloaded from the NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML) website, where links to the datasets were made available upon request.
c. Visible-Infrared Imager-Radiometer Suite (VIIRS) Our final dataset consists of SST fields derived from the VIIRS instrument launched on the Suomi-NPP satellite in October 2011. The SST product used here [Joint Polar Satellite System (JPSS) VIIRS sea surface temperature environmental data record (EDR) 5  ] was retrieved from the VIIRS ''moderate resolution bands,'' which have a nadir resolution of 750 m and, because of the way in which the instrument is configured, decreases very slowly to approximately 1600 m at the scan edge, a ground distance of approximately 1500 km from nadir  (Seaman et al. 2014; Schueler et al. 2013). For this study, we use only the best-quality data, quality level 1. This removes most clouds as well as a number of pixels that were cloud free. To compare the VIIRS data to the Oleander sections, we use an isotropic Barnes filter with ds 5 750 m to interpolate SST on an equidistant grid with a resolution of 750 m along a nominal Oleander section with start points and endpoints at 64.
b. Cohesion All our datasets have gaps. Because the VIIRS measurements are strongly affected by cloud cover, they have significantly more missing data along the Oleander track than the in situ sections. As a result, it is impossible to select a sufficient number of (nearly) complete VIIRS sections for our analysis. This is potentially problematic because data gaps, depending on their nature (i. e. , their number and length), affect the estimate of spectral densities. Thus, for selecting the VIIRS sections used, we seek an approach that balances the number of available sections with the possible error due to the gaps contained therein. Here, we introduce two measures to characterize the gaps. These measures are used in our study to assess the quality of a section and its potential for improving the ensemble mean.
We explored an alternative method for obtaining spectral densities, that of taking the Fourier transform from the biased autocorrelation function, but abandoned this approach in favor of the DFT. For complete time series, the autocorrelation method is equivalent to the DFT, but for data with missing values, it has the advantage that no interpolation is required across data gaps; hence, it appeared to be well suited to this project. However, tests with synthetic data (discussed in appendix B) indicate that the DFT gives more precise estimates than the autocorrelation function method for time series similar to those in our datasets.
2) STRUCTURE FUNCTIONS We also estimate the second-order structure functions for the temperature sections. Structure functions provide a different perspective on the data than spectral density. In addition, it may be possible to estimate the slope of the spectral density using (1), when the structure of the data (i. e. , the number and coherence of missing data points) makes it difficult (impossible) to employ standard techniques for estimating spectral densities directly (e. g. ,  McCaffrey et al. 2015 ). An advantage of structure functions is that it does not require interpolation over missing data points; hence, even time series with relatively few data points improve the estimate of the average structure function. Ensemble-averaged structure functions are calculated by averaging over the contributions from all data-point pairs in the dataset for a given point separation (rather than by averaging over the structure functions computed for each section).
Results In this section, we report the results of the analysis of the four datasets described in section 2. We start by comparing the three in situ datasets from the Oleander. a. TSG, TEX, and ADCP 1) AVERAGE SPECTRA Figure  3  shows the DFT spectral densities of the ADCP, TSG, and TEX sections in the Sargasso Sea and Gulf Stream regions. A very strict quality standard of Q. 0:999 is used for all in situ data due to the large number of high-quality sections available. With that choice the number of sections used is 522 and 527 for ADCP, 205 and 178 for TSG, and 236 and 236 for TEX in the Sargasso Sea and the Gulf Stream, respectively. Spectral slopes are estimated for wavelengths between 10 and 100 km [0. 1-0. 01 cycles per kilometer (cpkm) ]for all three datasets, and for the interval 1-10 km (1-0.
1 cpkm) for TSG and TEX, and are reported in Table  1. Generally, the Gulf Stream region is more energetic than the Sargasso Sea, and the slopes are steeper. Samelson and Paulson (1988)  and  Wang et al. (2010)  suggest that spectral densities of temperature and potential energy are proportional. If that assumption is valid, then the slopes suggest that the Gulf Stream is somewhat closer to a quasigeostrophic regime, where a slope of g 5 3 would be observed, whereas other processes are of relatively larger importance in the Sargasso Sea. On the other hand, it has been shown that the slopes of density (potential energy) and temperature spectra can diverge due to density compensation (e. g. ,  Rudnick and Ferrari 1999; Kolodziejczyk et al. 2015). Although the general shape of the spectra is similar for the three datasets, their details differ.
The ADCP sections are less energetic than the TEX and TSG sections, particularly at smaller scales ( & 10 km); thus, the ADCP spectra have steeper slopes. Qualitatively, this discrepancy can be explained by a response time of 5-10 min for the ADCP thermistor (appendix A; Fig. 3 ), which is expected based on the location of the temperature sensor within the ADCP. The simple model [(A1)] used for simulating the ADCP thermistor response overestimates the damping of small scales relative to that of larger scales, suggesting that the thermistor response is more complex than assumed by (A1). Other issues may contribute to the discrepancy, albeit likely less strongly: The data products made available involve different filtering and averaging, and the ADCP temperatures are measured in the sea chest, whereas the thermosalinograph samples from the engine intake.
2) STRUCTURE FUNCTIONS The structure functions computed for the same section as the spectral densities in Fig. 3  are shown in Fig. 4. In general, a picture similar to that associated with the spectral densities emerges, with the Gulf Stream region being more energetic and showing steeper slopes than the Sargasso Sea. The structure functions also reproduce the differences among the three datasets seen in the comparison of their spectral densities; that is, TSG and TEX are more energetic at smaller scales. Because (1) applies only when the spectral slope is constant and extends over an infinite frequency interval, it is not straightforward to compare slopes of the structure function at different separation intervals to spectral slopes at different wavelength. It has been suggested, however, that different high-and low-frequency regimes can be compared to small-and large-separation parts of the structure function if both parts are sufficiently well resolved  (McCaffrey et al.
3) SEASONAL CYCLE An understanding of the seasonal variability in the spectral slopes is important because the VIIRS data distribution is not uniform over the year. Figure  6  shows the TSG spectral densities averaged by season over the dataset. Interestingly, the seasonal cycles in the Sargasso Sea and Gulf Stream regions have a different, almost opposite phase. In the Sargasso Sea, spectral energy reaches a maximum in summer. The relatively large seasonal differences, in particular at smaller scales, may explain part of the increase in the confidence interval in the mean spectrum at smaller scales. This is consistent with the findings of  Luce and Rossby (2008) , who used the 150-kHz Oleander data to estimate the seasonal variability of the eddy population in the Sargasso Sea and found that eddy numbers peaked in summer and fall.
4) EFFECTS OF GAPS To test the impact of gaps in real data, we have artificially introduced gaps into the TSG sections in the same manner as described in appendix B and recalculated the spectra and spectral slopes. Figure  7  shows 2D histograms indicating the change in slope resulting from gaps in the Sargasso Sea and Gulf Stream regions. As with the synthetic data (appendix B), the spectral slopes are increasingly biased low as Q decreases and C increases, and this effect becomes more pronounced as the true spectral slope increases. These results motivated our choice to include only VIIRS sections with Q 2 C. 20:1 (dashed lines in Figs. 7a, b ) in the following section.
Another difference between the satellite and in situ data is that the satellite samples the entire area within a few minutes, whereas the Oleander requires about 1. 5 days for one section. The impact of this difference is likely minimal owing to the rapid ship speed of 8 m s 21 ; the Oleander crosses 30-km mesoscale features in 1 h. In summary, we note that TEX and VIIRS spectra diverge at different spatial scales but at the same energy levels in the Sargasso Sea and the Gulf Stream. This is a strong indication that sensor and retrieval noise is underlying these discrepancies. On the other hand, the satellite measures the ocean skin temperature, whereas the thermosalinograph samples at 5-6 m below the surface. Hence, it is possible that processes underlying the temperature fields differ at these depths.
2) STRUCTURE FUNCTION Structure functions for the VIIRS data are shown in Fig. 9. Since the structure function (  7 ) is insensitive to gaps, all sections with Q $ 0:5 are used, which increases the number of used sections to more than 150 in both regions. The structure functions show the same relations between VIIRS and TEX as the spectral densities. We further note that again, the slope of the structure functions reported in Table  2  is smaller than predicted by (1). We also show the structure functions using only data from daytime [71 (74) sections in the Sargasso Sea (Gulf Stream)] and nighttime  [83 (84)  sections in the Sargasso Sea  (Gulf Stream) ]. We find that although the shape does not differ much between day and night, the temperature field is more energetic at daytime at all scales.
Discussion In the present study, we calculate spectral densities and structure functions of surface temperature along sections between New Jersey and Bermuda. We use three in situ datasets: temperature measurements from an ADCP, a TSG, and its TEX. All instruments are mounted on the Oleander, a container ship making weekly round trips between New Jersey and Bermuda. In addition, we analyze satellite-derived SST from the VIIRS carried on the Suomi-NPP spacecraft. We compare two methodologies for computing spectral slopes from the datasets as well as with synthetic data, to estimate the effect of gaps, resulting from cloud cover in satellite-derived SST fields, on spectral slopes. These analyses, discussed in more detail in subsequent subsections, suggest that for SST in the Sargasso Sea and in the vicinity of the Gulf Stream, d the Oleander-mounted TEX and TSG provide consistent SST spectra down to scales of O(1 km) d the structure function is not a reliable estimator of the spectral slope in either region d spectra from the full-resolution VIIRS SST fields agree with in situ data on mesoscales but show elevated energy levels at smaller scales with possible physical and technical explanations a.
In situ data Our analysis indicates that temperature spectra estimated from TEX and TSG compare well down to scales of O(1 km). Spectra from TEX are more energetic at scales &5 km, which we attribute to the warming of TSG temperatures while water flows through the intake pipes. Despite these drawbacks, our analysis suggests that Oleander TSG temperatures can be used as a reliable substitute for characterizing the temperature structure on larger scales (but not for absolute temperature) if TEX data are not available. Interior TSG data from other ships may also be useful; however, because of possible differences in the system (e. g. , pipe length), the temperature modification may be different and damp the temperature structure at larger scales. Spectra estimated from the temperatures measured by the Oleander 75-kHz ADCP are significantly less energetic at submesoscales, in particular at scales & 10 km.
b. VIIRS data Spectra estimated from full-resolution VIIRS SST fields interpolated on a nominal Oleander track compare well with those from TEX for scales *5 km in the Gulf Stream region and *20 km in the Sargasso Sea region. At smaller scales, VIIRS spectra are more energetic and have flatter slopes. In both regions, the VIIRS spectra separate from the TEX spectra at a similar energy level, approximately 10 2 K 2 km. This coincidence points toward pixel-to-pixel noise in the VIIRS SST fields as a possible explanation for the discrepancy, which is a typical problem for full-resolution SST fields derived from infrared satelliteborne sensors  (Deschamps et al. 1981 (Deschamps et al. , 1984;; Obenour 2013). On the other hand, simulated VIIRS spectra, obtained by adding data gaps and noise consistent with that in VIIRS to the TEX temperatures (based on pixel-topixel temperature differences from VIIRS fields, white noise with an amplitude of 0.
1 K is assumed), cannot fully explain the difference between TEX and VIIRS spectra. Since VIIRS measures the skin temperature of the ocean, whereas TEX samples at the engine intake at a depth of 5-6 m, it is also possible that different physical processes affect the temperature records of the two instruments. For example, the VIIRS data are more likely to be affected by diurnal warming. In this regard, we also found a significant difference between the daytime and nighttime temperature structures in VIIRS, in particular in the Sargasso Sea. Since it takes about 1. 5 days for the Oleander to travel from New Jersey to Bermuda, the in situ sections include both, daytime and nighttime, the consequences of which are unclear. To better quantify the different causes for the difference between the in situ and VIIRS data requires further investigation.
c. Spectral density versus structure function By comparing spectral slopes estimated by DFT and slopes of the structure function from our datasets, as well as from synthetic data with known properties (appendix B), we have shown that (1) does not provide a good approximation of the relation between slopes of spectral density and those of the structure function for SST in the Gulf Stream and Sargasso Sea regions. Specifically, the structure functions were consistently flatter than expected from (1), and the error increases with the spectral slope. This is consistent with the findings of  Huang et al. (2010) : the structure function is not a good indicator of the spectral slope when significant amounts of energy are contained at the low-frequency end of the spectrum compared with the amount of energy at the high-frequency end.
An advantage of the structure function, on the other hand, is that it is not affected by data gaps; hence, for patchy datasets such as VIIRS SST fields for this region, the amount of usable data is significantly larger compared to DFT. For this reason, we found the structure function to be useful to contrast the structure of VIIRS temperature at daytime and nighttime and summer versus winter, which would have been prohibitive using spectra (e. g. , in the Sargasso Sea, only two VIIRS sections satisfied the imposed Q-C standard in winter). In conclusion, thermosalinograph and VIIRS data provide reliable spectra of the near-surface temperature of the ocean over a broad spectral range. However, several challenges remain, in particular at submesoscale and smaller scales for VIIRS. At such scales, the accuracy of the spectra is limited by sensor noise.
This problem is more pronounced in the relatively quiet Sargasso Sea than the Gulf Stream due to the different signal-to-noise ratios. Further research is needed to better understand the spatial and temporal variations of submesoscale processes in the ocean, and, in particular, to resolve to what extent the difference at scales & 20 km between VIIRS and thermosalinograph data can be attributed to noise versus differences in the temperature structure between the surface and 5-m depth. Despite these concerns, VIIRS is of significant value as a tool to study the structure of the upper ocean because of its global coverageclouds permitting-from early 2012 to present. Finally, we note that spectral estimates may not be a good measure of short-scale features, such as fronts that cover only a small fraction of the ocean surface but that are readily resolved in satellite-derived SST fields, because of the sharp discontinuities with which they are often associated.