dataesr/scientific-paragraphs-categorization huggingface dataset:
Yet, the low AOD over ocean seems to be in line with results published by  Smirnov et al. (2012). Nevertheless, ORAC does not rank highest over ocean, which may be due to the lack of independent validation data over open ocean which could confirm the low AOD observed by ORAC. Based on the current RR results, ADV ranks first, followed by SU with ORAC as third. Yet, the differences are so small, that the ranking may change when further improvements are implemented. Furthermore, the ranking may be influenced by uncertainties introduced by L3 sampling methods as discussed in  Sayer et al. (2010). Much of the difference between algorithms and their scoring may be due to cloud masking. The different cloud masks used by each of the algorithms slightly complicates the like for like comparison especially as common filtering may not completely account for possible differences in cloud masks or thresholds used at the 10x10 km 2 retrieval level.
As pointed out in the introduction, the comparison with AERONET data, which are well screened for cloud occurrence, does not provide a good test for how well clouds have been detected in satellite data and the results may be influenced by the occurrence of residual clouds. Several recommendations resulted from the RR exercise. One of them was that, although the ADV algorithm overall ranking was best, the coverage was a problem and needed to be improved. Such improvement could be found from increasing the maximum solar zenith angle used in the retrieval from 60 to 75 degrees which would give a similar coverage as other algorithms and from the implementation of a module to model the reflectance of bright surfaces such as applied by SU. The latter has been implemented in ORAC V2, together with the ADV post-processing step and initial results are better than those described here.
Conclusions A validation protocol and necessary tools to implement the protocol have been developed and were applied to 7 algorithms for aerosol retrieval using AATSR (4, one synergistic with SCIAMACHY), MERIS (2) and PARASOL data. For reference, these tools were also applied to MODIS and MISR data. The application of these tools, to L2 and to L3 data using different statistical methods and scoring based on a combination of methods, revealed the strengths and weaknesses of each algorithm as well as a scoring of both the Aerosol-cci and the reference algorithms. A crucial issue is the dependence of validation scores on data filtering -this led to the development of a common point filter to assure the comparison of equivalent datasets. Taking into account the results from the RR exercise, the improved algorithms will be used to provide a 1-year data set (2008) of Aerosol ECV products which, after validation using similar tools as described in this paper, will be offered to the climate modelling community for their validation and feedback as regards the use for climate studies.
Acknowledgements The results presented in this paper were achieved within the  Aerosol_cci  project funded by the  European Space Agency (ESA)  as part of the  ESA Climate Change Initiative (CCI). We acknowledge AERONET in situ aerosol data providers through which ground based measurements for the validation activities in the project were acquired, as well as ICARE and AEROCOM which played a crucial role in the data validation and data storage services. Aerosol retrieval work by sevral participants is supported by a range of other  ESA  projects than the  Aerosol-Climate Change Initiative  as well as  EU FP6  and  FP7  grants. ALAMO has been developed by  HYGEOS  and LOA with the collaboration of  ICARE  and with partial financial support of  CNES  "  Centre National d'Etudes Spatiales ".
Previous studies have described the observed features of global MLD distribution based on the climatological temperature and salinity (TS) data (e. g. ,  Levitus 1982; Monterey and Levitus 1997; Kara et al. 2003). de Boyer Montégut et al. (2004)  estimated the monthly variation in the global MLD distribution by processing individual profiles of the TS observations. These studies have clarified global features in the seasonal cycle of MLD climatology. Recently, observations by the Argo hydrographic array have facilitated the description of the interannualy-varying global MLD distribution (e. g. ,  Hosoda et al. 2010). However, the spatial resolution and temporal coverage of the Argo data are still limited, particularly since decent coverage is practically limited to the last century, which restricts applications to time scales of less than a decade.
g. ,  Oka et al. 2007) , this is arguably a very short period. Therefore, it is of value to identify the long-term basin-scale MLD variability by fully utilizing observational datasets available and our knowledge of the ocean dynamics (e. g. , ocean models). Ocean syntheses use the output of dynamical models combined with observations using statistical technique and hence can provide MLDs at every defined grid point and time step, although they are inevitably influenced by errors in the dynamical models, observations and assimilation methods. Due to the recent increase in a variety of observations and in response to advances in modeling and assimilation techniques, ocean syntheses have been improved to the level for practical use (e. g. ,  Lee et al. 2009). For example,  Toyoda et al. (2011)  analyzed the relative contributions of the 3 dominant physical processes to the interannual variability of the North Pacific eastern subtropical mode water (ESTMW; 24.
In this study, we focus on the interannual-decadal variability of the wintertime MLDs in the North Pacific represented in the ensemble mean of the reanalyses. The ensemble mean MLD field along with information on its uncertainty derived from the ensemble spread allows a more quantitative investigation than previous analyses (e. g. ,  Toyoda et al. 2011). The aforementioned study  (Toyoda et al. 2015)  focused on the validation of MLDs from ocean reanalyses using the Argo float data (e. g. ,  Hosoda et al. 2010). The current study, however, is not limited to the Argo era, but covers the full temporal record of the reanalyses, which allows analyzing the relationship between the MLD variability in the North Pacific and the interannual-decadal scale climate variability. Section 2 describes the ensemble mean MLD field derived from the reanalyses as well as other datasets used in this study.
Data Monthly MLD fields estimated from 2 observation-only analyses and 17 reanalyses (Table  1 ) are provided by the operational and research centers as an ORA-IP activity. These syntheses/reanalyses are all different in ocean general circulation model, resolution, surface forcing, ML parameterization, assimilated data and assimilation method (see details in  Toyoda et al. 2015). MLDs are obtained from the monthly mean TS fields on the original grids for the individual datasets and then interpolated onto the common longitude-latitude grids at one-degree intervals. In the present study, we use a density criterion for the MLD definition (e. g. ,  Levitus 1982) , i. e. , MLD is defined as the depth where potential density exceeds the 10-m depth value by 0. 125 kg m -3. Toyoda et al. (2015)  revealed that this definition results in an MLD field with less errors relative to definitions using other criteria for potential density (0.
03 kg m -3 ) or potential temperature (0. 2 and 0. 5 °C) in terms of both the seasonal cycle and interannual variability since errors due to monthly averaging of profiles and to weak thermal stratification at high latitudes are relatively small with this definition. Moreover, we use only wintertime maximum MLDs, since we consider that such MLDs are thought to be a most important factor for the thermocline layer dynamics via water mass formation and subduction. Note that the interannual variability in MLD is generally dominated by the interannual variability of the wintertime MLDs (except for the tropics). We pick out a wintertime maximum MLD in each location, year and dataset from January to April (July-October) in the Northern (Southern) Hemisphere. These months are chosen on the basis of the histogram for occurrence of the annual maxima of all the synthesis MLDs (Fig.
1a ). Although months when maximum MLDs occur can differ in each location We calculate the year-to-year wintertime PDO time series by averaging the monthly data from December of the preceding year to February of the target year. We also use the annual PDO time series defined as the average from July of the preceding year to June of the target year (with its center in winter) in order to compare with the above wintertime time series. Monthly time series of the WP index are obtained from the website of the National Weather Service Climate Prediction Center, National Ocean Atmosphere Administration (http://www. cpc. ncep. noaa. gov/data/teledoc/wp. shtml). Wintertime and annual WP time series are calculated in the same manner as the PDO time series. In addition, we use the JMA's historical observational data along the 137°E line in the North Pacific in order to evaluate our results.
Time series of temperatures in the STMW core in summer at 137°E are obtained from the JMA website (http://www. data. jma. go. jp/gmd/kaiyou/data/ shindan/b_1/stmw/npstmw137e. txt). Observation data of the geochemical parameters (nitrate concentration and apparent oxygen utilization (AOU)) are also obtained from the JMA website (http://www. data. jma. go. jp/gmd/kaiyou/ db/vessel_obs/data-report/html/ship/ship. php) and interpolated onto the 25. 2σ θ density surface through the Akima (1970) method after quality control in the MRI. Annual mean values are calculated from the monthly time series averaged over 28°N-32°N. We use the monthly values for sea level pressure (SLP) and latent and sensible heat fluxes from both the JRA-55  (Kobayashi et al. 2015)  and CORE  (Large and Yeager 2004)  datasets.
3 ) in the following analysis, in order to eliminate grid-scale un-eliminated data in the marginal seas (the Okhotsk, Japan and East China Seas). Before the analysis of the wintertime maximum MLDs of ENSMEAN, we compare those between ENSMEAN and the observation-only analyses (EN3v2a and ARMOR3D). Figure  4  shows the correlation coefficients among the three datasets. The correlations between EN3v2a/ARMOR3D and ENSMEAN are generally greater than the correlation between the observation-only products in most of the open ocean region. The comparison of the zonal mean values implies that this feature is pronounced at low-and mid-latitudes (Fig. 4d ). This is in contrast to the averaged correlation between the observation-only analyses and the individual reanalyses (dashed lines) which is lower than the correlation between the observation-only analyses.
g. , Fig. 5  of  Sugimoto and Hanawa (2009) ), which supports the large negative correlation between the PC-2 and wintertime WP index with zero time lag (Fig. 10d ). Over most of the North Pacific, the SST and THF anomalies have opposite signs (Fig. 12b ). For example, the negative SST anomalies correspond to the positive anomalies of THF release to the atmosphere in the south of Japan. However, in the eastern part of the Kuroshio Extension region (yellow box approximately), there exists a region where the anomalies have the same sign (positive) allowing the SST to increase the THF and force the atmosphere. Note that this result is only slightly dependent on the forcing dataset, differing little when the CORE dataset is used instead of the JRA-55 dataset as in Fig. 12. Qiu and Chen (2005)  indicated that the STMWs are relatively thick (thin) and thus the wintertime MLDs in the STMW formation region are relatively large (small) when the Kuroshio Extension jet is relatively strong and stable (weak and unstable).
Acknowledgments We thank three anonymous reviewers for their constructive comments. Thanks are extended to  Dr. H. Tsujino  and  Dr. K. Sakamoto  for their kind and valuable advices. This work was partly supported by the  Research Program on Climate Change Adaptation (RECCA)  of the  Ministry of Education, Culture, Sports, Science and Technology of the Japanese government (MEXT) , by the  Data Integration and Analysis System (DIAS)  of the MEXT, by the joint  UK DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ), by the  UK Public Weather Service Research Programme , and by the  European Commission funded projects MyOcean  ( FP7-SPACE-2007-1 ) and  MyOcean2  ( FP7-SPACE-2011-1 ). During the preparation of this article, our co-author Nicolas Ferry passed away. He was an active and supportive member of the ORA-IP and CLI-VAR-GSOP activities.
e. the filling-in of Fraunhofer lines, and the contribution of broadband absorption and scattering effects (mainly Rayleigh and Mie scattering). The main DOAS settings used in this study as well as the cross-sections included in the spectral fit are given in Table  1. They have been chosen in accordance with the recommendations made by the NDACC UV-Vis Working Group for the sake of harmonising the different data sets provided to the NDACC database  (Van Roozendael and Hendrick, 2012). DSCDs are the direct output of the QDOAS spectral fitting approach. Prior to further analysis, the NO 2 DSCDs are quality-checked based on the following: (1) their uncertainty, expressed as standard deviation (SD), and (2) the residual structure of the retrieval fit, expressed as root mean square error (RMS).
Both measures of dispersion, which can be interpreted as quality flags for the measurements, are calculated for each DSCD by the QDOAS software. An empirically derived threshold based on the 95 % confidence interval is set for both parameters to determine whether or not a measurement is an outlier, e. g. due to low signal-to-noise ratio (SNR), and needs to be rejected. On a total of 4226 DSCDs retrieved based on ZS-DOAS observations, 128 were rejected after the quality check. Beside NO 2 DSCDs, also the oxygen dimer (O 4 ) DSCDs have been retrieved. They are essential to determine the presence of aerosols and clouds, which can both affect the tropospheric NO 2 retrieval. O 4 has a well-known and nearly constant column and vertical distribution in the atmosphere, mainly depending on temperature and pressure, and thus on the altitude.
Stratospheric AMF Stratospheric AMFs have been calculated with the RTM package UVspec/DISORT  (Mayer and Killing, 2005). This code has been thoroughly validated in the framework of an intercomparison exercise between different RTMs for the interpretation of GB ZS-DOAS and MAX-DOAS observations  (Hendrick et al. , 2006; Wagner et al. , 2007). The radiative transfer equation (RTE) is numerically solved by the discrete ordinate method in a pseudo-spherical geometry and including multiple scattering. The wavelength used here is 457 nm, i. e. the middle of the NO 2 fitting window. Since stratospheric NO 2 is characterised by a strong diurnal variation due to photochemistry, the corresponding changes of the concentration along a given light path complicate the calculation of AMFs, especially at twilight.
Tropospheric AMF In contrast to the stratospheric contribution, tropospheric NO 2 concentrations can be highly variable in time and space in polluted regions. For an optimal simulation of tropospheric AMFs, realistic a priori profiles that take into account local pollution events are required. Therefore daily NO 2 and aerosol profiles retrieved from the BIRA-IASB MAX-DOAS observations performed during CINDI have been used to derive appropriate tropospheric NO 2 AMFs. These calculations were done with the bePRO package based on the LIDORT RTM  (Spurr et al. , 2008). This RT suite, dedicated to the retrieval of trace gas and aerosol vertical profiles, and AMF calculation, based on the Optimal Estimation Method  (Rodgers, 2000) , is extensively described in Clémer et al. (  2010 ) and  Hendrick et al.
The MAX-DOAS retrievals show similar day-to-day variations with respect to the ZS-DOAS and SAOZ retrievals. However, a positive bias of about 18 % on average can be observed for MAX-DOAS retrievals. The same feature can be seen in Fig. 9 , showing the retrieved NO 2 TVCD diurnal cycle of 2 subsequent days in the data set, i. e. 3 July 2009 (day 184; see Fig. 9a ) and 4 July 2009 (day 185; see Fig. 9b ). For most retrievals, MAX-DOAS data shows a positive offset while ZS-DOAS and SAOZ retrievals are very close to each other. Different air masses were sampled in both cases due to the different viewing geometries of the multi-axis and zenith-sky approach. This can lead to increased uncertainties especially if the horizontal distribution of NO 2 is inhomogeneous and in the presence of scattered clouds, as to be expected in a site like Cabauw.
, 1994) , which are frequently used for determination of the NO 2 residual amount in the reference spectrum, was meaningless in the case of the Cabauw data set. Only the observations in a limited SZA range (90-80 • ) with low tropospheric sensitivity could be used, since tropospheric pollution events can affect the straight-line fits of the Langley plot method. Along with the low frequency of ZS observations, i. e. each 15 to 20 min, too few data points remained in the plot to be statistically relevant. Furthermore, some tropospheric contamination could still be observed, even at high SZA. Constantin et al. (2013)  reported similar issues with the chemically modified Langley plot method, when applied on observations from a polluted site. For the selection of the SZA interval, a trade-off was discussed between having a sufficiently large set of observations while avoiding tropospheric contamination at lower SZA.
In case of low to moderate tropospheric content, it is however strongly recommended to apply both the MLE and the chemically modified Langley plot methods in order to further constrain the determination of the residual amount in the reference spectrum and to reduce the substantial uncertainties in this step. Another important error source is the determination of the stratospheric contribution. The assumption that the tropospheric contribution is negligible in the case of ZS observations at dawn and dusk does not always count in case of sites where frequent strong pollution events occur. Therefore, an approach was proposed to identify a non-polluted reference day and to assume that the retrieved stratospheric content for this day is representative for the whole data set. It is recommended, however, to use daily observations (or to take a weekly mean) of the stratospheric NO 2 amount in the absence of frequent tropospheric pollution events in order to reduce the uncertainties introduced by the temporal variance and/or seasonality of the stratospheric NO 2 content.
In  Chen et al. (2009)  a strategy was applied to determine the stratospheric contribution in case of a heavy polluted site. Due to severe tropospheric contamination at the measurement site in Shanghai (China), no clean reference day could be identified in the data set. Instead, measurements were done at Chongming Island, which lies to the northeast of Shanghai in the Pacific Ocean. Chongming can be considered as the area with the smallest tropospheric NO 2 pollution in the proximity of Shanghai. The SVCDs determined for a clean day at Chongming Island were eventually used to retrieve the TVCDs from the observations acquired in Shanghai. This strategy can be, however, an additional error source, because of the spatial and temporal variations of the stratospheric NO 2 content. It makes also difficult its application to any other station due to the need of these additional measurements in a clean site.
Both a higher frequency of ZS observations and the lack of frequent tropospheric contamination results in a sufficiently large and reliable data set to derive the RSCD by linear least-squares regression in the chemically modified Langley plot. A single noon spectrum, selected on 31 August 2013 at 11:40 LT, is used for the analysis of the whole time series. For the RSCD, a value of 2. 7 × 10 15 molec cm -2 is determined. (2) Due to the absence of frequent tropospheric pollution events at twilight, daily observations of the stratospheric contribution could be performed instead of retrieving the stratospheric content for a number of reference days, representative for parts of the data set. This strategy reduces the uncertainties introduced by the temporal variance and/or seasonality of the stratospheric NO 2 content.
The retrieved TVCDs have been compared again with correlative data sets from SAOZ and MAX-DOAS observations and the resulting monthly mean time series are shown in Fig. 10. At OHP, a marked seasonal cycle can be observed with a maximum in winter with mean values close to 3 × 10 15 molec cm -2 and a minimum in summer with mean values around 1. 7 × 10 15 molec cm -2. In general the three data sets are in good agreement for both low and high TVCDs: the correlation coefficients are respectively 0. 82 and 0. 88 for the comparison of ZS-DOAS with MAX-DOAS and with SAOZ. These results further support the good reliability of the ZS retrieval approach presented in this study.
The main error sources are characterised for the four principal steps of the retrieval approach: 1. Uncertainties due to the DOAS analysis and noise in the spectral measurements result in a relative error of approximately 14 and 3 % for low and high tropospheric VCD retrievals, respectively. It should be noted that DSCDs are quality-checked, including the removal of outliers, and compensated for multiple scattering events prior to the retrieval. 2. The NO 2 SCD residual amount in the fixed reference spectrum, measured on a non-polluted, clear-sky day around local noon, is determined based on the statistical minimum-amount Langley-extrapolation method. The related uncertainty can be substantial (22 % on average and up to 40 % in case of low NO 2 TVCDs). However, since a single RSCD is used for the analysis of the whole data set, potential errors scarcely affect the relative variation of the retrieved tropospheric VCDs.
The present study demonstrates that ZS observations, widely used for monitoring of the stratospheric composition for more than 3 decades, are also suitable for the retrieval of tropospheric NO 2 column amounts, despite the lower sensitivity to the troposphere when compared to MAX-DOAS observations. In order to further demonstrate the potential of the presented retrieval algorithm, it has been successfully applied on a 2-year data set acquired at OHP, being a background site from time to time affected by pollution events. This offers new perspectives for the exploitation of ZS UV-Vis observations at NDACC stations, and the applicability of the algorithm on data from other stations as well as longer time series will be further investigated. At present, there are far more ZS-DOAS than MAX-DOAS GB stations at various latitudes and much longer time series of ZS observations are available than for MAX-DOAS, mainly due to the novelty of the latter technique.
Acknowledgements. The CINDI Campaign was largely funded by the  ESA  project  CEOS  ( ESRIN 22202/09/I-EC ) and the  EU  project  ACCENT-AT2  ( GOCE- CT-2004-505337 ). We further acknowledge the support of the  European Union Seventh Framework Programme  via the  NORS  project ( FP7-284421 ) and the  ACTRIS  project ( FP7-262254 ). BIRA-IASB is also thankful for the support from the  Belgian Federal Science Policy Office  through the  AGACC-II  project ( SD/CS/07A ). M. P. Chipperfield, from the  University of Leeds , is gratefully acknowledged for providing us with the SLIMCAT data. The team of LATMOS wishes to thank the  French Centre National d'Etudes Spatiales (CNES) and Institut des Sciences de l'Univers (INSU). The authors also want to express their gratitude to the  KNMI staff at Cabauw  for their technical support and the offered infrastructure.