dataesr/scientific-paragraphs-categorization huggingface dataset:
This two step-based method was introduced by  Gouretski and Jancke (2000)  and  Johnson et al. (2001). For the secondary QC applied to the GLODAPv2 database, the crossover offsets were calculated using the running-cluster crossover routine  (Tanhua et al. , 2010) , with data from beneath 2,000 m to minimize effects of real variations. For pH T , crossover analysis was not possible because data only exist for a small fraction of the cruises. To pass the secondary QC, pH T measurements had to be concomitant with C T and/or A T for calculating offsets (see details in  Olsen et al. , 2016). For Mediterranean Sea data, the secondary QC always failed because none of the cruises inside the Mediterranean had an overlap with other cruises (e. g. , outside the Mediterranean) thus preventing the crossover analysis.
Hence, only "high-quality" GLODAPv2 data that passed the secondary QC were used to train and validate the CANYON methods except for the Mediterranean Sea where we used data that only passed the primary QC. The subset of the GLODAPv2 database used for our study (i. e. , the data that passed the secondary QC, except for the Mediterranean Sea data as explained above) contained 37,863 concurrent profiles of water-column (from the surface to a maximum sample depth of 8,000 m) hydrological properties together with nutrients concentration and/or parameters of the carbonate system (see Figure  1 ). These data were collected between 1972 and 2013 and were representative of the diversity of oceanic regions, i. e. , 25 % were collected in the North Atlantic, 10 % in the South Atlantic, 22 % in the North Pacific, 12 % in the South Pacific, 10 % in the Indian Ocean, 13 % in the Southern Ocean, 7 % in the Arctic Ocean, and ∼0.
2 % in the Mediterranean Sea (geographic boundaries are provided in Figure  S1 ). On the temporal scale, most of the data were acquired since the 1990's and more data were available for the spring and summer months (Figure  S2 ). There was a sampling bias according to latitude as data from autumn and winter months (i. e. , December to March for the Northern hemisphere, and May to August for the Southern hemisphere) were less represented at high than low latitudes (i. e. , >45 • North and South, respectively) in the GLODAPv2 database (Figure  S2 ). All the data used to train and validate CANYON were measurements recorded in the GLODAPv2 database, except the pCO 2 estimates that we calculated from A T and C T measurements using the R package "seacarb"  (Gattuso et al. , 2015 (Gattuso et al. , , 2016 ).
The carbonate system parameters were computed using the carbonic acid dissociation constants of  Lueker et al. (2000) , the hydrogen fluoride dissociation constant of  Perez and Fraga (1987) , the dissociation constant for bisulfate of  Dickson (1990) , and a ratio of total boron to salinity derived from  Uppström (1974). In situ measurements of salinity, temperature, hydrostatic pressure as well as the concentrations of PO 4 3-and Si(OH) 4 were used to calculate pCO 2. When not available in the GLODAPv2 database, the concentrations of PO 4 3-and Si(OH) 4 were estimated using our CANYON algorithm (see associated accuracies in Section Overall CANYON Performance). For the neural network development, vertical profiles of nutrients and carbonate system parameters from eight independent zones of the GLODAPv2 database (squares of 10 • latitude × 10 • longitude) were first removed from the general database to provide a more "independent data set" used for an independent validation of the algorithm developed in this study.
These zones were chosen in several major oceanic basins and were representative of the Sub-Equatorial Pacific, the Sub-Equatorial Indian, the North Atlantic Subtropical Gyre, the North Atlantic Subpolar Gyre, the North Pacific, the South Atlantic, the South Indian, and the South Pacific (Figure  1 ). The remaining profiles were then split into two subsets with 80 % and 20 % of the data, the so-called training and validation datasets, respectively (see the number of data for each variable in Table  1 ).
General Principle of Multi-Layered Perceptron (MLP) A multi-layer perceptron (MLP;  Bishop, 1995; Rumelhart et al. , 1988)  is an artificial neural network based on several layers (i. e. , the so-called input, hidden, and output layers) composed of neurons which are basically elementary transfer functions. These neurons are interconnected with the neurons of the preceding and following layers by weights (Figure  2 ), which are iteratively readjusted during the training phase of the MLP. The criterion for readjusting the weights is the minimization of a cost function defined as the quadratic difference between Details are given in Section The GLODAPv2 database. the reference measurements and the MLP-based outputs. This minimization is done through the back-propagation conjugategradient technique  (Hornik et al.
, 1989; Bishop, 1995) , an iterative optimization method adapted to the development of MLPs. To prevent overlearning  (Bishop, 1995) , the training data set is randomly split into two subsets called "learning" and "test" data sets (50 % of the training dataset each). Finally the validation data set is used to evaluate the final method performance. Moreover, the "independent data set" (see above in Section The GLODAPv2 Database) is used to check the general applicability of the method.
Statistical Evaluation of Method Performance Four statistics were chosen to evaluate the CANYON algorithms performance on the validation datasets. The coefficient of determination (r 2 ) and the slope of the linear regression between the CANYON-retrieved values and the corresponding GLODAPv2 measurements were computed. The statistics also included the MAE (Mean Absolute Error) and the RMSE (Root Mean Squared Error) to evaluate the errors and accuracies of each model: MAE = 1 N. N i = 1 |X CANYON i -X GLODAPv2 i | RMSE = 1 N. N i = 1 (X CANYON i -X GLODAPv2 i ) 2 Note that absolute uncertainties are expressed as values for NO 3 -, PO 4 3-, Si(OH) 4, pH T , A T , and C T parameters. For pCO 2 parameter, the relative uncertainties are expressed as percentages (e. g. , a relative uncertainty of 5 % is an absolute uncertainty of 20 µatm at 400 µatm).
Overall CANYON Performance Using the validation database (i. e. , 20 % of the general database minus the eight independent zones), we evaluated the performance of the method by comparing the CANYONretrieved nutrient concentrations and carbonate system parameters with the measurements in the GLODAPv2 database using the statistics from Section Statistical Evaluation of Method Performance. Scatterplots of CANYON-retrieved variables vs. GLODAPv2 measurements (Figure  3 ) show that the CANYON method predicts nutrient concentration and carbonate system parameters with good accuracy (i. e. , of 0. 93, 0. 066, and 3. 0 µmol kg -1 for the concentrations of NO 3 -, PO 4 3-, and Si(OH) 4 , respectively, and of 0. 019, 7 µmol kg -1 , 10 µmol kg -1 and 5. 1 % or 20 µatm at 400 µatm for pH T , A T , C T , and pCO 2 , respectively).
The determination coefficients of the seven linear models between the CANYON-retrieved and GLODAPv2-variables are comprised between 0. 982 and 0. 996 with slopes ranging from 0. 986 to 0. 999. In Figure  3  only very few data points diverge from the 1:1 line. A higher scatter is observed for low CANYON-retrieved NO 3 -(and PO 4 3-), which mostly corresponds to low surface nutrient concentrations inside and at the edges of the subtropical gyres. Moreover, the higher scatter observed near the surface than deeper for most variables is probably due to the higher inherent variability in the surface. To go further, the final CANYON-accuracies for the seven variables can be estimated using the merged accuracies of CANYON estimations and GLODAPv2 measurements (RMSE final = √ [RMSE CANYON 2 +RMSE GLODAPv2 2 ] ).
The accuracies of GLODAPv2 measurements are 0. 46, 0. 033, and 1. 1 µmol kg -1 , for NO 3 -, PO 4 3-, Si(OH) 4 , respectively, and 0. 005, 6 µmol kg -1 , and 4 µmol kg -1 , for pH T , A T , and C T , respectively from Olsen et al. (  2016 ) and 5. 6 % (22 µatm at 400 µatm) for pCO 2 from uncertainty propagation of the carbonate system calculations using seacarb errors  (Gattuso et al. , 2015 (Gattuso et al. , , 2016)). Thus, ultimately, the final global accuracies of CANYON are 1. 04, 0. 074, and 3. 2 µmol kg -1 for NO 3 -, PO 4 3-, and Si(OH) 4 concentrations, respectively, and 0. 020, 9 µmol kg -1 , 11 µmol kg -1 and 7. 6 % (30 µatm at 400 µatm) for pH T , A T , C T , and pCO 2 , respectively. The training and validation datasets of the neural networks used to retrieve carbonate system parameters were smaller than the datasets for the retrieval of nutrient concentrations (Figure  3  and Table  1 ).
It is thus possible that the carbonate system networks are less robust than the nutrient ones. In any case, all the MLPs could be updated in the future as more data become available; this seems especially important for the pH T database, which is presently the least populated. In order to assess the importance of this potential weakness, we developed a special neural network using all pH T data available, i. e. , all the data that passed the primary quality control (see details in Section The GLODAPv2 Database). The results of this special CANYON algorithm, based on more but a priori less accurate data used for training, are not improved when compared to our initial results (i. e. , RMSE of 0. 030). Given this and in order to maintain consistency among CANYON algorithms and their retrieval performance, all neural networks were trained using data that had passed the secondary quality control (except for the Mediterranean Sea, see details in Section The GLODAPv2 Database).
To identify possible trends, errors were plotted against each input variable for each retrieved nutrient concentration (Figure  4 ) and carbonate system parameter (Figure  5 ). Some errors are larger because of the small numbers of data (the intensity of the shading in each box refers to the number of data). Nevertheless, some clear trends are observed. The range of errors for Si(OH) 4 retrieval seems to be higher at high latitudes in the Southern hemisphere (i. e. , latitudes <-60 • ) and low temperatures, i. e. , the ranges of box-plot whiskers increased with both decreasing latitude and temperature in Figure  4. This corresponds to regions of significant Si(OH) 4 , mostly in the Southern Ocean, which suggests that the CANYON method is less accurate for Si(OH) 4 retrievals in the Southern Ocean than in other areas.
The C T estimates show a high error at high temperatures and unusually low (<34 psu) salinities. pCO 2 estimates exhibit an increased uncertainty at the extremes of the O 2 input (Figure  5 ), i. e. , the range of errors increases at low O 2 concentrations corresponding to high pCO 2 (Figure  S3 ), and at high O 2 concentrations corresponding to cold, low salinity polar surface waters. For CANYON-estimated NO 3 -, PO 4 3-, and C T , the upper layer (i. e. , ≤700 m) has the broadest error range. The Si(OH) 4 displays an opposite trend with error larger for deeper than surface waters (i. e. , ≥700 m). The CANYON-estimated A T , pH T , and pCO 2 errors are not affected by depth inputs. Finally, Figure  S3  shows that the CANYON retrieved variables are not biased against the range of in situ values to retrieve, except for a few extreme values where few data (i.
e. , lightly shaded boxes) were available in the training and validation databases. The above results indicate that hydrographic data and information about season (doy) and geolocation can certainly predict some aspects of the dynamics of biogeochemical variables at the surface (e. g. , nutrient and C T drawdown during the spring bloom, seasonal reset to preformed nutrient concentrations with winter ventilation) with O 2 being the most important predictor in CANYON for production and remineralization, particularly in the ocean interior. The year is the only variable that accounts in CANYON for the increase in anthropogenic CO 2. As a consequence, when the year was not included, CANYONestimated pH T , C T , and pCO 2 showed a clear remaining trend due to the missing information about the long-term changes in seawater CO 2 -carbonate chemistry (data not shown).
These results suggest that the GLODAPv2 data set for these specific zones could have been underrepresented in the training database with respect to the regional variability in nutrient concentrations and carbonate system parameters. above comparisons and the identification of spatiotemporal domains where CANYON performed less accurately could be used to identify more objectively the periods or regions that require more intense acquisition of discrete high-quality measurements. Indeed, the periods and regions where CANYON fails to reproduce well in situ observations are those where the variability (mostly seasonal) was not  well-represented in the training dataset. From Table  3  and Figure  6 , these regions are mainly the Sub-Equatorial Pacific, the North Pacific and the South Atlantic, and the periods are most likely late autumn and winter, when harsh sea conditions generally prevent ship-based collection of highquality measurements.
Further Results for Specific Applications Here we present further results important for specific applications. Indeed, one of a potential application of the CANYON method is the calibration of NO 3 -and pH T sensors mounted on BGC-Argo profiling floats (because the corresponding sensors may drift over long-term deployment). To overcome this problem, CANYON could be used to compute deep (e. g. , ≥1,000 m) reference measurements each time the float In the last row, comparable information is provided for the validation dataset (Section Overall CANYON Performance), as reference. In brackets figures the number of observations used to compute each RMSE. profiles. With this aim in mind, specific MLPs were developed to retrieve NO 3 -concentration and pH T only at depths between 950 and 2,050 m, i.
Example of Application: Illustration with HOT Database The CANYON method was further applied outside the GLODAPv2 domain, on which it was trained, using an independent dataset that contained similar variables. The Hawaii Ocean Time Series (HOT) database contains monthly vertical profiles of hydrological properties, O 2 , nutrient concentrations and carbonate system parameters from the deep-water station ALOHA since 1994  (Karl and Lukas, 1996; Dore et al. , 2003). The HOT temperature, salinity, and O 2 measurements from HOT were used as input variables to estimate the concentrations of nutrients and the carbonate system parameters. The overall agreement between the CANYON-simulated variables and their measured in situ counterparts is satisfactory, as shown by the absolute differences between the two datasets and the corresponding comparison statistics (Figure  7  and Table  4 ).
There are few systematic biases, such as the model underestimation of NO 3 -from 450 to 550 m (i. e. , the depth horizon of the nitracline) and the overestimation of PO 4 3-and C T in the upper 150 m (Figures  7b, d, l , respectively). Interestingly, running CANYON on this dataset unraveled O 2 outliers in the database. A first run of CANYON produced abnormally high differences between the retrieved and in situ values of the seven CANYON-estimated variables for profiles from 25 February 2003 to 29 March 2003. This corresponded to five O 2 profiles with abnormally high concentrations of O 2 (see black triangles in Figure  S4 ). These profiles were subsequently removed from the analysis to avoid contaminating the CANYON-retrieved data shown in Figure  7  and the corresponding statistics in Table  4.
CANYON also provided a way to fill a gap in the HOT dataset. Indeed, pH T had not been acquired during the 1999-2003 period, but the input variables needed to run CANYON had been measured. pH T could thus be estimated during that period (Figure  7g ) with a mean accuracy of 0. 033 units (i. e. , RMSE in Table  4 ). However, it is obviously not possible to compare the values predicted by CANYON with (non-existent) corresponding in situ measurements from 1999 to 2003 (Figure  7h ). Using the HOT temperature, salinity and O 2 measurements from the last year, i. e. , 2015, we estimated pH T for the 15 years to come (by changing only the year input in CANYON), and a decline is found in pH T of 0. 024 units over this 15-year period (i. e. , decrease of 0. 0016 ± 0. 0004 units year -1 ). This value is consistent with the decrease in pH T of 0.
Secondly, CANYON can also provide estimates, with known accuracies, of variables that are not presently measured by BGC-Argo floats. This is the case for PO 4 3-and Si(OH) 4 and the three other variables of the carbonate system than pH T. CANYON could thus be used as a cost-effective method for "filling the spatio-temporal gaps" of these variables by populating spatially and temporally their loosely resolved fields in oceanic waters. For these under-sampled variables, CANYON offers novel opportunities at global and local scales. For example, global fields of these variables provided by CANYON could support the initialization and validation of biogeochemical models which presently crucially lack reference data (e. g. ,  Doney et al. , 2009; Ilyina et al. , 2013). Thirdly, CANYON could also be used in combination with present measurements of the respective field nutrient concentrations and/or carbonate system parameters.
Beside quality control of these data, CANYON values could serve to identify unusual biogeochemical events that had not been covered by the global but sparse GLODAPv2 training data set, in cases where CANYON and field data diverge. Fourthly, and for more local approaches (e. g. , analysis of individual float time series), the possible derivation of pCO 2 from BGC-Argo float O 2 and ancillary measurements (with or without pH T ) potentially represents a new way to address regional and seasonal variability in CO 2 air-sea exchanges, and to reduce present uncertainties in the estimates of these fluxes. Moreover, estimating the three macronutrients (NO 3 -, PO 4 3-, and Si(OH) 4 ) from BGC-Argo float data could be of great value for better understanding the dynamics of biogeochemical events such as the development and subsequent collapse of phytoplankton blooms.
Fifthly, CANYON could contribute to design future observational programs by identifying areas and periods where data acquisition could most cost-effectively address variability that is presently unresolved. Indeed, the strict quality control of the input data in the present study (which used only GLODAPv2 data that passed the second quality check, see Section The GLODAPv2 Database) eliminated some specific regions from our training and validation datasets. This argues for developing field-based observation programs to conduct high-quality measurements in these areas. More generally, the spatio-temporal domains where CANYON provided the least satisfactory results likely corresponded to weaknesses in the GLODAPv2 database with respect to catching the inherent and natural variability of different variables, such as the Southern Ocean in winter.
Overall, the CANYON-type estimation of biogeochemical variables based on data provided by the global BGC-Argo program offers new avenues for marine biogeochemistry that are comparable to those that have been created for physical oceanography by the Argo network since the early 2000s. This novel approach could increase tremendously the value of biogeochemical measurements made on board ships and by BGC-Argo floats by combining the high quality of the first type of data with the broad spatio-temporal coverage of the second.
ACKNOWLEDGMENTS This study was supported by the remOcean project (funded by the  European Research Council , Grant Agreement No. 246777 ) and the  AtlantOS  project (funded by the  European Union 's  Horizon 2020 research and innovation program , Grant Agreement No. 2014-633211 ). This is a contribution to the  Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM)  project which is supported by the  US National Science Foundation  ( PLR-1425989 ). We want to thank  Tobias Steinhoff  ( GEOMAR, Kiel ), for helpful discussions on carbonate system calculations. We deeply acknowledge the work from analysts, investigators, and crew who collected the data at sea. We are also grateful to all who have contributed their data to GLODAPv2 project and acknowledge the huge effort made to gather all data to create the GLODAPv2 database.
69 (1,511) 0. 039 (1,490) 1. 8 (1,512) 0. 015 (833) 6 (841) 5 (900) 4. 3 (823) South Pacific 0. 54 (1,406) 0. 037 (1,406) 1. 7 (1,406) 0. 016 (754) 3 (760) 5 (762) 3. 0 (754) Validation dataset (20 %) 0. 93 (137,219) 0. 066 (128,264) 3. 0 (137,963) 0. 019 (54,161) 7 (53,072) 10 (62,678) 5. 1 (48,592).
New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude-longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid.
All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous Published by Copernicus Publications. 882 M. Stengel et al. : Cloud_cci datasets uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets.
Introduction Satellite-based datasets of geophysical variables are crucial for climate research as they represent observations of the Earth's climate system, which can be used for both the analysis of the climate and its variability as well as guidance for atmospheric model developments. These datasets evolve periodically by mainly two activities:  (1)  extending and improving the underlying radiance record by adding new satellite recordings and applying new inter-calibration to the entire record, and (  2 ) the development and application of more advanced retrieval systems and the utilization of additional or more frequent auxiliary data which often undergo regular updates themselves. In the last few decades, activities to process and reprocess global, high-quality cloud property datasets based on longterm satellite measurement records have been undertaken with increased effort.