dataesr/scientific-paragraphs-categorization huggingface dataset:
Consistency of continuous auxiliary datasets of the same quantity Often the retrieval of a land / ocean CDR is affected by perturbations in the measured bands due to atmospheric absorption or scattering, so an atmospheric correction needs to be applied. Examples of necessary atmospheric corrections include visible or thermal retrievals impacted by aerosol, water vapour, ozone or other trace gases (e. g. Popp 1995). A first step in algorithm development would be to assess the sensitivities of the measured reflectances to the various absorbing trace gases and to aerosol particles (e. g. Holzer  -Popp, et al. , 2002). This provides the basis for deciding which corrections can be neglected or made with a simple parameterization, and which need more precise corrections using an auxiliary dataset of distributions of the responsible agents influencing the signal.
When the auxiliary datasets come from the same sensor as the target CDR, accurate spatio-temporal matching (pixel colocation) would be possible. However, in cases where the auxiliary data come from different sensors, it may be necessary to deal with spatial and temporal mismatches, introducing a requirement for assessment of the associated additional uncertainties. Figure  5  shows a gridded map of differences of aerosol optical depth between the by-products of the ocean-colour atmospheric correction of MERIS data (processed using a NASA algorithm) and the corresponding CCI aerosol ECV product from AATSR, both for 865 nm (both sensors were on-board the same platform ENVISAT and thus exhibit zero time difference). The global average difference of AOD between both products of 0. 03 is acceptable for the purpose of aerosol corrections, but the variability is larger for the aerosol ECV product than for the ocean colour product (0.
10±0. 11 cf. 0. 13±0. 04 respectively), which has higher AOD values over the open ocean, but lower ones closer to land. Given the importance of AOD in oceancolour atmospheric correction (IOCCG, 2010), the aerosol-corrected ocean colour ECV can be regarded as consistent with the aerosol ECV in its global average, but not regionally. These results merit further investigations to identify the sources of the discrepancies and to assess the potential to improve the MERIS ocean-colour atmospheric correction algorithm by using concurrent auxiliary information on AOD from the main ECV aerosol product obtained from AATSR. retrieval algorithms or sensor calibrations, as well as sampling differences between aggregated datasets.
g. Reuter et al. , 2017). In creating a multi-sensor CDR covering a longer time period, a merging algorithm (EMMA,  Reuter et al. , 2013 Reuter et al. , , 2020) )  corrects potential remaining offsets of individual datasets to avoid jumps in the merged time series. In EMMA, the ensemble members have been bias corrected and brought to common a priori CO2 profiles before being combined to obtain the merged product. Similarly, spatial inconsistencies in one variable can often be assessed visually by looking at maps combined from independent pieces (different sensors, different overpass times of the same sensor with different observing geometries, different algorithms). In this case, inconsistencies are visible as artificial border lines or gradients that are larger than the noise in the image.
Mutual consistency between different quantities In testing multiple quantity consistency, the role of the underlying background knowledge becomes stronger since the physical processes connecting different ECVs need to be taken into account. One method to test the consistency of two ECVs is by looking at their correlations. For example, in the lower stratosphere, the strong physical dependency of lower stratospheric water vapour on tropical tropopause temperatures can be exploited to test the consistency between climate data records of temperature and stratospheric water vapour as highlighted by  Hegglin et al. (2014). This study proposed a new merging method that uses a chemistry-climate model as a transfer function between different satellite instrument records to create a CDR. The methodology allows the bias between instruments to be determined throughout the instrument's lifetime and not only for the overlap period (when old instruments may show first signs of degradation), hence improving characterization of systematic differences (or biases) between datasets.
By using the correlation between the newly merged stratospheric water vapour record and the zonal mean temperature from ERA-interim, visual inspection indicated that the new merging method led to physically more consistent results than the traditional one based on biascorrection of instruments during overlap periods. Figure  9  shows a comparison of the trends of the significant wave height (Hs) directly measured with altimetry (denoted ALT,  Queffeulou and Croize-Fillon, 2015)  and from the colocated model data from WW3 (denoted WW3 CoLoc) in which SSM/I ice concentrations have been used. Qualitatively, the regional patterns match between the two datasets, despite stronger trends in the altimeters (of up to 1 cm / y). At present the confidence interval for trends in wave heights is not known.
4 region (5°S-5°N, 190°E-240°E). This natural phenomenon is an ideal candidate for investigating multiple ECV consistency due to its relatively short timescale, large amplitude and multiple ECVs affected by it. This first attempt focusses on the main ENSO signatures at large scale. Physical or biological processes leading to spatio-temporal lags of a few months or a few degrees longitude between some variables have been neglected. This could be refined in future studies. ENSO variability is compared in several ocean (SST, SL, SSS, Chlor_a), atmosphere (CFChigh, TCWV, AOD550) and land (SM, burned area / fire) ECV products -see Table  A1  for the acronyms, more detailed information on the datasets and their correlations. All variables were interpolated to a common 1 by 1° grid, de-seasonalised by removing the corresponding monthly mean value and normalised by dividing by the standard deviations for their respective available time period.
Summary and conclusions Climate Data Records of Essential Climate Variables derived from satellite instruments provide essential information to monitor the state of the Earth system and its changing climate. A key requirement for these CDRs to be useful for Earth system science applications is that the CDRs are internally and mutually consistent. The discussion of a concept for assessing consistency and related methods on the retrieval and scientific level shows how consistency with regard to different categorical and continuous auxiliary datasets can be tested and how the assessment of single-variable selfconsistency and multiple quantity mutual consistency can be conducted. In all these methods a basic understanding of "the truth" needs to be employed. A relevant characteristic of an ECV and an appropriate metric (e.
g. bias, correlation, contingency matrix, …) for its evaluation need to be chosen. A tabular summary of different methods to assess consistency is given in Table  2. For each of the different metrics, a threshold needs to be defined to judge on consistency of two datasets. This may well differ from commonly applied thresholds for validation purposes since also other processes than consistency may affect the datasets. We suggest as a minimum requirement that each consistency study states the applied thresholds, as is done for the examples in this paper. Whereas the methods used to assess consistency rely on well-established tools for calibration and validation, placing them into the systematic context with relevance to consistency as done here, can serve as a practical guideline to consistency assessment.
A brief high-level analysis of the interdependencies of CCI ECVs at the retrieval and scientific levels (Table  1 ) is provided to understand where consistency is needed and thus needs to be checked. assessment of the current state of affairs regarding consistency assessment between the CDRs of the CCI program (Table  3 ) is compiled to outline possible further research needs. When discussing consistency, datasets from sources other than satellite data (e. g. Earth system models) are often required to comprehensively study an Earth system cycle, and their uncertainties also need to be considered, together with uncertainties in simplified or estimated budget equations. It is well understood that establishing consistency between two or more variables requires targeted analysis. Within and outside CCI much effort has been spent on quantifying the sensitivities and dependencies of the retrieved quantities.
shows at the top the resulting multisensor, multi-algorithm monthly mean XCO2 merged record for 2003-2018 for northern midlatitudes (30 o N-60 o N) with the known nearly linear increase in time and seasonal cycle and no remaining biases, while in the bottom panel differences between individual ensemble members and the merged product before the corrections are shown to be larger than the required XCO2 uncertainties of 0. 5 ppm. In this case, this threshold for the target uncertainties of the gap-corrected merged dataset is defined by the user requirement for the application of XCO2 trend analysis. Accepted for publication in Bulletin of the American Meteorological ociety. DOI S 10. 1175/BAMS-D-19-0127. 1.
The Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S) is also based largely on CCI standards. Such common standards are a prerequisite for the use of automated data services for accessing multiple data sources with little manual interaction, hence facilitating use of the data in scientific studies across multiple ECVs. On the retrieval level, one needs to limit contradictions in the use of auxiliary datasets (masks or continuous fields) of the same variables in separate processing chains. On the scientific level, consistency of multiple ECV CDRs means judging their relevant correlations, patterns, periodicity, trends, etc. (as appropriate for a given variable, process or cycle) in the light of underlying physical background knowledge (e. g. , by jointly confronting them with a model).
Through this link with background knowledge, "consistency" as defined in this study goes beyond "agreement" and relies rather on "compatibility". Finding inconsistencies in one or more ECV dataset(s) (i. e. patterns whose disagreements exceed underlying uncertainties, contradict physical principles or a well-founded model) often indicates errors in a dataset or model whose resolution can lead to new scientific understanding. This study also provides an overview of the technical consistency of CCI CDRs (common format and metadata standards, common data portal, harmonized documentation, common uncertainty reporting). An open issue in this regard is Accepted for publication in Bulletin of the American Meteorological ociety. DOI S 10. 1175/BAMS-D-19-0127. 1. harmonization.
We are also thankful for many other datasets from outside CCI and C3S which help cover all relevant ECVs: GOSAT Level 1 data from JAXA, GOSAT Level 2 data from NIES and NASA, OCO-2 Level 1 and Level 2 data from NASA, HOAPS data from EUMETSAT CM SAF, water vapour records from the G-VAP data archive, CAMS and ERA-5 data from ECMWF / Copernicus Atmosphere and Climate Change Services , SSM/I daily sea ice concentrations from IFREMER, and wind vectors from the Climate Forecast System Reanalysis. Accepted for publication in Bulletin of the American Meteorological ociety. DOI S 10. 1175/BAMS-D-19-0127. 1. Tables Accepted for publication in Bulletin of the American Meteorological ociety. DOI S 10. 1175/BAMS-D-19-0127. 1.
DOC atmospheric fluxes Atmospheric fluxes of DOC ranged between 0. 06 and 1. 78 mmol C m -2 d -1 , with high variability. The overall sampling lasted for 746 d. The deposition was lower than 0. 2 mmol DOC m -2 d -1 (Fig. 2  and Table  2 ) in half of the sampling days (52 %). In 2015, the lowest deposition rates (< 0. 1 mmol C m -2 d -1 ) were measured in July (Lmp09), October (Lmp13), and November (Lmp15). The highest deposition values (> 1. 2 mmol C m -2 d -1 ) occurred between March and April (Lmp02) and in June (Lmp06). Both periods were characterized by dry deposition (Fig. 2  and Table  2 ). High DOC fluxes (> 0. 6 mmol C m -2 d -1 ) were, however, also observed in March (Lmp01), May (Lmp04), and at the end of July (Lmp10) in correspondence with periods dominated by wet deposition. In 2015, the annual rainfall was 360 mm, slightly higher than the average annual rainfall for the island of Lampedusa (325 mm with 42 d of rain; data from http://www.
arpa. sicilia. it/ and http://www. eurometeo. com/italian/climate, last access: 10 June 2016). In 2016, the DOC deposition rates were rather low and with less variability compared to the previous year. DOC fluxes ranged between 0. 1 and 0. 3 mmol C m -2 d -1 from January to May (Lmp18 to Lmp23) and from June to August (Lmp27 to Lmp30). The highest DOC fluxes (> 0. 8 mmol C m -2 d -1 ) were observed in May (Lmp25) and between October and November (Lmp33; Fig. 2  and Table 2). In 2016, the annual rainfall was 378 mm (data from the local meteorological station of the Lampedusa Atmospheric Observatory). In 2017, DOC fluxes ranged between 0. 14 and 0. 92 mmol C m -2 d -1 from January to April (Lmp36 to Lmp41); these values are higher than those observed in the first 3 months of the previous year (Fig.
, 2010). The similarity among the depositions collected at the two sites (Lampedusa, in the central Med Sea, and the Frioul archipelago, in the northwestern Med Sea) suggests that the remote site of Lampedusa may be representative of DON and DOP deposition in the Med Sea, especially in the western basin. The data on atmospheric elemental ratios show that each deposition event is characterized by a specific elemental ratio, suggesting a high variability in DOM composition and the presence of multiple sources. Djaoudi et al. (2018)  observed an average value of DOC : DON : DOP molar ratios of 1228 : 308 : 1 in atmospheric DOM, collected in the northwestern Med Sea. In the surface Med Sea, the DOC : DON : DOP ratios range between 1050 : 84 : 1 in the western basin to 1560 : 120 : 1 in the eastern basin  (Pujo-Pay et al.
, 2005). The DOC input from the atmosphere is expected to increase the DOC concentration in the mixed layer by 0. 008-0. 079 µM C d -1 from June to August 2015 and by 0. 013-0. 014 µM C d -1 from June to August 2016, supplying 3 %-25 % of the daily BCD, assuming similar DOC concentrations and bacterial activity as during September. Even if we are aware that these assumptions are hardly met, in particular the estimate of DOC input to the whole Med Sea, based on the data collected on Lampedusa we think that these calculations can give an idea of the relevant role that the atmospheric input of DOC can play in sustaining heterotrophic prokaryote productivity in the surface layer, particularly when the upper water column is strongly stratified. The Mediterranean MLD seasonal variability is characterized by a basin scale deepening from November to March and an abrupt stratification in April, which is maintained throughout the summer and early autumn.
Conclusions Our data show that atmospheric input has a larger impact on the Med Sea than on the global ocean, and DOC fluxes from the atmosphere to the Med Sea can be up to 6 times larger than riverine input. Organic substances transported by Saharan dust on Lampedusa are primarily of natural origin, in particular from sea spray. Saharan dust can be an important carrier of organic substances. However, the load of DOC associated with dust is highly variable, and high DOC fluxes were also observed in the absence of dust deposition events. Atmospheric C : N : P molar ratios indicate that DOM is enriched in DOC and DON with respect to marine DOM, and the contribution of atmospheric deposition to the marine DOM stoichiometry in the Med Sea could be relevant, in particular during stratified periods.
Acknowledgements. The authors thank the analytical platform PACEM ( Mediterranean Institute of Oceanography ) for the analysis of organic and inorganic forms of nitrogen. Contributions from  Lorenzo De Silvestri  and  Francesco Monteleone  are gratefully acknowledged. Financial support. Part of this research was supported by the " Professionalità " project, funded by the  Fondazione Banca del Monte di Lombardia. Review statement. This paper was edited by  Christine Klaas  and reviewed by two anonymous referees.
Hydrological monitoring Two public hydrometric stations provide instantaneous discharges: one located on the Yonne river, 3. 3 km downstream of the Pierre Glissotte dam and the other on the Cure river, 28 km downstream of the Settons dam (Fig. 1B ). In addition, water sensors (Diver, ©Schlumberger) were installed at the upstream and downstream limits of the 6 study sites. They recorded water level variations every 15 minutes. Coupled with a DEM built from topographical surveys (section 3. 2), the upstream and downstream water altitude enabled the reach averaged water depth to be calculated for a given discharge using the HEC-RAS software. Water levels from water sensors were transformed into discharges with stage-discharge relationships built from our own discharge measurements (electro-magnetic current-meter) and extrapolation from the two public hydrometric stations (using the catchment area-discharge relationship found in  Bravard and Petit, 1997).
Morphological data and grain size distribution Longitudinal profiles and the cross sectional geometry of the study sites were surveyed with a Trimble total station S6. At least 13 cross sections spaced by 1. 5 times the bankfull width were made on each site. These topographical surveys were then used to calculate the bed slope and build a DEM on GIS (Arcgis software), which was then loaded into HEC-RAS software (section 3. 1). The grain size distribution of the riverbed was determined using Wolman's surface sampling method  (1954). We tried to respect a sampling step equating to twice the b-axis of the largest particle of the sampled unit  (Lejot, 2008; Belliard et al. , 2009). Three sub-reaches were delimited along the study reach as a function of their apparent geomorphological homogeneity (bankfull width and depth, grain size range, substrate arrangement) even if geomorphological variations within the same reach were generally very limited.
The relationship resulting from the stepwise multiple regression (Eq. (  4 )) also confirms the interest in considering stream impulse to predict the mean distance travelled by the particles. The calculation of the mean distances on the Aisne with this relationship is also rather good except for predictions of distances corresponding to large flow events (Fig. 8 ). This results in an underestimation of the distance, probably because of the different resolutions in the hydrological data. The Aisne instantaneous discharge data (used to calculate the SI) are indeed measured every hour whereas the Morvan data are measured every 15 minutes. It is then possible that the highest peak discharges, and consequently the maximum excess stream powers were less precisely recorded for the Aisne river.
Acknowledgments The authors would like to thank  Matthieu Moës  and the  Agence de l'Eau Seine-Normandie  (public water agency), for financing the research work leading to this paper. We are also very grateful to  Jean-René Malavoi  and  Electricity de France  (EDF) as well as the  PIREN Seine  for the financial support they provided for the purchase of research equipment and for fieldwork missions. We extend our sincere appreciation to  Jonathan Touche  for his help on the field and  Natasha Shields  for her assistance in.
Numerical simulation Groundwater flow and heat transport are simulated using the commercial code FEFLOW  [45]. This software is based on the finite element method and was applied for heat transport in related studies  [13, 37, [46] [47] [48] [49] [50]. The 2D numerical model domain used for all scenarios is illustrated in Figure  2. The domain covers a rectangle of 3000 m by 300 m. Only the injection well is simulated assuming that the production well is located far away from it and that there is no hydraulic interference among them both. The injection well is located at 150 m from the east and north boundaries. A horizontal mesh is applied using a triangular algorithm. The size of the elements ranges from several centimetres near the injection well to about three meters at the domain boundaries.
Fluorescence images were acquired with an Evolve 512 back-illuminated EMCCD camera (Photometrics) controlled by the MetaMorph software (Molecular Devices). For single molecule localization microscopy, the AF647 dye was exposed to a continuous 643 nm illumination with a constant laser power density of 1. 7 kW3cm -2 at the beam center. All dSTORM data were collected over 15,000 frames recorded with an exposure time of 50 ms (20 Hz). The blinking density was maintained all along the data collection by ramping up a 405-nm laser from 0. 1% to 3% of a typical maximal value of 50 W3cm -2. For diffraction-limited images, the sample was illuminated at 643 nm with 0. 5% of the maximal laser power (1. 7 kW3cm -2 ).
QUANTIFICATION AND STATISTICAL ANALYSIS Figures were prepared and statistics were performed using GraphPad Prism version 8. 00 for PC (GraphPad Software, https://www. graphpad. com:443/). The statistical details describing the quantification and measurements of localization patterns (including the definition of center, dispersion and precision of measures, the statistical tests used, the exact value of the n number of analyzed cells) can be found in the Results section and in the figure legends.
(G and H) Evolution of the longitudinal width of the parental (w, in black) and equatorial (W 1 and W 2 , in red) labeling patterns as a function of their diameter in R800 (G; n = 282) and D39 Dcps (H; n = 211) cells. The black line is a linear fitting performed on the ensemble of the data. (I) Distributions of the longitudinal width of labeling w among E, M, and L R800 cells (n(E) = 184, n(M) = 53, and n(L) = 45 cells) and D39 Dcps cells (n(E) = 132, n(M) = 32, and n(L) = 47 cells). (C, D, F, and I) Data are represented with boxplots showing the interquartile range (10 th and 90 th percentile), the median value, and whiskers for minimum and maximum values. p values from the U test of Mann-Whitney between different datasets are indicated with quadruple asterisks when the observed difference is statistically significant (p < 0.
(D) Distributions of the longitudinal width of the double bands observed in cells of type a, b2, and c in (A) and (B) (R800, n(a) = 194, n(b2) = 101, and n(c) = 72 cells; D39 Dcps , n(a) = 125, n(b2) = 26, and n(c) = 56 cells). (C and D) Data are represented with boxplots showing the interquartile range (10 th and 90 th percentile), the median value, and whiskers for minimum and maximum values. p values from the U test of Mann-Whitney between different datasets are indicated with triple or quadruple asterisks when the observed differences are statistically significant with p < 0. 001 or p < 0. 0001, respectively.
ACKNOWLEDGMENTS We thank members of the  Vernet and Bourgeois laboratories  for advice and encouragement;  O. Glushonkov ,  J. P. Kleman , and  S. Dufour  for advice and support regarding dSTORM;  G. Schoehn  for electron microscopy;  L. Espinosa  for preliminary tests performed on the IMM Microscopy platform; and  M. Winkler  for the D39 Dcps strains. Support for this work comes from the  Agence Nationale de la Recherche  ( ANR-16-CE11-0016  to C. M. ,  ANR-19-CE15-0011  to C. G. , and  Labex ARCANE   ANR-17-EURE-0003  to Y. -S. W. ). This work used the platforms of the  Grenoble Instruct-ERIC Centre (ISBG ;  UMS 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-05-02 ) and GRAL, financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ).