dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions An unprecedented BGC-Argo observation system was implemented in the Levantine area of the Mediterranean Sea in 2018-2019. It was supported by an equivalent and concomitant ship-based effort (3 seasonal surveys from May 2018 to March 2019) to elucidate the impact of physical forcing on the biogeochemical dynamics of the basin. The analysis of the floats data presented here unequivocally confirms phytoplankton response to the successive mixing events from December to March. This response was first hypothesized and observed in the 1990s, but, later on, substantiated only indirectly through models or satellite observations, which could fail in fully assessing the biogeochemical dynamic of the area (as suggested by the presented results). In the area where the lowest SSTs were recorded, BGC-Argo floats unambiguously observed significant NO 3 availability in the sunlit layer, followed by rapid accumulation of biomass on the surface.
Phytoplankton growth appears to be primarily driven by MLD dynamics as shown by the temporal match between MLD stratification and destratification events and CHL fluctuations in the upper layers. As hypothesized in previous studies, MLD deepening drives surface NO 3 availability, as observed by floats in the RG from January 2019 onwards. Floats data also revealed that the upper limit of the deep stock of NO 3 was closer to the surface (between 100 and 170 m) than in the surrounding regions and that an uplift of nutrients occurred at the end of the year (i. e. December 2018). Our results confirm that large-scale cyclonic circulation, uplifting density isolines, is likely to be the principal preconditioning cause of subsurface NO 3 increase in the RG region. Moreover, our results indicate that phytoplankton accumulation, as well as the environmental conditions that induce it, occur permanently during winter.
In other words, the spring event (observed here in March) delimits the end of the productive period, instead of starting it as in other temperate areas (e. g. the North Western Mediterranean Sea;  Kessouri et al. , 2018). BGC-Argo data accuracy is still not equivalent to that of classic ship-based observations, in particular for NO 3 and CHL. The results obtained here, however, are directly and indirectly confirmed by ancillary high-quality measurements. Moreover, the variations observed by the float data are sufficiently large in relation to the estimated errors to confirm the reliability of the general picture described above. On the whole, the BGC-Argo network has provided the first observational evidence of the evolution of NO 3 concentration in the surface layer of the RG region, showing the direct influence of MLD dynamics on this evolution.
In this context, the massive use of BGC-Argo floats, in support of and as a supplement to space and ship-based observation, is one of the potentialities of an expanded BGC-Argo network. To fully exploit these potentialities, more investigations are required to improve integration of the different sources of biogeochemical data and to enhance the use of coupled models assimilating both BGC-Argo and satellite observations (as in  Cossarini et al. [2019] ). Our analysis also demonstrates that BGC-Argo floats are a powerful tool for elucidating complex interactions between physical and biogeochemical dynamics, although they should be integrated, as far as possible, to "classic" observational systems such as ship surveys, moorings and satellites (see relevant discussion in D'  Ortenzio, Taillandier, et al.
Data Availability Statement The BGC-Argo data presented here are freely available at https://doi. org/10. 17882/42182#71394 or ftp://ftp. ifremer. fr/ifremer/argo/dac/. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www. argo. ucsd. edu, http://argo. jcommops. org). The PERLE data are available at SISMER (https://doi. org/10. 17600/18000865) and SEDOO (https:// mistrals. sedoo. fr/MERMeX/). Satellite data were provided by the Copernicus Marine Service and CNR-ISAC Rome, and are available from the Copernicus Marine Service website: https://marine. copernicus. eu.
Je remercie vivement les relecteurs anonymes pour leurs remarques et conseils très avisés. Cet article est le fruit d'un travail qui s'inscrit dans un programme de recherche pluridisciplinaire « Pour une épistémologie critique de l'exposome » au sein de l'institut Rabelais à Lyon (financement ANR-17-CONV-0002 PLASCAN). Yohan Fayet et Thibaut Serviant-Fine ont largement contribué à l'élaboration de cette réflexion. Je les remercie sincèrement et chaleureusement pour notre étroite et riche collaboration dans ce cadre. Je remercie aussi Séverine Louvel, Marc Billaud, Pierre-Olivier Méthot et Francesca Merlin pour leurs relectures et leurs commentaires très précieux d'une version antérieure de ce manuscrit.
Dès lors, il est difficile d'imaginer une diminution significative du volume qui se perd dans l'environnement. Certaines associations disposent maintenant de données locales très fines. Malheureusement, l'hétérogénéité de ces données rend leur compilation délicate. Tout l'enjeu est donc de développer des méthodes communes qui permettent d'exploiter et de comparer des bases de données issues des collectes bénévoles. Les associations bénéficient du soutien des pouvoirs publics pour travailler dans ce sens avec par exemple la mise en place depuis 2018 d'un atelier « déchets marins » et depuis 2020 d'un atelier « surveillance macrodéchets cours d'eau » au ministère de la Transition écologique. Ces ateliers constituent des espaces de discussion visant à faire émerger des idées d'actions concrètes en concertation avec l'État, ses établissements publics en charge de la mer et les associations de protection de l'environnement, au sein du Comité France Océan, sous l'autorité du Premier ministre  8.
Des indicateurs comme les altmetrics calculent l'impact sur Internet des travaux de recherche en se basant sur leur diffusion et les actions et interactions qu'ils engendrent sur les réseaux sociaux, les blogs, dans la presse, les bases de données bibliographiques, etc. (Dupré et al. , 2016). Le papier sur la présence des microplastiques sur les sommets pyrénéens (Allen et al. , 2019)  a un score d'altmetric de 1939 (au 17 janvier 2022), quand celui de Ter Halle et al. (2017)  sur les nanoplastiques dans les océans est de 55 à la même date. La différence de résonance médiatique s'explique moins par la portée scientifique du propos que par son contenu plus facile à percevoir et à appréhender. 4 D. Blot et al. : Nat. Sci. Soc.
Overview In this discussion paper, we focus on a topic that seems probably not sufficiently addressed in Life Cycle Assessment (LCA): the normalization  (Andreas et al. 2020). More precisely, we look again at the need for the normalization step in LCA and inconsistencies caused by this step. We highlight the importance of having the same data source for the system under study and the normalization references. While this is possible in a conventional normalization way for Economic Input-Output LCA (EIO-LCA), this remains a challenge for process-based LCA. We show how to overcome this limitation and discuss how a normalization by the geometric mean of the inventory database  (Hélias et al. 2020 ) ensures consistency.
Economic Input-Output LCA An EIO-LCA uses aggregated sectoral data and their interdependencies. Fractions of the impacts associated to the sectors of the economy are then attributed to the system under study with the Leontief's wellknown equation. With this top-down approach, we have the same model between the world (the totality of all economic sectors) and the system under study (parts of economic sectors). We can ensure consistency in the normalization process (subject to the calculating of this value from the EIO database). Unfortunately, the process-based LCA differs.
Process-based LCA A process-based LCA is more like a Lego® construction. The LCA practitioners take "bricks" from a Life Cycle Inventory (LCI) database and assemble them to build the system under study. Sometimes, they create a "brick", but this is far from being their primary activity. For each type of "brick" (a component or a process, called a dataset by LCI database providers) that is required, the practitioners take a certain number of them (i. e. the final demand). With this bottom-up approach, we do not have the information to shape the world as a whole. All the datasets into the LCI database describe what constitutes the world, but the quantity of each is missing. As an example, we can consider that the dataset "market for apple | apple (GLO)" from ecoinvent  (Wernet et al. 2016 ) allows us to have the average impact of an apple at the world level, but it does not allow us to know how many apples are produced in all the orchards of the world.
Implicitly, this says that we consider this arithmetic mean is a good descriptor of impacts, that we are in a "normal" world. But the LCA practitioner is not working in a "normal" world. Uncertainty calculations are generally done with log-normal distribution laws. In an LCI database, the distribution of substance emissions commonly follows a log-normal law  (Qin and Suh 2017). Some LCA results are interpreted in orders of magnitude (such as toxicities (Frischknecht and Jolliet 2019)) and therefore on a logarithmic scale. An LCA is the combination of elements following log-normal distributions. If no formal solution has been found for a sum of log-normal variables, there is a general agreement this is well approximated by a log-normal distribution  (Beaulieu et al. 1995). Although the log-normal law is less intuitive than the normal law, this distribution frequently occurs in nature (abundance of species, concentrations of minerals in the earth's crust, concentrations of pollutants in the atmosphere, dose-response curves, etc.
With !∏ ℎ ,  =1 # 1  , the geometric mean of ℎ !,# instead of  ! ℎ !,# , we have a normalization reference value that we only calculate with the LCI database, without having to deal with the final demand for the reference. This approach ensures consistency between the modelling of the system under study and the normalization references. This approach is especially relevant for process-based LCA, but can also be used with EIO-LCA.
0 # $ 0 # % = 51 + 𝑐𝑣 # (see equation Note in addition that the variability of the ratio 0 # $ 0 # % is reduced by the square root with respect to  #. It would be interesting to investigate further the values of these ratios with the EIO-LCA databases where  # $ and  # ' are then computable from the same data for all impact categories. Second remark: The geometric mean does not make the quantities commensurable. Indeed, for the purposes of normalization, the objective of being able to compare and communicate the results is not achieved by this normalization. This approach is interesting from the perspective of aggregating impacts. With some practice, reasoning with the geometric mean would be possible for an LCA practitioner but it would obviously be uncomfortable to communicate, the citizen-equivalent remaining the best solution for this purpose.
About the inventory database as data for normalization Third remark: As the LCI database is used, this is not a true external normalization. In this approach, there is a link between the system studied and the references, but the building of both from the same source does not make it an internal normalization. The normalization here is not based on the case under study, but on the used database. It therefore goes beyond the internal normalization and can therefore be considered as an external normalization. Fourth remark: No LCI database is perfect, some elements are missing and none represents the world as a whole. Over time, LCI databases are improved, but they are still perfectible. Using them to represent the world implies that the world is imperfectly modelled. But it is as imperfectly modelled as the system under study.
Consider one modelled with a database where infrastructures are not represented, as the agrifootprint database (e. g. see  Corrado et al. (2018) ). Using a reference including infrastructure is not really relevant: the biases are potentially different according to the impacts  (Heijungs et al. 2007 ), which will play a role in aggregation and may lead to biased conclusions. The best solution would obviously be a model of the system under study with infrastructures, but if missing they have to be missing in the reference as well. Consistency of the modelling is more important than the completeness of the reference for normalization purpose. Fifth remark: The LCA practitioner uses several databases / the LCA practitioner modifies the database. It is always risky to make an LCA with heterogeneous databases that do not necessarily have the same perimeters.
But in this case, it is enough to take as normalization value the geometrical mean of the union of the databases. If LCA practitioners substantially modify their databases, it is indeed necessary to recalculate the reference values and they will therefore have to do so (practitioners could also make their data public, these data would then be directly integrated into the inventory databases and the reference values would be calculated for them). Sixth remark: The results will change if another LCI database is used. This is a necessity to ensure consistency between the system under study and the normalization reference. In this case, there is no longer a single set of normalization values linked to a life cycle impact assessment (LCIA) method, but rather a set for each database-LCIA method pair.
Conclusion Multi-criteria decision-making is a complex field of research and there is no universal method that can be applied in all cases. The LCA presents an interesting particularity, with normalization we generally use external information to express the attributes (i. e. impacts) into a common scale. This makes the multi-criteria decision process more generic, independent of the case study. The other side of the coin is the introduction of an additional bias via different modelling between the system under study and the reference, especially for processbased LCA. Normalization by the geometric mean of the inventory database avoids this bias. That is argued considering that log-normal laws govern the phenomena and processes under investigation. In addition to this normalization issue, describing the database by its geometric means and comparing them to the cumulative impact values is a way to see the exhaustiveness of the database (subject to the exhaustiveness of the normalization value, see  Hélias et al.
Therefore, to achieve a target area of 100 km in which convective cells are fully developed, a limited area domain of at least 300 Â 300 km should be selected. Due to computational limitations, CPRCMs tend to use small domains compared to RCMs. This, in turn, implies that spectral nudging is only rarely required to further constrain the CPRCM large-scale atmospheric state toward the LBC  (von Storch et al. , 2000). Indeed,  Schaaf et al. (2017)  concluded that spectral nudging is not necessary for small CPRCM domain sizes of only several hundred kilometers in diameter. However, finding a closer match with the observations when nudging was used,  Collier and Mölg (2020)  decided to take advantage of nudging to produce a high-resolution climate dataset over Bavaria. In the rare cases when CPRCM domains are large such as over North America, the application of spectral nudging reduced 2-m temperature biases, improved precipitation patterns, but suppressed precipitation intensity over the western U.
| Need of high-resolution gridded observations As the resolution of climate models increases, the evaluation of climate simulations becomes a challenge due to the lack of high-quality high-resolution observational gridded datasets  (Piazza et al. , 2019; Prein & Gobiet, 2017). Indeed, many studies found large discrepancies between different observed gridded datasets despite that they are all based on weather stations  (Prein & Gobiet, 2017; Zittis et al. , 2017). This is especially true in low-density populated polar  (Revokatova et al. , 2021)  and mountainous regions where weather stations are sparsely distributed. In addition, weather stations are affected by precipitation undercatch  (Gao, Chen, & Jiang, 2020; Neff, 1977; Pollock et al. , 2018) , which is even more severe for snow measurements in winter  (Fortin et al.
Fortunately, such drawbacks were recently reduced with high spatio-temporal resolution radar data that are adjusted with gauges from weather stations over France  (Tabary et al. , 2012) , Germany  (Winterrath et al. , 2018) , the UK  (Yu, Li, et al. , 2020) , the United States  (Lin & Mitchell, 2005) , Netherlands  (Overeem et al. , 2009) , Sweden  (Berg et al. , 2016) , and Switzerland  (Wüest et al. , 2010). This merging technique allows producing reliable subdaily kilometer-scale gridded precipitation datasets. However, radar-based datasets rely on rather recent radars that are inhomogeneously distributed in space and time. Moreover, radar efficiency is distance-dependent and is affected by fixed-object echoes and mountain ranges, which limits their range. With the improved realism of CPRCMs  (Clark et al.
, 2016) , the study of specific meteorological processes that sometimes require the use of less conventional measurements is of greater interest. Amongst others, remote sensing products allow to investigate more deeply deep convection processes likely improved with CPRCMs. Doppler polarimetric radars provide the opportunity to evaluate cloud properties such as hydrometeor mixing ratios and falling speeds  (Carlin, 2015; Glushko & Yanovsky, 2010). However, these datasets are often 3D spatial datasets with high spatiotemporal resolution and are difficult to integrate on climate timescales  (Glushko & Yanovsky, 2010). Other remote sensing-based applications have been used for CPRCM cloud evaluation  (Brisson, Van Weverberg, et al. , 2016; Hentgen et al. , 2019; Keller et al. , 2016). The merging of multiple generations of satellites allows the retrieval of consistent cloud parameters over long-time periods on high spatio-temporal scales.
In  Argüeso et al. (2013) , gridded observations were found to be inadequate to bias correct CPRCMs because they were subjected to too frequent low intensity precipitation due to the spatial averaging of weather station data. Novel bias correction methods that directly use weather station data are now being developed to alleviate the problems associated with coarse-resolution or the lack of observational gridded datasets  (Argüeso et al. , 2013; Haerter et al. , 2015). Applying a nonlinear bias-correction to CPRCM simulated precipitation, Bannister et al. (  2019 ) obtained a precipitation dataset that is superior to precipitation estimates from reanalysis and two gridded datasets in the Himalayas. In addition, CPRCM simulations are often too short to apply bias correction. Bias correction approaches typically require simulations and observations spanning at least 30 years to account for the effects of natural climate variability in their calibration  (Berg et al.
, 2021; Ban et al. , 2014; Lind et al. , 2016). In an intercomparison of RCMs and CPRCMs covering most of Europe,  Berthou et al. (2020)  confirmed that precipitation that tends to be too frequent and too light with RCMs is largely corrected with CPRCMs over the UK, Switzerland and Germany. In a study comparing 23 CPRCM simulations, though individual CPRCMs gave contrasting results, the CPRCM ensemble means of the precipitation frequency and intensity are in much better agreement with different gridded datasets over France, Switzerland and Italy than those from the RCM ensemble  (Ban et al. , 2021). Reder et al. (2020)  found better agreement in the number of consecutive dry and wet days from a CPRCM than an RCM with observations over Switzerland, especially at higher elevations. Improvements in the precipitation frequency and intensity with CPRCMs were also found over China  (Gao, Chen, & Jiang, 2020; Guo et al.
In a CPRCM evaluation study over western Canada,  Li, Li, et al. (2019)  explained that higher peaks and lower valleys over the Canadian Rockies introduced orographic precipitation differences between their CPRCM and coarse-resolution observed gridded datasets, making a thorough evaluation difficult. Over the central Andes, an increase of CPRCM resolution led to smaller precipitation biases, with an improved performance as elevation increased  (Schumacher et al. , 2020)  that was also associated with a better structure of cloud systems  (Moya-Alvarez et al. , 2019). Performing a sensitivity study with a CPRCM using terrain height modifications,  Rasmussen and Houze (2016)  underlined the importance of the orographic control of the Andes on the initiation of convection and its upscale growth into MCSs in South America.
The use of a CPRCM was found to be useful to improve the simulation of orographic precipitation in many other regions. Indeed,  Mölg et al. (2020)  linked observed glacier meteorological elevation gradients to a CPRCM dataset that revealed the importance of mesoscale atmospheric circulation on the evolution of the Kersten Glacier near the Kilimanjaro Summit. El-Samra et al. (2018)  showed that a CPRCM improved the simulation of mean and extreme precipitation over the orographically complex terrain of Lebanon. Jing et al. (2020)  found that seasonal precipitation distributions, mean monthly precipitation, and precipitation variability were well simulated using a CPRCM mainly due to the better resolved complex terrain at high resolution over the United Arab Emirates. In a comparison of the orographic precipitation simulated at different resolutions over a southern Norway island,  Barstad and Caroletti (2013)  found that 1-km resolution is necessary for capturing the most intense precipitation events satisfactorily.
Some applications of CPRCM outputs in different impact studies were finally highlighted. Despite the recent interesting developments of CPRCMs, many of the initial hopes associated with their emergence  (Prein, Rasmussen, & Stephens, 2017 ) are yet to be confirmed, and CPRCMs do not always show an added value compared to conventional RCMs. One of the main challenges associated with the use of CPRCMs lies in their heavy computational requirements and demanding output storage sizes  (Schär et al. , 2020). Consequently, simulations are mainly performed over small domains, for short periods, with projections performed in time slice mode, and with ensembles of only few members to assess inter-model variability and robustness. In addition, the large datasets of CPRCM simulations and less-established simulation protocols lead to difficulties in the analysis, data storage, and data exchanges.
, 2017 (Beck et al. , , 2019)) , and alternative precipitation  (Beck et al. , 2020)  datasets are promising to provide more reliable observations in remote regions. Despite the CORDEX FPS projects that address specific scientific questions, additional community efforts would be welcome. To that extent, it would be interesting for the CPRCM community to agree on smaller domains (as the CORDEX domains are too large and restrict CPRCM simulations). In the coming years, better synergy with the NWP community in favor of the seamless approach would be necessary to improve the calibration of CPRCMs, where compensating deficiencies existing in RCMs may be less actual in more realistic CPRCMs  (Brisson, Van Weverberg, et al. , 2016). Future gain in computing power should soon allow to perform longer simulations over larger domains and additional simulations to increase ensemble sizes allowing a better characterization of biases and different sources of uncertainties.