dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Several reconstruction and reanalysis datasets were used to examine the link between autumn snow cover, ocean surface conditions and the NAO pattern in winter. We found evidence for a manifestation of a negative NAO after November with a strong west-to-east snow cover For future studies regarding seasonal prediction, we emphasize the use of the November snow dipole concerning a forecasting of the winter NAO state. Nevertheless, periods of weak correlation might occur again, especially since it is uncertain how the sea ice to snow relationship will change in the future, once the Arctic is ice free in summer or the local warming is strong enough to override the counterintuitive snow cover increase. Thus, further studies are needed to investigate the interplay between Arctic sea ice and continental snow distribution.
blockings (UBs). SLP distribution after El Niño events does not change considerably irrespective of the dataset and time period used. A strong Pacific signal shows the northern part of the Pacific-North American pattern (PNA) with negative SLP anomalies over the eastern North Pacific. https://doi. org/10. 5194/esd-2019-68 Preprint. Discussion started: 20 November 2019 c Author(s) 2019. CC BY 4. 0 License.
Acknowledgements Marco Rohrer  was supported by the  Swiss National Science Foundation  under Grant  143219. The Twentieth Century Reanalysis Project datasets are supported by the  U. S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment  ( DOE INCITE) program , and  Office of Biological and Environmental Research (BER) , and by the  National Oceanic and Atmospheric Administration Climate Program Office. The ECMWF 20th Century Reanalyses and model simulations are supported by the  EU   FP7  project  ERA-CLIM2. https://doi. org/10. 5194/esd-2019-68Preprint. Discussion started: 20 November 2019 c Author(s)  2019. CC BY 4. 0 License.
Data Availability The MERRA2 reanalysis data is publicly available at the NASA EARTHDATA repository (https://disc. gsfc. nasa. gov/daac-bin/FTPSubset2. pl). The ERA-20C reanalysis data is publicly available at the ECMWF data repository (https://apps. ecmwf. int/datasets/). The 20CRv2c reanalysis data is publicly available at the NOAA Earth System Research Laboratory repository (https://www. esrl. noaa. gov/psd/data/gridded/data. 20thC_ReanV2c. html). The blocking algorithm is publicly available at https://github. com/marco-rohrer/TM2D. The AMO reconstruction data is a publicly vailable at the NOAA Earth System Research Laboratory (https://www. esrl. noaa. gov/psd/data/timeseries/AMO/). The Niño 3. 4 reconstruction is publicly available at the GCOS Working Group on Surface Pressure repository (https://www.
Results Phenotypic variation for fruit firmness. The progeny in the 'Fercer' × 'X' population exhibited a wide range of fruit firmness; however, the distribution was bimodal with more individuals exhibiting soft fruit (Table  1 , Figs  1a  and S1 ). 'X' was assigned as the paternal parent of this population since the recorded paternal parent was found incorrect based on genotype data and the correct parent is unknown. 'Fercer' had a multi-year fruit firmness mean of ~67 g/mm 2 (Min 56, Max 82) which is aligned with the firm-fruited progeny group. A wide range of variation for fruit firmness was also observed for individuals from the INRA sweet cherry germplasm collection and RosBREED germplasm (Fig. 1b, c ). In the INRA sweet cherry germplasm collection, the majority of softfruited individuals were characterized as landraces as opposed to bred cultivars.
In the RosBREED germplasm, the majority of soft-fruited individuals were either mazzards or hybrids with mazzards. When the two INRA populations were compared for firmness for the one year that they were both phenotyped, the sweet cherry germplasm collection exhibited a wider phenotypic distribution compared to the F 1 population (Fig. 1 ). Within the three populations, the ANOVA analysis revealed highly significant effects for the different genotypes (Table  1 ). In all three populations, the broad-sense heritabilities were high (0. 73-0. 97), indicating that much of the phenotypic variation in these populations is genetically controlled (Table  1 ). The highest heritability for fruit firmness (0. 97) was obtained from the multi-year data for the 'Fercer' × 'X' population, likely because the environmental variation was low among years compared to the genetic variation as suggested by the bimodal phenotypic distribution.
The firmness of the RosBREED sweet cherry materials and sour cherry materials could be directly compared as they were phenotyped using the same instrumentation. Fruit from the sour cherry individuals exhibited a Table  1. Summary of fruit firmness data for three sweet cherry populations. Note: a Different methods for phenotyping were used for the INRA and RosBREED populations. b Phenotypic data is from seven years (2009-2013, 2015-2016). c Phenotypic data is from two years (2014-2015). d Phenotypic data is from two years (2011-2012). smaller range of firmness (98-197) compared to sweet cherry (137-397) (Figs  1  and S2 ). The fruit firmness of 95% of the sour cherry individuals was less than 170, indicating that almost all the sour cherries were softer than the sweet cherry landraces, bred cultivars, and their offspring.
S4 ). The effects on fruit firmness associated with 14 qP-FF4. 1 diplotypes were compared for the RosBREED germplasm. These 14 diplotypes, representing 10 haplotypes (H1, H2, H4 to H6, H8, H10 to H13), each consisted of firmness data from six to 104 individuals (Fig. 6b ). Fruit firmness ranged from a mean of 275 down to 162 for the softest fruit. The four diplotypes that had the firmest fruit all had one or two copies of H1, paired either with itself or with H4, H5 or H6. This suggests that in addition to H1 and H4, H5 and H6 can also be considered "firm" alleles. However, when H1 or H4 were paired with H2, the mean fruit firmness was reduced significantly. This is consistent with the dominant 'soft' effect of H2 observed in the 'Fercer' × 'X' progeny where H1H2 progeny had significantly softer fruit than H1H1 progeny.
For the two INRA populations (F 1 population and the sweet cherry germplasm collection), fruit firmness was measured using a Durofel (Setop Giraud Technologie, Cavaillon, France) texture analyzer on the day of harvest. A 3-mm probe was applied at two points on the fruit equator, the movement of the probe was recorded and the average of the two measures on ten fruits was used. Data were collected from seven years (2009-2013, 2015 and 2016) for the F 1 population and two years for the germplasm collection (2014 and 2015). For the RosBREED sweet cherry and sour cherry individuals, fruit firmness was measured from 25 fruits that were at room temperature using the compression test of BioWorks FirmTech 2 (Wamego, KS, USA). Compression was performed from the fruit cheek and with the stems still on the fruit.
The mean value of 25 measures were used. The data for the sweet and sour cherry individuals were collected for two years (sweet cherry, 2011 and 2012; sour cherry, 2011 and 2013). Since different methods were used to measure firmness in the RosBREED and INRA populations, the data for these populations were analyzed separately. The phenotypic data were analyzed using SAS version 9. 13 (SAS Institute Inc. ) and the model PROC MIXED was used to obtain the variance components. PROC CORR was performed to calculate the correlation coefficients of fruit firmness among different years. Broad-sense heritability (H 2 ) was calculated using estimates for the individuals based on the following random linear model: = + + + µ Y y g e ij i j ij where Y ij is the phenotypic value of the j th individual in i th year; µ is the mean value of fruit firmness; y i is the random effect of the i th year on the phenotype; g j is the random genotypic effect of j th individual; and e ij is the model residual.
For the INRA sweet cherry germplasm collection, marker data curation was described in Campoy et al. 55. A total of 1,215 SNP markers were retained after removing the following four SNP types: (1) SNPs failing to generate clear genotype clusters; (2) SNPs with missing genotypes greater than 5%; (3) SNPs showing high distortion for Hardy-Weinberg equilibrium (>0. 0001); and (4) SNPs with minor allele frequencies lower than 5%. For RosBREED pedigreed population, marker data curation was described in Cai et al. 24. A total of 1,617 SNPs were identified as robust markers and therefore used for QTL analysis. Genetic positions for these markers were determined by aligning and integrating these physical positions (based on Peach Genome v2. 0)  39  with the sweet cherry 'Regina' × 'Lapins' SNP linkage map  61.
The detailed QTL mapping methodology was as described in Castède et al. 27. The graphical presentation of QTL locations on the linkage groups was obtained using the MapChart software version 2. 2  62. A genome-wide association analysis was done for the INRA sweet cherry germplasm collection. This analysis tested the association between fruit firmness and the SNP markers on the chromosome where the firmness QTL was located. A total of 1215 SNP markers across the sweet cherry genome were used in the analysis and the SNPs associated with fruit firmness were identified according to a mixed linear model (MLM) using the software TASSEL version 5. 2. 61  63. Corrections for population structure and kinship were adopted in the model. Population structure was described in the study of Campoy et al.
55. The relative kinship matrix was calculated using SPAGeDi  64. The genome-wide significance cutoff was set at 4 × 10 -5 (0. 05/1215). QTL analysis in RosBREED sweet cherry pedigreed population was done using FlexQTL software that is designed for use with multiple pedigree-connected families  65. Markov Chain Monte Carlo (MCMC) simulation, implemented in FlexQTL software, was applied to obtain samples from the joint posterior distribution of the model. A total of 1,000 samples (500,000 iterations with thinning value of 500) were stored for each simulation and then used for statistical inference. The inference on the number of QTLs was based on a pairwise comparison of models differing from each other by one QTL. The Bayes factor parameters (2lnBF) were interpreted as non-significant (0-2), positive (2-5), strong (5-10) or decisive (>10) evidence for the presence of QTL.
Haplotypes were assigned using the PediHaplotyper software  66. The SNP phasing for the sour cherry haplotypes was described in Cai et al. 33. Unique haplotypes were named from H1 to H16 randomly. Statistical analyses for the association between haplotype (diplotype) and fruit firmness were performed using the software R version 3. 1. 3  67. QTL genotypes (QQ, Qq, qq) predicted by FlexQTL were used to deduce if a haplotype was associated with soft or firm fruit. As 'Q' is assigned to the higher phenotypic value, in this case increased firmness, QQ, Qq, and qq genotypes correspond to two "firm" alleles, one "firm" and one "soft" allele, and two "soft" alleles, respectively. Haplotypes were also deduced to be associated with soft or firm fruit based on comparison of diplotypes. In silico Candidate Genes.
The list of genes within the QTL interval and their functional annotations in sweet cherry were obtained from the sweet cherry genome  42  available on the Genome Database of Rosaceae website (GDR, https://www. rosaceae. org). The corresponding predicted cherry protein sequences obtained from GDR were blasted against the National Center for Biotechnology Information (NCBI) database to obtain gene ontology terms using BLASTP in the program Blast2GO  68  with an E-value cutoff of 0. 001. The sequences of the genes within the QTL interval were also blasted to the peach genome v2. 0 39 available at GDR and the tomato genome ITAG 2. 40  69  available at Sol genomics Network (https://solgenomics. net), and their best gene matches and annotations were extracted. Overall, three annotations (sweet cherry, peach and tomato) and gene ontology results from Blast2GO were considered for the identification of candidate genes for fruit firmness.
Candidate genes considered were those genes predicted to be involved in plant cell wall metabolism or various hormone (Ethylene, Brassinosteroid, Auxin, Gibberellin, and ABA) signaling pathways associated with fruit ripening. A circular plot of the mapping and candidate gene data was prepared with the Circos plotting tool  70. Alignment and analysis of the expansin genes was performed with ClustalW v2. 1 multiple sequences alignment tool. A dendrogram of the four genes was constructed using the Jukes-Cantor genetic distance model, and the neighbor-joining tree building method within the GeneiousV. 11. 1. 4 GUI software  71, 72.
Acknowledgements This project was supported in part by the  USDA-NIFA-Specialty Crop Research Initiative  project, RosBREED: Enabling marker-assisted breeding in Rosaceae ( 2009-51181-05808 ) and RosBREED 2: Combining disease resistance with horticultural quality in new rosaceous cultivars ( 2014-51181-22378 ). This project was also supported by the  CEP Innovation , the private partner of INRA breeding program. We thank  Dr. José Antonio Campoy  and  Mr. Guillaume Lalanne-Tisné  for the curation of  SNP data of 'Fercer' × 'X' progeny and the construction of the 'Fercer'  and 'X' parental maps. We also thank the technical staff of A3C team and  UEA Unit  for the management of trees as well as the phenotyping activities and the  INRA's 'Prunus Genetic Resources Center'  for preserving and managing the sweet cherry germplasm collections.
For more than twenty years, the multi-satellite DUACS system has been providing Near Real Time (NRT) and Delayed Time (DT) altimetric products. These data are ranging from along-track to multi-mission maps of Sea Level Anomaly (SLA) and Absolute Dynamic Topography (ADT). A reprocessing of 25 years of data, namely: DUACS DT2018, has been carried out and is available through the Copernicus Marine Environment Monitoring Service (CMEMS) and Copernicus Climate Change Service (C3S) since April 2018. Several changes have been implemented in the DT2018 processing in order to improve the quality of the products. Ne w altimeter standards and geophysical corrections has been used, refined data selection has been implemented and Optimal Interpolation (OI) parameters have been reviewed for global and regional map generation.
Introduction The multi-mission processing system of altimeter data so called DUACS (Data Unification and Altimeter Combination System) exists since 1997. Since then, it has produced Near Real Time (NRT), with a delay of a few hours to one day, and Delayed Time (DT), with a delay of a few months, altimetric products for the scientific community. The processing unit has been redesigned and regularly upgraded as the knowledge of altimetry processing has been refined  (Le Traon et al. , 1998; Ducet et al. , 2000; Dibarboure et al. , 2011; Pujol et al. , 2016). Every few years, a full reprocessing is performed by DUACS including all missions and taking into account recent improvements and recommendations from the altimetry community.
Altimeter standards DUACS system takes Level 2P (L2P) altimeter products as input data. These data are disseminated by CNES, CLS and EUMETSAT. They include the geophysical standards that allow the calculation of sea level anomalies (i. e. instrumental, geophysical, environmental corrections, Mean Sea Surface -MSS) and a validity flag that is used to remove spurious measurements. The DUACS DT2018 global reprocessing was an opportunity to take into account new recommendation and new correction from the altimetry community. The altimeter standards have been selected to be as consistent and homogeneous as possible between the different missions whatever the use (retrieval of mesoscale signals or climate applications). This selection has been made possible in the frame of the phase II of the ESA Sea Level Climate Change Initiative (SL_cci) project between 2014-2017.
Evolution of the DUACS processing The DUACS processing includes a first preprocessing step to acquire and homogenize the data from the different altimeter. Then, along-track product (L3) and multi-missions gridded products (L4) can be estimated. Finally, derived products are computed and disseminated to the users. This section does not aim to detail the entire data processing system but rather to expose major changes that occurred in this DT2018 version. For an advanced description of the DUACS processing, readers are advised to consult  Pujol et al. , 2016. Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-150 Manuscript under review for journal Ocean Sci. Discussion started: 8 January 2019 c Author(s) 2019. CC BY 4. 0 License.
Acquisition and preprocessing Processing sequence in DUACS can be divided into several steps: acquisition, homogenization, input data quality control, multi-mission cross calibration, along-track SLA generation, multi-mission mapping and final quality control. The acquisition and homogenization processes consist in retrieving altimeter and ancillary data and applying them with the most recent correction, models and reference recommended by expert (as described in section 2. 1 and 2. 2). This up-to-date selection is available in Table  1. The Input Data Quality Control is a process linked with the calibration/validation activities carried out for CNES, ESA and EUMETSAT. It is composed of several editing processes to detect and fix spurious measurements and to ensure a long-term stability of L2P products.
osi-saf. org) and gives us a maximum estimation of ice extent. In coastal areas, mainly due to a reduced quality of the mean sea surface (MSS) closer than 20 km to the coast, along-track SLA measurements for geodetic and drifting missions were drastically rejected in the DT2014  (Pujol et al. , 2016). In DT2018, with improved quality MSS solution, efforts were done to keep as much as possible valid measurements near the coast. The data selection strategy is based on a median filter applied in a 30km band from the coast  (Ollivier et al. , 2014). Number of valid data usable in DUACS system is now increased in a substantial proportion, especially for geodetic measurements. Figure  2  presents an example of the gain of measurements for the Cryosat-2 geodetic mission in DT2018 over the Mediterranean Sea.
2 and 2. 3. 1). It has a direct positive impact on the along track generation that will benefit of an extended coastal coverage. Globally the comparison of the difference at mono-mission and multi-mission crossovers provides good results in this new version. Compared to the DT2014 version, we observe at global scale a decrease of the mean of the difference at crossovers around 0. 3cm globally and up to 1cm locally (not shown here). It should be noted that for the Sentinel-3A mission the estimation of a precise MP was not possible for this reprocessing, due to the short time period (i. e. few months) available to compute it. Consequently, data from the Sentinel-3A mission are interpolated onto Theorical Track and the MSS is removed. Since then, a MP has been evaluated  (Dibarboure et al. , in prep and Pujol et al.
, 2018b)  and Sentinel-3A dataset will be reprocessed in a future CMEMS version in 2019. For non-repetitive missions (ERS-1 during its geodetic phase, Cryosat-2, Hayaing-2A, Jason-1 geodetic phase, Jason-2 geodetic phase, Saral-AltiKa geodetic phase), no MP can be estimated. The SLA is then derived along the real altimeter tracks using the gridded MSS. Last step of the along-track processing consists in noise reduction by law-pass Lanczos filtering, and subsampling. This process remains unchanged compared to the DT2014 version  (Pujol et al. , 2016). Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-150 Manuscript under review for journal Ocean Sci. Discussion started: 8 January 2019 c Author(s) 2019. CC BY 4. 0 License.
However, in the equatorial band (±20°N), the EKE in the DT2018 is less important (-17%). This is linked with the evolution of the noise measurement considered in the mapping process for all satellites. The consistency between altimeter geostrophic current and independent measurements is significantly improved in this area as discussed in section 3. 2. On coastal area, the DT2018 version presents less spurious peaks of high EKE. As already stated, this is linked with the improved altimetric correction and the variance SLA reduction. Considering the mean EKE time series, a global reduction of 26 cm² (17%) is observed for the DT2018 dataset. It is directly linked with the equatorial EKE reduction. What is also important to note is that the temporal evolution of the EKE in these products is less peaky than in DT2014.
Geostrophic current quality in the Mediterranean Sea DT2018 regional absolute geostrophic current in the Mediterranean basin has been assessed using drifter data for the period. Drifters released in the Mediterranean Sea in the frame of AlborEx  (Pascual et al. , 2017)   Table  6  summarizes the main statistical results for the whole basin. The DT2018 regional product presents a correlation coefficient with drifter data 4% larger than that obtained when using the DT2014 regional product. Moreover, the errors in the former are slightly reduce by 1% whilst its improvement in the explained variance reaches 14%. We repeated the analysis but for the different dynamical sub-regions of the basin (see Figure  11. a) reported by  Manca et al. (2004). This differentiation is based on the typical permanent features in the upper 200 m of the water column.
Other changes have been implemented in the DT2018 processing; the altimeter standard and geophysical corrections are upto-date with expert recommendation, mapping parameters have been refined including spatial and temporal correlation scale and measurements errors. We also focused on the improvement of coastal editing to gain many relevant sea level data, mainly for drifting altimeter. Additional sea level data have been used compared to the DT2014, especially Sentinel-3A measurements that have been used over a 6-month extended period. Having discussed these important changes, we have focused on the description of the impact on gridded sea level products. The SLA variability has been increased in energetic area (from 5 to 10%) and reduced locally along the coast (up to 50%). A 10% EKE decrease in the equatorial band is also observed and linked to the refined measurements errors in the area.
To estimate the spatial resolution of the gridded products, an evaluation has been carried out based on a spectral coherence approach. A full description of this approach can be found in  Ballarotta et al. , (in prep. ). Many products are derived from these global and regional gridded products and are strongly affected by the their quality: Lagrangian product  (FSLE d'Ovidio et al. 2015) , or eddy tracking (https://www. aviso. altimetry. fr/en/data/products/valueadded-products/global-mesoscale-eddy-trajectory-product. html) are a prominent examples. Medium term developments concern new Level-3 products that will be dedicated to data assimilation and CMEMS Monitoring Forecasting Centre. These products will be new in Delayed-Time mode. The Mean Dynamic Topography will also be updated, and the Black Sea area will be integrated.
DT2018 absolute geostrophic current has been assessed using drifter data for the time period 1993-2017. AOML (Atlantic Oceanographic & Meteorological Laboratory) database has been used for the comparison(Lumpkin et al. 2013). These insitu data are corrected from Ekman drift (Rio et al. , 2011)  but also from wind if drifters' drogue has been lost(Rio et al. ,    Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-150 Manuscript under review for journal Ocean Sci. Discussion started: 8 January 2019 c Author(s) 2019. CC BY 4. 0 License. 2012) so as to be compared with altimetry absolute geostrophic current. Positions and velocities of drifters are interpolated using a 3-day low-pass filter in order to remove high-frequency motions. Absolute geostrophic current derived from altimetry products are then interpolated onto drifter positions for comparison.