dataesr/scientific-paragraphs-categorization huggingface dataset:
C2. 4 Uncertainty assessment for E LUC Differences between the bookkeeping models and DGVMs originate from three main sources: the different methodologies, which among others lead to inclusion of the loss of additional sink capacity in DGVMs (see Appendix D1. 4), the underlying land-use/land-cover dataset, and the different processes represented (Table  A1 ). We examine the results from the DGVMs and of the bookkeeping method and use the resulting variations as a way to characterize the uncertainty in E LUC. Despite these differences, the E LUC estimate from the DGVMs multi-model mean is consistent with the average of the emissions from the bookkeeping models (Table  5 ). However, there are large differences among individual DGVMs (standard deviation at around 0. 5 GtC yr -1 ; Table  5 ), between the bookkeeping estimates (average difference 1850-2020 BLUE-updated H&N2017 of 0.
C3. 3 GOBM evaluation and uncertainty assessment for S OCEAN The ocean CO 2 sink for all GOBMs and the ensemble mean falls within 90 % confidence of the observed range, or 1. 5 to 2. 9 GtC yr -1 for the 1990s  (Ciais et al. , 2013)  after applying adjustments. An exception is the MPI model, which simulates a low ocean carbon sink of 1. 38 GtC yr -1 for the 1990s in simulation A owing to the inclusion of riverine carbon flux. After adjusting to the GCB's definition of S OCEAN by subtracting simulation B, the MPI model falls into the observed range with an estimated sink of 1. 69 GtC yr -1. The GOBMs and data products have been further evaluated using the fugacity of sea surface CO 2 (f CO 2 ) from the SOCAT v2021 database  (Bakker et al. , 2016 (Bakker et al. , , 2021)). We focused this evaluation on the root mean squared error (RMSE) between observed and modelled f CO 2 and on a measure of the amplitude of the interannual variability of the flux (modified after  Rödenbeck et al.
C4. 1 DGVM simulations The DGVM runs were forced by either the merged monthly Climate Research Unit (CRU) and 6-hourly Japanese 55-year Reanalysis (JRA-55) dataset or by the monthly CRU dataset, both providing observation-based temperature, precipitation, and incoming surface radiation on a 0. 5 • × 0. 5 • grid and updated to 2020  (Harris et al. , 2014 (Harris et al. , , 2020)). The combination of CRU monthly data with 6-hourly forcing from JRA-55  (Kobayashi et al. , 2015)  is performed with methodology used in previous years  (Viovy, 2016)  adapted to the specifics of the JRA-55 data. New to this budget is the revision of incoming short-wave radiation fields to take into account aerosol impacts and the division of total radiation into direct and diffuse components as summarized below. The diffuse fraction dataset offers 6-hourly distributions of the diffuse fraction of surface shortwave fluxes over the period 1901-2020.
Radiative transfer calculations are based on monthly-averaged distributions of tropospheric and stratospheric aerosol optical depth and 6-hourly distributions of cloud fraction. Methods follow those described in the Methods section of  Mercado et al. (2009) , but with updated input datasets. The time series of speciated tropospheric aerosol optical depth is taken from the historical and RCP8. 5 simulations by the HadGEM2-ES climate model  (Bellouin et al. , 2011). To correct for biases in HadGEM2-ES, tropospheric aerosol optical depths are scaled over the whole period to match the global and monthly averages obtained over the period 2003-2020 by the CAMS Reanalysis of atmospheric composition  (Inness et al. , 2019) , which assimilates satellite retrievals of aerosol optical depth. The time series of stratospheric aerosol optical depth is taken from the  Sato et al.
(1993)  climatology, which has been updated to 2012. Years 2013-2020 are assumed to be background years and so replicate the background year 2010. That assumption is supported by the Global Spacebased Stratospheric Aerosol Climatology time series  (1979 -2016; Thomason et al. , 2018). The time series of cloud fraction is obtained by scaling the 6-hourly distributions simulated in the Japanese Reanalysis  (Kobayashi et al. , 2015)  to match the monthly-averaged cloud cover in the CRU TS v4. 03 dataset  (Harris et al. , 2021). Surface radiative fluxes account for aerosol-radiation interactions from both tropospheric and stratospheric aerosols, and for aerosol-cloud interactions from tropospheric aerosols, except mineral dust. Tropospheric aerosols are also assumed to exert interactions with clouds.
C2. 2. All 17 DGVMs meet these three criteria. In addition, the DGVM results are also evaluated using the International Land Model Benchmarking system (ILAMB;  Collier et al. , 2018). This evaluation is provided here to document, encourage, and support model improvements through time. ILAMB variables cover key processes that are relevant for the quantification of S LAND and resulting aggregated outcomes. The selected variables are vegetation biomass, gross primary productivity, leaf area index, net ecosystem exchange, ecosystem respiration, evapotranspiration, soil carbon, and runoff (see Fig. B3  for the results and for the list of observed databases). Results are shown in Fig. B3  and discussed in Sect. 3. 6. 5. For the uncertainty for S LAND , we use the standard deviation of the annual CO 2 sink across the DGVMs, averaging to about ±0.
Each system uses a different transport model, which was demonstrated to be a driving factor behind differences in atmospheric inversion-based flux estimates, and specifically their distribution across latitudinal bands  (Gaubert et al. , 2019; Schuh et al. , 2019). The inversion systems prescribe same global fossil fuel emissions for E FOS , specifically the GCP's Gridded Fossil Emissions Dataset version 2021 (GCP-GridFEDv2021. 2; Jones et al. , 2021b) , which is an update through 2020 of the first version of GCP-GridFED presented by  Jones et al. (2021a). GCP-GridFEDv2021. 2 scales gridded estimates of CO 2 emissions from  EDGARv4. 3. 2 (Janssens-Maenhout et al. , 2019)  within national territories to match national emissions estimates provided by the GCP for the years 1959-2020, which were compiled following the methodology described in Appendix C1 based on all information available on 31 July 2021 (Robbie  Andrew, personal communication, 2021).
2 emissions from land use, land-use change, and forestry (E LUC ) The net CO 2 flux from land use, land-use change, and forestry (E LUC , called land-use change emissions in the rest of the text) includes CO 2 fluxes from deforestation, afforesta- tion, logging and forest degradation (including harvest ac- tivity), shifting cultivation (cycle of cutting forest for agri- culture, then abandoning), and regrowth of forests following wood harvest or abandonment of agriculture. Emissions from peat burning and drainage are added from external datasets (see Sect. C2. 1 below). Only some land-management activi- ties are included in our land-use change emissions estimates (Table.
The Puertos del Estado SAMOA coastal and port ocean forecast service delivers operational ocean forecasts to the Spanish Port Authorities since 01/2017 (originally set-up for 9 ports). In its second development phase (2019-2021), the SAMOA service has been extended to 31 ports (practically, the whole Spanish Port System). Besides, the next generation of the SAMOA service is being developed. Research is being focused on (1) updating atmospheric forcing (by combining the AEMET HARMONIE 2. 5 Km forecasts and the IFS-ECMWF ones), (  2 ) upgrading the circulation model (ROMS), and (3) testing new methodologies to nest SAMOA systems in the Copernicus IBI-MFC regional solution (with emphasis on its 3D hourly dataset). Evaluation of specific model upgrades is here presented. Model sensitivity tests have been assessed using the available in-situ and remoted sensed (i.
This product allows enhancement of the temporal frequency (from daily to hourly resolution) of the data imposed at boundaries. Moreover, specific physics upgrade was implemented in this configuration experiment. Table  1  summarises the main differences among the three different model sensitivity datasets. As denoted in Figure  3b , SAM_INI corresponds to the former operational version of SAMOA (active since October 2018 to October 2019), whereas SAM_ADV is the opera-tional set-up currently in production. SAM_H3D will be the forthcoming SAMOA set-up version (to be operationally released in 2022). All the SAMOA model set-up novelties have been extensively tested. The proposed sensitivity model runs were validated using in-situ and remote sensing observations (i. e. , from PdE coastal and deep-water mooring buoys for surface temperature, salinity and currents, tide gauges for sea level and HF-Radars for surface currents, available the latest only in some of the coastal model domains).
All these measurements have passed state-of-the-art Quality Control processes and can be accessed in NRT basis from the PORTUS  [24]  website. Performance of the different SAMOA model tests have been assessed by comparing the different models with the available observational datasets at each coastal and port domain (see in Table  2  the observational coverage available at each SAMOA domain). The following hourly-averaged error metrics has been computed for each dataset in each domain (when available observations): mean bias, correlation, Root-Mean Square Error (RMS) and Coefficient of Efficiency (COE). The metrics that will be shown in Tables 3-5 are representative of the whole analysed period in each sensitivity test. Metrics in Section 4. 1 were computed from time-series enclosing June 2019; whereas those for the Section 4.
The domain column identifies the SAMOA system. The Type denotes the sensor type used: TGS (Tide Gauge Station), CST (Coastal Buoy), EXT (Deep-water buoy). N counts the size of the sample (conditioned by the availability of hourly observation). Each error metric (Bias, Correlation, Root-Mean-Square Error (RMS) and Coefficient of Efficiency (COE)) provided for each model solution (SAM_INI (INI), SAM_ADV (ADV) and IBI_PHY(IBI)). The last row of each variable represents the average error metric of all the sensors given a specific test run. Bold numbers highlight the best performing dataset.
Impact in the SAMOA Solutions Related to Changes in the Physics and in the Temporal Frequency of the Imposed Data along Boundaries (SAM_H3D vs. SAM_ADV) As pointed in the previous section, SAM_ADV seemed to enhance coherency between the SAMOA systems and their parent solutions (IBI_PHY). However, differences were still high in some cases, affecting the whole domain and specially along the boundaries. The availability of new hourly-3D CMEMS IBI-MFC products, joint with the upgrade of some physical parametrizations, allowed building the SAM_H3D set-up at 5 systems that were compared with the SAM_ADV and the IBI_PHY parent solution. Their results are summarized in Tables  4  and 5  and Figures  5 6 7. were still high in some cases, affecting the whole domain and specially along the boundaries.
4 • ) bias is lower than IBI_PHY (-4. 6 • ); (ii) correlation and RMS are also better at SAM_H3D (0. 3, 74. 2 • ) than at IBI_PHY (0. 27, 84. 2 • ). Regarding surface current speed at Tarragona deep-water buoy, significant improvement in the correlation can be found (0. 35 (SAM_ADV) vs. 0. 36 (SAM_H3D) vs. 0. 25 (IBI_PHY)). RMS is similar for all three datasets (0. 16 vs. 0. 18 vs. 0. 17, respectively); but the bias is lower in IBI_PHY (0. 01 m/s), and higher in SAM_H3D (0. 05 m/s) than in SAM_ADV (0. 03 m/s). Tarragona and Gran Canaria are the two SAMOA that have coverage from Radar HF. Hence, special emphasis would be given. Error metrics have been computed at five specific points (Table  5 ), representative of the area (see their position at subpanels (c) in Figures  6  and 7 ). Figure  6  shows the monthly-averaged HF-radar measurements, model results and error metrics in Tarragona during March 2020.
Recommendations for Coastal Forecasting Services and Future SAMOA Perspectives This contribution has presented the SAMOA circulation system as a downstream service that benefits from Copernicus regional products. The authors recommend the use of products that ensure proper nesting between the parent solution and the local application. In this sense, the use of the hourly-3D IBI-MFC forecast dataset has contributed to improve the circulation and sea temperature fields (as shown in the SAM_H3D set-up). It has been shown, that the error metrics decrease when the boundary conditions have hourly-averaged resolution (be it sea-level, currents, sea temperature and salinity). This product ensures more consistency with the parent solution than the previous SAMOA releases (hourlyaveraged sea-level and barotropic currents; but daily-averaged total currents, temperature and salinity).
(i) The SAM_INI configuration, analogous to the one used in SAMOA-1, but substituting the former AEMET HIRLAM atmospheric forcing by the new HARMONIE 2. 5 km forcing data. (ii) The SAM_ADV set-up, focused on upgrading the open boundary condition scheme treatment and the computation of surface atmosphere-ocean fluxes through the COARE 3. 0 bulk formula. (iii) The SAM_H3D set-up, in which it is proposed (a) the use of the hourly-3D IBI-MFC forecast dataset, (b) the update of the bulk formula for surface fluxes (COARE 3. 5), (c) the use of a new advection scheme (HSIMT-TVD) for passive tracers and (d) the reduction of the wind-stresses due to the effect of surface currents.
Data Availability Statement: The SAMOA circulation forecast data and the observations used in this contribution can be downloaded from Puertos del Estado OpenDAP catalog: (https://opendap. puertos. es/thredds/catalog. html, accessed on 18 January 2022). The Copernicus Regional IBI-MFC solution can be downloaded at Copernicus Marine Environmental Monitoring Service (CMEMS) (http://marine. copernicus. eu/, accessed on 18 January 2022).
Acknowledgments: The authors acknowledge support from the SAMOA-2 initiative (2018-2021), co-financed by Puertos del Estado (Spain) and the Spanish Port Authorities. This contribution has been conducted using E. U. Copernicus Marine Service Information. Specifically, from its NRT forecast products at the IBI area. Likewise, ocean in-situ and HF-radar observations from the Puertos del Estado monitoring network are also duly acknowledged.
Thereby, NF-κB dimers such as p65 (RelA) and p50 are activated and migrate into the nucleus which results in gene transcription and the induction of inflammatory cytokines. Adapted from Heller S, Kölsch U, Magg T, et al. T Cell Impairment Is Predictive for a Severe Clinical Course in NEMO Deficiency. J Clin Immunol. 2020;40(3):421-434. Used with permission. IκBα, NF-κB inhibitor α; IKK, IκB kinase; IL-1R, interleukin-1 receptor; IRAK, interleukin-1 receptor-associated kinase; LPS, lipopolysaccharide; MyD88, myeloid differentiation primary response 88; TAB, TAK1-binding proteins; TAK-1, TGF-β-activated kinase 1; TLR, toll-like receptor; TRAF, TNF receptor-associated factor. The canonical NF-κB pathway can be disturbed through disease-causing mutations within multiple genes. Described correlations between phenotype and genotype are listed in the OMIM database (OMIM numbers: IκBα*164008, IKKα*600664, IKKβ*603258, IKKγ*300248, IRAK1*300283, IRAK4*606883, MyD88*602170, p50*164011, p65*164014).
Funding This work was supported by the  ANR (Agence Nationale de la Recherche ) for funding the  FEFS  project (contract number  ANR-18-CE21-0010 ) and the  Region Hauts-de-France  for funding the  Interreg Veg-I-Tec  project ( Programme Interreg V France-Wallonia-Flanders ,  GoToS3 ).
Context. Stellar ages are key to improving our understanding of different astrophysical phenomena. However, many techniques to estimate stellar ages are highly model-dependent. The lithium depletion boundary (LDB), based on the presence or absence of lithium in low-mass stars, can be used to derive ages in stellar associations of between 20 and 500 Ma. Aims. The purpose of this work is to revise former LDB ages in stellar associations in a consistent way, taking advantage of the homogeneous Gaia parallaxes as well as bolometric luminosity estimations that do not rely on monochromatic bolometric corrections. Methods. We studied nine open clusters and three moving groups characterised by a previous determination of the LDB age. We gathered all the available information from our data and the literature: membership, distances, photometric data, reddening, metallicity, and surface gravity.
We re-assigned membership and calculated bolometric luminosities and effective temperatures using distances derived from Gaia DR2 and multi-wavelength photometry for individual objects around the former LDB. We located the LDB using a homogeneous method for all the stellar associations. Finally, we estimated the age by comparing it with different evolutionary models. Results. We located the LDB for the twelve stellar associations and derived their ages using several theoretical evolutionary models. We compared the LDB ages among them, along with data obtained with other techniques, such as isochrone fitting, ultimately finding some discrepancies among the various approaches. Finally, we remark that the 32 Ori MG is likely to be composed of at least two populations of different ages.
The availability of photometric all-sky or wide sky surveys, such as Gaia DR2 (Gaia Collaboration 2018a), SDSS  (Alam et al. 2015) , 2MASS  (Skrutskie et al. 2006) , WISE  (Cutri 2013) , or Pan-STARRS  (Chambers et al. 2017 ) has signalled the dawning of a new era and allowed us to address several of the factors mentioned above from a new perspective. The high-quality astrometric data of Gaia DR2 represent a significant advance for at least three reasons: they allow us (a) to calculate, for the first time, individual distances for each lowmass cluster member, (b) to refine the membership criteria, and in some cases (c) to determine whether an object is a multiple system. Furthermore, the use of wide-area multi-wavelength imaging surveys with ranges from ultraviolet to infrared allows us to calculate the total flux of each object without assuming any monochromatic bolometric correction, which is a noteworthy source of systematic uncertainties in the LDB age.
Empirical bolometric corrections are derived from main-sequence stars and our objects have different log g values; in addition, bolometric corrections vary as a function of metallicity, especially for temperatures less than 3000 K  (Jeffries & Naylor 2001; Burke et al. 2004; Tognelli et al. 2015). In this work, we present, for the first time, a homogeneous analysis for all the stellar associations with former LDB ages aimed at creating a complete set of an LDB age scale. This analysis includes new photometric data in a broad range of wavelengths, individual accurate parallaxes from Gaia DR2, and the effects of metallicity and gravity. The work is structured as follows. In Sect. 2, we introduce the stellar associations under consideration. We provide information of the data and A70, page 2 of 64 evolutionary models used in Sect.
8 Å (see  Fig. A. 2 from Bayo et al. 2011). Unfortunately, the lithium data for the same cluster are not homogeneous, as they have been obtained by several authors using different analyses and with spectra with different resolving power or signal-to-noise ratio. We compiled the dataset using the astronomical databases Astrophysics Data System (ADS,  Murray et al. 1992) , SIMBAD  (Egret et al. 1991) , and VizieR  (Ochsenbein et al. 2000).
2018). We queried the Gaia DR2 5  archive without imposing any restriction, cross-matching the positions on the sky (right ascension and declination) from our sources using a 2. 5 radius. When finding two or more sources for the same input, we made certain which counterpart was the correct one and we carefully investigated the images and the entries using ESASky  (Baines et al. 2017)  and Aladin  (Bonnarel et al. 2000). We followed a different approach for the Hyades and the MGs because we lost a lot of sources in the cross-matching. This was due to two factors: (a) the proximity and high proper motion of the sources, and (b) our input positions on the sky are in epoch 2000, while Gaia DR2 uses as reference epoch 2015. 5. In such cases we identified each source using the 2MASS source name and the SIMBAD database, then we checked with ESASky and Aladin whether there was a Gaia DR2 counterpart or not.
Photometry We gathered photometric data from the literature. For each association, these came from studies carried out with different telescopes and instruments and, therefore, with heterogeneous uncertainties and different completeness and limiting magnitudes Additionally, we gathered data from all-sky or wide-area multi-wavelength imaging surveys, with ranges from the ultraviolet to the infrared, using, whenever possible, the Virtual Observatory SED Analyzer (VOSA,  Bayo et al. 2008) , together with the SIMBAD and VizieR databases. We cross-matched the celestial position of each source with the catalogues provided using a matching radius of 3. 0. The catalogues adopted for the analysis are the following: Tycho-2  (Høg et al. 2000)  B T V T bands; 2MASS  (Cutri et al. 2003 ) JHK s bands; CMC 14  (Evans et al.
NGC 1960 (M 36) We created the LDB sample collecting 32 members from  Jeffries et al. (2013). We used gri photometric data from  Jeffries et al. (2013) , and from our database  (Galindo-Guil et al. , in prep. ). The source 4165 is the only one without a counterpart in Gaia DR2. Candidate members identified as 3028, 3590, and 3596 have counterparts, but lack parallaxes and proper motions, and the source 3081 possesses negative parallax. Jeffries et al. (2013)  point out that 3080 shows evidence of being a binary system, and 3081 (the brightest object with lithium) could have depleted the ∼99% of its initial lithium content. From the VPD (Fig. A. 1 left), we considered as nonmembers  2188 , 2249 , 2663 , 3080, and 3150. Other sources, namely, 1859 , 1871 , 2696, and 3073, and 3073 , are inside the bulk of the confirmed members by the narrowest of margins.
The Pleiades We selected 35 sources from different works:  Marcy et al. (1994) ;  Rebolo et al. (1996) ;  Oppenheimer et al. (1997) ;  Stauffer et al. (1998b,a)  and  Dahm (2015) , together with photometry and lithium quantities from the DANCe database  (Bouy et al. 2015; Barrado et al. 2016 and other works: Steele et al. 1993; Bouvier et al. 1998; Zapatero Osorio et al. 1999; Martín et al. 2000; Stauffer et al. 2007 ). With the exception of Roque 5 and Roque 25, the rest of the sources have a counterpart in the Gaia DR2 catalogue; and with the exception of CFHT-Pl-18 and MHObd 3, all the sources have parallaxes and proper motions 11. Olivares et al. (2018)  studied the probability of a source being an equal-mass binary and eight sources fulfil their criterion: HCG 509, HHJ 6, HHJ 3, PPL-15, PPL-15, Roque 13, MHObd 3, CFHT-Pl-12, and CFHT-Pl-16.
A. 14a), as previously suggested  Stauffer et al. (1998b). Two sources, MHObd 1 and MHObd 3, present low signal-to-noise ratio lithium detections  (Stauffer et al. 1998b,a) , which would indicate that they are brown dwarfs or, at least, transition objects that have burned most of its initial lithium. IPL 43 has a low signal-to-noise ratio spectrum near 6700 Å that precludes an assessment of 11 CFHT-Pl-9 and MHObd 6 are the same source  (Stauffer et al. 1998b and Bouvier et al. 1998 ) even though the SIMBAD database treats them as two independent sources. We carried out a visual check of the charts from  Stauffer et al. (1998b)  and  Bouvier et al. (1998)  to confirm it. Also the counterpart of CFHT-Pl-10 in 2MASS catalogue is 2MASS J03443231+2525181. A70, page 14 of 64 the lithium equivalent width  (Dahm 2015).