dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017; Merryfield et al. , 2020). Their S2S project has the study of land initialization and configuration as one of its major activities. The LS4P research activities to address these scientific challenges are consistent with those of the WWRP/WCRP S2S project. The LS4P activity is also closely related to the TPE program. The TPE has closely worked with LS4P to provide and maintain a database to support this project, which is discussed in Sect. 3. 1 and Appendices C and D. The first phase of LS4P will be a joint effort with the TPE Earth System Model Inter-comparison Project (TPEMIP), which focuses on regional-scale Earth system modeling over the high-elevation Tibetan Plateau region. The LS4P initiative is also relevant to GEWEX Global Land Atmosphere System Study (GLASS) panel objectives because estimating the contribution of land memory to atmospheric predictability from convective to seasonal timescales is one of its main themes.
Observational data for LS4P Phase I (LS4P-I) The observational data provide the foundation for the LS4P research, are used for the LS4P model initialization of surface and boundary conditions, validation, and other relevant research activities, and are listed in Appendix B. Moreover, there are large amounts of observational data available in the Tibetan Plateau area, which are produced by the data groups, which are participating in LS4P and are available for the community to conduct further LS4P-related research, such as studying the causes of the LST/SUBT anomalies, the char-acteristics of the surface and atmospheric processes in the Tibetan Plateau, etc. The TPE has conducted comprehensive measurements over the Tibetan Plateau for more than a decade and has integrated the observational data into the National Tibetan Plateau Data Center  (Li et al.
LS4P does not require a specific number of ensemble members: each modeling group makes the decision based on their normal practice in performing their S2S simulations; When calculating the bias, it should be noted that the elevations of the T -2m observational data and model surface are usually not at the same levels, especially in high mountain regions. The observing stations tend to be situated in valleys and are generally at a lower elevation than the mean elevation of a model grid box. Before calculating the model bias, the model-simulated T -2m data must be adjusted with a proper lapse rate to the elevation height of the observational data as discussed in  Xue et al. (1996a)  and  Gao et al. (2017). The relationship between these two biases is evaluated to see whether they are consistent with the observed lag anomaly relationship, i.
Please note that there are no observed daily land surface temperature data available over the globe. The Tobs anomaly and Tbias are the averaged observed anomaly and model bias, respectively, over the entire area where the mask is intended to be applied, such as the Tibetan Plateau. Equation (  1a ) is applied for the situation when observed anomaly and model bias have the same sign, while Eq. (  1b ) is used when observed anomaly and model bias have different signs, regardless of whether the anomaly is positive or negative. Figure  2  shows schematic diagrams for imposed masks for surface temperature initialization under different conditions, which delineates the concept for the mask formulation. In this figure, a cold year (such as year 2003 that is used in the LS4P Phase I) is selected for demonstration.
Data uncertainty Observational T -2m/LST/SUBT data are crucial for model initialization of surface conditions and for model validation. However, ground measurements over high-elevation areas  Compared with the CMA data, the CAMS/CRU climatology is about 1. 8 • C cooler/1. 5 • C warmer, respectively. The biases for warm/cold years are even larger for CAMS/CRU (not shown), respectively. While the climatological bias for CFSR data is small, the bias for ERAI is still on the order of 1 standard deviation of the Tibetan Plateau T -2m variability (∼ 0. 7 • C). The bias is larger in MERRA-2, at about 4 • C. In addition, for cold/warm years, MERRA-2 and CFSR show opposite anomalies. The large surface temperature biases in the reanalysis data sets likely interact with temperature of the lower atmosphere.
Approaches to improving the LST/SUBT initialization and T -2m anomaly simulation In addition to the data that are used for LST/SUBT initial conditions, land models also have deficiencies in maintaining the anomalies that are imposed using an initial mask as discussed in Sect. 3. 2. In the LS4P-I experiment, most models are only able to partially produce the observed T -2m anomaly in May despite the imposed initial masks. The recent available daily Tibetan Plateau surface data from the LS4P data group show our imposed initial anomaly is not extreme, but models lost the imposed anomaly rather quickly. This section highlights some specific approaches undertaken by a few groups during their application of the LS4P-I protocol to improve the T -2m anomaly simulation. The surface soil (20-30 cm) in the central and eastern Tibetan Plateau contains a large amount of organic matter which greatly reduces the soil thermal conductivity and increases the soil heat capacity  (Chen et al.
g. , century-long simulations), realtime initialization for S2S prediction is not very closely related to the research objective the model centers intend to pursue. To conduct LS4P-type research, the modeling groups have to develop an approach in nudging the reanalysis data https://doi. org/10. 5194/gmd-14-4465-2021 Geosci. Model Dev. , 14, 4465-4494, 2021 for a real-time initialization. Nudging is one of the simplest data assimilation methods  (Hoke and Anthes, 1976)  and has been widely used in climate model evaluation and sensitivity studies (e. g. ,  Xie et al. , 2008; Sun et al. , 2019; Tang et al. , 2019)  to constrain the simulations towards a predefined reference (the reanalysis data in this case) and hence to facilitate time-specific comparisons between model and observations. For the LS4P simulations, E3SM and CESM2 used 1 month worth of nudging of the horizontal wind components (U and V ) with a 6 h relaxation timescale before the land mask for the initial LST perturbation was applied.
Although the T -2m anomaly covers large areas, our previous North American study has shown that only the LST/SUBT anomaly over high mountains (the Rockies) had a substantial impact on the subsequent drought over the South Great Plains  (Xue et al. , 2012). One of the LS4P groups, KIM, also tested the effect of the LST anomaly in other parts of East Asia but found their effects are incompatible with the Tibetan Plateau LST/SUBT effect. In addition to the year 2003, we also checked the May T -2m and June precipitation bias in the climatologies of the different models. The 13 ESMs shown in Fig. 6  have also provided their climatological data sets. Figure  7  shows the climatological biases for May T -2m and June precipitation from these ESMs. The patterns between the bias in the 2003 simulation and the bias in the model climatologies are generally consistent, which is important, because the climatological bias is substantial and affects the individual years as well.
In Phase I, through the LS4P RCM efforts in incorporating the TPE and TIPEX-III data, we also intend to simulate the water and energy cycle and atmospheric conditions in the Tibetan Plateau and their variability. These simulations will provide the data for better atmospheric and surface initialization along with obtaining an improved understanding of the atmospheric circulation and water cycle in the "Tibetan Water Tower". Thus far, the discussion has been focused on the modeling approach. A recent statistical study has shown that spring soil temperature in central Asia could be a predictor of summer heat waves over northwestern China  (Yang et al. , 2019). In addition, surface temperatures from five northern European observing stations have been used as predictors for longrange forecasting of monsoon rainfall over southwestern India  (Rajeevan et al.
However, we encourage/welcome different approaches to tackle this issue and for comparison with the approach presented in this study. One issue that hampers the application of the LST/SUBT approach for S2S prediction is data availability. The TPE has conducted comprehensive measurements over the high mountain Tibetan Plateau areas, which include a plateauscale observation network plus intensive networks at more local scales: these data consist of boundary-layer observations and land surface and deep-soil-layer measurements. These measurements have provided invaluable information to support the establishment of the LS4P and to foster further model development and the possible causes of land memory. Currently, such comprehensive measurements over high mountain areas are still lacking across the globe.
A. Uploading data into the National Tibetan Plateau Data Center using Filezilla To upload data into the National Tibetan Plateau Data Center, we recommend using "Filezilla". With Filezilla, the host, username and password are generated automatically for the Filezilla when the data are uploaded. The following procedure is based on "Filezilla". The procedure will utilize the following steps. 1. Log into the online National Tibetan Plateau Data Center (http://data. tpdc. ac. cn/en/, last access: 1 June 2021) using the aforementioned login details (see II). Login name: LS4P_group. 2. Go to "LS4P_group"/"personal center"; select "My Data" on the left bar, and then select "Submit Data". 3. You will see the webpage "CREATE META-DATA". Please fill in your data information, such as (i) overview (title, abstract, data file naming, file size, time range), (ii) reference, and (iii) keyword(s).
After completion, click "Save" to save the information. 4. Then select "Data Files". A new page will pop up, where you will find (i) the host ip address, (ii) the port number, (iii) the username, and (iv) the password to use for Filezilla. 5. On your local site, such as NCAR Cheyenne, open Filezilla at the directory where the data you would like to upload are located. Please use the information from (4) to remotely access the data center via Filezilla. 6. You will be at the root directory. The root directory is empty, and you need to create a folder using the naming method mentioned in (I), for example, UCLA_CFS-SSiB2 under the "root directory". If you have created the folder before, you will find it when you log back. 7. Then, from your Filezilla window, you can drag your data from your local site to the newly created folder/subfolder, such as Task1.
8. Send an email to Duo at panxd@itpcas. ac. cn. Then she will synchronize the data for you directly. 9. Click "submit" to submit the online data in the window which appeared in step 3. 10. Duo will send you a confirmation email to confirm/acknowledge the proper submission. By that time, you should be able to see your data. In case there is any problem/question, please contact Duo (panxd@itpcas. ac. cn) with cc to Ismaila (idiallo@ucla. edu) for help. B. Acquiring LS4P-I project data a. Log in to the online National Tibetan Plateau Data Center (http://data. tpdc. ac. cn/en/, last access: 1 June 2021), using the aforementioned login details (see II). b. Go to "LS4P_group"/"Personal Center". c. Select "My Data", and then select "Review" or "My Draft". d. You will see all the metadata belonging to the LS4P group.
Acknowledgements. LS4P is a project of the  Global Energy and Water Cycle Experiment (GEWEX) Global Atmospheric System Study (GASS)  under the auspices of the  World Climate Research Programme (WCRP). We appreciate the support of the  Pan-Third Pole Environment (Pan-TPE) program  (grant no. XDA20100000 ), the  Second Tibetan Plateau Scientific Expedition and Research (STEP) program  (grant no. 2019QZKK0200 ), the  U. S. National Science Foundation  (grant no. AGS-1849654 ), and the  U. S. DOE   E3SM  project at  LLNL  (contract no. DE-AC52-07NA27344 ) in organizing and coordinating the LS4P activity. Each  LS4P-I model group 's efforts are supported by the participants' home institutions and/or funding agencies. We also thank  Paul Dirmeyer  of the  Center for Ocean-Land-Atmosphere Studies, George Mason University ,  Thomas M.
Smith  of the National Environmental Satellite, Data, and  Information Service/NOAA ,  Catalina Oaida  for preparing the manuscript,  Matt Zebrowski 's technical assistance for this article, and  René Orth  of the  Max Planck Institute for Biogeochemistry  for providing very constructive and critical comments and detailed suggestions. Financial support. This research has been supported by the  National Science Foundation  (grant no. AGS-1849654 ), the  Pan-Third Pole Environment (Pan-TPE) program  (grant no. XDA20100000 ), the  second Tibetan Plateau Scientific Expedition and Research 45 (STEP) program  (grant no. 2019QZKK0200 ), and the  U. S. DOE.
Data availability. The LS4P data are stored by and will be distributed through the National Tibetan Plateau Data Center (Li et  al. , 2020, http://data. tpdc. ac. cn/en/) and the U. S. Lawrence Livermore National Laboratory (LLNL) Data Center Earth System Grid Federation (ESGF) node (Cinquini et al. , 2014, https://esgf-node. llnl. gov/projects/esgf-llnl). The evaluation/reference data sets from CAMS, CFSR, CMA, CRU, ERA-Interim, MERRA-2, and NARR as well as model data discussed in this paper are archived at https://doi. org/10. 5281/zenodo. 4383284 (Xue and Diallo, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-14-4465-2021-supplement.
Task 2 No 1981-2010 (climatology) Task 2 is the ESM climatology. Only major climate re- search centers provide this data set. Task 3 Yes Two months (late April-30 June 2003) Task 3 is the same as Task 1, but the mask is imposed over the Tibetan Plateau at the first time step of the ESM integration. Task 4 No Two months (late April-June 2003) Task 4 is the same as Task 1, but here the 2003 SST is replaced by the climatology (1981-2010) SST. and June precipitation in eastern China is just a necessary condition for LS4P to pursue its approach, i. e. , to propose a hypothesis. It is not sufficient to guarantee the model can improve the June precipitation prediction by using improved May T -2m initial conditions. Only Task 3, as discussed below, will serve this purpose. 3. 2. 2 Task 2A number of LS4P modeling groups are from big climate modeling centers, and, as such, already have the required climatological runs in their respective databases.
ATCC14028s can be purchased from the ATCC or under different designations from the National Collection of Type Cultures in London (NCTC 12023), the DSMZ in Braunschweig  (DSM 19587)  or the Collection de l'Institut Pasteur in Paris (CIP 104115). The same strain is also listed in the WFCC-Mircen World Data Centre for Microorganisms (WDCM 00031) (http://refs. wdcm. org/strainid. htm?p=1), and can be purchased from providers of laboratory chemicals (e. g. Sigma-Aldrich, Lenticule Disc). Sub-cultures from these various sources are also maintained at multiple universities and microbial diagnostic facilities around the globe, and are used as quality control standards for growth of Salmonella on laboratory media (ISO 6579-1:20170), including ring trials of laboratory proficiency. ATCC14028s has also been used to test the effects of mutational changes on infection and disease in animal models ('laboratory evolution')  [2] [3] [4].
ATCC14028s was the microbiological agent used for bioterrorism in 1984 by followers of Bhaghwan Shree Rashneesh who contaminated salad bars in multiple restaurants in Oregon with S. enterica in a failed attempt to influence local elections  [5]. 751 human cases of salmonellosis ensued whose bacterial isolates resembled ATCC14028s by microbiological criteria  [6]. Similar to the spread of Salmonella serovar Typhi over decades by Mr. N the milker  [7]  or Typhoid Mary  [8] , feces shed by these infected individuals into the sewage system could have URLs and accession codes. An EnteroBase workspace containing all 498 strains and their genomes and metadata can be found at http:// enterobase. warwick. ac. uk/a/44709. Links for public access are included in the figure legends. The nucleotide sequences and gene annotations of the genomic islands can be downloaded from figshare (https://dx.
The final dataset consisted of 156 ATCC14028s derivatives (Table  1 ). We stored them in Entero-Base as an Uberstrain  [15]  (GCF_000022165; EnteroBase barcode SAL_EA9729AA) with 155 sub-strains. Their cgSTs cluster in HierCC HC20_373, and the vast majority cluster in HC5_373 (140/155; 90%) or HC10_373 (152/155; 98%). Twenty percent of all pairs of these genomes were indistinguishable according to non-repetitive, single nucleotide polymorphisms (SNPs) in the core genome  (Fig 1A). The other 80% of the pairwise comparisons formed a fattailed distribution with a peak at 3 SNPs and a maximum of 16 SNPs. A fat-tailed distribution of core genomic diversity might represent the accumulation of mutations during laboratory storage and passage since 1960. Alternatively, such a distribution might reflect sequencing errors between different laboratories, or even between repeated sequencing in a single laboratory.
Map and pie charts were generated using the open-source D3. js  [65]  library GeographicLib  [66]  with the MIT/X11 license at https://github. com/ d3/d3-geo/blob/main/LICENSE. https://doi. org/10. 1371/journal. pgen. 1009820. g003 corn soy blend), rivers and lakes (Salinas river x 2; Salinas lake x 1) and humans (13 strains) in diverse states of the U. S. Other strains isolated in the early 2000s from global sources had been sequenced as part of the UoWUCC 10K genomes project  [14] , including several from humans in Ireland and Taiwan. We thereupon sequenced 22 additional genomes (Ireland: 14; Taiwan: 8) from later isolates from human salmonellosis in those two countries which had the same PFGE patterns; almost all fell into HC20_373. Lab infections with ATCC14028s were identified by the metadata associated with the genomes and by literature citations  [9, 10].
Comparisons of HC20_373 with other HC20 clusters HierCC is a recent development  [16] , and HC20 clusters have not yet been extensively characterized. We therefore investigated the general properties of Salmonella HC20 clusters to determine whether HC20_373 was unusual. The numbers of HC20 clusters in the EnteroBase Salmonella database are inversely correlated with the numbers of genomes per cluster, dropping from >31,000 HC20 clusters each containing only a single genome down to a single cluster (HC20_2; monophasic Typhimurium) containing >16,500 genomes (Figs  5A  and S1 ). HC20_373 belongs to a select group of 69 HC20 clusters which contained >400 genomes (Fig 5B) (0. 15% of 45,964 clusters; June, 2021). HC20_373 is also highly exceptional because it demonstrated less pairwise allelic diversity than all but one of the other large HC20 clusters (Fig  5B ).
For example, NCTC sold 125 ampoules of NCTC 12023 during 2016-2021, and CIP sold 46 ampoules of CIP 104115 during 2015-2021. The strain is also commercially available from Sigma-Aldrich. ATCC14028s genomes in the public domain include sequences from variants that were reisolated after laboratory experiments on natural selection, including long-term exposure to antibiotics or other forms of stress. Other variants were sequenced after experimental mutagenesis with transposon vectors and/or passage through experimental animals. Many Bayesian BEAST  [40]  temporal dating of ATCC14028s and its natural derivatives. The figure depicts a tipdated tree of 419 genomes from HC20_373 whose metadata included a tip date and which were not outliers in preliminary analyses (Table  4 ), rooted with the same outgroup genome from HC20_147 described in Fig 4.
Microevolution of the accessory genome Nine percent of natural isolates acquired a plasmid or bacteriophage after their escape from the laboratory, predominantly among bacteria isolated from infections of humans, poultry, and animals other than swine. None of their progeny seemed to be particularly fit because none became common. Occasional HGT of plasmids and lysogenic bacteriophages has also been described previously for serovar Agona  [55]. In the absence of data to the contrary, it is possibly safest to interpret such occasional HGT as marking the spread of selfish genes rather than the effects of natural selection for particular phenotypes. However, it might be interesting to investigate the frequency of these plasmids within databases of all conjugative plasmids that are currently being established  [56].
Bacterial isolates In May 2020, the EnteroBase Salmonella database  [15]  contained >255,000 assembled genomes. We used the Search functions in EnteroBase (https://enterobase. readthedocs. io/en/ latest//features/main-search-page. html) to identify all genomes assigned to HC20_373. Two exceptionally divergent draft genomes from laboratory derivatives were excluded from further analysis because the assemblies were of poor quality and others were excluded because their origins were uncertain (S1 Table ). The remaining 498 entries were saved as a publicly accessible workspace (http://enterobase. warwick. ac. uk/a/44709). The metadata were curated manually to ensure that they accurately reflected the original information in ENA. 155 genomes were assigned to the category ATCC 14028s derivative as described in Results.
We also assigned 17 genomes to laboratory infections due to publications  [9, 10]  (11 genomes), epidemiological investigations in France (3 genomes; see results) and/or based on public metadata (3 genomes). Other genomes were assigned to the other categories used to color-code Fig  4 , and all these assignments were stored in the "14028s" user-defined field within EnteroBase  [15]. Genomes assigned to "ATCC14028s derivative" were stored as substrains of Uberstrain SAL_EA9729AA, which has the Name 14028s and corresponds to the complete genome with accession number CP001363. 1 which was sequenced by Jarvik et al. [1]. On loading this workspace into an internet browser, this Uberstrain is collapsed by default and needs to be expanded by clicking on a triangle symbol in the Uberstrain column.
Genomic assembly and cgMLST assignments cgMLST assignments and HierCC clustering are performed automatically on each new genome by EnteroBase  [15]. Ten of the 498 genomes in HC20_373 had been downloaded directly from NCBI as complete genomes and the complete genome of NCTC 12023 Colindale (EnteroBase strain barcode SAL_FB4645AA) was assembled from long reads and polished according to short read sequences (S1 Text), whereas the remaining 487 genomes had been assembled by the EBAssembly pipeline V4. 1 implemented in EnteroBase  [14]  from short reads which had been downloaded from NCBI (453 genomes) or uploaded by EnteroBase users  (33). The backend pipeline of EnteroBase also automatically generates genomic annotations of all genomes with Prokka against a consistent gene naming scheme, and makes them publicly available for downloading, including all genomes referred to in this publication.
Core genome phylogeny The EToKi 'align' module  [15]  was used to remove repetitive sequence stretches and generate an alignment of the relaxed core genome against the reference genome of 14028s (CP001363. 1) for all genomes in HC20_373 plus an outgroup genome from HC20_147 (strain "SAP17-7699"; EnteroBase strain barcode SAL_AB1180AA). The alignment contains 4. 73 MB that are shared by at least 95% of the genomes and 462 SNPs. A RAxML v. 8. 2. 4 maximum-likelihood phylogeny of these 462 core SNPs was constructed with the 'EToKi phylo' module  [15] , and visualized with GrapeTree  [57]  (Fig 4 ; public interactive access at http://enterobase. warwick. ac. uk/a/54094).