dataesr/scientific-paragraphs-categorization huggingface dataset:
Growth rate The middle panel of Fig. 3  shows that while models capture the interannual variability of the growth rate quite well, they overestimate the mean growth rate compared to the observations. The correlation coefficient for the multi-model mean is at 0. 48 in CMIP6 and 0. 07 in CMIP5 which shows a large improvement in this area. The pronounced feature in 2009 is due to the introduction of the GOSAT data which changed the shape of the seasonal cycle and thus due to its calculation the monthly resolved annual growth rate. Fortunately, this feature gets averaged out when computing the annual growth rate and does not tangibly affect Comparison of time series from satellite XCO 2 (black), multi-model mean XCO 2 (orange) and surface CO 2 (red), and NOAA surface CO 2 station data (blue) at selected sites, with the coordinates noted in brackets above the time series and the altitudes shown in the map plot (see Table  1 ).
The multi-model mean for both XCO 2 and surface CO 2 was offset to have the same average value as the satellite XCO 2 for better comparison, and this offset is noted above each time series. CMIP6 and CMIP5 multi-model means are shown in the top (a) and bottom (b) panels, respectively. our conclusions. Figure  4  shows the global mean GR of XCO 2 for 2003-2014 and its standard deviation over all years depicted as error bars, with the observations shown in black and the multi-model mean in red. The annual mean GR of the satellite data is 1. 9 ± 0. 4 ppmv yr -1 , while the CMIP5 models (right) range from 1. 5 ± 0. 4 (MRI-ESM1) to 3. 0 ± 0. 9 ppmv yr -1 (CanESM2) with a multi-model mean of 2. 4 ± 0. 4 ppmv yr -1. In CMIP6 (left), the multi-model mean is slightly lower at 2. 3 ± 0. 3 ppmv yr -1 , and the spread has decreased by 0.
(2013)  to develop an emergent constraint on the sensitivity of tropical land carbon to climate change but were unable to find a  significant trend for this much shorter satellite-derived time series. The spatial variability of the GR is small, as CO 2 is long lived and well mixed in the atmosphere with a 1-year mean interhemispheric crossing time. Thus, there should be no significant regional changes on an annual level. Buchwitz et al. (2018)  found the growth rate of the satellite dataset to be in agreement with those quoted by NOAA ESRL's global and Mauna Loa time series, as well as robust over several latitude bands. Our own analysis also shows only very small regional differences in the growth rate (not shown). No significant changes to the annual growth rates due to the satellite spatial coverage were found.
While this effect was minimized by imposing the restriction of only computing the SCA of a year when at least 7 months of data are available, it is still a possibility. We therefore classify this SCA as an "effective SCA". However, the SCA does not seem to decrease significantly through sampling and the difference does not follow a trend in latitude, so a grid box comparison seems feasible. This paves the way for more comprehensive spatial investigations, which previously relied on data from ground-based stations with sparse spatial coverage. While the stations provide data in higher latitudes that the satellite dataset does not cover, in the tropics and midlatitudes the spatial coverage of the satellite is superior to the ground-based stations. A downside with this approach is the sparsity of the data when using observational sampling.
Furthermore, this becomes a com-putationally expensive operation, as the SCA will need to be calculated for each grid box. Another approach often used in model analysis is area averaging, e. g. , over different latitude bands like the tropics or the northern midlatitudes. Using surface flask measurements,  Wenzel et al. (2016)  found an increased SCA with rising CO 2 concentration for CMIP5 using model data from the full historical simulation (1850-2005) -CO 2 fertilization -and used this to establish an emergent constraint on the fertilization of terrestrial gross primary productivity (GPP). trend in the unsampled models, while the right one shows the trend when following the sampling of the satellite data. The SCA was computed after a weighted area average was determined on the XCO 2 time series.
Above 50 • N, SCIAMACHY measurements include more areas with 50 % or more coverage compared to GOSAT measurements. With a larger SCA in higher latitudes, it implies that SCIAMACHY would have a larger average SCA over this region compared to GOSAT, hence artificially generating a decreasing trend in the observed SCA, when moving from SCIAMACHY to GOSAT. Figure  9  shows the CMIP6 effective SCA trend with XCO 2 using SCIAMACHY and GOSAT masks obtained from masking out points with less than 50 % coverage. While the slopes remain largely the same, the mean effective SCA is higher in the models using the SCIAMACHY mask than when using the GOSAT mask. This mean SCA difference is larger than the total SCA difference within a model using the same sampling for the whole time period. Thus, when considering the observational time series and its full sampling, the trend intrinsic to the model is dominated by the negative SCA difference going from the SCIAMACHY to the GOSAT data coverage and thus changed to the negative trend seen in the observations.
Summary and conclusion In this paper, we have evaluated the performance of CMIP5 and CMIP6 ESMs with interactive carbon cycle (Tables  2  and 3 ) against column-integral CO 2 (XCO 2 ) data from satellite retrievals. Our analysis has compared ESM simulations to the 2003-2014 Obs4MIPs XCO 2 satellite dataset O4Mv3 retrieved from radiance spectra measured by the SCIA-MACHY/Envisat  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  and TANSO-FTS/GOSAT (2009-2014) satellite instruments. The O4Mv3 data product has a spatial resolution of 5 • × 5 • and monthly time reso-   [2003] [2004] [2005] [2006] [2007] [2008] [2009] [2010] [2011] [2012] [2013] [2014]. The evaluation of the CMIP models with the satellite data focused on the time series, the GR and the SCA XCO 2. All SCAs computed with a masked time series are considered to be "effective" SCAs due to the possibility of masking out peaks and troughs.
Finally, the connection between SCA and XCO 2 was investigated in the northern midlatitudes. Most models from both ensembles show a positive trend, i. e. , an increase of the SCA with XCO 2 , consistent with findings for surface CO 2  (Wenzel et al. , 2016). However, the satellite product shows a strong negative trend in contrast to the models and surface based observations. We have attributed this trend reversal to the sampling characteristics of the satellite products. The average effective SCA is higher for models sampled according to the SCIAMACHY/Envisat as opposed to the TANSO-FTS/GOSAT mean data coverage. As the early time series is based solely on the SCIA-MACHY/Envisat data and the last years only use data from TANSO-FTS/GOSAT, this introduces an artificial negative trend which dominates the positive trend shown by the unsampled models.
, 2017) , or the parameterizations of biological and physical processes not allowing the system to change rapidly enough  (Hoffman et al. , 2014). Along with a longer time series, newer satellites, such as OCO-2 or the planned Sentinel 7 bring higher resolutions and more data, potentially helping to fill the gaps and reduce the impact of the sampling we discussed in Sect. 5. 3. 2. Overall, the CMIP6 ensemble shows improved agreement with the satellite data in all considered quantities (mean XCO 2 , growth rate, SCA and trend in SCA), with the biggest improvement shown in the mean XCO 2 content of the atmosphere. The paper demonstrates the great potential of satellite data for climate model evaluation as this allows us to go beyond regional means or single point observations from in situ data and also enables the investigation of regional effects on SCA, such as the increase in SCA at higher latitudes.
37 ± 0. 71 ppmv yr -1 K -1 (CanESM5-CanOE) and a multi-model mean of 1. 14 ± 0. 37 ppmv yr -1 K -1. Code and data availability. The O4Mv3 XCO 2 data product is available via the Copernicus Climate Change Service (C3S, https: //climate. copernicus. eu/, Reuter, 2018) Climate Data Store (CDS) (https://cds. climate. copernicus. eu/, Reuter, 2018) , accessed August 2018. The surface flasks measurements were obtained online (ftp://aftp. cmdl. noaa. gov/data/trace_gases/co2/flask/surface/, Dlugokencky et al. , 2018) , last access: August 2018. Surface temperature anomalies were obtained from the GISTEMP Team, 2020: GISS Surface Temperature Analysis (GISTEMP) version 4, NASA Goddard Institute for Space Studies dataset at https://data. giss. nasa. gov/gistemp/ (GISTEMP Team, 2020, last access: 13 February 2020).
The MODIS IGBP land cover type classification was obtained from http://glcf. umd. edu/data/lc/, Friedl et al. , 2010b , last access: 31 January 2018). CMIP5 data are freely and publicly available from the Earth System Grid Federation (ESGF,  Williams et al. 2009) , with CMIP6 data DOIs given in Table  3. ESMValTool v2. 0  (Eyring et al. , 2020; Lauer et al. , 2020; Righi et al. , 2020)  is released under the Apache License, VERSION 2. 0. The latest release of ESMValTool v2 is publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 3401363 (Andela et al. , 2020a). The source code of the ESMValCore package, which is installed as a dependency of the ESMValTool v2, is also publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 3387139 (Andela et al. , 2020b). ESMValTool and ESMValCore are developed on the GitHub repositories available at https://github.
com/ESMValGroup, last access: 30 November 2020. The corresponding recipe that can be used to reproduce the figures of this paper will be included in ESMValTool v2 at the time of publication of the paper. Author contributions. BG led the writing and analysis of the paper. MB and MR provided the satellite dataset. VE, PC and PF contributed to the evaluation of the CMIP simulations. All authors contributed to the writing of the manuscript. Competing interests. The authors declare that they have no conflict of interest.
Figure2. Comparison of time series from satellite XCO 2 (black), multi-model mean XCO 2 (orange) and surface CO 2 (red), and NOAA surface CO 2 station data (blue) at selected sites, with the coordinates noted in brackets above the time series and the altitudes shown in the map plot (see Table1). The multi-model mean for both XCO 2 and surface CO 2 was offset to have the same average value as the satellite XCO 2 for better comparison, and this offset is noted above each time series. CMIP6 and CMIP5 multi-model means are shown in the top (a) and bottom (b) panels, respectively.
Acknowledgements. This work has been supported by  ESA Climate Change Initiative  ( CCI) Climate Modelling User Group (CMUG)  project, the  Horizon 2020  project  Climate-Carbon Interactions in the Current Century  ( 4C ) funded by the  EU  (grant agreement no. 821003 ) and the  EVal4CMIP  project funded by the  Helmholtz Society. The generation of the satellite XCO 2 data product has received funding from  ESA  ( GHG-CCI ) and the  EU (Copernicus Climate Change Service -C3S , led by ECMWF). We acknowledge the  World Climate Research Programme (WCRP) , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP6. We thank the climate modeling groups (listed in Tables 2 and 3 of this paper) for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access and the multiple funding agencies who support CMIP and ESGF.
We also thank both anonymous reviewers for their helpful comments on this paper and the handling editor,  Trevor Keenan , for taking on this paper. Financial support. This research has been supported by  Horizon 2020  (grant no. 4C(821003) ), the  European Space Agency  ( ESA Climate Change Initiative Climate Model User Group, ESA CCICMUG ) and the  Helmholtz Association  ( Advanced Earth SystemModel Evaluation for CMIP, EVal4CMIP ). The article processing charges for this open-access publication were covered by the  University of Bremen. Review statement. This paper was edited by  Trevor Keenan  and reviewed by two anonymous referees.
e. , only death processes). Herein, we will not derive the full theoretical background of the models, but we will focus on the technical aspects of the package design and the integration of the MC methods. Such a presentation was beyond the scope of previous articles (e. g. ,  Pagonis et al. , 2019 Pagonis et al. , , 2020)) , but it is likely of interest to a broader community. We structured our contribution as follows. The introduction continues with a brief paragraph on luminescence and the term 'cold light'. After that, we detail the rationales for our contribution by recalling conventional modeling approaches in the field. Readers familiar with these topics may safely skip this part. The subsequent section outlines the concept and the implementation of RLumCarlo, including code examples.
Modeling code for simulating luminescence production was often written with the tools at hand, e. g. , Mathematica (e. g. ,  Pagonis et al. , 2006) , which has led to a fragmentation of incompatible solutions. In 2016,  Friedrich et al. (2016)  introduced RLumModel  (Friedrich et al. , 2020) , pooling available kinetic (non-MC) models available for the luminescence production in quartz. A tantamount suite of R code was presented simultaneously by  Peng and Pagonis (2016). We will compare results from RLumModel and RLumCarlo at the end of this manuscript. MC simulations offer an alternative and are indispensable if the simulation of defect clusters in combination with the analysis of stochastic uncertainties is desired. Usually, the underlying models are very simple, but can be combined to describe complex systems.
The concept of RLumCarlo RLumCarlo implements energy-band models in a modular approach. Each model can simulate only an isolated effect (e. g. , a single curve, see below), but the package design allows various combinations, e. g. , in the form of clusters. Hence, RLumCarlo can evolve beyond a specific mathematical model through a combination of simple models. To that end, RLumCarlo differs fundamentally from RLumModel, where the collected models allow the simulation of complex phenomena and even entire measurement sequences  (Friedrich et al. , 2016)  but are self-contained by design. In other words, simulations cannot evolve beyond a specific mathematical model selected by the user. In RLumCarlo, the implemented energy-band models can simulate only isolated effects (e. g. , a single curve, see below), but the package design allows a combination in the form of clusters.
Throughout the text, we will use the word 'clusters' to (1) ascribe virtual units used in the MC simulation to run independent random processes (henceforth MC clusters) The model consists of one trap and one recombination center. Transitions take place from the excited state into the recombination center without involving the conduction band, and the recombination rate depends on the distance between electrons and holes. All symbols are detailed in the package manual, the package vignette, and the main text. Side note: For the creation of the plots we used scales and ggplot2 (https://github. com/ JohannesFriedrich/EnergyBandModels). and (2) to define groups of defects (defect clusters) e. g. , defined by their spatial distance. Only the latter carry a physical meaning.
Conceptional overview of the implementation The basic implementation of the MC processes as a software algorithm consists of two nested loops. The outer loop iterates over a time 0 < t ⩽ t max with t ∈ R > 0. The inner part loops over particles 0 < j ⩽ n with n ∈ Z. The model tests a random number, newly sampled with replacement in each run, against a threshold P. If the sampled random number is smaller than P, the absolute number of particles is reduced by one. The code below shows the basic algorithm outlined above for the radioactive decay, which we have chosen because it can be found in standard textbooks (e. g. ,  Landau and Binder, 2015). Below we used R code for illustrative reasons, while the package implementation is written in C++. n <-1000 t <-1:100 P <-0. 2 remaining <-numeric(length(t)) for (t in t) { for (j in 1:n) { if (runif(1) < P && n > 0) n <-n -1 } if (n > 0) remaining[t] <-n } For example, the algorithm starts with 1,000 particles.
Graphical feedback Terminal output In Figure  4 , we outline the basic layout of RLumCarlo. Two design decisions stand out: (1) Each stimulation mode/energy-band model combination has its own exported R function commencing with the prefix run_MC. (2) These functions interface one C++ function, each via Rcpp  (Eddelbuettel et al. , 2020)  (for an overview, see the vignette of RLumCarlo). The R functions provide a convenient user interface, and the C++ functions constitute the workhorse, as shown in the modeling core (Figure  4 ). While the apparent reason for using C++ was speed, the implementation could have been programmed more concisely, i. e. , completely in C++ instead of interfacing C++ with R. However, we wanted to allow code inspection by non-specialists from our field, who may wish to implement other models alike.
Currently we provide S3-generics for summary() and c(). The first one is also used internally by plot_RLumCarlo() to melt the array into a data. frame before plotting. The plot output adapts to the used stimulation mode provided via an attribute with each output object. A straightforward application for this kind of simulation is the study of the impact of physical parameters on the luminescence signal output and the estimation of the stochastic uncertainties, which cannot be achieved with the deterministic approach of differential equations. We provide more, always up-to-date examples with the package vignette, where we also compiled a table with meaningful physical parameter ranges for each model.
Comparison to RLumModel In the remainder, we want to compare simulation results from RLumCarlo, with other types of solutions, such as RLumModel which uses coupled differential equations to simulate luminescence production. RLumModel was selected since it was developed by some of the authors of this contribution. However, in theory, simple scripts using any existing models to simulate luminescence should work as well (as long as the models are comparable). In contrast to RLumCarlo, RLumModel input values for physical parameters are preset. RLum-Model encourages users to write a virtual luminescence signal measurement sequence, which is processed based on a pre-defined model with preset physical parameters. For the comparison, we have selected a TL curve simulated with the luminescence model for quartz by  Bailey (2001).
Acknowledgements We thank two anonymous reviewers for their thorough reviews and constructive suggestions, which helped to improve our manuscript. Furthermore, we are grateful to the CRAN team in general for their tireless efforts in keeping such a great resource alive and in particular for their patience during the initial submission of  RLumCarlo. Alex Roy Duncan  is thanked for his support on the package development during our stay in Westminster. The development of RLumCarlo benefited from the The R Journal Vol. 13/1, June 2021 ISSN 2073-4859.
Other analytical methods 8 The future of computational metabolomics in natural products discovery 8. 1 Toward a database of annotated structural motifs 8. 2  The integration of LC-MS/MS and 2D-NMR approaches 8. 3  Pathway and taxonomy supported metabolite annotation 8. 4  Comparative metabolomics and metabolite annotation 8. 5  Structural diversity and the limitations of spectrumbased analysis 8. 6  Breaking barriers 9 Conclusions & nal perspectives 10 Author contributions 11 Conicts of interest 12 Acknowledgements 13 Notes and references.
7 Whilst spectral databases containing reference spectra are growing for both MS and NMR, the matching rates for specialized metabolites to assign complete structures to spectral data remain low. 15 Therefore, we here argue that substructure-based metabolomics workows offer an interesting and feasible alternative since they target smaller parts of the molecules that are typically easier to structurally annotate. Most specialized metabolites in complex metabolite mixtures are not independent from each other: they can share common substructures or can be part of the same biosynthetic or biochemical pathway. Nature oen reuses the same building blocks to create ever increasingly complex structures with diverse functions. In metabolomics data, such building blocks are expected to transpire into spectral patterns because groups of atoms that are in a similar constitution and chemical environment are likely to produce similar spectral signals.
Substructure discovery by MS2LDA MS2LDA  16  was developed for unsupervised substructure discovery through the extraction of recurring spectral patterns, termed Mass2Motifs, from mass spectrometry fragmentation (MS/MS) spectra. It is inspired by the natural language processing algorithm Latent Dirichlet Allocation (LDA),  17  a method developed to decompose text documents into a series of topics, such as various categories for newspaper articles. This was a deviation from the more traditional document clustering techniques in that instead of attempting to place each document into a single topic it allowed documents to be made up of multiple topics. This resulted in the ability to decompose a set of text documents into a smaller set of more meaningful topics than would have been extracted with traditional document clustering.
However, the same Mass2Motifs will be rediscovered when similar sample types are analysed, motivating the development of an open database of structurally characterized Mass2Motifs (MotifDB 20 ) to which any user can add newly annotated Mass2Motifsknown as MotifSets. Where a MotifSet was measured from similar samples in the same ionization mode, it can be included in a new MS2LDA analysis. MS2LDA will extract these Mass2Motifs simultaneously with learning new Mass2-Motifs, accelerating the annotation process by simultaneously acknowledging known substructures and considering novel chemistry. MS2LDA is available in the GNPS 21 molecular networking pipeline as well as through a dedicated web application,  22  at which MotifDB is currently also hosted.
The latter are dened by determining the molecular ngerprints present from the SMILES representations of the molecules, resulting in vectors where the value in a particular position indicates whether or not a particular substructure is present (i. e. , aromatic ring or nitrogen-containing 5-membered ring). Then, rules are established to connect these substructures to the mass spectral features derived from the MS/MS spectra, thereby also using the hypothesis that the same substructure produces the same or similar mass spectral features independent of the other parts of the molecule that are connected to the substructure. The approach is inspired by the concept of association rule mining (ARM) that discovers interesting relations based on frequently co-occurring items. In the training process, a database of 8378 mass spectral features to substructure rules was established.