dataesr/scientific-paragraphs-categorization huggingface dataset:
0 (ref. 62 )) and Wilson and Reeder  63. Where we could not find distribution data for a given species in the IUCN database, we downloaded point occurrence data from the Global Biodiversity Information Facility (https://www. gbif. org/), using the R package rgbif (v. 2. 2. 0 (ref. 64)). We cleaned this data using the package CoordinateCleaner  65. We then merged the IUCN shapefiles with gbif rasters per clade and reprojected the resulting shapefile into a Behrmann cylindrical equal area projection (for a better comparison between cells close to the poles and cells close to the equator in terms of area). Finally, for each species, we plotted the range map to visually assess it and look for outliers that we removed if present.
Trait data For detailed descriptions of data collation, see Supplementary Information Section 3. For amphibians, we combined different trait databases  [66] [67] [68] [69]  and extracted snout-vent length (SVL), egg size, clutch size, clutches per year, age at sexual maturity (SM) and longevity (LG). For non-avian reptiles, we combined data  66, 67, [70] [71] [72] [73] [74]  and extracted SVL, hatchling body mass, clutch size, clutches per year, SM and LG. For mammals, we combined trait databases  73, [75] [76] [77] [78] [79] [80] [81] [82] [83] [84]  and extracted body mass (BM), neonate body mass, litter size, litters per year, LG, SM, gestation time and weaning age. Finally, for birds we combined data  52, 73, 77, 80, 81, [85] [86] [87] [88] [89] [90] [91] [92] [93] [94] [95] [96] [97] [98] [99] [100] [101] [102] [103]  and extracted BM, egg mass, litter size, litters per year, LG, SM, gestation time and weaning age.
Matching phylogenetic and trait data The phylogenies and combined trait databases did not follow the same taxonomies. We therefore looked up synonyms for all species without a direct match in the trait databases. To do so, we again used the ITIS (functions get_tsn and itis_getrecord, package taxize v0. 9. 99  (refs. 57,58)) and the package rangeBuilder (v1. 5 (ref. 59); function synonymMatch), which looks up synonyms in AmphibiaWeb  60  , the reptile database  61  , the BirdLife Taxonomic Checklist (v8. 0 (ref. 62)) and Wilson and Reeder  63. In addition, we looked up by hand species without a match after this process. Where several matches in the https://doi. org/10. 1038/s41559-023-02150-5 trait databases for a species with phylogenetic data were found, we averaged the trait values.
Bioregionalization and paleoreconstruction We used biogeographic models implemented in BioGeoBEARS (DEC, DIVALIKE and BAYAREALIKE  94  ) to estimate clades' biogeographic histories. These models infer past range evolution, that is, dispersal, vicariance and range contractions, over discrete areas that need to be defined a priori. For every clade, we thus identified relevant biogeographic barriers by calculating phylogenetic beta-diversity  95, 96  between raster cells (using an equal area projection, Behrmann projection with standard parallels at 30° to make raster cells between higher and lower latitudes comparable). To do this, we used the R package betapart v1. 5. 6 (ref. 97). We weighted the phylogenetic beta-diversity matrix with a geographical distance matrix (great-circle distances on latitude/longitude coordinates) and applied an unweighted pair group method with arithmetic mean following the methodology in Weil et al.
PFA To detect latent variables that structure life histories across taxonomic groups, we used a PFA  22  that allows for missing data (for more details, see Hassler et al. 100  and Weil et al. 11  ). This PFA is implemented in the Julia package PhylogeneticFactorAnalysis. jl v0. 1. 4 (ref. 23), which relies on a development version of BEAST 101 to be released with BEAST v1. 10. 5. Body size can influence life history traits through allometric constraints while evolving under different selection pressures. We therefore structured the PFA so that body size (as captured by SVL for amphibians and non-avian reptiles, and BM for birds and mammals) loaded only onto the first factor while all other traits loaded onto all factors, following Weil et al. 11. Hence, life history trait variation associated with body size was forced onto the first factor and the second factor we extracted captured size-independent patterns of life history covariation.
We then calculated differences between trait-dependent and trait-independent models, as well as distance-dependent and distance-independent models, and averaged across binarization splits per trait. This led to four values per clade (two traits (body size and life history) × two comparisons (trait dependent versus trait independent and distance dependent versus distance independent)). We used mixed-effects models (R packages lme4 v1. 1. 30 (ref. 103) and car v3. 1. 0 (ref. 104)) to assess how resolution and number of dispersal events in a clade's biogeographic history varied between the different model groups (trait-dependent, trait-independent, distance-dependent and distance-independent estimations), using clade identity as a random effect. Where these models were significant, we followed with contrasts among estimated marginal means (contrasting trait-dependent and trait-independent models, distance-dependent and distance-independent models, distancedependent and trait-dependent models, and distance-independent and trait-independent models, using a Šidák correction for multiple comparisons, in R package emmeans v1.
For multinomial regressions, we used the R packages nnet v7. 3. 17 (ref. 106) and mlogit v1. 1. 1 (ref. 107). In both cases, we used a stepwise selection process (in both directions, R package MASS v7. 3. 55 (ref. 106)) to identify those variables that were significantly related to the variables of interest. In the first case, where we tested for differences between clades in the maximal difference in dispersal rates between trait states, we used a two-step approach as there were too many predictor variables for the low number of observations (56 clades). We first tested the methodological variables (variables 4-8) and retained only those which were selected in the step-selection process for the following model, where we included the ecological variables (variables 1-3). There was no phylogenetic signal in the residuals of any of the models (function 'phylosig', R package phytools v1.
Predictor variables that were included in a single model were generally weakly correlated (-0. 35 < Pearson correlation < 0. 35), with some exceptions: crown age was moderately correlated with intra-clade trait variability (Pearson correlation 0. 44 and 0. 54 for life history and body size, respectively); the average number of dispersal events was moderately correlated with the percentage of oceanic dispersal (Pearson correlation -0. 41), as was average node resolution (Pearson correlation 0. 46). The average number of dispersal events was strongly correlated with average node resolution (Pearson correlation >0. 9), indicating that a lot of movement in biogeographic histories leads to more uncertain estimations. The R package 'ggtree' v3. 6. 2 (ref. 109) was used to prepare Figs. 1  and 2.
Variables that were not tested for a given dataset because we did not hypothesize them to causally influence the magnitude of trait effects are indicated by n. t. Life history 1 is the dataset presented in the main manuscript, including 47 clades in which we identified a fast-slow life history continuum (Extended Data Fig. 2 ); in Life history 2 we excluded clades where only one variable loaded onto the second factor of the phylogenetic factor analysis.
Acknowledgements We thank  J. Renaud  for providing part of the species distribution data, and  B. Bzeznik  and  M. Guéguén  for assistance with the cluster. M. González-Suárez ,  S. Diamond  and  F. Boucher  provided valuable insight during discussions. All BioGeoBEARS analyses were performed using the GRICAD infrastructure (https://gricad. univ-grenoble-alpes. fr), which is supported by  Grenoble  research communities. We thank  Swansea University  for covering the open access fees. This work is the result of a collaboration between  Université Grenoble Alpes  and  Swansea University , supported by  Initiative d'excellence (IDEX) International Strategic Partnership  and  Swansea University Strategic Partner Research (SUSPR)  scholarships.
Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5-10 years, creating potential for an information gap between report cycles. We base this update on the assessment methods used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report, updating the monitoring datasets and to produce updated estimates for key climate indicators including emissions, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth's energy imbalance, warming attributed to human activities, the remaining carbon budget and estimates of global temperature extremes.
The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi. org/10. 5281/zenodo. 7883758, Smith et al. , 2023). As they are traceable and consistent with IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human induced warming reached 1. 14 [0. 9 to 1. 4] °C over the 2013-2022 period and 1. 26 [1. 0 to 1. 6]    °C in 2022. Human induced warming is increasing at an unprecedented rate of over 0. 2 °C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 57 ± 5.
5°C of global warming in the first half of the 2030s  (Lee et al. , 2021b; Lee et al. , 2023; Riahi et al. , 2022) , it is important to have robust, trusted, and also timely climate indicators in the public domain to form an evidence base for effective science-based decision making. Cross-Chapter Box 1. 1, Table  1  in Chapter 1 of the AR6 report  (Chen et al. , 2021)  maps how the material assessed by WGI may be relevant for the global stocktake. When making their assessments, authors of IPCC reports assess published literature, but also apply established published analysis methods to assessed datasets, such as that produced by the latest climate model intercomparison projects  (Lee et al. ,   2021b). The authors combine and analyze both model and observational data as part of their expert assessment, making assessments of the trustworthiness and error characteristics of different datasets.
It is this synthetic analysis by IPCC authors that derives the estimates of key climate indicators. It is these same assessed approaches that we are implementing here to provide updates. The same approach, using the same datasets (updated by 2 years) and methods as employed in WGI, was used in the AR6 SYR (2023) report to provide an updated assessment of the latest atmospheric well mixed greenhouse gas concentrations (up to 2021) and decadal average change in global surface temperature (+1. 15°C [1. 00°C-1. 25°C] in 2013-2022 for global surface temperature). However, the assessment of human-induced warming was not updated (and therefore only covers warming up to the decade 2010-2019), nor was the remaining carbon budget updated, so the related information in the 2023 SYR report remained based on data up to the end of 2019.
In this work, we focus on providing updated estimates of key global indicators of the state of climate: the Earth's heat inventory, human-induced warming and the remaining carbon budget. To do this requires updates to emissions, greenhouse gas concentrations, effective radiative forcing, energy imbalance and surface temperature change, which are all important indicators of the direction of travel, giving important insights to the magnitude and the pace of global warming. This paper provides the basis for a dashboard of climate indicators grounded in IPCC methodologies and directly traceable to reports published as part of the AR6 cycle. We employ datasets that can be updated on a regular basis between the publication of IPCC reports. Note that there are other similar initiatives underway to update other AR6 cycle products; for example, the evolution of the WGI Interactive Atlas  (Gutierrez et al.
2021 ) is being developed under the Copernicus Climate Change Service (C3S) and has potential connections and synergies with this initiative that will be explored in the future. We track how specific global climate indicators have changed since reported in AR6. It breaks the change down into components from the march of time and any revisions to methods and/or data. For this first report, the methodologies are either identical or very close to those employed in AR6, keeping our results as consistent as possible. There are places where we need to depart as indicated in the text. These occasions are when a particular dataset is not updateable through 2022 and an alternative approach has had to be used. Our longer-term ambition is to rigorously track both real system change and methodological improvements between IPCC report cycles, thereby building consistency and awareness.
An example of why tracking methodological change is important, was the shift in the historical baseline used for global surface temperatures between SR1. 5 and AR6. Datasets and methods of evaluating global temperature changes altered between the fifth (AR5) assessment report and AR6, leading to a small shift in the historical temperature baseline. This was reflected in changes between AR5 and AR6, whereas SR1. 5 chose to broadly follow AR5 methods (see AR6 WGI Cross Chapter Box 2. 1,  Gulev et al. , 2021). Annual updates provide forewarning of possible future methodological shifts that subsequent IPCC reports may make as science advances and can detail their impact on perceived trends. We adopt the Global Carbon Budget ethos of a community-wide inclusive effort that synthesises work from across a large and diverse global scientific community in a timely fashion  (Friedlingstein et al.
Section 9 estimates preliminary global-scale indicators of climate extremes. These changes in extremes are not directly related to the other indicators but are included to showcase the possibility of extending the indicator set in future years. An important purpose of the exercise is to make these indicators widely available and understood. Plans for a web dashboard are discussed in Sect. 10, code and data availability in Sect. 11 and conclusions presented in Sect. 12. Data is available at https://doi. org/10. 5281/zenodo. 7883758,  Smith et al. (2023).
Each category of GHG emissions included here is covered by varying primary sources and datasets, with significant differences in uncertainties. Although many datasets cover individual categories, few extend across multiple categories, and only a minority have frequent and timely update schedules. Notable datasets include the Global Carbon Budget (GCB;  Friedlingstein et al. , 2022) , which covers CO2-FFI and CO2-LULUCF; the Emissions Database for Global Atmospheric Research (EDGAR;  Crippa et al. , 2022)  and the Potsdam Real-time Integrated Model for probabilistic Assessment of emissions Paths (PRIMAPhist;  Gütschow et al. , 2016; Gütschow and Pflüger 2023) , which cover CO2-FFI, CH4, N2O and UNFCCC F-gases; and the Community Emissions Data system (CEDS;  O'Rourke et al. , 2021) , which covers CO2-FFI, CH4, and N2O.
2020 ). Version 6 of EDGAR was used (with a fast-track methodology applied for the final year of data -2019), alongside the 2020 version of the GCB  (Friedlingstein et al. , 2020). CH4 and N2O emissions from biomass combustion from the Global Fire Emissions Database (GFED; Van Der Werf 2017) were added to EDGAR. CO2-equivalent emissions were calculated using global warming potentials with a 100-year time horizon AR6 WGI Chapter 7  (Forster et al. , 2021). Uncertainty ranges were based on a comparative assessment of available data and expert judgement, corresponding to a 90% confidence interval  (Minx et al. , 2021) : ±8% for CO2-FFI, ±70% for CO2-LULUCF, ±30% for CH4 and F-gases, and ±60% for N2O. (Note that the GCB assesses uncertainties for CO2-FFI as ±5%, and uses an absolute uncertainty range of ±2.
, 2020b). As in AR6 WGIII we use GFED for biomass combustion CH4 and N2O emissions, and GCB for net CO2-LULUCF emissions, taking the average of 3 bookkeeping models. There are three reasons for these specific data choices. First, national greenhouse gas emissions inventories tend to use improved, higher-tier methods for estimating emissions fluxes than global inventories such as EDGAR or CEDS  (Dhakal et al. , 2022; Minx et al. , 2021). As GCB and PRIMAP-hist integrate the most recent national inventory submissions to the UNFCCC, selecting these databases makes best use of the investments countries have made into data gathering infrastructures. Second, comprehensive reporting of F-gas emissions has remained challenging in national inventories (see  Minx et al. , 2021; Dhakal et al. , 2022).
However, as global F-gas concentrations are entirely anthropogenic substances they can be measured effectively and reliably in the atmosphere. We therefore follow the AR6 WGI approach in making use of direct atmospheric observations. Third, the choice of GCB data for CO2-FFI means we can integrate its projection of that year's CO2 emissions at the time of publication (i. e. , for 2022). No other dataset except GCB provides projections of CO2 emissions on this timeframe. At this point in the publication cycle (mid-year), the other chosen sources provide data points with a two-year time lag (i. e. , for 2021). While these data choices inform our overall assessment of GHG emissions, we provide a comparison across datasets for each emissions category, as well as between our estimates and an estimate derived from AR6 WGIII-like databases (i.
Non-methane short lived climate forcers In addition to GHG emissions, we provide an update of anthropogenic emissions of non-methane short-lived climate forcers (SLCFs) (SO2, BC, OC, NOx, VOCs, CO and NH3). Updating emissions of many short-lived climate forcing agents to 2022 based on established datasets is not possible as compiling global data can take several years. Yet, as SLCF emissions are needed in this paper to update effective radiative forcing (ERF) estimates through 2022, updated emission datasets, where they are available, are combined with projected data to make SLCF emission time series complete. As in  Dhakal et al. (2022) , sectoral emissions of SLCFs are derived from two sources. For fossil fuel, industrial, waste and agricultural sectors, we use the CEDS dataset that provided SLCF emissions for CMIP6  (Hoesly et al.
We do not provide a formal assessment of emissions uncertainty here as uncertainties in underlying datasets are not routinely quantified. Future updates of CEDS are expected to include uncertainties  (Hoesly et al. , 2018). Even though trends over recent years are uncertain, the general decline in SLCF emissions derived is supported by aerosol optical depth measurements (e. g. Quaas et al. , 2022). updated CO2, CH4 and N2O concentrations to 2021  (Lee et al. , 2023). In this update we extended the record to 2022 for all 52 gases. Ozone is an important greenhouse gas with strong regional variation both in the stratosphere and troposphere  (Szopa et al. , 2021). Its ERF arising from its regional distribution is assessed in Sect. 4, but following AR6 convention is not included with the GHGs discussed here.
, 2023b] , and multiplied N2O by 1. 0007 to be consistent with a NOAA-AGAGE average. NOAA CH4 in 2022 was used without adjustment since the NOAA and AGAGE global means CH4 are consistent within 2 ppb. Mixing ratio uncertainties for 2022 are assumed to be similar to 2019, and we adopt the same uncertainties as assessed in AR6 WGI. Many halogenated greenhouse gases are reported on a global mean basis from NOAA and/or AGAGE until 2020 or 2021 (SF6 is available in the NOAA dataset up to 2022). Where both NOAA and AGAGE data are used for the same gas, we take a mean of the two datasets. Where both networks are used and the last full year of data availability is different, the difference between the dataset mean and the dataset with the longer time series in this last year is used as an additive offset to the dataset with the longer time series.
Volcanic Volcanic ERF consists of contributions from stratospheric sulphate aerosol optical depth (sAOD; a negative forcing) and stratospheric water vapour (sWV, a positive forcing). The sAOD time series (at a nominal wavelength of 550 nm) is constructed from a combination of four datasets which have temporal overlap. We use ice-core deposition data from HolVol v1. 0  (Sigl et al. , 2022)  for 9500 BCE to 1900 CE. This has been extended backwards in time from the equivalent dataset used in AR6 (eVolv2k;  Toohey and Sigl, 2017)  which had temporal coverage of 500 BCE to 1900 CE. For 1850 to 2014 we use the CMIP6 volcanic sAOD dataset  (Dhomse et al. , 2020). Since 1979, the CMIP6 dataset was constructed using the Global Space-based Stratospheric Aerosol Climatology (GloSSAC) v1. 0  (Thomason et al.
, 2018). We use an updated, extended version of GloSSAC (v2. 2) providing sAOD up to 2021, which is itself an extension of the version used in AR6 (v2. 0) ending in 2018 Ångstrom exponent is not suggested for this conversion. The 745 nm band is used as this is reported to be more stable than the bands closer to 550 nm from OMPS LP  (Taha et al. , 2021). Other than for the 2013-2021 overlap between GloSSAC v2. 2 and OMPS LP in which only GloSSAC is used, we use a cross-fading approach to blend datasets in overlapping periods. Differences between datasets are minimal. sAOD is converted to a radiative effect using a scaling factor of -20 as in AR6  (Smith et al. , 2021b)  that is representative of CMIP5 and CMIP6 models. Effective radiative forcing is calculated with reference to the change in this radiative effect since "pre-industrial", defined as the mean of all available years before 1750 CE.
In other words, the mean of the pre-1750 period is defined as zero forcing. The January 2022 eruption of Hunga Tonga-Hunga Ha'apai (HTHH) was an exceptional episode in that it emitted large amounts of water vapour into the stratosphere  (Millán et al. , 2022; Sellitto et al. , 2022). Jenkins et al. (2023)  determined the HTHH eruption increased volcanic ERF by +0. 12 W m -2 due to sWV. The 2022 volcanic ERF has therefore been increased to account for this. sWV injections from other volcanic eruptions historically have been assumed to be negligible. This assumption for the whole Holocene is probably incorrect (1883 Krakatau may have also emitted substantial amounts of sWV  (Joshi and Jones, 2009) ), but at present no known proxy datasets for sWV injections from volcanic eruptions before the observational era exist.