dataesr/scientific-paragraphs-categorization huggingface dataset:
Preliminary follow-up rates Of the 2767 included participants, 2336 had at least one follow-up time point (that was in the range of T2 or later, that is, at least 1 month after baseline). The median duration of follow-up from baseline to time last seen was 14. 4 months (range 1-127 months; mean 22. 9±23. 2). The number of participants with available data and demographic characteristics for those with data at each time  point are shown in table  5. Follow-up data will be reported in more detail in subsequent publications.
77 78 he presence of such high rates of deliberate self-harm and suicidal thoughts and behaviour early in the course of care highlights the specific need for risk reduction strategies and ongoing management of these thoughts and behaviours during care and treatment. A report on suicide attempts in this cohort  79  found that the presence of a prior suicide attempt at baseline increases not only the risk for subsequent deliberate self-harm and suicidal thoughts and behaviours but also other negative outcomes including onset of alcohol or substance use disorder and bipolar disorder. This demonstrates the utility of this dataset in assessing prospective risk for multiple outcome domains in relation to baseline features.
Longitudinal assessment, using all available data will be another strength of this study, with standardised follow-up time points as well as a 'time last seen' time point capturing information across the full duration of time in care. The linkage of this longitudinal data with more in-depth assessments from related neuropsychological and neurobiological studies will further strengthen the richness of this dataset and increase the potential for greater understanding of the underpinnings of the development of mental illness in young people. There are important limitations of this study. Due to the method of data collection (ie, data extraction from clinical and research files), there is considerable variability in the quantity and quality of clinical information available. This has an adverse impact on the consistency of the measures.
Sources of information on LGM climate The PMIP3-CMIP5 model simulations were evaluated against two benchmark datasets: pollen-based reconstructions of seasonal temperature (mean annual temperature -MAT, mean temperature of the coldest month -MTCO, mean temperature of the warmest month -MTWA, growing season temperature indexed by growing degree days above a baseline of 0 • C), mean annual precipitation (MAP) and an index of soil moisture  (Bartlein et al. , 2011) ; and a compilation of sea-surface temperature (SST) reconstructions  (MARGO Project Members, 2009). In the  Bartlein et al. (2011)  dataset, reconstructions at individual pollen sites were averaged to produce an estimate for a 2 × 2 • grid; reconstruction uncertainties are estimated as a pooled estimate of the standard errors of the original reconstructions for all sites in each grid cell.
Although the  Bartlein et al. (2011)  dataset has good coverage for some regions, coverage was sparse in the tropics, and there were no reconstructions of LGM climate for Australia. Furthermore, not all of the six climate variables were reconstructed at every site, so statistical comparisons were more robust for some variables than others. The majority of the reconstructions included in the  Bartlein et al. (2011)  dataset used various sorts of statistical calibrations based on modern-day conditions and therefore do not account for the impact that changes in CO 2 have on water-use efficiency and hence plant distribution. Although  Bartlein et al. (2011)  were unable to demonstrate a statistically significant difference between statistical reconstructions and model-based inversions (which, in principle, account for the CO 2 effect on plant distribution), their analysis focused on the mid-Holocene where the CO 2 effect is small.
There is therefore some concern that the dataset may overestimate aridity at the LGM. Reconstructions which incorporate the effect of CO 2 are now available for Australia  (Prentice et al. , 2017). Cleator et al. (2020)  have used 3-D variational data assimilation techniques with a prior derived from the PMIP3-CMIP5 LGM simulations and the  Bartlein et al. (2011)  and  Prentice et al. (2017)  pollen-based reconstructions, and incorporating the  Prentice et al. (2017)  CO 2 correction, to produce a new global reconstruction of terrestrial climate at the LGM. In addition to accounting for potential effects of low CO 2 on moisture variables at the LGM, this reconstruction produces coherent estimates of seasonal climate variables at many more points than the original pollenbased reconstructions and also extends the geographic coverage.
5 • C) was added back to the matrix. This procedure effectively partitions the error variance; i. e. it assumes that at any given site, absolute uncertainty in SST cancels out in the anomaly calculation, while "relative" uncertainty associated with downcore measurement and nonlinearities in the calibration model is preserved. The data were then averaged within a 5 • × 5 • grid and differenced. The standard deviation associated with each grid point is calculated from the differenced ensemble dimension. In the present work, we also use other available reconstructions, all based on at least part of the initial MARGO Project Members (2009) reconstructions at the core sites: all are global reconstructions, obtained from this dataset via different methods, as summarised by  Paul et al. (2021). These datasets are from   et al.
Data-model comparisons The evaluation of the PMIP3-CMIP5 LGM simulations showed that large-scale climate features, such as the ratio of changes in land-sea temperature, high-latitude temperature amplification and precipitation scaling with temperature, were broadly consistent with modern observations  (Braconnot et al. , 2012; Izumi et al. , 2013; Harrison et al. , 2014 Harrison et al. , , 2015)). All PMIP3 and PMIP4 models simulate larger cooling over land than over oceans, on average for the tropics and for the globe. Figure  10  shows averages of model output sampled at sites for which there are reconstructions compared to the averages of the reconstructed values. Since the different reconstructions do not cover the same sites, the averages of the model values at reconstruction sites differ slightly for each dataset.
However, for all datasets, the multi-model relationship between the average cooling over land and that over the ocean is approximately linear. Figure  10  allows a comparison between model output and reconstructions averaged over land and over oceans, as well as a comparison of the ratio of the land cooling over the ocean cooling. Although the  Cleator et al. (2020)  dataset has a larger spatial coverage than the  Bartlein et al. (2011)  dataset, there is no significant difference between the two datasets for most of the temperature variables across common grid cells (Fig. 9 ). However, the new reconstructions have a reduced range at the warm end, especially between 0 and 40 • N, so that for the averages over the tropics, most simulations are recorded as within or warmer than the land-based reconstructions, while they are within or colder than the  Bartlein et al.
(2011)  reconstructions (Fig. 10 , left-hand side). The results for the global averages are fairly consistent for both land-based reconstructions (Fig. 10 , right-hand side) but the uncertainty is smaller for the  Cleator et al. (2020)  dataset. All in all, there are as many simulations within the range of globally averaged reconstructed temperatures of  Cleator et al. (2020)  as that of  Bartlein et al. (2011)  but the models outside this range tend to be on the warm side for the  Cleator et al. (2020)  dataset and both on the warm and cold sides for the  Bartlein et al. (2011)  dataset. The reconstructions of the LGM-Late Holocene SST anomalies provided by  Tierney et al. (2020)  are colder than the MARGO reconstructions in the tropics, and although this removes the apparent cold-bias shown by some simulations, this results in some simulations falling outside the window of reconstructed SSTs at the warm end.
This is even more the case if we compare the results to the data-assimilated product from  Tierney et al. (2020) , which has a global coverage (Fig. 10 , left-hand side, bottom line). The results from this latter dataset contrast the results from other global products, as shown in Supplement Fig. S3. These other global datasets  (Annan and Hargreaves, 2013 ; Kurahashi-Nakamura et al. , We have added the warmest estimate (GLOMAP2020) which includes uncertainties in Fig. 10  to obtain a more complete view of the available reconstructions as of 2020, keeping in mind that the authors of the GLOMAP2020 reconstruction estimate that it could be biased by 0. 5 to 1. 0 • C in the tropics. The simulated mean annual surface air temperature decreases over the tropical oceans stand between these two extremes.
This illustrates that model-related uncertainties are comparable with the uncertainties raising from the multiple approaches taken to reconstruct both the continental and oceanic temperatures. The ratio for the land-sea difference in changes in mean annual temperature in the tropics in the PMIP4 simulations is compatible with the ratio reconstructed from the  Bartlein et al. (2011)  and  MARGO Project Members (2009)  datasets. This is also the case if we consider the more recent reconstructions by  Cleator et al. (2020)  and  Tierney et al. (2020) , although the multi-model land-sea ratio appears to be smaller than that suggested by the reconstructions. This is the case for both the tropical and global averages. However, it would not be compatible with a landsea contrast based on the  Cleator et al.
(2020)  dataset and the GLOMAP2020 dataset, even if the warm bias pointed by its authors is taken into account. We are therefore left with large uncertainties on the topic of LGM cooling over land and oceans, from the reconstructions as well as from the models. The uncertainties based on the ensemble of model results and on the ensemble of continental and marine reconstructions are actually very similar. The amplification of temperature changes at high northern latitudes compared to the tropics is apparent over both the land and the ocean domains, although the amplification appears to be smaller in the new data syntheses (Fig. 11 ), except of the  Tierney et al. (2020)  data-assimilated product. For the ocean domain, this could reflect the influence of seasonal production on the extratropical sites, with indicators being more sensitive to summer changes or to changes in the seasonal production cycle.
Comparisons of the amplification over land areas with the  Bartlein et al. (2011)  dataset suggest that the simulated tropical cooling is too large in the PMIP3-CMIP5 simulations, whereas the extratropical cooling was both larger and smaller than that suggested by the reconstructions in both ensembles. Simulated tropical temperatures are more consistent with or warmer than the  Cleator et al. (2020)  reconstructions, suggesting that the apparent overestimation of tropical cooling in the PMIP3-CMIP5 simulations over land may reflect the paucity of tropical data points in  Bartlein et al. (2011). However, the discrepancies between the simulated and reconstructed extratropical land temperatures are still present: there are several PMIP3 and PMIP4 simulations that are much colder than the reconstructions and many which are warmer than the reconstructions.
The magnitude of the summer cooling is more consistent between the PMIP4 simulations and the  Cleator et al. (2020)  reconstructions than between the PMIP3 simulations and the  Bartlein et al. (2011)  reconstructions in North America, Eu- rope and extratropical Eurasia. Finally, the North Atlantic mean annual cooling simulated by the PMIP4 models spans a larger range that of the PMIP3 ensemble. While the PMIP3 ensemble mean showed temperatures within or above the reconstructed ranges from all oceanic datasets, the PMIP4 ensemble average stands within the range of the reconstructions, with six individual models being within this range, four above and three below. It is therefore quite difficult to determine the cause of the discrepancy in western Europe winter temperatures, which was previously assigned to an underestimation of the North Atlantic cooling.
(2020)  reconstructions. This is also the case for the PMIP4 models, but four simulations (IPSLCM5A2 and the three HadCM3 simulations) are now within the reconstructed range. This is probably related to the simulated tropical SSTs being colder in these simulations. The reconstructed changes in tropical precipitation over America are larger in the  Cleator et al. (2020)  dataset than in  Bartlein et al. (2011) , and both PMIP3 and PMIP4 models underestimate the reconstructed drying (Fig. 12 ). The PMIP4 models, however, all simulate the correct negative sign of the reconstructed precipitation change. There is a large difference between the estimates of precipitation change given by the  Bartlein et al. (2011)  and the  Cleator et al. (2019)  datasets for tropical Africa, with the  Cleator et al.
(2020)  reconstructions reducing the drying reconstructed by  Bartlein et al. (2011). The ranges of PMIP3 and PMIP4 results are broadly similar over this region, and there are the same number of models (four) within the reconstructed range of  Cleator et al. (2020) , while no model result was compatible with the  Bartlein et al. (2011)  reconstructions. All other models underestimate the change or even simulate an increase in precipitation. All in all, the simulated changes in precipitation are therefore more consistent with the newer dataset. Thus, there is no systematic improvement in the sim- The six data syntheses can be used to try and constrain the global MAT change from LGM to PI. There is a good correlation between the change in global average MAT over the reconstruction grid points and computed taking all the model grid points into account (Fig.
Using the terrestrial datasets as a constraint indicates a global cooling between -6. 7 and -3. 7 • C, while using  Tierney et al. (2020)  as a constraint indicates a global cooling of -4. 9 to -3. 2 • C, and using the MARGO Project Members (  2009 ) and GLOMAP2020 datasets constrains the global average to be above -3. 9 and -4. 4 • C, respectively. The constrained range (-6. 7 to -3. 2 • C) is larger than previous estimates (-4 to -6 • C). There is no obvious improvement in model performance at a regional scale between the PMIP3 and PMIP4 ensembles. In some cases (e. g. summer temperature over western Europe and extratropical Asia), the PMIP4 ensemble demonstrates a better ability to capture the changes depicted by the reconstructions; in some others (e. g. winter temperatures over Europe, mean annual precipitation over tropical America), the PMIP4 ensemble is still far from the reconstructed values.
Annan and Hargreaves (2013), who use theMARGO  Project Members (2009)  dataset, theBartlein et  al. (2019)  reconstructions on the continents, as well as the PMIP2 model output to generate a reconstruction of the sea-surface temperatures using multiple linear regression; -Kurahashi-Nakamura et al. (2017), who use the MARGO Project Members (2009) data, benthic δ 18 O and δ 13 C data as well as the MIT General Circulation Model (MITgcm) in combination with the method of Lagrange multipliers/adjoint method to generate a global reconstruction; -Paul et al. (2021), who produced the GLOMAP2020 dataset based on the floral and faunal assemblage data, as well as various sea-ice reconstructions from MARGO Project Members (2009), together with an optimal gridding method called DIVA to produce monthly global reconstructions.
A caveat given in Paul et al. (2021) about this reconstruction is that it may be too warm by 0. 5 to 1. 0 • C due to impacts of changes in seasonality and in the thermal structure of the ocean that are not taken into account in their reconstructions, as well as the impact due to heterogeneous spatial sampling. These datasets reflect different approaches and choices of initial datasets (only geochemical data for the Tierney et al. (2020)  reconstructions, for which the sites are often close to the coasts or only floral and faunal assemblages for GLOMAP2020), which yields a range a results with illustrate the uncertainty of the SST reconstructions. A crucial difference between the Tierney et al. (2020) synthesis and the other datasets used here is that the former implies more extensive tropical cooling during the LGM (-2.
6 • C us-ing the Cleator et al. (2020) datasets, between -4. 7 and -3. 3 • C for the Tierney et al. (2020) dataset and above -3. 9 and -4. 4 • C for the MARGO Project Members (2009) and GLOMAP2020 datasets, respectively. Taken altogether, these estimates span a larger range than previous estimates, which indicate changes in MAT of between 4 and 6 • C (Annan and Hargreaves, 2015; Friedrich et al. , 2016).
Data availability. The data shown in this paper can be found on the IPSL repository (https://doi. org/10. 14768/de241ea7-4c3d-4b56-8140-5de6940903be, Kageyama, 2021. ). The results from the PMIP3-CMIP5 and PMIP4-CMIP6 models can be found on the ESGF (Earth System Grid Federation) website. In particular, the CMIP6 lgm runs have the associated DOIs: AWI-ESM-1-1-LR: https://doi. org/10. 22033/ESGF/CMIP6. 9330 (last access: February 2020,  Shi et al. , 2020) ; INM-CM4-8: https://doi. org/10. 22033/ESGF/CMIP6. 5075 (last access: August 2019, Volodin et al. , 2019a); MIROC-ES2L: https://doi. org/10. 22033/ESGF/CMIP6. 5644 (last access: June 2019, Ohgaito et al. , 2019); MPI-ESM1-2-LR: https://doi. org/10. 22033/ESGF/CMIP6. 6642 (last access: July 2019, Jungclaus et al. , 2019). The associated piControl simulations have the following DOIs: AWI-ESM-1-1-LR: https://doi.
- Annan and Hargreaves (2013), who use theMARGO  Project Members (2009)  dataset, theBartlein et  al. (2019)  reconstructions on the continents, as well as the PMIP2 model output to generate a reconstruction of the sea-surface temperatures using multiple linear regression; -Kurahashi-Nakamura et al. (2017), who use the MARGO Project Members (2009) data, benthic δ 18 O and δ 13 C data as well as the MIT General Circulation Model (MITgcm) in combination with the method of Lagrange multipliers/adjoint method to generate a global reconstruction; -Paul et al. (2021), who produced the GLOMAP2020 dataset based on the floral and faunal assemblage data, as well as various sea-ice reconstructions from MARGO Project Members (2009), together with an optimal gridding method called DIVA to produce monthly global reconstructions.
We performed an additional sensitivity analysis using the Global Exposure Mortality Model (GEMM)  (Burnett et al. , 2018). For the GEMM analysis, we also used age stratified population data from GWPv3. Premature death is calculated the same as shown in Eq. 4 ; however, the relative risk differs. For the GEMM model, the relative risk can be calculated as shown in Eq. 6. Eq. 6 Here z = max (0,PM 2. 5 ˗PM 2. 5,Threshold ); θ, π, , α, and PM 2. 5,Threshold depends on disease category and are from  Burnett et al. (2018). Similar to the Eq. 5 , if the concentrations are below the threshold (2. 4 µg m -3 , Burnett et al. (  2018 ) ), then premature deaths are computed as zero; however, the GEMM has a lower threshold than the GBD method. For GBD, we do not consider age-specific mortality rates or risks.
For GEMM, we calculate age-specific health impacts with age-specific parameters in the exposure response function ( Table  S13  ). We combine the age-specific results of the exposure-response function with age distributed population data from GPW (CIESIN, 2017) and a national mortality rate across all ages to assess age-specific mortality. We calculated total premature deaths using annual average total PM 2. 5 concentrations derived from satellite-based estimates at the resolution of 0. 1°×0. 1° from  van Donkelaar et al. (2016). Application of the remote-sensing based PM 2. 5 at the 0. 1°×0. 1° resolution rather than direct use of the GEOS-Chem model concentrations at the 2°×2. 5° resolution helps reduce uncertainties in the quantification of PM 2. 5 exposure inherent in coarser estimates  (Punger and West, 2013).
Updates to the SIMPLE Model With the combination of the new dataset, which expands across urban areas on three continents, the SIMPLE parameterization for ASOA  (Hodzic and Jimenez, 2011)  is updated in the standard GEOS-Chem model to reproduce observed ASOA in Fig. 5  a. The parameterization operates as represented by Eq. 9. Eq. 9 SOAP represents the lumped precursors of ASOA, k is the reaction rate coefficient with OH (1. 25×10 -11 cm 3 molecules -1 s -1 ), and [OH] is the OH concentration in molecules cm -3. SOAP emissions were calculated based on the relationship between ∆SOA/∆CO and R aromatics /∆CO in Fig. 5  a. First, we calculated R aromatics /∆CO ( Eq. 10 ) for each grid cell and time step as follows: Eq. 10 Where E and k stand for the emission rate and reaction rate coefficient with OH, respectively, for benzene (B), toluene (T), and xylenes (X).
S6  ), were used to determine the emission ratio versus saturation concentration. Finally, for VOCs between log 10 (c*) 0 and 2 (SVOCs), the volatility distributions from  Robinson et al. (2007)  for non-fossil fuel POA and from  Worton et al. (2014)  for fossil fuel POA were used to convert the normalized POA mass concentration ( Table  S9  ) to VOC emission ratios. Note, the emission ratio versus saturation concentration for New York City, 2015, was similar to (b), as the emissions were similar ( Fig. 2  ) and the BTEX for New York City is the same as NE US ( Table  S5  ). S4  ). For (b) through (d), cities marked with * have no HCHO, PAN, or hydrocarbon data. S5  through Table  S7  ), for the campaigns studied here. For all figures, red shading is the ±1σ uncertainty of the slope, and the bars are ±1σ uncertainty of the data (see Sect.
which is available from Global Burden of Disease (GBD) Study 2015 database (IHME, 2016). Population (Pop) was obtained from Columbia University Center for International Earth Science Information Network (CIESIN) for 2010 (CIESIN, 2017). Relative risk, RR, can be calculated as shown in Eq. 5. Eq. 5 α, β, and ρ values depend on disease category and are calculated from Burnett et al. (2014) (see Table.
Acknowledgements This study was partially supported by grants from  NASA   NNX15AT96G ,  NNX16AQ26G ,  Sloan Foundation   2016-7173 ,  NSF   AGS-1822664 ,  EPA STAR 83587701-0 ,  NERC NE/H003510/1 ,  NERC   NE/H003177/1 ,  NERC   NE/H003223/1 ,  NOAA NA17OAR4320101 ,  NCAS R8/H12/83/037 ,  Natural Science and Engineering Research Council of Canada (NSERC ,  RGPIN/05002-2014 ), and the  Fonds de Recherche du Québec -Nature  et technologies (FRQNT,  2016-PR-192364 ). This manuscript has not been formally reviewed by  EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. We thank  Katherine Travis  for useful discussions. We acknowledge  B J.
Data Availability TexAQS measurements are available at https://esrl. noaa. gov/csl/groups/csl7/measurements/2000TexAQS/LaPorte/DataDownload/ and upon request. NEAQS measurements are available at https://www. esrl. noaa. gov/csl/groups/csl7/measurements/2002NEAQS/. MILAGRO measurements are available at http://doi. org/10. 5067/Aircraft/INTEXB/Aerosol-TraceGas. CalNex measurements are available at https://esrl. noaa. gov/csl/groups/csl7/measurements/2010calnex/Ground/DataDownload/. ClearfLo measurements are available at https://catalogue. ceda. ac. uk/uuid/6a5f9eedd68f43348692b3bace3eba45. SEAC 4 RS measurements are available at http://doi. org/10. 5067/Aircraft/SEAC4RS/Aerosol-TraceGas-Cloud. WINTER measurements are available at https://data. eol. ucar. edu/master_lists/generated/winter/. KORUS-AQ measurements are available at http://doi.