dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population We used the Echantillon Généraliste des Bénéficiaires (EGB) database, a permanent, representative and anonymised sample of people affiliated with the three major National Health Insurance funds  [12]. These funds cover more than 90% of the French general population, divided into salaried workers, agricultural workers and farmers, and self-employed workers. The EGB was created in 2005 by a national random sampling of 1/97th of the French population, stratified for age and sex; it records information on their healthcare consumption and includes data on reimbursement claims for drugs purchased in the community, classified according to the Anatomical Therapeutic Chemical (ATC) index. The EGB is a dynamic cohort, where every 3 months, registered births and foreign immigrants taking up employment in France and their eligible dependents are added to the sample.
Most of these conditions share commonalities in pathogenesis, aetiology, and risk factors, which are potentially modifiable. With no intervention, their cumulative financial burden is projected to greatly increase in the next decades  [14]. Administrative databases, such as drug prescription data, have frequently been used to identify people with chronic diseases in prevalence estimates  [15]  or epidemiological studies for comorbidity adjustments  [16]. In the EGB database, three types of data can be used to identify patients with chronic diseases. In 2013, Huber et al. [17]  developed an updated approach with a special focus on the unambiguous assignment of drug prescriptions to chronic diseases. For chronic diseases, they only included ATC codes, which are exclusively used for the treatment of specific diseases.
From 2009 to 2014, we considered that a person had a chronic disease if they had been dispensed at least three drugs in the corresponding ATC category at different times over the calendar year (this threshold of three dispensations was used on various French studies on diabetes  [18]  and other chronic diseases  [19] ). Since 2006, data from the French Medical Information System (Programme de Médicalisation des Systèmes d'information), which covers all French public and private hospitals except military and psychiatric hospitals, have been available for all individuals included in the EGB. In France, each hospital stay is registered in a hospital discharge database from the French Medical Information System and diagnoses are coded using the International Classification of Diseases (ICD-10 [20]) codification either as a primary, related, or significant associated diagnosis.
Cchronic diseases were identified using these diagnosis data from 2009 to 2014. We used long-term illness (LTI) status data from the French social insurance system to identify people with chronic diseases. LTI status is granted to people with long-term and costly diseases, exempting them from copayments for any associated medical treatment  [21]. As with hospital discharge data, chronic diseases were identified from 2009 to 2014 using the ICD-10 classification. These three sources of data (drug dispensing, hospital discharge and LTI) are available in the EGB database and linked with a unique individual identifier. They were combined to identify prevalent and incident cases for our 10 chronic disease groups.
Healthcare expenditure data We used reimbursement data to calculate, for each individual in the database, an aggregate healthcare expenditure in 2014, including primary care and consultations with specialists, (reimbursed) medicines, medical procedures, biological tests, medical devices, emergency care, and hospital inpatient care. This pricing of ambulatory care also takes into account possible co-payment from the patient, except for over-the-counter drugs which are not available in the EGB database. Regarding the hospital sector, this cost-evaluation only takes into account the part of the cost which is reimbursed to hospitals through the diagnosis-related group payment system (through which we can clearly assign a diagnosis using the reason of admission). Diagnosis-related group rates were used as proxies of case costs for public and private not-for-profit hospital stays.
Estimating the cost of chronic diseases In a bottom-up design, healthcare consumption is collected at individual patient level and illness costs are modelled at the same level. Compared to a top-down approach, in which total expenditure for a given area or policy is divided by the number of patients with a given disease, the bottom-up approach provides greater accuracy  [22]. However, in the French healthcare system, healthcare expenditure on patients, particularly in the ambulatory care system, cannot be directly linked to one specific diagnosis. To overcome this limitation, we chose to estimate the cost associated with each chronic disease using regression models. The marginal costs associated with one disease were estimated on individual-level data as the mean marginal difference in the predicted outcome (total individual healthcare expenditure in 2014) with the chronic disease independent variables switched on or off.
This makes it possible to estimate the 'counterfactual' of what the cost would have been in the absence of chronic disease, while leaving the other model parameters unchanged. This approach is commonly used to estimate incremental costs for diseases and risk factors  [23] [24] [25]. A useful modelling framework in such cases is a twostage model  [26]. Two-stage models are appropriate for analysing zero-inflated cost data with skewness  [27] , which is typical in medical expenditure data  [26]. Our dataset included both many people with zero healthcare spending and some with extremely high spending. In our two-stage model, the first part P (cost > 0) was modelled using a logit model; then, conditional on the healthcare spending being positive, the value of the spending was modelled using gamma regressions with log link  [28, 29].
In order to limit this effect, we added the 'other LTI' variable in the two-step regression model, controlling for the presence of a chronic disease outside of the list of the 10 selected chronic diseases, and reducing unobserved heterogeneity. Thirdly, this study was performed on a medicoadministrative database from France, whose healthcare system comprises a fully integrated network of public hospitals, private hospitals, doctors and other medical service providers. The individual receiving care is generally reimbursed for all medical treatment on the basis of a price established by the Social Security administration, unlike practices in other countries. Access to care in France is very easy, with a large decisional autonomy allowed to both the physician and the patient, who are free to adapt treatments as they wish  [34].
As alternative reference datasets, data from the NCEP 1 reanalysis and the CRU dataset are shown in the two lowest rightmost panels. The global averaged annual mean bias ("bias") and root mean square deviation (RMSD) compared with ERA-Interim are given above the individual panels. ents in temperature, for example in areas with high topography such as the Himalayas and the sea ice edge in the Southern Ocean. In the AMIP simulations (left two columns in Fig. 1 ), the MPI-ESM shows only very modest changes in the simulated mean near-surface temperature bias, whereas particularly EC-Earth3 and CNRM-AM-PRE6 show considerable improvements compared with their CMIP5 versions over North America. The cold biases over large parts of Antarctica found in CNRM-CM5 are also reduced in the EMBRACE version of the model, possibly related to updates in the turbulence scheme and the increased vertical resolution in the lower troposphere.
In most of the analyzed models, a warm bias over Central Africa and northern South America is still present in the EMBRACE simulations. Particularly in these two tropical regions, however, the observational uncertainties are large as can be seen by comparison of ERA-Interim and the Climate Research Unit (CRU) dataset  (Harris et al. , 2014)  showing differences of up to 2-3 • C. Only the temperature bias found in the simulations from the CNRM and HadGEM when compared to ERA-Interim are larger than this estimate of the observational uncertainty in these regions. In the coupled simulations, large biases are still present in the Southern Ocean, in particular along the coast of Antarctica. The coupled EMBRACE models are slightly worse than (HadGEM) or do not systematically outperform (EC-Earth, MPI-ESM) their CMIP5 counterparts in reproducing the ERA-Interim global near-surface temperature distribution in terms of RMSE.
Columns 1 and 3 show the original CMIP5 model versions, columns 2 and 4 the EMBRACE-updated models. The domain-averaged annual mean ("mean"), linear pattern correlation ("corr"; leftmost two columns), mean bias ("mean"), and root mean square error (RMSE; rightmost two columns) compared with ERA-Interim are given above the individual panels. the ERA-Interim reanalysis. Also shown are the alternative observation-based datasets GPCP-SG (precipitation) and the NCEP 1 reanalysis  (Kalnay et al. , 1996 ; near-surface temperature and zonal wind speed). In the observations, a precipitation maximum is seen on the western coast of India, with a relative minimum on the lee side of the Western Ghats. Further maxima are seen along the coast of Myanmar and Laos (eastern coast of the Bay of Bengal) and along the foothills of the Himalayas.
Latitudinal cross sections of precipitation, 2 m temperature, and key radiation variables averaged from 10 • W to 10 • E for the JJAS season are shown in Fig. 10. A maximum in 2 m temperatures (Fig. 10a ) coincides with a minimum in sea level pressure (Fig. 10b ) associated with the Saharan heat low (around 22 • N). While there is some discrepancy between the simulated 2 m temperature and the two observationally based datasets (ERA-Interim and CRU), all models capture the sharp increase in temperature around 15 • N although maximum temperatures over the Sahara can vary by 5 • C across models. Most models also capture the location and intensity of the Saharan heat low fairly well. Sur- The years used to calculate the averages shown are given above each panel ("yrs"). Rightmost two columns: differences relative to TRMM.
11 ) emphasizes the dominant timescale of precipitation variability over West Africa. This variability is associated with westward-propagating AEWs and MCSs embedded within these waves. Both TRMM and GPCP show large variance in precipitation on these timescales, stretching from the Darfur mountains west across the Sahel region, with Despite the relatively similar time mean (climatological) precipitation from TRMM and GPCP-1DD, the precipitation variabilities from TRMM and GPCP-1DD show large differences. As the base TRMM observational data are at 0. 25 • of spatial resolution and 3-hourly temporal resolution, whereas the GPCP-1DD data are at a spatial resolution of 1 • and the highest temporal resolution is daily mean values, we would expect TRMM to sample the high temporal and spatial variability in convective precipitation in this region more accurately than GPCP-1DD.
In this analysis, we therefore use TRMM as our main reference dataset. EC-Earth appears to capture the northern band of precipitation variability quite well, although this is degraded in EC-Earth3 west of the 0 • meridian. Both the HadGEMs and MPI-ESMs fail to capture sufficient precipitation variability on these timescales over land compared with TRMM, with significant variability only occurring over the tropical ocean regions. All three coupled EMBRACE models show less precipitation variability than their CMIP5 counterparts. Such findings emphasize the need for an improved representation of wave-precipitation interactions in all coupled models before they can provide robust estimates of changes in intra-seasonal rainfall over this region. Higher model resolution is generally considered an important route for improving weather timescale variability in climate models  (Jung et al.
The findings from this analysis may offer pointers for an optimal resolution for other models to aim at with respect to simulating sub-seasonal precipitation variability and seasonal mean rainfall. Figure  12  shows 3-10-day band-pass-filtered precipitation variance for JJAS over Africa from two observational datasets (TRMM 3B42 and GPCP-1DD) and the six EC-Earth resolutions. The two observations differ markedly with respect to the absolute magnitude of variance on these timescales. This is partly expected as the observational datasets feature a rather different horizontal resolution (0. 25 • vs. 1 • ). They do, however, exhibit some agreement in the spatial distribution of maxima and minima in precipitation variability, with a broad region of high variability stretching from Sudan west across to the Atlantic coast.
GPCP- 1DD indicates a northerly maximum in variability over West Africa around 12 • N associated with AEWs growing on the northern flank of the AEJ. Both datasets indicate a maximum in variability at the Atlantic coast around 10 • N and relative maxima over the Ethiopian Highlands and Darfur mountains. In EC-Earth, precipitation variability increases (and improves compared to the observations) as model resolution increases from T159 to T511. Beyond T511 there is little further increase in variability. In particular, as resolution increases from T159 to T511, higher variability appears eastwards back across the AEW wave track towards Ethiopia. There is also a clear increase in variability (wave activity and intensity) at the Atlantic coast. Perhaps surprisingly, this increase in precipitation variability is not seen in the 850 hPa meridional wind variability (not shown), which is a typical measure of the AEW activity.
For AMIP simulations all SST fields match the observations by design, except for HadGEM3-A, which deviates slightly due to using daily SST and sea ice fields from  Reynolds et al. (2007). Observed SSTs have a relative minimum on the Equator, ∼ 0. 5 • C cooler than the SSTs at 7-8 • S and ∼ 1 • C cooler than SSTs at 7-8 • N. Maximum SSTs are north of the Equator, ∼ 0. 5 • C warmer than at similar latitudes south of the Equator. Observed precipitation shows a distinct maximum at ∼ 8 • N, with values of 7 mm day -1 (GPCP) to 8 mm day -1 (CMAP, TRMM). A second, weaker maximum (3-4 mm day -1 depending on the observational dataset) is seen at 8 • S. A precipitation minimum is located on the Equator coincident with the SST minimum. The AMIP models reproduce this structure of precipitation, with only small deviations from observations.
We analyze total cloud amount, cloud liquid path (LWP) and ice water path (IWP), and surface and TOA solar radiation. Analysis is only performed for the AMIP simulations as the main findings also apply to the coupled models. Figure  16  shows cross sections from 65 to 30 • S of simulated zonal mean DJF total cloud cover, LWP and IWP, TOA outgoing (SWUP), and surface downwelling (SWD) shortwave radiation compared to satellite observations. For LWP and IWP, ERA-Interim reanalysis data are also included as a second observationally based estimate  (Dee et al. , 2011). Observed cloud cover increases from ∼ 60 % at 30 • S to more than 90 % around 60 • S. Most models capture this poleward increase, although all except HadGEM3-A exhibit a systematic negative bias (of 5-15 %) across the band ∼ 45 to 65 • S.
HadGEM3-A has the most accurate cloud cover and is a clear improvement over HadGEM2-A. CNRM-AM-PRE6 also shows improvement compared against its CMIP5 version. EC-Earth3 shows a small improvement, while the MPI-ESM shows little change. The impact of clouds on solar radiation can be summarized by cloud optical depth, which is a function of the cloud water content and the effective radius of cloud liquid droplets and ice crystals integrated over cloud depth  (Slingo, 1989). Here, vertically integrated LWP values are compared to observed estimates (LWP and IWP are not available from HadGEM3-A or EC-Earth). LWP observations are based on the University of Wisconsin (UWisc;  O'Dell et al. , 2008)  satellite passive microwave dataset, and IWP observations are MODIS collection 6 data  (Platnick et al.
, 2003). Similar to Jiang et   1986 -2005 (HadGEMs 1986 -2004 , MPI models 1980 -1999)   al. (  2012 ), the MODIS IWP data representing in-cloud values have been multiplied with the observed ice cloud fraction for comparison with the grid-box averages provided by the models. Due to the large differences across remotely sensed LWP and IWP datasets, values from UWisc and MODIS should be viewed as indicative at best. We include LWP and IWP estimates from ERA-Interim as a second constraint to provide some measure of this uncertainty. Our main motivation is to show the large range in both LWP and IWP across models, which may partly be due to the weak observational constraint. South of ∼ 40 • S, LWP differs by a factor of ∼ 2 across models, with IWP showing an even larger inter-model spread (up to a factor of ∼ 3).
No radiation data from EC-Earth are available. AM-PRE6 if UWisc data are used as the observational constraint. To gain more insight into the relationship between cloud cover and reflected SW radiation, Fig. 17  shows scatter plots of monthly mean TOA SWUP and surface SWD each plotted against monthly mean cloud cover for all available DJF months over the 20-year simulation period. Observations are from  CERES-EBAF (2001 -2014)  for SWUP and SWD and MODIS-L3 for cloud cover  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). The figure is constructed as follows: for each ocean grid point in the band 30 to 65 • S, monthly cloud cover is binned into 5 % width bins (from 0 and 100 %) and for each cloud cover occurrence the corresponding SWUP and SWD are saved to that bin.
0. The newly added ESMValTool namelist "namelist_lauer18esd. xml" includes the diagnostics that can be used to reproduce the figures in this paper. This version will be available from the ESMValTool webpage at http://www. esmvaltool. org/ and from github (https://github. com/ ESMValTool-Core/ESMValTool). Users who apply the software resulting in presentations or papers are kindly asked to cite the ESM-ValTool documentation paper  (Eyring et al. , 2016b)  along with the software DOI (https://doi. org/10. 17874/ac8548f0315) and version number. The climate community is encouraged to contribute to this effort and to join the ESMValTool development team for the con-tribution of additional diagnostics for ESM evaluation. Data from the CMIP5 models are publically available through the Earth System Grid Federation (ESGF), and the EMBRACE model runs can be made available on request from the host modeling groups.
Figure1. Bias in 20-year annual mean near-surface air temperature for the period1986 -2005  (MPI AMIP models: 1980 -1999). Shown are the differences between the 20-year climatology from ERA-Interim and the following from left to right: (1) the AMIP simulations from the CMIP5 models, (2) the corresponding EMBRACE models, (3) the coupled historical simulations from the CMIP5 models, and (4) the corresponding EMBRACE models. As alternative reference datasets, data from the NCEP 1 reanalysis and the CRU dataset are shown in the two lowest rightmost panels. The global averaged annual mean bias ("bias") and root mean square deviation (RMSD) compared with ERA-Interim are given above the individual panels.
Figure2. Bias in annual mean precipitation rate (mm day -1 ) for the20-year period 1986 -2005  (MPI AMIP models: 1980 -1999)  as the difference between the Global Precipitation Climatology Project (GPCP) and from left to right (1) the AMIP simulations from the CMIP5 models, (2) the corresponding EMBRACE models, (3) the coupled historical simulations from the CMIP5 models, and (4) the corresponding EMBRACE models. Data from CMAP are shown as a second reference dataset in the lowermost rightmost panel. The global averaged annual mean bias ("bias") and root mean square deviation (RMSD) compared with GPCP are given above the individual panels.
Figure5. Leftmost two columns: seasonal mean zonal wind speed at 850 hPa for JJAS from reanalysis data (ERA-Interim, NCEP 1) and the coupled simulations averaged over the years1986 -2005   (HadGEM: 1986 -2004)). Rightmost two columns: differences relative to ERA-Interim. Columns 1 and 3 show the original CMIP5 model versions, columns 2 and 4 the EMBRACE-updated models. The domain-averaged annual mean ("mean"), linear pattern correlation ("corr"; leftmost two columns), mean bias ("mean"), and root mean square error (RMSE; rightmost two columns) compared with ERA-Interim are given above the individual panels.
Figure9. Leftmost two columns: seasonal mean wind speed at 925 hPa for JJAS from reanalysis data (ERA-Interim and NCEP 1) and the coupled simulations averaged over the years1986 -2005   (HadGEM: 1986 -2004)). Rightmost two columns: differences relative to ERA-Interim. Columns 1 and 3 show the original CMIP5 model versions, columns 2 and 4 the EMBRACE-updated models. The domain-averaged annual mean ("mean"), linear pattern correlation ("corr"; leftmost two columns), mean bias ("mean"), and root mean square error (RMSE; rightmost two columns) compared with ERA-Interim are given above the individual panels.
dust composition did not vary with size for this set of samples. Because of its high atmospheric concentration, light absorption by mineral dust can be competitive with black and brown carbon even during atmospheric transport over heavy polluted regions, when dust concentrations are significantly lower than at emission. The AAE values of mineral dust are higher than for black carbon (∼ 1) but in the same range as light-absorbing organic (brown) carbon. As a result, depending on the environment, there can be some ambiguity in apportioning the aerosol absorption optical depth (AAOD) based on spectral dependence, which is relevant to the development of remote sensing of light-absorbing aerosols and their assimilation in climate models. We suggest that the sample-to-sample variability in our dataset of MAE values is related to regional differences in the mineralogical composition of the parent soils.
The percent error on mass concentrations is estimated to 5 %. Elemental concentrations for the major constituents of mineral dust (Na, Mg, Al, Si, P, S, Cl, K, Ca, Fe, Ti, Mn) were obtained by wavelength-dispersive X-ray fluorescence (WD-XRF) of the Nuclepore filters using a PW-2404 spectrometer by Panalytical. Excitation X-rays are produced by a Coolidge tube (I max = 125 mA, V max = 60 kV) with an Rh anode; the primary X-ray spectrum can be controlled by inserting filters (Al, at different thickness) between the anode and the sample. Each element was analyzed three times, with specific conditions (voltage, tube filter, collimator, analyzing crystal and detector). Data collection was controlled by the SuperQ software provided with the instrument. The elemental mass thickness (µg cm -2 ), that is, the analyzed elemental mass per unit surface, was obtained by comparing the elemental yields with a sensitivity curve measured in the same geometry on a set of certified mono-or bi-elemental thin layer standards by Micromatter Inc.
The energy range was scanned from 6850 to 7800 eV at a step resolution varying between 0. 2 eV in proximity to the Fe K absorption edge (at 7112 eV) to 2 eV in the extended range. Samples were analyzed in fluorescence mode without prior preparation. One scan acquisition lasted approximately 30 min and was repeated three times to improve the signal-to-noise ratio. The same analytical protocol was applied to five standards of Fe(III)-bearing minerals (Table  1 ), including iron oxides (hematite, goethite) and silicates (illite, montmorillonite, nontronite). The standard spectra were used to deconvolute the dust sample spectra to quantify the mineralogical status of iron. The linear deconvolution was performed with the Athena IFEFFIT freeware analysis program  (Ravel and Newville, 2005). This provided the proportionality factors, α i , representing the mass fraction of elemental iron to be assigned to the ith standard mineral.