dataesr/scientific-paragraphs-categorization huggingface dataset:
Time-slice historical ERF simulations (in support of science questions 2. 1 and 2. 3) The quantification of pre-industrial to present-day ERFs due to different drivers (such as in  Myhre et al. , 2013a, Fig. 8. 17 ) is used widely. The AerChemMIP time-slice experiments will provide the data to generate a consistent table of presentday ERFs for the reactive gases and aerosols. The ERFs are calculated by comparing the change in net TOA radiation fluxes between two runs with the same prescribed SSTs, but with NTCF emissions or WMGHG (methane, nitrous oxide, halocarbon) concentrations perturbed from their pre-industrial to present-day values. Internal variability (mainly clouds) generates considerable interannual variability in ERFs; therefore, at least 30 years of simulation are needed to characterise the present-day ERF from some species  (Forster et al.
Natural emissions simulations (in support of science question 2. 4) Climate change will affect the natural emissions of NTCFs and reactive WMGHGs. These natural emissions will have a radiative effect and so feed back onto climate change. To simplify the experimental setup, the experiments detailed here simply double the natural emissions. The radiative effects of natural WMGHGs (e. g. methane from natural sources) are not calculated as these can be obtained from experiment piClim-CH4. The control simulation is the 30-year 1850 fixed SST piClim-control as for the time-slice ERFs. Each experiment parallels the 30-year control, except that the emission fluxes from an interactive parameterisation are doubled (see Table  7 ). For models that do not interactively parameterise particular emissions, the fluxes from the 1850 climatological dataset should be doubled.
Diagnostics The AerChemMIP-specific diagnostics are designed to answer the following questions. How large are forcing, feedback and response associated with reactive gases and aerosols in the models participating in CMIP6 historical and scenario simulations? Which processes and mechanisms need to be represented in the models for a credible description of climate-chemistry-aerosol interactions? How well do models reproduce the observed spatial distribution and historical evolution of NTCF concentrations, depositions, optical properties, and observable interactions with climate? To guide the diagnostic process, the data request is structured according to overarching analysis subjects. These are detailed in the subsections below: Climate response, Forcing, Feedbacks, Chemistry-climate interactions, Air Quality, and Evaluation of model performance.
Considerable experience has been gained in previous model intercomparison exercises (namely CCMVal, CCMI, AeroCom, ACCMIP, and Hemispheric Transport of Air Pollution (HTAP)), but all too often model versions were different from those used in CMIP. AerChemMIP provides a unique opportunity to generate a complete dataset, requested directly from those GCMs providing climate sensitivity and scenario information to CMIP6. A specific problem may be the expected diversity in model complexity, as mentioned in Sect. 3. Models may contain interactive aerosols, tropospheric chemistry, stratospheric chemistry and any combination of these. AerChem-MIP requests all output unless unavailable from an individual model configuration with good reason. The diagnostics requested for the AerChemMIP experiments are assembled in an Excel sheet in the supplementary material for this paper (also available at https://wiki.
met. no/aerocom/aerchemmip/diagnostics), and the definitive and detailed request will be found in the CMIP6 data request (https://earthsystemcog. org/projects/ wip/CMIP6DataRequest). Since the AerChemMIP model versions are requested to also perform the DECK experiments, the data request contains suggestions for output limitations for these experiments (see the final data request for details). Here we provide an overview of the analysis subjects mentioned above. Suggestions for the best practice of diagnosing processes and outputting variables are given in some cases, in particular where previous model intercomparison projects failed to harmonise model output. The specific AerChemMIP request is grouped into eight tables and these are to be found in the Excel file and the CMIP6 data request: aerfixed (grid and land information), aermonthly-3d (essential monthly tracer and budget fields for all AerChemMIP simulations), aermonthly-2d (essential daily tracer fluxes, radiative forcing components and physical climate variables for all AerChemMIP simulations), aerdaily (boundary layer characterisation and ozone-aerosol diagnostics for evaluation), aerhourly (air quality index parameters), aer-6h (backscatter and extinction for aerosol vertical structure evaluation, dynamical parameters geopotential height and vorticity), aerzonal-vert and aerzonal (zonal mean gas concentrations for chemistry-climate interaction studies).
Climate response The characterisation of the climate response to NTCF forcing requires a set of diagnostics, which are fairly standard to all CMIP experiments. They include the variables that altogether describe the state of the atmosphere, the ocean and cryosphere, and land surfaces including essential biosphere and carbon cycle parameters. Specific attention should be devoted to the CFMIP Observation Simulator Package (COSP), which AerChemMIP models are encouraged to install. To facilitate the exploita-tion of A-train satellite data in numerical models, the COSP system has been developed that allows simulation of the signal that CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) would see in a model-generated world. A better understanding of cloudaerosol interactions may be possible if models add specific diagnostic aerosol calls, which would allow analysis together with the COSP diagnostic package output.
Air quality The simulations in AerChemMIP provide the opportunity to retrieve from historical and scenario runs air quality related parameters which relate the broadly used CMIP emissions to a transient description of climate to air quality metrics. An ensemble of models can be used to establish consequences for air quality. Most interest is on particulate matter concentrations and high ozone peaks. Since air pollution standards have been defined as exceedances for a given time window, we request hourly data at surface level for some few key substances, such as ozone, PM 2. 5 and NO 2. From these frequencies of daily maximum, diurnal cycles in different climate regimes, boundary layer characteristics can be obtained, which do characterise the model also in terms of chemical reactivity but also with respect to boundary layer mixing.
These include 2-D hourly (surface level ozone, PM 2. 5 , and NO 2 ), 3-D monthly mean concentrations of aerosol species, ozone and ozone precursors (including methane, CO, NO 2 , OH, and VOCs), column data (ozone), AOTs at different wavelengths, and deposition rates (including wet and dry deposition of nitrates and sulfates, dust, and BC). The hourly and 6-hourly model output (contained in aerhourly and aer-6h tables) is requested specifically for the DECK AMIP simulations  and is not needed for other experiments. The other variables are included as being essential for forcing, feedback, chemistry-climate interactions and air quality analysis. Outputting such variables will hence serve multiple purposes. Of particular importance are variables as assembled in the framework of the Global Atmospheric Watch (GAW) and observations for Model Intercomparison Projects (obs4MIP)  (Teixeira et al.
, 2014; Ferraro et al. , 2015). In addition, other datasets such as from the SPARC Data Initiative  (Hegglin et al. , 2017)  for the stratosphere and from the ESA CCI  (Hollmann et al. , 2013)  for the troposphere will be valuable for comparisons. Note that both CCMI and AeroCom will contribute with their model evaluation experience and will feed selected observational data sources into the Earth System Model Evaluation Tool (ESMValTool;  Eyring et al. , 2016b; Righi et al. , 2015). The ESMValTool will run -together with other evaluation tools such as the Program for Climate Model Diagnosis and Intercomparison (PCMDI) metrics package (PMP,  Gleckler et al. , 2016)  -alongside the Earth System Grid Federation (ESGF) as soon as the output is submitted to the CMIP archive so that evaluation results can be made available at a time much faster than in CMIP5  (Eyring et al.
Detection and Attribution MIP (DAMIP) There is some overlap between the AerChemMIP coupled model experiments and those requested in DAMIP. For example, AerChemMIP requires the extra historical runs from DAMIP to increase the ensemble size to at least three members. The DAMIP historical experiments use the pre-industrial as the control and have one class of species evolving at a time (e. g. hist-aer parallels historical, but with only aerosol forcing evolving), with all others fixed to pre-industrial levels. In contrast, AerChemMIP uses historical as the control and fixes one class of species at a time to pre-industrial levels (e. g. hist-piAer). The DAMIP hist-stratO3 run has only stratospheric ozone evolving, using either the prescribed CMIP6 ozone dataset  (Hegglin et al. , 2017)  or ozone output from the previous historical run.
Data availability The climate model output from AerChemMIP experiments described in this paper will be distributed through the Earth System Grid Federation (ESGF) with DOIs assigned. As in CMIP5, the model output will be freely accessible through data portals after registration. In order to document CMIP6's impact and enable ongoing support of CMIP, users are obligated to acknowledge CMIP6 and the participating modelling groups (see details on the CMIP Panel website at http://www. wcrp-climate. org/index. php/wgcm-cmip/ about-cmip). In order to run the experiments, datasets for natural and anthropogenic forcings are required. These forcing datasets are described in separate invited contributions to this Special Issue. The forcing datasets will be made available through the ESGF (https://pcmdi.
Acknowledgements. CRESCENDO project members ( Bill Collins ,  Michael Schulz ,  Olivier Boucher , and  Veronika Eyring ) acknowledge funding received from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 641816  (CRESCENDO). This work benefitted also from Norwegian research council projects no. 235548  ( Role of SLCF in Global Climate Regime ) and no. 229796  ( AeroCom-P3 ). Edited by: F. O'Connor Reviewed by: two anonymous referees.
Semi-parametric approaches based on generalized estimating equation (GEE) are widely used to analyse correlated outcomes. Most available softwares had been developed for longitudinal settings. In this paper, we present a R package CRTgeeDR for estimating parameters in marginal regression in cluster randomized trials (CRTs). Theory for adjusting for missing at random outcomes by inverse-probability weighting methods (IPW) based on the use of a propensity score had been largely studied and implemented. We exhibit that in CRTs most of the available softwares use an implementation of weights that lead to a bias in estimation if a non-independence working correlation structure is chosen. In CRTgeeDR, we solve this problem by using a different implementation while keeping the consistency properties of the IPW.
Introduction We describe the R (R Core Team 2015) package CRTgeeDR, for estimating coefficients of regression in a marginal mean model. The methods is designed to analyze data collected in cluster randomized trials (CRTs) where 1) observations within a cluster may be correlated, 2) observations in separate clusters are independent, 3) a monotone transformation of expectation of the outcome is linearly related to the explanatory variables, 4) the variance is a function of the expectation, and 5) treatment is randomized at a cluster level. The estimation approach generalizes the Generalized Estimating Equation (GEE)  (Zeger and Liang 1986)  for fitting marginal generalized linear models to clustered data with possibly informative missingness of the outcome. It combines existing methods for accommodating missing data that use inverse probability weighting (IPW)  (Robins, Rotnitzky, and Zhao 1995)  and for increasing precision of estimation by appropriate use of baseline covariates (AUG)  (Stephens, Tchetgen Tchetgen, and DeGruttola 2012).
We have developed a method that combines the IPW and the AUG that is doubly robust (DR), which implied that the resulting estimator is consistent if either the outcome model or missing data model are correctly specified -that is, they reflect the true data generation processes  Prague et al. (2015). Below we illustrate the use of the software on a real dataset and clarify its benefits. The package CRTgeeDR not only implements the DR estimator but also the standard GEE, the IPW and the AUG. Regarding IPW, our package differs from most of those currently available in that it avoids the bias that can result from conventional implementation applied to CRTs. Lin, Rodriguez, and SAS (2015)  pointed out that implementation of GEE for complete longitudinal data in the current version of SAS (GENMOD procedure) requires use of an independence correlation structure if the observation of the outcome at one time point depends on covariates obtained at another time point; this problem had been corrected in the new GEE procedure in SAS/STAT 13.
2 (SAS Institute Inc. 2015) but not in R to date. Tchetgen Tchetgen, Glymour, Weuve, and Robins (2012)  made a similar comment regarding the analysis of incomplete longitudinal data in which time-varying covariates and previous outcome values are needed to model the missingness process. This article clarifies this issue for CRTs and propose an implementation that allows for unbiased IPW (and thus DR) estimation with non-independence working correlation structure. GEE-based approaches for estimating the coefficients in marginal models, in particular the marginal effect of an intervention, have been implemented in only a limited number of software and R packages for general use. Of note, most of these software have initially been developed to deal with correlated longitudinal data rather than data from CRTs.
There are three R packages on CRAN, which will solve GEEs and produce standard errors: whereas gee (Carey, Lumley, and Ripley 2012) and geepack  (Halekoh, Højsgaard, and Yan 2006)  are computationally demanding, the package geeM allows a fast estimation through the use of sparse matrix representation  (McDaniel, Henderson, and Rathouz 2013). When interest lies in adjusting for MAR outcomes using the IPW, all the packages mentioned above require specification of weights. These weights can be computed using packages such as ipw (van der Wal and Geskus 2011) or directly plugged from a user-defined function. These approaches require that the missing data process be correctly specified. Some packages, such as drgee  (Zetterqvist and Sjölander 2015) , implement doubly robust approaches for uncorrelated data arising from observational studies.
However, the two approaches for CRTs are conceptually different, therefore, the comparison between the two will not be discussed in this article where the focus is on software implementation. The paper is organized as follows. Section 2 introduces the theory of the Doubly robust estimator and section 3 describes the features of the CRTgeeDR and the estimating function denoted GeedrEstimation. Section 4 compares the performance of CRTgeeDR to geepack and geeM for the IPW in CRTs and illustrates that the DR is also consistent and more efficient than the IPW. Section 5 illustrates the analysis of a dataset on sanitation in developing countries  (Guiteras et al. 2015a ) and illustrates the benefit of using the DR approach compared to standard GEE. Section 6 presents a discussion.
In particular, V i = φF 1/2 i C(α)F 1/2 i , where F 1/2 i = diag(h(µ ij )) j=1,. ,n i and C(α) is the working correlation structure with non-diagonal terms α. For example, for an independence correlation structure α is zero; for exchangeable structure, all the elements of α are identical. Parameters α could also depend on the treatment assignment C(α(A i )) but we do not consider this possibility in our implementation. In the package CRTgeeDR, we estimate the α and φ parameters using moment estimators from the Pearson residuals and the Pearson Chi-Square statistic as in  McDaniel et al. (2013). In the absence of missing data, W i = I is set to identity, and the standard GEE is performed by CRTgeeDR. In existing packages such as geepack and geeM, the Equation 1 is implemented as 0 = n i=1 D i V -1 i (Y i -µ i ), with V -1 i = φF 1/2 i W 1/2 i C(α)W 1/2 i F 1/2 i to ensure the invertibility of V i.
The R package CRTgeeDR The R package CRTgeeDR contains the functions described below. Examples and description are also available in the package documentation. • geeDREstimation to estimate the regression coefficients in the mean marginal models, • getPSPlot and getOMPlot, to plot the distribution of inverse probability weights and the adjustment of outcome model in each treatment group, • summary, to summarize results, • fitted and predict to extract fitted or predicted values into a new dataset. Package CRTgeeDR also contains the simulated datasets data. sim mimicking the data from a CRT in HIV risk reduction after STI/HIV intervention and documented code to analyse these data.
The main function for estimation in the package CRTgeeDR The call function for performing estimation is geeDREstimation: In this call, the first group of parameters is related to the standard GEE and computation of the variance options, the second group is related to the PS description and IPW options, and finally the third group is related to the OM description and AUG options. The marginal model, to be estimated on the R dataframe data, is given in formula. The link function, g, depends on the nature of the outcome, which is specified in the attribute family. The name of the outcome nameY, the clustering variable id, the binary treatment nameTRT (with the convention 1 is treated and 0 is control), and the missing indicator nameMISS must be specified if they differ from default values. The algorithm iterates between the estimation of nuisance parameters and regression parameters with a stopping rule based on stabilization of estimates (tolerance can be set by the user; default is tol= 10 -5 or max.
Of note these graphs are only interpretable for continuous outcome. In addition, for the PS model the function getPSPlot provides a histogram of the weights. If weights are too large then the IPW approach is likely to be unstable. Thus, the user should compute weights externally using, for example, stabilized weights with the associated package ipw (van der Wal and Geskus 2011) or other approaches such as described in  Wang and Paik (2011). Finally, the user can access the glm objects created during the PS and OM steps as outputs named ps. model, om. model. trt and om. model. ctrl from the main function geeDREstimation.
One covariate is of interest: X ij ∼ N (2, 1). We simulate correlated outcome with exchangeable structure, and correlation between individuals is set to 0. 05. This is done by using a cluster-level bridge distribution b i ∼ B(0. 05). Data generation process is as follow: logit[P (Y ij |A i , X ij )] = -0. 5 + 0. 3A i + 0. 4X ij + 0. 4X ij A i + b i , logit[P (R ij = 1|A i , X ij )] = 4. 0 -0. 3A i -0. 8X ij -0. 8X ij A i (3) We simulated R=10,000 replicates. The observed average proportion of missing observations is around 25% and the observed average intraclass correlation is 0. 08. The missingness is associated strongly with individual covariates and, thus, the weights differ between individuals in the same cluster. The true value of the odd-ratio for the marginal effect of treatment is computed for each dataset k without missing data by obtaining the counterfactual values with and without treatment under this model: OR k = E(Y ij = 1|A i = 1)/E(Y ij = 0|A i = 1) E(Y ij = 1|A = 0)/E(Y ij = 0|A i = 0).
Then, the true OR is given by 1 R R k=1 OR k =2. 56 with associated parameter for marginal intervention effect in the marginal regression β A = 0. 941. For each dataset, we first ran the analysis on the dataset without missing data for the standard GEE and the AUG using CRT-geeDR. Then, we ran the analysis on the dataset with missing data for the IPW using geepack and geeM and for the standard GEE, the IPW, the AUG and the DR using CRTgeeDR. Two types of DR are presented here, DR1 is the estimator using the correct models for the OM and the PS and Table  1  shows the bias, empirical standard error, sandwich standard error and coverages for each analysis using independence (-I) and exchangeable (-E) working correlation structure. The code to replicate this experiment is available in Web-Supplementary material.
The models for the PS and OM for analysis are described in the Table  1. They both can be considered as correctly specified, except for DR2 for which the PS omits treatment-covariate interaction terms and in that sense is misspecified. The results for standard GEE are unbiased on the datasets without missing data (<0. 003 for GEE-I and GEE-E with all packages) and biased in presence of rMAR outcomes (-0. 257 for GEE-I and GEE-E with package CRTgeeDR and for the other packages as well) implying that logit(µ ij ) = β 0 + β A A i PS used for IPW and DR (true): logit(P (R ij |A i , X ij )) = γ 0 + γ A A i + γX ij + γ I X ij A i PS used for DR2 (omitting interactions in PS): logit(P (R ij |A i , X ij )) = γ 0 + γ A A i + γX ij used for AUG and DR (fitted for each group logit(P (Y ij |A i = a, X ij )) = ξ + ξ A A i + ξX ij Table  1 : Comparison of the standard GEE, the IPW, the AUG and the DR analysis with the package CRTgeeDR, geepack and geeM using independence and exchangeable working correlation structure.
True value for the parameter β A is 0. 91 (OR=2. 56). The bias, the empirical and the estimated standard errors (SE) and the coverages for parameter β A are computed over 10000 replicates. The true data generation process for outcome and missingness is provided in Equation  3. The PS and OM models for analysis are correctly specified and given in the footnote of the table. the missingness is informative. Using the IPW-I corrects for this bias regardless of the package used for estimation (0. 003 for CRTgeeDR and geeM and 0. 008 for geepack). All packages give a similar estimated standard error leading to acceptable coverage close to their nominal value of 95%. When using an exchangeable correlation structure, the coverage (93. 7%) remains close to the nominal value for IPW-E using CRTgeeDR, but it drops to 19.
2015). The code for analysis is available in Web-Supplementary material. To illustrate the use of the package CRTgeeDR, we provide instructions for the DR estimator: R> DR<-geeDREstimation(OUTCOME~TRT, id=CLUSTER, data = Sanitation, family = binomial("logit"), corstr = "exchangeable", typeweights (µ ij ) = β 0 + β A A i PS: logit(P (R ij |A i , X IND ij , X C ij ) = γ 0 + γ A A i + 10 k=1 γ IND k X IND ijk + 5 k=1 γ C k X C ijk OM: logit(P (Y ij |A i = a, X IND ij , X C ij ) = ξ 0 + 10 k=1 ξ aIND k X IND ijk + 5 k=1 ξ aC k X C ijk (for each group a) Table  3 : Effects of the supply side-market vs. control on the probability of hygienic latrine ownership in the sanitation data analysis (Guiteras et al. 2015a) using the standard GEE, the IPW adjustment (IPW and DR), and the augmentation for imbalance (AUG and DR) assuming outcomes are rMAR.