dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , the Earth System Model Evaluation Tool, ESMValTool;  Eyring et al. , 2020;  the PCMDI Metrics Package (PMP);  Gleckler et al. , 2016; the NCAR Climate Variability Diagnostics Package; Phillips et al. , 2014; and  the International Land Model Benchmarking (ILAMB) package;  Collier et al. , 2018). In the next section, the history and initial objectives of the obs4MIPs project are briefly summarized. Section 3 describes the needs and efforts to expand the scope of obs4MIPs beyond its initial objectives, particularly for including a wider range of observational resources in preparation for CMIP6. Section 4 provides an updated accounting of the obs4MIPs dataset holdings; descriptions of a number of new features, including updated dataset specifications, dataset indicators, and accommodation for supplementary material; and a brief description of the alignment and intersection of obs4MIPs and CMIP model evaluation activities.
Background In late 2009, the Jet Propulsion Laboratory (JPL, NASA) and the Program for Climate Model Diagnostics and Intercomparison (PCMDI, United States Department of Energy, DOE) began discussions on ways to better utilize global satellite observations for the systematic evaluation of climate models, with the fifth phase of the WCRP's CMIP5 in mind. A 2 d workshop was held at PCMDI in October 2010 that brought together experts on satellite observation, modeling, and climate model evaluation  (Gleckler et al. , 2011). The objectives of the meeting were to (1) identify satellite datasets that were well suited to provide observation reference information for CMIP model evaluation; (2) define a common template for documentation of observations, particularly with regard to model evaluation; and (3) begin considerations of how to make the observations and technical documentation readily available to the CMIP model evaluation community.
D. Waliser et al. : Obs4MIPs: status for CMIP6 The name "obs4MIPs" was suggested to uniquely identify the data in the ESGF archive and distinguish it from the diversity of other information hosted there. Along with outlining the initial objectives and tenets of the pilot effort, a first set of about a dozen NASA satellite observation datasets was identified and deemed particularly appropriate for climate model evaluations relevant to CMIP and associated IPCC assessment reports, based on their maturity and long-standing community use. The initial set included temperature and humidity profiles from the Atmospheric InfraRed Sounder (AIRS) and the Microwave Limb Sounder (MLS), ozone profiles from the Tropospheric Emission Spectrometer (TES), sea surface height (SSH) from TOPEX/Jason (jointly with CNES -Centre National d'Etudes Spatiales), sea surface temperature (SST) from the Advanced Microwave Sounder Radiometer-E (AMSR-E, jointly with JAXA -Japanese Aerospace Exploration Agency), shortwave and longwave all-sky and clear-sky radiation fluxes at the top of the atmosphere from the Clouds and the Earth's Radiant Energy System (CERES), cloud fraction from MODIS, and column water vapor from the Special Sensor Microwave Imager (SSMI).
All these initial datasets were global, or nearly so, and had monthly time resolution spanning record lengths between 8 and 19 years. By late 2011 these datasets were archived, with their associated technical notes, on the JPL ESGF node. Further information on the development and scope of the obs4MIPs effort during this period was captured in  Teixeira et al. (2014). With the success of this pilot effort, NASA and DOE sought to broaden the activity and engage more satellite teams and agencies by establishing an obs4MIPs Working Group early in 2012 that included members from DOE, three NASA centers, and NOAA. In the subsequent year, this working group helped identify and shepherd a number of additional datasets into the obs4MIPs project. These included ocean surface wind vectors and speed from QuikSCAT, precipitation from the Tropical Rainfall Mapping Mission (TRMM) and the Global Precipitation Climatology Project (GPCP), aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR), aerosol extinction profiles from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), surface radiation fluxes from CERES, and sea ice from the National Snow and Ice Data Center (NSIDC).
Two of the datasets included higherfrequency sampling, with TRMM providing both monthly and 3-hourly values and GPCP providing both monthly and daily values. All of the datasets contributed to obs4MIPs thus far are gridded products, and many cover a substantial fraction of the Earth. Most of the data discussed above were provided on a 1 • × 1 • (longitude × latitude) grid which was an appropriate target for the CMIP5 generation of models. More recently, data are being included at the highest gridded resolution available rather than mapping them to another grid. Calculation of monthly averages, which may be nontrivial especially for data derived from polar-orbiting instruments, is determined on a case-by-case basis and is described in the "Tech Note" of each product. Most of the products that have been introduced into obs4MIPs to date are based on satellite measurements, but other gridded products based on in situ measurements are envisioned to become a part of an expanding set of gridded products available via obs4MIPs.
Expanding the scope and contributions Since its inception, obs4MIPs has continually engaged the climate modeling and model evaluation communities and endeavored to make them aware of its progress. Awareness and community support were fostered in part through the publications and workshops mentioned above  (Gleckler et al. , 2011; Teixeira et al. , 2014; Ferraro et al. , 2015) , as well as through the WCRP, the Committee on Earth Observing Satellites (CEOS), and the Coordination Group of Meteorological Satellites (CGMS) through their Joint Working Group on Climate (JWGC). 2  The JWGC published in 2017 an inventory 3  integrating information on available and planned satellite datasets from all CGMS and CEOS agencies. The inventory is updated annually and serves as one resource of candidate datasets that might be suitable for obs4MIPs.
, 2017; Webb et al. , 2017 , for recent summary information), and it was aligned with the obs4MIPs strategy and goals. Crucially, this alignment included the use of CF-compliant format, hosting the data on the ESGF, and having a focus on observed quan-tities and diagnostics that are fully consistent with outputs from the CFMIP Observations Simulator Package (COSP;  Bodas-Salcedo et al. , 2011)  for the evaluation of clouds and radiation in numerical models. Based on this relatively close alignment, CFMIP provided over 20 satellite-based observed quantities as contributions to obs4MIPs. These include a number of cloud and aerosol variables from CALIPSO, CloudSat, and the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite missions as well as the International Satellite Cloud Climatology Project (ISCCP).
ESA established the Climate Modeling User Group (CMUG) 5  to provide a climate system perspective at the center of its Climate Change Initiative (CCI)  6  and to host a dedicated forum bringing the Earth observation and climate modeling communities together. Having started at approximately the same time as obs4MIPs with overlapping goals, communication between the two activities was established at the outset. Through the CCI, a number of global datasets were being produced that overlapped with the model evaluation goals of obs4MIPs, and CMUG/CCI succeeded in making early contributions to obs4MIPs. These included an SST product developed from the Along Track Scanning Radiometers (ATSR) aboard ESA's ERS-1, ERS-2, and Envisat satellites, specifically the ATSR Reprocessing for Climate (ARC) product, as well as the ESA GlobVapour project merged MERIS and EUMETSAT's SSM/I water vapor column product.
The growing international and multiagency interest in obs4MIPs and its initial success meant there was potential to broaden the support structure of obs4MIPs and further expand international involvement. The establishment of the WCRP Data Advisory Council (WDAC) 7  in late 2011 provided a timely opportunity to foster further development. During 2012, as the WDAC developed its priorities and identified initial projects to focus on, obs4MIPs was proposed as an activity that could contribute to the objectives of the WDAC and could be served by WDAC oversight and promotion. Based on this proposal and ensuing discussions, a WDAC Task Team on Observations for Model Evaluation (subsequently here, simply the "Task Team") was formed in early 2013. The terms of reference for the Task Team included (1) establishing data and metadata standards for observational and reanalysis datasets consistent with those used in major climate model intercomparison efforts, (2) encouraging the application of these standards to observational datasets with demonstrated utility for model evaluation, (3) eliciting community input and providing guidance and oversight to establish criteria and a process by which candidate obs4MIPs datasets might be accepted for inclusion, (4) assisting in the coordination of obs4MIPs and re-lated observation-focused projects (e.
, 2015). The meeting, held in late spring of 2014 at NASA Headquarters, was sponsored by DOE, NASA, and WCRP. It brought together over 50 experts in both climate modeling and satellite data from the United States, Europe, Japan, and Australia. The objectives for the meeting included the following: (1) review and assess the framework, working guidelines, holdings, and ESGF implementation of obs4MIPs in the context of CMIP model evaluation; (2) identify underutilized and potentially valuable satellite observations and reanalysis products for climate model evaluation, in conjunction with a review of CMIP model output specifications, and recommend changes and additions to datasets and model output to achieve better alignment; and (3) provide recommendations for new observation datasets that target critical voids in model evaluation capabilities, including important phenomena, subgrid-scale features, higher temporal sampling, in situ and regional datasets, and holistic Earth system considerations (e.
g. , carbon cycle and composition). Apart from recommendations of specific datasets to include in obs4MIPs in preparation for CMIP6, there were several consensus recommendations that have driven subsequent and recent obs4MIPs developments and expansion activities: expand the inventory of datasets hosted by obs4MIPs, include higher-frequency datasets and higher-frequency model output, provide more information on the degree of correspondence between model and observations. - For more details on the discussion and associated recommendations, see  Ferraro et al. (2015). In the following section, we highlight the considerations and progress that have been made towards these and other recommendations for expanding and improving obs4MIPs.
Improvements and implementation status for CMIP6 With the recommendations of the planning meeting in hand and with CMIP6 imminent, a number of actions were taken by the obs4MIPs Task Team and the CMIP Panel (a WCRP group that oversees CMIP). For the most part, these have provided the means to widen the inventory, to make the process of contributing datasets to obs4MIPs more straightforward, and to develop additional features that benefit the users.
Additional obs4MIPs datasets CMIP6-endorsed MIPs were required to specify the model output they needed to perform useful analyses  (Eyring et al. , 2016) , and these formed what is now the CMIP6 data request  (Juckes et al. , 2020 ). The obs4MIPs Task Team responded by encouraging and promoting a wider range of observationbased datasets and released a solicitation for new datasets in the fall of 2015 that added emphasis on higher frequency, as well as basin-to global-scale gridded in situ data. The solicitation also placed a high priority on data products that might be of direct relevance to the CMIP6-endorsed model intercomparison projects. 10 The outcomes of the solicitation and status of the obs4MIPs holdings are described below. As of August 2019, the holdings for obs4MIPs 11  include over 80 observational datasets.
12 The datasets include contributions from NASA, ESA, CNES, JAXA, and NOAA, with the data being hosted at a number of ESGF data nodes, including PCMDI (LLNL), IPSL, GSFC (NASA), GFDL (NOAA), British Atmospheric Data Centre (BADC), and the German Climate Computing Center (DKRZ). Along with the previously discussed datasets, there are additional SST and water vapor products, and outgoing longwave radiation (OLR) and sea ice datasets. Some of these include both daily and monthly sampled data. There are a number of datasets that have been provided through the ESA CCI effort, including aerosol optical thickness contribution from the ATSR-2 and AATSR missions, ocean wind speed from SSM/I, total column methane and CO 2 from ESA, and a near-surface ship-based CO 2 product from the Surface Ocean CO2 Atlas (SOCAT); the latter three are particularly important for the carbon cycle component of Earth system models.
A new and somewhat novel dataset is expected to be contributed which will provide regional OLR data based on the Geostationary Earth Radiation Budget (GERB) instrument aboard EUMETSAT's geostationary operational weather satellites. In this case, the data coverage is for Europe and Africa only but with sampling that resolves the diurnal cycle. In the fall of 2015, the Task Team raised awareness of obs4MIPs by explicitly inviting the observational community to contribute to obs4MIPs. The call, which was communicated by WCRP and through other channels, set the end of March 2016 as the deadline for submission. The call made explicit the desire to include observational datasets that had a regional focus, provided higher-frequency sampling, and in particular were aligned with CMIP6 experimentation and model output  (Eyring et al.
, 2016). The response to this call resulted in proposals for nearly 100 new datasets, with several notable new contribution types. This includes proposals for a number of in situ gridded products, merged in situ and satellite products, and regional datasets. Examples include global surface temperature, multivariate ocean and land surface fluxes, sea ice and snow, ice sheet mass changes, ozone, complete regional aggregate water and energy budget products, soil moisture, cloud, aerosol, temperature and humidity profiles, surface radiative flux, and chlorophyll concentrations. Not long after polling the observational community about possible additions to obs4MIPs, efforts began in earnest within the CMIP community to dramatically expand the CMIP5 model output lists for CMIP6. This expansion was primarily driven by the more comprehensive experimental design for CMIP6 and desire for more in-depth model diagnosis and secondarily by the greater availability of observations.
It soon became clear that despite risks of slowing the momentum of obs4MIPs, it was better to postpone the inclusion of new datasets until the data standards for CMIP6 were solidified. This took more than 2 years (given CMIP6's scope and complexity), and only when that effort was largely completed in late 2017 was it possible to begin working to ensure that obs4MIPs data standards would remain technically close to those of CMIP.
Obs4MIPs data specifications (ODS) The primary purpose of obs4MIPs is to facilitate comparison of observational data to model output from WCRP intercomparison projects, notably CMIP. To accomplish this, the organization of CMIP and obs4MIPs data must be closely aligned, including the data structure and metadata requirements and how they are ingested into the Earth System Grid Federation (ESGF) infrastructure, which is relied on for searching and accessing the data. The original set of obs4MIPs dataset contributions adhered to guidelines (ODS V1. 0, circa 2012) that were based on the CMIP5 data specifications. Now, the obs4MIPs data specifications have been refined to be largely consistent with the CMIP6 data specifications, which will not change until the community begins to configure a next generation (CMIP7).
Updates to the Obs4MIPs Data Specifications (ODS2. 1) include accommodation via global attributes that allow for unique identification of datasets and associated institutions, source types, and dataset versions (i. e. , types of observa-D. Waliser et al. :  Obs4MIPs: status for CMIP6 tions). 13 In addition, the global attributes are constructed to facilitate organization of the obs4MIPs datasets and in particular for providing a useful set of options (or facets) for data exploration via the ESGF search engine. Meeting the obs4MIPs (or CMIP6) data requirements is facilitated by using the Climate Model Output Rewriter (CMOR3;  Doutriaux et al. , 2017). 14 Use of CMOR3 is not required for producing obs4MIPs data, but it is strongly recommended because CMOR3 ensures that the necessary metadata for distributed data searching are included.
The version of CMOR used in the initial phase of obs4MIPs was designed for model output, and some special adaptations were required when applying it to various gridded observations. Fortunately, during the period when the CMIP6/obs4MIPs data standards were being developed, important improvements were made to CMOR3 which included streamlining how it could be used for processing gridded observations. With the updates to ODS2. 1 and CMOR3 completed, 15  new and revised datasets are once again being added to obs4MIPs, and with additional enhancements in place (Sect. 4. 1-4. 4), that effort is expected to be the main priority for obs4MIPs throughout the research phase of CMIP6. For data providers interested in contributing to obs4MIPs, please see "How to Contribute" on the obs4MIPs website. 16 Efforts to further improve the process, as well as additional considerations for future directions, are discussed in Sect.
Obs4MIPs dataset indicators Obs4MIPs has implemented a set of dataset indicators that provide information on a dataset's technical compliance with obs4MIPs standards and its suitability for climate model evaluation. The motivation for including this information is twofold. First, the indicators provide users with an overview of key features of a given dataset's suitability for model evaluation. For example, does the dataset adhere to the key requirements of obs4MIPs (e. g. , having a technical note and adhering to the obs4MIPs data specifications that is required to enable ESGF searching)? Similarly, are model and observation comparisons expected to be straightforward (e. g. , is direct comparison with model output possible or will it require the use of special coding applied to the model output to make it comparable)? Another relevant consideration is the degree to which the dataset has previously been used for model evaluation and whether publications exist that document such use.
Second, the indicators allow for a wider spectrum of observations to be included in obs4MIPs. In the ini-tial stages of obs4MIPs, only relatively mature datasetsthose already widely adopted by the climate model evaluation community -were considered acceptable. While this helped ensure the contributions were relevant for model evaluation, it also limited the opportunity for other or newer datasets to be exposed for potential use in model evaluation. The establishment of the indicators will facilitate the monitoring and characterization of the increasingly broad set of obs4MIPs products hosted on the ESGF and will guide users in determining which observational datasets might be best suited for their purposes. There are six indicators grouped into three categories: two indicators are associated with obs4MIPs technical requirements, three indicators are related to measures of dataset maturity and suitability for climate model evaluation, and one indicator is a measure of the comparison complexity associated with using the observation for model evaluation.
These indicators, grouped by these categories, along with their potential values are given in Fig. 2a. Each of the values is color coded so that the indicators can be readily shown in a dataset search as illustrated by Fig. 2b. In the present framework, still to be fully exercised, the values of the indicators for a given dataset are intended to be assigned, in consultation with the dataset provider, by the obs4MIPs Task Team. Note that the values of the indicators can change over time as a dataset and/or its use for model evaluation matures or as the degree to which the dataset aligns with obs4MIPs technical requirements improves. To accommodate this, the values of the indicators will be version-controlled via the obs4MIPs GitHub repository. Additional information on the indicators and how they are assigned can be found on the obs4MPs website.
Obs4MIPs dataset supplemental information As a result of the obs4MIPs-CMIP6 meeting in 2015  (Ferraro et al. , 2015) , many data providers and users made the case that obs4MIPs should accommodate optional inclusion of ancillary information with a dataset. Ancillary information might include quantitative uncertainty information, codes that provide transfer functions or forward models to enable a closer comparison between models and observations, the ability to include data flags, verification data, additional technical information, etc. Note that with the new obs4MIPs data specifications, "observational ensembles" (which provide a range of observationally based estimates of a variable that might result from reasonable processing choices of actually measured quantities) are accommodated as a special dataset type and are not relegated to "Supplemental Infor-mation".
The inclusion of Supplemental Information for an obs4MIPs dataset is optional, and the provision for accommodating such information is considered a "feature" of the current framework of obs4MIPs (see example in Sect. 4. 5). In the future, there may be better ways to accommodate such information, as one particular limitation is that the Supplemental Information is not searchable from the ESGF search engine, although its existence is readily apparent and accessible once a particular dataset is located via a search. Additional information for data providers on how to include supplementary information is available on the obs4MPs website.
Example datasets and model and observation comparison Here we illustrate how the obs4MIPs conventions and infrastructure are applied using CERES outgoing longwave radiation and TES ozone. First, following the obs4MIPs data specifications (ODS2. 1; Sect. 4. 2), data contributors provide some basic "registered content" (RC; see footnote 14) which includes a "source_id", identifying the common name of the dataset (e. g. , CERES) and version number (e. g. , v4. 0). The source_id (CERES-4-0) identifies at a high level the dataset version, which in some cases (as with CERES) applies for more than one variable. Another attribute is "region" which for CERES is identified as "global". Controlled vocabulary (CV) provides many options for the region attribute as defined by the CF conventions. Yet another example is the "Nominal Resolution", providing an approximate spatial resolution which in the case of the CERES-4-0 data is "1 × 1 • ".