The Earth System Grid Federation (https://esgf.nci.org.au/projects/esgf_nci/) is a collaboration of international institutions that maintains the infrastructure to manage and disseminate the Coupled Model Intercomparison Project (CMIP) and related MIP datasets. Further information on ESGF can be found on the PCDMI website: https://esgf.llnl.gov/ ESGF data published through NCI from the Australian climate community include data under the four streams: 1. CMIP5 2. CORDEX 3. GeoMIP 4. PMIP3 For further information on each see the linked child pages pages. All CMIP5-era Australian published data is contained under the project rr3 on the global filesystems.

The US CLIVAR Working Group on Large "Initial-Condition" Earth System Model Ensembles (LEs) was formed in March 2018. The intent of the working group is to spearhead a dedicated effort across the national and international climate communities to advance progress on identifying anthropogenic influences on weather and climate amidst the "noise" of internal variability. The timeliness and cross-cutting nature of the problem, together with promising new observational and modelling approaches, means this working group has an unprecedented opportunity to make substantive progress on this tractable issue. Reference: https://usclivar.org/working-groups/large-ensemble-working-group? The Australian contribution to the LE-MIP includes output derived from the CSIRO-MK3-6-0 model and covers historical and rcp85 scenarios, described in Rotstayn, L. D., Jeffrey, S. J., Collier, M. A., Dravitzki, S. M., Hirst, A. C., Syktus, J. I., and Wong, K. K. (2012). Aerosol- and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations, Atmos. Chem. Phys., 12, 6377-6404, doi:10.5194/acp-12-6377-2012. Note that the first 10 ensembles or experiments of LE-MIP were published as one model within the CMIP5 multi-model submission, however, the metadata has been modified somewhat to reflect the LE-MIP set. The LE-MIP parameter list/set is a smaller subset of that submitted to CMIP5, however, daily sets are available for all years compared to typically only a few specific year ranges in CMIP5.

The EDGAR (Emission Database for Global Atmospheric Research) database project is a comprehensive task carried out jointly by the National Institute for Public Health (RIVM) and the Netherlands Organization for Applied Scientific Research (TNO) and stores global emission inventories of direct and indirect greenhouse gases from anthropogenic sources including halocarbons and aerosols both on a per country and region basis as well as on a grid (see http://www.rivm.nl/edgar/). For the ISLSCP Initiative II data collection, gridded global annual anthropogenic emissions for the greenhouse gases CO2, CH4, N2O are provided on a 1.0 degree by 1.0 degree grid for the years 1970, 1980, 1990, and 1995 and for the tropospheric ozone precursor gases CO, NOx, NMVOC (Non-Methane Volatile Organic Compounds) and SO2 for the years 1990 and 1995. There are 2 *.zip data files with this data set.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) LEOLSTCMG30 version 1 Climate Modeling Grid (CMG) product provides Land Surface Temperature (LST) derived from the Low Earth Orbit (LEO) satellite data record from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments as well as LST error estimates for both day and night. The product will include global LST produced on CMG at monthly timesteps from 2002 to 2020. The MEaSUREs LEOLST product is generated by regridding the monthly LST CMG products from MODIS (MYD21C3.061) and VIIRS (VNP21C3.002). The product will be available on 0.25, 0.5, and 1 degree optimized climate grids with well characterized per-pixel uncertainties. A low-resolution browse is also available showing LST as an RGB (red, green, blue) image in PNG format. Known Issues * Users should be aware that in v001 of the Low Earth Orbit Land Surface Temperature Monthly Global Gridded product (LEOLSTCMG30) the nighttime LST error estimates (LST_Night_err_*) in the one degree, half degree, and quarter degree SDS layers were erroneously filled with zero values and should not be used for any scientific data analyses.

sstClim is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (http://cmip-pcmdi.llnl.gov/cmip5/). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 6.2a sstClim (6.2a Control SST Climatology) - Version 1: AMIP-style experiment with control run climatological SSTs and sea ice. Experiment design: http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf List of output variables: http://cmip-pcmdi.llnl.gov/cmip5/docs/standard_output.pdf Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

The Ground-Based Global Positioning System (GPS) Meteorology Integrated Precipitable Water Vapor (IPW) data set measures atmospheric water vapor using ground-based GPS receivers. The data contain observations from several hundred locations around the globe every 30 minutes from 2002-05-01 to 2016-11-28. However, most locations lie within the continental United States. The data set was formed in response to the need for improved moisture observations to support weather forecasting, climate monitoring, and research. The data set contains total precipitable water estimates, GPS total signal delay, GPS hydrostatic signal delay, GPS wet signal delay, surface temperature, surface pressure, mean-weighted surface temperature, and the wet delay mapping function. The GPS-IPW network processes data from both NOAA and other agency CORS (Continuously Operating Reference Sites) sites. All sites are equipped with a GPS receiver and many are equipped with a surface meteorological instrumentation package. GPS satellite observation are combined with GPS satellite orbit and earth orientation parameters to estimate GPS signal delay (Zenith Total Delay -- ZTD). Signal delays are then combined with surface meteorological information are used to estimate total precipitable water. For sites without surface meteorology sensors, data from nearby ASOS (Automated Surface Observing System) systems were used. Data set variables and their resolution: total precipitable water; 0.001 m, GPS total signal delay; 0.001m, GPS hydrostatic signal delay; 0.001m, GPS wet signal delay; 0.001m, surface temperature; 0.1 K, surface pressure; 0.1 hpa, mean-weighted surface temperature; 0.1 K, wet delay mapping function; 0.1 (dimensionless). Late updated in November 2016 with no plans for updating at this time due to funding.

amip is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (http://cmip-pcmdi.llnl.gov/cmip5/). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 3.3 amip (3.3 AMIP) - Version 1: AMIP (1979 - at least 2008). Impose SSTs and sea ice from observations but with other conditions as in experiment 3.2 historical. Experiment design: http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf List of output variables: http://cmip-pcmdi.llnl.gov/cmip5/docs/standard_output.pdf Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.