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We present an overview of state-of-The-Art chemistry-climate and chemistry transport models that are used within phase 1 of the Chemistry-Climate Model Initiative (CCMI-1). The CCMI aims to conduct a detailed evaluation of participating models using process-oriented diagnostics derived from observations in order to gain confidence in the models' projections of the stratospheric ozone layer, tropospheric composition, air quality, where applicable global climate change, and the interactions between them. Interpretation of these diagnostics requires detailed knowledge of the radiative, chemical, dynamical, and physical processes incorporated in the models. Also an understanding of the degree to which CCMI-1 recommendations for simulations have been followed is necessary to understand model responses to anthropogenic and natural forcing and also to explain intermodel differences. This becomes even more important given the ongoing development and the ever-growing complexity of these models. This paper also provides an overview of the available CCMI-1 simulations with the aim of informing CCMI data users. ; This work has been supported by NIWA as part of its government-funded, core research. Olaf Morgenstern acknowledges support from the Royal Society Marsden Fund, grant 12-NIW-006, and under the Deep South National Science Challenge. The authors wish to acknowledge the contribution of NeSI high-performance computing facilities to the results of this research. New Zealand's national facilities are provided by the New Zealand eScience Infrastructure (NeSI) and funded jointly by NeSI's collaborator institutions and through the Ministry of Business, Innovation & Employment's Research Infrastructure programme (https://www.nesi.org.nz). The SOCOL team acknowledges support from the Swiss National Science Foundation under grant agreement CRSII2_147659 (FUPSOL II). CCSRNIES's research was supported by the Environment Research and Technology Development Fund (2-1303) of the Ministry of the Environment, Japan, and computations were performed on NEC-SX9/A(ECO) computers at the CGER, NIES. Wuhu Feng (NCAS) provided support for the TOMCAT simulations. Neal Butchart, Steven C. Hardiman, and Fiona M. O'Connor and the development of HadGEM3-ES were supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). Neal Butchart and Steven C. Hardiman also acknowledge additional support from the European Project 603557-STRATOCLIM under the FP7-ENV.2013.6.1-2 programme. Fiona M. O'Connor acknowledges additional support from the Horizon 2020 European Union's Framework Programme for Research and Innovation CRESCENDO project under grant agreement no. 641816. Slimane Bekki acknowledges support from the European Project 603557-STRATOCLIM under the FP7-ENV.2013.6.1-2 programme and from the Centre National d'Etudes Spatiales (CNES, France) within the SOLSPEC project. Kane Stone and Robyn Schofield acknowledge funding from the Australian Government's Australian Antarctic science grant program (FoRCES 4012), the Australian Research Council's Centre of Excellence for Climate System Science (CE110001028), the Commonwealth Department of the Environment (grant 2011/16853), and computational support from National computational infrastructure INCMAS project q90. The CNRM-CM chemistry–climate people acknowledge the support from Météo-France, CNRS, and CERFACS, and in particular the work of the entire team in charge of the CNRM/CERFACS climate model.

The Montreal Protocol (MP) controls the production and consumption of carbon tetrachloride (CCl4 or CTC) and other ozone-depleting substances (ODSs) for emissive uses. CCl4 is a major ODS, accounting for about 12% of the globally averaged inorganic chlorine and bromine in the stratosphere, compared to 14% for CFC-12 in 2012. In spite of the MP controls, there are large ongoing emissions of CCl4 into the atmosphere. Estimates of emissions from various techniques ought to yield similar numbers. However, the recent WMO/UNEP Scientific Assessment of Ozone Depletion [WMO, 2014] estimated a 2007-2012 CCl4 bottom-up emission of 1-4 Gg/year (1-4 kilotonnes/year), based on country-by-country reports to UNEP, and a global top-down emissions estimate of 57 Gg/ year, based on atmospheric measurements. This 54 Gg/year difference has not been explained. In order to assess the current knowledge on global CCl4 sources and sinks, stakeholders from industrial, governmental, and the scientific communities came together at the "Solving the Mystery of Carbon Tetrachloride" workshop, which was held from 4-6 October 2015 at Empa in Dübendorf, Switzerland. During this workshop, several new findings were brought forward by the participants on CCl4 emissions and related science. • Anthropogenic production and consumption for feedstock and process agent uses (e.g., as approved solvents) are reported to UNEP under the MP. Based on these numbers, global bottom-up emissions of 3 (0-8) Gg/year are estimated for 2007-2013 in this report. This number is also reasonably consistent with this report's new industry-based bottom-up estimate for fugitive emissions of 2 Gg/year. • By-product emissions from chloromethanes and perchloroethylene plants are newly proposed in this report as significant CCl4 sources, with global emissions estimated from these plants to be 13 Gg/year in 2014. • This report updates the anthropogenic CCl4 emissions estimation as a maximum of ~25 Gg/year. This number is derived by combining the above fugitive and by-product emissions (2 Gg/year and 13 Gg/year, respectively) with 10 Gg/year from legacy emissions plus potential unreported inadvertent emissions from other sources. • Ongoing atmospheric CCl4 measurements within global networks have been exploited for assessing regional emissions. In addition to existing emissions estimates from China and Australia, the workshop prompted research on emissions in the U.S. and Europe. The sum of these four regional emissions is estimated as 21±7.5a Gg/year, but this is not a complete global accounting. These regional top-down emissions estimates also show that most of the CCl4 emissions originate from chemical industrial regions, and are not linked to major population centres. • The total CCl4 lifetime is critical for calculating top-down global emissions. CCl4 is destroyed in the stratosphere, oceans, and soils, complicating the total lifetime estimate. The atmospheric lifetime with respect to stratospheric loss was recently revised to 44 (36-58) years, and remains unchanged in this report. New findings from additional measurement campaigns and reanalysis of physical parameters lead to changes in the ocean lifetime from 94 years to 210 (157-313) years, and in the soil lifetime from 195 years to 375 (288-536) years. • These revised lifetimes lead to an increase of the total lifetime from 26 years in WMO [2014] to 33 (28-41) years. Consequently, CCl4 is lost at a slower rate from the atmosphere. With this new total lifetime, the global top-down emissions calculation decreases from 57 (40-74) Gg/year in WMO [2014] to 40 (25-55) Gg/year. This estimate is relatively consistent with the independent gradient top-down emissions of 30 (25-35) Gg/year, based upon differences between atmospheric measurements of CCl4 in the Northern and Southern Hemispheres. In addition, this new total lifetime implies an upper limit of 3-4 Gg/year of natural emissions, based upon newly reported observations of old air in firn snow. These new CCl4 emissions estimates from the workshop make considerable progress toward closing the emissions discrepancy. The new industrial bottom-up emissions estimate (15 Gg/year total) includes emissions from chloromethanes plants (13 Gg/year) and feedstock fugitive emissions (2 Gg/year). When combined with legacy emissions and unreported inadvertent emissions, this could be up to 25 Gg/year. Top-down emissions estimates are: global 40 (25-55) Gg/year, gradient 30 (25-35) Gg/year, and regional 21 (14-28) Gg/year. While the new bottom-up value is still less than the aggregated top-down values, these estimates reconcile the CCl4 budget discrepancy when considered at the edges of their uncertainties. ; Peer reviewed

30 pags., 11 figs., 5 tabs. ; We quantify the stratospheric injection of brominated very short-lived substances (VSLS) based on aircraft observations acquired in winter 2014 above the Tropical Western Pacific during the CONvective TRansport of Active Species in the Tropics (CONTRAST) and the Airborne Tropical TRopopause EXperiment (ATTREX) campaigns. The overall contribution of VSLS to stratospheric bromine was determined to be 5.0 ± 2.1 ppt, in agreement with the 5 ± 3 ppt estimate provided in the 2014 World Meteorological Organization (WMO) Ozone Assessment report (WMO 2014), but with lower uncertainty. Measurements of organic bromine compounds, including VSLS, were analyzed using CFC-11 as a reference stratospheric tracer. From this analysis, 2.9 ± 0.6 ppt of bromine enters the stratosphere via organic source gas injection of VSLS. This value is two times the mean bromine content of VSLS measured at the tropical tropopause, for regions outside of the Tropical Western Pacific, summarized in WMO 2014. A photochemical box model, constrained to CONTRAST observations, was used to estimate inorganic bromine from measurements of BrO collected by two instruments. The analysis indicates that 2.1 ± 2.1 ppt of bromine enters the stratosphere via inorganic product gas injection. We also examine the representation of brominated VSLS within 14 global models that participated in the Chemistry-Climate Model Initiative. The representation of stratospheric bromine in these models generally lies within the range of our empirical estimate. Models that include explicit representations of VSLS compare better with bromine observations in the lower stratosphere than models that utilize longer-lived chemicals as a surrogate for VSLS. ; The CONTRAST field deployment was supported by the U.S. NSF, and the ATTREX field deployment was supported by the National Aeronautics and Space Administration (NASA). P. A. W., R. J. S., T. P. C., J. M. N., and D. C. A. received support from NSF, NASA Atmospheric Composition Modeling and Analysis Program (ACMAP), and the NASA Modeling, Analysis, and Prediction (MAP). D. C. A. also received support from the NASA Upper Atmospheric Research Program. J. M. N. was also supported by the NASA Postdoctoral Program at the NASA Goddard Space Flight Center, administered by Universities Space Research Association under contract with NASA. R. V. acknowledges funding from NSF awards AGS‐1261740 and AGS‐1620530. CONTRAST data are publicly available at "http://data.eol.ucar.edu/master_list/?project= CONTRAST." ATTREX data are publicly available at "https://espoarchive.nasa.gov/archive/browse/attrex/id4/GHawk." The National Center for Environmental Prediction (NCEP) meteorological data are available at "https://doi.org/10.5065/D6M043C6." CCMI outputs from CESM1‐WACCM and CESM1‐CAM4Chem are archived by the National Center for Atmospheric Research (NCAR) at "www.earthsystemgrid.org," and NCAR is sponsored by NSF. CCMI output from the EMAC‐L90MA‐SD simulation is available at "https://doi.org/10.5281/zenodo.1204495." All other CCMI simulations are archived by the British Atmospheric Data Centre at "http://badc.nerc.ac.uk/". Output from CAM‐chem‐SD is available as "NCAR/ACD CAMChem 1 Degree Forecast" at "http://catalog.eol.ucar.edu/contrast/model/CAMChem_NCAR_1deg/." WACCM and CAM‐Chem are components of the Community Earth System Model (CESM), which is also supported by NSF. Computing resources were provided by NCAR's Climate Simulation Laboratory, sponsored by NSF and other agencies. This research was enabled by the computational and storage resources of NCAR's Computational and Information System Laboratory (CISL). R. S. and K. A. S., with ACCESS‐CCM, acknowledge support from Australian Research Council's Centre of Excellence for Climate System Science (CE110001028), the Australian Government's National Computational Merit Allocation Scheme (q90), and Australian Antarctic science grant program (FoRCES 4012). CCSRNIES research was supported by the Environment Research and Technology Development Fund (2‐1303 and 2‐1709) of the Ministry of the Environment, Japan, and computations were performed on NEC‐SX9/A(ECO) computers at the CGER, NIES. The EMAC simulations have been performed at the German Climate Computing Centre (DKRZ) through support from the Bundesministerium für Bildung und Forschung (BMBF). DKRZ and its scientific steering committee are gratefully acknowledged for providing the HPC and data archiving resources for the consortial project ESCiMo (Earth System Chemistry integrated Modelling). The TOMCAT modeling was supported by NERC NCAS and the SISLAC project (NE/R001782/1), and the simulations were performed on the Archer and Leeds HPC Systems.