Suchergebnisse

49 pags, 10 figs, 2 tabs. -- Supplementary data is available at the Publisher's web ; The goal of the Tropospheric Ozone Assessment Report (TOAR) is to provide the research community with an up-to-date scientific assessment of tropospheric ozone, from the surface to the tropopause. While a suite of observations provides significant information on the spatial and temporal distribution of tropospheric ozone, observational gaps make it necessary to use global atmospheric chemistry models to synthesize our understanding of the processes and variables that control tropospheric ozone abundance and its variability. Models facilitate the interpretation of the observations and allow us to make projections of future tropospheric ozone and trace gas distributions for different anthropogenic or natural perturbations. This paper assesses the skill of current-generation global atmospheric chemistry models in simulating the observed present-day tropospheric ozone distribution, variability, and trends. Drawing upon the results of recent international multi-model intercomparisons and using a range of model evaluation techniques, we demonstrate that global chemistry models are broadly skillful in capturing the spatio-temporal variations of tropospheric ozone over the seasonal cycle, for extreme pollution episodes, and changes over interannual to decadal periods. However, models are consistently biased high in the northern hemisphere and biased low in the southern hemisphere, throughout the depth of the troposphere, and are unable to replicate particular metrics that define the longer term trends in tropospheric ozone as derived from some background sites. When the models compare unfavorably against observations, we discuss the potential causes of model biases and propose directions for future developments, including improved evaluations that may be able to better diagnose the root cause of the model-observation disparity. Overall, model results should be approached critically, including determining whether the model performance is acceptable for the problem being addressed, whether biases can be tolerated or corrected, whether the model is appropriately constituted, and whether there is a way to satisfactorily quantify the uncertainty. ; A portion of the work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the NASA Aeronautics and Space Administration. A portion of the work was carried out the National Center for Atmospheric Research, which is operated by the University Corporation for Atmospheric Research under sponsorship of the National Science Foundation. PY acknowledges support from the Faculty of Science and Technology, Lancaster University. JB and UI acknowledge NordForsk under the Nordic Programme on Health and Welfare Project #75007: Understanding the link between air pollution and distribution of related health impacts and welfare in the Nordic countries (Nordic Welf Air); and the H2020-LCE project: Role of technologies in an energy efficient economy – model based analysis policy measures and transformation pathways to a sustainable energy system (REEEM), Grant agreement no.: 691739. GZ acknowledges the New Zealand Government's Strategic Science Investment Fund (SSIF) through the NIWA programme CACV. This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N027736/1] and the Natural Environment Research Council [grant number NE/N003411/1]. ; Peer reviewed

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.

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.