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Abstract. Over the past decade Europe has been hit by a number of severe flood events. Reviews of recent large flood events in England and France have indicated that there is room for improvement in the emergency planning for floods. Methods that can be used for the systematic assessment and improvement of emergency plans are extensively documented in readily available literature. However, those that do exist are often limited to appraising the content of the plans rather than the process that the plan should guide. This paper describes research to develop a systematic method for assessing and improving emergency plans, which is called the FIM FRAME method. The development of the method was informed by research carried out with stakeholders in France, the Netherlands and England, as well as an appraisal of available tools that can be used to develop and improve plans, and an analysis of a selection of flood emergency plans from the three countries. One of the fundamental requirements of the FIM FRAME method was that it should be able to be applied by the relevant stakeholders to a range of emergency plans that mainly focus on flooding. The method comprises a series of steps (known as Appraise, Tackle and Implement) that can assist stakeholders with assessing and improving emergency plans. The method was piloted in the three countries and then refined following feedback from end users. This paper describes the development of the FIM FRAME method and its application in three case studies affected by different types of floods.

BACKGROUND: Involving adults lacking capacity (ALC) in research on end of life care (EoLC) or serious illness is important, but often omitted. We aimed to develop evidence-based guidance on how best to include individuals with impaired capacity nearing the end of life in research, by identifying the challenges and solutions for processes of consent across the capacity spectrum. METHODS: Methods Of Researching End of Life Care_Capacity (MORECare_C) furthers the MORECare statement on research evaluating EoLC. We used simultaneous methods of systematic review and transparent expert consultation (TEC). The systematic review involved four electronic databases searches. The eligibility criteria identified studies involving adults with serious illness and impaired capacity, and methods for recruitment in research, implementing the research methods, and exploring public attitudes. The TEC involved stakeholder consultation to discuss and generate recommendations, and a Delphi survey and an expert 'think-tank' to explore consensus. We narratively synthesised the literature mapping processes of consent with recruitment outcomes, solutions, and challenges. We explored recommendation consensus using descriptive statistics. Synthesis of all the findings informed the guidance statement. RESULTS: Of the 5539 articles identified, 91 met eligibility. The studies encompassed people with dementia (27%) and in palliative care (18%). Seventy-five percent used observational designs. Studies on research methods (37 studies) focused on processes of proxy decision-making, advance consent, and deferred consent. Studies implementing research methods (30 studies) demonstrated the role of family members as both proxy decision-makers and supporting decision-making for the person with impaired capacity. The TEC involved 43 participants who generated 29 recommendations, with consensus that indicated. Key areas were the timeliness of the consent process and maximising an individual's decisional capacity. The think-tank (n = 19) refined equivocal recommendations including supporting proxy decision-makers, training practitioners, and incorporating legislative frameworks. CONCLUSIONS: The MORECare_C statement details 20 solutions to recruit ALC nearing the EoL in research. The statement provides much needed guidance to enrol individuals with serious illness in research. Key is involving family members early and designing study procedures to accommodate variable and changeable levels of capacity. The statement demonstrates the ethical imperative and processes of recruiting adults across the capacity spectrum in varying populations and settings.

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.