Abstract. Three types of reference simulations, as recommended by the Chemistry-Climate Model Initiative (CCMI), have been performed with version 2.51 of the European Centre for Medium-Range Weather Forecasts -Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model: hindcast simulations , hindcast simulations with specified dynamics , i.e. nudged towards ERA-Interim reanalysis data, and combined hindcast and projection simulations . The manuscript summarizes the updates of the model system and details the different model set-ups used, including the on-line calculated diagnostics. Simulations have been performed with two different nudging setups, with and without interactive tropospheric aerosol, and with and without a coupled ocean model. Two different vertical resolutions have been applied. The on-line calculated sources and sinks of reactive species are quantified and a first evaluation of the simulation results from a global perspective is provided as a quality check of the data. The focus is on the intercomparison of the different model set-ups. The simulation data will become publicly available via CCMI and the Climate and Environmental Retrieval and Archive (CERA) database of the German Climate Computing Centre (DKRZ). This manuscript is intended to serve as an extensive reference for further analyses of the Earth System Chemistry integrated Modelling (ESCiMo) simulations.

Simulations from eleven coupled chemistry‐climate models (CCMs) employing nearly identical forcings have been used to project the evolution of stratospheric ozone throughout the 21st century. The model‐to‐model agreement in projected temperature trends is good, and all CCMs predict continued, global mean cooling of the stratosphere over the next 5 decades, increasing from around 0.25 K/decade at 50 hPa to around 1 K/decade at 1 hPa under the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. In general, the simulated ozone evolution is mainly determined by decreases in halogen concentrations and continued cooling of the global stratosphere due to increases in greenhouse gases (GHGs). Column ozone is projected to increase as stratospheric halogen concentrations return to 1980s levels. Because of ozone increases in the middle and upper stratosphere due to GHG‐induced cooling, total ozone averaged over midlatitudes, outside the polar regions, and globally, is projected to increase to 1980 values between 2035 and 2050 and before lower‐stratospheric halogen amounts decrease to 1980 values. In the polar regions the CCMs simulate small temperature trends in the first and second half of the 21st century in midwinter. Differences in stratospheric inorganic chlorine (Cly) among the CCMs are key to diagnosing the intermodel differences in simulated ozone recovery, in particular in the Antarctic. It is found that there are substantial quantitative differences in the simulated Cly, with the October mean Antarctic Cly peak value varying from less than 2 ppb to over 3.5 ppb in the CCMs, and the date at which the Cly returns to 1980 values varying from before 2030 to after 2050. There is a similar variation in the timing of recovery of Antarctic springtime column ozone back to 1980 values. As most models underestimate peak Cly near 2000, ozone recovery in the Antarctic could occur even later, between 2060 and 2070. In the Arctic the column ozone increase in spring does not follow halogen decreases as closely as in the Antarctic, reaching 1980 values before Arctic halogen amounts decrease to 1980 values and before the Antarctic. None of the CCMs predict future large decreases in the Arctic column ozone. By 2100, total column ozone is projected to be substantially above 1980 values in all regions except in the tropics.

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Abstract.A transient simulation with the interactively coupled chemistry-climate model (CCM) E39/C has been carried out which covers the 40-year period between 1960 and 1999. Forcing of natural and anthropogenic origin is prescribed where the characteristics are sufficiently well known and the typical timescales are slow compared to synoptic timescale so that the simulated atmospheric chemistry and climate evolve under a "slowly" varying external forcing. Based on observations, sea surface temperature (SST) and ice cover are prescribed. The increase of greenhouse gas and chlorofluorocarbon concentrations, as well as nitrogen oxide emissions are taken into account. The 11-year solar cycle is considered in the calculation of heating rates and photolysis of chemical species. The three major volcanic eruptions during that time (Agung, 1963; El Chichon, 1982; Pinatubo, 1991) are considered. The quasi-biennial oscillation (QBO) is forced by linear relaxation, also known as nudging, of the equatorial zonal wind in the lower stratosphere towards observed zonal wind profiles. Beyond a reasonable reproduction of mean parameters and long-term variability characteristics there are many apparent features of episodic similarities between simulation and observation: In the years 1986 and 1988 the Antarctic ozone holes are smaller than in the other years of that decade. In mid-latitudes of the Southern Hemisphere ozone anomalies resemble the corresponding observations, especially in 1985, 1989, 1991/1992, and 1996. In the Northern Hemisphere, the episode between the late 1980s and the first half of the 1990s is dynamically quiet, in particular, no stratospheric warming is found between 1988 and 1993. As observed, volcanic eruptions strongly influence dynamics and chemistry, though only for Correspondence to: M. Dameris (Martin.Dameris@dlr.de) few years. Obviously, planetary wave activity is strongly driven by the prescribed SST and modulated by the QBO. Preliminary evidence of realistic cause and effect relationships strongly suggests that detailed process-oriented studies will be a worthwhile endeavour.

Abstract. In addition to CO2, the climate impact of aviation is strongly influenced by non-CO2 emissions, such as nitrogen oxides, influencing ozone and methane, and water vapour, which can lead to the formation of persistent contrails in ice-supersaturated regions. Because these non-CO2 emission effects are characterised by a short lifetime, their climate impact largely depends on emission location and time; that is to say, emissions in certain locations (or times) can lead to a greater climate impact (even on the global average) than the same emission in other locations (or times). Avoiding these climate-sensitive regions might thus be beneficial to climate. Here, we describe a modelling chain for investigating this climate impact mitigation option. This modelling chain forms a multi-step modelling approach, starting with the simulation of the fate of emissions released at a certain location and time (time-region grid points). This is performed with the chemistry–climate model EMAC, extended via the two submodels AIRTRAC (V1.0) and CONTRAIL (V1.0), which describe the contribution of emissions to the composition of the atmosphere and to contrail formation, respectively. The impact of emissions from the large number of time-region grid points is efficiently calculated by applying a Lagrangian scheme. EMAC also includes the calculation of radiative impacts, which are, in a second step, the input to climate metric formulas describing the global climate impact of the emission at each time-region grid point. The result of the modelling chain comprises a four-dimensional data set in space and time, which we call climate cost functions and which describes the global climate impact of an emission at each grid point and each point in time. In a third step, these climate cost functions are used in an air traffic simulator (SAAM) coupled to an emission tool (AEM) to optimise aircraft trajectories for the North Atlantic region. Here, we describe the details of this new modelling approach and show some example results. A number of sensitivity analyses are performed to motivate the settings of individual parameters. A stepwise sanity check of the results of the modelling chain is undertaken to demonstrate the plausibility of the climate cost functions.