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 (1950–2011), hindcast simulations with specified dynamics (1979–2013), i.e. nudged towards ERA-Interim reanalysis data, and combined hindcast and projection simulations (1950–2100). 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 set-ups, 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.

>We analyse simulations performed for the Chemistry-Climate Model Initiative (CCMI) to estimate the return dates of the stratospheric ozone layer from depletion caused by anthropogenic stratospheric chlorine and bromine. We consider a total of 155 simulations from 20 models, including a range of sensitivity studies which examine the impact of climate change on ozone recovery. For the control simulations (unconstrained by nudging towards analysed meteorology) there is a large spread (±20 DU in the global average) in the predictions of the absolute ozone column. Therefore, the model results need to be adjusted for biases against historical data. Also, the interannual variability in the model results need to be smoothed in order to provide a reasonably narrow estimate of the range of ozone return dates. Consistent with previous studies, but here for a Representative Concentration Pathway (RCP) of 6.0, these new CCMI simulations project that global total column ozone will return to 1980 values in 2049 (with a 1σ uncertainty of 2043–2055). At Southern Hemisphere mid-latitudes column ozone is projected to return to 1980 values in 2045 (2039–2050), and at Northern Hemisphere mid-latitudes in 2032 (2020–2044). In the polar regions, the return dates are 2060 (2055–2066) in the Antarctic in October and 2034 (2025–2043) in the Arctic in March. The earlier return dates in the Northern Hemisphere reflect the larger sensitivity to dynamical changes. Our estimates of return dates are later than those presented in the 2014 Ozone Assessment by approximately 5–17 years, depending on the region, with the previous best estimates often falling outside of our uncertainty range. In the tropics only around half the models predict a return of ozone to 1980 values, around 2040, while the other half do not reach the 1980 value. All models show a negative trend in tropical total column ozone towards the end of the 21st century. The CCMI models generally agree in their simulation of the time evolution of stratospheric chlorine and bromine, which are the main drivers of ozone loss and recovery. However, there are a few outliers which show that the multi-model mean results for ozone recovery are not as tightly constrained as possible. Throughout the stratosphere the spread of ozone return dates to 1980 values between models tends to correlate with the spread of the return of inorganic chlorine to 1980 values. In the upper stratosphere, greenhouse gas-induced cooling speeds up the return by about 10–20 years. In the lower stratosphere, and for the column, there is a more direct link in the timing of the return dates of ozone and chlorine, especially for the large Antarctic depletion. Comparisons of total column ozone between the models is affected by different predictions of the evolution of tropospheric ozone within the same scenario, presumably due to differing treatment of tropospheric chemistry. Therefore, for many scenarios, clear conclusions can only be drawn for stratospheric ozone columns rather than the total column. As noted by previous studies, the timing of ozone recovery is affected by the evolution of N₂O and CH₄. However, quantifying the effect in the simulations analysed here is limited by the few realisations available for these experiments compared to internal model variability. The large increase in N₂O given in RCP 6.0 extends the ozone return globally by ∼ 15 years relative to N₂O fixed at 1960 abundances, mainly because it allows tropical column ozone to be depleted. The effect in extratropical latitudes is much smaller. The large increase in CH₄ given in the RCP 8.5 scenario compared to RCP 6.0 also lengthens ozone return by ∼ 15 years, again mainly through its impact in the tropics. Overall, our estimates of ozone return dates are uncertain due to both uncertainties in future scenarios, in particular those of greenhouse gases, and uncertainties in models. The scenario uncertainty is small in the short term but increases with time, and becomes large by the end of the century. There are still some model–model differences related to well-known processes which affect ozone recovery. Efforts need to continue to ensure that models used for assessment purposes accurately represent stratospheric chemistry and the prescribed scenarios of ozone-depleting substances, and only those models are used to calculate return dates. For future assessments of single forcing or combined effects of CO₂, CH₄, and N₂O on the stratospheric column ozone return dates, this work suggests that it is more important to have multi-member (at least three) ensembles for each scenario from every established participating model, rather than a large number of individual models.

We present version 14 of the photolysis module JVAL. Taking atmospheric conditions as input, JVAL calculates photolysis rate coefficients, i.e. the speed of dissociation of atmospheric molecules in the sunlight. Computational efficiency is obtained through the use of parameters for polynomial curve fitting and lookup tables. Physical changes compared to the previous version include a parameterization of the Lyman-alpha absorption, and an update of the UV/VIS cross sections to the most recent recommended values. JVAL also includes the auxiliary program JVPP (JVal PreProcessor) which pre-calculates these parameters based on the absorption cross sections and quantum yields of the atmospheric molecules. It is possible to use JVAL either as a stand-alone program or as a module inside the Modular Earth Submodel System (MESSy). JVAL is a community model published under the GNU General Public License.

The modeling study presented here aims to estimate how uncertainties in global hydroxyl radical (OH) distributions, variability, and trends may contribute to resolving discrepancies between simulated and observed methane (CH₄) changes since 2000. A multi-model ensemble of 14 OH fields was analyzed and aggregated into 64 scenarios to force the offline atmospheric chemistry transport model LMDz (Laboratoire de Meteorologie Dynamique) with a standard CH₄ emission scenario over the period 2000–2016. The multi-model simulated global volume-weighted tropospheric mean OH concentration ([OH]) averaged over 2000–2010 ranges between 8.7×10⁵ and 12.8×10⁵ molec cm⁻³. The inter-model differences in tropospheric OH burden and vertical distributions are mainly determined by the differences in the nitrogen oxide (NO) distributions, while the spatial discrepancies between OH fields are mostly due to differences in natural emissions and volatile organic compound (VOC) chemistry. From 2000 to 2010, most simulated OH fields show an increase of 0.1–0.3×10⁵ molec cm⁻³ in the tropospheric mean [OH], with year-to-year variations much smaller than during the historical period 1960–2000. Once ingested into the LMDz model, these OH changes translated into a 5 to 15 ppbv reduction in the CH₄ mixing ratio in 2010, which represents 7 %–20 % of the model-simulated CH₄ increase due to surface emissions. Between 2010 and 2016, the ensemble of simulations showed that OH changes could lead to a CH₄ mixing ratio uncertainty of $>\pm \mathrm{30}$ ppbv. Over the full 2000–2016 time period, using a common state-of-the-art but nonoptimized emission scenario, the impact of [OH] changes tested here can explain up to 54 % of the gap between model simulations and observations. This result emphasizes the importance of better representing OH abundance and variations in CH₄ forward simulations and emission optimizations performed by atmospheric inversions.

Simulated stratospheric temperatures over the period 1979–2016 in models from the Chemistry‐Climate Model Initiative are compared with recently updated and extended satellite data sets. The multimodel mean global temperature trends over 1979–2005 are −0.88 ± 0.23, −0.70 ± 0.16, and −0.50 ± 0.12 K/decade for the Stratospheric Sounding Unit (SSU) channels 3 (~40–50 km), 2 (~35–45 km), and 1 (~25–35 km), respectively (with 95% confidence intervals). These are within the uncertainty bounds of the observed temperature trends from two reprocessed SSU data sets. In the lower stratosphere, the multimodel mean trend in global temperature for the Microwave Sounding Unit channel 4 (~13–22 km) is −0.25 ± 0.12 K/decade over 1979–2005, consistent with observed estimates from three versions of this satellite record. The models and an extended satellite data set comprised of SSU with the Advanced Microwave Sounding Unit‐A show weaker global stratospheric cooling over 1998–2016 compared to the period of intensive ozone depletion (1979–1997). This is due to the reduction in ozone‐induced cooling from the slowdown of ozone trends and the onset of ozone recovery since the late 1990s. In summary, the results show much better consistency between simulated and satellite‐observed stratospheric temperature trends than was reported by Thompson et al. (2012, https://doi.org/10.1038/nature11579) for the previous versions of the SSU record and chemistry‐climate models. The improved agreement mainly comes from updates to the satellite records; the range of stratospheric temperature trends over 1979–2005 simulated in Chemistry‐Climate Model Initiative models is comparable to the previous generation of chemistry‐climate models.

Decadal trends and interannual variations in the hydroxyl radical (OH), while poorly constrained at present, are critical for understanding the observed evolution of atmospheric methane (CH₄). Through analyzing the OH fields simulated by the model ensemble of the Chemistry–Climate Model Initiative (CCMI), we find (1) the negative OH anomalies during the El Niño years mainly corresponding to the enhanced carbon monoxide (CO) emissions from biomass burning and (2) a positive OH trend during 1980–2010 dominated by the elevated primary production and the reduced loss of OH due to decreasing CO after 2000. Both two-box model inversions and variational 4D inversions suggest that ignoring the negative anomaly of OH during the El Niño years leads to a large overestimation of the increase in global CH₄ emissions by up to 10 ± 3 Tg yr⁻¹ to match the observed CH₄ increase over these years. Not accounting for the increasing OH trends given by the CCMI models leads to an underestimation of the CH₄ emission increase by 23 ± 9 Tg yr⁻¹ from 1986 to 2010. The variational-inversion-estimated CH₄ emissions show that the tropical regions contribute most to the uncertainties related to OH. This study highlights the significant impact of climate and chemical feedbacks related to OH on the top-down estimates of the global CH₄ budget.