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Dimethyl sulfide (DMS) is an important trace gas emitted from the ocean. The oxidation of DMS has long been recognised as being important for global climate through the role DMS plays in setting the sulfate aerosol background in the troposphere. However, the mechanisms in which DMS is oxidised are very complex and have proved elusive to accurately determine in spite of decades of research. As a result the representation of DMS oxidation in global chemistry-climate models is often greatly simplified. Recent field observations, laboratory and ab initio studies have prompted renewed efforts in understanding the DMS oxidation mechanism, with implications for constraining the uncertainty in the oxidation mechanism of DMS as incorporated in global chemistry-climate models. Here we build on recent evidence and develop a new DMS mechanism for inclusion in the UKCA chemistry-climate model. We compare our new mechanism (CS2-HPMTF) to a number of existing mechanisms used in UKCA (including the highly simplified 3 reactions, 2 species, ST mechanism used in CMIP6 studies) and to a range of recently developed mechanisms reported in the literature through a series of global and box model experiments. Global model runs with the new mechanism enable us to simulate the global distribution of hydroperoxyl methyl thioformate (HPMTF), which we calculate to have a burden of 2.6–26 Gg S (in good agreement with the literature range of 0.7–18 Gg S). We show that the sinks of HPMTF dominate uncertainty in the budget, not the rate of the isomerisation reaction forming it, and that based on the observed DMS/HPMTF ratio, rapid cloud uptake of HPMTF worsens the model-observation comparison. Our box model experiments highlight that there is significant variance in simulated secondary oxidation products from DMS across mechanisms used in the literature, with significant divergence in the sensitivity of these products to temperature exhibited; especially for methane sulfonic acid (MSA). Our global model studies show that our updated DMS scheme performs better than the current scheme used in UKCA when compared against a suite of surface and aircraft observations. However, sensitivity studies underscore the need for further laboratory and observational constraints.

Coupled surface-atmosphere high-resolution simulations were carried out to understand radiation fog development and persistence in a city surrounded by complex terrain. The controls of mesoscale meteorology and microscale soil moisture heterogeneity on fog were investigated using case studies for the city of Christchurch, New Zealand. Numerical model simulations from the synoptic to micro- scale were carried out using the Weather Research and Forecasting (WRF) model and the Parallelised Large-Eddy Simulation Model (PALM). Heterogeneous soil moisture, land use, and topography were included. The spatial heterogeneity of soil moisture was derived using Landsat 8 (https://www.usgs.gov/landsat-missions/landsat-8, last access: 10 October 2022) satellite imagery and ground-based meteorological observations. Eight simulations were carried out under identical meteorological conditions. One contained homogeneous soil moisture and one contained heterogeneous soil moisture derived from Landsat 8 imagery. For the other six simulations, the soil moisture heterogeneity magnitudes were amplified following the observed spatial distribution to aid our understanding of the impact of soil moisture heterogeneity. Our results showed that soil moisture heterogeneity did not significantly change the general spatial structure of near-surface fog occurrence, even when amplified. However, compared to homogeneous soil moisture, spatial heterogeneity in soil moisture leads to significant changes in radiation fog duration. The resulting changes in fog duration can be more than 50 minutes, although such changes are not directly correlated with spatial variations in soil moisture. The simulations showed that the mesoscale (104 to 2 × 105 m) meteorology controls the location of fog occurrence, while soil moisture heterogeneity alters fog duration at the microscale (10−2 to 103 m). Our results highlight the importance of including soil moisture heterogeneity for accurate spatiotemporal fog forecasting.

With low concentrations of tropospheric aerosol, the Southern Ocean offers a “natural laboratory” for studies of aerosol–cloud interactions. Aerosols over the Southern Ocean are produced from biogenic activity in the ocean, which generates sulfate aerosol via dimethylsulfide (DMS) oxidation, and from strong winds and waves that lead to bubble bursting and sea spray emission. Here, we evaluate the representation of Southern Ocean aerosols in the Hadley Centre Global Environmental Model version 3, Global Atmosphere 7.1 (HadGEM3-GA7.1) chemistry–climate model. Compared with aerosol optical depth (AOD) observations from two satellite instruments (the Moderate Resolution Imaging Spectroradiometer, MODIS-Aqua c6.1, and the Multi-angle Imaging Spectroradiometer, MISR), the model simulates too-high AOD during winter and too-low AOD during summer. By switching off DMS emission in the model, we show that sea spray aerosol is the dominant contributor to AOD during winter. In turn, the simulated sea spray aerosol flux depends on near-surface wind speed. By examining MODIS AOD as a function of wind speed from the ERA-Interim reanalysis and comparing it with the model, we show that the sea spray aerosol source function in HadGEM3-GA7.1 overestimates the wind speed dependency. We test a recently developed sea spray aerosol source function derived from measurements made on a Southern Ocean research voyage in 2018. In this source function, the wind speed dependency of the sea spray aerosol flux is less than in the formulation currently implemented in HadGEM3-GA7.1. The new source function leads to good agreement between simulated and observed wintertime AODs over the Southern Ocean; however, it reveals partially compensating errors in DMS-derived AOD. While previous work has tested assumptions regarding the seawater climatology or sea–air flux of DMS, we test the sensitivity of simulated AOD, cloud condensation nuclei and cloud droplet number concentration to three atmospheric sulfate chemistry schemes. The first scheme adds DMS oxidation by halogens and the other two test a recently developed sulfate chemistry scheme for the marine troposphere; one tests gas-phase chemistry only, while the second adds extra aqueous-phase sulfate reactions. We show how simulated sulfur dioxide and sulfuric acid profiles over the Southern Ocean change as a result and how the number concentration and particle size of the soluble Aitken, accumulation and coarse aerosol modes are affected. The new DMS chemistry scheme leads to a 20 % increase in the number concentration of cloud condensation nuclei and cloud droplets, which improves agreement with observations. Our results highlight the importance of atmospheric chemistry for simulating aerosols and clouds accurately over the Southern Ocean.

A set of Python-based tools, WRF4PALM, has been developed for offline-nesting of the PALM model system 6.0 into the Weather Research and Forecasting (WRF) modelling system. Time-dependent boundary conditions of the atmosphere are critical for accurate representation of microscale meteorological dynamics in high resolution real-data simulations. WRF4PALM generates initial and boundary conditions from WRF outputs to provide time-varying meteorological forcing for PALM. The WRF model has been used across the atmospheric science community for a broad range of multidisciplinary applications. The PALM model system 6.0 is a turbulence-resolving large-eddy simulation model with an additional Reynolds averaged Navier–Stokes (RANS) mode for atmospheric and oceanic boundary layer studies at microscale (Maronga et al., 2020). Currently PALM has the capability to ingest output from the regional scale Consortium for Small-scale Modelling (COSMO) atmospheric prediction model. However, COSMO is not an open source model which requires a licence agreement for operational use or academic research (). This paper describes and validates the new free and open-source WRF4PALM tools (available on ). Two case studies using WRF4PALM are presented for Christchurch, New Zealand, which demonstrate successful PALM simulations driven by meteorological forcing from WRF outputs. The WRF4PALM tools presented here can potentially be used for micro- and mesoscale studies worldwide, for example in boundary layer studies, air pollution dispersion modelling, wildfire emissions and spread, urban weather forecasting, and agricultural meteorology.

We studied sulfate aerosols over the Southern Ocean using the atmosphere-only climate model HadGEM3-GA7.1. The model contains biases in the aerosol seasonal variability over the Southern Ocean (40°S to 60°S), which cascade to uncertainties in aerosol-cloud interactions. Aerosols over the Southern Ocean are primarily natural in origin, such as sea spray aerosol and sulfate aerosol formed by phytoplankton-produced dimethyl sulfide (DMS).The current sulfate chemistry scheme implemented in the model simplifies the oxidation pathways for DMS, which has been identified as a major source of the seasonal bias present. The simulations performed here incorporate a comprehensive sulfate scheme in both the gas and aqueous-phase. An intermediate complexity biogeochemical dynamic model, MEDUSA, simulated a global climatology of seawater DMS, which is compared with a seawater DMS observational dataset from 2011. We compared the seasonality of sulfate aerosols over the Southern Ocean, and the global distribution using the two seawater DMS climatologies. Simulated aerosols over the Southern Ocean were evaluated against satellite and in-situ observations. The results show the impact of seawater DMS on sulfate aerosols and their influence on cloud formation.

With low concentrations of tropospheric aerosol, the Southern Ocean offers a "natural laboratory" for studies of aerosol–cloud interactions. Aerosols over the Southern Ocean are produced from biogenic activity in the ocean, which generates sulfate aerosol via dimethylsulfide (DMS) oxidation, and from strong winds and waves that lead to bubble bursting and sea spray emission. Here, we evaluate the representation of Southern Ocean aerosols in the Hadley Centre Global Environmental Model version 3, Global Atmosphere 7.1 (HadGEM3-GA7.1) chemistry–climate model. Compared with aerosol optical depth (AOD) observations from two satellite instruments (the Moderate Resolution Imaging Spectroradiometer, MODIS-Aqua c6.1, and the Multi-angle Imaging Spectroradiometer, MISR), the model simulates too-high AOD during winter and too-low AOD during summer. By switching off DMS emission in the model, we show that sea spray aerosol is the dominant contributor to AOD during winter. In turn, the simulated sea spray aerosol flux depends on near-surface wind speed. By examining MODIS AOD as a function of wind speed from the ERA-Interim reanalysis and comparing it with the model, we show that the sea spray aerosol source function in HadGEM3-GA7.1 overestimates the wind speed dependency. We test a recently developed sea spray aerosol source function derived from measurements made on a Southern Ocean research voyage in 2018. In this source function, the wind speed dependency of the sea spray aerosol flux is less than in the formulation currently implemented in HadGEM3-GA7.1. The new source function leads to good agreement between simulated and observed wintertime AODs over the Southern Ocean; however, it reveals partially compensating errors in DMS-derived AOD. While previous work has tested assumptions regarding the seawater climatology or sea–air flux of DMS, we test the sensitivity of simulated AOD, cloud condensation nuclei and cloud droplet number concentration to three atmospheric sulfate chemistry schemes. The first scheme adds DMS oxidation by halogens and the other two test a recently developed sulfate chemistry scheme for the marine troposphere; one tests gas-phase chemistry only, while the second adds extra aqueous-phase sulfate reactions. We show how simulated sulfur dioxide and sulfuric acid profiles over the Southern Ocean change as a result and how the number concentration and particle size of the soluble Aitken, accumulation and coarse aerosol modes are affected. The new DMS chemistry scheme leads to a 20% increase in the number concentration of cloud condensation nuclei and cloud droplets, which improves agreement with observations. Our results highlight the importance of atmospheric chemistry for simulating aerosols and clouds accurately over the Southern Ocean.

Climate models consistently predict an acceleration of the Brewer-Dobson circulation (BDC) due to climate change in the 21st century. However, the strength of this acceleration varies considerably among individual models, which constitutes a notable source of uncertainty for future climate projections. To shed more light upon the magnitude of this uncertainty and on its causes, we analyze the stratospheric mean age of air (AoA) of 10 climate projection simulations from the Chemistry Climate Model Initiative phase 1 (CCMI-I), covering the period between 1960 and 2100. In agreement with previous multi-model studies, we find a large model spread in the magnitude of the AoA trend over the simulation period. Differences between future and past AoA are found to be predominantly due to differences in mixing (reduced aging by mixing and recirculation) rather than differences in residual mean transport. We furthermore analyze the mixing efficiency, a measure of the relative strength of mixing for given residual mean transport, which was previously hypothesized to be a model constant. Here, the mixing efficiency is found to vary not only across models, but also over time in all models. Changes in mixing efficiency are shown to be closely related to changes in AoA and quantified to roughly contribute 10 % to the long-term AoA decrease over the 21st century. Additionally, mixing efficiency variations are shown to considerably enhance model spread in AoA changes. To understand these mixing efficiency variations, we also present a consistent dynamical framework based on diffusive closure, which highlights the role of basic state potential vorticity gradients in controlling mixing efficiency and therefore aging by mixing.

Previous multi-model intercomparisons have shown that chemistry-climate models exhibit significant biases in tropospheric ozone compared with observations. We investigate annual-mean tropospheric column ozone in 15 models participating in the SPARC/IGAC (Stratosphere-troposphere Processes and their Role in Climate/International Global Atmospheric Chemistry) Chemistry-Climate Model Initiative (CCMI). These models exhibit a positive bias, on average, of up to 40–50 % in the Northern Hemisphere compared with observations derived from the Ozone Monitoring Instrument and Microwave Limb Sounder (OMI/MLS), and a negative bias of up to ~ 30 % in the Southern Hemisphere. SOCOLv3.0 (version 3 of the Solar-Climate Ozone Links CCM), which participated in CCMI, simulates global-mean tropospheric ozone columns of 40.2 DU – approximately 33 % larger than the CCMI multi-model mean. Here we introduce an updated version of SOCOLv3.0, SOCOLv3.1, which includes an improved treatment of ozone sink processes, and results in a reduction in the tropospheric column ozone bias of up to 8 DU, mostly due to the inclusion of N2O5 hydrolysis on tropospheric aerosols. As a result of these developments, tropospheric column ozone amounts simulated by SOCOLv3.1 are comparable with several other CCMI models. We apply Gaussian process emulation and sensitivity analysis to understand the remaining ozone bias in SOCOLv3.1. This shows that ozone precursors (nitrogen oxides (NOx, carbon monoxide, methane and other volatile organic compounds) are responsible for more than 90 % of the variance in tropospheric ozone. However, it may not be the emissions inventories themselves that result in the bias, but how the emissions are handled in SOCOLv3.1, and we discuss this in the wider context of the other CCMI models. Given that the emissions data set to be used for phase 6 of the Coupled Model Intercomparison Project includes approximately 20 % more NOx than the data set used for CCMI, further work is urgently needed to address the challenges of simulating sub-grid processes of importance to tropospheric ozone in the current generation of chemistry-climate models.

We evaluate how the coupled aerosol-chemistry-climate model SOCOL-AER represents the influence of the 1991 eruption of Mt. Pinatubo on stratospheric aerosol loading, aerosol microphysical processes, radiative effects, and atmospheric chemistry. The aerosol module includes comprehensive sulfur chemistry and microphysics, in which the particle size distribution is represented by 40 size bins spanning radii from 0.39 nm to 3.2 μm. Radiative forcing is computed online using aerosol optical properties calculated according to Mie theory. SOCOL-AER simulations are compared with satellite and in situ measurements of aerosol parameters, temperature reanalyses, and ozone observations. In addition to the reference model configuration, we performed a series of sensitivity experiments looking at different processes affecting the aerosol layer. An accurate sedimentation scheme is found to be essential to prevent particles diffusing too rapidly to high and low altitudes. The aerosol radiative feedback and the use of a nudged quasi-biennial oscillation help to keep aerosol in the tropics and significantly affect the evolution of the stratospheric aerosol burden, which improves the agreement with observed aerosol mass distributions. Changes in the aerosol distribution affected by an inclusion of Van der Waals forces to the particle coagulation scheme suggest improvements in particle effective radius, although other parameters (such as aerosol longevity) deteriorate. Modification of the Pinatubo emission rate also improves some aerosol parameters, while worsens others compared to observations. Observations themselves are highly uncertain and render it difficult to conclusively judge the necessity of further model reconfiguration. In conclusion, our results show that SOCOL-AER is capable of predicting the most important global-scale atmospheric and climate effects following volcanic eruptions, which is also a prerequisite for improved understanding of anthropogenic effects from sulfur emissions.

To simulate the impacts of volcanic eruptions on the stratosphere, chemistry-climate models that do not include an online aerosol module require temporally and spatially resolved aerosol size parameters for heterogeneous chemistry and aerosol radiative properties as a function of wavelength. For phase 1 of the Chemistry-Climate Model Initiative (CCMI-1) and, later, for phase 6 of the Coupled Model Intercomparison Project (CMIP6) two such stratospheric aerosol data sets were compiled, whose functional capability and representativeness are compared here. For CCMI-1, the SAGE-4λ data set was compiled, which hinges on the measurements at four wavelengths of the SAGE (Stratospheric Aerosol and Gas Experiment) II satellite instrument and uses ground-based Lidar measurements for gap-filling immediately after the Mt. Pinatubo eruption, when the stratosphere was optically opaque for SAGE II. For CMIP6, the new SAGE-3λ data set was compiled, which excludes the least reliable SAGE II wavelength and uses CLAES (Cryogenic Limb Array Etalon Spectrometer) measurements on UARS, the Upper Atmosphere Research Satellite, for gap-filling following the Mt. Pinatubo eruption instead of ground-based Lidars. Here, we performed SOCOLv3 (Solar Climate Ozone Links version 3) chemistry-climate model simulations of the recent past (1986–2005) to investigate the impact of the Mt. Pinatubo eruption in 1991 on stratospheric temperature and ozone and how this response differs depending on which aerosol data set is applied. The use of SAGE-4λ results in heating and ozone loss being overestimated in the lower stratosphere compared to observations in the post-eruption period by approximately 3 K and 0.2 ppmv, respectively. However, less heating occurs in the model simulations based on SAGE-3λ, because the improved gap-filling procedures after the eruption lead to less aerosol loading in the tropical lower stratosphere. As a result, simulated temperature anomalies in the model simulations based on SAGE-3λ for CMIP6 are in excellent agreement with MERRA and ERA-Interim reanalyses in the post-eruption period. Less heating in the simulations with SAGE-3λ means that the rate of tropical upwelling does not strengthen as much as it does in the simulations with SAGE-4λ, which limits dynamical uplift of ozone and therefore provides more time for ozone to accumulate in tropical mid-stratospheric air. Ozone loss following the Mt. Pinatubo eruption is overestimated by 0.1 ppmv in the model simulations based on SAGE-3λ, which is a better agreement with observations than in the simulations based on SAGE-4λ. Overall, the CMIP6 stratospheric aerosol data set, SAGE-3λ, allows SOCOLv3 to more accurately simulate the post-Pinatubo eruption period.

Ozone fields simulated for the Chemistry-Climate Model Initiative (CCMI) will be used as forcing data in the 6th Coupled Model Intercomparison Project (CMIP6). Here we assess, using reference and sensitivity simulations produced for phase 1 of CCMI, the suitability of CCMI-1 model results for this process, investigating the degree of consistency amongst models regarding their responses to variations in individual forcings. We consider the influences of methane, nitrous oxide, a combination of chlorinated or brominated ozone-depleting substances (ODSs), and a combination of carbon dioxide and other greenhouse gases (GHGs). We find varying degrees of consistency in the models' responses in ozone to these individual forcings, including some considerable disagreement. In particular, the response of total-column ozone to these forcings is less consistent across the multi-model ensemble than profile comparisons. The likely cause of this is lower-stratospheric transport and dynamical responses exhibiting substantial inter-model differences. The findings imply that the ozone fields derived from CCMI-1 are subject to considerable uncertainties regarding the impacts of these anthropogenic forcings.

Through the 21st century, anthropogenic emissions of the greenhouse gases N2O and CH4 are projected to increase, thus increasing their atmospheric concentrations. Consequently, reactive nitrogen species produced from N2O and reactive hydrogen species produced from CH4 are expected to play an increasingly important role in determining stratospheric ozone concentrations. Eight chemistry-climate model simulations were performed to assess the sensitivity of stratospheric ozone to different emissions scenarios for N2O and CH4. Global-mean total column ozone increases through the 21st century in all eight simulations as a result of CO2-induced stratospheric cooling and decreasing stratospheric halogen concentrations. Larger N2O concentrations were associated with smaller ozone increases, due to reactive nitrogen-mediated ozone destruction. In the simulation with the largest N2O increase, global-mean total column ozone increased by 4.3 DU through the 21st century, compared with 10.0 DU in the simulation with the smallest N2O increase. In contrast, larger CH4 concentrations were associated with larger ozone increases; global-mean total column ozone increased by 16.7 DU through the 21st century in the simulation with the largest CH4 concentrations and by 4.4 DU in the simulation with the lowest CH4 concentrations. CH4 leads to ozone loss in the upper and lower stratosphere by increasing the rate of reactive hydrogen-mediated ozone loss cycles, however in the lower stratosphere and troposphere, CH4 leads to ozone increases due to photochemical smog-type chemistry. In addition to this mechanism, total column ozone increases due to H2O-induced cooling of the stratosphere, and slowing of the chlorine-catalyzed ozone loss cycles due to an increased rate of the CH4 + Cl reaction. Stratospheric column ozone through the 21st century exhibits a near-linear response to changes in N2O and CH4 surface concentrations, which provides a simple parameterization for the ozone response to changes in these gases.

We present an overview of state-of-the-art chemistry-climate and -transport models that are used within the Chemistry-Climate Model Initiative (CCMI). 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, 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 recommendations for simulations have been followed is necessary to understand model response to anthropogenic and natural forcing and also to explain inter-model 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 simulations with the aim to inform CCMI data users.

We find that wintertime temperature anomalies near 4 hPa and 50° N/S are related, through dynamics, to anomalies in ozone and temperature, particularly in the tropical stratosphere, but also throughout the upper stratosphere and mesosphere. These mid-latitude anomalies occur on timescales of up to a month, and are related to changes in wave-forcing. A change in the meridional circulation extends from the middle stratosphere into the mesosphere and forms a temperature-change quadrupole from equator to pole. We develop a dynamical index based on detrended, deseasonalised mid-latitude temperature. When employed in multiple linear regression, this index can account for up to 40 % of the total variability of temperature and ozone and a doubling of the total coefficient of determination in the equatorial stratosphere above 20 hPa. Further, the uncertainty on all multiple-linear regression coefficients can be reduced by up to 45 % and 25 % in temperature and ozone, respectively, and so this index is an important tool for quantifying current and future ozone recovery.