Oceans dominate emissions of dimethyl sulfide (DMS), the major natural sulfur source. DMS is important for the formation of non-sea salt sulfate (nss-SO 4 2− ) aerosols and secondary particulate matter over oceans and thus, significantly influence global climate. The mechanism of DMS oxidation has accordingly been investigated in several different model studies in the past. However, these studies had restricted oxidation mechanisms that mostly underrepresented important aqueous-phase chemical processes. These neglected but highly effective processes strongly impact direct product yields of DMS oxidation, thereby affecting the climatic influence of aerosols. To address these shortfalls, an extensive multiphase DMS chemistry mechanism, the Chemical Aqueous Phase Radical Mechanism DMS Module 1.0, was developed and used in detailed model investigations of multiphase DMS chemistry in the marine boundary layer. The performed model studies confirmed the importance of aqueousphase chemistry for the fate of DMS and its oxidation products. Aqueous-phase processes significantly reduce the yield of sulfur dioxide and increase that of methyl sulfonic acid (MSA), which is needed to close the gap between modeled and measured MSA concentrations. Finally, the simulations imply that multiphase DMS oxidation produces equal amounts of MSA and sulfate, a result that has significant implications for nss-SO 4 2− aerosol formation, cloud condensation nuclei concentration, and cloud albedo over oceans. Our findings show the deficiencies of parameterizations currently used in higher-scale models, which only treat gas-phase chemistry. Overall, this study shows that treatment of DMS chemistry in both gas and aqueous phases is essential to improve the accuracy of model predictions. ) contribute to the formation of new aerosol particles as well as secondary particulate matter and are, thus, important for human health and the Earth's climate (1). Globally, anthropogenic sulfur emissions in the form of sulfur dioxide (SO 2 ) dominate atmospheric production of gaseous H 2 SO 4 and particle-phase sulfate. However, the main natural source of sulfur is the oxidation of dimethyl sulfide (DMS) emitted by oceans (2), which is the most important precursor for non-sea salt sulfate (nss-SO 4 2− ) aerosols over the open ocean (3). Sulfate aerosols strongly influence the climate both by direct negative radiative forcing (4) and as a dominant source of cloud condensation nuclei (CCN) over the open ocean (5). Because oceans cover about 70% of Earth's surface (6) and have generally low albedo, DMS oxidation plays a major role in influencing the natural radiative forcing of sulfate aerosols as well as cloud properties (3).Investigations of the effect of DMS oxidation on natural sulfate aerosol concentrations and cloud and aerosol properties require an accurate, reduced DMS oxidation scheme in chemical transport models (CTMs) and global climate models (GCMs). Current parameterizations use fixed yields of SO 2 and methyl sulfonic acid (MSA) to calculate new nss...

A detailed and extended chemical mechanism describing tropospheric aqueous phase chemistry (147 species and 438 reactions) is presented here as Chemical Aqueous Phase Radical Mechanism (CAPRAM) 2.4 (MODAC mechanism). The mechanism based on the former version 2.3 [Herrmann et al., 2000] contains extended organic and transition metal chemistry and is formulated more explicitly based on a critical review of the literature. The aqueous chemistry has been coupled to the gas phase mechanism Regional Atmospheric Chemistry Modeling (RACM) [Stockwell et al., 1997], and phase exchange accounted for using the resistance model of Schwartz [1986]. A method for estimating mass accommodation coefficients (α) is described, which accounts for functional groups contained in a particular compound. A condensed version has also been developed to allow the use of CAPRAM 2.4 (MODAC mechanism) in higher‐scale models. Here the reproducibility of the concentration levels of selected target species (i.e., NOx, S(IV), H2O2, NO3, OH, O3, and H+) within the limits of ± 5% was used as a goal for eliminating insignificant reactions from the complete CAPRAM 2.4 (MODAC mechanism). This has been done using a range of initial conditions chosen to represent different atmospheric scenarios, and this produces a robust and concise set of reactions. The most interesting results are obtained using atmospheric conditions typical for an urban scenario, and the effects introduced by updating the aqueous phase chemistry are highlighted, in particular, with regard to radicals, redox cycling of transition metal ions and organic compounds. Finally, the reduced scheme has been incorporated into a one‐dimensional (1‐D) marine cloud model to demonstrate the applicability of this mechanism.

No abstract

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together sixteen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America on common emissions and boundary conditions. With the advent of online-coupled models providing new capability to quantify the effects of feedback processes, the main aim of this study is to compare the response of coupled air quality models to simulate levels of O 3 over the two continental regions. The simulated annual, seasonal, continental and sub-regional ozone surface concentrations and vertical profiles for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. Results show a general model underestimation of the annual surface ozone levels over both continents reaching up to 18% over Europe and 22% over North America. The observed temporal variations are successfully reproduced with correlation coefficients larger than 0.8. Results clearly show that the simulated levels highly depend on the meteorological and chemical configurations used in the models, even within the same modeling system. The seasonal and sub-regional analyses show the models' tendency to overestimate surface ozone in all regions during autumn and underestimate in winter. Boundary conditions strongly influence ozone predictions especially during winter and autumn, whereas during summer local production dominates over regional transport. Daily maximum 8-hour averaged surface ozone levels below 50-60 g m-3 are overestimated by all models over both continents while levels over 120-140 g m-3 are underestimated, suggesting that models have a tendency to severely under-predict high O 3 values that are of concern for air quality forecast and control policy applications.