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The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post‐process model outputs to match observations, existing approaches usually focus on limited, low‐order statistics, or break either the spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases. Instead of pre‐assuming the spatiotemporal characteristics of model biases, we apply discriminative neural networks to distinguish historical climate simulation samples and observation samples. The evidences based on which the discriminative neural networks make distinctions are applied to train the domain adaptation neural networks to bias correct climate simulations. We regularize the domain adaptation neural networks using cycle‐consistent statistical and dynamical constraints. An application to daily precipitation projection over the contiguous United States shows that our methodology can correct all the considered moments of daily precipitation at approximately 1° resolution, ensures spatiotemporal consistency and inter‐field correlations, and can discriminate between different dynamical conditions. Our methodology offers a powerful tool for disentangling model parameterization biases from their interactions with the chaotic evolution of climate dynamics, opening a novel avenue toward big‐data enhanced climate predictions.

An atmospheric release of hazardous material, whether accidental or intentional, can be catastrophic for those in the path of the plume. Predicting the path of a plume based on characteristics of t...

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal forecast, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge cost in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. In this study, we develop a probabilistic deep neural network model, drawing on a wealth of existing climate simulations to enhance seasonal forecast capability and forecast diagnosis. By leveraging complex physical relationships encoded in climate simulations, our probabilistic forecast model demonstrates favorable deterministic and probabilistic skill compared to state-of-the-art dynamical forecast systems in quasi-global seasonal forecast of precipitation and near-surface temperature. We apply this probabilistic forecast methodology to quantify the impacts of initialization errors and model formulation deficiencies in a dynamical seasonal forecasting system. We introduce the saliency analysis approach to efficiently identify the key predictors that influence seasonal variability. Furthermore, by explicitly modeling uncertainty using variational Bayes, we give a more definitive answer to how the El Nino/Southern Oscillation, the dominant mode of seasonal variability, modulates global seasonal predictability.

From 2012 to 2016, California experienced one of the worst droughts since the start of observational records. As in previous dry periods, precipitation-inducing winter storms were steered away from California by a persistent atmospheric ridging system in the North Pacific. Here we identify a new link between Arctic sea-ice loss and the North Pacific geopotential ridge development. In a two-step teleconnection, sea-ice changes lead to reorganization of tropical convection that in turn triggers an anticyclonic response over the North Pacific, resulting in significant drying over California. These findings suggest that the ability of climate models to accurately estimate future precipitation changes over California is also linked to the fidelity with which future sea-ice changes are simulated. We conclude that sea-ice loss of the magnitude expected in the next decades could substantially impact California’s precipitation, thus highlighting another mechanism by which human-caused climate change could exacerbate future California droughts., Persistent atmospheric ridging in the North Pacific steered storms away and led to the California drought of 2012-16. Here the authors use simulations to show that sea-ice changes trigger reorganization of tropical convection resulting in drying over California.

Probability distribution functions (PDFs) of model inputs that affect the transport and dispersion of a trace gas released from a coastal California nuclear power plant are quantified using ensemble simulations, machine-learning algorithms, and Bayesian inversion. The PDFs are constrained by observations of tracer concentrations and account for uncertainty in meteorology, transport, diffusion, and emissions. Meteorological uncertainty is calculated using an ensemble of simulations of the Weather Research and Forecasting (WRF) model that samples five categories of model inputs (initialization time, boundary layer physics, land surface model, nudging options, and reanalysis data). The WRF output is used to drive tens of thousands of FLEXPART dispersion simulations that sample a uniform distribution of six emissions inputs. Machine-learning algorithms are trained on the ensemble data and used to quantify the sources of ensemble variability and to infer, via inverse modeling, the values of the 11 model inputs most consistent with tracer measurements. We find a substantial ensemble spread in tracer concentrations (factors of 10 to 103), most of which is due to changing emissions inputs (about 80 %), though the cumulative effects of meteorological variations are not negligible. The performance of the inverse method is verified using synthetic observations generated from arbitrarily selected simulations. When applied to measurements from a controlled tracer release experiment, the inverse method satisfactorily determines the location, start time, duration and amount. In a 2 km × 2 km area of possible locations, the actual location is determined to within 200 m. The start time is determined to within 5 min out of 2 h, and the duration to within 50 min out of 4 h. Over a range of release amounts of 10 to 1000 kg, the estimated amount exceeds the actual amount of 146 kg by only 32 kg. The inversion also estimates probabilities of different WRF configurations. To best match the tracer observations, the highest-probability cases in WRF are associated with using a late initialization time and specific reanalysis data products.

A computational Bayesian inverse technique is used to quantify the effects of meteorological inflow uncertainty on tracer transport and source estimation in a complex urban environment. We estimate a probability distribution of meteorological inflow by comparing wind observations to Monte Carlo simulations from the Aeolus model. Aeolus is a computational fluid dynamics model that simulates atmospheric and tracer flow around buildings and structures at meter-scale resolution. Uncertainty in the inflow is propagated through forward and backward Lagrangian dispersion calculations to determine the impact on tracer transport and the ability to estimate the release location of an unknown source. Our uncertainty methods are compared against measurements from an intensive observation period during the Joint Urban 2003 tracer release experiment conducted in Oklahoma City. The best estimate of the inflow at 50 m above ground for the selected period has a wind speed and direction of 4.6 − 2.5 + 2.0 m s −1 and 158.0 − 23 + 16 , where the uncertainty is a 95% confidence range. The wind speed values prescribed in previous studies differ from our best estimate by two or more standard deviations. Inflow probabilities are also used to weight backward dispersion plumes and produce a spatial map of likely tracer release locations. For the Oklahoma City case, this map pinpoints the location of the known release to within 20 m. By evaluating the dispersion patterns associated with other likely release locations, we further show that inflow uncertainty can explain the differences between simulated and measured tracer concentrations.

International audience; After performing a first multi-model exercise in 2015 a comprehensive and technically more demanding atmospheric transport modelling challenge was organized in 2016. Release data were provided by the Australian Nuclear Science and Technology Organization radiopharmaceutical facility in Sydney (Australia) for a one month period. Measured samples for the same time frame were gathered from six International Monitoring System stations in the Southern Hemisphere with distances to the source ranging between 680 (Melbourne) and about 17,000 km (Tristan da Cunha). Participants were prompted to work with unit emissions in pre-defined emission intervals (daily, half-daily, 3-hourly and hourly emission segment lengths) and in order to perform a blind test actual emission values were not provided to them. Despite the quite different settings of the two atmospheric transport modelling challenges there is common evidence that for long-range atmospheric transport using temporally highly resolved emissions and highly space-resolved meteorological input fields has no significant advantage compared to using lower resolved ones. As well an uncertainty of up to 20% in the daily stack emission data turns out to be acceptable for the purpose of a study like this. Model performance at individual stations is quite diverse depending largely on successfully capturing boundary layer processes. No single model-meteorology combination performs best for all stations. Moreover, the stations statistics do not depend on the distance between the source and the individual stations. Finally, it became more evident how future exercises need to be designed. Set-up parameters like the meteorological driver or the output grid resolution should be pre-scribed in order to enhance diversity as well as comparability among model runs.

We investigate the sensitivity of precipitation characteristics (mean, extreme, and diurnal cycle) to a set of uncertain parameters that influence the qualitative and quantitative behavior of cloud and aerosol processes in the Community Atmosphere Model (CAM5). We adopt both the Latin hypercube and Quasi-Monte Carlo sampling approaches to effectively explore the high-dimensional parameter space and then conduct two large sets of simulations. One set consists of 1100 simulations (cloud ensemble) perturbing 22 parameters related to cloud physics and convection, and the other set consists of 256 simulations (aerosol ensemble) focusing on 16 parameters related to aerosols and cloud microphysics. In the cloud ensemble, six parameters having the greatest influences on the global mean precipitation are identified, three of which (related to the deep convection scheme) are the primary contributors to the total variance of the phase and amplitude of the precipitation diurnal cycle over land. The extreme precipitation characteristics are sensitive to a fewer number of parameters. Precipitation does not always respond monotonically to parameter change. The influence of individual parameters does not depend on the sampling approaches or concomitant parameters selected. Generally, the Generalized Linear Model is able to explain more of the parametric sensitivity of global precipitation than local or regional features. The total explained variance for precipitation is primarily due to contributions from the individual parameters (75–90% in total). The total variance shows a significant seasonal variability in midlatitude continental regions, but very small in tropical continental regions.

We systematically explore the ability of the Community Atmospheric Model version 5 (CAM5) to simulate the Madden-Julian Oscillation (MJO), through an analysis of MJO metrics calculated from a 1100-member perturbed parameter ensemble of 5 year simulations with observed sea surface temperatures. Parameters from the deep convection scheme make the greatest contribution to the variance in MJO simulation quality with a much smaller contribution from parameters in the large-scale cloud, shallow convection, and boundary layer turbulence schemes. Improved MJO variability results from a larger lateral entrainment rate and a reduction in the precipitation efficiency of deep convection that was achieved by a smaller autoconversion of cloud to rainwater and a larger evaporation of convective precipitation. Unfortunately, simulations with an improved MJO also have a significant negative impact on the climatological values of low-level cloud and absorbed shortwave radiation, suggesting that structural in addition to parametric modifications to CAM5's parameterization suite are needed in order to simultaneously well simulate the MJO and mean-state climate.

Probability distribution functions (PDFs) of model inputs that affect the transport and dispersion of a trace gas released from a coastal California nuclear power plant are quantified using ensemble simulations, machine learning algorithms, and Bayesian inversion. The PDFs are constrained by observations of tracer concentrations and account for uncertainty in meteorology, transport, diffusion, and emissions. Meteorological uncertainty is calculated using an ensemble of simulations of the Weather Research and Forecasting (WRF) model that samples five categories of model inputs (initialization time, boundary layer physics, land surface model, nudging options, and reanalysis data). The WRF output is used to drive tens of thousands of FLEXPART dispersion simulations that sample a uniform distribution of six emissions inputs. Machine learning algorithms are trained on the ensemble data, and used to quantify the sources of ensemble variability and to infer, via inverse modeling, the values of the 11 model inputs most consistent with tracer measurements. We find a substantial ensemble spread in tracer concentrations (factors of 10 to 103), most of which is due to changing emissions inputs (about 80 %), though the cumulative effects of meteorological variations are not negligible. The performance of the inverse method is verified using synthetic observations generated from arbitrarily selected simulations. When applied to measurements from a controlled tracer release experiment, the most likely inversion results are within about 200 meters of the known release location, 5 and 50 minutes of the release start and duration times, respectively, and 22 % of the release amount. The inversion also estimates probabilities of different combinations of WRF inputs of matching the tracer observations.

Simulations using IPCC (Intergovernmental Panel on Climate Change)-class climate models are subject to fail or crash for a variety of reasons. Quantitative analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation crashes within the Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4). About 8.5% of our CCSM4 simulations failed for numerical reasons at combinations of POP2 parameter values. We applied support vector machine (SVM) classification from machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicted model failures in an independent validation ensemble, as assessed by the area under the receiver operating characteristic (ROC) curve metric (AUC > 0.96). The causes of the simulation failures were determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations were the major sources of the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations across the relevant parameters. Our method can also be used to quantify, predict, and understand simulation crashes in other complex geoscientific models.

[1] Modern climate models contain numerous input parameters, each with a range of possible values. Since the volume of parameter space increases exponentially with the number of parameters N, it is generally impossible to directly evaluate a model throughout this space even if just 2–3 values are chosen for each parameter. Sensitivity screening algorithms, however, can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination. This can aid both model development and the uncertainty quantification (UQ) process. Here we report results from a parameter sensitivity screening algorithm hitherto untested in climate modeling, the Morris one-at-a-time (MOAT) method. This algorithm drastically reduces the computational cost of estimating sensitivities in a high dimensional parameter space because the sample size grows linearly rather than exponentially with N. It nevertheless samples over much of the N-dimensional volume and allows assessment of parameter interactions, unlike traditional elementary one-at-a-time (EOAT) parameter variation. We applied both EOAT and MOAT to the Community Atmosphere Model (CAM), assessing CAM's behavior as a function of 27 uncertain input parameters related to the boundary layer, clouds, and other subgrid scale processes. For radiation balance at the top of the atmosphere, EOAT and MOAT rank most input parameters similarly, but MOAT identifies a sensitivity that EOAT underplays for two convection parameters that operate nonlinearly in the model. MOAT's ranking of input parameters is robust to modest algorithmic variations, and it is qualitatively consistent with model development experience.

This document presents the large scale water budget statistics of a perturbed input-parameter ensemble of atmospheric model runs. The model is Version 5.1.02 of the Community Atmosphere Model (CAM). These runs are the “C-Ensemble” described by Qian et al., “Parametric Sensitivity Analysis of Precipitation at Global and Local Scales in the Community Atmosphere Model CAM5” (Journal of Advances in Modeling the Earth System, 2015). As noted by Qian et al., the simulations are “AMIP type” with temperature and sea ice boundary conditions chosen to match surface observations for the five year period 2000-2004. There are 1100 ensemble members in addition to one run with default inputparameter values.

Emission rates of greenhouse gases (GHGs) entering into the atmosphere can be inferred using mathematical inverse approaches that combine observations from a network of stations with forward atmospheric transport models. Some locations for collecting observations are better than others for constraining GHG emissions through the inversion, but the best locations for the inversion may be inaccessible or limited by economic and other non-scientific factors. We present a method to design an optimal GHG observing network in the presence of multiple objectives that may be in conflict with each other. As a demonstration, we use our method to design a prototype network of six stations to monitor summertime emissions in California of the potent GHG 1,1,1,2-tetrafluoroethane (CH2FCF3, HFC-134a). We use a multiobjective genetic algorithm to evolve network configurations that seek to jointly maximize the scientific accuracy of the inferred HFC-134a emissions and minimize the associated costs of making the measurements. The genetic algorithm effectively determines a set of "optimal" observing networks for HFC-134a that satisfy both objectives (i.e., the Pareto frontier). The Pareto frontier is convex, and clearly shows the tradeoffs between performance and cost, and the diminishing returns in trading one for the other. Without difficulty, our method can be extended to design optimal networks to monitor two or more GHGs with different emissions patterns, or to incorporate other objectives and constraints that are important in the practical design of atmospheric monitoring networks.

Local and global sensitivity and uncertainty methods are applied to a box model of the dimethylsulfide (DMS) oxidation cycle in the remote marine boundary layer in order to determine the key physical and chemical parameters and sources of uncertainty. The model considers 58 uncertain parameters, and simulates the diurnal gas-phase cycles of DMS, SO2, methanesulfonic acid (MSA), and H2SO4 for clear-sky summertime conditions observed over the Southern Ocean. The results of this study depend on many underlying assumptions, including the DMS mechanism, simulation conditions, and probability distribution functions of the uncertain parameters. A local direct integration method is used to calculate first-order local sensitivity coefficients for infinitesimal perturbations about the parameter means. Key parameters identified by this analysis are related to DMS emissions, vertical mixing, heterogeneous removal, and the DMS+OH abstraction and addition reactions. MSA and H2SO4 are also sensitive to numerous rate constants, which limits the ability of using parameterized mechanisms to predict their concentrations. Of the chemistry, H2SO4 is highly sensitive to the rate constants for a set of nighttime reactions that lead to its production through a non-SO2 path initiated by the oxidation of DMS by NO3. For the global analysis, the probabilistic collocation method is used to propagate the uncertain parameters through the model. The concentrations of DMS and SO2 are uncertain (1-σ) by factors of 3.5 and 2.5, respectively, while MSA and H2SO4 have uncertainty factors that range between 4.1 and 8.6. The main sources of uncertainty in the four species are from DMS emissions and heterogeneous scavenging, but the uncertain rate constants collectively account for up to 59% of the total uncertainty in MSA and 43% in H2SO4. Of the uncertain DMS chemistry, reactions that form and destroy CH3S(O)OO and CH3SO3 are identified as important targets for reducing the uncertainties.

One objective of the Climate Science for a Sustainable Energy Future (CSSEF) program is to develop the capability to thoroughly test and understand the uncertainties in the overall climate model and its components as they are being developed. The focus on uncertainties involves sensitivity analysis: the capability to determine which input parameters have a major influence on the output responses of interest. This report presents some initial sensitivity analysis results performed by Lawrence Livermore National Laboratory (LNNL), Sandia National Laboratories (SNL), and Pacific Northwest National Laboratory (PNNL). In the 2011-2012 timeframe, these laboratories worked in collaboration to perform sensitivity analyses of a set of CAM5, 2◦ runs, where the response metrics of interest were precipitation metrics. The three labs performed their sensitivity analysis (SA) studies separately and then compared results. Overall, the results were quite consistent with each other although the methods used were different. This exercise provided a robustness check of the global sensitivity analysis metrics and identified some strongly influential parameters.

The vibration–rotation–tunneling (VRT) spectrum of a low-frequency intermolecular vibration of (D 2 O) 5 was recorded near 0.9 THz (30.2 cm −1 ). From an analysis of the relative intensities in the compact Q-branch region, the ground-state C-rotational constant is estimated to be 975±60 MHz, consistent with ab initio structural predictions. The precisely determined B -rotational constant ( B =1750.96±0.20 MHz) agrees well with previous results. Efforts to resolve possible bifurcation tunneling fine structure, such as that observed in VRT spectra of (D 2 O) 3 , revealed no such effects. This constrains the splittings to be less than 450 kHz, or roughly 3 times smaller than required by previous results.

We report the first observation of five water trimer isotopomers using terahertz laser spectroscopy coupled with a pulsed slit jet expansion technique. A single c-type vibration−rotation-tunneling (VRT) band has been observed for each isotopomer between 40 and 50 cm-1. By considering all the experimental data, including results from isotopic substitution experiments, analyses of bifurcation tunneling splittings, and rotational analyses of the VRT bands, it has been possible to determine unambiguously the isotopic composition and structure of each isotopomer. We have also extended the measurements of the 41.1 cm-1 band in (D2O)3, first reported by Suzuki and Blake [Suzuki, S.; Blake, G. A. Chem. Phys. Lett. 1994, 229, 499]. All six VRT bands have been assigned to pseudorotational transitions, which principally involve flipping of the “free” hydrogen or deuterium atoms in the trimer from above to below the plane of the OOO ring. Interestingly, four of the six bands have been assigned to the same transition....

This paper presents a step in the development of a top-down method to complement the bottom-up inventories of halocarbon emissions in California using high frequency observations, forward simulations and inverse methods. The Scripps Institution of Oceanography high-frequency atmospheric halocarbons measurement sites are located along the California coast and therefore the evaluation of transport in the chosen Weather Research Forecast (WRF) model at these sites is crucial for inverse modeling. The performance of the transport model has been investigated by comparing the wind direction and speed and temperature at four locations using aircraft weather reports as well at all METAR weather stations in our domain for hourly variations. Different planetary boundary layer (PBL) schemes, horizontal resolutions (achieved through nesting) and two meteorological datasets have been tested. Finally, simulated concentration of an inert tracer has been briefly investigated. All the PBL schemes present similar results that generally agree with observations, except in summer when the model sea breeze is too strong. At the coarse 12 km resolution, using ERA-interim (ECMWF Re-Analysis) as initial and boundary conditions leads to improvements compared to using the North American Model (NAM) dataset. Adding higher resolution nests also improves the match with the observations. However, no further improvement is observed from increasing the nest resolution from 4 km to 0.8 km. Once optimized, the model is able to reproduce tracer measurements during typical winter California large-scale events (Santa Ana). Furthermore, with the WRF/CHEM chemistry module and the European Database for Global Atmospheric Research (EDGAR) version 4.1 emissions for HFC-134a, we find that using a simple emission scaling factor is not sufficient to infer emissions, which highlights the need for more complex inversions.

[1] Representation of clouds remains among the largest uncertainties in climate models and thus climate projections. Clouds vary significantly over different climate regimes and are controlled by different dynamics and physics. Using the cloud simulator output from perturbed-parameter ensemble climate runs with prescribed monthly sea surface temperature, this study examines the performance of the Community Atmosphere Model version 4 (CAM4) in simulating clouds over different tropical regions. Perturbing 28 selected parameters shows that model performance is quite sensitive to parameter values in different cloud regimes. Carefully adjusting these parameters could lead to a better simulation of clouds over many regions compared with the default model. Latin hypercube runs that pseudo-randomly sample the 28 parameters simultaneously have much wider spread and more spatial variations than the runs with parameters varied One-At-a-Time (OAT), suggesting the importance of non-linearities and interactions among parameters associated with different physical processes. The perturbed parameters have a relatively large impact on the mean bias compared to the pattern error.

Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's weather and climate, and other complex systems. It entails much more than attaching defensible error bars to predictions: in particular it includes assessing low-probability but high-consequence events. To achieve these goals with models containing a large number of uncertain input parameters, structural uncertainties, etc., raw computational power is needed. An automated, self-adapting search of the possible model configurations is also useful. Our UQ initiative at the Lawrence Livermore National Laboratory has produced the most extensive set to date of simulations from the US Community Atmosphere Model. We are examining output from about 3,000 twelve-year climate simulations generated with a specialized UQ software framework, and assessing the model's accuracy as a function of 21 to 28 uncertain input parameter values. Most of the input parameters we vary are related to the boundary layer, clouds, and other sub-grid scale processes. Our simulations prescribe surface boundary conditions (sea surface temperatures and sea ice amounts) to match recent observations. Fully searching this 21+ dimensional space is impossible, but sensitivity and ranking algorithms can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination. Bayesian statistical constraints, employing a variety of climate observations as metrics, also seem promising. Observational constraints will be important in the next step of our project, which will compute sea surface temperatures and sea ice interactively, and will study climate change due to increasing atmospheric carbon dioxide.

[1] Numerically efficient parameterizations based on theories of binary, ternary and ion-induced aerosol nucleation (BN, TN and IN) enable online calculations of new particle formation in high resolution atmospheric models. These parameterizations are evaluated interactively in a 3D global chemical transport model that simulates gaseous sulfuric acid, ammonia and the ion-pair production rate. The BN and IN parameterizations produce new particles only in the cold upper troposphere, with IN giving relatively lower nucleation rates due to parameterization errors. In contrast, the TN parameterization predicts new particle formation throughout the troposphere, but gives unrealistically high nucleation rates. New and revised TN theories from recent studies instead give much lower rates and indicate that TN is not likely in the lower troposphere. Nucleation parameterizations can be useful for global modeling applications, but the current schemes should be used cautiously given their large uncertainties.

The gas-phase products of dimethylsulfide (DMS) oxidation are simulated in the global 3D Model of Atmospheric Transport and Chemistry (MATCH). The focus is on the sensitivities to the assumed mechanisms of DMS oxidation chemistry in large-scale models, and hence volcanic and anthropogenic sources of SO 2 are ignored. Four representations of DMS chemistry are considered, including two comprehensive mechanisms (about 50 reactions) and two parameterized schemes (four and five reactions). The gas-phase yields of DMS, SO 2 , methanesulfonic acid (MSA) and H 2 SO 4 are compared between these four cases as a measure of the sensitivity to uncertain DMS chemistry. Among the four cases, DMS is largely invariant, while SO 2 using parameterized chemistry is three times higher than its levels using comprehensive chemistry in the tropical upper troposphere. For MSA and H 2 SO 4 , there are order-of-magnitude inter-mechanistic differences at high and low altitudes in the extratropics, respectively. The differences are attributed to fixed branching yields and the absence of important intermediate species and pathways in the parameterized mechanisms. Regional budgets are also analyzed within the remote Southern Ocean and central tropical Pacific. While the DMS budget varies primarily between the regions, the SO 2 , MSA and H 2 SO 4 budgets vary strongly between the regions and mechanisms. The DMS oxidation products, therefore, are as sensitive to the assumed mechanisms as to the external conditions between the tropics and extratropics. To distinguish between the mechanism cases, the MATCH simulations are also compared to campaign measurements. The simulated values of DMS and SO 2 are found to approximate the observations, but do not provide mechanism differentiation. The gas-phase H 2 SO 4 and MSA simulations differ more extensively from the observations, yet the comprehensive chemistry cases give a better fit to the MSA measurements. Better fits to the MSA observations are also achieved for the two mechanisms that consider dimethylsulfoxide (DMSO) as an MSA precursor.

[1] Tropospheric distributions of H2SO4-H2O binary nucleation rates are calculated from a 3D model with DMS chemistry. Two mechanisms are used that include and exclude an SO2-independent H2SO4 production path. In the lower troposphere, the SO2-independent path generates extra H2SO4 and, consequently, much higher aerosol nucleation rates. However, these rates are less than 10−5 particles cm−3 s−1 in most of the lower troposphere, which indicates that DMS oxidation does not lead to appreciable aerosol formation there. In the upper troposphere, binary nucleation is fairly insensitive to the mechanism differences, with both yielding rates that exceed 1 particle cm−3 s−1 in the upper tropics. These large upper tropospheric nucleation rates imply that the DMS cycle may have served as an important source of aerosols to the troposphere and stratosphere during preindustrial and volcanically-quiescent periods, or during modern periods in clean marine regions where anthropogenic SO2 and H2SO4 are relatively low.

[1] A one-dimensional model of dimethylsulfide (DMS) oxidation chemistry and simultaneous observations from Flight 24 of the First Aerosol Characterization Experiment are used to test the oxidation of DMS by OH and the gas-phase production of SO2, H2SO4, and methanesulfonic acid (MSA) in the remote marine atmosphere. The model includes a comprehensive chemical mechanism (55 sulfur reactions, 28 sulfur species), vertical mixing, scavenging by background aerosols, and surface losses and emissions. Model parameter uncertainties have been estimated and are used to compute probability distribution functions of observable model outputs using a Monte Carlo method. Seven mechanistic scenarios are considered, which include a baseline case incorporating our best current knowledge and six cases that test novel MSA production reactions involving a newly proposed MSA isomer and the oxidation of methanesulfenic (MSEA) and methanesulfinic (MSIA) acids. The results show that for each of the seven scenarios the modeled DMS and SO2 concentrations agree statistically with the Flight 24 observations. For MSA, however, the observations are a factor of 104 to 105 larger than the baseline mean model predictions and lie 3–4 orders of magnitude outside of the one-sigma model uncertainty range. Statistical agreement between the boundary layer MSA observations and the model is achieved only for the mechanism scenarios that invoke new MSA production pathways, with the best agreement occurring when MSA is produced from the oxidation of MSIA or through a path involving the DMS-OH adduct and MSA isomer. For H2SO4 this study finds that even though the majority of the observations lie within the one-sigma model uncertainty range, the baseline scenario systematically underproduces H2SO4 in the boundary layer. These systematic differences are removed when the production of SO3 is enhanced through a pathway that is independent of SO2. This provides evidence for an efficient H2SO4 production pathway that does not involve SO2 as a precursor. Sensitivity studies are also presented, the results of which suggest observables that are most effective at distinguishing between our seven DMS mechanistic scenarios.

Current guidelines focus on a particular risk factor and specify criteria for categorizing persons into a small number of treatment groups.To compare current guidelines with individualized guidelines (that use readily available characteristics from each person to calculate the risk reduction expected from treatment and to identify persons for treatment in ranked order of decreasing expected benefit), in the context of blood pressure management.Analysis of person-specific, longitudinal data.The ARIC (Atherosclerosis Risk in Communities) Study.Persons aged 45 to 64 years without preexisting cardiovascular disease who currently do not receive antihypertensive treatment.Treatment according to the criteria of the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7 guidelines); individualized guidelines, or treatment in decreasing order of expected benefit; and random care, or treatment of persons selected at random.Number of myocardial infarctions (MIs) and strokes and medical costs.Compared with treating people according to random care, individualized guidelines could prevent the same number of MIs and strokes as JNC 7 guidelines at savings that are 67% greater than using JNC 7 guidelines, or it could prevent 43% more MIs and strokes for the same cost as treatment according to JNC 7 guidelines. The superiority of individualized guidelines was not sensitive to a wide range of assumptions about costs, treatment effectiveness, level of risk for cardiovascular disease in the population, or effects on workflow. The degree of superiority was sensitive to the accuracy of the method used to rank patients and to its span (the proportion of the population for whom all of the outcomes of interest can be calculated).Specific results apply to the effects of blood pressure management on MI and stroke in the ARIC Study population. The methods for calculating individual benefits require quantitative evidence about the relationships among risk factors, long-term outcomes, and treatment effects.Use of individualized guidelines can help to increase the quality and reduce the cost of care.

The 14th International Congress on Combustion By-Products and Their Health Effects was held in Umeå, Sweden from June 14th to 17th, 2015. The Congress, mainly sponsored by the National Institute of Environmental Health Sciences Superfund Research Program and the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, focused on the "Origin, fate and health effects of combustion-related air pollutants in the coming era of bio-based energy sources". The international delegates included academic and government researchers, engineers, scientists, policymakers and representatives of industrial partners. The Congress provided a unique forum for the discussion of scientific advances in this research area since it addressed in combination the health-related issues and the environmental implications of combustion by-products. The scientific outcomes of the Congress included the consensus opinions that: (a) there is a correlation between human exposure to particulate matter and increased cardiac and respiratory morbidity and mortality; (b) because currently available data does not support the assessment of differences in health outcomes between biomass smoke and other particulates in outdoor air, the potential human health and environmental impacts of emerging air-pollution sources must be addressed. Assessment will require the development of new approaches to characterize combustion emissions through advanced sampling and analytical methods. The Congress also concluded the need for better and more sustainable e-waste management and improved policies, usage and disposal methods for materials containing flame retardants.