ALBERT

Description: Atmospheric nanoparticles (NPs) are important intermediates in the transition from gas-phase molecules to new aerosols that can activate into cloud droplets. Through increases in the emissions of sulfur-containing gases, human activities have likely increased the number of NPs produced in the atmosphere. To have significant impacts, however, sulfur pollution must be transported away from the surface, where NP formation is inefficient, to higher altitudes. To characterize this anthropogenic influence, tagged tracers are implemented in a global atmospheric transport model. The tagged tracers are used to track the contributions of sulfur from five sources (anthropogenic, oceanic, volcanic, aircraft, and stratospheric) to the gas-phase burdens of SO2 and H2SO4(g), and the rates of forming atmospheric NPs. Because NPs may be produced by a variety of mechanisms, three different aerosol nucleation schemes (binary, ternary and ion-induced) are used in the model calculations. Of the SO2 in the global troposphere, the tagged tracers indicate that about 69% originates from anthropogenic surface emissions, 20% from the oceans and 10% from de-gassing volcanoes. The same sources contribute about 56%, 24% and 19%, respectively, to the global tropospheric H2SO4(g) burden. The anthropogenic contribution for H2SO4(g) is reduced because anthropogenic SO2 produces H2SO4(g) less efficiently than oceanic and volcanic sulfur. Regardless of the underlying nucleation assumptions, the simulations show a pronounced influence of anthropogenic sulfur on atmospheric NP formation, particularly in the Northern Hemisphere. Utilizing the tagged H2SO4(g) contributions, anthropogenic sulfur is estimated to account for roughly 69% of the NP formation in the Northern Hemisphere, 31% in the Southern Hemisphere and 56% across the global troposphere. In the key region of the upper troposphere, anthropogenic and oceanic sulfur both make sizeable contributions to NP formation (54% and 37%, respectively). The tagged tracer contributions suggest that human activities have probably more than doubled the NP production rate in the atmosphere from preindustrial to modern times.

Description: 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.

Description: 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.

Description: 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.

Description: A study of the current significant uncertainties in dimethylsulfide (DMS) gas-phase chemistry provides insight into additional research needed to decrease these uncertainties. The DMS oxidation cycle in the remote marine boundary layer is simulated using a diurnally-varying box model with 56 uncertain chemical and physical parameters. Two analytical methods (direct integration and probabilistic collocation) are used to determine the most influential parameters (sensitivity analysis) and sources of uncertainty (uncertainty analysis) affecting the concentrations of DMS, SO2, methanesulfonic acid (MSA), and H2SO4. The key parameters identified by the sensitivity analysis are associated with DMS emissions, mixing in to and out of the boundary layer, heterogeneous removal of soluble sulfur-containing compounds, and the DMS+OH addition and abstraction reactions. MSA and H2SO4 are also sensitive to the rate constants of numerous other reactions, which limits the effectiveness of mechanism reduction techniques. Propagating the parameter uncertainties through the model leads to concentrations that are uncertain by factors of 1.8 to 3.0. The main sources of uncertainty are from DMS emissions and heterogeneous scavenging. Uncertain chemical rate constants, however, collectively account for up to 50–60% of the net uncertainties in MSA and H2SO4. The concentration uncertainties are also calculated at different temperatures, where they vary mainly due to temperature-dependent chemistry. With changing temperature, the uncertainties of DMS and SO2 remain steady, while the uncertainties of MSA and H2SO4 vary by factors of 2 to 4.

Description: Simulations using IPCC-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 apply 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 predicts 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 are determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations are 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.

Description: 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.