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Organic aerosol concentration and composition over Europe: Insights from comparison of regional model predictions with aerosol mass spectrometer factor analysis
A detailed three-dimensional regional chemical transport model (Particulate Matter Comprehensive Air Quality Model with Extensions, PMCAMx) was applied over Europe, focusing on the formation and chemical transformation of organic matter. Three periods representative of different seasons were simulated, corresponding to intensive field campaigns. An extensive set of AMS measurements was used to evaluate the model and, using factor-analysis results, gain more insight into the sources and transformations of organic aerosol (OA). Overall, the agreement between predictions and measurements for OA concentration is encouraging, with the model reproducing two-thirds of the data (daily average mass concentrations) within a factor of 2. Oxygenated OA (OOA) is predicted to contribute 93% to total OA during May, 87% during winter and 96% during autumn, with the rest consisting of fresh primary OA (POA). Predicted OOA concentrations compare well with the observed OOA values for all periods, with an average fractional error of 0.53 and a bias equal to −0.07 (mean error = 0.9 μg m−3, mean bias = −0.2 μg m−3). The model systematically underpredicts fresh POA at most sites during late spring and autumn (mean bias up to −0.8 μg m−3). Based on results from a source apportionment algorithm running in parallel with PMCAMx, most of the POA originates from biomass burning (fires and residential wood combustion), and therefore biomass burning OA is most likely underestimated in the emission inventory. The sensitivity of POA predictions to the corresponding emissions' volatility distribution is discussed. The model performs well at all sites when the Positive Matrix Factorization (PMF)-estimated low-volatility OOA is compared against the OA with saturation concentrations of the OA surrogate species C* ≤ 0.1 μg m−3 and semivolatile OOA against the OA with C* > 0.1 μg m−3.
Polymer Brushes under High Load
Polymer coatings are frequently used to provide repulsive forces between surfaces in solution. After 25 years of design and study, a quantitative model to explain and predict repulsion under strong compression is still lacking. Here, we combine experiments, simulations, and theory to study polymer coatings under high loads and demonstrate a validated model for the repulsive forces, proposing that this universal behavior can be predicted from the polymer solution properties.
Status and future of numerical atmospheric aerosol prediction with a focus on data requirements
Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite- and ground-based platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol-climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term "requirements" is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of aerosol prediction activities. The criteria on which the requirements for aerosol observations are based are also outlined. Assimilation and evaluation aspects are discussed from the perspective of the user requirements.
Long-term predictability of mean daily temperature data
We quantify the long-term predictability of global mean daily temperature data by means of the Rényi entropy of second order K2. We are interested in the yearly amplitude fluctuations of the temperature. Hence, the data are low-pass filtered. The obtained oscillatory signal has a more or less constant frequency, depending on the geographical coordinates, but its amplitude fluctuates irregularly. Our estimate of K2 quantifies the complexity of these amplitude fluctuations. We compare the results obtained for the CRU data set (interpolated measured temperature in the years 1901-2003 with 0.5° resolution, Mitchell et al., 20051) with the ones obtained for the temperature data from a coupled ocean-atmosphere global circulation model (AOGCM, calculated at DKRZ). Furthermore, we compare the results obtained by means of K2 with the linear variance of the temperature data.
Aerosol activation characteristics and prediction at the central European ACTRIS research station of Melpitz, Germany
Understanding aerosol particle activation is essential for evaluating aerosol indirect effects (AIEs) on climate. Long-term measurements of aerosol particle activation help to understand the AIEs and narrow down the uncertainties of AIEs simulation. However, they are still scarce. In this study, more than 4 years of comprehensive aerosol measurements were utilized at the central European research station of Melpitz, Germany, to gain insight into the aerosol particle activation and provide recommendations on improving the prediction of number concentration of cloud condensation nuclei (CCN, NCCN). (1) The overall CCN activation characteristics at Melpitz are provided. As supersaturation (SS) increases from 0.1% to 0.7%, the median NCCN increases from 399 to 2144cm-3, which represents 10% to 48% of the total particle number concentration with a diameter range of 10-800nm, while the median hygroscopicity factor (κ) and critical diameter (Dc) decrease from 0.27 to 0.19 and from 176 to 54nm, respectively. (2) Aerosol particle activation is highly variable across seasons, especially at low-SS conditions. At SSCombining double low line0.1%, the median NCCN and activation ratio (AR) in winter are 1.6 and 2.3 times higher than the summer values, respectively. (3) Both κ and the mixing state are size-dependent. As the particle diameter (Dp) increases, κ increases at Dp of 1/440 to 100nm and almost stays constant at Dp of 100 to 200nm, whereas the degree of the external mixture keeps decreasing at Dp of 1/440 to 200nm. The relationships of κ vs. Dp and degree of mixing vs. Dp were both fitted well by a power-law function. (4) Size-resolved κ improves the NCCN prediction. We recommend applying the κ-Dp power-law fit for NCCN prediction at Melpitz, which performs better than using the constant κ of 0.3 and the κ derived from particle chemical compositions and much better than using the NCCN (AR) vs. SS relationships. The κ-Dp power-law fit measured at Melpitz could be applied to predict NCCN for other rural regions. For the purpose of improving the prediction of NCCN, long-term monodisperse CCN measurements are still needed to obtain the κ-Dp relationships for different regions and their seasonal variations.
Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0
Sea level rise (SLR) is one of the major impacts of global warming; it will threaten coastal populations, infrastructure, and ecosystems around the globe in coming centuries. Well-constrained sea level projections are needed to estimate future losses from SLR and benefits of climate protection and adaptation. Process-based models that are designed to resolve the underlying physics of individual sea level drivers form the basis for state-of-the-art sea level projections. However, associated computational costs allow for only a small number of simulations based on selected scenarios that often vary for different sea level components. This approach does not sufficiently support sea level impact science and climate policy analysis, which require a sea level projection methodology that is flexible with regard to the climate scenario yet comprehensive and bound by the physical constraints provided by process-based models. To fill this gap, we present a sea level model that emulates global-mean long-term process-based model projections for all major sea level components. Thermal expansion estimates are calculated with the hemispheric upwelling-diffusion ocean component of the simple carbon-cycle climate model MAGICC, which has been updated and calibrated against CMIP5 ocean temperature profiles and thermal expansion data. Global glacier contributions are estimated based on a parameterization constrained by transient and equilibrium process-based projections. Sea level contribution estimates for Greenland and Antarctic ice sheets are derived from surface mass balance and solid ice discharge parameterizations reproducing current output from ice-sheet models. The land water storage component replicates recent hydrological modeling results. For 2100, we project 0.35 to 0.56m (66% range) total SLR based on the RCP2.6 scenario, 0.45 to 0.67m for RCP4.5, 0.46 to 0.71m for RCP6.0, and 0.65 to 0.97m for RCP8.5. These projections lie within the range of the latest IPCC SLR estimates. SLR projections for 2300 yield median responses of 1.02m for RCP2.6, 1.76m for RCP4.5, 2.38m for RCP6.0, and 4.73m for RCP8.5. The MAGICC sea level model provides a flexible and efficient platform for the analysis of major scenario, model, and climate uncertainties underlying long-term SLR projections. It can be used as a tool to directly investigate the SLR implications of different mitigation pathways and may also serve as input for regional SLR assessments via component-wise sea level pattern scaling.
A parameterization of the heterogeneous hydrolysis of N2O5 for mass-based aerosol models: Improvement of particulate nitrate prediction
The heterogeneous hydrolysis of N2O5 on the surface of deliquescent aerosol leads to HNO3 formation and acts as a major sink of NOx in the atmosphere during night-time. The reaction constant of this heterogeneous hydrolysis is determined by temperature (T), relative humidity (RH), aerosol particle composition, and the surface area concentration (S). However, these parameters were not comprehensively considered in the parameterization of the heterogeneous hydrolysis of N2O5 in previous mass-based 3-D aerosol modelling studies. In this investigation, we propose a sophisticated parameterization (NewN2O5) of N2O5 heterogeneous hydrolysis with respect to T, RH, aerosol particle compositions, and S based on laboratory experiments. We evaluated closure between NewN2O5 and a state-of-the-art parameterization based on a sectional aerosol treatment. The comparison showed a good linear relationship (R Combining double low line 0.91) between these two parameterizations. NewN2O5 was incorporated into a 3-D fully online coupled model, COSMO-Muscat, with the mass-based aerosol treatment. As a case study, we used the data from the HOPE Melpitz campaign (10-25 September 2013) to validate model performance. Here, we investigated the improvement of nitrate prediction over western and central Europe. The modelled particulate nitrate mass concentrations ([NO3-]) were validated by filter measurements over Germany (Neuglobsow, Schmücke, Zingst, and Melpitz). The modelled [NO3-] was significantly overestimated for this period by a factor of 5-19, with the corrected NH3 emissions (reduced by 50 %) and the original parameterization of N2O5 heterogeneous hydrolysis. The NewN2O5 significantly reduces the overestimation of [NO3-] by ∼ 35 %. Particularly, the overestimation factor was reduced to approximately 1.4 in our case study (12, 17-18 and 25 September 2013) when [NO3-] was dominated by local chemical formations. In our case, the suppression of organic coating was negligible over western and central Europe, with an influence on [NO3-] of less than 2 % on average and 20 % at the most significant moment. To obtain a significant impact of the organic coating effect, N2O5, SOA, and NH3 need to be present when RH is high and T is low. However, those conditions were rarely fulfilled simultaneously over western and central Europe. Hence, the organic coating effect on the reaction probability of N2O5 may not be as significant as expected over western and central Europe.
Anharmonic strong-coupling effects at the origin of the charge density wave in CsV3Sb5
The formation of charge density waves is a long-standing open problem, particularly in dimensions higher than one. Various observations in the vanadium antimonides discovered recently further underpin this notion. Here, we study the Kagome metal CsV3Sb5 using polarized inelastic light scattering and density functional theory calculations. We observe a significant gap anisotropy with 2Δmax/kBTCDW≈20, far beyond the prediction of mean-field theory. The analysis of the A1g and E2g phonons, including those emerging below TCDW, indicates strong phonon-phonon coupling, presumably mediated by a strong electron-phonon interaction. Similarly, the asymmetric Fano-type lineshape of the A1g amplitude mode suggests strong electron-phonon coupling below TCDW. The large electronic gap, the enhanced anharmonic phonon-phonon coupling, and the Fano shape of the amplitude mode combined are more supportive of a strong-coupling phonon-driven charge density wave transition than of a Fermi surface instability or an exotic mechanism in CsV3Sb5.
A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)
Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models.
Neural partial differential equations for chaotic systems
When predicting complex systems one typically relies on differential equation which can often be incomplete, missing unknown influences or higher order effects. By augmenting the equations with artificial neural networks we can compensate these deficiencies. We show that this can be used to predict paradigmatic, high-dimensional chaotic partial differential equations even when only short and incomplete datasets are available. The forecast horizon for these high dimensional systems is about an order of magnitude larger than the length of the training data.