Aerosol activation and cloud processing in the global aerosol-climate model ECHAM5-HAM

A parameterization for cloud processing is presented that calculates activation of aerosol particles to cloud drops, cloud drop size, and pH-dependent aqueous phase sulfur chemistry. The parameterization is implemented in the global aerosol-climate model ECHAM5-HAM. The cloud processing parameterization uses updraft speed, temperature, and aerosol size and chemical parameters simulated by ECHAM5-HAM to estimate the maximum supersaturation at the cloud base, and subsequently the cloud drop number concentration (CDNC) due to activation. In-cloud sulfate production occurs through oxidation of dissolved SO2 by ozone and hydrogen peroxide. The model simulates realistic distributions for annually averaged CDNC although it is underestimated especially in remote marine regions. On average, CDNC is dominated by cloud droplets growing on particles from the accumulation mode, with smaller contributions from the Aitken and coarse modes. The simulations indicate that in-cloud sulfate production is a potentially important source of accumulation mode sized cloud condensation nuclei, due to chemical growth of activated Aitken particles and to enhanced coalescence of processed particles. The strength of this source depends on the distribution of produced sulfate over the activated modes. This distribution is affected by uncertainties in many parameters that play a direct role in particle activation, such as the updraft velocity, the aerosol chemical composition and the organic solubility, and the simulated CDNC is found to be relatively sensitive to these uncertainties

The evolution of the global aerosol system in a transient climate simulation from 1860 to 2100

The evolution of the global aerosol system from 1860 to 2100 is investigated through a transient atmosphere-ocean General Circulation Model climate simulation with interactively coupled atmospheric aerosol and oceanic biogeochemistry modules. The microphysical aerosol module HAM incorporates the major global aerosol cycles with prognostic treatment of their composition, size distribution, and mixing state. Based on an SRES A1B emission scenario, the global mean sulfate burden is projected to peak in 2020 while black carbon and particulate organic matter show a lagged peak around 2070. From present day to future conditions the anthropogenic aerosol burden shifts generally from the northern high-latitudes to the developing low-latitude source regions with impacts on regional climate. Atmospheric residence- and aging-times show significant alterations under varying climatic and pollution conditions. Concurrently, the aerosol mixing state changes with an increasing aerosol mass fraction residing in the internally mixed accumulation mode. The associated increase in black carbon causes a more than threefold increase of its co-single scattering albedo from 1860 to 2100. Mid-visible aerosol optical depth increases from pre-industrial times, predominantly from the aerosol fine fraction, peaks at 0.26 around the sulfate peak in 2020 and maintains a high level thereafter, due to the continuing increase in carbonaceous aerosols. The global mean anthropogenic top of the atmosphere clear-sky short-wave direct aerosol radiative perturbation intensifies to −1.1 W m^−2 around 2020 and weakens after 2050 to −0.6 W m^−2, owing to an increase in atmospheric absorption. The demonstrated modifications in the aerosol residence- and aging-times, the microphysical state, and radiative properties challenge simplistic approaches to estimate the aerosol radiative effects from emission projections

Sources of uncertainties in modelling black carbon at the global scale

Our understanding of the global black carbon (BC) cycle is essentially qualitative due to uncertainties in our knowledge of its properties. This work investigates two source of uncertainties in modelling black carbon: those due to the use of different schemes for BC ageing and its removal rate in the global Transport-Chemistry model TM5 and those due to the uncertainties in the definition and quantification of the observations, which propagate through to both the emission inventories, and the measurements used for the model evaluation. The schemes for the atmospheric processing of black carbon that have been tested with the model are (i) a simple approach considering BC as bulk aerosol and a simple treatment of the removal with fixed 70% of in-cloud black carbon concentrations scavenged by clouds and removed when rain is present and (ii) a more complete description of microphysical ageing within an aerosol dynamics model, where removal is coupled to the microphysical properties of the aerosol, which results in a global average of 40% in-cloud black carbon that is scavenged in clouds and subsequently removed by rain, thus resulting in a longer atmospheric lifetime. This difference is reflected in comparisons between both sets of modelled results and the measurements. Close to the sources, both anthropogenic and vegetation fire source regions, the model results do not differ significantly, indicating that the emissions are the prevailing mechanism determining the concentrations and the choice of the aerosol scheme does not influence the levels. In more remote areas such as oceanic and polar regions the differences can be orders of magnitude, due to the differences between the two schemes. The more complete description reproduces the seasonal trend of the black carbon observations in those areas, although not always the magnitude of the signal, while the more simplified approach underestimates black carbon concentrations by orders of magnitude. The sensitivity to wet scavenging has been tested by varying in-cloud and below-cloud removal. BC lifetime increases by 10% when large scale and convective scale precipitation removal efficiency are reduced by 30%, while the variation is very small when below-cloud scavenging is zero. Since the emission inventories are representative of elemental carbon-like substance, the model output should be compared to elemental carbon measurements and if known, the ratio of black carbon to elemental carbon mass should be taken into account when the model is compared with black carbon observation

Cloud microphysics and aerosol indirect effects in the global climate model ECHAM5-HAM

The double-moment cloud microphysics scheme from ECHAM4 has been coupled to the size-resolved aerosol scheme ECHAM5-HAM. ECHAM5-HAM predicts the aerosol mass and number concentrations and the aerosol mixing state. This results in a much better agreement with observed vertical profiles of the black carbon and aerosol mass mixing ratios than with the previous version ECHAM4, where only the different aerosol mass mixing ratios were predicted. Also, the simulated liquid, ice and total water content and the cloud droplet and ice crystal number concentrations as a function of temperature in stratiform mixed-phase clouds between 0 and –35°C agree much better with aircraft observations in the ECHAM5 simulations. ECHAM5 performs better because more realistic aerosol concentrations are available for cloud droplet nucleation and because the Bergeron-Findeisen process is parameterized as being more efficient. The total anthropogenic aerosol effect includes the direct, semi-direct and indirect effects and is defined as the difference in the top-of-the-atmosphere net radiation between present-day and pre-industrial times. It amounts to –1.8 W m^−2 in ECHAM5, when a relative humidity dependent cloud cover scheme and present-day aerosol emissions representative for the year 2000 are used. It is larger when either a statistical cloud cover scheme or a different aerosol emission inventory are employed

The importance of temporal collocation for the evaluation of aerosol models with observations

This is the final version of the article. Available from European Geosciences Union (EGU) and Copernicus Publications via the DOI in this record.It is often implicitly assumed that over suitably long periods the mean of observations and models should be comparable, even if they have different temporal sampling. We assess the errors incurred due to ignoring temporal sampling and show that they are of similar magnitude as (but smaller than) actual model errors (20–60 %). Using temporal sampling from remote-sensing data sets, the satellite imager MODIS (MODerate resolution Imaging Spectroradiometer) and the ground-based sun photometer network AERONET (AErosol Robotic NETwork), and three different global aerosol models, we compare annual and monthly averages of full model data to sampled model data. Our results show that sampling errors as large as 100 % in AOT (aerosol optical thickness), 0.4 in AE (Ångström Exponent) and 0.05 in SSA (single scattering albedo) are possible. Even in daily averages, sampling errors can be significant. Moreover these sampling errors are often correlated over long distances giving rise to artificial contrasts between pristine and polluted events and regions. Additionally, we provide evidence that suggests that models will underestimate these errors. To prevent sampling errors, model data should be temporally collocated to the observations before any analysis is made. We also discuss how this work has consequences for in situ measurements (e.g. aircraft campaigns or surface measurements) in model evaluation. Although this study is framed in the context of model evaluation, it has a clear and direct relevance to climatologies derived from observational data sets.This work was supported by the Natural Environmental Research Council grant nr NE/J024252/1 (Global Aerosol Synthesis And Science Project). Computational resources for the ECHAM-HAM runs were made available by Deutsches Klimarechenzentrum (DKRZ) through support from the Bundesministerium für Bildung und Forschung (BMBF). The ECHAM-HAMMOZ model is developed by a consortium composed of ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford, the Finnish Meteorological Institute and the Leibniz Institute for Tropospheric Research, and managed by the Center for Climate Systems Modeling (C2SM) at ETH Zurich. P. Stier would like to acknowledge funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) ERC project ACCLAIM (grant agreement no. FP7-280025). HadGEMUKCA was run on the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk). The development of the UKCA model (www.ukca.ac.uk) was supported by the UK’s Natural Environment Research Council (NERC) through the NERC Centres for Atmospheric Science (NCAS) initiative. MIROC-SPRINTARS was run on the SX-9 supercomputer at NIES (CGER) in Japan. The figures in this paper were prepared using David W. Fanning’s coyote library for IDL. The authors thank an anonymous reviewer and in particular Andrew Sayer for useful comments that helped improve the manuscript

Links between satellite-retrieved aerosol and precipitation

This is the final version of the article. Available from EGU via the DOI in this recordMany theories have been proposed detailing how aerosols might impact precipitation, predicting both increases and decreases depending on the prevailing meteorological conditions and aerosol type. In convective clouds, increased aerosol concentrations have been speculated to invigorate convective activity. Previous studies have shown large increases in precipitation with increasing aerosol optical depth, concluding an aerosol effect on precipitation. Our analysis reveals that these studies may have been influenced by cloud effects on the retrieved aerosol, as well as by meteorological covariations. We use a regime-based approach to separate out different cloud regimes, allowing for the study of aerosol–cloud interactions in individual cloud regimes. We account for the influence of cloud properties on the aerosol retrieval and make use of the diurnal sampling of the TRMM satellite and the TRMM merged precipitation product to investigate the precipitation development. We find that whilst there is little effect on precipitation at the time of the aerosol retrieval, in the 6 h after the aerosol retrieval, there is an increase in precipitation from cloud in high-aerosol environments, consistent with the invigoration hypothesis. Increases in lightning flash count with increased aerosol are also observed in this period. The invigoration effect appears to be dependent on the cloud-top temperature, with clouds with tops colder than 0 °C showing increases in precipitation at times after the retrieval, as well as increases in wet scavenging. Warm clouds show little change in precipitation development with increasing aerosol, suggesting ice processes are important for the invigoration of precipitation.This work was supported by a UK Natural Environment Research Council (NERC) DPhil studentship and funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. FP7-280025

Satellite observations of cloud regime development: the role of aerosol processes

This is the final version of the article. Available from European Geosciences Union via the DOI in this record.Many different interactions between aerosols and clouds have been postulated, based on correlations between satellite retrieved aerosol and cloud properties. Previous studies highlighted the importance of meteorological covariations to the observed correlations. In this work, we make use of multiple temporally-spaced satellite retrievals to observe the development of cloud regimes. The observation of cloud regime development allows us to account for the influences of cloud fraction (CF) and meteorological factors on the aerosol retrieval. By accounting for the aerosol index (AI)-CF relationship, we reduce the influence of meteorological correlations compared to “snapshot” studies, finding that simple correlations overestimate any aerosol effect on CF by at least a factor of two. We find an increased occurrence of transitions into the stratocumulus regime over ocean with increases in MODIS AI, consistent with the hypothesis that aerosols increase stratocumulus persistence. We also observe an increase in transitions into the deep convective regime over land, consistent with the aerosol invigoration hypothesis. We find changes in the transitions from the shallow cumulus regime in different aerosol environments. The strength of these changes is strongly dependent on Low Troposphere Static Stability and 10 m windspeed, but less so on other meteorological factors. Whilst we have reduced the error due to meteorological and CF effects on the aerosol retrieval, meteorological covariation with the cloud and aerosol properties is harder to remove, so these results likely represent an upper bound on the effect of aerosols on cloud development and CF.This work was supported by a UK Natural Environment Research Council (NERC) DPhil studentship and funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement no. FP7-280025

Magneto-Optics of Exciton Rydberg States in a Monolayer Semiconductor

We report 65 tesla magneto-absorption spectroscopy of exciton Rydberg states in the archetypal monolayer semiconductor WSe$_2$. The strongly field-dependent and distinct energy shifts of the 2s, 3s, and 4s excited neutral excitons permits their unambiguous identification and allows for quantitative comparison with leading theoretical models. Both the sizes (via low-field diamagnetic shifts) and the energies of the $ns$ exciton states agree remarkably well with detailed numerical simulations using the non-hydrogenic screened Keldysh potential for 2D semiconductors. Moreover, at the highest magnetic fields the nearly-linear diamagnetic shifts of the weakly-bound 3s and 4s excitons provide a direct experimental measure of the exciton's reduced mass, $m_r = 0.20 \pm 0.01~m_0$.Comment: To appear in Phys. Rev. Lett. Updated version (25 jan 2018) now includes detailed supplemental discussion of Landau levels, Rydberg exciton energies, exciton mass, Dirac Hamiltonian, nonparabolicity, and dielectric effect

Incivility in Comparison: How Context, Content, and Personal Characteristics Predict Exposure to Uncivil Content

Incivility, that is, the breaking of social norms of conversation, is evidently prevalent in online political communication. While a growing literature provides evidence on the prevalence of incivility in different online venues, it is still unclear where and to what extent Internet users are exposed to incivility. This paper takes a comparative approach to assess the levels of incivility across contexts, content and personal characteristics. The pre-registered analysis uses detailed web browsing histories, including public Facebook posts and tweets seen by study participants, in combination with surveys collected during the German federal election 2021 (N = 739). The level of incivility is predicted using Google's Perspective API and compared across contexts (platforms and campaign periods), content features, and individual-level variables. The findings show that incivility is particularly strong on Twitter and more prevalent in comments than original posts/tweets on Facebook and Twitter. Content featuring political content and actors is more uncivil, whereas personal characteristics are less relevant predictors. The finding that user-generated political content is the most likely source of individuals' exposure to incivility adds to the understanding of social media's impact on public discourse

Experiences with a weighted decision tree learner

Machine learning algorithms for inferring decision trees typically choose a single “best” tree to describe the training data. Recent research has shown that classification performance can be significantly improved by voting predictions of multiple, independently produced decision trees. This paper describes an algorithm, OB1, that makes a weighted sum over many possible models. We describe one instance of OB1, that includes all possible decision trees as well as naïve Bayesian models. OB1 is compared with a number of other decision tree and instance based learning algorithms on some of the data sets from the UCI repository. Both an information gain and an accuracy measure are used for the comparison. On the information gain measure OB1 performs significantly better than all the other algorithms. On the accuracy measure it is significantly better than all the algorithms except naïve Bayes which performs comparably to OB1