Abstract

The response of clouds to changes in the aerosol concentration is complex and may differ depending on the cloud type, the aerosol regime, and environmental conditions. In this study, a novel technique is used to systematically modify the environmental conditions in realistic convection-resolving simulations for cases with weak and strong large-scale forcing over central Europe with the Consortium for Small-Scale Modeling (COSMO) model. Besides control runs with quasi-operational settings, initial and boundary temperature profiles are modified with linear increasing temperature increments from 0 to 5 K between 3 and 12 km AGL to represent different amounts of convective available potential energy (CAPE) and relative humidity. The results show a systematic decrease of total precipitation with increasing cloud condensation nuclei (CCN) concentrations for the cases with strong synoptic forcing caused by a suppressed warm-rain process, whereas no systematic aerosol effect is simulated for weak synoptic forcing. The effect of increasing CCN tends to be stronger in the simulations with increased temperatures and lower CAPE. While the large-scale domain-averaged responses to increased CCN are weak, the precipitation forming over mountainous terrain reveals a stronger sensitivity for most of the analyzed cases. Our findings also demonstrate that the role of the warm-rain process is more important for strong than for weak synoptic forcing. The aerosol effect is largest for weakly forced conditions but more predictable for the strongly forced cases. However, more accurate environmental conditions are much more important than accurate aerosol assumptions, especially for weak large-scale forcing.

Abstract

An ice nucleation parameterization based on classical nucleation theory, with aerosol-specific parameters derived from experiments, has been implemented into a global climate model—the Community Atmosphere Model (CAM)-Oslo. The parameterization treats immersion, contact, and deposition nucleation by mineral dust, soot, bacteria, fungal spores, and pollen in mixed-phase clouds at temperatures between 0° and −38°C. Immersion freezing is considered for insoluble particles that are activated to cloud droplets, and deposition and contact nucleation are only allowed for uncoated, unactivated aerosols. Immersion freezing by mineral dust is found to be the dominant ice formation process, followed by immersion and contact freezing by soot. The simulated biological aerosol contribution to global atmospheric ice formation is marginal, even with high estimates of their ice nucleation activity, because the number concentration of ice nucleation active biological particles in the atmosphere is low compared to other ice nucleating aerosols. Because of the dominance of mineral dust, the simulated ice nuclei concentrations at temperatures below −20°C are found to correlate with coarse-mode aerosol particle concentrations. The ice nuclei (IN) concentrations in the model agree well overall with in situ continuous flow diffusion chamber measurements. At individual locations, the model exhibits a stronger temperature dependence on IN concentrations than what is observed. The simulated IN composition (77% mineral dust, 23% soot, and 10⁻⁵% biological particles) lies in the range of observed ice nuclei and ice crystal residue compositions.

Abstract

Waterbelt climate states with an ice-free tropical ocean provide a straightforward explanation for the survival of advanced marine species during the Cryogenian glaciations (720–635 million years ago). Previous work revealed that stable waterbelt states require the presence of highly reflective low-level mixed-phase clouds with a high abundance of supercooled liquid in the subtropics. However, the high uncertainty associated with representing mixed-phase clouds in coarse-scale general circulation models (GCMs) that parameterize atmospheric convection has prohibited assessment of whether waterbelt states are a robust feature of Earth’s climate. Here we investigate whether resolving convective-scale motion at length scales of hectometers helps us to assess the plausibility of a waterbelt scenario. First, we show that substantial differences in cloud reflectivity among GCMs do not arise from the resolved atmospheric circulation. Second, we conduct a hierarchy of simulations using the Icosahedral Nonhydrostatic (ICON) modeling framework, ranging from coarse-scale GCM simulations with parameterized convection to large-eddy simulations that explicitly resolve atmospheric convective-scale motions. Our hierarchy of simulations supports the existence of highly reflective subtropical clouds if we apply moderate ice nucleating particle (INP) concentrations. Third, we test the sensitivity of cloud reflectivity to the INP concentration. In the presence of high but justifiable INP concentrations, cloud reflectivity is strongly reduced. Hence, the existence of stable waterbelt states is controlled by the abundance of INPs. We conclude that explicitly resolving convection can help to constrain Cryogenian cloud reflectivity, but limited knowledge concerning Cryogenian aerosol conditions hampers strong constraints. Thus, waterbelt states remain an uncertain feature of Earth’s climate.

Abstract

In climate and weather models, the quantitative description of aerosol and cloud processes relies on simplified assumptions. This contributes major uncertainties to the prediction of global and regional climate change. Therefore, models need good parameterizations for heterogeneous ice nucleation by atmospheric aerosols. Here the authors present a new parameterization of immersion freezing on desert dust particles derived from a large number of experiments carried out at the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) cloud chamber facility. The parameterization is valid in the temperature range between −12° and −36°C at or above water saturation and can be used in atmospheric models that include information about the dust surface area. The new parameterization was applied to calculate distribution maps of ice nuclei during a Saharan dust event based on model results from the regional-scale model Consortium for Small-Scale Modelling–Aerosols and Reactive Trace Gases (COSMO-ART). The results were then compared to measurements at the Taunus Observatory on Mount Kleiner Feldberg, Germany, and to three other parameterizations applied to the dust outbreak. The aerosol number concentration and surface area from the COSMO-ART model simulation were taken as input to different parameterizations. Although the surface area from the model agreed well with aerosol measurements during the dust event at Kleiner Feldberg, the ice nuclei (IN) number concentration calculated from the new surface-area-based parameterization was about a factor of 13 less than IN measurements during the same event. Systematic differences of more than a factor of 10 in the IN number concentration were also found among the different parameterizations. Uncertainties in the modeled and measured parameters probably both contribute to this discrepancy and should be addressed in future studies.

Abstract

Based on results of 11 yr of heterogeneous ice nucleation experiments at the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) chamber in Karlsruhe, Germany, a new empirical parameterization framework for heterogeneous ice nucleation was developed. The framework currently includes desert dust and soot aerosol and quantifies the ice nucleation efficiency in terms of the ice nucleation active surface site (INAS) approach.

The immersion freezing INAS densities n _S of all desert dust experiments follow an exponential fit as a function of temperature, well in agreement with an earlier analysis of AIDA experiments. The deposition nucleation n _S isolines for desert dust follow u-shaped curves in the ice saturation ratio–temperature (S _i –T) diagram at temperatures below about 240 K. The negative slope of these isolines toward lower temperatures may be explained by classical nucleation theory (CNT), whereas the behavior toward higher temperatures may be caused by a pore condensation and freezing mechanism. The deposition nucleation measured for soot at temperatures below about 240 K also follows u-shaped isolines with a shift toward higher S _i for soot with higher organic carbon content. For immersion freezing of soot aerosol, only upper limits for n _S were determined and used to rescale an existing parameterization line.

The new parameterization framework is compared to a CNT-based parameterization and an empirical framework as used in models. The comparison shows large differences in shape and magnitude of the n _S isolines especially for deposition nucleation. For the application in models, implementation of this new framework is simple compared to that of other expressions.

Abstract

The contribution of heterogeneous ice nucleation to the formation of cirrus cloud ice crystals is still not well quantified. This results in large uncertainties when predicting cirrus radiative effects and their role in Earth’s climate system. The goal of this case study is to simulate the composition, and thus activation conditions, of ice nucleating particles (INPs) to evaluate their contribution to heterogeneous cirrus ice formation in relation to homogeneous ice nucleation. For this, the regional model COSMO—Aerosols and Reactive Trace Gases (COSMO-ART) was used to simulate a synoptic cirrus cloud over Texas on 13 April 2011. The simulated INP composition was then compared to measured ice residual particle (IRP) composition from the actual event obtained during the NASA Midlatitude Airborne Cirrus Properties Experiment (MACPEX) aircraft campaign. These IRP measurements indicated that the dominance of heterogeneous ice nucleation was mainly driven by mineral dust with contributions from a variety of other particle types. Applying realistic activation thresholds and concentrations of airborne transported mineral dust and biomass-burning particles, the model implementing the heterogeneous ice nucleation parameterization scheme of Ullrich et al. is able to reproduce the overall dominating ice formation mechanism in contrast to the model simulation with the scheme of Phillips et al. However, the model showed flaws in reproducing the IRP composition.

Abstract

Prediction of weather is a main goal of atmospheric science. Its importance to society is growing continuously due to factors such as vulnerability to natural disasters, the move to renewable energy sources, and the risks of climate change. But prediction is also a major scientific challenge due to the inherently limited predictability of a chaotic atmosphere, and has led to a revolution in forecasting methods as we have moved to probabilistic prediction. These changes provide the motivation for Waves to Weather (W2W), a major national research program in Germany with three main university partners in Munich, Mainz, and Karlsruhe. We are currently in the second 4-yr phase of our planned duration of 12 years and employ 36 doctoral and postdoctoral scientists. In the context of this large program, we address what we have identified to be the most important and challenging scientific questions in predictability of weather, namely, upscale error growth, errors associated with cloud processes, and probabilistic prediction of high-impact weather. This paper presents some key results of the first phase of W2W and discusses how they have influenced our understanding of predictability. The key role of interdisciplinary research linking atmospheric scientists with experts in visualization, statistics, numerical analysis, and inverse methods will be highlighted. To ensure a lasting impact on research in our field in Germany and internationally, we have instituted innovative programs for training and support of early-career scientists, and to support education, equal opportunities, and outreach, which are also described here.

Abstract

This study presents results from a model intercomparison project, focusing on the range of responses in deep convective cloud updrafts to varying cloud condensation nuclei (CCN) concentrations among seven state-of-the-art cloud-resolving models. Simulations of scattered convective clouds near Houston, Texas, are conducted, after being initialized with both relatively low and high CCN concentrations. Deep convective updrafts are identified, and trends in the updraft intensity and frequency are assessed. The factors contributing to the vertical velocity tendencies are examined to identify the physical processes associated with the CCN-induced updraft changes. The models show several consistent trends. In general, the changes between the High-CCN and Low-CCN simulations in updraft magnitudes throughout the depth of the troposphere are within 15% for all of the models. All models produce stronger (~+5%–15%) mean updrafts from ~4–7 km above ground level (AGL) in the High-CCN simulations, followed by a waning response up to ~8 km AGL in most of the models. Thermal buoyancy was more sensitive than condensate loading to varying CCN concentrations in most of the models and more impactful in the mean updraft responses. However, there are also differences between the models. The change in the amount of deep convective updrafts varies significantly. Furthermore, approximately half the models demonstrate neutral-to-weaker (~−5% to 0%) updrafts above ~8 km AGL, while the other models show stronger (~+10%) updrafts in the High-CCN simulations. The combination of the CCN-induced impacts on the buoyancy and vertical perturbation pressure gradient terms better explains these middle- and upper-tropospheric updraft trends than the buoyancy terms alone.