The relevance of nanoscale biological fragments for ice nucleation in clouds

Most studies of the role of biological entities as atmospheric ice-nucleating particles have focused on relatively rare supermicron particles such as bacterial cells, fungal spores and pollen grains. However, it is not clear that there are sufficient numbers of these particles in the atmosphere to strongly influence clouds. Here we show that the ice-nucleating activity of a fungus from the ubiquitous genus Fusarium is related to the presence of nanometre-scale particles which are far more numerous, and therefore potentially far more important for cloud glaciation than whole intact spores or hyphae. In addition, we quantify the ice-nucleating activity of nano-ice nucleating particles (nano-INPs) washed off pollen and also show that nano-INPs are present in a soil sample. Based on these results, we suggest that there is a reservoir of biological nano-INPs present in the environment which may, for example, become aerosolised in association with fertile soil dust particles

Aerosols in the Pre-industrial Atmosphere

Purpose of Review: We assess the current understanding of the state and behaviour of aerosols under pre-industrial conditions and the importance for climate. Recent Findings: Studies show that the magnitude of anthropogenic aerosol radiative forcing over the industrial period calculated by climate models is strongly affected by the abundance and properties of aerosols in the pre-industrial atmosphere. The low concentration of aerosol particles under relatively pristine conditions means that global mean cloud albedo may have been twice as sensitive to changes in natural aerosol emissions under pre-industrial conditions compared to present-day conditions. Consequently, the discovery of new aerosol formation processes and revisions to aerosol emissions have large effects on simulated historical aerosol radiative forcing. Summary: We review what is known about the microphysical, chemical, and radiative properties of aerosols in the pre-industrial atmosphere and the processes that control them. Aerosol properties were controlled by a combination of natural emissions, modification of the natural emissions by human activities such as land-use change, and anthropogenic emissions from biofuel combustion and early industrial processes. Although aerosol concentrations were lower in the pre-industrial atmosphere than today, model simulations show that relatively high aerosol concentrations could have been maintained over continental regions due to biogenically controlled new particle formation and wildfires. Despite the importance of pre-industrial aerosols for historical climate change, the relevant processes and emissions are given relatively little consideration in climate models, and there have been very few attempts to evaluate them. Consequently, we have very low confidence in the ability of models to simulate the aerosol conditions that form the baseline for historical climate simulations. Nevertheless, it is clear that the 1850s should be regarded as an early industrial reference period, and the aerosol forcing calculated from this period is smaller than the forcing since 1750. Improvements in historical reconstructions of natural and early anthropogenic emissions, exploitation of new Earth system models, and a deeper understanding and evaluation of the controlling processes are key aspects to reducing uncertainties in future

The study of atmospheric ice-nucleating particles via microfluidically generated droplets

Ice-nucleating particles (INPs) play a significant role in the climate and hydrological cycle by triggering ice formation in supercooled clouds, thereby causing precipitation and affecting cloud lifetimes and their radiative properties. However, despite their importance, INP often comprise only 1 in 10³–10⁶ ambient particles, making it difficult to ascertain and predict their type, source, and concentration. The typical techniques for quantifying INP concentrations tend to be highly labour-intensive, suffer from poor time resolution, or are limited in sensitivity to low concentrations. Here, we present the application of microfluidic devices to the study of atmospheric INPs via the simple and rapid production of monodisperse droplets and their subsequent freezing on a cold stage. This device offers the potential for the testing of INP concentrations in aqueous samples with high sensitivity and high counting statistics. Various INPs were tested for validation of the platform, including mineral dust and biological species, with results compared to literature values. We also describe a methodology for sampling atmospheric aerosol in a manner that minimises sampling biases and which is compatible with the microfluidic device. We present results for INP concentrations in air sampled during two field campaigns: (1) from a rural location in the UK and (2) during the UK’s annual Bonfire Night festival. These initial results will provide a route for deployment of the microfluidic platform for the study and quantification of INPs in upcoming field campaigns around the globe, while providing a benchmark for future lab-on-a-chip-based INP studies

Automated rain rate estimates using the Ka-band ARM zenith radar (KAZR)

The use of millimeter wavelength radars for probing precipitation has recently gained interest. However, estimation of precipitation variables is not straightforward due to strong signal attenuation, radar receiver saturation, antenna wet radome effects and natural microphysical variability. Here, an automated algorithm is developed for routinely retrieving rain rates from the profiling Ka-band (35-GHz) ARM (Atmospheric Radiation Measurement) zenith radars (KAZR). A 1-dimensional, simple, steady state microphysical model is used to estimate impacts of microphysical processes and attenuation on the profiles of radar observables at 35-GHz and thus provide criteria for identifying situations when attenuation or microphysical processes dominate KAZR observations. KAZR observations are also screened for signal saturation and wet radome effects. The algorithm is implemented in two steps: high rain rates are retrieved by using the amount of attenuation in rain layers, while low rain rates are retrieved from the reflectivity–rain rate (<i>Z</i>_<i>e</i>–<i>R</i>) relation. Observations collected by the KAZR, rain gauge, disdrometer and scanning precipitating radars during the DYNAMO/AMIE field campaign at the Gan Island of the tropical Indian Ocean are used to validate the proposed approach. The differences in the rain accumulation from the proposed algorithm are quantified. The results indicate that the proposed algorithm has a potential for deriving continuous rain rate statistics in the tropics

Automated rain rate estimates using the Ka-band ARM Zenith Radar (KAZR)

The use of millimeter wavelength radars for probing precipitation has recently gained interest. However, estimation of precipitation variables is not straightforward due to strong signal attenuation, radar receiver saturation, antenna wet radome effects and natural microphysical variability. Here, an automated algorithm is developed for routinely retrieving rain rates from the profiling Ka-band (35-GHz) ARM (Atmospheric Radiation Measurement) zenith radars (KAZR). A 1-dimensional, simple, steady state microphysical model is used to estimate impacts of microphysical processes and attenuation on the profiles of radar observables at 35-GHz and thus provide criteria for identifying situations when attenuation or microphysical processes dominate KAZR observations. KAZR observations are also screened for signal saturation and wet radome effects. The algorithm is implemented in two steps: high rain rates are retrieved by using the amount of attenuation in rain layers, while low rain rates are retrieved from the reflectivity–rain rate (Ze–R) relation. Observations collected by the KAZR, rain gauge, disdrometer and scanning precipitating radars during the DYNAMO/AMIE field campaign at the Gan Island of the tropical Indian Ocean are used to validate the proposed approach. The differences in the rain accumulation from the proposed algorithm are quantified. The results indicate that the proposed algorithm has a potential for deriving continuous rain rate statistics in the tropics

A general approach to double-moment normalization of drop size distributions

Normalization of drop size distributions (DSDs) is reexamined here. First, an extension of the scaling normalization that uses one moment of the DSD as a scaling parameter to a more general scaling normalization that uses two moments as scaling parameters of the normalization is presented. In addition, the proposed formulation includes all two-parameter normalizations recently introduced in the literature. Thus, a unified vision of the question of DSD normalization and a good model representation of DSDs are given. Data analysis of some convective and stratiform DSDs shows that, from the point of view of the compact representation of DSDs, the double-moment normalization is preferred. However, in terms of physical interpretation, the scaling exponent of the single-moment normalization clearly indicates two different rain regimes, whereas in the double-moment normalization the two populations are not readily separated. It is also shown that DSD analytical models ( exponential, gamma, and generalized gamma DSD) have the same scaling properties, indicating that the scaling formalism of DSDs is a very general way of describing DSDs

Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling

Radar Doppler spectra measurements are exploited to study a riming event when precipitating ice from a seeder cloud sediment through a supercooled liquid water (SLW) layer. The focus is on the "golden sample" case study for this type of analysis based on observations collected during the deployment of the Atmospheric Radiation Measurement Program's (ARM) mobile facility AMF2 at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The presented analysis of the height evolution of the radar Doppler spectra is a state-of-the-art retrieval with profiling cloud radars in SLW layers beyond the traditional use of spectral moments. Dynamical effects are considered by following the particle population evolution along slanted tracks that are caused by horizontal advection of the cloud under wind shear conditions. In the SLW layer, the identified liquid peak is used as an air motion tracer to correct the Doppler spectra for vertical air motion and the ice peak is used to study the radar profiles of rimed particles. A 1-D steady-state bin microphysical model is constrained using the SLW and air motion profiles and cloud top radar observations. The observed radar moment profiles of the rimed snow can be simulated reasonably well by the model, but not without making several assumptions about the ice particle concentration and the relative role of deposition and aggregation. This suggests that in situ observations of key ice properties are needed to complement the profiling radar observations before process-oriented studies can effectively evaluate ice microphysical parameterizations