A High Speed Particle Phase Discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber

© Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.A new instrument, the High-speed Particle Phase Discriminator (PPD-HS), developed at the University of Hertfordshire, for sizing individual cloud hydrometeors and determining their phase is described herein. PPD-HS performs an in situ analysis of the spatial intensity distribution of near-forward scattered light for individual hydrometeors yielding shape properties. Discrimination of spherical and aspherical particles is based on an analysis of the symmetry of the recorded scattering patterns. Scattering patterns are collected onto two linear detector arrays, reducing the complete 2-D scattering pattern to scattered light intensities captured onto two linear, one-dimensional strips of light sensitive pixels. Using this reduced scattering information, we calculate symmetry indicators that are used for particle shape and ultimately phase analysis. This reduction of information allows for detection rates of a few hundred particles per second. Here, we present a comprehensive analysis of instrument performance using both spherical and aspherical particles generated in a well-controlled laboratory setting using a vibrating orifice aerosol generator (VOAG) and covering a size range of approximately 3-32 μm. We use supervised machine learning to train a random forest model on the VOAG data sets that can be used to classify any particles detected by PPD-HS. Classification results show that the PPD-HS can successfully discriminate between spherical and aspherical particles, with misclassification below 5% for diameters >3μm. This phase discrimination method is subsequently applied to classify simulated cloud particles produced in a continuous flow diffusion chamber setup. We report observations of small, near-spherical ice crystals at early stages of the ice nucleation experiments, where shape analysis fails to correctly determine the particle phase. Nevertheless, in the case of simultaneous presence of cloud droplets and ice crystals, the introduced particle shape indicators allow for a clear distinction between these two classes, independent of optical particle size. From our laboratory experiments we conclude that PPD-HS constitutes a powerful new instrument to size and discriminate the phase of cloud hydrometeors. The working principle of PPD-HS forms a basis for future instruments to study microphysical properties of atmospheric mixed-phase clouds that represent a major source of uncertainty in aerosol-indirect effect for future climate projections..Peer reviewe

Ice clouds: from ice crystals to their response in a warming climate

Atmospheric ice crystals form from a variety of different sources and at different temperatures. Between 0 °C and -38 °C, liquid water and ice crystals can coexist. Cloud ice initiated by freezing of cloud droplets at these temperatures needs to be catalyzed by an ice nucleating particle (INP). Further growth then often involves collisions of ice crystals and cloud droplets or depositional growth of the ice crystals at the expense of the cloud droplets due to the lower water vapor pressure over the ice crystal than over the liquid droplet surface. At temperatures colder than -38 °C, ice can not only originate from freezing of cloud droplets, but also by freezing of deliquesced aerosols and direct deposition of water vapor onto an INP. The complexity of the ice formation processes is reflected in the spread of simulated cloud ice contents in the current generation of global climate models (GCM). This work describes the implementation and first results of a new cloud microphysics scheme in the ECHAM6-HAM2 GCM aimed to reduce the number of weakly constrained parameters involved in the representation of cloud ice formation and evolution. It does no longer rely on heuristic conversion rates between in-cloud ice crystals and precipitating snow but uses only one single, prognostic ice category which better represents the spectrum of ice crystals in clouds. Because precipitating snow is no longer diagnosed, the trajectory of ice crystals must be fully prognostic. Numerical stability of vertical advection is achieved by an adaptive time step in the microphysics routine which leads to an increase in computation time of roughly 25%. The new scheme significantly reduces the conceptual complexity of the model. Tuning parameters for the ice crystal fall speeds and the conversion to snow are no longer needed. With the introduction of a new cloud cover parameterization the high bias of high cloud cover in the base model version ECHAM6.3-HAM2.3 could be reduced. Overall, the new model is in reasonable agreement with observations in key variables while some deficiencies remain. New model diagnostics are introduced to disentangle the relative importance of ice formation pathways to provide a sound cause-and-effect relation between the simulated cloud fields and the process parameterizations. This analysis revealed that immersion and contact freezing in supercooled liquid clouds only dominate ice formation in roughly 5% of the simulated clouds, a small fraction compared to roughly 64% of the clouds governed by freezing in the cirrus temperature regime below -38 °C. Furthermore, we could demonstrate that even in the mixed-phase temperature regime between -38 °C and 0 °C, the dominant source of ice is the sedimentation of ice crystals that originated in the cirrus regime. The new scheme is used to assess changes in the cloud fields in response to a warming climate. The equilibrium response of the global mean surface temperature to an instantaneous doubling of atmospheric carbon dioxide concentrations is found to be 3.8 °C which is within the spread of the current generation of GCMs but substantially larger than the base model version ECHAM6.3-HAM2.3 with a value of 2.5 °C. This difference could be narrowed down to different cloud optical depth feedbacks and needs further investigation. Even though clouds are predominantly glaciated already below temperatures of roughly -5 °C, the cloud phase feedback is suppressed. Since most cloud ice is formed in clouds with a large vertical extent and high optical thickness, phase transitions do not significantly increase the optical depth of the cloud

Elucidating ice formation pathways in the aerosol-climate model ECHAM6-HAM2

Cloud microphysics schemes in global climate models have long suffered from a lack of reliable satellite observations of cloud ice. At the same time there is a broad consensus that the correct simulation of cloud phase is imperative for a reliable assessment of Earth's climate sensitivity. At the core of this problem is understanding the causes for the inter-model spread of the predicted cloud phase partitioning. This work introduces a new method to build a sound cause-and-effect relation between the microphysical parameterizations employed in our model and the resulting cloud field by analysing ice formation pathways. We find that freezing processes in supercooled liquid clouds only dominate ice formation in roughly 6 % of the simulated clouds, a small fraction compared to roughly 63 % of the clouds governed by freezing in the cirrus temperature regime below −35 ∘C. This pathway analysis further reveals that even in the mixed-phase temperature regime between −35 and 0 ∘C, the dominant source of ice is the sedimentation of ice crystals that originated in the cirrus regime. The simulated fraction of ice cloud to total cloud amount in our model is lower than that reported by the CALIPSO-GOCCP satellite product. This is most likely caused by structural differences of the cloud and aerosol fields in our model rather than the microphysical parametrizations employed.ISSN:1680-7375ISSN:1680-736

Prognostic parameterization of cloud ice with a single category in the aerosol-climate model ECHAM(v6.3.0)-HAM(v2.3)

A new scheme for stratiform cloud microphysics has been implemented in the ECHAM6-HAM2 general circulation model. It features a widely used description of cloud water with two categories for cloud droplets and raindrops. The unique aspect of the new scheme is the break with the traditional approach to describe cloud ice analogously. Here we parameterize cloud ice by a single category that predicts bulk particle properties (P3). This method has already been applied in a regional model and most recently also in the Community Atmosphere Model 5 (CAM5). A single cloud ice category does not rely on heuristic conversion rates from one category to another. Therefore, it is conceptually easier and closer to first principles. This work shows that a single category is a viable approach to describe cloud ice in climate models. Prognostic representation of sedimentation is achieved by a nested approach for sub-stepping the cloud microphysics scheme. This yields good results in terms of accuracy and performance as compared to simulations with high temporal resolution. Furthermore, the new scheme allows for a competition between various cloud processes and is thus able to unbiasedly represent the ice formation pathway from nucleation to growth by vapor deposition and collisions to sedimentation. Specific aspects of the P3 method are evaluated. We could not produce a purely stratiform cloud where rime growth dominates growth by vapor deposition and conclude that the lack of appropriate conditions renders the prognostic parameters associated with the rime properties unnecessary. Limitations inherent in a single category are examined.ISSN:1991-9603ISSN:1991-959

A Modeling Study on the Sensitivities of Atmospheric Charge Separation According to the Relative Diffusional Growth Rate Theory to Nonspherical Hydrometeors and Cloud Microphysics

Collisional charge transfer between graupel and ice crystals in the presence of cloud droplets is considered the dominant mechanism for charge separation in thunderclouds. According to the relative diffusional growth rate (RDGR) theory, the hydrometeor with the faster diffusional radius growth is charged positively in such collisions. We explore sensitivities of the RDGR theory to nonspherical hydrometeors and six parameters (pressure, temperature, liquid water content, sizes of ice crystals, graupel, and cloud droplets). Idealized simulations of a thundercloud with two‐moment cloud microphysics provide a realistic sampling of the parameter space. Nonsphericity and anisotropic diffusional growth strongly control the extent of positive graupel charging. We suggest a tuning parameter to account for anisotropic effects not represented in bulk microphysics schemes. In a susceptibility analysis that uses automated differentiation, we identify ice crystal size as most important RDGR parameter, followed by graupel size. Simulated average ice crystal size varies with temperature due to ice multiplication and heterogeneous freezing of droplets. Cloud microphysics and ice crystal size thus indirectly determine the structure of charge reversal lines in the traditional temperature‐water‐content representation. Accounting for the variability of ice crystal size and potentially habit with temperature may help to explain laboratory results and seems crucial for RDGR parameterizations in numerical models. We find that the contribution of local water vapor from evaporating rime droplets to diffusional graupel growth is only important for high effective water content. In this regime, droplet size and pressure are the dominant RDGR parameters. Otherwise, the effect of local graupel growth is masked by small ice crystal sizes that result from ice multiplication.ISSN:0148-0227ISSN:2169-897

A High Speed Particle Phase Discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber

A new instrument, the High Speed Particle Phase Discriminator (PPD-HS) developed at the University of Hertfordshire, for sizing individual cloud hydrometeors and determining their phase is described herein. PPD-HS performs an in-situ analysis of the spatial intensity distribution of near forward scattered light for individual hydrometeors yielding shape properties. Discrimination of spherical and aspherical particles is based on an analysis of the symmetry of the recorded scattering patterns. Scattering patterns are collected onto two linear detector arrays, reducing the complete 2D scattering pattern to scattered light intensities captured onto two linear, one dimensional strips of light sensitive pixels. Using this reduced scattering information, we calculate symmetry indicators that are used for particle shape and ultimately phase analysis. This reduction of information allows for detection rates of a few hundred particles per second. Here, we present a comprehensive analysis of instrument performance using both spherical and aspherical particles, generated in a well-controlled laboratory setting using a Vibrating Orifice Aerosol Generator (VOAG) and covering a size range of approximately 3–32 micron. We use supervised machine learning to train a random forest model on the VOAG data sets that can be used to classify any particles detected by PPD-HS. Classification results show that the PPD-HS can successfully discriminate between spherical and aspherical particles, with misclassification below 5 % for diameters > 3 micro meter. This phase discrimination method is subsequently applied to classify simulated cloud particles produced in a continuous flow diffusion chamber setup. We report observations of small, near-spherical ice crystals at early stages of the ice nucleation experiments, where shape analysis fails to correctly determine the particle phase. Nevertheless, in case of simultaneous presence of cloud droplets and ice crystals, the introduced particle shape indicators allow for a clear distinction between these two classes independent of optical particle size. We conclude that PPD-HS constitutes a powerful new instrument to size and discriminate phase of cloud hydrometeors and thus study microphysical properties of mixed-phase clouds, that represent a major source of uncertainty in aerosol indirect effect for future climate projections.ISSN:1867-861

The ICON-A model for direct QBO simulations on GPUs (version icon-cscs:baf28a514)

Classical numerical models for the global atmosphere, as used for numerical weather forecasting or climate research, have been developed for conventional central processing unit (CPU) architectures. This hinders the employment of such models on current top-performing supercomputers, which achieve their computing power with hybrid architectures, mostly using graphics processing units (GPUs). Thus also scientific applications of such models are restricted to the lesser computer power of CPUs. Here we present the development of a GPU-enabled version of the ICON atmosphere model (ICON-A), motivated by a research project on the quasi-biennial oscillation (QBO), a global-scale wind oscillation in the equatorial stratosphere that depends on a broad spectrum of atmospheric waves, which originates from tropical deep convection. Resolving the relevant scales, from a few kilometers to the size of the globe, is a formidable computational problem, which can only be realized now on top-performing supercomputers. This motivated porting ICON-A, in the specific configuration needed for the research project, in a first step to the GPU architecture of the Piz Daint computer at the Swiss National Supercomputing Centre and in a second step to the JUWELS Booster computer at the Forschungszentrum Julich. On Piz Daint, the ported code achieves a single-node GPU vs. CPU speedup factor of 6.4 and allows for global experiments at a horizontal resolution of 5 km on 1024 computing nodes with 1 GPU per node with a turnover of 48 simulated days per day. On JUWELS Booster, the more modern hardware in combination with an upgraded code base allows for simulations at the same resolution on 128 computing nodes with 4 GPUs per node and a turnover of 133 simulated days per day. Additionally, the code still remains functional on CPUs, as is demonstrated by additional experiments on the Levante compute system at the German Climate Computing Center. While the application shows good weak scaling over the tested 16-fold increase in grid size and node count, making also higher resolved global simulations possible, the strong scaling on GPUs is relatively poor, which limits the options to increase turnover with more nodes. Initial experiments demonstrate that the ICON-A model can simulate downward-propagating QBO jets, which are driven by wave-mean flow interaction.ISSN:1991-9603ISSN:1991-959