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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:1867-8610

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.

ETH/CSCS has the duty of providing stable computing resources for its customers' applications. In climate research this requirement often requires stable, reproducible execution of a particular application over multiple years. Since the underlying computing platform – in our case CSCS Piz Daint – changes with each upgrade, ensuring such continuity for users is at best challenging. ESiWACE2 is putting a large effort to evaluate containers to see if they provide the platform stability needed. The object of this milestone, One model ported using containers, is to illustrate first-hand the viability of the container paradigm. The PRACE-supported QUBICC project which uses the Icosahedral Non-hydrostatic (ICON) model to simulate the evolution of the atmosphere in order to analyse the quasi-biennial oscillation (QBO) – the roughly 2-year shift from prevailing westerly mean flow to easterly and back – is one such application. The underlying high global resolution (2.9–5km) simulations require extensive computing resources over long periods of time, where the system needs to remain as static as possible. High performance is key to these simulations in order to complete them in reasonable time, so the overhead for running them inside a container has to be small. While QUBICC simulations currently run on 'bare-metal', i.e., not within containers, ETH/CSCS and MPI-M are preparing the container infrastructure to support these simulations over the long term where machine upgrades are unavoidable. The ICON model was one of many models addressed in the Container Hackathon for Modellers, a three-day event to 'jump start' the insertion of models into Docker-based containers. The results of this event are exhaustively documented in ESiWACE-2 Deliverable D2.8. Of all the applications ported to containers during the hackathon, the ICON team arguably made the least progress. The work was subsequently continued by the ETH/CSCS User Support team: Rafael Samiento (ETH/CSCS) methodically extended the initial hackathon work and upgraded to the code base on the QUBICC branch mc10_rrtmgp_remerge from the icon-cscs repository. The realisation can be found in a subsequent section. The port of the ICON for QUBICC using containers was thus a complete success as illustrated by the performance comparison (see Appendix A.1) with the base code without containers, using a medium resolution simulation on 64 to 160 GPU nodes of Piz Daint: The overhead of running with containers turns out to be minimal. The resulting container infrastructure is highly useful for CSCS customers who need ICON to remain in a stable configuration for periods much longer than the normal upgrade cycle. This milestone documents work contributing to Principle Objective (1) Enable leading European weather and climate models to leverage the available performance of pre-exascale systems with regard to both compute and data capacity in 2021. It enables the long simulations common in climate modelling on the rapidly changing environment of TIER0 supercomputers. By using Docker and Sarus, it makes use of the emerging container technology and thus contributes to the specific objective (b)Establish new technologies for weather and climate modelling".

ETH/CSCS has the duty of providing stable computing resources for its customers' applications. In climate research this requirement often requires stable, reproducible execution of a particular application over multiple years. Since the underlying computing platform – in our case CSCS Piz Daint – changes with each upgrade, ensuring such continuity for users is at best challenging. ESiWACE2 is putting a large effort to evaluate containers to see if they provide the platform stability needed. The object of this milestone, One model ported using containers, is to illustrate first-hand the viability of the container paradigm. The PRACE-supported QUBICC project which uses the Icosahedral Non-hydrostatic (ICON) model to simulate the evolution of the atmosphere in order to analyse the quasi-biennial oscillation (QBO) – the roughly 2-year shift from prevailing westerly mean flow to easterly and back – is one such application. The underlying high global resolution (2.9–5km) simulations require extensive computing resources over long periods of time, where the system needs to remain as static as possible. High performance is key to these simulations in order to complete them in reasonable time, so the overhead for running them inside a container has to be small. While QUBICC simulations currently run on 'bare-metal', i.e., not within containers, ETH/CSCS and MPI-M are preparing the container infrastructure to support these simulations over the long term where machine upgrades are unavoidable. The ICON model was one of many models addressed in the Container Hackathon for Modellers, a three-day event to 'jump start' the insertion of models into Docker-based containers. The results of this event are exhaustively documented in ESiWACE-2 Deliverable D2.8. Of all the applications ported to containers during the hackathon, the ICON team arguably made the least progress. The work was subsequently continued by the ETH/CSCS User Support team: Rafael Samiento (ETH/CSCS) methodically extended the initial hackathon work and upgraded to the code base on the QUBICC branch mc10_rrtmgp_remerge from the icon-cscs repository. The realisation can be found in a subsequent section. The port of the ICON for QUBICC using containers was thus a complete success as illustrated by the performance comparison (see Appendix A.1) with the base code without containers, using a medium resolution simulation on 64 to 160 GPU nodes of Piz Daint: The overhead of running with containers turns out to be minimal. The resulting container infrastructure is highly useful for CSCS customers who need ICON to remain in a stable configuration for periods much longer than the normal upgrade cycle. This milestone documents work contributing to Principle Objective (1) Enable leading European weather and climate models to leverage the available performance of pre-exascale systems with regard to both compute and data capacity in 2021. It enables the long simulations common in climate modelling on the rapidly changing environment of TIER0 supercomputers. By using Docker and Sarus, it makes use of the emerging container technology and thus contributes to the specific objective (b)Establish new technologies for weather and climate modelling".

ETH/CSCS has the duty of providing stable computing resources for its customers' applications. In climate research this requirement often requires stable, reproducible execution of a particular application over multiple years. Since the underlying computing platform – in our case CSCS Piz Daint – changes with each upgrade, ensuring such continuity for users is at best challenging. ESiWACE2 is putting a large effort to evaluate containers to see if they provide the platform stability needed. The object of this milestone, One model ported using containers, is to illustrate first-hand the viability of the container paradigm. The PRACE-supported QUBICC project which uses the Icosahedral Non-hydrostatic (ICON) model to simulate the evolution of the atmosphere in order to analyse the quasi-biennial oscillation (QBO) – the roughly 2-year shift from prevailing westerly mean flow to easterly and back – is one such application. The underlying high global resolution (2.9–5km) simulations require extensive computing resources over long periods of time, where the system needs to remain as static as possible. High performance is key to these simulations in order to complete them in reasonable time, so the overhead for running them inside a container has to be small. While QUBICC simulations currently run on 'bare-metal', i.e., not within containers, ETH/CSCS and MPI-M are preparing the container infrastructure to support these simulations over the long term where machine upgrades are unavoidable. The ICON model was one of many models addressed in the Container Hackathon for Modellers, a three-day event to 'jump start' the insertion of models into Docker-based containers. The results of this event are exhaustively documented in ESiWACE-2 Deliverable D2.8. Of all the applications ported to containers during the hackathon, the ICON team arguably made the least progress. The work was subsequently continued by the ETH/CSCS User Support team: Rafael Samiento (ETH/CSCS) methodically extended the initial hackathon work and upgraded to the code base on the QUBICC branch mc10_rrtmgp_remerge from the icon-cscs repository. The realisation can be found in a subsequent section. The port of the ICON for QUBICC using containers was thus a complete success as illustrated by the performance comparison (see Appendix A.1) with the base code without containers, using a medium resolution simulation on 64 to 160 GPU nodes of Piz Daint: The overhead of running with containers turns out to be minimal. The resulting container infrastructure is highly useful for CSCS customers who need ICON to remain in a stable configuration for periods much longer than the normal upgrade cycle. This milestone documents work contributing to Principle Objective (1) Enable leading European weather and climate models to leverage the available performance of pre-exascale systems with regard to both compute and data capacity in 2021. It enables the long simulations common in climate modelling on the rapidly changing environment of TIER0 supercomputers. By using Docker and Sarus, it makes use of the emerging container technology and thus contributes to the specific objective (b)Establish new technologies for weather and climate modelling".