Ice multiplication from ice-ice collisions in the high Arctic: sensitivity to ice habit, rimed fraction, ice type and uncertainties in the numerical description of the process

Atmospheric models often fail to correctly reproduce the microphysical structure of Arctic mixed-phase clouds and underpredict ice water content even when the simulations are constrained by observed levels of ice nucleating particles. In this study we investigate whether ice multiplication from breakup upon ice-ice collisions, a process missing in most models, can account for the observed cloud ice in a stratocumulus cloud observed during the Arctic Summer Cloud Ocean Study (ASCOS) campaign. Our results indicate that the efficiency of this process in these conditions is weak; increases in fragment generation are compensated for by subsequent enhancement of precipitation and subcloud sublimation. Activation of collisional breakup improves the representation of cloud ice content, but cloud liquid remains overestimated. In most sensitivity simulations, variations in ice habit and prescribed rimed fraction have little effect on the results. A few simulations result in explosive multiplication and cloud dissipation; however, in most setups, the overall multiplication effects become substantially weaker if the precipitation sink is enhanced through cloud-ice-to-snow autoconversion. The largest uncertainty stems from the correction factor for ice enhancement due to sublimation included in the breakup parameterization; excluding this correction results in rapid glaciation, especially in simulations with plates. Our results indicate that the lack of a detailed treatment of ice habit and rimed fraction in most bulk microphysics schemes is not detrimental for the description of the collisional breakup process in the examined conditions as long as cloud-ice-to-snow autoconversion is considered.

Maximisation numérique et mesures acoustiques des précipitations

Knowledge of extreme precipitation phenomena is of crucial importance to the safety of civil engineering works and for electricity production management in a country of lakes and mountains like Switzerland. In order to study the distribution in space and the evolution of strong rain episodes, the work presented here relies on the complementary approaches of field observation and numerical simulation. The experimental portion of this project relies on a novel, acousticbased, rain metrology instrument. Based on the results, a methodology for the determination of raindrop size distribution (disdrometer facility) and rainfall rate (rain gauge facility) has been developed and is described in the present dissertation. In addition, numerical modelling and simulation methods were developed with the aim of calculating — for a given watershed topography — the Probable Maximum Precipitation (PMP). The method relies on the separation of the different phenomenological contributions and on the climatic characterization of atmospheric situations leading to extreme rain events. Boundary and initial conditions are represented by theoretical profiles of the wind speed, wind direction, temperature, and water contents, turbulent energy and dissipation rate variables. The numerical model calculates the consequent wind and rain fields within the simulation domain for the desired atmospheric situation. The hydrodynamic code (CFX4) is based on the finite volume approach and is particularly adapted to complex geometries, allowing an excellent representation of the topography. The code is partially open and several specific atmospheric models were implemented. Microphysics schemes considered are Kessler's warm classic scheme (1969) and the Caniaux detailed scheme (1993). The latter includes solid ice particles, aggregates and graupel and allows the simulation of convective as well as orographic cloud system precipitation cycles. Sensitivity studies of the results with respect to the dominant parameters of each situation, lead to a maximisation procedure successfully applied to convective as well as frontal precipitation. The work shows that the maximisation method consisting of maximising severe events into critical events can be more effective than using statistical approaches. Use of this method compensates for the relative lack of measurement facilities in many regions.

EPFL2004

The impact of secondary ice processes on orographic mixed-phase clouds

Alexis Berne, Paraskevi Georgakaki, Athanasios Nenes, Georgia Sotiropoulou, Etienne Gabriel Henri Vignon

Ground based and airborne observations of orographic mixed-phase clouds (MPCs) forming over mountain top research stations have long reported a discrepancy between the measured ice crystal number concentrations (ICNCs) and the concentration of ice nucleating particles, the former being several orders of magnitude higher (e.g., Lloyd et al., 2015). Additionally, model simulations of Alpine clouds are frequently found to underestimate the amount of ice compared with observations (Farrington et al., 2016). Although surface-based processes such as blowing snow and hoar frost have been suggested to explain this discrepancy, the potential role of secondary ice production (SIP) processes – especially mechanical breakup of cloud ice and droplet fragmentation during freezing – has been less studied. In this study we utilize the Weather Research and Forecasting model (WRF) to explore the potential contribution of SIP processes on the orographic MPCs observed during the Cloud and Aerosol Characterization Experiment (CLACE) 2014 campaign at the mountain-top site of Jungfraujoch in the Swiss Alps. The only SIP mechanism included in the default version of WRF is the Hallett–Mossop process (H-M), which is however ruled out since the recorded temperatures were generally colder than -8 ˚C. We modified the default WRF to include parameterizations of two additional SIP mechanisms, namely the collisional break-up (BR) upon collisions between ice particles and droplet shattering (DS), in order to investigate if the performance of the model is improved. Simulations suggest that the DS mechanism is not a significant source of ICNCs. The BR mechanism however is quite active, elevating the predicted ICNCs by up to 3 orders of magnitude, which is consistent with observations. The initiation of the BR mechanism is primarily associated with the occurrence of seeder-feeder situations, which are widespread phenomena over Switzerland (Proske et al., 2021). Including a source of ice crystals from the effect of blowing snow episodically affects cloud ICNCs; the numbers reaching cloud base is not large, but the concentrations are multiplied through the action of the BR mechanism. Our findings highlight the importance of considering both secondary ice and an “external” seeding mechanism – primarily falling ice from above and to a lesser degree blowing ice from the surface - in weather-prediction models in order to predict correctly the amount of liquid and ice in MPCs, which is in turn critical for the accurate representation of radiation processes and precipitation patterns. Farrington, R. J., Connolly, P. J., Lloyd, G., Bower, K. N., Flynn, M. J., Gallagher, M. W., Field, P. R., Dearden, C., and Choularton, T. W. (2016). Comparing model and measured ice crystal concentrations in orographic clouds during the INUPIAQ campaign. Atmos. Chem. Phys., 16, 4945–4966, https://doi.org/10.5194/acp-16-4945-2016 Lloyd, G., Choularton, T. W., Bower, K. N., Gallagher, M. W., Connolly, P. J., Flynn, M., Farrington, R., Crosier, J., Schlenczek, O., Fugal, J. and Henneberger, J. (2015). The origins of ice crystals measured in mixed-phase clouds at the high-alpine site Jungfraujoch. Atmos. Chem. Phys., 15, 12953–12969. https://doi.org/10.5194/acp-15-12953-2015 Proske, U., Bessenbacher, V., Dedekind, Z., Lohmann, U., and Neubauer, D. (2021). How frequent is natural cloud seeding from ice cloud layers ( < −35 °C) over Switzerland?. Atmos. Chem. Phys., 21, 5195–5216. https://doi.org/10.5194/acp-21-5195-2021

2021

Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study

Luca Pozzoli, Julia Schmale

While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008–2009 and 2014–2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, O3, BC, and SO ), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for CH4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.

2022