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Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement: Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months. [ABSTRACT FROM AUTHOR]

A general formulation for the distribution problem is presented, which is applicable to frequency distributions of subgrid-scale variables, hydrometeor size distributions, as well as to probability distributions characterizing data uncertainties. The general formulation is presented based upon two well-known basic principles: the maximum-entropy principle and Liouville equation. The maximum-entropy principle defines the most likely general distribution form, if necessary constraints are specified. This paper proposes to specify these constraints as the output variables to be used in a host model. Once a general distribution form is defined, the problem of temporal evolution of the distribution reduces to that of predicting a small number of parameters characterizing it. This paper derives prognostic equations for these parameters from the Liouville equation. The developed formulation, which is applicable to a wide range of atmospheric modelling problems, is specifically applied to condensation growth of cloud droplets as a demonstration. [ABSTRACT FROM AUTHOR]

A general formulation for the distribution problem is presented, which is applicable to frequency distributions of subgrid-scale variables, hydrometeor size distributions, as well as to probability distributions characterizing data uncertainties. The general formulation is presented based upon two well-known basic principles: the maximum--entropy principle and Liouville equation. The maximum-entropy principle defines the most likely general distribution form, if necessary constraints are specified. This paper proposes to specify these constraints as the output variables to be used in a host model. Once a general distribution form is defined, the problem of temporal evolution of the distribution reduces to that of predicting a small number of parameters characterizing it. This paper derives prognostic equations for these parameters from the Liouville equation. The developed formulation, which is applicable to a wide range of atmospheric modelling problems, is specifically applied to condensation growth of cloud droplets as a demonstration. [ABSTRACT FROM AUTHOR]

The time dependence of ice-nucleating particle (INP) activity is known to exist, yet for simplicity it is often omitted in atmospheric models as an approximation. Hitherto, only limited experimental work has been done to quantify this time dependency, for which published data are especially scarce regarding ambient aerosol samples and longer timescales. In this study, the time dependence of INP activity is quantified experimentally for six ambient environmental samples. The experimental approach includes a series of hybrid experiments with alternating constant cooling and isothermal experiments using a recently developed cold-stage setup called the Lund University Cold-Stage (LUCS). This approach of observing ambient aerosol samples provides the optimum realism for representing their time dependence in any model. Six ambient aerosol samples were collected at a station in rural Sweden representing aerosol conditions likely influenced by various types of INPs: marine, mineral dust, continental pristine, continental-polluted, combustion-related and rural continental aerosol. Active INP concentrations were seen to be augmented by about 40 % to 100 % (or 70 % to 200 %), depending on the sample, over 2 h (or 10 h). Mineral dust and rural continental samples displayed the most time dependence. This degree of time dependence observed was comparable to, but weaker than, that seen in previous published works. A general tendency was observed for the natural timescale of the freezing to dilate increasingly with time. The fractional freezing rate was observed to decline steadily with the time since the start of isothermal conditions following a power law. A representation of time dependence for incorporation into schemes of heterogeneous ice nucleation that currently omit it is proposed. Our measurements are inconsistent with the simplest purely stochastic model of INP activity, which assumes that the fractional freezing rate of all unfrozen drops is somehow constant and would eventually overpredict active INPs. In reality, the variability of efficiencies among INPs must be treated with any stochastic theory. [ABSTRACT FROM AUTHOR]

A new empirical parameterization (EP) for multiple groups of primary biological aerosol particles (PBAPs) is implemented in the aerosol cloud model (AC) to investigate their roles as ice-nucleating particles (INPs). The EP describes the heterogeneous ice nucleation by (1) fungal spores, (2) bacteria, (3) pollen, (4) detritus of plants, animals, and viruses, and (5) algae. Each group includes fragments from the originally emitted particles. A high-resolution simulation of a midlatitude mesoscale squall line by AC is validated against airborne and ground observations. Sensitivity tests are carried out by varying the initial vertical profiles of the loadings of individual PBAP groups. The resulting changes in warm and ice microphysical parameters are investigated. Overall, PBAPs have little effect on the ice phase, especially in the convective region. In the stratiform region, increasing the initial PBAP loadings by a factor of 100 resulted in less than 60% change in ice number concentrations. The total ice concentration is mostly controlled by various mechanisms of secondary ice production (SIP). However, when SIP is artificially prohibited in sensitivity tests, increasing the PBAP loading by a factor of 100 has no significant effect on the ice phase. Further sensitivity tests revealed that PBAPs have little effect on surface precipitation as well as on shortwave and longwave flux. [ABSTRACT FROM AUTHOR]

The time dependence of ice-nucleating particle (INP) activity is known to exist, yet for simplicity it is often omitted in atmospheric models as an approximation. Hitherto only limited experimental work has been done to quantify this time dependency, for which published data are especially scarce regarding ambient aerosol samples and longer time scales. In this study, the time dependence of INP activity is quantified experimentally for ambient environmental samples. The experimental approach includes a series of hybrid experiments with alternating constant cooling and isothermal experiments using a recently developed cold-stage setup called the Lund University Cold-Stage (LUCS). This approach of observing ambient aerosol samples provides the optimum realism for representing their time dependence in any model. Six ambient aerosol samples were collected representing aerosol conditions likely influenced by these types of INPs: marine, mineral dust, continental pristine, continental polluted, combustion-related and rural continental aerosol. Active INP concentrations were seen to be augmented by about 40% to 100% (or 70% to 200%), depending on the sample, over 2 (or 10) hours. This degree of time dependence observed was comparable to that seen in previous published works. Our observations show that the minority of active ice nuclei (IN) with strong time dependency on hourly time scales display only weak time dependence on short time scales of a few minutes. A general tendency was observed for the natural time scale of the freezing to dilate increasingly with time. The fractional freezing rate was observed to steadily declines exponentially with the order of magnitude (logarithm) of the time since the start of isothermal conditions. A representation of time dependence for incorporation into schemes of heterogeneous ice nucleation that currently omit time dependence is proposed. [ABSTRACT FROM AUTHOR]

We have implemented and evaluated a secondary organic aerosol scheme within the chemistry transport model TM5-MP in this work. In earlier versions of TM5-MP the secondary organic aerosol was emitted as Aitken sized particle mass emulating the condensation. In the current scheme we simulate the formation of SOA from oxidation of isoprene and monoterpenes by ozone and hydroxyl radicals which produce semi-volatile organic compounds and extremely low-volatility compounds. Subsequently, SVOC and ELVOC can condense on particles. Furthermore, we have introduced a new particle formation mechanism depending on the concentration of ELVOCs. For evaluation purposes, we have simulated the year 2010 with the old and new scheme, where we see an increase in simulated production of SOA from 39.9 Tg y-1 with the old scheme to 52.5 Tg y-1 with the new scheme. For more detailed analysis, the particle mass and number concentrations and their influence on the simulated aerosol optical depth are compared to observations. Phenomenologically, the new particle formation scheme implemented here is able to reproduce the occurrence of observed particle formation events. However, the concentrations of formed particles are clearly lower as is the subsequent growth to larger sizes. Compared to the old scheme, the new scheme is increasing the number concentrations across the observation stations while still underestimating the observations. The total aerosol mass concentrations in the US show a much better seasonal cycle and removal of a clear overestimation of concentrations. In Europe the mass concentrations are lowered leading to a larger underestimation of observations. Aerosol optical depth is generally slightly increased except in the northern high latitudes. This brings the simulated annual global mean AOD closer to observational estimate. However, as the increase is rather uniform, biases tend to be reduced only in regions where the model underestimates the AOD. Furthermore, the correlation against satellite retrievals and ground-based sun-photometer observations are improved. Although the process based approach to SOA formation causes reduction in model performance in some areas, overall the new scheme improves the simulated aerosol fields. [ABSTRACT FROM AUTHOR]

For decades, measured ice crystal number concentrations have been found to be orders of magnitude higher than measured ice nucleating particles in moderately cold clouds. This observed discrepancy reveals the existence of secondary ice production (SIP) in addition to the primary ice nucleation. However, the importance of SIP relative to primary ice nucleation remains highly unclear. Furthermore, most weather and climate models do not represent well the SIP processes, leading to large biases in simulated cloud properties. This study demonstrates a first attempt to represent different SIP mechanisms (frozen raindrop shattering, ice-ice collisional break-up, and rime splintering) in a global climate model (GCM). The model is run in the single column mode to facilitate comparisons with the Department of Energy (DOE)'s Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE) observations. We show the SIP importance in the four types of clouds during M-PACE (i.e., multilayer, and single-layer stratus, transition and front clouds), with the maximum enhancement in ice crystal number concentration by up to 4 orders of magnitude in the moderately-cold clouds. We reveal that SIP is the dominant source of ice crystals near the cloud base for the long-lived Arctic single-layer mixed-phase clouds. The model with SIP improves the occurrence and phase partitioning of the mixed-phase clouds, reverses the vertical distribution pattern of ice number concentration, and provides a better agreement with observations. The findings of this study highlight the importance of considering the SIP in GCMs. [ABSTRACT FROM AUTHOR]