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

Dietlicher, Remo; Neubauer, David; Lohmann, Ulrike

doi:10.5194/gmd-2017-164

Cited by 6 publications

(16 citation statements)

References 23 publications

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“…The two-moment ice microphysics scheme by Lohmann et al (2007), used in the default version of ECHAM6.3-HAM2.3, was succeeded by the Predicted bulk Particle Properties (P3) scheme by Morrison and Milbrandt (2015) that was ported to ECHAM-HAM by Dietlicher et al (2018Dietlicher et al ( , 2019. It replaces an earlier method of artificially separating ice particles into different size classes (Levkov et al, 1992), rendering the use of the tuning parameter for the rate of snow formation unnecessary (Dietlicher et al, 2019).…”

Section: Model Descriptionmentioning

confidence: 99%

Cirrus cloud thinning using a more physically based ice microphysics scheme in the ECHAM-HAM general circulation model

et al. 2022

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Abstract. Cirrus cloud thinning (CCT) is a relatively new radiation management proposal to counteract anthropogenic climate warming by targeting Earth's terrestrial radiation balance. The efficacy of this method was presented in several general circulation model (GCM) studies that showed widely varied radiative responses, originating in part from the differences in the representation of cirrus ice microphysics between the different GCMs. The recent implementation of a new, more physically based ice microphysics scheme (Predicted Particle Properties, P3) that abandons ice hydrometeor size class separation into the ECHAM-HAM GCM, coupled to a new approach for calculating cloud fractions that increases the relative humidity (RH) thresholds for cirrus cloud formation, motivated a reassessment of CCT efficacy. In this study, we first compared CCT sensitivity between the new cloud fraction approach and the original ECHAM-HAM cloud fraction approach. Consistent with previous approaches using ECHAM-HAM, with the P3 scheme and the higher RH thresholds for cirrus cloud formation, we do not find a significant cooling response in any of our simulations. The most notable response from our extreme case is the reduction in the maximum global-mean net top-of-atmosphere (TOA) radiative anomalies from overseeding by about 50 %, from 9.9 W m−2 with the original cloud fraction approach down to 4.9 W m−2 using the new cloud fraction RH thresholds that allow partial grid-box coverage of cirrus clouds above ice saturation, unlike the original approach. Even with this reduction with the updated cloud fraction approach, the TOA anomalies from overseeding far exceed those reported in previous studies. We attribute the large positive TOA anomalies to seeding particles overtaking both homogeneous nucleation and heterogeneous nucleation on mineral dust particles within cirrus clouds to produce more numerous and smaller ice crystals. This effect is amplified by longer ice residence times in clouds due to the slower removal of ice via sedimentation in the P3 scheme. In an effort to avoid this overtaking effect of seeding particles, we increased the default critical ice saturation ratio (Si,seed) for ice nucleation on seeding particles from the default value of 1.05 to 1.35 in a second sensitivity test. With the higher Si,seed we drastically reduce overseeding, which suggests that Si,seed is a key factor to consider for future CCT studies. However, the global-mean TOA anomalies contain high uncertainty. In response, we examined the TOA anomalies regionally and found that specific regions only show a small potential for targeted CCT, which is partially enhanced by using the larger Si,seed. Finally, in a seasonal analysis of TOA responses to CCT, we find that our results do not confirm the previous finding that high-latitude wintertime seeding is a feasible strategy to enhance CCT efficacy, as seeding in our model enhances the already positive cirrus longwave cloud radiative effect for most of our simulations. Our results also show feedbacks on lower-lying mixed-phase and liquid clouds through the reduction in ice crystal sedimentation that reduces cloud droplet depletion and results in stronger cloud albedo effects. However, this is outweighed by stronger longwave trapping from cirrus clouds with more numerous and smaller ice crystals. Therefore, we conclude that CCT is unlikely to act as a feasible climate intervention strategy on a global scale.

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Section: Model Descriptionmentioning

confidence: 99%

Cirrus cloud thinning using a more physically based ice microphysics scheme in the ECHAM-HAM general circulation model

et al. 2022

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“…In addition, arbitrary unphysical parameter, such as the ice‐to‐snow autoconversion threshold size, should be avoided in a further model development. Taking this into account, incorporation of a single ice parameterization (Morrison & Milbrandt, ) into GCMs could be one of the solutions for better describing ice morphology while ensuring continuity of ice growth (Dietlicher et al, ; Eidhammer et al, ; Zhao et al, ).…”

Section: Summary and Future Workmentioning

confidence: 99%

Prognostic Precipitation in the MIROC6‐SPRINTARS GCM: Description and Evaluation Against Satellite Observations

Michibata

Suzuki

Sekiguchi

et al. 2019

J Adv Model Earth Syst

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A comprehensive two‐moment microphysics scheme is incorporated into the MIROC6‐SPRINTARS general circulation model (GCM). The new scheme includes prognostic precipitation for both rain and snow and considers their radiative effects. To evaluate the impacts of applying different treatments of precipitation and the associated radiative effect, we perform climate simulations employing both the traditional diagnostic and new prognostic precipitation schemes, the latter also being tested with and without incorporating the radiative effect of snow. The prognostic precipitation, which maintains precipitation in the atmosphere across multiple time steps, models the ratio of accretion to autoconversion as being approximately an order of magnitude higher than that for the diagnostic scheme. Such changes in microphysical process rates tend to reduce the cloud water susceptibility as the autoconversion process is the only pathway through which aerosols can influence rain formation. The resultant anthropogenic aerosol effect is reduced by approximately 21% in the prognostic precipitation scheme. Modifications to the microphysical process rates also change the vertical distribution of hydrometeors in the manner that increases the fractional occurrence of single‐layered warm clouds by 38%. The new scheme mitigates the excess of supercooled liquid water produced by the previous scheme and increases the total mass of ice hydrometeors. Both characteristics are consistent with CloudSat/CALIPSO retrievals. The radiative effect of snow is significant at both longwave and shortwave (6.4 and 5.1 W/m2 in absolute values, respectively) and can alter the precipitation fields via energetic controls on precipitation. These results suggest that the prognostic precipitation scheme, with its radiative effects incorporated, makes an indispensable contribution to improving the reliability of climate modeling.

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“…We refer to Figure 1 of Morrison and Milbrandt () or Figure 1 of Dietlicher et al. () for graphical context of how the m‐D relationship varies for each PSD size range. For each range, all m‐D relationships follow a power law:

m = a D^{b} .

For spherical particles, the parameter a is proportional to the bulk density of ice ( ρ ), defined as particle mass divided by the volume of a sphere for a given D , and is given by

a = ρ \frac{π}{6} .

…”

Section: Scheme Developmentmentioning

confidence: 99%

Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework

Stanford

Morrison

Varble

et al. 2019

J Adv Model Earth Syst

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Ice microphysics parameterizations in models must make major simplifications relative to observations, typically employing empirical relationships to represent average functional properties of particles. However, previous studies have established that ice particle properties vary even in similar cloud types and thermodynamic environments, and it remains unclear how this so-called "natural variability" impacts simulated deep convection. This uncertainty is addressed by implementing a stochastic framework into the Predicted Particle Properties microphysics scheme in the Weather Research and Forecasting model. The approach stochastically varies the coefficients of the mass-size (m-D) relationship (m=aD b ) for unrimed and partially rimed ice. Using guidance from aircraft in situ measurements obtained during the Midlatitude Continental Convective Clouds Experiment (MC3E), the scheme samples from distributions of the prefactor (a) and the exponent (b) of the m-D relationship. Simulations of two MC3E deep convective cases indicate that the stochastic m-D scheme produces considerable variability of anvil cirrus cloud optical depth (τ) distributions, even for the same ice water path (IWP). Thus, the stochastic scheme produces variable cloud radiative forcing that is independent of IWP. This τ-IWP relationship variability is nonexistent using the deterministic m-D ensemble. Additional sensitivity tests are performed in which the fallspeed-size relationship (V=cD d ) is stochastically varied, resulting in variable precipitation amounts and rain rate distributions. Results are presented in the context of satellite and precipitation observations and include comparison with other ensemble configurations using perturbed initial and lateral boundary conditions and small-amplitude noise added to the potential temperature field. Plain Language SummaryRepresenting snowflakes, hail, and other ice crystal types in weather and climate models is a challenging task, but properly doing so is often important in order to produce accurate forecasts. In reality, ice crystals in clouds take on many different shapes and sizes, but current models do not fully account for this variability. Thus, we implement a new method in a weather model that accounts for some of this variability in ice crystal shape and size, guided by observations obtained from aircraft flying through clouds. Our results show that accounting for variable ice crystal size and shape can alter how much sunlight reaches the surface during storms that produce expansive cloud cover. In addition, we find that altering the speed at which ice crystals fall to the ground changes the amount of precipitation accumulated during a precipitation event. These results thus provide guidance on how representing the wide range of different ice crystal shapes and sizes in weather models can impact forecasts of weather and predictions of climate.

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

Cited by 6 publications

References 23 publications

Cirrus cloud thinning using a more physically based ice microphysics scheme in the ECHAM-HAM general circulation model

Cirrus cloud thinning using a more physically based ice microphysics scheme in the ECHAM-HAM general circulation model

Prognostic Precipitation in the MIROC6‐SPRINTARS GCM: Description and Evaluation Against Satellite Observations

Sensitivity of Simulated Deep Convection to a Stochastic Ice Microphysics Framework

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