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

Abstract

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.

Technical Note: assessing predicted cirrus ice properties between two deterministic ice formation parameterizations

“…The cirrus model in ECHAM-HAM is called from the cloud microphysics scheme and calculates the number of new ice crystals that form in in-situ cirrus. It uses a sub-stepping approach to simulate the temporal evolution of the ice saturation ratio in an adiabatically ascending air parcel during the formation stage of a cloud (Kuebbeler et al, 2014;Tully et al, 2022c). In this note, we extracted only the cirrus sub-model code from ECHAM-HAM to formulate a box model of ice nucleation within cirrus, see Section 2.2 and Section 2.3.…”

“…In this note, we extracted only the cirrus sub-model code from ECHAM-HAM to formulate a box model of ice nucleation within cirrus, see Section 2.2 and Section 2.3. In its full form, the cirrus sub-model calculates the competition of water vapor between deposition onto pre-existing ice particles and phase transitions by homogeneous or heterogeneous nucleation processes that form new ice crystals (Tully et al, 2022c). Muench and Lohmann (2020) introduced a new method to more easily distinguish between new ice formation mechanisms by categorizing them by either threshold or continuous processes.…”

“…It simulates the temporal evolution of S i during the adiabatic ascent of a theoretical air parcel. S i evolves through the balance between the updraft velocity, which is used as an input parameter to the sub-model, and a fictitious downdraft that quantifies the effect of vapor deposition onto newly-formed or pre-existing ice crystals (Tully et al, 2022c). This quantity is termed the "effective updraft velocity".…”

“…To simulate deterministic, AF-based ice formation onto externally mixed accumulation and coarse mode mineral dust particles (Stier et al, 2005;Zhang et al, 2012), we also defined two "freezing modes", respectively, following Muench and Lohmann (2020). Full ice nucleation competition (Gasparini and Lohmann, 2016;Tully et al, 2022c) was also tested by adding additional modes for homogeneous nucleation of liquid sulphate aerosol, immersion freezing of internally mixed mineral dust particles, and pre-existing ice. However, the results for these latter tests are not shown in this note as the competition between ice formation mechanisms as well as vapor deposition onto preexisting ice crystals did not change the outcome of our box model compared to our dust deposition-only tests.…”

“…cirrus) contain a higher level of uncertainty (Storelvmo, 2017;Bellouin et al, 2020). For cirrus, the subject of this technical note, accurately simulating ice formation mechanisms is still an open research topic that impedes further understanding of cirrus climate impacts, including assessing potential climate intervention strategies (Storelvmo et al, 2013;Zhou and Penner, 2014;Penner et al, 2015;Gasparini and Lohmann, 2016;Krämer et al, 2016;Kärcher, 2017;Storelvmo, 2017;Gasparini et al, 2020;Krämer et al, 2020;Kärcher et al, 2022;Tully et al, 2022c).…”