The Impact of Resolving Subkilometer Processes on Aerosol‐Cloud Interactions of Low‐Level Clouds in Global Model Simulations

Terai, C. R.; Pritchard, Michael S.; Blossey, Peter N.; Bretherton, Christopher S.

doi:10.1029/2020ms002274

Cited by 20 publications

(27 citation statements)

References 85 publications

(200 reference statements)

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“…Our interpretation is that the model used by Terai et al. (2020) has been the first to hit the sweet spot between global coverage, resolution, and model physics required to understand the balance between precipitation suppression and evaporation enhancement.…”

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confidence: 91%

“…Terai et al. (2020) describe the latest development, which endows the Parishani et al. (2017) ultraparameterized model with a representation of aerosol–cloud interactions.…”

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confidence: 99%

“…If the behavior of the ultraparameterized model is indeed due to the closing of this gap in the model physics, then Terai et al. (2020) have taken an important step toward identifying the appropriate level of complexity for modeling the climate impacts of aerosols, including their role in regional climate projections.…”

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confidence: 99%

“…In Terai et al. (2020), the CRM running within each GCM column has a resolution approaching large eddy simulations (LESs) suitable for boundary‐layer clouds. LES at this resolution is known to be able to represent entrainment, albeit with quantitative uncertainty, through a combination of the resolved dynamics and subgrid turbulence parameterization.…”

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confidence: 99%

“…As a proof of concept, the more realistic cloud physics behavior in the Terai et al. (2020) model is highly encouraging, but some drastic trade‐offs were needed to get an ERF estimate. The host GCM is too coarse to resolve regional circulations crucial to aerosol–circulation interactions, likely a step backwards in representing regional circulations accurately given moderate improvements in the Asian summer monsoon (Goswami & Goswami, 2017; Johnson et al., 2016; Ramu et al., 2016) and substantial improvements in the ITCZ (Song & Zhang, 2020) with increasing GCM resolution.…”

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confidence: 99%

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The Fall and Rise of the Global Climate Model

Mülmenstädt

Wilcox

2021

J Adv Model Earth Syst

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Global models are an essential tool for climate projections, but conventional coarse‐resolution atmospheric general circulation models suffer from errors both in their parameterized cloud physics and in their representation of climatically important circulation features. A notable recent study by Terai et al. (2020, https://doi.org/10.1029/2020ms002274 documents a global model capable of reproducing the regime‐based effect of aerosols on cloud liquid water path expected from observational evidence. This may represent a significant advance in cloud process fidelity in global models. Such models can be expected to give a better estimate of the effective radiative forcing of the climate. If this advance in cloud process representation can be matched by advances in the representation of circulation features such as monsoons, then such models may also be able to navigate the complex tangle between spatially heterogeneous aerosol–cloud interactions and regional circulation patterns. This tight link between aerosol and circulation results in anthropogenic perturbations of climate variables of societal importance, such as regional rainfall distributions. Upcoming global models with km‐scale resolution may improve the regional circulation and be able to take advantage of the Terai et al. (2020, https://doi.org/10.1029/2020ms002274 improvement in cloud physics. If so, an era of significantly improved regional climate projection capabilities may soon dawn. If not, then the improvement in cloud physics might spur intensified efforts on problems in model dynamics. Either way, based on the rapid changes in aerosol emissions in the near future, learning to make reliable projections based on biased models is a skill that will not go out of style.

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confidence: 91%

“…Terai et al. (2020) describe the latest development, which endows the Parishani et al. (2017) ultraparameterized model with a representation of aerosol–cloud interactions.…”

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%