Exploring Impacts of Size‐Dependent Evaporation and Entrainment in a Global Model

Self Cite

Clouds are critical for weather and climate prediction. The multiple scales of cloud processes make simulation difficult. Often models and measurements are used to develop empirical relationships for large‐scale models to be computationally efficient. Machine learning provides another potential tool to improve our empirical parameterizations of clouds. To explore these opportunities, we replace the warm rain formation process in a General Circulation Model (GCM) with a detailed treatment from a bin microphysical model that causes a 400% slowdown in the GCM. We analyze the changes in climate that result from the use of the bin microphysical calculation and find improvements in the rain onset and frequency of light rain compared to high resolution process models and observations. We also find a resulting change in the cloud feedback response of the model to warming, which will significantly impact the climate sensitivity. We then replace the bin microphysical model with several neural networks designed to emulate the autoconversion and accretion rates produced by the bin microphysical model. The neural networks are organized into two stages: the first stage identifies where tendencies will be nonzero (and the sign of the tendency), and the second stage predicts the magnitude of the autoconversion and accretion rates. We describe the risks of overfitting, extrapolation, and linearization by using perfect model experiments with and without the emulator. We can recover the solutions with the emulators in almost all respects, and get simulations that perform as the detailed model, but with the computational cost of the control simulation.

Section: Discussionmentioning

confidence: 99%

Machine Learning the Warm Rain Process

Gettelman

Gagne

Chen

et al. 2021

Self Cite

“…For example, Karset et al. (2020) have added a size dependence to the parameterization of entrainment and evaporation and find a small impact on the radiative forcing due to aerosol‐cloud interactions.…”

Section: Introductionmentioning

confidence: 99%

“…Conversely, there are also studies that implement more detail into parameterizations and find small effects. For example, Karset et al (2020) have added a size dependence to the parameterization of entrainment and evaporation and find a small impact on the radiative forcing due to aerosol-cloud interactions.…”

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confidence: 99%

Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics

Proske

Ferrachat

Klampt

et al. 2023

In climate modeling, our task is to represent an immensely complex system which we wish to understand and learn about. Model development inherently faces tradeoffs between epistemic values like tractability, interpretability, validation potential, and specificity (Beucler et al., 2021;Larsen et al., 2016;Undorf et al., 2022), which manifest themselves in the ever present question of where to draw the line for details. Understanding nature requires a simplification of the mechanisms at play, but wanting to be precise calls for more details that increase complexity (Tapiador et al., 2019). For the term "model complexity" we here follow the "loose definition" mentioned in García-Callejas and Araújo (2016) that a model becomes more complex the more difficult to comprehend its computations are and the more processes/computations there are (Baartman et al. (2020) andRandall et al. (2003)).Climate modeling has followed the mirror view (Parker, 2022), meaning that it wants to mirror the Earth system in the model representation. Different modeling approaches would be following for example, the predictive or

“…In models with limited resolution, numerical diffusion leads to artificial mixing, perhaps great enough to drown out any parameterized microphysics–turbulence interaction that would represent the enhanced evaporation effect. For a given combination of model resolution and physics, the strength of the artificial mixing could give an indication of how parameterizable the aerosol‐enhanced evaporation effect is in that model (Karset et al., 2020; Sato et al., 2018). Indications are that Terai et al.…”

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confidence: 99%

“…In models with limited resolution, numerical diffusion leads to artificial mixing, perhaps great enough to drown out any parameterized microphysics-turbulence interaction that would represent the enhanced evaporation effect. For a given combination of model resolution and physics, the strength of the artificial mixing could give an indication of how parameterizable the aerosol-enhanced evaporation effect is in that model (Karset et al, 2020;Sato et al, 2018). Indications are that Terai et al (2020) have found a combination of resolution and physics that mitigates the numerical diffusion problem; quantifying the numerical diffusion in other combinations of resolution and turbulence scheme (be it ultraparameterization, higher-order closures, EDMF, or any other) could identify other viable combinations.…”

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confidence: 99%

The Fall and Rise of the Global Climate Model

Mülmenstädt

Wilcox

2021

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.