Another Piece of Evidence for Important but Uncertain Ice Multiplication Processes

Hoose, Corinna

doi:10.1029/2022av000669

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…Randall et al (2003) state that "the cloud parameterization problem is overwhelmingly complicated," and is becoming more so, because both numerical and conceptual complexity are rapidly increasing. The development of CMP parameterizations may even be ahead of fundamental research (Morrison et al, 2020), as Hoose (2022) states for the process of secondary ice production. This may be for example, because the need to improve climate models is often employed as a justifying value (Ward, 2021) that encourages experimentalists to frame their research as contributing to parameterization development (Sundberg, 2007).…”

Section: Meltingmentioning

confidence: 99%

Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics

Proske

Ferrachat

Klampt

et al. 2023

J Adv Model Earth Syst

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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

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Section: Meltingmentioning

confidence: 99%

Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics

Proske

Ferrachat

Klampt

et al. 2023

J Adv Model Earth Syst

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“…This suggests that SIP might be a prevalent process in the moderately cold polar conditions. Along with observations, modeling studies across scales have also emphasized the significance of incorporating SIP parameterizations in regional and global climate model simulations for an accurate representation of the phase partitioning and radiative properties of Arctic MPCs (e.g., Fridlind & Ackerman, 2018;Sotiropoulou et al, 2020, 2022. Recent physically based SIP parameterizations tested in GCMs were developed by pooling experimental observations and further considering the physics of collisions or applying correction factors to account for the simplified laboratory setups (Phillips et al, 2017(Phillips et al, , 2018.…”

Section: Introductionmentioning

confidence: 99%

RaFSIP: Parameterizing Ice Multiplication in Models Using a Machine Learning Approach

Georgakaki,

Nenes

2024

J Adv Model Earth Syst

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Accurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms.

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“…The importance of SIP has been widely acknowledged in laboratory [23][24][25][26] , eld [27][28][29] , remote sensing [30][31][32][33] , and modeling studies [34][35][36] worldwide 37 . The most commonly invoked SIP processes include the Hallett-Mossop (HM) or rime-splintering process 38,39 , ice-ice collisional break-up (BR) 40,41 , and dropletshattering during freezing (DS) 42,43 .…”

Section: Introductionmentioning

confidence: 99%

Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling

Georgakaki,

Billault-Roux,

Foskinis

et al. 2023

Preprint

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Recent years have shown that secondary ice production (SIP) is ubiquitous, affecting all clouds from polar to tropical regions. SIP is not described well in models and for this may vastly underpredict ice crystal number concentrations in warm mixed-phase clouds. Through a synergy of modeling, remote sensing and in-situ measurements carried out in an orographic environment during the Cloud-AerosoL InteractionS in the Helmos background TropOsphere (CALISHTO) campaign, we show that SIP can have a profound impact on the vertical distribution of hydrometeors and precipitation, especially in seeder-feeder configurations which are encountered in multi-layered cloud systems. The mesoscale model simulations coupled with a radar simulator strongly support a unique signature that is characteristic of SIP; because of this, our study opens the possibility of using the vast global archive of cloud radar data for systematically inferring SIP signatures and frequency of occurrence.

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Another Piece of Evidence for Important but Uncertain Ice Multiplication Processes

Cited by 6 publications

References 20 publications

Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics

Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics

RaFSIP: Parameterizing Ice Multiplication in Models Using a Machine Learning Approach

Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling

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