How important are secondary ice processes – preliminary results from FOR-ICE

Ickes, Luisa; Costa-Surós, Montserrat; Eriksson, Patrick; Frostenberg, Hannah; Georgakaki, Paraskevi; Gonçalves, M.; Hallborn, Hanna; Lewinschal, Anna; May, Eleanor G.; Nenes, Athanasios; Neubauer, David; García‐Pando, Carlos Pérez; Proske, Ulrike; Sotiropoulou, Georgia

doi:10.5194/egusphere-egu23-10696

Cited by 2 publications

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“…The incorporation of the RaFSIP framework in the microphysics scheme of 3 GCMs, namely ECHAM‐HAM, NorESM2, and EC‐Earth, participating in the CMIP assessments is one of the undergoing studies we are currently addressing (Costa‐Surós et al., 2023; Ickes et al., 2023). The modular nature of the new SIP parameterization allows for seamless integration with the dynamical core of each GCM model, provided that the input features are well located inside the stratiform cloud microphysics routine.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

Georgakaki,

Nenes

2024

J Adv Model Earth Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…The incorporation of the RaFSIP framework in the microphysics scheme of 3 GCMs, namely ECHAM-HAM, NorESM2, and EC-Earth, participating in the CMIP assessments is one of the undergoing studies we are currently addressing (Ickes et al, 2023;Costa-Surós et al, 2023). The modular nature of the new SIP parameterization allows for seamless integration with the dynamical core of each GCM model, provided that the input features are well located inside the stratiform cloud microphysics routine.…”

Section: Discussionmentioning

confidence: 99%

RaFSIP: Parameterizing ice multiplication in models using a machine learning approach

Georgakaki,

Nenes

2023

Preprint

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Representing single or multi-layered mixed-phase clouds (MPCs) accurately in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Ice multiplication, or secondary ice production (SIP), can increase the ice crystal number concentration (ICNC) in MPCs by several orders of magnitude, affecting cloud properties and processes. Here, we propose a machine-learning approach, called Random Forest SIP (RaFSIP), to parameterize the effect of SIP on stratiform MPCs. The RaFSIP scheme uses few input variables available in models and considers rime splintering, ice-ice collisional break-up, and droplet-shattering, operating at temperatures between 0 and -25 ˚C. The training dataset for RaFSIP was derived from two-year pan-Arctic simulations with the Weather Research and Forecasting (WRF) model with explicit representations of SIP processes. The RaFSIP scheme was evaluated offline against WRF simulation outputs, then integrated within WRF. The parameterization exhibits stable performance over a simulation year, and reproduced predictions of ICNC with explicit microphysics to within a factor of 3. The coupled WRF-RaFSIP scheme can replicate regions of enhanced SIP and accurately map ICNCs and liquid water content, particularly at temperatures above -10 ˚C. Uncertainties related to the RaFSIP representation of MPCs marginally affected surface cloud radiative forcing in the Arctic, with radiative biases of lower than 3 Wm-2 compared to simulations with explicit SIP microphysics. Training from a few high-resolution model grid points did not limit the predictive skill of RaFSIP, with the approach opening up new avenues for model simplification and process description in GCMs by physics-guided machine learning algorithms.

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How important are secondary ice processes – preliminary results from FOR-ICE

Cited by 2 publications

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

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

RaFSIP: Parameterizing ice multiplication in models using a machine learning approach

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