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\begin{document}

\title{RaFSIP: Parameterizing ice multiplication in models using a machine
learning approach}



\author[1]{Paraskevi Georgakaki}%
\author[2]{Athanasios Nenes}%
\affil[1]{École Polytechnique Fédérale de Lausanne}%
\affil[2]{Ecole Polytechnique Fédérale de Lausanne}%


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  \date{May 23, 2024}


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\begin{abstract}
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 @\selectlanguage{english}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 @\selectlanguage{english}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.%
\end{abstract}%



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\textbf{Hosted file}

\verb`968242_0_supp_11167696_rxg0xn.docx`  available at \url{https://authorea.com/users/542999/articles/655698-rafsip-parameterizing-ice-multiplication-in-models-using-a-machine-learning-approach}

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