Exploring the Dynamics of an Arctic Sea Ice Melt Event Using a Coupled Atmosphere‐Ocean Single‐Column Model (AOSCM)

Abstract

Improving model performance through parameterization development is a difficult, time-consuming but potentially very rewarding task. It usually involves disentangling the model behavior, considering existing compensating errors (Dommenget & Rezny, 2018), unbalanced physics representations and numerical issues, before benefits of new, more advanced and physically based schemes are realized. As an intermediate step in understanding the model behavior, and to focus on specific parts of the model, there is a long tradition of using single-column models (SCM) for atmospheric processes (e.g., Holtslag et al., 2013;Neggers et al., 2012), and ocean vertical mixing (e.g., Li et al., 2019). More recently, Bogenschutz et al. (2020) seamlessly integrated an SCM in a general circulation model and Hourdin et al. ( 2021) applied a SCM to find optimal parameterization parameters as part of model tuning.Improving the representation of the polar regions in weather and climate models has been a challenging task (Jung et al., 2016). Additional small scale processes and properties of the climate system become relevant at high

Nudging allows direct evaluation of coupled climate models with in-situ observations: A case study from the MOSAiC expedition

“…We attribute this to an inconsistent treatment of surface coupling: the IFS uses separate skin and surface temperatures, whereas surface temperatures in the coupled model are updated on the FESOM side using a scheme modelled after ECHAM6, which does not distinguish skin and surface temperatures. Effectively enforcing T skin = T surf ace in the IFS by setting the skin layer conductivity to 10 10 Wm −2 K −1 (as discussed in Hartung et al (2022)) largely fixes this issue (downward pointing triangles in Fig. 8).…”