Leveraging RALI‐THINICE Observations to Assess How the ICOLMDZ Model Simulates Clouds Embedded in Arctic Cyclones
Lea Raillard, Étienne Vignon, Gwendal Rivière, Jean‐Baptiste Madeleine, Yann Meurdesoif, Julien Delanoe͏̈, Arnaud Caubel, Olivier Jourdan, Antoine Baudoux, S. Fromang, Philippe Conesa
Abstract
Abstract Despite their essential role in the high‐latitude climate, the representation of mixed‐phase clouds is still a challenge for Global Climate Models (GCMs)'s cloud schemes. In this study we propose a methodology for robustly assessing Arctic mixed‐phase cloud properties in a climate model using airborne measurements. We leverage data collected during the RALI‐THINICE airborne campaign that took place near Svalbard in August 2022 to evaluate the simulation of mid‐level clouds associated with Arctic cyclones. Simulations are carried out with the new limited‐area configuration of the ICOLMDZ model which combines the recent icosahedral dynamical core DYNAMICO and the physics of LMDZ, the atmospheric component of the IPSL‐CM Earth System Model. Airborne radar and microphysical probes measurements are then used to evaluate the simulated clouds. A comparison method has been set‐up to guarantee as much as possible the spatiotemporal co‐location between observed and simulated cloud fields. We mostly focus on the representation of ice and liquid in‐cloud contents and on their vertical distribution. Results show that the model overestimates the amount of cloud condensates and exhibits a poor cloud phase spatial distribution, with too much liquid water far from cloud top and too much ice close to it. The downward gradual increase in snowfall flux is also not captured by the model. This in‐depth model evaluation thereby pinpoints priorities for further improvements in the ICOLMDZ cloud scheme.