EGU23-10617, updated on 26 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Impacts of a layer snow density evolution scheme on the Arctic snow simulation based on the CICE sea-ice model

Hao Yin1,2 and Jie Su1,2
Hao Yin and Jie Su
  • 1College of Oceanic and Atmospheric Science, Ocean University of China, Qingdao 266100
  • 2Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao 266100

Snow plays a vital role in the coupled ocean-ice-atmosphere system with its unique thermodynamic properties. Snow density is set to be a constant (about 320 kg·m-3) in most present sea ice models, ignoring the seasonal evolution of density and relevant thermodynamic regimes. We introduced the layered snow density evolution scheme into the Los Alamos Sea Ice Model (CICE), making it possible to access the diagnostic time-varying snow density. Forcing by ERA5, the modeled results of both CICE and its one-dimension submodule Icepack were compared with buoy observations (Ice Mass Balance buoys, IMB), remote sensing (The advanced Microwave Scanning Radiometer 2, AMSR2), as well as model results from SnowModel-LG, one of the popular snow models with the most sophisticated physical processes.The monthly average snow density absolute bias between the results of the improved CICE and SnowModel-LG, is about 30±13 kg·m-3 in most months except Jul. and Aug. Relatively fresh snow density is found in SnowModel-LG results because most of the winter snow has melted in these two months, while old snow still remains in CICE. This causes a 100~200 kg·m-3 differences of snow density in the two results in this period. The annual mean (1990~2018) contribution of strain compaction, fresh snowfall, and wind compaction on the density evolution is about 1:-16:17.5, respectively, with the effects of the latter two compensating each other and out to a value of the same magnitude as the first component. Verification of the 1D results with 42 IMBs observations showed great agreements among snow depth (Hs), ice thickness (Hi), and snow/ice temperature (Ts/Ti) in the standard Icepack run with constant snow density. Several improvements were found in the new simulations of Hs (reduce 30% of 3 cm overestimation), Hi (reduce 34% of 0.04 m overestimation), and Ts/Ti (increase 50% of 1.4°C underestimation for snow and 10% of 0.7°C underestimation for ice, respectively) with the layered snow scheme in the winter seasons. In 2D CICE model, the implement of new snow parameterization improved the simulation of Hs in the Central Arctic (CA, north of 80°N) obviously. The overestimated 5 cm Hs under the standard CICE run can be reduced about 10% in the new experiment relative to the AMSR2 retrieval snow depth data in winter (Nov.-Apr.) from 2013 to 2018.

How to cite: Yin, H. and Su, J.: Impacts of a layer snow density evolution scheme on the Arctic snow simulation based on the CICE sea-ice model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10617,, 2023.