Comment on tc-2021-353

doi:10.5194/tc-2021-353-rc1

Abstract

The Los Alamos sea ice model (CICE) is being tested in standalone mode for its suitability for seasonal time scale prediction. The prescribed atmospheric forcings to drive the model are from the NCEP Climate Forecast System Reanalysis (CFSR). A built-in mixed layer ocean model in CICE is used. Initial conditions for the sea ice and the mixed layer ocean in the control experiments are also from CFSR. The simulated sea ice extent in the Arctic in control experiments is generally in good agreement with observations in the warm season at all lead times up to 12 months, suggesting that CICE is able to provide useful ice edge information for seasonal prediction. However, the ice thickness forecast has a positive bias stemming from the initial conditions and often persists for more than a season, limiting the model’s seasonal forecast skill. In addition, thicker ice has a lower melting rate in the warm season, both at the bottom and top, contributing to this positive bias. When this bias is removed by initializing the model using ice thickness data from satellite observations while keeping all other initial fields unchanged, both simulated ice edge and thickness improve. This indicates the important role of ice thickness initialization in sea ice seasonal prediction.

Highlights

Sea ice concentration observations from passive microwave satellites show that ice coverage has decreased rapidly in the Arctic in recent decades, and in the Antarctic in recent years, the two regions with greatest warming on earth in recent decades 15 (Chapman and Walsh, 2003)
We investigate the sensitivity of prediction skill of the standalone CICE when initialized with different ice thickness products, as suggested in various studies mentioned earlier, in order to be close to the potential predictability (Palmer, 2006), where the model is 45 assumed to be perfect and the only source of error arises from the uncertainties in the initial conditions
We carry out numerical experiments to evaluate sea ice prediction skills at seasonal time scales in a standalone CICE ice model incorporating a mixed layer ocean model

Summary

Introduction

Sea ice concentration observations from passive microwave satellites show that ice coverage has decreased rapidly in the Arctic in recent decades, and in the Antarctic in recent years, the two regions with greatest warming on earth in recent decades 15 (Chapman and Walsh, 2003). Global climate models (e.g., IPCC, 2014, 2021) suggest that further reduction in sea ice coverage and thickness will occur in the coming decades. Sea ice in the polar oceans has a major impact on the regional energy balance but on the global climate as well. Change in sea ice is one of the most sensitive 20 and visible indicators of our changing climate. Reliable sea ice prediction is important for forecasting in the polar regions and is expected to improve predictability at mid-latitudes at subseasonal to seasonal (S2S) time scales due to teleconnections (e.g., Randall et al, 1998; Jaiser et al, 2012; Li et al, 2014)

Objectives

Methods

Results

Conclusion