Sensitivities and uncertainties in a coupled regional atmosphere‐ocean‐ice model with respect to the simulation of Arctic sea ice

Abstract

[1] A series of sensitivity experiments using a coupled regional atmosphere-ocean-ice model of the Arctic has been conducted in order to identify the requirements needed to reproduce observed sea-ice conditions and to address uncertainties in the description of Arctic processes. The ability of the coupled model to reproduce observed summer ice retreat depends largely on a quasi-realistic ice volume at the beginning of the melting period, determined by the relationship between winter growth and summer decay of ice. While summer ice decay is strongly affected by the parameterization of the sea-ice albedo, winter ice growth depends significantly on the parameterization of lateral freezing. Reciprocal model biases due to uncertainties in the atmospheric energy fluxes can be compensated to a certain extent. However, potential underlying weaknesses of the model cannot be eliminated that way. Since lateral freezing also determines the ice concentration during winter, and thus the heat loss of the ocean and the near-surface air temperature, the model tuning possibilities are limited. A large uncertainty in the model relates to the simulation of long-wave radiation most likely as a result of overestimated cloud cover. The results suggest that uncertainties in the descriptions for Arctic clouds, snow, and sea-ice albedo, and lateral freezing and melting of sea ice, including the treatment of snow, are responsible for large deviations in the simulation of Arctic sea ice in coupled models. Improved descriptions of these processes are needed to reduce model biases and to enhance the credibility of future climate change projections.

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

“…Small changes in the ice albedo scheme may lead to significant changes in the simulation of summer sea ice extent (e.g. Dorn et al, 2007Dorn et al, , 2009). This result called for a reconsideration of the physical basis of the sea ice albedo models, which might explain in part why the rapid reduction of Arctic sea ice is better captured by the models used for the latest assessment report AR5 Massonet et al, 2012).…”

Limitations of a coupled regional climate model in the reproduction of the observed Arctic sea-ice retreat

“…Previous experiments with HIRHAM-NAOSIM showed that the model needs about 6-10 yr to arrive at a quasistationary cyclic state of equilibrium in sea-ice volume (Dorn et al, 2007). This is why the ensemble members were initialized with restart fields taken after not less than 6 yr of coupled simulation, assuming that sea ice and upper ocean are then sufficiently adjusted.…”

“…The coupled HIRHAM-NAOSIM system was applied for the first time by Rinke et al (2003) and subsequently by Dorn et al (2007), who also gave a detailed description of the basic model setup. Over the last years, a few parameterizations in HIRHAM-NAOSIM were replaced by more sophisticated schemes.…”

“…An ensemble of 7 hindcast simulations for the period 1948-2008 were carried out using NCEP/NCAR reanalysis data (Kalnay et al, 1996) as HIRHAM's lateral boundary forcing as well as HIRHAM's lower and NAOSIM's upper boundary forcing outside the overlap area of the two model domains as described by Dorn et al (2007). All ensemble members were equally started on 1 January 1948 and run through 31 December 2008, but the initial ocean and sea-ice fields were taken from different years of a preceding coupled spin-up run (runA to runF) or from the final state of one of the ensemble members (runG) as listed in Table 1.…”

Influence of parallel computational uncertainty on simulations of the Coupled General Climate Model

“…Generally, CGCM's uncertainties, which usually result from the nonlinear interaction of the component of the climatic system (Tebaldi et al, 2004;Held et al, 2002), have two types. The first one is due to uncertainty of the physical parameterization (Meehl et al, 2007;Moss and Schneider, 2000;Wittenberg and Anderson, 1998;Dorn et al, 2007), and the other one is from amplification of the computational errors (Cousins and Xue, 2001;Wang et al, 2007;Chen et al, 2008). Those results suggest that uncertain processes or parameterization schemes could cause a quite large uncertainty of the climate simulations.…”