Potential decadal predictability and its sensitivity to sea ice albedo parameterization in a global coupled model

Abstract

Decadal prediction is one focus of the upcoming 5th IPCC Assessment report. To be able to interpret the results and to further improve the decadal predictions it is important to investigate the potential predictability in the participating climate models. This study analyzes the upper limit of climate predictability on decadal time scales and its dependency on sea ice albedo parameterization by performing two perfect ensemble experiments with the global coupled climate model EC-Earth. In the first experiment, the standard albedo formulation of EC-Earth is used, in the second experiment sea ice albedo is reduced. The potential prognostic predictability is analyzed for a set of oceanic and atmospheric parameters. The decadal predictability of the atmospheric circulation is small. The highest potential predictability was found in air temperature at 2 m height over the northern North Atlantic and the southern South Atlantic. Over land, only a few areas are significantly predictable. The predictability for continental size averages of air temperature is relatively good in all northern hemisphere regions. Sea ice thickness is highly predictable along the ice edges in the North Atlantic Arctic Sector. The meridional overturning circulation is highly predictable in both experiments and governs most of the decadal climate predictability in the northern hemisphere. The experiments using reduced sea ice albedo show some important differences like a generally higher predictability of atmospheric variables in the Arctic or higher predictability of air temperature in Europe. Furthermore, decadal variations are substantially smaller in the simulations with reduced ice albedo, which can be explained by reduced sea ice thickness in these simulations.

Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution

“…volume with respect to that of sea ice concentration (i.e. area or extent), found in all seasonal to interannual predictability studies seems to disappear when considering longer time scales, as shown by the very weak predictability of SIV, in contrast to the highly predictable sea ice extent (SIE) found on decadal means by Koenigk et al (2012).…”

“…Longer-term Arctic sea ice predictability has been investigated using perfect model assumption, in which Global Climate Models (GCMs) ensemble integrations are initialized from a reference model integration (Koenigk and Mikolajewicz 2009;Koenigk et al 2012;Tietsche et al 2013). These studies examined the upper limit of initial-value predictability and its sensitivity to the Arctic sea ice mean state , which is an important issue in the observed rapidly changing Arctic sea ice conditions.…”

“…Therefore, the understanding of their variability and predictability requires consideration at a smaller spatial scale in order to highlight regional discrepancies. A few studies have investigated the spatial distribution of the sea ice cover predictability, concluding that the sea ice cover is more predictable in the Atlantic sector than in the rest of the Arctic (Koenigk and Mikolajewicz 2009;Koenigk et al 2012). This higher predictability is attributed to sea ice anomalies advection processes in the transpolar drift stream impact area, i.e.…”

Recent advances in understanding the Arctic climate system state and change from a sea ice perspective: a review

“…Sector (Koenigk et al, 2012), due to a strong correlation with the meridional overturning circulation in the North Atlantic Ocean. Such results suggest that the outlook is positive for future climate prediction in the Arctic.…”

“…Koenigk et al (2012) have indicated that the increasing ocean heat transport strongly contributes to the reduced sea ice cover in the Barents and Kara Sea region, and thus hypothetically also contributes to the Arctic temperature amplification of the global climate warming (see Sect. 2.1).…”