Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6–LIM3 global ocean–sea ice model

Abstract

Abstract. This study assesses the impact of different sea ice thickness distribution (ITD) discretizations on the sea ice concentration (SIC) variability in ocean stand-alone NEMO3.6–LIM3 simulations. Three ITD discretizations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by K-means clustering both in the Arctic and Antarctica between 1979 and 2014. We focus on two seasons, winter (January–March) and summer (August–October), in which correlation coefficients across clusters in individual months are largest. In the Arctic, clusters are computed before and after detrending the series with a second-degree polynomial to separate interannual from longer-term variability. The analysis shows that, before detrending, winter clusters reflect the SIC response to large-scale atmospheric variability at both poles, while summer clusters capture the negative and positive trends in Arctic and Antarctic SIC, respectively. After detrending, Arctic clusters reflect the SIC response to interannual atmospheric variability predominantly. The cluster analysis is complemented with a model–data comparison of the sea ice extent and SIC anomaly patterns. The single-category discretization shows the worst model–data agreement in the Arctic summer before detrending, related to a misrepresentation of the long-term melting trend. Similarly, increasing the number of thin categories reduces model–data agreement in the Arctic, due to a poor representation of the summer melting trend and an overly large winter sea ice volume associated with a net increase in basal ice growth. In contrast, more thin categories improve model realism in Antarctica, and more thick ones improve it in central Arctic regions with very thick ice. In all the analyses we nonetheless identify no optimal discretization. Our results thus suggest that no clear benefit in the representation of SIC variability is obtained from increasing the number of sea ice thickness categories beyond the current standard with five categories in NEMO3.6–LIM3.

Effects of Increasing the Category Resolution of the Sea Ice Thickness Distribution in a Coupled Climate Model on Arctic and Antarctic Sea Ice Mean State

“…These results are consistent with previous work indicating that thinnest categories are most sensitive to thermodynamic processes, while thickest categories are most sensitive to dynamic processes (Hunke, 2014;Moreno-Chamarro et al, 2020). We expect the impact of higher ITD category resolution to decrease over time as Arctic sea ice becomes thinner on average, as demonstrated by the results of the future scenario SSP3-7.0.…”

“…Massonnet et al (2011) found that increasing the number of ITD categories improved the seasonal to interannual variability of Arctic sea ice extent and retreat at basin-scales. In contrast, Moreno-Chamarro et al (2020) found that increasing the number of thin categories resulted in poorer comparisons of Arctic sea ice concentration and extent with observations when all other model settings were kept constant. Massonnet et al (2019) more broadly investigated the impact of the discretization and resolution of thick ice categories on representation of sea ice over the historical period.…”

Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean

“…11l), with several lines clearly being outliers, which were checked to match jpl = 1. This is because the multi-category sea ice thickness takes into account the subgrid-scale variations in sea ice properties (Thorndike et al, 1975;Massonnet et al, 2019;Moreno-Chamarro et al, 2020) and is therefore significantly different from the singlethickness category (jpl = 1). For instance, the presence of thin sea ice categories in multi-category sea ice schemes al-lows for greater melt rates compared to a single-category scheme (Uotila et al, 2017).…”

Sensitivity of NEMO4.0-SI3 model parameters on sea ice budgets in the Southern Ocean

“…9l), with several lines being clearly outliers, which were checked to match jpl=1. This is because the multi-category sea ice thickness takes into account the subgrid-scale variations in sea ice properties (Thorndike et al, 1975;Massonnet et al, 2019;Moreno-Chamarro et al, 2020) and is therefore significantly different from the single thickness category (jpl=1). For instance, the presence of thin sea-ice categories in multi-category sea-ice schemes allows for greater melt rates compared to a single-category scheme (Uotila et al, 2017).…”