Abstract/Summary

The evolution of Arctic sea ice thickness and volume over the satellite era are poorly understood because of the difficulties in both modelling and observing the sea ice. The intricate coupling that occurs between the ice, the ocean and the atmosphere makes modelling the sea ice cover accurately a very complex task. The harsh conditions and remoteness of the polar regions as well as the continual changes in the sea ice cover mean that sea ice thickness is difficult to observe consistently year-round. Further work is needed in understanding the changes that have occurred in the Arctic sea ice cover, not only to understand where the changes have occurred, but how and why they have occurred, so that we can understand what is driving these changes, and what changes are likely to occur in the future. In this thesis, we produce a new Arctic sea ice reanalysis in an attempt to ascertain where sea ice models are performing poorly, why this is happening, and identify possible areas of focus for future sea ice model development in order to obtain better estimates of regional and Pan Arctic changes in the sea ice cover. In the past decade groundbreaking new satellite observations of the Arctic sea ice cover have been made, allowing researchers to understand the state of the Arctic sea ice system in greater detail than before. The derived estimates of sea ice thickness are useful but limited in time and space. In this thesis the results from a new sea ice data assimilation system are presented. Observations assimilated (in various combinations) are monthly mean sea ice thickness and monthly mean sea ice thickness distribution from Cryosat-2, and NASA Team and Bootstrap daily sea ice concentration. This data assimilation system couples the Centre for Polar Observation and Modelling’s (CPOM) version of the Los Alamos Sea Ice Model (CICE) to the Localised Ensemble Transform Kalman Filter (LETKF) from the Parallel Data Assimilation Framework (PDAF) library. The impact of assimilating a sub-grid scale sea ice thickness distribution is of particular novelty. The sub-grid scale sea ice thickness distribution is a fundamental component of sea ice models, playing a vital role in the dynamical and thermodynamical processes, yet very little is known of its true state in the Arctic. Observations of summer sea ice thickness are assimilated for the first time, which has not previously been possible in Arctic sea ice reanalyses. We find that assimilating Cryosat-2 products for the mean thickness and the sub-grid scale thickness distribution can have significant consequences on the modelled distribution of the ice thickness across the Arctic and particularly in regions of thick multi-year ice. The assimilation of sea ice concentration, mean sea ice thickness and sub-grid scale sea ice thickness distribution together performed best when compared to a subset of Cryosat-2 observations held back for validation. Regional model biases are reduced: the thickness of the thickest ice in the Canadian Archipelago is decreased, but the thickness of the ice in the Central Arctic is increased. When comparing the assimilation of mean thickness with the assimilation of sub-grid scale thickness distribution, it is found that the latter leads to a significant change in the volume of ice in each category. Estimates of the thickest ice improve significantly with the assimilation of sub-grid scale thickness distribution alongside mean thickness. We find that our reanalyses of Arctic sea ice over the satellite era (1981-2020) substantially improve the estimates of sea ice extent, primarily in winter where the model estimates of sea ice concentration are much too high in the Arctic peripheral seas, causing an overestimation of sea ice extent in the stand-alone model. The differences in estimated sea ice concentration in two different sets of observations (NASA Team and Bootstrap) lead to very different estimates of sea ice volume depending on which is assimilated. These differences are reduced when ice thickness is also assimilated, demonstrating both the importance of assimilating ice thickness, and the drawbacks of only using sea ice concentration observations to make reanalysis estimates of short or long term changes in ice volume. We find that the distribution of the ice thickness in the Arctic is considerably different in our reanalyses in comparison to a model simulation that does not use assimilation, with a reduction in very thick ice and a reduction in the overall range of grid cell mean ice thickness. We also find strong disagreement between the free run of the model, observation and reanalysis on the timing of the seasonal sea ice cycle, with observations favouring earlier starts to both the melt and growth season. The assimilation of recently produced year-round sea ice thickness estimates is performed for the first time and shows the greatest improvement for ice thickness estimates in comparison with independent observations. Our work demonstrates the importance of, and need for, further work in sea ice observations and sea ice data assimilation in order to improve our understanding of the Arctic sea ice cover.