Search

The atmospheric mechanisms responsible for the loess grain‐size variations in central Asia are less clear, and an increasing number of studies have found that the coarse‐grained component is likely supplied by the surface circulation rather than by the westerlies (westerlies circulation). In this study, we attempt to determine the atmospheric dynamics patterns in the Afghan‐Tajik Basin during the past 800 kyr, based on the coarse grain‐size fraction of loess, combined with a study of modern dust transport processes. We suggest that the coarse grain‐size fraction can be used to indicate the intensity of near‐surface winds, while on glacial‐interglacial time scales, the variations of loess grain size in Tajikistan are dominated by Northern Hemisphere ice volume via its effect on the strength of the Siberian High. In addition, we suggest that atmospheric greenhouse gases concentration affected the loess grain size during interglacials via their influence on the temperature of southern high latitudes.

Sea ice cover and thickness have substantially decreased in the Arctic Ocean since the beginning of the satellite era. As a result, sea ice strength has been reduced, allowing more deformation and fracturing and leading to increased sea ice drift speed. We use the version 3.6 of the global ocean–sea ice NEMO-LIM model (Nucleus for European Modelling of the Ocean coupled to the Louvain-la-Neuve sea Ice Model), satellite, buoy and submarine observations, as well as reanalysis data over the period from 1979 to 2013 to study these relationships. Overall, the model agrees well with observations in terms of sea ice extent, concentration and thickness. The seasonal cycle of sea ice drift speed is reasonably well reproduced by the model. NEMO-LIM3.6 is able to capture the relationships between the seasonal cycles of sea ice drift speed, concentration and thickness, with higher drift speed for both lower concentration and lower thickness, in agreement with observations. Model experiments are carried out to test the sensitivity of Arctic sea ice drift speed, thickness and concentration to changes in sea ice strength parameter P*. These show that higher values of P* generally lead to lower sea ice deformation and lower sea ice thickness, and that no single value of P* is the best option for reproducing the observed drift speed and thickness. The methodology proposed in this analysis provides a benchmark for a further model intercomparison related to the relationships between sea ice drift speed and strength, which is especially relevant in the context of the upcoming Coupled Model Intercomparison Project 6 (CMIP6). David Docquier and Antoine Barthélemy work on the PRIMAVERA project (PRocess-based climate sIMulation: AdVances in high-resolution modelling and European climate Risk Assessment), which is funded by the European Commission’s Horizon 2020 programme, grant agreement no. 641727. François Massonnet is funded by the Belgian Fonds National de la Recherche Scientifique (FNRS) and was funded by the Ministerio de Economía, Industria y Competitividad (MINECO). Neil F. Tandon is supported by the Canadian Sea Ice and Snow Evolution (CanSISE) Network. Olivier Lecomte is a research assistant within the Belgian FNRS. The present research benefited from computational resources made available on the Tier-1 supercomputer of the Fédération Wallonie-Bruxelles, infrastructure funded by the Walloon Region under the grant agreement no. 1117545. Computational resources have also been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under grant no. 2.5020.11. We would like to thank Hugues Goosse, Martin Vancoppenolle, Jonathan Raulier and Véronique Dansereau for their very helpful comments regarding this study. We also acknowledge Pierre-Yves Barriat for his help in using computing resources at UCL and Damien François for his advice in improving Python scripts. Finally, we thank the editor Dirk Notz and the two anonymous reviewers for helping to improve the original paper.

Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice-ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an Ensemble Kalman Filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged sea ice) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the assimilation of synthetic data (i.e., model-generated data) and, secondly, through the assimilation of satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.