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Publications

Juan C. Acosta Navarro, Pablo Ortega, Javier García-Serrano, Virginie Guemas, Etienne Tourigny, Rubén Cruz-García, François Massonnet, and Francisco J. Doblas-Reyes

The sea-ice extent in the Arctic region hit an absolute record low during November and December of 2016. In the first 15 years of the 21st century, approximately 40% of Barents and Kara Seas were covered with sea-ice during the months of November and December.

Full Article: https://zenodo.org/record/2557464#.XGVc2FVKiuU

Ponsoni, Leandro Georges Lemaître Centre for Earth and Climate Research (TECLIM), Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium ; Massonnet, François; Fichefet, Thierry; Chevallier, Matthieu; Docquier, David

The ocean–sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of 14 reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites, and ice boreholes. Second, based on the same reanalyses, we intend to characterize the timescales (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective timescales and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the timescales and length scales: reanalyses built with sea ice data assimilation present shorter timescales and length scales. The mean timescales and length scales for reanalyses with data assimilation vary from 2.5 to 5.0 months and 337.0 to 732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9 to 7.8 months and 846.7 to 935.7 km, respectively.

Full Article: https://www.zenodo.org/record/2571613#.XG1kd1T7S70

Massonnet François; Sandu Irina;

Poster presented at the YOPP Arctic Science workshop (Helsinki, 14-16 Jan 2019)

Full Article: https://www.zenodo.org/record/2540486#.XFlRpp37S70

Cruz-García, Rubén Barcelona Supercomputing Center (BSC-CNS), Barcelona, Spain ; Guemas, Virginie; Chevallier, Matthieu; Massonnet, Fraçois

Arctic sea ice plays a central role in the Earth’s climate. Changes in the sea ice on seasonal-to-interannual timescales impact ecosystems, populations and a growing number of stakeholders. A prerequisite for achieving better sea ice predictions is a better understanding of the underlying mechanisms of sea ice predictability.

Full Article: https://www.zenodo.org/record/2540630#.XFlSj537S70

Blockley, Edward W.; Peterson, K. Andrew;

Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue.

Full Article: https://www.zenodo.org/record/1477991#.W-1wqlT7S70

Goessling, Helge; Jung, Thomas;

We introduce a verification score for probabilistic forecasts of contours – the Spatial Probability Score (SPS). Defined as the spatial integral of local (Half) Brier Scores, the SPS can be considered the spatial analogue of the Continuous Ranked Probability Score (CRPS).

Full Article: https://www.zenodo.org/record/1470466#.W-1voFT7S70

Hartung, Kerstin; Svensson, Gunilla; Struthers, Hamish; Deppenmeier, Anna-Lena; Hazeleger, Wilco;

Original research paper.

Full Article: https://www.zenodo.org/record/1470535#.W-1wAlT7S70

Zampieri, Lorenzo; Goessling, Helge F.; Jung, Thomas;

The need for reliable forecasts for the sea ice evolution from weeks to months in advance has substantially grown in the last decade. Sea ice forecasts are of critical importance to manage the opportunities and risks that come with increasing socioeconomic activities in the rapidly changing Arctic, which, despite the reduction of the sea ice cover, remains an extreme environment.

Full Article: https://www.zenodo.org/record/1471501#.W-1wXlT7S70

Keen, Ann; Blockley, Ed;

We present a method for analysing changes in the modelled volume budget of the Arctic sea ice as the ice declines during the 21st century. We apply the method to the CMIP5 global coupled model HadGEM2-ES to evaluate how the budget components evolve under a range of different forcing scenarios.

Full Article: https://www.zenodo.org/record/1478001#.W-1w01T7S70

Tummon, F; Day, J; Svensson, G;

The polar prediction problem is inherently multidisciplinary and requires cooperation across a wide community. Thus, an international group of agencies specifically designed a 10-day training course to bring together a wide group of students and lecturers to cover important topics related to polar prediction.

Full Article: https://www.zenodo.org/record/1341815#.W-1vUlT7S70