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Publications

David Docquier; Jeremy Grist; Malcolm Roberts; Christopher Roberts; Tido Semmler; Leandro Ponsoni; François Massonnet; Dmitry Sidorenko; Dmitry Sein; Doroteaciro Iovino; Alessio Bellucci; Thierry Fichefet;

Arctic sea-ice area and volume have substantially decreased since the beginning of the satellite era. Concurrently, the poleward heat transport from the North Atlantic Ocean into the Arctic has increased, partly contributing to the loss of sea ice. Increasing the horizontal resolution of general circulation models (GCMs) improves their ability to represent the complex interplay of processes at high latitudes. Here, we investigate the impact of model resolution on Arctic sea ice and Atlantic Ocean heat transport (OHT) by using five different state-of-the-art coupled GCMs (12 model configurations in total) that include dynamic representations of the ocean, atmosphere and sea ice. The models participate in the High Resolution Model Intercomparison Project (HighResMIP) of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Model results over the period 1950–2014 are compared to different observational datasets. In the models studied, a finer ocean resolution drives lower Arctic sea-ice area and volume and generally enhances Atlantic OHT. The representation of ocean surface characteristics, such as sea-surface temperature (SST) and velocity, is greatly improved by using a finer ocean resolution. This study highlights a clear anticorrelation at interannual time scales between Arctic sea ice (area and volume) and Atlantic OHT north of 60 ◦N in the models studied. However, the strength of this relationship is not systematically impacted by model resolution. The higher the latitude to compute OHT, the stronger the relationship between sea-ice area/volume and OHT. Sea ice in the Barents/Kara and Greenland–Iceland–Norwegian (GIN) Seas is more strongly connected to Atlantic OHT than other Arctic seas.

Full Article: https://www.zenodo.org/record/3244826

Bojovic, Dragana; Terrado, Marta;

Presentation of APPLICATE user engagement activities at the shared JPI Climate and Climateurope booth at the European Climate Change Adaptation Conference at ECCA

Full Article: https://www.zenodo.org/record/3243906

Bojovic, Dragana; Terrado, Marta; Christel, Isadora; Doblas-Reyes, Francisco Javier; Jóhannsson, Halldór; Cristini, Luisa; Jung, Thomas

Proceedings of the 5th International Climate Change Adaptation Conference, Cape Town, South Africa, 18 -21 June 2018

Full Article: https://www.zenodo.org/record/2590955#.XRMzbrrAO70

Bojovic, Dragana; Terrado, Marta

Conference report on the APPLICATE User Group meeting that took place on the 16 January 2018 in Barcelona, Spain

Full Article: https://www.zenodo.org/record/2590947#.XRMyXLrAO70

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

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

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