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Publications

Voldoire, Aurore; Saint-Martin, David; Sénési, Stéphane; Decharme, Bertrand; Alias, Antoinette; Chevallier, Matthieu; Colin, Jeanne; Guérémy, Jean-François; Michou, Martine; Moine, Marie-Pierre; Nabat, Pierre; Roehrig, Romain; Salas y Melia, David; Séférian, Roland; Valcke, Sophie; Beau, Isabelle; Belamari, Sophie; Berthet, Sarah; Cassou, Christophe; Cattiaux, Julien; Deshayes, Julie; Douville, Hervé; Ethé, Christian; Franchisteguy, Laurent; Geoffroy, Olivier; Lévy, Claire; Madec, Gurvan; Meurdesoif, Yann; Msadek, Rym; Ribes, Aurélien; Sanchez-Gomez, Emilia; Terray, Laurent; Waldman, Robin

Paper describing the main characteristics of the CNRM-CM6-1, the coupled global climate model developed jointly by CNRM and CERFACS for the CMIP6, and comparing its simulated climate with the CMIP5 version CNRM-CM5.1.

Full Article: https://www.zenodo.org/record/3582911#.XhMwQWXAO70

Jung, Thomas

The General Assembly of the H2020 Project EU-PolarNet convened for its annual meeting at the Institute of Geography and Spatial Planning of the University of Lisbon. Together with updates and presentations from other projects from the EU Arctic Cluster, the APPLICATE project was featured and developments in the project were presented.

Full Article: https://www.zenodo.org/record/3568087#.XhMv_GXAO70

Jung, Thomas

At the end of February 2019 the European Climate Research Alliance hosted an ECRA General Assembly in Brussels. The presentations and discussions during the meeting referred to the overarching theme "Climate Change and Actionable Information". The APPLICATE project coordinator presented first results and updates from the project.

Full Article: https://www.zenodo.org/record/3568076#.XhMvq2XAO70

Jung, Thomas

The school was held at the Abisko Scientific Research Station in northern Sweden. It brought together 29 Ph.D. students and early-career researchers from 16 countries. It was a joint initiative from the World Weather Research Programme (WWRP)-Polar Prediction Project, the World Climate Research Program (WCRP)-Polar Climate Predictability Initiative, and the Bolin Centre for Climate Research. In this framework, an overview presentation of the APPLICATE Project was given as part of the school programme.

Full Article: https://www.zenodo.org/record/3568049#.XhMvXWXAO70

Jung, Thomas

Presentation of an overview of the APPLICATE Project's purposes and objectives in occasion of the General Assembly of the H2020 fellow Arctic Cluster Project EU-PolarNet. The meeting took place on Monday, April 3rd 2017 in Prague (Czech Republic).

Full Article: https://www.zenodo.org/record/3568047#.XhMvGWXAO70

Jung, Thomas

Keynote presentation of the APPLICATE Project's purposes and objectives to the Steering Group Meeting of the Polar Prediction Project, in occasion of their 8th meeting at College Park in Maryland, USA.

Full Article: https://www.zenodo.org/record/3568042#.XhMuxWXAO70

Cruz-García, Rubén; Ortega, Pablo; Acosta Navarro, Juan C.; Massonnet, François; Doblas-Reyes, Francisco J.

Initialization is a key step when performing climate predictions, and for this the use of the latest high-quality observations and their assimilation in the model realm is of paramount importance. Less attention has been paid to other essential aspects of initialization that are equally important. For example, inconsistencies between the initial conditions (ICs) used for the different model components can cause important initialization shocks, hindering the prediction capacity during the first weeks of the forecast. In this study we investigate this and other different contributions to the forecast error in a seasonal prediction system with the EC-Earth general circulation model where sea ice is initialized via Ensemble Kalman filter assimilation of European Space Agency (ESA) derived sea ice concentrations. Large initial forecast errors in Arctic sea ice appear in regions of high observational uncertainty and little model spread, a combination that brings the assimilation, and in turn the ICs, close to the model attractor and far from the observations. We also investigated the development of the model drift during the first forecast month, and how it competes with the initial shock due to the inconsistency in ICs. After 24 (19) days the drift, as characterized by the systematic model error, becomes the largest contributor to the forecast error for the May (November) initialized forecasts, while the initial inconsistency dominates in the previous days. However, there are regions like the Greenland Sea for which the impact of the ICs inconsistency is still present after one month. Moreover, the development of both types of errors is sensitive to the month of initialization: the shock is more pronounced in November than in May. The main differences between both months relate to the systematic error, which is much higher in November, as well as to the direction of the shock with respect to the seasonal trend. In both cases the shock leads to sea ice melting, but, unlike in May, in November it happens in a context of sea ice expansion. The results stress that this opposing effect during November might be enhancing the generation of the drift. Our findings also highlight the importance of looking at high frequency data to disentangle the evolution of errors within the first forecast month, whose effects are harder to detect with the monthly averages.

Full Article: https://www.zenodo.org/record/3567552#.XhMtBmXAO70

Svensson, Gunilla

Presentation at the MOSAiC workshop March 11, 2019 in Pottsdam, Germany

Full Article: https://www.zenodo.org/record/3567127#.XhMsl2XAO70

Svensson, Gunilla; Hartung, Kerstin; Struthers, Hamish

Presentation at AMS 15th Conference on polar meteorology and oceanography

Full Article: https://www.zenodo.org/record/3567125#.XhMsOGXAO70

Ponsoni, Leandro; Massonnet, François; Docquier, David; Van Achter, Guillian; Fichefet, Thierry

This work evaluates the statistical predictability of the Arctic sea ice volume (SIV) anomaly – here defined as the detrended and deseasonalized SIV – on the interannual time scale. To do so, we made use of 6 datasets, from 3 different atmosphere-ocean general circulation models, with 2 different horizontal grid resolutions each. Based on these datasets, we have developed a statistical empirical model which in turn was used to test the performance of different predictor variables, as well as to identify optimal locations from where the SIV anomaly could be better reconstructed and/or predicted. We tested the hypothesis that an ideal sampling strategy characterized by only a few optimal sampling locations can provide in situ data for statistically reproducing and/or predicting the SIV interannual variability. The results showed that, apart from the SIV itself, the sea ice thickness is the best predictor variable, although total sea ice area, sea ice concentration, sea surface temperature, and sea ice drift can also contribute to improving the prediction skill. The prediction skill can be enhanced further by combining several predictors into the statistical model. Feeding the statistical model with predictor data from 4 well-placed locations is enough for reconstructing about 70% of the SIV anomaly variance. An improved model horizontal resolution allows a better trained statistical model so that the reconstructed values approach better to the original SIV anomaly. On the other hand, if we look at the interannual variability, the predictors provided by numerical models with lower horizontal resolution perform better when reconstructing the original SIV variability. As per 6 well-placed locations, the statistical predictability does not substantially improve by adding new sites. As suggested by the results, the 4 first best locations are placed at the transition Chukchi Sea–Central Arctic–Beaufort Sea (158.0◦W, 79.5◦N), near the North Pole (40◦ E, 88.5◦ N), at the transition Central Arctic–Laptev Sea (107◦E, 81.5◦N), and offshore the Canadian Archipelago (109.0◦W, 82.5◦N), in this respective order. We believe that this study provides recommendations for the ongoing and upcoming observational initiatives, in terms of an Arctic optimal observing design, for studying and predicting not only the SIV values but also its interannual variability.

Full Article: https://www.zenodo.org/record/3566808#.XhMr3GXAO70