Deliverable No. 4.2: Evaluation of the contribution of the Arctic observing system to forecast skill from short/medium-range-to-seasonal time scales

• Main Authors: Daniela Flocco, Edward Hawkins, Irina Sandu, Heather Lawrence, Niels Bormann, Leandro Ponsoni, François Massonnet
• Other Authors: Edward Hawkin, Flocco, Daniela, Hawkins, Edward, Sandu, Irina, Lawrence, Heather, Bormann, Niel, Ponsoni, Leandro, Massonnet, François
• Format: Book Part
• Language: English
• Published: 2020
• Subjects: Sea ice Predictability Climate models; Arctic; Sea ice
• Online Access: https://hdl.handle.net/11588/913523; https://ec.europa.eu/research/participants/documents/downloadPublic/WUQ5dkd5K2tZakZBVEZsV3VsR0EwTVhQL1pRTk9aUEFUeUFPaStyUFBHMDhlejhOblorUlBnPT0=/attachment/VFEyQTQ4M3ptUWVPZVFoajlwNEtNS0xZMEtPNFRGZ3g=

APPLICATE – GA 727862 Deliverable 4.2 APPLICATE WP4 (‘Support for Arctic observing system design’) demonstrated that numerical models can be successfully used to assess the importance of current Arctic observations for predictive skill in the Arctic and beyond and for guiding the design of future Arctic observing system. This deliverable summarizes part of this effort made in Task 4.2. This task aimed (i) to assess the importance of current Arctic observing systems for predictive skill from hours to months ahead, (b) to identify which enhancements to the observing systems themselves would contribute to improved predictive skill in the Arctic and beyond and (c) to guide the design of future observing systems in Arctic region. These objectives were accomplished by making use of data denial experiments, in which the quality of the initial conditions of weather predictions is artificially degraded by removing the contribution of a certain observation type, and of historical simulations with various coupled global circulation models. Coordinated idealized ‘data denial’-type experiments performed with two coupled climate models HadGEM3-GC3 and EC-EARTH, were used to quantify the impact of accurate sea ice thickness initialization on the Arctic sea-ice and atmospheric circulation forecast skill up to a year ahead. These idealized ‘data denial’ experiments consisted in initializing ensemble simulations using degraded sea ice thickness information and comparing the loss of skill with respect to control simulations assumed as the ‘truth’. This provided a theoretical understanding on the improvement of sea ice and atmospheric predictions linked to the sea ice thickness assimilation in coupled prediction systems. It was shown that an accurate sea ice initialization on the 1st of January improves the skill in all performed experiments. In particular, there is a high skill gain for the sea ice volume and extent in January-February and a re-emergence of skill gain from September onwards. The 2m air temperature shows a ...