Deliverable 6.11 Climate model initialization V2
Creators: name: Kruschke, Tim
name: Karami, Mehdi Pasha
name: Navarro, Juan C. Acosta
name: Lapin, Vladimir
name: Ortega, Pablo
name: Counillon, François
name: Gustafsson, David
Publisher: name: Zenodo
Year: 2021
Subjects: Arctic
INTAROS
Climate Model
Climate Prediction
Sea Ice Concentration
River Discharge
Observation Systems

description: This report describes the final results of the work performed in Task 6.1, which has the main goal of improving the skill of climate predictions, investigating the benefits related to the exploitation of INTAROS data. Such benefits demonstrate a clear potential for users of Arctic data and stakeholders of climate prediction. A key ingredient to skillful seasonal-to-decadal climate prediction is the use of high-quality observational data, that cover a sufficiently long period, typically at least a few decades to test robustly their impact. This emphasizes the need - from a user perspective - to sustain and continue the production of the various iAOS products. The works in the task made use of three different datasets produced in INTAROS, namely CERSAT sea-ice concentrations, SMOS sea-thickness, and Arctic-HYCOS river discharges. The results found in Task 6.1 are: 1. CERSAT sea-ice concentrations were successfully used to assess the skill of SMHI’s quasi-operational decadal climate predictions with EC-Earth3 regarding September Northern Hemisphere sea-ice area for a lead time of 11 months (based on the period 1992-2020; correlation of 0.83) and the quality of new assimilation experiments providing potentially better initial conditions for climate predictions (correlation of 0.9 including long-term trend; 0.58 for detrended data, i.e. interannual variability). 2. CERSAT sea-ice concentrations were assimilated for BSC’s seasonal climate prediction system employing EC-Earth3. It is shown that the assimilation of sea-ice concentrations does not yield significant benefit for winter seasonal predictions (started on 1 November) but do have a remarkable positive impact on summer seasonal predictions (started on 1 May) regarding the sea-ice edge but also remote North Atlantic SSTs. The latter is shown to be the result of a so-called atmospheric bridge translating the improved sea-ice representation via more realistic large-scale atmospheric variability into the SST-signal. 3. Anomalies derived from sea-ice concentrations as well as SMOS and ENVISAT CCI sea-ice thickness estimates were assimilated in NorCPM, the seasonal-to-decadal climate prediction system developed at NERSC. It is shown that the assimilation of sea-ice concentrations is particularly beneficial for predictions along the sea-ice edge while sea-ice thickness is more important for the central Arctic. Hence, the assimilation of both is complementary and yields the best overall result. Here, the assimilation of SMOS data provides significantly better results compared to ENVISAT CCI. 4. Arctic-HYCOS river discharge is part of the iAOS product, driving from WP2. It has been assimilated to produce a pan-Arctic hydrological analyses and subsequent forecasts with the Arctic-HYPE model. The functionality of this workflow is demonstrated via a use-case addressing the Republic of Sacha (Yakutia), in Far-East Russia, where a sub-set of the Arctic-HYPE model is used for spring flood and river ice breakup forecasting in the major Yakutia rivers.