1. Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK GEOMAR (6) Mojib Latif (WP lead) Wonsun Park %Thomas Martin %Fritz Krüger MPG (2) Johann Jungclaus Katja Lohmann UHAM (1) Detlef Stammer Armin Köhl Xueyuan Liu Andrey Vlasenko DMI (7) Steffen M. Olsen (CT/WP lead) Jacob L. Høyer Gorm Dybkjær Torben Schmith Berlin 14-15 October 2014

2. Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Suitability of the ocean observing system components for initialization Impact of Arctic initialization on forecast skill Initialization of prediction systems with ocean observations Initialization of prediction systems with ocean observations WP 3.1 Mojib Latif MPG+GEOMAR WP 3.2 Steffen M. Olsen DMI+UHAM

3. Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Suitability of the ocean observing system components for initialization WP 3.1 Objectives Investigate and quantify the benefit of different components of the ocean observing system for prediction systems (decadal) Identify necessary enhancements and potential reductions in the present system Methodology Perfect model approach, hindcast experiments using the Kiel Climate Model Re-start simulations with truncated ocean initial conditions corresponding to different ocean regions and observing systems

4. Deliverables D9 (GEOMAR, month 12): Report on the setup of coupled model and hindcasts conducted with initial conditions corresponding to ARGO-like sampling. (Mojib Latif & Fritz Krüger submitted October 2013) D 26 (GEOMAR, month 24): Report on hindcasts conducted with initial conditions extended to include ”RAPID”, and on the feasibility of decadal forecasts with the current ocean observing system Delayed until April 2015 (Wonsun Park) D 39 (GEOMAR, month 36): Report on hindcasts conducted with satellite information (Wonsun Park) D 58 (GEOMAR, month 44): Report on the identifications of potential needs that are not captured by the present ocean observing system for enhancing decadal predictions. (Wonsun Park)

5. Done: –Control run finished –Idealized sampling experiments delivered To be done in 2014/2015: –Perfect predictability experiments with atmospheric perturbations –Repeat these experiments with ARGO-like sampling –Extension by including “data” from the RAPID array –Repetition of the predictability experiments only with satellite information (SST) Suitability of the ocean observing system components for initialization

6. Impact of Arctic initialization on forecast skill WP 3.2 Objectives To address in detail the Arctic region of sparse data coverage Establish the impact of Arctic data and initialization of the Arctic region on forecast skill Explore the potential to constrain the state of the Arctic Ocean using the transport monitoring system at the GSR. Methodology / Tasks 3.2.1 Model assessment, arctic sector, GSR, processes limiting the skill 3.2.2Improving model skill by parameter optimization using climate observations over the Arctic sector. 3.2.4 Perfect-model approach to potential predictability, data withholding 3.2.3 Improve data availability: deliver an Arctic Surface Temperature dataset 3.2.5 Evaluation of model predictive skill

7. Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Deliverables D10 (DMI, month 12): Assessment of model build-up, storage and release of Arctic Ocean freshwater pools. Done (Steffen Olsen) D27 (UHAM, month 24): Report on the documentation and description of improved model parameters. Finalized October 2014, no further work planned D28 (DMI, month 24): Report on the documentation and description of the new Arctic Ocean dataset combining SST and IST. Report ready Monday, dataset available for distribution shortly (Gorm Dybkjær) D40 (DMI, month 36): Report on the establishment of impact of the Arctic region initialization, and on the sources of predictive skill from data withholding experiments. No results yet (Schmith & Olsen). D51 (DMI, month 44): Assessment of the value of the GSR flux monitoring time series for confining the initial state of the upper Arctic Ocean. Initiated (Steffen)

8. Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Task 3.2.4 Perfect-model approach to potential predictability - data withholding experiment Aim: We will verify and quantify the potential predictability linked to initialization of the upper Arctic ocean in an ideal model experiment using the EC-Earth coupled climate model. Approach: Sources of forecast skill will be identified by comparing the potential predictive skill across a perfect model ensemble with data withholding experiments. Status: A new control simulation with present day CO2 levels are underway but presently postpone the production of model results. Considerations: We have shown (D32.10) potential mechanisms and modes for build up and release of upper Arctic Ocean anomalies which contribute to the understanding of potential predictability.

9. Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Task 3.2.3 Data set of sea surface and ice surface temperatures (D32.28) Technical report (D32.10) describing methods and data sources ready to be submitted to the project office. Single sensor (infrared) consistent dataset 1982-2009 (1972-2014 next year) Includes uncertainties (model validation and assimilation) Superior resolution in time and space - marginal ice zone/edge To be considered Reduced subsets based on user requirements (weakly mean, night-time data)

10. Left: March mininimum temperature, 2007. Right: September minimum temperature, 2007. Mean September difference temperature 2mt (ERA interim) – skin (reprocessed data set)

11. Task 3.2.2 Improve the skill of the EU FP7 THOR adjoint assimilation system Reema Agrawal,Detlef Stammer, Armin Köhl Climate observations will be used to better constrain uncertain model parameters in the adjoint model The THOR coupled assimilation system, which is now established as the CESAM (CEN Earth System Assimilation Model) is tuned using a multivariate data assimilation technique to bring the mode closer to real world observations The Simultaneous Perturbation Stochastic Approximation (SPSA) method is used for assimilating annual mean data (pseudo and ERA-interim reanalysis data). The advantage of using SPSA method is its ease of implementation, robustness to noise in cost function and cost effectiveness. Identical twin testing demonstrates that the method reliably finds an estimate of parameters Tuning the model to reanalysis data by adjusting parameters yields a reduction in air temperature and the net surface heat flux. Deliverable D32.27 Future: No further work planed for this Task

12. Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Control on sub-polar dynamics including AMOC variability limited to phases of accumulation of freshwater in the Arctic

13. The research leading to these results has received funding from the European Union 7th Framework Programme (FP7 2007-2013), under grant agreement n.308299 NACLIM www.naclim.euwww.naclim.eu