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1. NACLIM: North Atlantic Climate Predictability of the Climate in the North Atlantic/European sector related to North Atlantic/Arctic Ocean temperature and sea ice variability and change Brief overview Detlef Stammer S 1

2. Overarching NACLIM Goals To quantify the uncertainty of state-of-the-art climate forecasts by evaluating the ability to model the most important oceanic and atmospheric processes in the North Atlantic and Arctic Oceans, and by comparing key quantities with observations. To optimize the present North Atlantic observation system by evaluating the impact of its components on the quality and quality control of model forecasts, and their value in determining the present ocean state and its past variability. To quantify the impact on oceanic ecosystems and on European urban societies of predicted North Atlantic/Arctic Ocean variability. To critically assess the usefulness of climate forecast parameters for stakeholders in society, politics and industry.

3. Overarching goals To quantify the uncertainty of state-of-the-art climate forecasts by evaluating the ability to model the most important oceanic and atmospheric processes in the North Atlantic and Arctic Oceans, and by comparing key quantities with observations.

4. Scientific goals Establishing the impact of an Arctic initialization on the forecast skill in the North Atlantic/European sector Quantifying the benefit of the different ocean observing system components for the initialization of decadal climate predictions Monitoring of volume, heat and fresh water transports across key sections in the North Atlantic Assessing the atmospheric predictability related to the North Atlantic/Arctic Ocean surface state Assessing the predictability and quantifying the uncertainty in forecasts of the North Atlantic/Arctic Ocean surface state Quantifying the impact of predicted North Atlantic upper ocean state changes on socio-economic systems in European urban societies Quantifying the impact of predicted North Atlantic upper ocean state changes on the oceanic ecosystem

5. UHAM Contribution to WP 1 and WP 3 Core Theme 1: Predictability of key oceanic and atmospheric quantities related to the North Atlantic/Arctic Ocean surface state • WP 1.2 Predictability of the atmosphere related to the ocean’s surface state • WP 1.3 Mechanisms of ocean surface state variability Core Theme 3: Initialization of prediction systems with ocean observations • WP 3.2 Impact of Arctic initialization on forecast skill

6. WP 1.2 Predictability of the atmosphere related to the ocean’s surface state Objectives • Identify the sea surface temperature (SST), surface salinity, and sea ice patterns that optimally influence the atmosphere in the North Atlantic/European sector on seasonal to decadal time scales and quantify their climatic impacts. • Assess the ability of climate models to reproduce these impacts, identify their potential predictability, and use observations to downscale the model predictions from global to local scales. • Quantify the impact of Arctic changes on polar meso-cyclone activity.

7. WP 2.1 UHAM Task 1.2.1 Identification of the atmospheric response to ocean surface state changes • The EU FP7 THOR adjoint assimilation system will be used to identify sensitivities of predictable elements over northern Europe, such as air temperature or precipitation, on parameters in the North Atlantic and the Arctic, such as SST, sea surface salinity or sea ice thickness and concentration. The sensitivity information will be used subsequently to unravel the processes affecting the important climate parameters over northern Europe and underlying time scales. Task 1.2.2 Attribution and assessment of the boundary forced changes • The EU FP7 THOR adjoint assimilation system will be used to identify optimal SST or freshwater perturbation patterns that can lead to maximum impact on the atmosphere (fastest growing, or singular modes). These patterns will also help to identify where observations will have a maximum impact on constraining or improving predictions.

8. WP 1.3 Mechanisms of ocean surface state variability Objectives • Characterize the time-space sea surface variability in the Arctic/North Atlantic region. • Identify the mechanisms underpinning this variability and link them to indices of variability of the ocean circulation. • Provide information on the respective roles of the atmosphere and the ocean in this variability and identify feedback mechanisms between ocean anomalies and the overlaying atmosphere.

9. WP 1.3 UHAM Task 1.3.1 Characterize the spatial patterns of the ocean surface state (sea ice, SST) variability on seasonal to decadal time scales • Large scale patterns as well as regional indices of sea ice cover (SIC) and SST variability will be retrieved based on available observations, reanalysis products and outputs of hindcast simulations. [UHAM, UPMC] • The skill of the state-of-the-art climate and ocean models and reanalyses in representing the most important patterns of surface variability will be evaluated against independent observations. [UHAM, UPMC] • Model outputs will be used to infer relationships between the observed parameters of sea ice variability (concentration, extent) and other climate relevant parameters such as the ice thickness. [UHAM, UPMC]

10. WP 1.3 UHAM Task 1.3.2 Link ocean surface state variability to key ocean quantities • Statistical relationships between the surface state changes that most influence the atmosphere on seasonal to decadal time scales (coll. with 1.2) and ocean variability will be established both in the available observations, in hindcast ocean simulations and reanalysis (the EU FP7 THOR assimilation system), and in state-of-the-art climate models. The ocean processes contributing to these relationships and to the predictability of the surface changes will be documented. [UPMC, UHAM]. • The influence of changes in the AMOC, the gyre circulation, the inflow of Atlantic and Pacific water to the Arctic, and the redistribution of heat and fresh water in the ocean surface layer will be established. The analysis will benefit from a comparison of ocean simulations at different resolutions (eddy permitting to eddy resolving) so that the impact of resolving mesoscale processes could be estimated. [UPMC, UHAM]

11. WP 3.2 Impact of Arctic initialization on forecast skill Objectives • To establish the impact of Arctic data and initialization of the Arctic region on forecast skill for the North Atlantic/European sector. • To construct a 15-year dataset of combined satellite sea surface and sea ice surface temperatures (SST and IST) covering the entire Arctic Ocean and demonstrate the data impact on forecast skill. • To explore the potential to constrain the state of the Arctic Ocean by integrating flux monitoring time series at the Greenland Scotland Ridge (GSR), which have been established in previous projects (incl. EU FP7 THOR project).

12. WP 3.2 Impact of Arctic initialization on forecast skill Task 3.2.1 Model assessment • Establish the potential to diagnose the upper Arctic freshwater reservoir from time series of ocean transports across the GSR in coupled climate models, and determine the potential impact of assimilating anomalous transport characteristics at the Ridge. [DMI, UHAM] Task 3.2.2 Improve the skill of the EU FP7 THOR adjoint assimilation system • Climate observations obtained over the Arctic Sector will be used to better constrain uncertain model parameters in the adjoint model. This step will help to better estimate initial conditions for a coupled climate model. [UHAM]

13. WP 3.2 Impact of Arctic initialization on forecast skill Task 3.2.4 Generation of perfect and real model ensembles • Perform an assimilated ensemble initializing the integrations by relaxing the ocean and sea ice state towards the time varying control state for an initial period. Five ensemble members should be considered yielding 3x5 integrations, 10 years long, in total 150 years. [DMI, UHAM] Task 3.2.5 Evaluation of predictive skill • Consider additional cases where only sea-ice or ocean stratification is withheld (2x150 years of simulations). [DMI, UHAM]

14. UHAM Workplan WP 2.1 (Reema Agarwal with help of Ion Matei) Task 1.2.1 Task 1.2.2 WP 1.3 (input from Nikolay Koldunov) Task 1.3.1 Task 1.3.2 WP 3.2 (Xueuyan Stephanie Liu) Task 3.2.1 Task 3.2.2

15. 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.eu