Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK CT3 Hamburg 22-23/ GEOMAR (6) Mojib Latif (CT/WP lead) Wonsun Park Thomas Martin MPG (2) Johann Jungclaus Katja Lohmann UHAM (1) Detlef Stammer Armin Köhl DMI (7) Steffen M. Olsen (CT/WP lead) Jacob L. Høyer Rasmus T. Tonboe Torben Schmith (tbc)

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 WP 3.2 Steffen M. Olsen

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 Ideal model World hindcast experiments (using the adjoint assimilation system of UHAM - an environment for climate model initialization ?) Re-start simulations with truncated ocean initial conditions corresponding to different ocean regions and observing systems

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Deliverables D9 (GEOMAR, month 12): Report on the setup of coupled model and hindcasts conducted with initial conditions corresponding to ARGO-like sampling 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 D 39 (GEOMAR, month 36): Report on hindcasts conducted with satellite information 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.

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Impact of Arctic initialization on forecast skill WP 3.2 Objectives Establish the impact of Arctic data and ini- tialization of the Arctic region on forecast skill Construct a 15 year combined SST/IST dataset for the Arctic Ocean Explore the potential to constrain the state of the Arctic Ocean by remote observations – flux monitoring system at the GSR. Methodology This WP address in detail the Arctic region of sparse data coverage. Work is organized along three parallel tracks including - ideal model experiments (data withholding, potential predictability) - improving data availability and - explore the use of remote transport measurements.

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. D27 (UHAM, month 24): Report on the documentation and description of improved model parameters. D28 (DMI, month 24): Report on the documentation and description of the new Arctic Ocean dataset combining SST and IST. 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. 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.

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Verify the list of CT3 people and their involvement – done! Explore overlapping synergies with CT1 ongoing – ongoing! Decide on the level of internal coordination and WP specific meetings in addition to the annual meetings – done! - joint activities with CT1 on overarching themes CT2 work includes a complete work package on joint model-observational data comparison (WP 2.3, UHAM+FMI). Possibilities for involvement.

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Combined satellite SST and IST for the Arctic Ocean DMI is experienced with SST and Ice Surface Temperature data processing through Eumetsat (OSI-SAF), ESA (CCI) and EU (MyOcean) projects. Arctic SST reanalysis product (1985-present) will be available from end of the year (within other project) 15 years combined SST and Ice Surface Temperature data record will be developed within NACLIM, based upon AVHRR observations. Both Level 3 (with gaps) and level 4 (gap-free) fields will be produced. Special attention will be on error characterization and uncertainties Objective: to demonstrate the impact of improved data on the forecast skills.

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Combined satellite SST and IST for the Arctic Ocean Level 3 example of Ice, Sea and Marginal Ice Zone – Surface Temperatures from METOP AVHRR References: Tonboe, R. T., Dybkjær, G. and Høyer, J. L.Simulations of the snow covered sea ice surface temperature and microwave effective temperature, Tellus, 63A, 1028–1037, 2011 Høyer, J. L., Ioanna Karagali, Gorm Dybkjær, Rasmus Tonboe, Multi sensor validation and error characteristics of Arctic satellite sea surface temperature observations, Remote Sensing of Environment, Volume 121, June 2012, Pages , ISSN , /j.rse Dybkjær, G., Høyer, J., Tonboe, R., Arctic surface temperatures from Metop AVHRR compared to in situ ocean and land data. In press, Ocean Sci., 9, doi: /osd , 2012.

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Combined satellite SST and IST for the Arctic Ocean Long term satellite datasets with uncertainties for model validation and assimilation: ice surface temperature and ice concentration

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK de Steur et al (in prep) Preindustrial

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Freshwater content relative to S=34.8 Arctic Ocean+BaffinSubpolar North AtlanticGIN Seas 8000 km 3 HistoricalRCP8.5

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK

The Arctic FW reservoir appears weakly constrained Distributions may suggest two modes? - no significant atmospheric mode identified FWC (10 3 km 3 )

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK Difference in SLP between high and low anomalies in FWC changes Changes in FWC are driven by multi annual variations in AO Results are consistent with the concept of a cumulative process of uncorrelated variability with AO constituting the signal. If so, the autocorrelation of the FWC is practically unlimited and predictable but this was not what we expected to establish… No relation – no concern !

Steffen M Olsen, Polar Oceanography, DMI, Copenhagen DK The research leading to these results has received funding from the European Union 7th Framework Programme (FP ), under grant agreement n NACLIM