1. Tolman March 17, 2015YOPP webinar, 1/8 Sea ice at NCEP/EMC YOPP report out, with special thanks to Bob Grumbine Hendrik L. Tolman Director, Environmental Modeling Center NOAA / NWS / NCEP Hendrik.Tolman@NOAA.gov

2. Tolman March 17, 2015YOPP webinar, 2/8 Overview  Products versus modeling OPC need for real-time Arctic Services. CPC outlook for ice.  Modeling and analysis. Ice concentration analysis  Since 1997, 1/12° resolution.  Used as model input. Ice drift model  Since 1978, 16 day forecast  Used by FWO Anchorage.

3. Tolman March 17, 2015YOPP webinar, 3/8 Ice modeling  Present ice in models at NCEP: NAM: ice/no ice field (constant in forecast), moving to ice concentration. GFS: ice thickness evolves, concentration fixed, no velocity. CFS-v2: ice thickness, concentration and velocity evolve.  Post-processing by CPC for seasonal products. WAVEWATCH III: constant ice concentration as model input.  Model allows for evolving ice input. RTOFS/HYCOM: Global: energy loan sea ice model. Arctic Cap Nowcast Forecast System (ACNFS, NAVO/NRL, data available at NCEP) Los Alamos CICE model two-way coupled to HYCOM.

4. Tolman March 17, 2015YOPP webinar, 4/8 RTOFS-Global  RTOFS-Global Arctic cap model with CICE code will be integrated with RTOFS-Global, when this model is updated to Navy GLOFS 3.1  Better ice model, buy  Still very limited skill in short term forecast.  In-house development of KISS model (Keep Ice’S Simplicity)

5. Tolman March 17, 2015YOPP webinar, 5/8 Ice modeling  In the pipeline: KISS.  V0: (2012) concentration and thickness fixed (e.g., GFS).  V1: (2013) velocity from drift model, thickness and concentration evolve with thermodynamics only.  V2: (2014+) ice advection, thickness classes.  Justification for developing KISS: Predictability strongly linked to thermodynamics, secondary to ice drift.  Sea ice drift model (virtual) ice edge at 72h forecast is as accurate as ACFNM full ice model at 24h forecast.

6. Tolman March 17, 2015YOPP webinar, 6/8 Ice model development  Key elements for ice modeling / predictability: Coupled problem ocean-ice-atmosphere.  See Canadian experience for Gulf of St. Lawrence. Need to control flux biases in coupled system.  10 W/m2 bias grows/thaws 1m ice per year! Ensemble should improve predictability, as random flux errors are averaged out. Metrics need to be developed to make validation relevant to real-world users.  Tentative STI-R2O funding for two year project. EMC to build model with above features (regional  global). Partnering with GFDL (ice models, validation).

7. Tolman March 17, 2015YOPP webinar, 7/8 Prototype model plan MonthsActivities 1-2 Set up NMMB, HYCOM, static ice “solo” in NEMS. archive based flux biases Ice in ESMF 3-4 5-6 Build and validate deterministic coupled system with flux bias correction for 5-7 day forecast Validation metrics 7-8 KISS v2 9-10 11-12 13-14 Setup ensemble system 15-16 17-18 Test, validate and calibrate ensemble system 19-20 21-22 23-24Coupled demonstration system, (  day 10+ ?)

8. Tolman March 17, 2015YOPP webinar, 8/8