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Tolman March 17, 2015 YOPP webinar, 1/8
Sea ice at NCEP/EMCYOPP report out,
with special thanks to Bob Grumbine
Hendrik L. TolmanDirector, Environmental Modeling CenterNOAA / NWS / NCEP
Tolman March 17, 2015 YOPP 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.
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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.
Tolman March 17, 2015 YOPP 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)
Tolman March 17, 2015 YOPP 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.
Tolman March 17, 2015 YOPP 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).
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Prototype model plan
Months Activities
1-2 Set up NMMB, HYCOM, static ice “solo” in NEMS.
archive based flux biases Ice in
ESMF3-4
5-6 Build and validate deterministic coupled system with flux bias correction for 5-7 day
forecast
Validation metrics
7-8
KISS v29-10
11-12
13-14Setup ensemble system
15-16
17-18
Test, validate and calibrate ensemble system19-20
21-22
23-24 Coupled demonstration system, ( day 10+ ?)
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