1. 1 Agenda Topic: Sea Ice Modeling Presented By: Bob Grumbine (NWS/NCEP) Contributors: Hendrik Tolman

2. 2 Operational System Attribute(s) System NameAcronymAreal Coverage Horz Res Cycle Freq Fcst Length (hr) Sea ice drift modelicedriftArctic25km116d Ice concentration analysisiceconc(ant)arctic1/121N/A CFS-v2 ice modelSISGlobal4various RTOFS-Global ice model (Oct 2015)CICEGlobal1/1218d SystemAttributes iceconc Blended product mainly using SSMI-(S) data. CFS-v2 Using ice concentration analyses as initialization System Data Assimilation or Initialization Technique

3. 3 Why System(s) are Operational  Primary stakeholders and requirement drivers Traditionally AK region and OPC. NIC less interested in modeling, more in analysis, this started changing in 2007. Emerging Arctic service requirements.  What products are the models contributing to? Stand-alone products for service centers (concentration). Ice in most weather, wave and ocean models.  What product aspects are you trying to improve with your development plans? Going from analysis products to full ice modeling (SUS, CICE, KISS). Weather scale ice predictability. Monthly-seasonal ice outlooks (thaw-out, freeze in).  Top 3 System Performance Strengths Ice analyses have been used world wide for 20+ years. Drift model “virtual ice edge” = skillfull ice edge prediction.  Top 3 System Performance Challenges Availability of appropriate ice metrics. Ice predictability on short time scales. Ice model issues may limit benefits of global atmosphere – ocean coupling

4. 4 System Evolution Over the Next 5 Years  Major forcing factors Emerging requirements for Arctic services. YOPP and PPP. NGGPS and coupled modeling.  Science and development priorities YOPP / PPP / NGGPS coupled regional Arctic ensemble development (ocean – atmosphere – ice – [waves] ) Coupled ice for Lake-effect weather (GLERL-NOS focus)  What are you top challenges to evolving the system(s) to meet stakeholder requirements? Science focus on ice physics, not on predictability. On weather time scales, ice edge predictability is abbismal. Predictability at week 2, 1 month, seasonal ?  Potential opportunities for simplification going forward NGGPS coupled experiment on regional model to effectively use resource. Building in NEMS  move techniques into unified global modeling system in FY18 and beyond as appropriate. Merging aspects of SIS – CICE – KISS.

5. 5 Top 3 Things You Need From the UMAC 1.Ice modeling becomes more essential with focus on coupled atmosphere – ocean modeling. Ice needs an advocate to properly resource as it is often seen as a “non-core” requirement for the NWS. 2.Push community efforts Physical-model based ice products and forecast guidance, away from observational focus. Predictability research to complement ice physics research. Development of appropriate ice metrics. 3.Assessment of ice needs / place in coupled modeling approaches.