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Coupled Modeling for Seasonal to Interannual Prediction

Explore the operational system of coupled modeling for seasonal to interannual prediction presented by Suru Saha from EMC/NCEP, including primary stakeholders, system attributes, and future system evolution plans.

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Coupled Modeling for Seasonal to Interannual Prediction

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  1. Coupled Modeling for Seasonal to Interannual Presented By Suru Saha (EMC/NCEP) Contributors: Jack Woollen, Daryl Kleist, Dave Behringer, Steve Penny, Xingren Wu, Bob Grumbine, Mike Ek, Jiarui Dong, ShrinivasMoorthi, Xu Li, Sarah Lu, Yu-Tai Hou, Arun Chawla, Henrique Alves (all EMC)

  2. Operational System Attribute(s) System Data Assimilation or Initialization Technique

  3. Why System(s) are Operational • Primary stakeholders and requirement drivers • The need for seasonal prediction is great , given societal vulnerabilities, such as: • the impact of El Nino on the prediction of US temperature and precipitation (especially, since this winter 2015/16 may shape up to be a major event) • the drought community, which require accurate predictions for water management purposes • industry, such as energy, transportation, etc. • Participation in the National and International MME • What products are the models contributing to? • At CPC: Global Hazards Assessment, Seasonal Prediction (lead 0.5-12.5 months) of US T&P and global SST, ENSO Diagnostic Discussion, MJO, Drought Outlook, African Desk products, Seasonal Hurricane Outlooks, Arctic Sea-ice prediction. • Outside NCEP: Commercial, Regional Climate Centers, etc. • What product aspects are you trying to improve with your development plans? • Better guidance through improved forecast skill. • Increased availability of products. • Top 3 System Performance Strengths • Strength 1: ENSO Prediction (6-month, Nino 3.4 SST) • Strength 2: MJO Prediction out to 45 days • Strength 3: Prediction of Global SST and oceanic precipitation • Top 3 System Performance Challenges • Challenge 1 : Keeping up with constant changes in the observing system • Challenge 2: Keeping up with changing CO2 concentrations in the system • Challenge 3: Keeping up with changing computer systems

  4. System Evolution Over the Next 5 Years • Major forcing factors • We need a unified approach to global modeling for all spatial and temporal resolutions for better management of modeling activities at NCEP. • Science and development priorities • UGCS Unified Global Coupled System • Atmosphere-Land-Ocean-Seaice-Waves-Aerosol-Ionosphere interactions • Timely Reanalysis and Reforecast for each implementation • What are you top challenges to evolving the system(s) to meet stakeholder requirements? • Challenge 1 : Using a unified hybrid ensemble approach to DA of the coupled system • Challenge 2 : Testing the following models within the NEMS framework: • MOM5.1/MOM6 and/or HYCOM for the ocean • SIS2/CICE/KISS for sea-ice • WAVEWATCH III for waves • Noah-MP for land • GOCART for aerosol • WAM for ionosphere • Challenge 3 : Making the next CFS a community model • Potential opportunities for simplification going forward • Unified global modeling allows for better use of resources with respect to core / physics / coupling development, but is can we unified on the different scales involved.

  5. Top 3 Things You Need From the UMAC Help with finding the required resources for: Computing Archival Dissemination Where and how should this system be run. Should a focus be on unified global modeling, and how can this e approached as a community modeling effort.

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