1. Earth System Prediction Capability ESPC ESPC Demonstrations 1 Extreme Weather Events: Predictability of Blocking Events and Related High Impact Weather at Lead Times of 1-6 Weeks (Stan Benjamin, ESRL) Seasonal Tropical Cyclone Threat: Predictability of Tropical Cyclone Likelihood, Mean Track, and Intensity from Weekly to Seasonal Timescales (Melinda Peng, NRL MRY) Arctic Sea Ice Extent and Seasonal Ice Free Dates: Predictability from Weekly to Seasonal Timescales (Phil Jones, LANL) Coastal Seas: Predictability of Circulation, Hypoxia, and Harmful Algal Blooms at Lead Times of 1-6 Weeks (Gregg Jacobs, NRL SSC) Open Ocean: Predictability of the Atlantic Meridional Overturning Circulation (AMOC) from Monthly to Decadal Timescales for Improved Weather and Climate Forecasts (Jim Richman, NRL SSC)

2. Earth System Prediction Capability ESPC Extreme Weather Events: Predictability of Blocking Events and Related High Impact Weather at Lead Times of 1-6 Weeks Challenges & Approach Objectives and ThrustsSchedule and Key Performers Stan Benjamin Chief, Assimilation and Modeling Branch, GSD, Earth Systems Research Laboratory, 325 Broadway, Boulder, CO (303)497-6387 Stan.benjamin@noaa.govStan.benjamin@noaa.gov NOAA/ESRL, NOAA/CPC, NASA/GISS, NRL/MRY, DOE/LLNL Year 1: Identify participating groups and experimental coupled model projects. Year 2: Coordinate workshops and develop common cases studies, skill metrics, output criteria, data management and analysis plans. Challenges: Several possible causes are postulated for onset and cessation, each with their own sources of predictability and technical approach. These include MJO interaction, ET wave configurations, TCs with strong extratropical transition (NH fall), SSW events, and early season snow cover or melting. Approach: Current NCEP blocking index: Tibaldi and Molteni (1990, Tellus) Proposed Θ-PV blocking index: Pelly and Hoskins (2003, J. Atmos. Sci.) Develop time-integrated blocking statistics Develop additional skill metrics for 6-month coupled model forecasts Reinvestigate predictability of statistics of blocking (not individual events) with higher-resolution coupled models and assess root causes Accomplishments FY13 Start leveraging ongoing work at contributing agencies Objectives: Application of current understanding of the blocking process to the assessment of current diagnostic tools applicable to both the initial (or analyzed or reanalyzed) and model predicted states. Thrusts: Diagnosing longer-term weather anomalies from atmospheric blocking - defined here as either ridge or trough quasi-stationary events with duration of at least 4 days to 2+ months Predicting seasonal statistics (below/normal/above average conditions) at various lead times up to six months. Predicting individual events (onset/ persistence, structure/ cessation) Predicting Outcomes (floods, droughts, fires, extreme temps, snow/ice)

3. Earth System Prediction Capability ESPC Seasonal Tropical Cyclone Threat: Predictability of Tropical Cyclone Likelihood, Mean Track, and Intensity from Weekly to Seasonal Timescales Challenges & Approach Objectives and ThrustsSchedule and Key Performers Melinda Peng, Atmospheric Dynamics and Prediction Branch Code 7530, Marine Meteorology Division, Naval Research Laboratory, #7 Grace Hopper Ave, Monterey, CA 93943 (831) 656-4704 melinda.peng@nrlmry.navy.milmelinda.peng@nrlmry.navy.mil Extend weather prediction to monthly prediction with ocean coupling (NRL, NCEP/EMC, NCAR) Apply higher-resolution climate prediction system to seasonal predictions (NCAR, GFDL, LANL, NCEP/CPC, IRI, NASA) Investigate seasonal prediction capability with new, innovative global systems (NRL, GFDL, ESRL, NCAR) Year 1: Assess skill variability of current seasonal predictions globally. Year 2: Coordinate workshops and develop common cases studies, skill metrics, output criteria, data management and analysis plans. Challenges: The number and regional distribution of landfalling cyclones is largely controlled by large-scale circulations such as the subtropical high, monsoon trough, etc. which carry forward their own predictability characteristics, and the intensity of the landfalling TC determines its impact on coastal regions. Many coupled global systems have large biases in different geographic regions and in different component models. Approach: While there are many viable statistic models for seasonal prediction of TCs, this demonstration system will focus on dynamic models only and will rely on ensemble techniques to provide conditional probabilities. Future efforts will combine ensemble and statistical guidance with the dynamic model forecasts. Accomplishments FY13 Start leveraging ongoing work at contributing agencies Objectives: Prediction of ocean basin scale tropical cyclone genesis events, track distributions, and potential intensity range at seasonal timescales. Thrusts: Initial value, short range prediction improvements for track and structure. Boundary value, longer range probabilistic forecasts of maximum likelihood genesis, track, intensity weekly and longer statistics. Landfall probability with the accompanying potential intensity and precipitation. This will provide information for resource management including advanced evacuation plans, ship routing, dam and river runoff control, and utility planning. Design of ensemble and multi-model ensemble systems. Goswami et al (2003) Maloney and Hartmann (2000)

4. Earth System Prediction Capability ESPC Arctic Sea Ice Extent and Seasonal Ice Free Dates: Predictability from Weekly to Seasonal Timescales Challenges & Approach Objectives and ThrustsSchedule and Key Performers Phil Jones, Climate, Ocean and Sea Ice Modeling T-3 MS B216 Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545 505-500-2699 pwjones@lanl.govpwjones@lanl.gov CCSM-team (UW-PSC, NCAR, LANL), SEARCH collaborators, AOMIP collaborators, NRL-SSC, NRL-MRY, ESRL, GFDL, NASA, NSIDC. Year 1: Identify participating groups and experimental coupled model projects. Year 2: Coordinate workshops and develop common cases studies, skill metrics, output criteria, data management and analysis plans. Challenges: While models reproduce historical record well when forced with observations (reanalysis) in a bulk sense, the level of fidelity needed for Arctic shipping and other observations is poorly characterized. Hints of predictability at longer time scales (1-2 years) as a forced problem but requires further definition of skill metrics. Predictability may be greatly reduced in likely future thin ice regime. Approach: Repeat analysis of existing CCSM Arctic perfect model ensembles with focus on different regions of Arctic (previous study was basin-wide). Perform perturbed ensembles of retrospective (hindcast) studies initialized to different historical initial conditions and quantify predictability against historical record. Use CCSM/CESM in fully coupled mode with high fidelity ocean or slab ocean mode to assess underlying processes. Accomplishments FY13 Start leveraging ongoing work at contributing agencies Objectives: Further explore limits of predictability of sea ice extent and volume, and freeze and melt onset dates, at 3-12 month lead times. Extend prediction to regional scale areas of interest (e.g. Northern and Northwest passages). Extend forecast variables to other ice and atmosphere properties such as ice thickness and movement, marginal ice zone, snow, fog, etc. Thrusts: Adequacy of current sea ice models that produce accurate hindcasts for use as forecast models when forcing is less well characterized. Predictability and suitability of different approaches at different forecast timescales. Explicit and ensemble prediction as ice thins and system memory and persistence is reduced.

5. Earth System Prediction Capability ESPC Coastal Seas: Predictability of Circulation, Hypoxia, and Harmful Algal Blooms at Lead Times of 1-6 Weeks Challenges & Approach Objectives and ThrustsSchedule and Key Performers Gregg Jacobs, Oceanography Division, Code 7320, Bldg. 1009, Naval Research Laboratory, Stennis Space Center, MS 39529 (228) 688-4720 Gregg.jacobs@nrlssc.navy.milGregg.jacobs@nrlssc.navy.mil NRL/SSC, NOAA/NOS/NCCOS, NOAA/GFDL, TAMU, MSU, LUMCON, Dalhousie U., NCSU, VIMS, Rutgers, WHOI, NCAR/NESL /CGD, UMD/CES Year 1: Identify participating groups and experimental coupled model projects. Assess statistical and dynamical approaches, applicability for U.S. and global forecast problems. Year 2: Coordinate workshops and develop common cases studies, skill metrics, output criteria, data management and analysis plans. Challenges: Rainfall/ runoff residence times and nutrient loading changes from watershed to coastal waters is not well characterized in forecast models. Fresh water enhancement of coastal seas stratification and hypoxia is difficult to efficiently represent numerically in a unified vertical coordinate system. Upwelling, driven by 3-dimensional air and ocean circulations, and modified by waves, bathymetry, and topography, also a major cause of HABs and hypoxia. Approach: Weekly to monthly relevant physical processes can be dynamically predicted linked to global systems such as blocking, MJO and ENSO. Seasonal conditions can be statistically predictable where good historical records exist. Accomplishments FY13 Start leveraging ongoing work at contributing agencies Objectives: To establish, at a range of lead times beyond present synoptic weather and ocean prediction scales, the forecast skill in Harmful Algal Blooms (HABs) and hypoxia. Linkage from physical conditions to HABs and hypoxia, and thus predictability, varies across different regions. Thrusts: Physical earth system effects currently represented in global forecasts that lead to conditions conducive to HABS and hypoxia. Impacts of the globally forecasted conditions leading to local conditions particular to areas in which HABS and hypoxia are significant concerns (downscaling). Impacts of globally forecasted effects on local area biology and chemistry.

6. Earth System Prediction Capability ESPC Open Ocean: Predictability of the Atlantic Meridional Overturning Circulation (AMOC) from Monthly to Decadal Timescales for Improved Weather and Climate Forecasts Challenges & Approach Objectives and ThrustsSchedule and Key Performers Jim Richman, Oceanography Division, Code 7323, Bldg. 1009, Naval Research Laboratory, Stennis Space Center, MS 39529 (228) 688-4933 james.richman@nrlssc.navy.miljames.richman@nrlssc.navy.mil NRL/SSC, NRL/MRY, CESM, NCAR, LANL, UCSD-SIO, NASA/GISS, NCEP, US AMOC Science Team, Duke U., AOML, NOAA /GFDL, WHOI, Texas A&M Year 1: Identify participating groups and experimental coupled model projects. Year 2: Coordinate workshops and develop common cases studies, skill metrics, output criteria, data management and analysis plans. Challenges: It is not clear what is predictable about the AMOC. The AMOC is thought to be an important driver for the oceanic meridional heat flux and sea surface temperature, although the link between the AMOC and climate is not clear. Recent climate model studies have shown a slowdown in the AMOC with possible impacts on European regional seasonal climate, ENSO and hurricanes in the Atlantic Ocean. Approach: Leverage existing USGCRP and IPCC AR5 simulations assessing AMOC and meridional mass, heat and salt transport Additionally assess ocean reanalyses fields against predictions at various timescales from the Estimating the Circulation and Climate of the Ocean (ECCO) and HYCOM groups for the strength and depth of the AMOC along with the variability and trends. Accomplishments FY13 Start leveraging ongoing work at contributing agencies Objectives: Assess predictability of basin-scale three-dimensional ocean circulation from monthly to decadal timescales using the RAPID dataset as validating observations. Thrusts: Build upon the existing IPCC AR5 experiments to assess basic predictability of the net transport and sensitivity to forcing. Conduct high resolution coupled model simulations to look at detailed structure and air-ocean feedback. Conduct close collaboration with observational community to identify knowledge gaps in underlying processes and design new field efforts.