U.S. Navy Global Ocean Forecast System (GOFS)

Transcription

1 U.S. Navy Global Ocean Forecast System (GOFS) Patrick Hogan, Eric Chassignet, James Cummings GODAE OceanView (GOVST) GOVST-IV Meeting 5-9 November 2012 Rio De Janerio, Brazil

2 HYCOM-NCODA Prediction Systems 1/12 Global 1/25 Gulf of Mexico 1/12 Arctic Cap Available at via OPenDAP or LAS Global 1/12 Analysis: 2003 present Gulf of Mexico 1/25 Analysis: present

3 Global Ocean Forecast System (GOFS) GOFS 2.6 1/8 Navy Coastal Ocean Model (NCOM (Global configuration) GOFS 3.0 1/12 Hybrid Coordinate Ocean Model (HYCOM) + NCODA OI GOFS 3.05 GOFS 3.0 with NCODA 3DVar and CICE GOFS 3.1 GOFS 3.05 with ISOP instead of MODAS GOFS 3.5 1/25 GOFS 3.1 with tides GOFS 4.0 Coupled GOFS 3.5 ocean-wwiii (waves) ESPC (Earth System Prediction Capability)

4 Large Scale Prediction The Nowcast/Forecast Systems Global Ocean Forecast System (GOFS) 3.0 1/12 global HYCOM/NCODA NCODA-MVOI MODAS synthetics Energy-loan ice model GOFS /12 global HYCOM/NCODA NCODA-3DVAR MODAS synthetics Two-way coupled to Los Alamos CICE

5 GOFS 3.1 Large Scale Prediction 1/12 global HYCOM/NCODA NCODA-3DVAR Improved Synthetic Ocean Profiles (ISOP) Two-way coupled to Los Alamos CICE GOFS 3.5 The Nowcast/Forecast Systems 1/25 global HYCOM/NCODA NCODA-3DVAR Improved Synthetic Ocean Profiles (ISOP) Two-way coupled to Los Alamos CICE

6 Why 1/25 horizontal resolution for global HYCOM? Surface EKE (cm 2 /s 2 ) Drifters 1/25 non-assim 1/12 non-assim Doubling the resolution from 1/12 to 1/25 in HYCOM increases surface EKE to levels comparable to drifters (left), and deep EKE and KEM are also increased to levels consistent with deep current meters (Thoppil et al., 2010)

7 Real-time 1/12 Arctic Cap HYCOM/CICE/NCODA Nowcast/Forecast System Community Ice CodE (CICE) HYbrid Coordinate Ocean Model (HYCOM) Hourly coupling the Earth System Modeling Framework (ESMF) 1/12 bipolar horizontal grid pole ward of 40 N. ~ 3.5 km near the North Pole and 6.5 km near 40 N Ice thickness, ice concentration, ice speed and drift in addition to ocean variables. Atmospheric 3-hour 0.5 NOGAPS forcing NCODA 3DVar (ice concentration in addition to ocean observations)

8 Nome Alaska Oil Resupply Mission Used ACNFS Products for Guidance Last seasonal fuel barge was delayed getting into Nome, AK due to an early November 2011 winter storm Russian ice breaking tanker was contracted to deliver 1.3 million gallons of fuel Coast Guard ice breaker Healy was used to provide an escort through the ice ACNFS nowcasts and forecasts were used by National Weather Service (Anchorage) to provide guidance for the convoy ACNFS ice thickness (m) 13 January 2012

9 Los Alamos CICE model was successfully two-way coupled with global HYCOM Global HYCOM/NCODA/CICE Arctic Cap Nowcast/Forecast System that has recently passed Operational Testing Animation of ice thickness (m) for 1 to 30 January 2012 Overlaid black line is the independent National Ice Center ice edge This is a prototype for Global Ocean Forecast System (GOFS) 3.1

10 Global HYCOM/NCODA/CICE in the southern hemisphere Sea ice concentration (%) around Antarctica near extreme ice extents 22 July winter 1 January summer Overlaid black line is the independent National Ice Center ice edge

11 M 2 barotropic tidal elevation: TPXO vs 1/12 Global HYCOM TPXO cm HYCOM Lines of constant phase (overlaid in white) are plotted every 45 The accuracy of HYCOM barotropic tides is comparable to other non-assimilative shallow water tide models (Shriver et al., 2012; JGR-O)

12 M 2 barotropic tidal elevation error: TPXO vs 1/12 Global HYCOM Total error Due to errors in tidal amplitude Due to errors in tidal phase Barotropic tides interacting with bathymetry in a stratified ocean generate the internal tides Phase errors in the barotropic tide are large in the strong internal tide generation regions in the Pacific which help to explain why the tide amplitudes agree well but the RMS error is large (Shriver et al., 2012; JGR-O)

13 M 2 internal tide amplitude: along-track altimetry data vs 1/12 Global HYCOM Altimetry-based analysis cm HYCOM The black boxes denote key generation regions. The amplitude of the internal tide in HYCOM compares well with the altimeter observations However, the RMS error is approximately 50% of the amplitude (Shriver et al., 2012; JGR-O)

14 Skill Score RMS Error Improvements underway for the tides in HYCOM Current Model New Southern Ocean BC A large difference between the data-assimilative TPXO model and HYCOM is the treatment of the floating ice shelves around Antarctica Using the TPXO tides as a boundary condition at the floating ice shelves reduces the rms difference (a and b) and improves the skill (c and d) over much of the globe, not just the Southern Ocean.

15 Demonstrated HYCOM/NCODA with tidal forcing on 1/12 domain August 2008 animation of the daily variance of hourly steric SSH Tides no data assimilation Data assimilation - no tides Data assimilation with tides Transient waves from the insertion of NCODA analysis increments Strong generation of internal tides at hot spots that can propagate 1000s of km away from generation regions need a global model with tides

16 Demonstrated HYCOM/NCODA with tidal forcing on 1/12 domain August 2008 animation of differences between the variances of steric SSH of (tides only + DA without tides) minus (DA with tides) Data assimilation does not appear to be adversely affecting the tidal solution Tides do not adversely affect the large scale circulation

17 Ensemble Evaluation 8 different global simulations that differed by some parameter setting or technique. For example, 5 used Cooper-Haines, 3 used MODAS synthetics. A couple used 35 layers instead of 27. Some used an updated version of NCODA and one used mixed layer depth to modify the MODAS synthetic, etc.

18 SSH: Global Ensemble Variance vs. Time Variance 18

19 SSS: Global Ensemble Variance vs. Time Variance

20 SST: Global Ensemble Variance vs. Time Variance

21 What is Earth System Prediction Capability (ESPC)? Coupled global analysis and prediction framework at accuracies and timescales beyond traditional synoptic weather forecasts. More than just a model. An approach towards advanced understanding and systems-based prediction leveraging multiple U.S. national efforts

22 ESPC (Earth System Prediction Capability) Approach Improve model physics through Coupled modeling Improved parameterizations Improve data assimilation through Joint observational retrievals New hybrid DA approaches Increase forecast information through Stochastic prediction National multi-model ensembles Seamless prediction Increase system resolution affordably through Efficient computational architectures Efficient numerics/ discretization

23 ESPC Demonstrations (Proposed Titles) Extreme Weather Events: Predictability of Blocking Events and 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) 23

24 Arctic Sea Ice Extent and Seasonal Ice Free Dates: Predictability from Weekly to Seasonal Timescales Objectives and Thrusts 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. Challenges & Approach 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. Schedule 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 pwjones@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. Accomplishments FY13 Start leveraging ongoing work at contributing agencies 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.

25 Open Ocean: Predictability of the Atlantic Meridional Overturning Circulation (AMOC) from Monthly to Decadal Timescales for Improved Weather and Climate Forecasts Objectives and Thrusts 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. Challenges & Approach 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. Schedule and Key Performers Jim Richman, Oceanography Division, Code 7323, Bldg. 1009, Naval Research Laboratory, Stennis Space Center, MS (228) james.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. Accomplishments FY13 Start leveraging ongoing work at contributing agencies Approach: Leverage existing USGCRP and IPCC AR5 simulations assessing AMOC and meridional mass, heat and salt transport Additionally assess ocean reanalysis 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.

26 Thanks!