Update on Climate Modeling at NOAA Applied Research CentersARCs

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Update on Climate Modeling at NOAA Applied
Research Centers(ARCs)

• Anjuli Bamzai
• NOAA OAR/OGP
• Climate Dynamics Experimental Prediction(CDEP)
  Program
• CAB Mtg _at_ NSF
• Nov 6, 2003

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NOAA ARCs

• Primary Mission
• (i) Advance the Nations intraseasonal,
  seasonal-to-interannual climate prediction effort
  through development and implementation of
  forecast systems based on state-of-art coupled
  ocean-land-atmosphere climate models
• (ii) Assess decadal predictability
• (iii) Contribute towards development of
  improved models for prediction across all
  timescales
• (iv) Foster the development of new prediction
  and application techniques
• Method Sponsor a critical mass of focused RD
  on the above at a few institutions - the ARCs
• Clients Forecasters at CPC, IRI users from
  applications and assessment community e.g. RISA,
  IRI
• ARCs do NOT undertake model development per se,
  rather ARCs rely on high end modeling centers
  (GFDL, EMC, NCAR, NASA GMAO) that are then
  customized for SI prediction. ARCs scientists
  deal with issues such as ODA, multi-model
  ensembling, addressing bias and model drift,
  coupling techniques, forecasting using Tier 1,
  Tier 2 etc
  

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ARCs

• Climate Diagnostics Center (CDC - NOAA/OAR) -
  Randy Dole
• Center for Ocean - Atmosphere Prediction Studies
  (COAPS FSU) - Jim OBrien
• Center for Ocean - Land - Atmosphere Studies
  (COLA - IGES) - Jim Kinter
• Center for Science in the Earth System (CSES -
  Univ. Washington) - Ed Sarachik, Ed Miles
• Experimental Climate Prediction Center (ECPC -
  Scripps) - John Roads
• Geophysical Fluid Dynamics Laboratory (GFDL -
  NOAA/OAR) - Ants Leetmaa, Tony Rosati, Gabriel
  Lau
• NASA Seasonal-to-Interannual Prediction Project
  (NSIPP - NASA/GSFC, now GMAO) - Michele
  Rienecker, Max Suarez, Siegfried Schubert
• Environmental Modeling Center (NOAA/NWS/NCEP) -
  Steve Lord, Dave Behringer
• Modeling and Prediction Research Group
  (International Research Institute for Climate
  Prediction) - Steve Zebiak

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Integrated Approach to SI Prediction

• Experimental Prediction
• Model and Forecast System Development
• Diagnostics and Attribution
• Regional Applications
• 5. Predictability research, Reanalysis,
  Paleoclimate, long term climate change

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Integrated Approach to SI Prediction 1.
Experimental Prediction

• There is a scientific basis to expect
  improvements in the quality and precision of
  forecasts from a week to a season in advance.
• Beyond about 10 days, major source for
  predictive skill is tropical heating.
• Both the analysis and forecast probability
  distributions can be improved by advances in
  ensemble predictions and data assimilation.
• Accurate estimation of extreme event risks will
  require large ensembles of model runs.

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Integrated Approach to SI Prediction

• 2. Model and Forecast System Development
• Global and Regional Models
• Leverage on base capabilities at high-end
  modeling centers (EMC, GFDL, NASA, NCAR)
• Common Modeling Framework - ESMF
• Global Forecast Systems
• IRI, NCEP/EMC, GFDL, NASA/GMAO, COLA all in Tier
  1 Tier2
• ECPC GFM, CAM Presently only in Tier 2
• Multi-model ensemble forecast system
• Sub seasonal forecast system
• Multiple-Physics Ensemble
• Recent EMC fully coupled model results look
  promising
• Ocean model initialization via data assimilation
• ODASI consortium

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ODASI ConsortiumCOLA GFDL IRI LDEO NCEP/EMC
NASA/GMAO

• Intercomparisons (models, methodologies,
  assimilation parameters)
• Infrastructure (observational data streams)
• Sensitivity experiments
• Validation (forecast experiments)
• Expert Team for the Ocean Observing System
  (Mike Johnsons Climate Obs. program).

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IRI Example Dynamical Multi-model Forecast System
ATMOSPHERE
HISTORICAL DATA Extended simulations Observation
s
SST
GLOBAL ATMOSPHERIC MODELS ECHAM4.5(MPI) NCEP
(MRF9) CCM3.2(NCAR) NASA GMAO COLA2.x
PERSISTED GLOBAL SST
Persisted SST Ensembles 3 Mo. lead
POST PROCESSING -Multimodel Ensembling
FORECAST SST TROP. PACIFIC (NCEP
dynamical) TROP. ATL, INDIAN (statistical) EXTRATR
OPICAL (damped persistence)
Forecast SST Ensembles 3/6 Mo. lead
REGIONAL MODELS
AGCM INITIAL CONDITIONS UPDATED ENSEMBLES
(10) WITH OBSERVED SST
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Integrated Approach

• 3. Diagnostics and Attribution (SI timescales)
• Climate model diagnostics consortium
• CDC, NASA/GMAO, IRI, CPC, EMC, COLA,
  ECPC, GFDL)
• - Attribution for the observed seasonal
  climate anomalies and assessment of their
  potential predictability from SSTs
• - Comparison of atmospheric responses to SSTs
  for different AGCMs
• - Diagnostics analysis of AGCM biases and
  their atmospheric responses to SSTs

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Comparing Surface Temperature Anomalies for DJF
2002/03
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Integrated Approach4. Regional Applications

• Agriculture (SE Climate consortium)
• Fire weather (SIO/ECPC)
• Water resource (GAPP, SIO/CAP, UW/CSES,
  CDC)
• Fishery and ecosystem (UW/CSES)
• Extremes (e.g., Drought several ARCs)
• Western Water Assessment (CDC)

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RISA Project Locations (in black)
ARCs-RISA collaboration (in blue) CAP, CDC,
COAPS, CSES
Water resources management fisheries, drought,
snowpack fire prediction,
Pacific Northwest
western water assessment, water policy, snowpack,
drought
CDC
CLIMAS
California Applications Project
fire forecasting health, drought stream flow fcst
SE Climate Consortium
agricultural crop modeling extension development,
drought fire forecasting, frost-freeze
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Reservoir Management Decision Calendar Water
Year Planning

Next Water Year
Planning
Jan
Feb
Mar
Apr
May
Jun
July
Aug
Sep
Oct
Oct
Nov
Dec
Aug
Sep
Andrea J. Ray, Robert S. Webb, John D. Wiener,
2001 Photos US Bureau of Reclamation,
NOAA-CIRES Western Water Assessment
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Challenges

• Develop a paradigm for climate forecasts
  services for the Nation through better forecasts
  as well as improved regional climate applications
• RD effort towards new/ improved ISIP
  products (fire forecasting, drought forecasting,
  extended stream flow prediction, agriculture)
• Research/Resource Issues
• Improved Skill potential predictability, trends
  and SI predictability in a changing climate
• Improved climate information integrate science
  communities. Facilitate two-way communication
  with users (through partnerships with CPC, IRI,
  RISA, etc)
• Improved component models, computing resources to
  run these in higher resolution, more ensembles to
  better resolve pdf

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S-I Prediction Skill Whats Missing?
Models are plagued by systematic errors that may
be masking predictability and are failing to
predict situations that are known to be
predictable. A major effort is needed to 1.
Make better use of the current generation of
models (e.g., multi-model approach, interactive
ensembles) 2. Improve the models (e.g.,
empirical correction or improved
parameterizations through innovative use of
observational data) Predictions are overwhelmed
by initial shock and model climate drift. A major
effort is needed to 1. Develop appropriate
initialization strategies that can address the
problem of initial shock 2. Make optimal use of
observational data to limit the effects of model
climate drift
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