1. Update on Climate Modeling at NOAA Applied Research Centers(ARCs)
Anjuli Bamzai
NOAA OAR/OGP
Climate Dynamics & Experimental Prediction(CDEP) Program
CAB NSF
Nov 6, 2003
CSES: Center for Science in the Earth System

2. NOAA ARCs Primary Mission:
(i) Advance the Nation’s 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 R&D 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

3. ARCs
Climate Diagnostics Center (CDC - NOAA/OAR) - Randy Dole
Center for Ocean - Atmosphere Prediction Studies (COAPS FSU) - Jim O’Brien
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

4. 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

5. 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.

6. 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

7. ODASI Consortium COLA 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 Johnson’s Climate Obs. program).

8. IRI Example: Dynamical Multi-model Forecast System
ATMOSPHERE
SST
HISTORICAL DATA
Extended simulations
Observations
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)
EXTRATROPICAL
(damped persistence)
NOAA climate operating plan: Experimental Product - FY2003
Forecast
SST
Ensembles
3/6 Mo. lead
REGIONAL
MODELS
AGCM INITIAL CONDITIONS
UPDATED ENSEMBLES (10+)
WITH OBSERVED SST

9. 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

10. Comparing Surface Temperature Anomalies for DJF 2002/03

11. Integrated Approach 4. 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)
Close linkage/coordination between CDEP (Physical Science) and RISA (Regional Applications Research)
Several ARCs are also part of RISAs (CSES, CDC, CAP)

12. 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
California Applications Project
CLIMAS
fire forecasting
health, drought
stream flow fcst
SE Climate Consortium
agricultural crop modeling
extension development, drought
fire forecasting, frost-freeze

13. Reservoir Management Decision Calendar
Water Year Planning Next Water Year Planning
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
July
Aug
Sep
Oct
Provide for late Summer/early Fall irrigation while maintaining target flows
Next water year runoff unknown, reserve water until February snowpack data
Winter season precipitation forecast for Fall release decisions
Winter releases based on Jan/Feb snowpack data
Winter/Spring forecast for Winter release decisions
Peak Flow Augmentation fill curve
Summer season forecast for Peak Augmentation planning
This is a reservoir management decision calendar for Upper Colorado reservoir managers
Management of water represents a formidable challenge. Managers must retain as much water as possible in reservoirs to meet the traditional needs of irrigation, hydropower generation, and domestic consumption as well as emerging needs such as an adequate water supply for recreational uses, water quality standards, regulations for maintenance of aquatic ecosystems, and the special needs for the protection of threatened or endangered species. Reservoir space must also be maintained to protect downstream homes, farms, and businesses from flooding.
Water year instead of calendar year
Different types of forecasts during year with different scales
When information is important and where science can have most impact
Research issues: hydrologic flows and endangered species; southwest monsoon prediction; river-basin hydro-meteorology
Week 2 forecasts for Peak Augmentation
Peak Flow Augmentation releases
Plan releases for Summer irrigation & hydropower
Week 2 forecasts for Summer irrigation & hydropower release decisions
Provide for Summer irrigation & hydropower needs while maintaining target flows
Planning processes
Operational issues
Climate & weather forecasts
Andrea J. Ray, Robert S. Webb, John D. Wiener, 2001
Photos: US Bureau of Reclamation, NOAA-CIRES Western Water Assessment

14. Challenges
Develop a paradigm for climate forecasts & services for the Nation through better forecasts as well as improved regional climate applications
R&D 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
Is research to operation the only model? Is this a practical model? Who is to deliver services?
long term R&D efforts lead to significant success such as the prediction of the 97/98 El Nino. How to measure the progress on quarterly base? Take weather forecasting as guide:
need basic skill score for tracking, e.g., c.c. for 500mb Z;
Measure for improvement in forecasting capability, I.e., development of new products
Simultaneous approach of multiple potential products, not all but some of them will be successful-nature of R&D
Incl. predictability studies
big institution’s directions impact long term program vision and objectives. Program/matrix management may not be able to influence big institution’s directions. Because they have their own mission.
if we build, they will come. Is this sufficient? We are mostly hard core physical scientists. Working with RISA is a good start. Need to lean to listen to the users.
Many science communities, e.g., CLIVAR, GEWEX, GWC, ocean, atmosphere, weather, land, have interests, goals, and/or claim turf in S-I prediction. Resource is the motivation. It is a challenge to minimize overlapping, combining strength, avoiding turf battle.
Include. PMEL and NDBC reps on ODASI consortium, NASA interests in satellite obs. requirements-interagency

15. S-I Prediction Skill : What’s 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