EarthSunSolar SystemUniverse

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The NASA Modeling, Analysis and Prediction Program
Don Anderson NASA HQ Sience Mission
Directorate Earth-Sun Division Manager, Modeling,
Analysis and Prediction Lead, Climate Variability
and Change Focus Area Manager, Atmospheric
Effects of Aviation Research My Background
Planetary-gtSpace-gtEarth Science
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Satellite Observations Provide Global Input to
Models from Process, t, to Global
TRMM
Aqua
CloudSat
GRACE
CALIPSO
TOPEX
Meteor/ SAGE
Landsat
NOAA/POES
SeaWiFS
Aura
Jason
Terra
SORCE
ICESat
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Results from Antarctic Peninsula

• Following Larsen ice shelf break-up glaciers
  accelerated 8x
• ICESat shows thinning by 38 m (blue lines)

Scambos et al., GRL 2004
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10,000 Years of Ice Gone in 1 Month Collapse of
the Larsen B Ice Shelf
Larsen B breakup, 31 January to 7 March 2002
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Modeling Paradigm of the Future - Frameworks
Integration

• Technological Trends
• Environmental modeling and prediction (climate,
  NWP,...)
• Science requires detailed representation of
  individual physical processes - accuracy,
  compatibility with observations
• Systems are integration of diverse components
  into a comprehensive coupled environmental model
  and prediction system
• Computing technology...
• Science requires use of scalable computing
  architectures
• Hardware advances means that models can run on
  desktops, even laptops
• ? increase in hardware and software complexity

• The solution
• Earth System Modeling Framework Brings together
  major national modeling centers
• ESMF - an environment for assembling geophysical
  components into applications.
• ESMF - a toolkit that components use to
• increase interoperability
• improve performance portability
• abstract common services

GEOS5 AGCM is first model completely implemented
with ESMF
Platforms
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Where we are going Modern models integrate
components from different sources ESMF
accelerates development cycle
NASA AGCM for climate and weather
GFDL dynamics
GMAO physics
Add in the assimilation components and the
satellite data ? science future mission design
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Climate Variability and Chaos Even large scale
circulation patterns are influenced by
uncertainties - initial conditions, external
factors and unresolved scales
Modeling Uncertainty - the need for ensembles
Model simulations of past droughts over the U.S.
Great Plains show substantial sensitivity to
initial conditions, reflecting the chaotic nature
of climate variability.
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MAP NRA the MAP Modeling Environment Components
Added as Program Evolves
MAP Modeling Environment
Crosscutting Themes Focus Areas Model, Analysis,
Prediction Program / Multi-investigator proposals
ESMF
Core Integration Team

External NRA Proposals
GISS Model E
CMAI
GMI
ECCO II
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Data System Architecture for MAP Modeling
Environment
Flight Operations,
Science Data
Distribution,
Processing,
Data Capture,
Access,
Data
Info Mgmt, Data
Initial Processing,
Transport
Interoperability,
Backup Archive
Data Acquisition
to DAACs
Archive, Distribution
Reuse
Research
Users
Tracking
Spacecraft
Data
Relay Satellite
DAACs
(TDRS)
NASA
ESIPs
Integrated
Data Processing Mission Control
Services
Network (NISN) Mission Services
Education
REASoNs
Users
Ground Stations
WWW
Value-Added
Science Teams
Providers
Intl Partners
Interagency
Polar Ground Stations
Data
Centers
Data
Centers
Project Columbia
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Long-term data assimilation feeds into climate
models

Biomass
Ocean
Carbon
Atmosphere
CO2
Land
Aerosols
Clouds
Precipitation
Long-term Observations
Statistics and analysis

• Modeled climate forcings and feedbacks
• Projections of future climate states
• Global Regional data product for assessments

Algorithms
Many Runs
Large Data Sets
Higher Resolution
Data assimilation, High-end climate modeling and
computing
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• Integrating Multi-Sensor Observations to Improve
  Models
• Leverage international, multi-agency field
  campaigns (process-focused intensive observing
  periods) to test, improve model physics
• Cross-reference with multi-year, global
  satellite data sets to understand, improve
  coupled model performance, simulations of
  interactive climate processes, document biases
• Regional model development and validation of
  downscaling of global forecasts for regional
  climate assessment and decision-making

Ocean, Land and Atmosphere Process
studies Long-term in-situ Observation
Data Satellite Remote Sensing TRMM rainfall,
CERES surface fluxes, AMSR cloud water / ice,
Cloudsat and CALIPSO cloud / aerosol vertical
profiles, Quikscat wind stress, AIRS, AMSU, HSB
thermal moisture profiles
Space / time precipitation distribution
atmospheric stability Stratiform cloud production
Linkage to National and International
Programs -GCRP GEWEX/CEOP (Land hydrology focus)
-WCRP and US CLIVAR (Global oceans and land)
Model Problems / Challenges
Inter Annual Variability Dynamical Feedback to
Climate System Cloud radiative forcing /
feedback Conv / ocean evap feedback, surface
wind stress
CERES - SW anomaly for Jan 1998
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From Precipitation Climatology to Improved
Climate Prediction through better closure of
water budget accompanying quantification of
accelerations/decelerations in atmospheric
surface branches of water cycle
Improved Climate Prediction
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Next Steps Multi-center/agency ESMF Sensor Web
integration of real time OSSEs toward optimal
observations-modelgtforecast/prediction SWMF
lt-gtESMF gt Mud-to-Sun Why? Why Not?