Post on 16-Jan-2016
transcript
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->Space->Earth Science
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Satellite Observations Provide Global Input to Models from Process, t, to Global
Aqua
Terra
TRMM
SORCE
SeaWiFS
Aura
Meteor/SAGE
GRACE
ICESat
CloudSat
Jason
CALIPSO
TOPEX
Landsat
NOAA/POES
• Following Larsen ice shelf break-up glaciers accelerated 8x
• ICESat shows thinning by 38 m (blue lines)
Results from Results from Antarctic Antarctic PeninsulaPeninsula
Scambos et al., GRL
2004
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
Modeling Paradigm of the Future - Frameworks & Integration
Technological TrendsEnvironmental 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 solutionEarth 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
i. increase interoperabilityii. improve performance portabilityiii. abstract common services
AGCMDYNAMICS
GWDFVCORE
SURFACE
LAND LAKE
OCEAN
RADIATION
ATM PHYSICS
SOLAR
IR
MOIST
TURB Atm CHEM
AEROSOL
Ocn CHEM
OGCM
HYDRO
VEG DYN
ESM
CAP
GLACIERSEAICE
GEOS5 AGCM is first model completely implemented with ESMF
Platforms
GMI chemistry
Where we are going: Modern models integrate components from different sources ESMF accelerates development cycle
GMAO LSM
GMAO physics
GFDL dynamics
NASA AGCM for climate and weather
GMU ocean
LANL sea ice model
Add in the assimilation components and the satellite data science + future mission design
GMAO ocean biology
Climate Variability and Chaos: Even large scale circulation patterns are influenced by uncertainties - initial conditions, external factors and unresolved scales
Ensemble Member 9
Ensemble Member 10
Ensemble Member 11 Ensemble Member 12
Ensemble Member 13
Ensemble Member 14
Model simulations of past droughts over the U.S. Great Plains show substantial sensitivity to initial conditions, reflecting the chaotic nature of climate variability.
Modeling Uncertainty - the need for ensembles
MAP NRA & the MAP Modeling Environment Components Added as Program Evolves
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Crosscutting ThemesFocus Areas
Model, Analysis, Prediction Program / Multi-investigator proposals
CoreIntegration
Team
ExternalNRA
Proposals
ES
MFMAP
ModelingEnvironment
CMAI GMI ECCO IIGISS
Model E
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NASA-Coordinated Satellite Systems(GEOSS Precurser?)
Flight Operations,Data Capture,Initial Processing,Backup Archive
DataTransportto DAACs
Science DataProcessing,Info Mgmt, DataArchive, & Distribution
Distribution,Access,Interoperability,Reuse
Spacecraft
NASAIntegratedServicesNetwork(NISN)MissionServices
WWWValue-Added
Providers
InteragencyData
Centers
Int’l Partners& DataCenters
Data Acquisition
GroundStations
Tracking& Data
Relay Satellite(TDRS)
ResearchUsers
EducationUsers
ScienceTeams
Data Processing
&MissionControl
Polar Ground Stations
Data System Architecture for MAP Modeling Environment
DAACs ESIPs
REASoNs
Project Columbia
Long-term Observations
• Modeled climate forcings and feedbacks
• Projections of future climate states
• Global & Regional data product for assessments
Data assimilation, High-end climate modeling and computing
Higher Resolution
Large Data Sets
Many Runs
Long-term data assimilation feeds into climate models
Ocean
Atmosphere CO2
Land
Carbon
Biomass
Aerosols
Precipitation
Clouds
Algorithms
Statistics and analysis
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
Linkage to National and International Programs…-GCRP GEWEX/CEOP (Land hydrology focus)
-WCRP and US CLIVAR (Global oceans and land)
Space / time precipitation distribution
atmospheric stability
Stratiform cloud production
Inter Annual Variability & Dynamical
Feedback to Climate System
Cloud radiative forcing / feedback
Conv / ocean evap feedback, surface wind stress
Model Problems / Challenges
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
CERES - SW anomaly for Jan 1998
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From Precipitation Climatology to Improved Climate PredictionFrom Precipitation Climatology to Improved Climate Predictionthrough better closure of water budget & accompanying quantification of through better closure of water budget & accompanying quantification of
accelerations/decelerations in atmospheric & surface branches of water cycleaccelerations/decelerations in atmospheric & surface branches of water cycle
Improved Improved ClimateClimate
PredictionPrediction
QuantifyQuantifyStorages &Storages &
FluxesFluxes
Incorporating
Incorporating
Microphysics
Microphysics
Next Steps:Multi-center/agency ESMFSensor Web: integration of real time OSSEs toward optimal observations-model=>forecast/predictionSWMF <->ESMF => ‘Mud-to-Sun’ Why? Why Not?