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

3

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?