1. 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

4. Following Larsen ice shelf break-up glaciers accelerated 8x ICESat shows thinning by 38 m (blue lines) Results from Antarctic Peninsula Scambos et al., GRL 2004

5. 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

7. 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 i.increase interoperability ii.improve performance portability iii.abstract common services AGCM DYNAMICS GWDFVCORE SURFACE LAND LAKE OCEAN RADIATION ATM PHYSICS SOLAR IR MOIST TURBAtm CHEM AEROSOL Ocn CHEM OGCM HYDRO VEG DYN ESM CAP GLACIER SEAICE GEOS5 AGCM is first model completely implemented with ESMF Platforms

8. 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

9. 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 11Ensemble 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

10. MAP NRA & the MAP Modeling Environment Components Added as Program Evolves Program or Branch #1 Modeling 1 Program or Branch #2 Modeling 2 Program or Branch #3 Modeling 3 Program or Branch #N Modeling N Crosscutting Themes Focus Areas Model, Analysis, Prediction Program / Multi-investigator proposals Core Integration Team External NRA Proposals ESMF MAP Modeling Environment CMAIGMIECCO II GISS Model E …

12. Flight Operations, Data Capture, Initial Processing, Backup Archive Data Transport to DAACs Science Data Processing, Info Mgmt, Data Archive, & Distribution Distribution, Access, Interoperability, Reuse Spacecraft NASA Integrated Services Network (NISN) Mission Services WWW Value-Added Providers Interagency Data Centers Int’l Partners & Data Centers Data Acquisition Ground Stations Tracking & Data Relay Satellite (TDRS) Research Users Education Users Science Teams Data Processing & Mission Control Polar Ground Stations Data System Architecture for MAP Modeling Environment DAACs ESIPs REASoNs Project Columbia

13. 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 CO 2 Land Carbon Biomass Aerosols Precipitation Clouds Algorithms Statistics and analysis

14. 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

15. blank 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 ImprovedClimatePrediction Quantify Storages & Fluxes Incorporating Microphysics

16. Next Steps: Multi-center/agency ESMF Sensor Web: integration of real time OSSEs toward optimal observations- model=>forecast/prediction SWMF ESMF => ‘Mud-to-Sun’ Why? Why Not?