GMAO Global Modeling and Assimilation Office NASA/GSFC NSIPP & DAO merger - a new focus activity for NASA’s global modeling and data assimilation The Office will be a core resource for NASA’s Earth Science Enterprise in the development and use of satellite observations. Our main thrust will be to use comprehensive global models and data assimilation to maximize the impact of satellite observations in climate and weather prediction. Models and assimilation systems are integrating tools, essential to realizing the value of satellite technologies.

Use models and assimilation tools to GOALS Use models and assimilation tools to advance understanding of climate variability and change help interpret and extrapolate the information from satellite observations bring information in satellite observations to improve models help define and design future satellite missions Support ESE science goals through provision of targeted products Maximize the impact of satellite observations in short term climate, weather and air quality predictions and other national applications Transition satellite assimilation capabilities to operations Contribute to the science of observing systems: evaluate existing and potential new data types - sensitivity of products and forecasts to different data types Advance chemical constituent/aerosol modeling & assimilation Advance subseasonal-to-decadal climate prediction - the ultimate test of models used for climate change Agency Partnerships: JCSDA, ESMF, CLIVAR CPTs, NCEP, NCAR, GFDL, NOAA/OGP/CDEP

Atmospheric Modeling New approach: unified modeling capability - weather capable climate model and climate-reliable weather model Single Goddard modeling system FVcore + evolving physics: GSFC developments with NCAR, GFDL collaborations Include GISS modelE under a common Goddard model “toolkit” (with Code 930) Model for weather and climate: Improve moist processes in AGCMs - for climate and for assimilating cloud and precipitation data - preparation for GPM - modeling of hydrological cycle Next generation model: Embedded CRM - assimilation of high-resolution imagery data, clouds and precipitation data - use of Geostationary data; preparation for GPM,NPP, GIFTS Non-hydrostatic model Development and validation in collaboration with other centers and general community Climate Process Modeling Teams (NCAR, GFDL, GSFC and their collaborators) GMAO Atmosphere Working Group (GISS, GSFC, NASA Centers, GMAO science team)

A unified atmospheric model for climate and weather prediction Enabling Technology: Earth System Modeling Framework  interoperability ESMF development ESMF maintenance, enhancement, optimization NSIPP/Goddard Physics Prognostic Clouds (NSIPP, GISS, Sud) NCAR GWD Stratosphere and Mesosphere Physics: bring assimilation to GWD, physics development CLIVAR CPTs to improve parameterizations Finite Volume Dynamical Core Embedded Cloud Resolving Model nonhydrostatic model FvCore under ESMF 3Q 03 GMAO unified model 4Q 03 unified model in DAS 3Q 04 Cloud & precip assim Improved Convection physics 4Q 05 Improved physics 2006 Improved model for cloud assimilation 2008 Weather in climate model 2010 Improved assim & simulation for Aura GOCART aerosol Microphysics stratospheric chemistry Fully coupled Trop & strat chemistry Shared components possible GISS modelE Updated 1Q 04 NCCS & GMAO: Parallelized & ESMF-compliant GISS ModelE

http://gmao.gsfc.nasa.gov/public_science/models/GMAO_AMIP/index.php

Using TRMM storm top and rain rate statistics to evaluate and improve the GMAO Convective Parameterization (RAS) Monthly-Mean Convective Storm Top Frequency for RAS in the NSIPP-2 Model vs. TRMM as a Function of Rain Rate Height (km) Mon Mean Rain Rate (mm h-1) Y3amip01 TRMM Warm rain Missing shallower cloud tops 30o N/S Ocean Only Vertical Rain Rate Profiles Y3amip01_Tok land Y3amip01_Tok ocean TRMM ocean TRMM land Monthly Area-Mean Rain Rate (mm h-1) Sigma model is missing shallower convective cloud tops present during heavier rain rates has a larger percentage of warm rain than TRMM model has reasonable vertical rain rate structure rain rate too high Robertson & Bacmeister

Unification of LAND SURFACE MODELs Dynamic vegetation module: species succession SiB2, SiB3, CLM, outside collaborations Dynamic vegetation module: phenology SiB2, SiB3, CLM, GISSLSM outside collaborations Coupling Physics, Boundary Layer Physics Mosaic, CLM, PLACE Transpiration Physics: stomatal conductance, photosynthesis physics SiB2, SiB3, CLM Other evaporation: interception loss, bare soil evaporation All models Snow Physics Catchment, SSib Land Surface Hydrology: runoff generation, soil layering, subgrid variability Catchment, TOPLATS, PLACE River Routing In-house, outside collaboration Albedo, thermal emissivity All models Carbon, geochemical tracers SiB2, SiB3, CLM, outside collaborations LSM unification: Water balance Energy balance Carbon balance Evolution of surface and subsurface states Subsurface Thermodynamics Catchment, CLM Vegetation Physics Other Physical Processes

CGCMv1 NSIPP1 AGCM 2 x 2.5 x 34L Poseidon v4 OGCM 1/3 x 5/8 x 27L Mosaic LSM

CGCM: 2 = 0.48 Obs: 2 = 0.82

Predictability of Extreme weather events: High resolution models Large ensembles to resolve the tail of the pdf Large-Scale Conditions Favoring Extreme Winter Weather in the SE U.S. (note strong ENSO signal) Probability Density Function of Extreme Winter Storms in SE U.S. (DJF 1949-1998) 300mb Height Observations Red - El Nino winters Blue - La Nina winters Dotted - observations Solid - model (9 ensemble members) Frequency Higher probability of extreme weather NSIPP Model Simulations Strength The NSIPP model is able to mimic nature. Extreme Winter storms are more likely to occur during El Nino winters Schubert, Suarez GSFC/GMAO (NSIPP)

Schubert et al.

Next GMAO Assimilation system (GEOS5) Joint Analysis System with NCEP - Accelerate the utilization and operational implementation of new satellite data types NASA Development NOAA Development ESE Science Interests with NWP constraints on quality & performance Primarily driven by operational NWP Input Observations Common Data Processing/QC NASA metrics NOAA metrics ModelAnalysis Interface (ESMF) Analysis Algorithm Input Model State Flexible background error formulation in grid-space Advanced observation operators (radiances, winds) Increased observation count (~106/6-hr cycle) Run-time choices for NASA/NOAA applications Interoperability NCEP AGCM GMAO AGCM AnalysisModel Interface (ESMF)

Land surface models and assimilation Predictability of JJA precipitation associated with SST associated with SST & soil wetness Theoretical estimates of prediction skill Large ensembles used to assess potential data impacts INITIALIZED NOT INITIALIZED OBSERVATIONS Test of land initialized by observed forcing Precipitation anomalies: the 1988 drought 10 5 3 2 1 0.7 0.5 0.3 0.2 0.1 0.0 - 0.1 - 0.2 - 0.3 - 0.5 - 0.7 - 1 - 2 - 3 - 5 mm/day Forecast experiments with simple land initialization to test simulation results NSIPP also places much emphasis on soil moisture assimilation. Early predictability studies showed the potential for increased skill in the summertime precipitation forecasts over the central US if information on surface soil wetness is available. While waiting for soil moisture data from AMSR-E on Aqua, Randy Koster has conducted some initial experiments of initializing the LSM soil moisture by running the LSM offline with prescribed forcing. The initial tests seem to confirm the theoretical predictability results. Here the -ve numbers are colored blue, and indicate a deficit in precipitation. Whereas the forecasts with the LSM initialization does not reproduce the observations, the improvement in the drought forecast over the central US is clear. One can expect that, just as for the ocean, the initialization of the land surface by assimilating observations of soil moisture will improve upon these results. Improvements in areas consistent with theoretical results Koster & Suarez NASA/GMAO

Anomaly correlation of forecast SSH with TOPEX altimetry May starts Altimeter data not used in initialization Altimeter data used in initialization Forecast Lead 1 month 3 month 6 month 9 month Kurkowski, Keppenne, Kovach

GMAO Interactions with CCSM and GFDL CLIVAR CPTs - atmospheric physics ESMF partnership Finite volume dynamical core Data assimilation input to climate model development WACCM - chemistry-climate coupling (GMI) chemistry assimilation