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A National Program for Analysis of the Climate System Presented by Siegfried Schubert

A National Program for Analysis of the Climate System Presented by Siegfried Schubert Global Modeling and Assimilation Office NASA/GSFC. Where do we go from here*?. Need to expand analysis system to include all the relevant components of the climate system.

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A National Program for Analysis of the Climate System Presented by Siegfried Schubert

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  1. A National Program for Analysis of the Climate SystemPresented by Siegfried Schubert Global Modeling and Assimilation Office NASA/GSFC

  2. Where do we go from here*? Need to expand analysis system to include all the relevant components of the climate system Need to support research and development on high priority climate analysis problems (e.g. inhomogeneities in observational network) Need to foster and maintain critical expertise on climate analysis problems Need to ensure necessary infrastructure exists for periodic reanalyses of the climate as warranted by improved observations and analysis systems * workshop at Maryland June 2000

  3. A Proposal for a US National Program• on-going research and development program with periodic data generation and distribution• infrastructure that facilitates “outside” participation• an interagency approach that capitalizes on the strengths and expertise of various organizations • development strategy tailored to different time scales

  4. “A coordinated national program should be implemented and funded to develop consistent, long-term assimilated data sets for the study of climate and global change” National Research Council Report, 1991

  5. Basic Elements of the Program A core group of scientists and support personnel dedicated to work on high priority research and development Funding announcements targeted to support community research and development focused on climate analysis problems Partnerships with selected organizations to help with production, and to facilitate validation and data distribution

  6. Overview -Outline of Development Strategy atmospheric driven -Infrastructurerequirements -Partnerships key organizations/capabilities

  7. Development strategy• satellite era (~1979-present) denoted byR1979• upper air era - period with a substantial but changing upper air network (~ 1950 – present) :R1950• historical era - the period defined by the availability of a minimal set of surface observations (~1850 – present) :R1850 recognizes the differing needs of the broad user communitydevelopment issues, quality and scope of the data products are strongly tied to the availability of observations.data sets continued into the future, thus providing a consistent basis for short and long-term climate change evaluations.

  8. Satellite era - R1979Focused on obtaining the best comprehensive, consistent, high-resolution global data with emphasis on improving the representation of the hydrological cycle and related physical processes. Utilize latest state-of-the-art data assimilation system emphasis is on link between 4DDA and model development•Efforts that utilize 4DDA output to improve/assess modelperformance with a focus on hydrological cycle•Extend new assimilation techniques (e.g. precip/cloud)to use historical data•Support efforts to apply Global LDAS to reanalysis (transfer to interactive/in-line system)•Improve ocean surface fluxes•Develop DAS techniques that are “moisturefriendly”•Improve stratosphere, assimilate constituents,aerosols•Support efforts to “clean-up” satellite radiance data•Assess impact of resolution in model and analysis •Observing system impact studies

  9. Data Assimilation and Model Development Regress analysis increments where Schubert and Chang (1996) Leith 1978, Klinker and Sardeshmukh 1992, DelSole and Hou 1999

  10. Variability Mean Precipitation Assimilation: A. Hou, GMAO

  11. MJO Variability in Precipitation TRMM Observations Precip Assimilation

  12. Composition • Stratospheric ozone modeling and assimilation • Air quality and pollutant transport • gases and aerosols • Carbon cycle: • inferring carbon sources and sinks from atmospheric inversions • Driving for land and ocean biology • S. Pawson - GMAO/ReSTS

  13. Composition • Requirements: • large-scale meteorology • winds for transport • temperature for reaction rates • sub-grid-scale transport • e.g. convective fluxes • surface parameters • precipitation, radiative fluxes for photosyntesis, etc.

  14. Upper-air era: R1950Strives for improved global estimates of interannual to decadal variability by improving the consistency of the global data over the last one-half century in the face of major changes to the observing system. 4DDA may be different from operational systems. Emphasis is on continuity and estimating low frequency signals.•bias estimation and correction •observing system impact and model/datasensitivity studies •link with AMIP runs and role of boundary forcing •analysis techniques that best incorporate past andfuture data (optimize low frequency signal)•improved use of surface observations•support/coordinate with SST dataset development•development of “optimal” consistent observationaldatasets•recovery of historical observations

  15. Long-term Trends in the NCEP/NCAR ReanalysisGlenn H. WhiteEMS/NCEP/NWS/NOAA

  16. NCEP/NCAR Reanalysis Leading EOFs 200mb Height (JFM 1950-2000) ENSO AO PNA AAO

  17. NSIPP AGCM Simulation: Leading EOFs 200mb Height (JFM 1950-2000) ENSO AO PNA AAO

  18. NCEP/NCAR Reanalysis and NSIPP AGCM Simulation: Leading PCs 200mb Height (JFM 1950-2000) ENSO AO PNA AAO

  19. Historical era: R1850Will focus on obtaining the best and longest possible consistent record of a limited number of surface, near surface, and upper air fields for the study of global climate variability and climate change on time scales from about a month to a century and a half. Very experimental, may be very different from usual 4DDA•methods that obtain maximum information fromvery sparse observations•methods optimal for estimating lowfrequency and trend information•innovative techniques for using surfaceobservations•bias estimation and correction•recovery of historical observations•development of “optimal” consistent observational datasets•support/coordinate with SST dataset development•link with AMIP runs and role of boundary forcing

  20. What happened in the dust bowl? (1930s) Courtesy Jim Hansen

  21. Ensemble mean Observations “Dust Bowl” NSIPP AGCM forced with observed SST

  22. Infrastructure issues/requirements Must ensure that the various efforts/components feed into the development of the new climate analysis system and the production of new climate data sets • need to define role of major centers (NCEP, GMAO, …) -production activities -relationship to in-house/agency priorities -how will development be coordinated? -how to allow access to systems by community? • need to define roles of various organizations (data distribution, validation, etc) • how can the “outside” community contribute in a meaningful and timely fashion?

  23. System Integration and testing Research and development Validation Production and monitoring Product Quality Control Storage and Distribution Research and development Product Evaluation

  24. Infrastructure - exampleEstablish a test-bed computing and analysis “Climate Data Assimilation Facility” (CDAF) • allow researchers to test ideas and methods utilizing complete or simplified versions of state-of-the-art dataassimilation systems and models. • Port and develop user-friendly full and simplified versions of the latest global data assimilation systems being developed at NOAA and NASA.• Take advantage of national Earth System Modeling Framework. • Development of synthetic observations for researchers toaddress problems in a controlled environment.

  25. Proposed Key Partnerships/LinkagesValidationPartner with key groups and research communities e.g. PCMDI, CDC, CPC, NCAR, polar, ocean, land communitiesInvite proposals from general communityPart of “core” development activitySupport development of validation datasetsDistribution NCAR, CDC, PCMDI, CPC, GDAACData recovery, quality control (NCAR, NCDC, community)Distributed data analysis(??, COLA-GDS)Link to other assimilation effortsoff-line land (LDAS) ocean data assimilation (GODAE, ODASI) other US “reanalysis” effortscoordinate with European effort

  26. Paul R. Houser, NASA/GSFC Hydrological Sciences Paul.Houser@gsfc.nasa.gov Consistent Global Intercomparison Observed Forcing Land Data Assimilation CEOP NASA-EOS/IDS 2000-2003 Project P.R.Houser, M.Rodell, U.Jambor, J. Gottschalck, J.Radakovich, K.Arsenault, M.Bosilovich, B.Cosgrove, J.K.Entin, J. Walker,J.Meng,K. Mitchell, and H.L.Pan Objective:1/8 degree (~10km) global land modeling and assimilation system: uses all relevant observed forcing, storages, and validation. Expand current N. American LDAS to the globe. Eventual 1km global resolution goal Benefits: Enable improved land-atmosphere understanding, hydrological and climate prediction, transfer research to application, and enable consistent inter-site comparison (i.e. GEWEX). Land Modeling: Use multiple state-of-the-art water-energy-carbon land modeling systems. Land Observation: Use best available observed forcing from surface and remote sensing platforms. Data Assimilation: Merge a wide range of surface information to constrain and improve model trajectory. Validation/Calibration: Compare GLDAS to independent observations, i.e. streamflow. Open Collaboration: Encourage international participation through code and data access, and cooperative evaluation. LDAS Concept: NLDAS(North-America), ELDAS(European), GLDAS(Global), and many others (I.e. GSWP) Coupled Connections: GLDAS is the off-line land-surface development strategy for DAO, NCEP, NCAR, and NSIPP.

  27. GODAE: the vision A global system of observations, communications, modelling and assimilation that will deliver regular, comprehensive information on the state of the oceans in a way that will promote and engender wide utility and availability of this resource for maximum benefit to the community Primary GODAE products: -coherent, organized data sets: more effective assembly and availability improved data utility improved data quality - synoptic ocean analyses -- a hierarchy of products at different resolution for different applications: Navy applications, Seasonal prediction, ... - short-range forecasts - high-quality scientific (re)-analyses: global, consistent 4D analyses of T, S and flow fields - statistical attributes and errors for products

  28. Status Funded Two-Year Joint NASA/NOAA Proposal (2003/04) “Development of a Science and Implementation Plan for the National Program for Analysis of the Climate System” P. Arkin, E. Kalnay, S. Schubert, K. Trenberth, J. Laver • Recruit members of a science working group • Hold several workshops with relevant communities • Develop a Science and Implementation Plan • Provide NOAA, NASA, and NSF and others with summary of planned program, costs, and recommendations for phased implementation

  29. Background: Land Surface Observations Precipitation:Remote-Sensing: SSM/I, TRMM, AMSR, GOES, AVHRR In-Situ: Surface Gages and Doppler Radar Radiation:Remote-Sensing: MODIS, GOES, AVHRR In-Situ: DOE-ARM, Mesonets, USDA-ARS Surface Temperature:Remote-Sensing: AVHRR, MODIS, SSM/I, GOES In-Situ: DOE-ARM, Mesonets, NWS-ASOS, USDA-ARS Soil Moisture:Remote-Sensing: TRMM, SSM/I, AMSR, HydroStar, ESTAR, NOHRSC, SMOS In-Situ: DOE-ARM, Mesonets, Global Soil Moisture Data Bank, USDA-ARS Groundwater:Remote-Sensing: GRACE In-Situ: Well Observations Snow Cover, Depth & Water:Remote-Sensing: AVHRR, MODIS, SSM/I, AMSR, GOES, NWCC, NOHRSC In-Situ: SNOTEL Streamflow:Remote-Sensing: Laser/Radar Altimiter In-Situ: Real-Time USGS, USDA-ARS Vegetation:Remote-Sensing: AVHRR, TM, VCL, MODIS, GOES In-Situ: Field Experiments Others: Soils, Latent & Sensible heat fluxes, etc.

  30. U.S. Participation in GODAE • Operational Oceanography: FNMOC, NAVOCEANO, NRL, HyCOM consortium, ... • Seasonal forecast Initialization: NOAA/NCEP, NSIPP, NOAA/CDEP ODASI Consortium • Climate analyses: ECCO (SIO, JPL, MIT), UMD, ...

  31. Results from GEOS precipitation assimilation experiments using a variational continuous assimilation (VCA) scheme with the forecast model as a weak constraint GEOS = Goddard Earth Observing System • Assimilation of 6h surface rain rates to estimate model tendency corrections in moisture/temperatureusing an observation operator based on 6h integration of a column-model of moist physics with prescribed large-scale forcing • Continuous application of 6h moisture/temperature tendency corrections in data assimilation cycles to obtain dynamical consistency • The scheme operates effectively as an online model bias estimation and correction for rain and moisture every 6h. • Unlike nudging or physical initialization, it is a statistical variational algorithm using background and observation error statistics

  32. Impact on cloud, radiation, and humidity analyses Better precipitation leads to improved IR cloud-radiative forcing, as verified against CERES TOA measurements Improved latent heating and vertical motion lead to better upper-tropospheric humidity, as verified against HIRS2 Channel 12 Tb January 1998 • GEOS control has a moist/cold bias relative to HIRS2 channel 12 (top) • Rainfall assimilation leads to a drier upper-troposphere & reduces the err.std.dev by 11% • 94% reduction in tropical-mean bias • 51% reduction in spatial error std deviation

  33. Impact on analysis error statistics (01 May – 31 Aug 98) Reduced OLR errors verified against CERES Reduced precipitation errors verified against GPCP NCEP GDAS Minus GPCP (Aug 98) Time Correlation with GPCP rain Time Correlation with CERES OLR mm/day GEOS TRMM Minus GPCP (Aug 98) RMS Error against GPCP rain RMS Error against CERES OLR GEOS TRMM GEOS CONTROL NCEP GDAS GEOS TRMM GEOS CONTROL NCEP GDAS mm/day

  34. Global Climate Data Assimilation Future Generation Re-analyses Next Generation Re-analyses First Generation Re-analyses 105 104 103 102 101 100 10-1 Reduced impact of observing system changes Improved coupling to encompass Earth System New satellite observations Improved model physics and higher resolution Current Generation Re-analyses Reduced impact of observing system changes Improved QC, error stats, bias correction, data recovery New satellite observations Improved Hydro. Cycle, higher resolution Time scale (days) Improved stratosphere Constituent assimilation New analysis system, higher resolution “bug” fixes Fixed analysis system Improved archive of observations Global satellite observations 1990 2000 2010 2020

  35. Reanalysis and Composition Ongoing activities in GMAO • ReSTS (Reanalysis for Studies of Trace Species) for 1991-present using GEOS-4 • Support for chemistry-transport modeling: • Stratospheric chemistry (GSFC: ozone) • Tropospheric pollutants (Harvard: GEOS-Chem) • Ozone assimilation, including stratospheric distributions and inferred tropospheric ozone • Aerosol modeling and assimilation • Proposed effort for carbon cycle, including atmosphere, land and oceans

  36. Example from ReSTS: Jan. 2, 1994 Temperature and water vapor Water is a “passive” tracer in the lower to middle stratosphere. There is downward transport of moist air from high-level sources (methane oxidation) inside the polar vortex; low values elsewhere arise from dehydration in the cold tropical tropopause region. Here, the advected water is shown with the temperature analysis as the polar vortex splits. Two “moist” vortex centers emerge. Specific humidity [mg/kg] (shading)

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