A National Program for Analysis of the Climate System

  

  Presented by

Siegfried Schubert

Global Modeling and Assimilation OfficeNASA/GSFC

 

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

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 

“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

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

Overview 

-Outline of Development Strategyatmospheric driven

-Infrastructure  requirements

-Partnershipskey organizations/capabilities

Development strategy 

 • satellite era (~1979-present) denoted by R1979  

• 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.

Satellite era - R1979 

Focused 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 model

performance 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 “moisture friendly” • 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

Data Assimilation and Model Development

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Fi = E,P,etcSchubert and Chang (1996)

Regress analysis increments

Leith 1978, Klinker and Sardeshmukh 1992, DelSole and Hou 1999

Precipitation Assimilation: A. Hou, GMAO

Mean Variability

MJO Variability in Precipitation

Precip Assimilation TRMM Observations

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

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.

Upper-air era: R1950 

Strives 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/data sensitivity studies• link with AMIP runs and role of boundary forcing • analysis techniques that best incorporate past and future

data (optimize low frequency signal)• improved use of surface observations• support/coordinate with SST dataset development• development of “optimal” consistent observational datasets• recovery of historical observations

Long-term Trends in the NCEP/NCAR ReanalysisGlenn H. White

EMS/NCEP/NWS/NOAA

NCEP/NCAR Reanalysis Leading EOFs 200mb Height (JFM 1950-2000)

ENSO AO

PNA AAO

NSIPP AGCM Simulation: Leading EOFs 200mb Height (JFM 1950-2000)

ENSO AO

PNA AAO

NCEP/NCAR Reanalysis and NSIPP AGCM Simulation: Leading PCs 200mb Height (JFM 1950-2000)

ENSO AO

PNA AAO

Historical era: R1850 

Will 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 from very sparse observations • methods optimal for estimating low frequency and trend information • innovative techniques for using surface observations • 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

What happened in the dust bowl?

Courtesy Jim Hansen

(1930s)

NSIPP AGCM forced with observed SST

Observations

Ensemble mean

“Dust Bowl”

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?

Research and development

System Integration and testing

Validation

Production and monitoring

Product Quality Control

Storage and Distribution

Product Evaluation

Research and development

Infrastructure - example 

 Establish 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 data assimilation 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 to address problems in a controlled environment.

Proposed Key Partnerships/Linkages 

ValidationPartner 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 datasets

DistributionNCAR, CDC, PCMDI, CPC, GDAAC

Data 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

Observed ForcingObserved Forcing Consistent Global IntercomparisonConsistent Global Intercomparison

Model Integration

Data

Insertion of Data into the Model

Land Data AssimilationLand Data Assimilation

Obs Model4DDAImproved products,

predictions, understanding CEOP

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.

Paul R. HouserPaul R. Houser,, NASA/GSFC Hydrological Sciences NASA/GSFC Hydrological SciencesPaul.Houser@gsfc.nasa.govPaul.Houser@gsfc.nasa.gov

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

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

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

BBackground: ackground: Land Surface ObservationsLand 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.

950km Swath

3 dayReturn Period

30km Resolution

GlobalCoverage

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, ...

Assimilation of 6h surface rain rates to estimate model tendency corrections in moisture/temperature using 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

Results from GEOS precipitation assimilation experimentsusing a variational continuous assimilation (VCA) scheme with the forecast model as a weak constraint

GEOS = Goddard Earth Observing System

Impact on cloud, radiation, and humidity analyses Improved latent heating and vertical motion

lead to better upper-tropospheric humidity, as verified against HIRS2 Channel 12 Tb

Better precipitation leads to improved IR cloud-radiative forcing, as verified against CERES TOA measurements

94% reduction in tropical-mean bias 51% reduction in spatial error std deviation

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%

Impact on analysis error statistics (01 May – 31 Aug 98)

Time Correlation with GPCP rain

RMS Error against GPCP rain

GEOS TRMM

GEOS CONTROL

NCEP GDAS

RMS Error against CERES OLR

Time Correlation with CERES OLR

Reduced precipitation errors verified against GPCP Reduced OLR errors verified against CERES

GEOS TRMM

GEOS CONTROL

NCEP GDAS

NCEP GDAS Minus GPCP (Aug 98)

GEOS TRMM Minus GPCP (Aug 98)

mm/day

mm/day

Global Climate Data Assimilation

1990 2000 2010 2020

105

104

103

102

101

100

10-1

Tim

e sc

ale

(day

s)

First Generation Re-analyses

Fixed analysissystem

Improved archive ofobservations

Global satellite observations

Current Generation Re-analyses

Improvedstratosphere

Constituentassimilation

New analysis system, higher resolution

“bug” fixes

Next Generation Re-analyses

Reduced impactof observing system changes

Improved QC,error stats, biascorrection, datarecovery

New satelliteobservations

ImprovedHydro. Cycle,higher resolution

Reduced impactof observing system changes

Improved coupling toencompassEarth System

New satelliteobservations

Improvedmodel physicsand higherresolution

Future Generation Re-analyses

Reanalysis and CompositionOngoing 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

Example from ReSTS: Jan. 2, 1994

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.

Temperature and water vapor

Specific humidity [mg/kg] (shading)