March 29, 2005 Presentation for NOAAs Climate Working Group Tom Karl, Observations and Analysis Clim

1
March 29, 2005Presentation for NOAAs Climate
Working GroupTom Karl, Observations and
Analysis Climate Program ManagerJohn Bates, SDS
Co-LeadMitch Goldberg, SDS Co-Lead
NOAAs Scientific Data Stewardship
ProgramImplementation, Governance, Performance
Measures and Linkages
1
2
Outline

• Background
• Defining Scientific Data Stewardship and CDRs
• NOAA Scientific Data Stewardship Program
  Governance and Management
• Program Drivers and Performance Measures
• Linkages to Other Programs
• Questions?

3
Background

• Climate Goal/Climate Program
• Element 1 Observations and Analysis
• Scientific Data Stewardship (SDS)
• Funds to support SDS
• Reprogrammed from EDSM (ESDIM)
• Presidents FY06 Total EDSM Budget 9.384M
• Funding shared among several programs including
  SDS
• SDS from EDSM 2.5M
• OGPs Applied Research Center for Data Set
  Development
• 1.4M (Maybe more or less, depending on FY06
  distribution)
• FY06 PBA 100 Requirement for SDS is 3.5M
• FY07 - FY12 100 requirement substantially more

4
Background

• NOAAs Scientific Data Stewardship rooted in
  NRC dialogue and reports
• NOAA/NRC SDS leads
• Bates
• Goldberg

5
Defining Scientific Data Stewardship

• Data Stewardship is a subset of Data Management
  and consists of the application of rigorous
  analyses and oversight to ensure that data sets
  meet the needs of users (NOSC definition). For
  environmental satellite observations, SDS
  priorities include
• Observing System Performance Monitoring
• Documenting measurement practices and processing
  practices (metadata)
• Providing feedback on observing system
  performance, including recommending corrective
  action for errant or non-optimal operations.
• Climate Data Records
• Reprocessing (incorporate new data, apply new
  algorithms, perform bias corrections,
  integrate/blend data sets from different sources
  or observing systems)
• Inter-comparison of data sets for validation

6
Defining Scientific Data Stewardship
Notional Functions of Scientific Data Stewardship
for Climate
Scientific Data StewardshipReal time and
retrospective management of climate data
Climate Data Records
Network Performance Monitoring
Model Re-analyses
Observations Metadata
Climate Quality Products
Reference Data Sets (Reprocessing)
Analyses and Quality Control
Archives
Feedbacks
Climate Analyses
Stewardship Teams
7
Defining CDRs
Climate Data Records
Data (Direct Remotely Sensed)
Time-tagged Geo-Referenced
Sensor DataRecords (SDRs)
Converted to Bio-Geophysical Variables
Homogenization and Calibration
EnvironmentalData Records(EDRs)
Fundamental Climate Data Records (FCDRs)
Climate Data Records or Homogenized Time Series
Converted to Bio-Geophysical Variables
Thematic Climate Data Records(TCDRs)
8
NOAAs Scientific Data Stewardship Program
FY06 EDSM 2.5M C2D2 1 to 1.5M
Governance and Management Structure
Scientific Data Stewardship Program Management
NOAA Climate Program Climate Board
NOAA (5-10)
C2D2 NCDC ORA
NOAA Observing System Council
NOAA Science Advisory Board
Scientific Data Stewardship CLASS Working Group
Climate Working Group
External (1-2)
NOAA, Other Agencies External Scientific (90)
Operational CDR Generation Data Mgmt.
Research to Operations
Research Climate Data Science Teams
CDR Generation NOAA Other Agencies Universities P
rivate Sector
CLASS NOAA IT Infrastructure operators
FCDR Teams Observations Scoping Requirement
Systems
TCDR Teams RD Products and Services Theme Areas
NOAAs Scientific Data Stewardship Program
Implementation, Governance, Performance Measures
Linkages
8
Currently exists
FY05
FY06
9
Program Drivers

• What is ..
• Changing
• Causing the Change
• The Impact

10
Program DriversClimate Program Essential Global
Climate Variables (from CCSP GCOS)

• The following essential atmospheric variables
  are required over land, sea and ice
• 1.1 Surface
• a. Air temperature
• b. Precipitation
• c. Air pressure
• 1.2 Upper-air
• a. Earth radiation budget (including solar
  irradiance)
• b. Upper-air temperature (including MSU
  radiances)
• c. Wind speed and direction
• d. Water vapor
• e. Cloud properties
• 1.3 Composition
• a. Carbon dioxide
• b. Methane
• c. Ozone

1. Atmospheric Variables

• g. Evaporation evapotranspiration

• d. Surface radiation budget
• e. Wind speed and direction
• f. Water vapor

• d. Other long-lived greenhouse gases
• e. Aerosol properties

Including nitrous oxide (N2O),
chloroflurorocarbons (CFCs), hydrofluorocarbons
(HFCs), sulpher hexaflouride (SF6), and
perfluorocarbons (PFCs).
11
Program DriversEssential Global Climate
Variables (from CCSP GCOS)

• 2.1 Surface
• a. Sea-surface temperature
• b. Sea-surface salinity
• c. Sea level
• d. Sea state
• e. Sea ice
• f. Current
• g. Ocean color (for biological activity)
• h. Carbon dioxide partial pressure
• i. ocean surface wind wind stress
• j. Surface air temp/humidity
• k. Precip (fresh water/salinity flux)
• l. Evaporation
• m. Fresh water flux from rivers ice melt
• n. CO2 flux across the air sea interface
• o. Geothermal heat flux ocean bottom

2. Ocean Variables

• 2.2 Sub-surface
• a. Temperature
• b. Salinity
• c. Current
• d. Nutrients
• e. Carbon
• f. Ocean tracers
• g. Phytoplankton

12
Program DriversEssential Global Climate
Variables (from CCSP GCOS)
3. Terrestrial Variables

• a. Snow cover
• b. Glaciers and ice caps
• c. Permafrost and seasonally-frozen ground
• d. Albedo
• e. Land cover (including vegetation type)

• f. Fraction of absorbed photosynthetically
  active radiation (FAPAR)
• g. Leaf area index (LAI)
• h. Biomass
• i. Land surface temp

13
Performance Measures for CDRs
SOFTWARE ENGINEERING CRITERIA
SCIENTIFIC CRITERIA
(PERFORMANCE)
Metadata
Time-dependent biases
Data formats and data models
Reproducibility
Data archive access
Multiple Observing Systems
Standards, NARA, EGDC, EFA, etc.
Multiple analysis teams
Version control
Error structure
SOCIETAL IMPACTS
New knowledge
Outreach network building
New tools techniques
New products services
Assessments decision support
14
Linking Ice Ages to Space Age
Minor Observing Changes
Major Observing Changes
Direct versus Indirect Observables
15
Scientific Criteria
Performance Measures for Climate Data Records

• All CDRs address time-dependent biases and
  random errors in the data set and are
  reproducible by independent analysis teams.
• Bronze --- A single time series produced by a
  single analysis team from a single observing
  system (e.g. MSU) or Silver or Gold level
  failures to meet
• Silver --- Multiple time series
• produced by multiple analysis teams based a
  common observing system
• OR
• produced from multiple independent observing
  systems by a single analysis team (e.g. MSU vs.
  Radiosondes)
• Gold --- Multiple time series produced by
  multiple analysis teams from multiple independent
  observing systems.
• Note Trends within data sets must be larger
  than the differences among data sets to reach
  Gold or Silver status.

16
Scientific Criteria
Notional Status of Global Scale CDRs - Atmosphere
16
17
Scientific Criteria
Notional Status of Global Scale CDRs - Ocean
17
18
Notional Status of Global Scale CDRs
Terrestrial Variables
Scientific Criteria
18
19
Secondary VariablesExternal Forcing or Feedback
Variables
Scientific Criteria
3. Terrestrial Variables

• a. fire occurrence
• b. volcanic effects (on surface)
• c. biodiversity
• d. chemical (fertilizer/pesticide gas exchange)
• e. waste disposal other contaminants
• f. earthquakes, tectonic motions
• g. nutrients and soil microbial activity
• h. coastal zones/margins
• i. erosion, sediment transport
• j. land surface structure topography

2. Ocean Variables

• a. Organic inorganic effluents (into ocean)

20
Working Group Report on Implementation Plans for
the CCSP Deliverable
Linkage to Other ProgramsRe-analysis
Re-analyses of historical climate data for key
atmospheric features. Implications for
attribution of causes of observed change

• CDR production enables
• Identification, reduction and removal of
  discontinuities in Re-analyses
• Reducing impact of changes in the observing
  system

21
Linkage to Other Programs

• General CDR Development
• Quality control and bias correct satellite
  radiance data
• Develop, acquire, and quality control needed
  forcing data sets, e.g. SST, land use, solar
  variability, green house gases, aerosols
• Homogenize key observations, especially upper
  air data that have suffered from a rapidly
  changing observing system

22
Linkage to Other Programs
CDR Relevant Strategies data streams aimed at
developing improved Re-analyses for

• Modern Satellite Era (1979-present)
• Upper-air Era (approx. 1950-present)
• Surface Era (approx. 1900-present)

23
Linkage to Other GEOSS Programs
23
24
IEOS Near-Term Deliverables and Potential
Regional Linkages
International Project Management, (GEO)
Regular Monitoring Reports for NOAAs Earth
Observation Partnership of the Americas (EOPA)
Programmatic Guidance (e.g., GEO, GCOS, GTOS,
WHYCOS, IGBP, IGOS, etc.
IWGEO Near-TermDeliverables
Data Management
Activities/Products
User Requirement Document
Climate Liaison Team (Stakeholder Consultation)
Application of Data ProductsImproved Earth
System Understanding and Forecasting
Validation of Earth System Modeling
Projections Prediction (including Extreme
Events)
24
24
25
Questions
26
Data Management (Analysis)
Collection - Internet, private networks,
satellite, media Ingest/Management -
Collect/create/maintain metadata - Catalog and
inventory Quality Control - Basic-missing data,
identifying and correcting suspected errors
including - Model/Data intercomparison -
Observing system performance monitoring -
Developing Climate Data Records Preservation/Archi
ve - Sort/Reformat - Storage - Backup
Analysis - Monitor climate indicators - Data
products (including integration) - Reprocessing
in time and space (means and extremes) -
Data filtering (filter high frequency
noise) - Trend analysisDiscovery/Access -
Interactive browse on-line, near-line - Via
customer services Data Delivery - On-line,
Internet - Media

CLASS


CLASS
ScientificDataSteward-ship

CLASS
Overarching the above data management components
are interoperable systems, effective user
feedback and standards/protocols
27
(No Transcript)
28
(No Transcript)
29
(No Transcript)
30
(No Transcript)