NOAA Climate Research: Climate Variability

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NOAA Climate Research Climate Variability
NOAA Research Overview
Climate Variability
California floods during 1998 El Nino
Drought in Great Plains, ca. 1935
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Mission-critical research
Mission-critical research

• Climate variability research is central to NOAAs
  mission
• to provide
• Environmental assessments
• and predictions
• Stewardship of marine resources
• Questions public and decision-makers ask NOAA
• Are we likely to see more or fewer storms with
  El Niño?
• How unusual is this drought, and what are its
  causes?
• What is the precipitation outlook for next
  season?

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Support for Observing Systems
NOAAs global and regional observing systems are
crucial in supporting monitoring,
interpretations, and predictions of climate
variability.
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Relationships to USGCRP/CCRI
Relationships to USGCRP and CCRI

• USGCRP Draft Strategic Plan Objectives
• Climate Variability and Change
• Improve detection, attribution, and projections
  of climate change.
• Extend and improve predictions of major modes of
  climate variability.
• Assess the potential for changes in extreme
  events at regional and local scales.
• Characterize the mechanisms and estimate the
  likelihood of abrupt climate change and expected
  global and regional manifestations.
• Improve effectiveness of interactions between
  producers and users of climate information.
• NOAA Research contributes to all five major
  objectives.

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Relationships to USGCRP/CCRI

• CCRI some suggested key products
• Improved knowledge of natural vs. human caused
  climate change.
• Increased understanding of the potential for
  extreme weather events as a result of climate
  change.
• New tools for integrated assessment and risk
  management.

Increased understanding of natural variability is
essential for reducing climate science
uncertainties, e.g. in key climate forcing
mechanisms, improving climate predictions,
attributing the causes for observed variations,
and assessing the potential for abrupt climate
change.
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Primary Research Foci
NOAA Climate Variability Priorities

• Major areas for near-term emphasis (next 2-5
  years)
• Improve predictions of major modes of climate
  variability.
• Increase understanding and capabilities to
  predict short-term climate variability,
  decadal-to-centennial variability, and their
  inter-relationships.
• Develop climate prediction capabilities for
  high-impact events.
• Establish quantitative estimates for risks of
  abrupt change.
• Develop monitoring/forecast products for regional
  applications and risk reduction.

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Core Activities
NOAA Core Components Climate Variability
Research

• Office of Global Programs
• Climate Dynamics and Experimental Prediction
  (CDEP)
• Climate Variability and Predictability Programs
  (CLIVAR)
• Global Water Cycle Program/GAPP
• Climate Change Data and Detection Program (CCDD)
• Climate Observations and Services Program (COSP)
• Weather-Climate Connection
• Study of Environmental Arctic Change (SEARCH)
• NOAA Research Laboratories
• NWS Climate Prediction Center - routine
  monitoring/predictions

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CDEP
Climate Dynamics and Experimental Prediction
(CDEP)

• Objectives
• Contribute to the development and implementation
  of a coupled ocean-land-atmosphere forecast
  system based on dynamical models
• Foster the development of new prediction and
  application techniques
• Focus
• Seasonal-to-interannual (S-I) prediction research
  and applications
• Method
• Sponsor a sustained critical mass of focused
  applied research and development at a few
  institutions - the Applied Research Centers
  (ARCs).
• Support the International Research Institute for
  Climate Prediction (IRI), which provides
  international climate assessments and predictions.

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CDEP
CDEP Product example
JAS
OND
Web tool enabling real-time comparisons of Sea
Surface Temperature (SST) anomaly forecasts by
different methods for three month seasons
July-September (JAS) October-December (OND)
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CDEP Deliverables
CDEP Deliverables

• Improved seasonal forecast systems through
  advances in ocean data assimilation, seasonal
  diagnostics and regional modeling capabilities.
• Model-based approaches for developing climate
  forecast products for regional applications and
  risk reduction.
• Decision support tools to enable quantitative
  assessments of regional implications of global
  climate variability on time scales from seasonal
  to centennial.
• International climate assessments and predictions
  through the IRI.

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CLIVAR
Climate Variability and Predictability (CLIVAR)

• Objectives
• Understand mechanisms producing different
  patterns of natural climate variations and their
  global and regional manifestations.
• Assess predictability of these climate modes
  through observational and modeling studies.
• Foci
• El Niño - Southern Oscillation (ENSO), Pacific
  Decadal Oscillation (PDO), Arctic Oscillation
  (AO), North Atlantic Oscillation (NAO), Tropical
  Atlantic Variability, and North American monsoon
  system.
• Abrupt climate change (Atlantic thermohaline
  circulation).
• Method
• Sponsor investigator research/observational
  experiments in key regions CLIVAR-Pacific,
  CLIVAR-Atlantic, CLIVAR-Americas.
• Support interagency national (USCLIVAR) and
  international programs.
• Implement Climate Model Process Teams (CPTs) to
  develop and improve climate model representations
  of physical processes.

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Indian Ocean warming
Research example CLIVAR
Wintertime Response of Climate Model to warm
Indian Ocean
Temperature
Precipitation
Models responses to warm Indian Ocean reinforces
La Niña tendency for dry, warm conditions in SE
and along east coast, as well as part of the
northern plains. Probable contributing factor to
U.S. and SW Asia (Afghan) droughts.
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CLIVAR deliverables
CLIVAR Deliverables

• Improved climate prediction and projection
  capability for global climate variability and
  change on seasonal, interannual, decadal, and
  centennial time scales.
• Assessments of decadal predictability of climate
  modes.
• Accelerated improvements in modeling of physical
  processes through the CPTs. Initial efforts
  will focus on high priority areas cloud - water
  vapor feedbacks, and Arctic snow and ice
  feedbacks.

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Water Cycle Program
Global Water Cycle Program/GAPP

• Objectives
• Develop a capability to predict water cycle
  variables
• on monthly-to-seasonal time scales.
• Interpret climate predictions for water
  management.
• Assess risks to water systems through improved
• climate projections.
• Foci
• Impact of land surface processes (vegetation and
  soil moisture) on predictability of summer
  precipitation.
• Initial focus on Americas, GEWEX Americas
  Prediction Project (GAPP).
• Method
• Sponsor investigator research/ modeling/
  observational experiments.
• Coordinated with larger national and
  international programs in the Global Water and
  Energy Cycle Experiment (GEWEX).

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Research example Water Cycle Program

Research Example Water Cycle Program
Impact of Land Surface Processes (Vegetation and
Soil Moisture) on predictability of summer
precipitation
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Water cycle Products/deliverables
Global Water Cycle/GAPP Deliverables

• Assessment of risks and vulnerabilities for river
  basins arising from variations and changes in
  the water cycle.
• Improved experimental forecasts of precipitation
  and water cycle extremes on seasonal time scales.
• Reduced uncertainty in climate model projections
  of long-term changes in the water cycle
  components.
• Closure of the water budget globally and
  regionally over all time scales.
• Integrated water cycle data products (satellite,
  in-situ) such as an integrated water vapor
  product.
• Surface water cycle in-situ data sets and
  assimilation products for the validation of
  regional and globalclimate models.

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Climate Change Data and Detection
Climate Change Data and Detection (CCDD)

• Objectives
• Data set development and enhancement
• Detection and attribution (NOAA-DOE)
• Paleoclimatology (NOAA-NSF)
• Foci
• Produce time series for trends and variability.
• Characterize the range of natural variability.
• Establish linkages between paleo and instrumental
  records.
• Method
• Sponsor data set development.
• Support data recovery/data archeology efforts.