NOAA Climate Research: Climate Variability

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NOAA Climate Research Climate Variability
NOAA Research Overview
Climate Variability
Drought in Great Plains, ca. 1934
California floods during 1998 El Nino
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Mission-critical research
Climate variability research is critical to
NOAAs mission To understand and predict
changes in the Earths environment and conserve
and manage coastal and marine resources And,
specifically, Strategic Goal 2 Understand
climate variability and change to enhance
societys ability to respond.
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NOAA Strategic Plan Performance Measures
CV Overall Strategy

• Four components
• Monitor and Observe
• Understand and Describe
• Assess and Predict
• Engage, Advise, and Inform
• For the success of the overall program, it is
  vital that these components be linked together.

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Measures of success
Measures of Success

• Understand and describe
• Increased number of new research findings and
  progress toward their implementation into NOAA
  operations.
• Decreased degree of uncertainty of climate system
  processes.

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CCSP priority
U.S. Climate Change Science Plan Chapter 4
Climate Variability and Change

• Major Research Questions
• To what extent can uncertainties due to climate
  feedbacks be reduced?
• 2. What are limits of climate predictability, and
  how can climate predictions and climate change
  projections be further improved?
• What is the likelihood of abrupt climate changes?
• 4. How do extreme events respond to climate
  variability and change?
• 5. How can information on climate variability and
  change be most efficiently developed and
  communicated to serve societal needs?

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CV Research Priorities
Research Priorities

• NOAA CV programs emphasize priority areas
  described in the CCSP and NOAA SP.
• Increase understanding of climate feedbacks
  (CLIVAR NOAA labs SEARCH).
• 2. Clarify limits to climate predictability
  improve climate predictions (CLIVAR CDC other
  NOAA labs).
• Increase understanding mechanisms for abrupt
  climate change (CLIVAR ATL, SEARCH).
• 4. Response of extreme events to climate
  variability and change (Weather-Climate
  Connection CDC, ETL, AOML).
• 5. Develop climate information to serve societal
  needs (CDC overall program, especially where
  linked to RISAs, CDEP, NWS, and IRI).

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CV Resource allocation
CV Resource Allocation (FY03 est.) (in M)
CLIVAR 20.6 SEARCH 2.0 WX-CLIM
.9 AOML 4.2 CDC 2.5 ETL .2 FSL
.2 PMEL 2.4 ENSO obs (PMEL) 4.7
35.3 CLIVAR amount includes 6.1 M for
sustained ocean obs., and OGP-sponsored research
funding for CLIVAR Pacific, CLIVAR Atlantic, and
CLIVAR Pan-American Climate Studies (PACS).
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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.
Total Climate Variability contributions to the
Climate Observation Program are 13.1M (CLIVAR
Obs 5.1M, OAR Labs 8M).
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Core Activities
NOAA Base Components Climate Variability
Research

• Office of Global Programs
• Climate Variability and Predictability (CLIVAR)
• - CLIVAR Atlantic
• - CLIVAR Pacific
• - CLIVAR Pan-American Climate Studies (PACS)
• Climate Observations and Services Program (COSP)
• Study of Environmental Arctic Change (SEARCH)
• Weather-Climate Connection
• NOAA Research labs

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NOAA OGP CLIVAR Program
NOAA/OGP CLIVAR Program

• CLIVAR Atlantic
• CLIVAR Pacific
• CLIVAR Pan-American Climate Studies (PACS)

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CLIVAR
Climate Variability and Predictability

• Overall Objectives
• Develop an understanding of 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 Variability (PDV), Arctic Oscillation
  (AO), North Atlantic Oscillation (NAO), Tropical
  Atlantic Variability, and North American monsoon
  system.
• Abrupt climate change (Atlantic thermohaline
  circulation).
• Method
• Sponsor PI research/field experiments in key
  regions CLIVAR-Pacific, CLIVAR-Atlantic,
  CLIVAR-PACS.
• Support interagency national and international
  programs.
• Implement Climate Model Process Teams (CPTs) to
  develop and improve climate model representations
  of physical processes.

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CLIVAR Atlantic aims to describe changes in, and
assess predictability of, three major climate
phenomena

• Tropical Atlantic Variability (TAV)
• North Atlantic Oscillation (NAO)
• Meridional Overturning Circulation (MOC)

Figure courtesy of Science (2002)
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CLIVAR Atlantic research examples

• Coupled modeling - TAV, NAO
• P. Chang and R. Saravanan
• S.-P. Xie
• J. Marshall
• Coupled modeling - tropical teleconnections
• M. Hoerling and J. Hurrell
• Ocean modeling - TAV (including subtropical
  cells), MOC
• G. Halliwell and R. Weisberg
• Atmospheric analysis - NAO
• J. Hurrell et al.
• R. Miller et al.
• M. Baldwin and T. Dunkerton
• Data set development and analysis - TAV, MOC, NAO
• P. Niiler
• L. Yu and R. Weller

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CLIVAR Pacific Objectives

• Improve understanding of Pacific basin-scale
  atmosphere-ocean variability, its predictability
  on seasonal and longer timescales, and
  anthropogenic impacts. Particular foci include
  ENSO and Pacific Decadal Variability. This
  requires further comprehensive analysis, testing
  and improvement of coupled models.
• Document time-varying T, S, currents in the upper
  ocean at 300 km, 10 day resolution over the
  entire basin north of 40o S for a 15 year period,
  with higher resolution in boundary currents and
  near the equator. Apply ODA to provide a
  three-dimensional time-dependent analysis based
  on this data.
• Document time-varying vertical and lateral fluxes
  and air-sea exchange of heat, fresh water, and
  momentum over the corresponding period.
• Improve physical parameterizations in OGCMs,
  AGCMs and NWP models via process studies and via
  ODA, which as a by-product identifies systematic
  errors in the atmospheric forcing of the ocean or
  the assimilating ocean model.

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PACS Science Objectives

• PACS seeks to extend the scope and improve the
  skill of climate prediction over the Americas on
  subseasonal to interdecadal time scales with an
  emphasis on summer precipitation. Specific
  objectives
• Improve understanding and provide more realistic
  simulations of coupled ocean-atmosphere-land
  processes, with emphasis on
• the response of planetary-scale atmospheric
  circulation and precipitation patterns to
  potentially predictable surface boundary
  conditions
• the mechanisms that couple climate variability
  over ocean and land
• the seasonally varying climatological mean state
  of the ocean, atmosphere, and land surface
• The effects of land surface processes and
  orography on the variability of seasonal rainfall
• Determine the predictability of warm-season
  precipitation anomalies over the Americas on
  seasonal and longer time scales.
• Advance the development of the climate observing
  and prediction system for seasonal and longer
  time scales.

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PACS Foci

• PACS focuses on the phenomena that are crucial
  for organizing seasonal rainfall patterns
• - the oceanic ITCZs
• - the continental scale monsoon systems,
• - the tropical and extratropical storm tracks
• For implementation, three regional process study
  domains are defined
• - Eastern Pacific (EPIC and VEPIC)
• - North American Monsoon System (NAME)
• - South American Monsoon System (MESA)

NAME
EPIC
MESA
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North American Monsoon ExperimentPhenomena of
Interest
III
II
I
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PACS Achievements

• Established enhanced pibal upper air sounding
  network and auxiliary raingauge networks to
  augment existing networks over Pan-America for
  studying low-level flow and precipitation
• Enhanced observing system in eastern Pacific with
  extension of 95W TAO line and stratus mooring
• Initiated the PIRATA array of moored buoys in the
  tropical Atlantic

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PACS Deliverables

• Measured improvements in coupled climate models
  capability to predict North and South American
  climate variability months to seasons in advance
• Infrastructure to monitor and predict the North
  and South American monsoon systems
• More comprehensive understanding of Pan-American
  summer climate variability and predictability
• Contributions to assessments of climate
  variability and long-term climate change for
  regions within North and South America
• Strengthened multinational scientific
  collaboration across Pan-America

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CLIVAR deliverables
CLIVAR milestones/deliverables

• Improved climate predictions for global climate
  variability on S/I and longer time scales.
• Contributions to the development of the sustained
  global climate observing system, esp. ocean
  observations.
• Data sets from process field campaigns.
• Improved physical understanding of climate
  feedbacks.
• Assessments of predictability of climate modes.
• Accelerated improvements in modeling of physical
  processes through the CPTs. Initiate a CPT
  focusing on deep atmospheric convection (Q4,
  OGP).
• Conduct the South American Low Level Jet
  Experiment (Q2, OGP)
• Enhance observations in Mexico and the southwest
  U.S. for NAME experiment (Q4, OGP)

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SEARCH
Study of Environmental Arctic Change (SEARCH)

• Objectives
• Identify causes for observed multi-decadal trends
  of interrelated changes in the Arctic
  (atmosphere, ice, ocean, land).
• Clarify potential for feedbacks (albedo, fresh
  water export,
• release of carbon from permafrost/methane
  hydrates)
• Determine implications for abrupt changes.
• Assess impacts to ecosystem and society.
• Foci
• Interannual to decadal time scales.
• Arctic/subarctic ocean fluxes relationship to
• thermohaline variability.
• Expand on limited observations to track key
  variables
• incorporate into models.
• Method
• Implement and sustain environmental observations.
• Data analysis and process research.

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SEARCH Products
Examples SEARCH Products
Atmosphere-Ocean-Land Surface Interactions
and Feedbacks over Arctic
Prototype Observing Array
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SEARCH Products/deliverables
SEARCH deliverables

• Temperature, radiation and ice data to support
  analyses of ice/albedo feedback, ocean
  thermohaline circulation, Arctic shipping routes,
  marine mammal management.
• Atmospheric data to enhance model physics and
  improve prediction of Arctic Oscillation, US
  temperature and hydrologic forecasts
• Long-term data to detect decadal changes,
  demonstrate links to mid-latitudes

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Weather-Climate Connection
Weather-Climate Connection

• Objectives
• Improve understanding and predictions of links
  between climate variations and high impact
  weather phenomena
• Improve regional observing capabilities
• Develop stronger link between climate research
  and user needs
• Infuse new science and technology into NOAA
  operations
• Foci
• Improve predictions on weekly to seasonal time
  scales.
• Initial focus on tropical-midlatitude
  interactions over the Pacific and their regional
  impacts on U.S.
• Method
• Observational, diagnostic, and modeling studies
  at regional scales to assess predictability and
  realize the potential for operational prediction.
• Research coordinated with services (NWS) and end
  users.

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Research example Wx-Clim. Connection
Weather-climate Connection
40 of rain/ 7 days
Pineapple Express

MJO

• Where will storm track be for the next few weeks?
  
• When will an arctic outbreak affect the east
  coast?
• When will the rain (drought, heat wave, etc.)
  end?
• How will a climate shift affect the weather in a
  particular region?

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Week 2 reliability and skill score derived from
an Ensemble MRF Reforecast Experiment (CDC) (23
years of training data, cross validated)
Bottom line Big gains can be made in forecast
skill by statistically correcting forecasts from
a frozen model. Validations over last two years
indicate that skills of U.S. T, p week two (8-14
day) forecasts derived by this method are
superior to official 6-10 day forecasts.
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How many years of training data are needed?
Results suggest that most of the gains can be
achieved by conducting reforecasts for 5-10
years, with forecasts run every 4-5 days.
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Weather-Climate Connection deliverables
Weather-Climate Connection Milestones/Deliverable
s

• Improved regional forecast capabilities of U.S.
  temperatures and precipitation from a week to a
  season.
• Climate prediction capabilities for high-impact
  events, including droughts and major floods.
• Enhanced data sets and analyses to identify and
  interpret weather-climate connections between the
  tropics and mid-latitudes.
• Develop modified and improved practices for
  biweekly and/or monthly U.S. temperature and
  precipitation outlooks (Q2 CPC/CDC).

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Research Laboratories
NOAA Research

• Objectives
• Carry out long-term research central to NOAAs
  mission
• Provide sustained support for NOAA climate
  observations and services (e.g., NWS Climate
  Prediction Center)
• Deliver products for decision support
• Regular and timely provision of climate obs. and
  predictions
• Foci
• Develop national capabilities to describe,
  interpret, and predict climate variations,
  emphasizing major climate phenomena such as ENSO,
  droughts, and floods.
• Provide and interpret ocean data
• Develop capabilities to monitor and predict the
  ocean environment on time scales from days to
  decades.

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Research Example AOML
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PMEL Recent Accomplishments

• Monitor and observe The TAO array has provided
  accurate, high resolution, real-time data for
  tracking the evolution of the 2002-2003 El Niño.
• Understand and describe TAO data and related
  shipboard measurements have supported
  approximately 50 refereed journal publications
  per year on climate variability and change.
• Assess and predict The TAO array is the backbone
  of the ENSO observing system providing real-time
  data essential for accurate analyses and
  forecasts of evolving climatic conditions in the
  tropical Pacific. TAO data were fundamental to
  the successful NCEP forecast in January 2002 that
  an El Niño was developing.
• Engage, advise, inform a) PMEL scientists serve
  on many national and international committees
  promoting awareness of climate science and NOAAs
  climate mission. c) TAO web pages, providing
  valuable information to the general public,
  educators, government policy makers and private
  businesses, received nearly 25 million hits in
  the past year.

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Test of Bridge Hypothesis in GFDL Model (CDC,
GFDL) - obs. vs. model correlations - 1950-99
Observed SSTs (FMA) correlated with Niño Index
(NDJ)
Mixed Layer Model correlations - SSTs
(FMA) specified in Niño region, MLM elsewhere
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The Perfect Ocean for Drought (CDC, CPC)
Observed Temperature and Precipitation
anomalies (June 1998 - May 2002)
Model-simulated Temperature and Precipitation
Anomalies given SSTs over this period
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Research lab deliverables
NOAA Research FY03 Milestones/Deliverables

• Determine the origins and assess the
  predictability of the 1998-2002 U. S. drought,
  leading to improved drought forecasts (Q2, CDC)
• Continue internationally coordinated studies to
  determine the role of the Tropical Atlantic on
  global climate (Q3, AOML)
• Develop a website dedicated to ongoing, real-time
  predictions of tropical convection associated
  with the MJO (Q3, CDC).
• Provide data for operational forecasting and
  analyses of the 2002-2003 El Niño and for
  comparisons with previous events (Q4, PMEL).