The Original Vision of PACS and the Need for PACS Climate Observations

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The Original Vision of PACS and the Need for PACS
Climate Observations

• Bob Weller
• EPIC/PACS workshop
• Boulder Sept 15-18, 2003

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GOALS 1994
Shukla, Chair David Anderson David Halpern Mike
Wallace Ed Sarachik Michael Gill
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GOALS 1994

• Understand global climate variability on
  seasonal-to-interannual time scales,
• Determine the spatial and temporal extent to
  which this variability is predictable,
• Develop the observational, theoretical, and
  computational means to predict this variability
  and
• Make enhanced climate predictions on
  seasonal-to-interannual time scales.

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GOALS 1994

• Hypothesis Variations in the upper ocean and
  SST, soil moisture, sea ice, and snow exert a
  strong influence on S-I variations in atmospheric
  circulation and thus, the predictability of the
  circulation.
• Approach Both understanding variability and
  predicting climate at S-I time scales will
  require accurate measurements of global surface
  and upper-ocean conditions as well as improved
  models to simulate their future evolution.
• Phased program - start first in the global
  tropics, initially the Pacific, where there are
  the major thermal sources and sinks for the
  atmosphere, move on later to higher latitudes.

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GOALS 1994 High priority science

• Structure and dynamics of the annual cycle of the
  coupled system
• Relationship of climate variability to annual
  cycle
• Role of slowly varying surface conditions in
  determining interannual variations
• What determines low-level convergence of moisture
  in the tropics what are the thermal sources for
  the atmosphere?
• The nature of tropical-extratropical
  interactions, especially the role of tropical SST
  anomalies.
• Improvements to coupled models, espec. for
  clouds, mixing, convection,
• Interactions between interannual and interdecadal
• Role of synoptic fluctuations in S-I variability
• Determine the global upper ocean and land surface
  obs needed to initialize coupled S-I prediction
  models

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GOALS 1998(Webster, Branstator, Chelton, Lukas,
Neelin, Rasmusson, Shukla, Shuttleworth,
Trenberth, Weller)
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GOALS 1998

• Understand global climate variability on
  seasonal-to-interannual time scales,
• Determine the spatial and temporal extent to
  which this variability is predictable,
• Develop the observational, theoretical, and
  computational means to predict this variability
  and
• Make enhanced climate predictions on
  seasonal-to-interannual time scales.

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GOALS 1998 - The Six Elements

• Long-term observations and analyses,
• Process studies,
• Empirical and diagnostic studies,
• Modeling,
• Applications and human dimensions,
• Data management.

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GOALS 1998 - Tasks

• Provide the research infrastructure to prospect
  for predictability within the global
  ocean-atmosphere-land-ice system in order to
  incrementally improve S-I prediction skill,
• Describe, analyze, diagnose the processes that
  determine variability and the spatial and
  temporal links in that variability,
• Develop pilot long-term monitoring systems of S-I
  variability of the coupled ocean-atmosphere
  system,
• Conduct process studies to improve understanding
  of coupled ocean-atmosphere system,
• Develop, improve, and evaluate models of the
  coupled ocean-atmosphere-land system to be used
  for prediction.
• Provide ongoing evaluation of climate monitoring
  systems and prediction products from IRI, NCEP,
  CDEP and other operational centers .
• Advise on the need for new products for multiple
  applications and help develop products addressing
  applications and human dimensions.

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• To observe
• Ocean
• State variables
• Upper ocean temperature
• Upper ocean currents
• Sea level
• Upper ocean salinity
• Optical absorption
• Sea ice extent, concentration, thickness
• External variables
• Wind stress
• Net surface shortwave radiation
• Surface incoming longwave radiation
• Surface air temperature
• Surface humidity
• Precipitation

• Air
• State variables
• Wind structure
• Thermal structure
• Surface air temperature
• Sea level pressure
• Water vapor structure
• Columnar water vapor and liquid water content
• Precipitation
• Cloud cover and height
• External variables
• Sea surface temperature
• Net radiation at the top of the atmosphere
• Land surface properties

• Land
• State variables External variables
• Soil moisture Precipitation
• Snow cover and depth Net surface SW and LW
  radiation
• Vegetation type, biomass, and vigor Surface wind
• Water runoff Surface humidity
• Ground temperature Evaporation
• 
  Evapotranspiration

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• To observe
• Ocean
• State variables
• Upper ocean temperature
• Upper ocean currents
• Sea level
• Upper ocean salinity
• Optical absorption
• Sea ice extent, concentration, thickness
• External variables
• Wind stress
• Net surface shortwave radiation
• Surface incoming longwave radiation
• Surface air temperature
• Surface humidity
• Precipitation

• Air
• State variables
• Wind structure Aerosols
• Thermal structure
• Surface air temperature
• Sea level pressure
• Water vapor structure
• Columnar water vapor and liquid water content
• Precipitation
• Cloud cover and height
• External variables
• Sea surface temperature
• Net radiation at the top of the atmosphere
• Land surface properties

• Land
• State variables External variables
• Soil moisture Precipitation
• Snow cover and depth Net surface SW and LW
  radiation
• Vegetation type, biomass, and vigor Surface wind
• Water runoff Surface humidity
• Ground temperature Evaporation
• 
  Evapotranspiration

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The PACS domain .
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Anomalous rain (red ), tropospheric temperature
(gold ) during warm phase of ENSO
Also, ENSO Impacts on storm tracks, extreme events
Feb-May, correlation between Brazil precip and
SST (red )
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Seasonal, ENSO variability meridional asymetry
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The role of SST gradients and anomalies
Esbensen et al
EPIC PACS 97-98/TEPPS, EPIC Enhanced
Monitoring, EPIC 2001
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Challenge - boundary layer, air-sea coupling,
predicting SST and anomalies
1997-1998 surface met, 10N, 125W
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1997-1998 surface fluxes, 10N, 125W
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1997-1998 Easterly regime 1999-2003 Southerly
regime
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Enhanced monitoring 1999-2003
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PACS-SONETsounding stations
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Satellite obs - Chelton, Frielich, Bates,
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Another challenge - ocean color
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Advances - active scatterometers, TMI
Enhanced monitoring, process studies
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Not all the answers are in for the eastern
tropical Pacific - PUMP
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Esbensen strikes again! The Sao Paulo VAMOS plan
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S-I variability and predictability in the Americas
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North American Monsoon Experiment (NAME)
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Moisture flow onto central North America Any
from the Gulf of Mexico?
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Surface drifter tracks
Gulf of Mexico Many observing efforts IntraAmeri
can Seas initiative
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South American Monsoon Experiment (MESA)
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SALLJEX
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Other influences of adjacent ocean regions
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Does this answer the need?
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Forcing from remote and adjacent seas
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On Synoptic to intraseasonal time scales

• Southerly cross-equatorial flow (V)
• Equatorward shift of rainfall
• Cyclonic surface circulation in Gulf of Mexico
• Northeast displacement of Bermuda High

• Northerly cross-equatorial flow (V)
• Southward shift of rainy area
• Southwest displacement of Bermuda High

Composite of daily values during January of
1979-1993
rainrate
rainrate
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The large-scale link of the SE US Summer Droughts
to the North and South American Monsoon
H
Composite of 10 wet years in SE US
Composite of 10 dry years in SE US
H
H. Wang, Derived from 39-year NCEP reanalyses
(1958-1996)
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Challenges

• ENSO - extending, improving predictability and
  understanding of impacts
• 97-98 ENSO, remote response but not exactly like
  82-83
• 02-03 ENSO, but little impact along American
  coasts
• The ENSO observing system - improve it to better
  understand physics and to improve models -
  process studies like PUMP and/or additional
  long-term climate obs
• Extending climate observations along the American
  coasts
• Observing, diagnosing the teleconnections and
  local coupling
• Ongoing evaluation of models, predictions

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S-I climate variability in Chile atmospheric
teleconnection from western Pacific ENSO
modulation of basin scale atmospheric
circulation ocean path - coastal Kelvin waves
carry ENSO signal south, Rossby waves go west
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Challenges

• The Gulf of Mexico (NW S. Atlantic, SW S.
  Atlantic, E. N. Pacific)
• What role do adjacent seas play in S-I
  variability in America
• Many observing systems in some areas for diverse
  purposes - coordination for climate obs? Climate
  quality?
• The expansion to the global tropics, extratropics
  (remote seas)
• Building the PACS/GAPP climate obs system
• Pacific, Gulf of Mexico, Tropical Atlantic,
  Indian
• Bndy forcing, ocean and land
• The basin boundaries (adjacent seas, orography)
• Expansion to extratropics
• Building on EPIC, NAME, MESA, VOCALS, GCIP
• Conversion of process studies, enhanced
  monitoring
• Into climate obs - a credible record of SI
  variability and data base for empircial studies
  and reanalyses
• Into improved sampling strategies
• Carrying forward links to operational agencies
• Cal/val and motivate improvements to models,
  remote sensing

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Challenges

• Are climate obs in jeopardy? Is there an
  opportunity?
• Will we have the right in-situ obs
• Operationalizing climate obs
• The societal value of climate obs
• Will we have the right satellite obs
• Scatterometer, precip, altimetry, color,
  salinity, radiation
• The operationalization of NOAA satellite missions
• The planning process for climate obs in jeopardy?
• NRC panels gave way to project specific panels
• Focused, productive
• Able to stand back and provide oversight?
• Reorganization in NOAA
• Research to operations transitions
• Performance metrics
• Reduction in outside input to planning process

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Opportunities

• Productive PACS/GAPP science
• Process studies
• Modeling
• Indian Ocean obs beginning to mature
• High level focus on climate obs
• High level focus on improved predictability
• Climate Process Teams
• Data archiving
• PACS/GAPP partnership

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The observing system and prediction
It is up to you to direct the provision of a
climate observing system which will be

• the research infrastructure to prospect for
  predictability to improve prediction skill,
• which includes long-term monitoring systems of
  the variability of the coupled ocean-atmosphere
  -land system,
• which is needed in order to develop, improve, and
  evaluate models of the coupled ocean-atmosphere-la
  nd system to be used for prediction,
• and which will provide ongoing evaluation of
  climate monitoring systems and prediction
  products from IRI, NCEP, CDEP and other
  operational centers ,
• and be critical to attribution of variability.