Ground-based Observing
System for Climate Change
Monitoring
Tom Ackerman
Lecture II.6
Topics
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Why do we need ground-based
observations for climate and what
would we do with them?
What is measurement synergy all
about?
To be added
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Results from Monday discussion
GCOS Position on Ground-based
Observations for Climate
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Primary tool is satellite(s)
Surface and in situ observations only
needed for
• Calibration and validation
• Measurement of parameters that cannot be
measured by satellite (“biodiversity,
groundwater, carbon sequestration, subsurface
ocean”)
• Long time series for diagnosis of global change
(“surface temperature, precipitation and water
resources, weather and other natural hazards,
… pollutants”)
GCOS Position – Surface networks
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GCOS surface network (GSN)
• Measure in situ (surface) observations of
surface temperature, humidity, wind speed and
direction
• Mention “surface radiation (e. g., sunshine
duration)”
• Approximately 1000 stations world wide
• Over ocean – volunteer ships, buoys (fixed and
floating)
• Baseline Surface Radiation Network (BSRN)
should be enhanced (currently a volunteer
network of 39 stations
BSRN Locations
GCOS Position – Surface networks
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GCOS surface network (GSN)
GCOS Upper Air Network (GUAN)
• Radiosonde T, q, wind (~150 stations)
• Cloud properties from surface observer
• Commercial aviation
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Global Atmospheric Watch (GAW)
• CO2, CH4, O3, other GHGs, aerosol
• ~33 stations, some ship measurements
So, what’s wrong with this picture?
A Minority Report and Proposal
Acknowledgements to
Jim Coakley (OSU) and Chuck Long (PNNL)
Two underlying premises

Understanding and addressing climate
change will be a critical societal problem for
the next several decades
• The most important issues will be water availability
and energy production and use
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Climate change is a global problem and must
be attacked with globally integrated models
and observations
Some Limitations of Satellite
Observations
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Surface fluxes of energy and
moisture
Properties of low-level clouds in
multi-layered systems
Aerosols below and mixed with cloud
layers
Absorption of solar energy by
aerosols
More?
Role of ground-based observations
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Continuous observations of cloud, aerosol and
radiative properties at high temporal resolution
that facilitate the understanding of cloud and
aerosol processes (Improvement of atmospheric
physics)
Alternative climatologies of radiation, cloud, and
aerosol quantities crucial to climate change
science (Model and satellite comparison)
Critical datasets to test the surface and cloud
properties retrieved from satellite observations
(Evaluation)
A means for evaluating the performance of
satellite instruments over the lifetime of the
satellite (Calibration)
Level 1 stations
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Basic surface observation site – BSRN
type
Measurements of
• Surface energy fluxes
• Surface state variables
• Column aerosol properties
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Co-located with sonde sites
Order of 1000 to 2000 sites globally
Site capital cost $75K
Site operational costs: $16K/year
Level 2 Sites
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Includes Level 1 site instruments
Incorporates highly reliable remote
sensing instruments to measure
•
•
•
•
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Column water vapor
Cloud base height
Aerosol backscatter profile
Cloud fraction and occurrence
Order of 100 to 200 sites globally
Additional capital cost: $200K
Additional site operational cost: $4K
Level 3 Sites
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Includes Level 2 site instruments
Like US ARM and European sites
High end remote sensing sites to measure
• Surface properties
• Vertical profiles of aerosol and cloud properties and
thermodynamic variables
• Spectrally-resolved radiances and fluxes
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Focus on high spatial and temporal resolution
Order of 10 to 20 sites globally
Additional capital cost: $1.5M
Additional site operational cost: Undetermined
Increasing
Instrument Complexity
Data volume
Cost
Operator training
CAP sites
Process
Best obs possible
Augmented Sites
Climatology
Measured cloud properties
Enhanced meteorology
Basic surface radiation and meteorology sites
Monitoring
Sfc energy budget, aerosol OD, statistical cloud properties
Strategy
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Integrate concept into climate observing system
plans such as GCOS
Build on existing networks
• Sustained support for current sites
• Funding to expand observations to under-represented
areas (oceans, southern hemisphere) => need much
better geographical representation
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Enable science
• Shared and accessible data => archive!
• Algorithm development and operational application
From observations to science …
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Three level plan implies a progression of
understanding and interpretation
• More sophisticated measurements clarify what
we can learn from less sophisticated
measurements
• Multiple measurements provide additional data
that allow us to do more detailed comparisons
with models
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Top level sites are the key to making the
entire scheme work!
The Atmospheric Radiation
Measurement Program
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Sites are prototypes of the Level 3
sites
What have we learned from ARM?
Note: Sites in Europe (e. g., Cabauw, Lindenberg, Palaiseau,
Chilbolton) are as well instrumented and scientifically useful
as the ARM sites. ARM is a more operational program, so it
offers the best model for us to consider here.
Atmospheric Radiation
Measurement Program
Goal
Improve the treatment of cloud and radiation
physics in global climate models in order to
improve the climate simulation capabilities of
these models
Two fundamental science questions
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If we can specify the
properties of a cloud field,
can we compute the
radiative fluxes?
Requires knowledge of cloud
properties (3D structure, water
path, phase, size, etc.)
Two fundamental science questions
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If we can specify the largescale atmospheric fields,
can we predict the cloud
field properties?
Requires 3D field of state
properties and cloud field
properties
ARM Program Components
Infrastructure
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Development of ground-based remote sensing
facilities
Continuous data acquisition and archival
Data analysis
Physical modeling
Parameterization development and testing
Science
ARM History
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Program initiated in 1990
• initial science grants
• Site planning and instrument selection
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USA facility (SGP) data available since 1994
Full instrumentation in 1996
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Tropical western Pacific site
• Manus 1996
• Nauru 1998
• Darwin 2003
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North Slope of Alaska (Pt. Barrow) since 1998
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ARM Mobile Facility first deployment in 2005
Southern Great Plains
Central Facility
Southern Great Plains Central Facility
North Slope of Alaska
North Slope of Alaska – Barrow facility
Tropical Western Pacific
Darwin
Tropical
Western
Pacific Sites
Manus, PNG
Nauru
Darwin
The ARM Mobile Facility at Pt.
Reyes
What have we learned in ARM about
…
Operating ground-based sites
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Continuous measurements
• Can be done but difficult in remote locations
• Measurement synergy is key to science success
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Instrument improvement and development
• Interaction with science community is critical
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Expensive
• But not when compared with science return
Data acquisition and archiving
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Science success depends on a good archive
Good => reliable, easy to use, responsive
Best metric is user satisfaction
Second best metric is data outflow
Data analysis
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Data must be calibrated and quality-controlled
Continuous data
• Multiple instruments
• Multiple sites
• Multiple seasons and years
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Scientists will find more things to do with the
data than you ever imagined
High-resolution atmospheric modeling
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Most profitable area of scientific investigation
• GEWEX Cloud System Studies (GCSS) link
• Weather forecasting centers (ECMWF, NCEP,
etc.)
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Data used for
• Model initialization
• Model evaluation
• Model development
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Classification studies
Evaluating and improving climate
models
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Most difficult problem
• Requires a lot of data
• Dependent on clever data analysis and understanding
• Critical issues of spatial and temporal sampling
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We are making significant progress!
• Model evaluation
• Cloud and convection parameterizations
(more about this later!)
Retrievals, Synergy and Redundancy
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In the beginning, there was one measurement
and one retrieved property
Lidar backscatter => Cirrus optical depth
Required
closure
assumptions!
Retrievals, Synergy and Redundancy
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Then we added a
measurement of
Window
Radiance
Lidar + I(10 mic)
=>  and IWP
Synergy!
Retrievals, Synergy and Redundancy
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Then we added
an
Interferometer
Lidar + I()
=> , IWP, Deff
Synergy!
Redundancy!
Retrievals, Synergy and Redundancy
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Then we added a
mm radar
Radar + Lidar + I()
=> multiple values
of , IWP, Deff
Synergy!
Redundancy!
Choice!
Retrievals, Synergy and Redundancy
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Multiple measurements allow us to
retrieve more pieces of information
Observational redundancy allows us to
address issues of instrument noise
Multiple measurements allow us to
perform complex retrievals that optimize
information based on instrument
operational characteristics
A priori information based on our
knowledge of atmospheric phenomena can
also be folded into the retrieval process