David H. Bromwich1 Ola Persson2,

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Atmosphere Observational Needs for Model
Validation

• David H. Bromwich1 Ola Persson2,
• and John J. Cassano3
• 1Polar Meteorology Group
• Byrd Polar Research Center
• The Ohio State University
• Columbus, Ohio, USA
• 2National Oceanic and Atmospheric Administration
• Earth System Research Laboratory
• Cooperative Institute for Research in
  Environmental Sciences
• 3University of Colorado
• Cooperative Institute for Research in
  Environmental Sciences
• Department of Atmospheric and Oceanic Sciences

2
Just What Is Arctic?
We Select

• Arctic Ocean
• Greenland Ice Sheet and
• The rivers which empty into the Arctic Ocean and
  maintain its near surface stratification

Thus our Arctic extends from 45N to the North
Pole
3
Selected Weather and Climate Features and their
Resolvability with Current Arctic Observations

• Climate Modes
• e.g., Arctic Oscillation/NAO, PNA
• Yes, we can resolve these large-scale features
  with current observations
• Weather
• e.g., Synoptic-scale cyclones
• Reasonably, but some shortcomings see next
  slide
• Mesoscale Phenomena
• Polar lows, sea breezes, barrier winds, katabatic
  winds
• Insufficiently resolved
• Sea Ice
• Extent, fractional coverage, thickness, albedo,
  snow cover, melt ponds
• Yes Yes No
  ? ? ??
• Land
• Snow cover, SWE, permafrost, vegetation, lakes
• Yes ? ?
  Yes ?

4
The storm of 19 October 2004 as depicted by the
NCEP/NCAR global reanalysis. Contours represent
isobars of sea level pressure at increments of 3
hPa. from visualization package of NOAA Climate
Diagnostics Center
The Figure shows an intense storm depicted in the
NCEP/NCAR reanalysis for 19 October 2004. This
storm, which led to flooding of downtown Nome,
Alaska, has a central pressure of 949 hPa in the
reanalysis. The actual central pressure deduced
by the National Weather Service was as low as 941
hPa.
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3 Critical Components for an Integrated Arctic
Observing System

• Remote Sensing Observations
• only way to obtain comprehensive regional
  coverage
• Numerical Modeling
• Fills gaps in the system
• Maintains physical consistency in the system
• In-situ Observations
• Provides the ground truth to calibrate the system

6
Typical distribution of COSMIC GPS radio
occultation soundings (green dots) over a 24-hour
period over the Arctic.
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Greenland Climate Network (GC-Net)
HUM
TUN
GIT
NGP
NAE
NAU
SUM
CP1
KAR
CP2
JAR1,2,3,SWC
NSE
DY2
SDL
SDM
Steffen and Box (2001), JGR
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IASOA Observatories
Data of interest to the IASOA consortium include
measurements of standard meteorology, greenhouse
gases, atmospheric radiation, clouds, pollutants,
chemistry, aerosols, and surface energy
balances. 
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Tara Ice Station
Tara Arctic 2007-2008 is a specific project for
IPY. The boat will undertake a Nansen-like
crossing of the Arctic Ocean, drifting from the
north of Siberia to the Fram Straight during
almost two year trapped in the ice. The Tara
Arctic ice station will provide permanent
facilities for science fieldwork, in-situ
observations and maintenance possibilities of
probes and automated buoys.
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Arctic System Reanalysis (ASR)an NSF-Funded IPY
Project

• Rapid climate change appears to be happening in
  the Arctic. A more comprehensive picture of the
  coupled atmosphere/land surface/ ocean
  interactions is needed.
• 2. Global reanalyses encounter many problems at
  high latitudes. The ASR would use the best
  available description for Arctic processes and
  would enhance the existing database of Arctic
  observations. The ASR will be produced at
  improved temporal resolution and much higher
  spatial resolution.
• 3. The ASR would provide fields for which direct
  observation are sparse or problematic
  (precipitation, radiation, cloud, ...) at higher
  resolution than from existing reanalyses.
• 4. The system-oriented approach would provide a
  community focus including the atmosphere, land
  surface and sea ice communities.
• 5. The ASR would provide a convenient synthesis
  of Arctic field programs (SHEBA, LAII/ATLAS, ARM,
  ...)

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Optimizing the Arctic Observing Network Using the
ASR Framework Observing System Experiments
(OSEs) are numerical model-based experiments to
test the impact of existing observations.
Sometimes called data denial experiments.
Observing System Simulation Experiments (OSSEs)
are numerical experiments that test impacts of
future observing systems, e.g., new satellite
sensors and AWS. Determine the observations
needed to optimize the observing system.
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Key Points for SAON

• New Data Sources
• Weather and Climate Applications
• Combining Remote Sensing, Modeling and In-situ
  Observations
• Data Assimilation
• Bringing Observations, Modeling and Data Users
  together
• Arctic System Reanalysis as a Synthesis
• Better Integrated Use of Resources
• User Friendly Data Handling

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Atmospheric Model Evaluation

• Evaluate over a variety of polar surface types
• Ice sheet
• Sea ice / ocean
• Non-ice covered land
• Evaluate atmospheric state
• Temperature, pressure, winds, humidity,
• Evaluate atmospheric processes and relationships
• Surface energy budget
• Cloud processes
• Are we getting the right answer for the right
  reasons?
• Do the relationships occurring in the data also
  occur in the models?

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Example ARCMIP Evaluation
Comparison to ERA40

• Need to evaluate models on several scales
• - At largest scales need to compare model to
  reanalyses
• - At smaller scales can compare model to point
  observations, although care is needed

Comparison with SHEBA surface observations
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Comparison to SHEBA sensible heat flux
Comparison to SHEBA latent heat flux - Large
accumulated errors in almost all models are of
concern in coupled simulations
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ARCMIP comparisons of sensible heat flux
relationship - Only one model is able to decrease
the magnitude of the Hs for very stable
conditions as in the observations
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Analysis of relationship between variables SWD
and CWP
It is important to not only evaluate the model
state but to evaluate if the model reproduces
observed relationships between variables
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Conclusions

• Care needs to be taken when evaluating variables
  that are the result of many interacting, complex
  processes
• It is useful to evaluate the different processes
  that are responsible for the final state
• Evaluation of processes and relationships between
  variables can provide additional insight into
  model performance
• Useful to highlight aspects of the model that
  need improvement

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