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Observing and
modeling global
warming impacts
Mark Patterson
Mark Brush
Marjorie Friedrichs
Global change science in the
coastal zone has a problem
•
Coastal zone often
changes faster than our
ability to observe it
•
HMS Challenger-style
oceanography: sail, stop,
dip, repeat, doesn’t work
well for coastal zone
•
Most data are heavily
aliased
What is aliasing?
•
•
•
•
•
•
Occurs when a ‘signal’ in time or space isn’t sampled often enough
Signal can be anything: temperature, chlorophyll, # fish/river, shoreline changes
Analyses of underlying rates of change are corrupted!
Can make predictions of models problematic
Q: Why care about aliasing for climate change research?
A: We are looking for small absolute changes that may have big integrated
impacts. We need to see small rates of change in widely varying signals.
Observing systems are the new costeffective way to collect long-term
data useful in climate change research
VECOS, Tidewatch are VIMS’ contribution to regional and
global efforts
Observing system technology tries to solve the aliasing
problem
Models
Predictions
Sample data for dissolved oxygen in the lower York River in 2007 from the ACROBAT towed
instrument platform and the continuous vertical profiler deployed at the US Coast Guard pier.
Source: I.C. Anderson, M.J. Brush, L.W. Haas, and H.I. Kator (VIMS).
Examples of models already making useful predictions are:
Current and weather in the Gulf of Mexico using a feature analysis program:
https://oceanography.navy.mil/legacy/web/cgi-bin/search.pl/0-ussouthcom/metoc/*/110+40/*/19
Models are mathematical
simplifications of how
nature works
Predictions can be used “operationally” in real-time
or to guide decisions now that affect future
outcomes (what ifs?)
Models need observing
system data for:
assimilation &
validation
Models relevant to climate
change in the coastal zone
come in different flavors
• Empirical probabilistic models (jellyfish in
the Bay, HABS, coral bleaching)
• Data assimilative forecasting (water depth/
salinity) and “what ifs?” (inundation maps)
• Species specific models (blue crab)
• Ecosystem models (Ches. Bay Program
efforts - “what ifs?”)
Model predictions end
up on OOS web sites
•
Increased pressure from funding agencies to do this - no more
modeling for the sake of modeling
•
Products often occur side-by-side with more traditional
meteorological forecasts
•
Brave new world of “experimental products” - how to present the
GUI to the public, how to limit liability, how to partner with
stakeholders
•
Explosive growth of modeling has led to increased research on
understanding how model tuning procedures and model complexity
affects performance - expect more “bake offs”
earch
ms
dress
the
ion of
y
g, and
e
ECOSYSTEM MODELING PUBLICATIONS
NUMBER OF PUBLICATIONS .
y in
he way
ork in
use of
last
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131
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50
40
30
20
Riley
(1946, 1947)
10
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1940
Chesapeake Bay Modell
1950
1960
1970
1980
1990
2000
Number of aquatic science publications found using the search term “ecosystem
model” in the ASFA literature database. Publication dates of the first two
versions of the Chesapeake Bay Program water quality model are noted
(HydroQual 1987; Cerco & Cole 1994); the model continues to be updated
today. From Brush (in prep).
Concluding remarks
•
Climate change research will be enhanced by the
marriage of observing systems with models
•
Institutional learning curve for marine labs, states, and
Congress on the true expense of observing systems
•
Expect “consensus forecasting”, common now in
weather and climate arena to migrate to “biological
forecasting”
•
Rapid consensus needed on how to inform the public
and managers on how “forecasts” should be interpreted
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Don’t be such a scientist!
Dead zones - the most global change water quality problem
in the coastal ocean
Excess nutrients
Too many algae
Algae die
(fertilizers, cars)
www.shiftingbaselines.org
Oxygen gets
used up
Decompose
Sink to bottom