observing and modeling global warming...
TRANSCRIPT
Observing and modeling global warming impacts
Mark PattersonMark 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 cost-effective 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
Ocean Observing Systems and Modeling for Global Changes
p. 3
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).
models, which then make predictions we can test with the observing system, in near real-time.
The operation of the model can be improved and refined by data collected by the observing
system, a technique called data assimilation. New sensors, many developed at VIMS or with its
industry partners, are now available to detect and count fishes (or underwater threats to VA DoD
installations), toxins in the water at extremely low concentrations, and harmful microbes. New
techniques to analyze the data from the sensor network have been developed. Neural networks, a
kind of computer program that can make sense of data similar to how the human brain works,
can recognize commercial underwater species, or underwater threats from terrorists (US Patent
7,221,621 to William & Mary). The synergy between these technologies: network, sensors
advances, ways to analyze the data, and mathematical modeling of nature, is profound, and will
change the way we conduct science and homeland security, in the coastal zone.
An example of how large scale measurements in space and time can contribute to our
understanding of water quality are two systems recently deployed in the Chesapeake Bay near
VIMS: the ACROBAT profiling instrument, and an autonomous vertical profiling instrument
(see figure below). These devices give us a picture of dissolved oxygen, a key index of water
quality, that can’t be obtained any other way.
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-timeor 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?”)
Ocean Observing Systems and Modeling for Global Changes
p. 5
oxygen), models of the open ocean have more often been used to study the role of the oceans in
climate change, particularly their ability to take up and store human-produced CO2 (Doney et al.
2001).
Since ecosystem models are
driven by environmental
variables such as water
temperature, depth, and
loading of nutrients and
sediments from both rivers
and precipitation, all of
which are projected to
increase in Virginia from
future climate change, they
are ideally suited to studying
the potential response of
Virginia’s marine waters to
these changes. A schematic
of a typical ecosystem model
is shown in the figure to the
left, with the major variables
affected by climate change
highlighted in yellow. Each
arrow is underlain by a series
of mathematical equations
that define the relationships
between the various
components of the model. Projected increases in loading of nutrients and sediments from both
rivers and rainfall will increase water column stocks of nutrients and reduce light penetration.
Projected increases in sea level rise will also reduce light penetration that is critical for
photosynthesis by aquatic plants. Projected increases in temperature will increase the rates of all
biological and chemical processes and probably favor smaller organisms with greater rates of
nutrient cycling and larger oxygen demands. Models allow us to run a series of “what if?
scenarios” with these projected changes to determine how they may affect the balance among
phytoplankton and benthic plants, particularly survival of submerged seagrasses, rates of oxygen
consumption and extent of hypoxia and anoxia, and the flow of energy up the food web to
commercially and recreationally important finfish and shellfish.
Potential Responses
Virginia needs a sustained bay and coastal observing system, with its observations feeding into
models that predict the local weather, storm inundation, seafood distribution and abundance, and
when and where harmful algal blooms and degraded water quality are to be found. This
observing system will help protect life and property during storms, help resource managers make
effective decisions, provide underwater security to the numerous military and federal facilities in
Schematic of a typical coastal ecosystem model showing major groups
and flows between groups, which can be expanded into any number of
species. Major inputs to the model which are affected by climate change
are shown with yellow boxes and arrows.
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”
Ocean Observing Systems and Modeling for Global Changes
p. 4
Water level in the Chesapeake Bay, very useful to the shipping industry:
http://tidesandcurrents.noaa.gov/
Harmful algal bloom forecasting system in the Gulf of Mexico, very useful for health
authorities and the shellfish industry: http://www.csc.noaa.gov/crs/habf/
Coral bleaching (a stress response exhibited by corals to warm water), with predictions
available in Google Earth: http://coralreefwatch.noaa.gov/satellite/ge/index.html
Stinging jellyfish distributions in the Chesapeake Bay:
http://coastwatch.noaa.gov/seanettles/sn_maps.html.
As Virginia’s climate changes, society needs continuous data, gathered on a long-term basis, of
what is happening on, in and around the water. Economists who have studied the value of these
observing systems in protecting society and managing our resources have concluded they are a
sound investment.
Predicting the effect of climate change using ecosystem models
Computer models have great potential to help us predict the effects of climate change on marine
ecosystems, both in Virginia and around the world. Models are mathematical simplifications of
real ecosystems. They work by piecing together known relationships between environmental
variables like temperature, light, and nutrient supply and biological responses like growth,
respiration, and feeding rates of organisms. While it would never be possible to study the
response of Chesapeake Bay to an experimental addition of nutrients while at the same time
heating the bay to simulate climate warming, computer models allow us to do just that in a
“virtual laboratory”.
Ecosystem models have a long history in
marine science and can be traced all the way
back to Gordon Riley’s pioneering work in
the late 1940’s (figure at right). The use of
models has literally exploded over the last
decade with the development of powerful
personal computers and the increasing
demand to predict responses at the
ecosystem level. Models began as research
tools to understand how natural systems
function, and quickly expanded to address
major management questions such as the
effect of nutrient addition on degradation of
the coastal zone. The Chesapeake Bay
Program’s linked models of water
circulation, watershed nutrient loading, and
water quality response are arguably the
best-known examples of the use of models to inform management policy, particularly in setting
goals for reduction of watershed nutrient and sediment loads to improve bay water quality
(Linker et al. 2001). Although coastal and estuarine models have typically been used to study
nutrient-fueled eutrophication (dense growth of plant life and death of animal life from lack of
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1940 1950 1960 1970 1980 1990 2000
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Riley
(1946, 1947)
ECOSYSTEM MODELING PUBLICATIONS
Chesapeake Bay Model
131
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).
l
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
Collatera
l
Without a
Paddle
Napoleon Dynam
ite
65 40 19 25 0.4 10
Pew Ocea
n Report
38% 96%58% 3%3 <0.1
ProductionMarketing Don’t be such a scientist!
Millions
Dead zones - the most global change water quality problemin the coastal ocean
www.shiftingbaselines.org
Too many algaeExcess nutrients (fertilizers, cars)
Algae die
Sink to bottomDecomposeOxygen gets
used up