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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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).

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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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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