Transcript 355o8

Overview of 355 Themes
and Concepts
Environmental Problems are generally
characterize by noisy and ambiguous data.
Understanding errors and data
reliability/bias is key to implementing good
policy
Making a model of the data is an advanced
technique that is sorely needed in this field
Goals of this Course
• To gain practice in how to frame a problem
• To practice making toy models of various data
waveforms
• To understand the purpose of making a model
• To understand the limitations of modeling and
that models differ mostly in the precision of
predictions made
• Provide you with a mini tool kit for analysis
Sequence for Environmental Data
Analysis
• Conceptualization of the problem  which data
is most important to obtain; how to obtain a
random/representative data set?
• Methods and limitations of data collection 
know your biases (e.g. Sunshine Moonbeam)
• Presentation of Results => data organization
and reduction; data visualization; statistical
analysis
• Compare different models
Statistical Distributions
• Why are they useful?
• How to construct a frequency distribution
and/or a histogram of events.
• Frequencies are probabilities
• How the law of large numbers manifests itself
 central limit theorem; random walk;
expectation values
Statistical Distributions
Mapping dispersion units into probabilities of an event occurring
Some Tools
• Linear Regression  predictive power
lies in scatter; the “r” value is
unimportant for scientific analysis
• Slope errors (cell C18 in Excel) are
important and must be factored in to
determine the total uncertainty of your
prediction
• Identify anomalous points by sigma
clipping (+/- 1.8 s (1-cycle)
More Tools
• Chi square test – measures goodness
of fit
• Understand how to determine your
expected frequencies
• Chi square minimization used to find
best fitting model
• Chi square statistic used to accept or
reject the null hypothesis (that the data
is consistent with the model plus
random fluctuations)
More Tools
• Moving average technique applied to
noisy data
• Z-test: determine significance between
two mean values for two distributions
KS Test
• Most powerful for comparing two distributions
• Statistic is the maximum difference between 2
cumulative frequency distributions
• Data does not need to be normally distributed
• Best means to compare data distribution
against a model
• Can’t be used for sample sizes less than 10
Arrival Statistics (Poisson)
• Events have to be discrete
• A good measure of the average event
rate allows the probability that N events
will occur over some time period to be
determined
• Large values of l produces a
distribution that is normal.
Green House Effect
Long wavelength
absorption properties of our
atmosphere increase the
surface temperature- Water
vapor is the dominant
effect, followed by CO2
Methane
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Potential role of methane is larger than CO2
GWP = 21
Scales with population growth
Released from permafrost
Released from hydrate deposits
Emissions now rising again due to global
wetlands returning from prolonged drought
Difficulty of Climate Change
Detection
• Data is noisy
• Temporal baseline of data is not long
enough
• Multi decadal climate cycles seem to be
very important
• Oceans act as a buffer that delays the
overall effect
Predator Prey Relations
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Non linear in nature  small changes in one
part of the system can produce rapid population
crashes
Density dependent time lags are important
(what causes them?)
“Equilibrium” is intrinsically unstable
Logistic growth curve makes use of carrying
capacity concept, K
Negative feedback occurs as you approach K
R selected vs. K selected mammals
P vs H
Understand why graphical representations
look like this:
 What drives the lag time?
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Human Population Projections
What assumptions are used?
Does human population growth respond to
the carrying capacity concept?
World population growth rate is in
continuous decline (but still positive)  will
this continue indefinitely?
Oscillatory model may be most realistic
What role does increased life expectancy
have? 
Estimation Techniques
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Extremely useful skill  makes you
valuable
Devise an estimation plan  what factors
do you need to estimate
Scale from familiar examples when
possible
Perform a reality check on your estimate
Applied Ecology
 Know what the terms mean and
understand what an iterative solution is:
Applied Ecology II
 Understand from the point of view of the
framework (e.g. the equations) why stability is
very hard to achieve
 What role does finite reproductive age play?
 What makes human growth special within this
framework.
 Understand concepts of equilibrium occupancy
and demographic potential
 Why is error assessment so important here?
Skewed Distributions
This is a probability distribution function and one can still
use the area under the curve or area between x values to
determine probabilities via numerical integration
Time Series Analysis
 Much of environmental data analysis
or modeling represents the time
evolution of some observed quantity.
 Long term trends with cyclical
oscillations and/or short term regular
deviations; plus random variations
Value of time Series Analysis
Gas Prices: The long term trend is steep and rises
above the fluctuations
Climate: The long term trend is overwhelmed by
the fluctuations
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You want to uncover the long term trend
that may be buried under the fluctuations
Determining the amplitude of the
fluctuations helps to determine if any recent
events are aberrant
Two cases: Gas prices; Climate Cycles:
Multiple Sine Wave Fits
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Can often reproduce the behavior seen in
complex time series
The Data Rules
Always, The
always
plot
data
Average ALWAYS
value for this data
set your
is
totally meaningless
 Never, never NEVER put data through
some blackbox reduction routine without
examining the data themselves
 The average of some distribution is not
very meaningful unless you also know the
dispersion. Always calculate the dispersion
and then know how to use it!
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More Data Rules
Always compute the level of significance
when comparing two distributions
 Always know your measuring errors. If
you don't them you are not doing science.
 Always calculate the dispersion in any
correlative analysis. Remember that a
correlation is only as good as the
dispersion of points around the fitted line.
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The Biggest Rules
Always require someone to back up their
"belief statements" with credible data.
 Change the world. Stop being a passive
absorber of some one else's belief system.
 Frame all environmental problems
objectively and seek reliable data to
resolve conflicts and make policy
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And Now For Something
Completely Different:
Global climate change, species extinction,
oil depletion, world food crises, global
inequity, environmental justice, depletion
of mineral resources, blogs, sustainability,
alternative energy solutions, alternative
fuels, more blogs, Obama, Hillary, McCain,
whatever …
 WTF? How did all of this happen?
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Your World Upon Graduation
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The Fossil Fuel Legacy
Engineering the Planet
What Compels us to do so?
Consumption: Pros and Cons
• This depends on how you want to index
consumption – personal consumption/affluence
is different than production/consumption that
indirectly leads to better society infrastructure
and services.
• What matters is the rate of consumption relative
to the resource base. Main problem is that
short term market growth, which we value, wants
high rates.
• Sustainability demands lower rates  this is the
clash of values.
Key Historical Moments
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We are special (different than other animals)
We are uniquely positioned at the center of the
Universe (reflects our “special-ness”)
The Universe is ordered, logical and rational –
Age of Reason humankind is unbounded
The Newtonian world shows us the machine and
it is precise (we can now engineer the planet)
The notion of uncertainty, as a valid and integral
scientific concept, arises too late in this process
 we already have truth pathways established
Essence of Science
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Knowledge based on measurement means
that knowledge is both uncertain and
subject to change when new and better
measurements are made – there is no
room for absolute truth in this
methodology
Problems can then only be solved by
objective means that rely on real data and
not bias or wishful thinking.
Choice Pathways
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Which world does humanity want to live
in?
One that is based on a belief system
that is then projected on to the natural
world to support that belief (this is the
BIAS)
One where scientific methodology and
thinking is used to enable, on a planet
wide scale, the enlightenment motto that
all men are created equal
Relationship with the Land is key
• Three possibilities
• The Land is Sacred  “Indigienous
Model” your ancestors are buried in it
• The Land is shared  “European Model”
 lots of people, not much land
• The Land is Owned  “American Model”
 lots of land, I can piss on it if I want,
afterall, its mine.
Continued Economic Development
Requires high Energy Use
• 1900  100 Million Capitalists to build
markets
• 2003  2.5 Billion new capitalists
• Energy is the core of the “environmental
problem”; Environment is the core of the
energy problem
• The energy-environment intersection is the
core of the sustainable-prosperity problem
Resolution?
• We need to stop be driven by market
economics and start to recognize that
energy and environment is a shared
resource.
• 20 Million college students should march
on Washington demanding this: