>> BRUCE: Good evening and welcome to NOAA's Climate
Stewards Education Project November webinar.
This evening, we're pleased to present Ms. Tarlise Townsend
from Michigan's Ford School of Public Policy.
She will be presenting understanding uncertainty in
the context of climate change.
Ms. Townsend is currently a Ph.D student at the University
of Michigan's Ford School of Public Policy.
After completing her Bachelors of Science in Neuroscience and
her Bachelors of Art in Dramatic Studies,
Tarlise spent a year studying risk and uncertainty
communication at Berlin's Max Plank Institute for Human
Development where she was a DAD scholar.
She then took what she learned into the field as a Henry Lewis
Scholar working in Vietnam's ministry of Science and
Technology.
She and her colleagues used field experience to investigate
how rural Vietnamese perceived climate change and uncertainty.
Future projects, she looks forward to studying ways to
link behavioral science with policy.
And Tarlise, your mic is hot and you now have the calm.
>> TARLISE: Let me accept the calm.
Wonderful.
Well, thanks, Bruce and hi everyone.
I'm so excited that you could join the conversation this
evening.
I'm going to talk about uncertainty in climate change
and something that's comes up a lot in the national climate
change whether we should be concerned about climate change.
But it's turns out that uncertainty is incapable in
forecasts and so we really need to have some 1 understanding of
what it's all about and what we can do with it.
So I'll start by addressing number of the questions that
your students might raise or that you might want to present
when you talk with them about climate change.
And then, we will move on to talk about some of the ways
that materials for the public are often non-transparent and
in their communication of information,
and ways to avoid being misled by that information.
So, let's jump at -- >> BRU I apologize.
Tarlise is going to be going through her whole presentation
first.
If you have any questions that you would like to ask her,
please feel free to type them into your question box and we
will address them at the end of the presentation.
If they reference to a specific slide,
please note that slide in your question.
Thanks Tarlise.
>> TARLISE: And sorry, I don't have numbers on my slides.
should have added those in.
Okay, so, uncertainty is a tricky term,
right because it has this very colloquial and everyday
meaning.
And in addition to a scientific and in fact,
even within science, there many ways of categorizing
uncertainties.
So, what do we mean when we talk about it?
Well, i's not uncommon for people to interpret a phrase
like scientific uncertainty as meaning scientists are just
ignorant, that they don't have the knowledge or the confidence
maybe to make any convincing predictions.
And then compounding that issue is that there's this tendency
to assume scientific uncertainty is a
laugh.
So here is a phrase t often hear,
if they can't predict the week's weather accurately,
how can they possibly predict long-term climate changes?
So one problem with this of course is that whether in
climate models are different.
And so what we're trying to predict is the specific daily
weather forecast is very different from that of a
long-term climate projection.
So this is an apples and oranges comparison.
But also listed in a statement like this is this idea that
just because there is uncertainty,
scientists can't say anything at all with confidence?
So you can't take anything away from their claims.
And here is a cartoon illustrating this perception
which I actually found accidentally while googling
around.
But it's from a pretty prominent skeptic's blog.
And so these two guys are in the climate research division
and one says to the other, I figure there is a 40 percent
chance of fog and a ten percent chance of we know what we're
talking about.
So again, you see here completion of weather and
climate projections.
And then, you can also sense this sort of implication that
these scientists are just kind of bummed away and incompetent,
and ignorant.
So this is the problem.
But the good news is t there's considerable evidence
that if people understand that A,
uncertainty is inherit to science,
it's normal in good science.
And B, that because of randomness in the environment,
because of
3 climate system,
there will always be amount of uncertainty.
If people understand those things,
they are more likely to be accepting of uncertainty.
So given that, let's talk a little bit about what
scientific uncertainty is and where it comes from.
So we will start with a pretty course distinction but I think
it's actually pretty useful for categorizing unknowns.
notes.
So this was set out by his guy in 1921.
And has been very influential.
So now he basically said, let's distinguish risk from
uncertainty.
So risk he said was this situation where you know what
your probabilities are.
So using this jar of marbles as an example, you know,
we have five red marbles, five blue marbles.
We know what our probability is selecting marbles.
It's 5.
And then, there is this other thing.
This is a situation where you're probabilities are known
or where your uncertainties are not quantifiable.
So and then we know that in this jar are marbles but we
don't know for sure how many are red, how many are blue.
We don't even know if red and blue are your only options.
Maybe they are yellow marbles in here.
So, where 90 in risk is quantifiable.
90 in uncertainty is quantifiable.
We don't know what the probabilities are and Night
argues that really on that type of uncertainty is an entirely
different from his definition of risk.
So okay, why does this matter?
It matters a lot in the case of climate change.
And this is because climate change 4 is introducing a lot
of -- there's really a shift towards uncertainly away from
that risk.
So you can think about night yum risk like a car ensurer,
right which makes decisions about how much to charge you
each month based on historical data, right.
You know how many accidents do people like you typically get
into?
But with climate change, t climate's changing.
So that's the point.
So historical data is not nearly as useful to us.
We can't predict what's c up and so we really don't know
what the probability is of the possible future outcomes
are.
And there are many other reasons for the climate change.
But what does that mean?
Well, when you know what your probability is,
you can make decisions that maximize your likelihood of
success.
So think of a gambler who is very skilled and can play the
probabilities well.
But if you don't know what your most likely outcome is or if
you don't know what even worst case scenario,
you plan effectively.
It is a whole different model of decision making.
And so this is what's happening in climate change.
And we can come back a little bit later to what kinds of
markings might be decrease demanding increase in
uncertainty in climate change.
But I think this can distinguish is a really
important one to hold on to.
Having said that, I'm now going to muddle the back up and refer
to both Nitrian risk and just Nitrian uncertainty as
uncertainty in the rest of the talk but you will hear me talk
about unquantifiable uncertainty.
Okay.
So where is uncertainly coming from in climate change
projections?
So, one way of breaking it down which is again, of course,
but again, really useful I think and it is three sources
probably.
So model uncertainty, natural variabilities and policy
answer.
So model uncertainties are the closest thing to what people
typically think about when they think of scientific
uncertainty.
It's what results from us not knowing exactly how the climate
system and probably systems work because our climate models
are models, right.
They approximate this climate system,
but there is really a lot that we don't know about the
complexities and interactions among the variables within the
climate system and the system that's interacting with.
So you know, this is where you all will encourage your
students to go on and be scientists who are getting the
next generation of great models because this is an area where
we do have potential for narrowing the knowledge gap.
The second major source of uncertainty is natural
variability.
And unlike model uncertainty, this is really something that
we don't have control over and that we can't really get to the
bottom of.
So natural variability refers to chaotic behavior of the
climate system.
So these are things that no matter how good our scientific
knowledge gets, we will never be able to predict because they
are a product of randomness within the climate
system.
And then, a final important but I think 6 often forgotten
source of uncertainty is the uncertainty that we create,
right.
It's policy answer.
So to the extent to which we are committed to eliminate
greenhouse gases will influence how severe an extensive climate
change as to impacts are.
And I also think it's important to think not just mitigation
measures but also measures that are resilient to changes.
Great.
So there's this experience of theology that every unquote
every natural disaster is at least a little bit man-made.
So when a flood occurs, it will be extensive harm.
It's not just a product o the water arrives but also,
how good the resilience is how building are and so on.
So although they don't have a ton of power to reduce the
uncertainty coming from natural variability,
we really do have a lot of space for improved -- for
moving forward and closing the knowledge gap on model
uncertainty.
And then, also you k growing the next generation of
leaders to make the right policy choice.
So we've talked a little bit about what uncertainty is and I
also want to address one thing that it's not.
Uncertainty does not reflect a lack of consensus among
scientists that climate change is occurring.
Or that its largely depleted.
Despite this, studies have found that this is a really
common misinterpretation of science or uncertainty and
climate change.
It seems that people often mistake that as suggesting that
scientists are broadly in disagreement on climate change.
7 advantage of by groups that are
interested in presenting or slowing climate policies to go
make the change.
So this is just one example of political strategy n wrote that
memo in 2003 that voter in there is no consensus about the
global warming.
And the memo was focused on whether we are willing this
climate and the argument was that he administration should
take advantage of this belief in disagreement among
scientists in order to slow -- slow acts on climate change.
And it turns out that it's actually a pretty clever
strategy because the misunderstanding of uncertainty
as implying a lack of scientist consensus has really serious
implications.
So one study found conceived consensus was the
strongest predictor of the acceptance of climate science
among a wide variety of doctors.
And then, another study found that nearly 60 percent of the
variance and support for climate policy was explained by
whether or not the individual scientists were agreeing on the
occurrence of climate change and it's being caused by
humans.
So clearly this misunderstanding can have some
pretty worrisome consequences, right?
The good news is that there's some initial evidence that it
may be -- the issue may be relatively easy to remedy
although I'm always a little suspicious of findings like
that.
But a study from a couple o years ago found that basically,
it's just telling people about the science that consensus on
climate change had considerable impact on a
eliminating doubts in climate change.
An androgenetic climate change.
So this probably explains then why you may have seen this 97
percent value around so often.
So this is you know, 97 percent of climate scientists agree
climate change occurring and is largely human caused.
And so this is a great strategy, right,
communicate the consensus.
So here's this screen shot from NASA's website.
They communicate that 97 percent value and then go on to
list associations that have agreed,
that have made statements on climate change.
So I said before that signs tip uncertainty is often
misunderstood as suggesting ignorance or incompetence among
scientists that you know, it's misunderstood,
meaning a lack of consensus.
So with all of these dangers of misinterpretation of
uncertainty, maybe, it would just be better if we leave
uncertainly out of the picture entirely.
Maybe we should just communicate certainty or act as
if we're certain in our predictions.
And this sounds a b conspiratorial I think to say
so explicitly but I actually think it is a pretty common and
subconscious reaction, or tendency.
And you know, oftentimes when we want somebody to believe us
about something, we often, we may tend to overstate our
confidence about that thing.
And yet, I would caution us against that starkly because I
think not communicating the uncertainty in prediction is
actually more dangerous than communicating it.
So think about it this way: If 9 you convey an uncertain
prediction in a way that's deterministic,
so you claim to be 100 percent certain that this thing will
occur, you might be right a good portion of the time.
But you're also bound to p false some of the time, right,
and that of course creates a serious threat to your
audience's trust, to your credibility.
And this phenomenon actually has been found in a wide
variety of context around the world.
So here's an example from Brazil.
This group of researchers was in Brazil and they were trying
to help farmers there make better agricultural decisions
by providing them seasonable forecast information,
but they decided to provide that information to
deterministically to leave uncertainty out of those
predictions.
Maybe they worried that farmers would doubt their expertise if
they mentioned uncertainty or maybe because they thought they
would just not understand the certainty and or maybe it was
too complex.
But of course when t happens,
the authors right forecast were deterministic and
the first year that events did not unfold actually as
predicted, people stopped trusting them.
So you think, you know, overstating our certainty is a
risky business.
Let's get that slide actually.
Okay, so, an interesting thing about uncertainty, you know,
you may ask like, why talk about it in the first place.
Why even contemplate providing it to people?
Well, not only to reserve credibility,
it turns out that uncertainty can 10 actually be really
useful in decision making.
So, you know, first, like think about why we have weather
forecast and climate projection in the first place, right?
So in the case of weather forecast,
it may be useful to influence the clothes you wear that day
or whether the to spread salt in preparation for a potential
freeze, whether to evacuate the city in the context of an
impending storm.
All of those short term decisions.
And then, the long-term climate forecasts may be, you know,
useful to you in terms of thinking about what kind of
planet policy to support or to increase your city's resilience
on your individual life, how to reduce your own carbon
footprint.
So in all of these cases, the forecast information is used
for decision making.
And in all of those forecasts, there's going to be some
uncertainty.
The good news is that that uncertainty information
actually has been shown to aid decision making by reducing
risk exposure, reducing financial losses and really
having a lot of other accuracy and decision making.
So okay, why?
So, an example from North Dakota, 1997,
the National weather Service had been providing grant work
predictions in the form of two deterministic values.
And Grant was using predictions to make
preparations or the flood to avoid as much damage as
possible.
And yet, they actually suffering more than a billion
dollars of damage.
You can see from this aerial photograph.
so what happened?
Well, it 1 the forecast that were provided
up until the last few days, the accuracy improved in the last
few days.
But by then, it was too late to make preparations.
Those deterministic forecast were several times actually
lower than what the actual forecast was.
The interesting thing though is that the actual flow crest was
within the error range of the predictive value,
within the air or range of the data.
The analysis that the National Weather Service had produced
but that information was not provided.
Right.
So the argument from city officials was are was that,
hey if we had gotten that uncertainty information,
we could have known that there was a wider variety of possibly
outcomes.
And we could have prepared that in a party line.
But okay, in that case, that's like city managers and people
who are dealing with these kinds of predictions on a
regular basis.
And so you might say, like okay, sure,
they can use uncertainties to make their decisions but can
uncertainty really be beneficial to lay people.
But actually, the answer to be pretty consistently.
These images from a 2009 study by these guys,
Wilson and Kaplan.
And Dave's prediction o undergraduates,
a five day temperature forecast,
So one of the groups of undergrads,
this image thing you see in the top left here and these were
deterministic predictions.
So you can see like throughout the day,
you know on March 18 here, see kind of decrease in
temperature and then, it rises towards mid-day put still even
1 time,
those predictions are deterministic.
Now, another group same information but,
with these arrow rangers included.
Right, so you can see the 50 percent range there in red and
then, the 90 percent decline.
And Wilson and Kaplan asked the undergrads to make a decision
using this information.
And what they found was that the folks who had received the
uncertainty information did better.
And, that was not dependent on undergraduate's thinking.
So even the humanities majors and you know,
and whether we're working with statistics on a regular basis,
they were aided by the uncertainty of the information.
And the decisions that Wilson and Kaplan and other studies
have asked are the real world implication.
So this could really matter.
And it's especially relevant again in the context of climate
change because again, we are seeing with climate change,
this widening range and growing variabilities of outcome and
the ubiquity of uncertainty.
So we have to be able to make decisions in the context of
uncertainty.
So some might argue why should we make decisions under
uncertainty?
It seems a little hard.
Yeah, it may be it can be useful to decision-making but
what if you just don't make the decision until there is no
uncertainty any more?
Why don't we just wait until we have more information and we
just make the best decision in the context of certainty,
right, that seems easier.
If you are thinking 1 limiting greenhouse gases some
people argue why spend the money on that now before we
know exactly what's the outcomes of climate change and
exactly how serious the impact.
So this was a statement here that you see here on the screen
that was proposed to several Americans.
So they were asked whether they agreed with the following:
Until we are sure that global warming is going to be a
problem, we should not take any steps that have economic costs.
>> I think it was like 30 percent of people agreed with
us and it is a common reframe that we hear among our policy
makers and political leaders.
But there are two responses that I think are really
important to discuss here.
So one is the issue of your use of uncertainty that we talked
about before.
So again because of that natural variability in the
environment of however long you wait or however long we wait,
or however much scientific progress we made,
there will always be uncertainty in the climate
system and things that we can not predict.
So there will never be a time when you know,
uncertainty is not there.
Some sitting around and kind of waiting for it to go away is
problematic, especially in the context of climate change
because the only situation in which uncertainty would go away
is in the event that happened.
And you can't find any homeowner's insurance after the
fire, right.
So at that point, it would be easier.
The other issue referred to this idea of the uncertainty
monster.
So you 1 tons of suggestions that
because there is some concern in climate change,
climate projection.
That suggest that climate change really won't pose a
problem for us, like oh, there is uncertainty about exactly
what's going to happen.
So that means there must be some chance that means than
there must be some chance that nothing will happen and we
should be pretty hopeful about the outcome.
It actually seems like the reverse is true.
So, 2014, actually two 2014 papers by this guy,
Levin and his colleague, did some call out medical modeling
basically showing that in this current state of final
projections, the more uncertainty increases,
the more damages we can actually expect to see as a
result of climate change.
So this means that rather than being a reason for us to step
back or just sort or optimistic,
more uncertainty should actually demand more urgency in
tackling climate change.
So it should not be a source of adoption or reason for
inaction.
So uncertainty is not going away.
It is not a reason to not worry.
So we need to learn how to work in the face of it.
But it can be so easy to not be motivated in the face of
uncertainty, right.
So going back to the marble example,
a number of studies have found this effect called ambiguity in
people where basically you know,
if you give people the choice between a jar with known
probabilities like the one on the left,
even if those probabilities are really, really unfavorable,
the red is candy and the blue is blue and
15 every 100 blue.
So if the possibilities run favorable for you,
people would rather chose that jar than the jar with the
probabilities are because ambiguity is really sort of
scary thing.
And it can lead to t paralysis which we are all
probably a little familiar with in our lives.
So you can sort of think about how this would work in climate
change , right.
People were maybe thinking, how do I even know what to prepare
for?
How do I know if the actions I take could even make a
difference?
But this is not a reaction that you want, right.
This is not the way that we should be tackling climate
change.
So I have a few suggested strategies for how to respond
to ambiguity diversion.
One is to provide people with very clear action items that
have high efficacy.
And so what do I mean by efficacy.
So two things: Self efficacy is the person's feeling that they
can take the action.
This would be like if I'm thinking about shrinking my
carpet footprint, do I feel like I have the time to
research ways to do it?
Do I feel like I have the energy to make the changes in
my habits or the money to buy more efficient, appliance ,
our car whatever.
And then, the second type of efficacy is response efficacy.
And this is the individual's perception that the action or
actions make a difference.
So that would for me trying to continuing to shrink my carpet
footprint but that would be the feeling like doing so would
actually make a difference to 16 the grand scheme of
things.
And this of course is a big challenge for an individual
decision making in the context of climate change, right,
because you're one fish in a big,
big climate change causing pot.
Okay, but still, action i with high efficacy.
Secondly, no regrets strategies.
So given that we can't in many cases,
we don't know the exact probability distribution of
many outcomes related to climate change,
we can't make decisions the way that we used to be able to.
We just maximum our success.
So instead, we will e share strategies through robust
to a variety of different outcomes.
So you know, if you're thinking about how to make your city
more resilient, you might consider limiting building in
certain areas.
If you say you're a coastal city and you're thinking about
sea level rise.
So that would be extremely beneficial l and save you from
a lot of damage to those buildings in the context of
sealable rise but it is a no regret strategy because it is
still protects the city and the members of the city even in the
context of the climate.
And then, lastly, I w suggest moving beyond people
because they actually may reinforce ambiguity aversion.
So this is a big counter-intuitive.
I think a lot of times, if we're worried about something
and we want to get others to be worried about something,
your appeals may seem like kind of the natural strategies.
But I think evidence is showing that they are actually not the
best way to people and you can kind of
imagine why this would create kind of hopelessness,
and sort of intuitive, potentially paralyzing itself.
Okay, so we have talked a little bit about what
uncertainty in climate change is.
And you know how it is than just a reflection of a
scientific competence or of consensus around climate
change.
And we talked about the fact that we really need to get used
to uncertainties, ,and kind of learn to work with it.
And one more piece of learning how to work with it is
understanding uncertainty and information that's provided to
us.
So,unfortunately, a lot of information is not always
conveyed transparently whether by the media who often are not
ever trained or by scientists who may not be supervised to do
a lot of communication with public or other groups.
So I'm going to walk through just a few really common
non-transparent methods of uncertainty communication and
some of the strategies based on those.
Okay, so let's start wi framing.
So these come in a wide variety of forms and you may have heard
of an example like access which is called a type of framing
effect called risky choice framing.
And it's often, you often get in examples from us so you can
present a group participants this study,
the policy information.
Mr. Roe needs surgery.
Nine in a thousand people die from the surgery and then,
you are asked by the participant to rank that
surgery on a scale of 1 to 5 of 18 riskiness,
how risky it is.
And then, you can provide the other half of your study
participants with the same information but framed in the
opposite way.
So you would say something like Mr. Smyth needs surgery.
991 in a thousand people survive this surgery.
What we find really quite consistently,
is that when the frame focuses on a number of the deaths,
people perceive the surgery as more risky.
So even though the information is logically equivalent in both
statements, like a thousand minus 9, is 991.
you see these differences in how people interpret that
information .
and what's going on basically that people respond
to what's emphasized in the State.
So it's really important t aware of this kind of framing
in fact .
and when you're a communicator ,
to try to find ways to communicate transparently,
right.
So a lot of those is by using some kind of visualization that
compares -- that shows both side of the statement.
,Right.
So here in this topic example 1-A ,
you can see an icon array and I like icon arrays a lot.
I know it's kind of boring but basically with me too is that
you have a thousand dots here and a thousand people who have
the surgery and you can see the bottom right here,
you got nine deaths representing those folks that
died.
So now you can see actually the nine compared to the 991.
And what happened is that when people are provided a
visualization like this, those framing effects go away.
Oh, and by the way, there is a website that I really like or
19 actually creating your ow icon arrays using icons and
it's super easy to use.
it was created by a guy here at the University of Michigan.
So if you ever want to use it, the Webb side is
www.iconarray.com.
And I think it's nice for helping to visualize some of
these statistics.
So is an example of framing and climate change information that
was found to have a different kind of effect on people.
So look at the two sets used in this study.
So again, these two censuses were provided to different
groups.
And in the top, we have i 10 to 30 % likely that global
arming of two to three Celsius will make a quarter of all
species extinct.
And then on the other hand, it is 70 to 90 percent likely that
global warming is 2 degrees Celsius will not make a quarter
of all species extinct.
And actually, I apologize , I should have the colors shifted,
right because the group on -- the statement on the top is
really more negative .
It's focusing on the negative possible outcomes.
Whereas the one on the bottom, we'll call it the positive
brand.
So you can kind of think in a way of the top of this .
So the authors of this study, they specifically wanted to
find out do these statements have different affects on
people in terms of how willing they are to take I want to find
out does do these statements have different affects on
people in terms of off willing they to take from a fireman all
the behaviors after hearing the results.
So here are the results.
We will focus 2 in the green box.
We have this intention to take up environmental behavior on a
like, hurt scale so higher means higher tension .
And basically the people who heard the positive assignment
which was the -they were talking about the likelihood
that these species would not go extinct due to 2 to 3-degrees
of warming .
those guys actually came out with significantly higher
environmental behaviors intentions than those guys who
had the negative frame.
So this links us back to the comment before about the fear .
Here you can see that your fears may have to be less
motivating than the positive framing.
The other thing , what's called gain framing in psychology .
you're focusing on what can be gained on in this case,
saving these animals with the potential of going extinct.
But I'm thinking the other take-away here is that framing
matters.
It does have an effect on people .
and so ,you know, as you're talking with your students
about how to interpret information, you know,
they should be prepared to face these kinds of frames and to
always think about the other side so that they are not
influenced unduly.
And again, one way of neutralizing a framing effect
would be some kind of visual like this one.
So I want to look at framing in one other form .
and this is one that I think people are actually much less
aware of.
It's gotten a lot less attention in the literature.
But I think it's important and it's interesting.
So this is the verbal framin 21 of confidence in our food
or ranges of likely values , a prediction about something.
So let's actually look at the third example on this slide.
So this author and the economist read,
a rise of around 2 to 3 degrees could mean that 30 percent of
species could be at greater risk or extinction.
The reason is then really going to focus on that 30 percent
because that's what's being emphasized in the statement.
And it's true that it's probably good for people to be
aware if that is the worst case scenario or the most likely
worst case scenario But it can also be a little bit misleading
, right ,because that percent value probably is not
actually what's most likely to happen.
Because the author was presumably describing some kind
of competence .
And confidence says something like, hey,
we're 95 percent confident that somewhere in between and I'm
making these numbers up but somewhere in between 20 and 30
percent would be at a higher risk of extinction.
So the thing about confidence is that typically the
probability decreases as you move towards the bound of
confidence hurdle.
So actually, the likelihood that something in the middle of
that change like 25 percent would actually happen,
if that would be true , you know, like amount of species,
portion of species at greater risk of reduction.
That likelihood is higher than the likelihood to the 20
percent or the 30 percent.
So by focusing on the 30 percent in this statement,
you're really shifting people toward one end of the
confidence hurdle.
And can be very broad.
So, framing just according to one value can actually really
influence people.
You see some here some o examples on this slide,
The World Bank report was written,
the Sahara Middle East, we will seen temperatures as high as 45
degrees Celsius and then a more skeptical article in Forbes and
outcome they are talking about some other prediction.
This is an outcome which means they will not have more than
two degrees of warming.
So these are different takes of takes on this kind of framing
effect.
But I think again, they a very important to be aware of.
Okay, so I want to shift a from framing now so that we can
continue briefly on this issue of confidence and our goals and
probabilities, particularly in the context of visuals of
graphics.
So it's a little blurry but here we have this 5 day
temperature forecast for the high.
So this would say like on Sunday, February 6th,
the high temperature is most likely to be 13-degrees
Celsius.
And then, they got this error range here again.
So the 11 to 14 range.
But the issue here again is that this 11 to 14,
these are really emphasized just visually.
So they need to look actually much more probable than they In
this case, like okay, the difference between 13 degrees
Celsius and 14 degrees Celsius is not so much.
But in other contents, you imagine actually that
over-emphasizing the outer bounds ,
the confidence and probability of that thing 23 happening is
actually a lot lower and really problematic.
So here are some examples of kind of best practices in this
and these are things to keep an eye out for and for your
students to keep an eye out for it.
You can no longer ignore information within each of
these graphics on the methods of depiction.
But what we have on the top left is from an ICCC Report and
basically they are using the black lines to indicate the
most likely value which is well done because that really stands
out.
And then, the blue and orange spaces indicate your 50 percent
confidence So you know, the things within that state are
pretty likely to happen but not as likely as the labels in
black.
And then, the less li things are there in the white
bars ,.and the least likely outcomes are outside of the
bars themselves.
But that color coding really indicates the probabilities i a
much more transparent way than the previous example.
And then, a couple of other examples.
So here's one prediction from the Bank of England where they
are talking about -- it looks like the U-K for that
,projections for 2011.
And basically what they are saying is look,
we can't make a clear prediction.
There's a lot of uncertainty but notice that despite the
fact that the range of possible outcomes is pretty broad.
The shading indicates that the probabilities decreases as you
move further and further to the extremes.
The final example down here is from is hurricane trajectory
mapping or 24 prediction.
And again, the point here is that you want people -so the
people who are sitting in the red zone are most likely to be
extremely likely to be enclosed by hurricane force winds.
The people n the dark green out there are potentially likely
but much less so.
And the point is, that you want to be sure those green people
are being notified of that status.
You also don't want to freak them, right , And so,
a much more transparent way of creating or communicating
uncertainty to people is by showing these gradations and
I'm sorry, teaching further and further away from your most
likely values.
So let's move now from talking about prediction intervals to
talking about point probabilities.
And we'll look at them in a form that we're all actually
very familiar with which is the daily weather forecast.
So I'm looking at Ann H right now.
And so my little town, this Thursday,
we got a 40 percent chance of rain.
And that's a statement t hear all the time and I'm sure
almost all of you hear all time.
But what does it actually mean?
So here are four options and you can think about what you
agree with.
I think its most The first, it will rain on Thursday on 40
percent of the time.
It will rain on Thursday for 40 percent of the region.
It will rain on 40 percent of the days like Thursday.
Or ten meteorologists tend to believe it will rain on
Thursday.
People were asked this in New York and in a number of other
countries.
And actually the 2 Yorkers did pretty well
although Americans happened sing you at looking at New York
informed about the weather patterns, I guess.
But what most people will say is that it will rain Thursday
for 40 percent of the time.
That's the correct interpretation of the forecast.
The actual answer though is C, that it will rain on 40 percent
of the days like Thursday.
Okay, so you looked a wrong.
What do we conclude from this?
Do we take that away that people are just ignorant,
that they are unable understand risk and uncertainty
information?
And so we should just kind of give up?
Actually, no.
It turns out actually that the statement of the salt is
lacking.
So this claim, there's a 40 % chance of rain tomorrow.
You actually can't or Thursday obviously in our case.
You actually can't figure out the right answer from this
statement alone because missing what's called a
reference box.
So what is the 40 percent referring to?
Are we talking about 40 percent of the time,
like 40 percent of hours Thursday;
40 percent of meteorologists who agree or disagree,
40 percent of your the region, we have no idea.
It's not provided.
What is this pretty percent chance?
So that information isn't given and that's why we can't bill at
the right answer.
The more transparency means we would include this random box.
And it rains with conditions like Thursday or conditions
like those on Thursday.
So the moral of the stroy here is that should 26 always pay
attention to the reference class.
And look to see if a reference class is included in the
statistic.
So one way to test whether you have reference classification
is to think about whether you can transform the probability
or the percentage, if that's what you're given into a
natural frequency statement.
So you can say, if we can consider a hundred days of
conditions like Thursdays, it rained into 40 of them.
If you can turn it into natural frequencies like that where you
have a hundred of something, then,
you have a reference class there.
If you can't do that, it has not been conveyed,
that actually could potentially be a misleading step,
certainly nontransparent.
But you may think like all right,
is it really that important that we understand the precise
definition of the weather forecast.
We know how to operate based on whether forecast,
that's all we need to know.
I think that is a valid argument.
Here is an example where I think it matters in terms of
understanding projections.
So this is an example from the U-K climate projection briefing
from 2009.
And they are talking about the number of dry spells likely to
affect the U-K and under a medium membership scenario.
But that's less important here.
If you can kind of focus less on the data and more on how
they are conveying the data.
So what do they have here on the left?
It's inside the green box, the tenth percentile.
So what could have been said here is something like,
for any given year in the 2080's,
there is a 90 percent c that the number of dry spells
will be greater than whatever.
So I think looking at the map, it looks like London is there
in the green, so like greater than nine dry spells for that
year.
There is a lot, actually.
But that would create a reference class problem.
Again you're at 90 percent of what?
Instead, these guys tell u what that 90 percent is
actually coming from.
So they say, this number of dry spells is likely to be exceeded
every nine and ten years.
And that in ten years is your reference point.
So that's much more transparent and accessible.
And then, you can see is you have this likely to be exceeded
every nine and ten years, likely to be exceeded every one
in ten years.
So actually, that is a kind of 80 percent competence level
there, right provided for one of these data points.
So moving away now from reference classes,
let's move back again to an example from the medical
domain.
This is probably not something I guess that many of you will
share with your students.
But I think it is a really nice example of this issue.
So in 1995, the U-K made t official announcement about the
new generation of contraceptive pills that had just come out.
And they said, "Contraceptive pills double the risk of venous
thromboembolism which is blood clots.
They double the risk of blood clots and as you can imagine,
this created a lot of fear.
And in fact thousands of women stopped taking the pill in
response to this warning.
So you can see in 2 graph,
we had a decline in abortions in England and Whales that was
pretty consistent.
Five years prior to this announcement and then,
there is an upswing.
And the belief is that this was caused by reduction of use of
the pill.
But, you know, you might argue well, hey,
if the risk of blood clot was increasing by a hundred percent
or was doubling, then, surely, it was worth it, right?
Well, considering a more transparent format of the risk
communication, you can see that maybe a different story was
actually underlying that percent increase.
So a transparent format of the same data would have said,
contraceptive pills increase the risk of venous of blood
clots from one out of every 7,
000 women to two out of every 7,000 women.
So it is not double, right?
It has increased by 100 percent which is by another one woman.
But the story that t transparent format tells us is
very different.
And what this tells us is that you should always be skeptical
when you're given a relative risk increase or a relative
risk change, rather than an absolute risk change.
So a elative risk change is where you're talking about,
you're saying oh, the increase is 100 percent higher than
before.
So you're not told what before is.
You're not told what your base rate is.
And without the base rate or without that absolute risk
change, absolute numbers, you end up really not having very
much information at all.
Taking an example of this from climate change,
here is a title, an article from 2014 in The
Guardian.
And the article is entitled, "Heat Related Deaths will Rise
257 Percent: by 2050 Because Of Climate Change." So once again,
that risk increase really sounds pretty big, right?
But once again, we're only giving the relative risk change
here.
So there is this really c statistician in the U-K David
Spiegehalter who actually should check out his website.
It 's basically a blog a reports on examples of
miscommunication of uncertainty.
And sometimes they can be quite starkey but they are always
really interesting and smart.
So it's understanding uncertainty .
org.
So what did he do?
So he was a little skeptical about that article and when in
fact he looked at the base rates of heat deaths and cool
deaths and found that, okay, heat deaths were really
uncommon.
Cool deaths were very common.
And that difference was so broad that even the 257 percent
increase in heat deaths, heat-related deaths would be
completely outweighed by the decrease in cold deaths that
was projected to occur during climate change in this same
time line.
And so, the net effect t would actually be life-saving.
So obviously I'm not trying to comply here that climate
uncertainty does not pose a problem for us but rather,
-- that climate change does not pose a problem for us,
but rather, that you know, this kind of nontransparent
communication comes from everywhere,
and we need to be sensitive to it.
We need to 3 informed when we're reading
this stuff.
Okay, so, we been through quite a lot in the past hour.
And I will now, I got some additional slides if anyone
wants to hear more but also open it up to questions.
Thank you so very much for being here and for hanging
around.
>> BRUCE: Thanks Tarlise.
Appreciate that.
So, if you folks have some questions for Charlie,
please feel free to type them in to the box on your control
panel on the right hand side of your screens.
I actually do have a question for you.
How do climate communicators reconcile the need to show the
severity of climate change and not incite fear and potentially
alienate people?
And I guess if you don't have a you know,
quite an example about climate communicator,
you can use other examples of course but I think that balance
is what is sort of being asked about?
>> TARLISE: , I think this is really difficult.
And these are things that psychologists and folks from
other fields are continuing to research.
And I don't have the -- they found some great solutions.
But you know, I do think that -- so I mention that reduce
motivation to act.
There is evidence now that they catch people's attention.
So that they are goo catching attention but once
they got your attention, are not so great at -- I been
wondering if you know, a combination of your appeal sort
of an initial kind of startling facts would be welcome behind
them with some of those really motivating strategies that I 31
mentioned before that are high efficacies,
that are no regrets, sort of got nothing to lose.
But I think a really important aspect of this is going to be
some kind of real -- some kind of community element because
there is this sort of nihilism I think about making
for individuals about making significant like mitigation
related changes in their lives.
And I think in order to be motivated,
there's got to be some type of strong pressure from some sort
of social network.
So, scientists are working to test the effects of information
where you can basically if you like tell a household that they
been using more energy than their neighbors for instance,
there's been some evidence that peer pressure that results from
that will drive that household to reduce their energy use as
well.
So I think sort of harnessing the potential for not fear but
for concern to grab attention and then follow that up with
action items that have high efficacy and that are more
manageable within people's lives.
>>BRUCE: I have just a follow up sort of a statement from one
of our sort of long-term NOAA climate stewards here.
Where Terry says that for children,
it's very important particularly for younger
children, younger than 5th and 6th grade,
that it's not -- that they that they may not be developmentally
ready for some of the drastic predictions of climate change
and that that can create fear and hopelessness rather than a
desire to take action.
Have you seen anything to this 32 degree of relating some
aspects of uncertainty to different audiences?
>> TARLISE; Yeah, this is really' -- no, I mean,
so I actually have seen nothing on -- I have not come across
any rigorous research on the affects of uncertainty
communication with children.
Like trying to teach them kind of reasoning.
So this is a really interesting area for study.
I mean, I think what I encountered mostly in terms of
like climate change communication with kids,
is that the focus there is this encouragement that the focus be
on sort of habits setting early on so like that maybe we don't
need to spur children into action using concern anywhere
because their habits are not set in a negative way yet.
So if we can really focus less on trying to inform them about
why they need to change their behavior and focus more on
studying sustainable behaviors and then informing them about
why sustainability is important,
that that would be useful.
But that's like a pretty uninformed statement.
So I can -- I would be really curious to hear what you guys
having worked with children on these issues would say whether
there are some good strategies and what has not worked.
>> BRUCE: Just a note, Terry mentioned that it was in fact a
gentleman name David Sobell who has done some studies on this
out of Antioch University.
>> TARLISE: h, interesting.
I'll make a note.
>> BRUCE: And we do have another note from another long
3 who has mentioned that in the
updated Against All Odds insights statistics video
series from the Alenburg Foundation,
our meteorologist explains that a 40 percent chance of rain
means that 40 percent of the forecast area will see rain.
>> TARLISE: Yeah, I would be really curious to see that.
So, I have seen like one paper that questions this definition
that I gave.
Although that's the one that's been -- so I'm not a
cryptologist t or meteorologist so I cannot speak to it
specifically but this is one that has been used in these
studies.
I think regardless is not the most common response that
people give us 40 percent of the time.
But I think the point is not that right,
the point is rally that not like whether people are
right or wrong.
It's the fact that there is no way for people to know based on
the way the information is communicated and so that's why
it's so important to have a reference.
>> BRUCE: Got-cha.
Very true.
So, what would be your -- overall,
if you had to just sum things up into just a few take away
messages, like some of your most important points that you
feel you would like for people to walk away with from today,
what would those be?
>> Yeah, I mean, I think there is sort of stated on this slide
in a way of being cautious of some of the assumptions that
people -- that people who are not sort of thinking explicitly
about what uncertainty means on a regular basis,
that they may come to the table with, that you 34 know, oh,
it implies disagreement among scientists.
To be able to be aware of those things,
be able to identify them then, you know,
with your students, especially if your students are displaying
those things, being able to counter those.
And then in digesting information or in consuming
statistical or uncertainty information to really
constantly by thinking, okay is there a frame here?
How can I counter that?
You know, is there a reference class in this information?
And if not, you know, this percentage could be totally
meaningless to me.
And you will see -- I think once you become conscious of
this, if you're not already you will start seeing issues of
reference class everywhere.
And then, you know, demanding absolute risk information,
and that also is something that comes up frequently and outside
of climate projection.
>> BRUCE: I have another few statements just from some of
our educators who have tuned in.
Kinglsey Miller states that we don't usually talk about
climate change with kids in grades K through 8.
They are not at a stage in their life where they can
really understand the cause effect and talk about the
environment as a whole and protecting inspiration about
the world around them is the first step in wanting to
protect it.
And then, Laura Tucker mentions hat she just had a recent
experience with negative information turning kids off.
Last May, she gave a climate change presentation to about
400 plus, 4 through 8 graders to
35 academy where kids learned to
be climate ambassadors and to help in planting trees.
She mentions mentions very positive and wonderful
experience about three quarters of the students want to attend
the academy this fall.
So if somebody from Climate Change program gave a very dire
presentation with doom and gloom,
pegged with guilty and made the kids feel bad that if they
don't quit driving cars and eating meat,
the result is only signed up.
So, I think that will Laura shows an interesting very sort
of pragmatic result of presenting positive messaging
and action against the sort of doom and gloom.
But I suppose there is a balance between those which can
be reached depending up on developmental level of your
audience.
>> TARILSE: Yeah, absolutely and I would go back to this
point that I touched on very briefly before which is that --
and I also speak to the question of like how do we
convey this, the severity of the situation without shutting
people down with these fear appeals.
And that's -- there is t evidence hey are using what's
called a gain frame can effective in a lot of scenario
.
And people are still trying to work out like when it is not
more effective.
And sort of what the theory is that projects limits more
effective but basically a gain frame is where you're saying
okay, if you take this option, here's what you can gain.
So that you can in the case of the animals potentially going
exatinct, you know,
X percent of I'm not communicating this well but
like potentially endanged species.
And so you can still talk about like how consequential this is
but more in terms of the positives rather than of
shutting down.
>> BRUCE: Thanks Tarlise, I really appreciate that
perspective.
One thing that I'd like to do now is Tarlise has mentioned
some additional resources that she has recommended folks take
a look at.
And Tarlise, would you like t say one or two things about
these resources?
And these will be made available to everyone they are
currently already on the Google archives site.
TARLISE: n sure, yeah and I don't have them right in front
of me now so I will have to pull a little bit from memory.
>> BRUCE: You should be seeing them on your screen right now.
>> TARLISE: I can take my own slide down.
>> BRUCE: Yes, right.
>> TARLISE: Okay, right.
So, this World Bank Report is pretty long but I think it is
really nice and for the most part,
very accessible overview of the uncertainty of the world that
it plays in climate change.
And the kind of investment issue is not so central
actually.
So basically, these documents are discussing how do we make
decisions under the context of this deep uncertainty,
unquantifiable uncertainty that's growing in the context
of climate change, is how does this force you to change the
way we approach decision making?
The CRED Guide, the Center for Research On Environmental 37
Decisions, the group is called CRED.
This is very accessible, v easy sort of overview of a lot
of the sites stuff that I talked about today and some
additional findings that offers a really nice guide for talking
about climate change.
Not focused on uncertainty but more broadly.
And then, the National Research Council basically did this
assessment of how weather climate forecasting could be
better or how the forecast can be better communicated if
uncertainty were incorporated.
And so they talked about incorporating psychology but
also the policy issues, into that.
So they give a lot of g examples.
So these are helpful resource force anyone that used them.
Thanks so much.
I just want the reiterate a couple of notes here before we
log off which is again, now, every one who is attended this
evening has already in their in-box,
you should have a post webinar poll.
Society really appreciate if right after we log off,
if you go right to that e-mail and just chance this
very quickly.
Don't forget.
Monday November 30th, 8:00 p.m.
eastern time will be our next climate P stewards book
discussion club faith, science and climate change.
We are going to be talking about Pope Francis and on the
environment and of course, applications are now being
accepted for the 2016 steward you ship community.
Go to NOAA Climate Steward home page,
watch the video or download it for additional information.
And for those over you who are not familiar with the page,
there it is right here.
Just google NOAA Climate Stewards if you are not
familiar with it.
I'd like to thank you Tarlise for giving us a really great
presentation this evening.
Very informative, really expanding our perspective of
what we think about when we think about uncertainty and
risk and aspect of presenting that information to various
audiences.
It was a great presentation.
>> TARLISE: Well, thank you and thank you guys so very much for
having me and thanks to everyone who took part.
>> BRUCE: And with that, we will end this evening's
broadcast and thanks again.
We look forward to seeing you in a month from now where we're
going to be focusing on climate models,
assimilations and games for the classroom and for other venues.
So thanks again and we will see you soon.
Have a good evening.