>> What I hope to speak to today is the discussion that
you heard about global climate models and how they fit into
climate research in general.
I'll be talking about different spatial scales and time scales,
but most of the focus will go on the larger spatial scales
and on multi-decadal time scales.
So here is a brief outline in my talk.
I'll start by speaking a dynamical climate models,
talk about how they are just one tool in our scientific
toolbox but an important one.
Let's spend a little bit of time just circling back to the
phrase "climate" itself and how it's one word that has many
meanings.
And we often hear about the discussions of weather versus
climate, and I'll rephrase the proposition a little
differently some of the questions about
whether we can about long-term climate change
where we can't weather precisely a month or
two in advance day to day.
That will then lead into questions about what climate
models can and cannot address and how we use these tools,
and then I'll finish up by talking a little about or
asking you as well what makes a better climate model.
So, first thing I want t out is that progress in climate
science -- and there's lot in past few decades -- does
not occur by models alone,
even though that's what I will be talking most about tonight.
But basically, the major advances that have been made
and come about by a synthesis of three different types of
information: understanding what
is happened in the past, an improved understanding or
improved theory about the way things work and the different
climate factors interact with one another and computer
modeling itself, ways to test these hypotheses.
So when these t together and tell a compelling
and consistent story, when my progress has moved
forward.
So we'll use the a sturdy, three-legged stool.
And again, I mostly about the large spatial
scales because one can detect climate changes
most readily.
So I'll be talking more about global change or how the artic
will warm more quickly than the tropics or how underneath the
areas that are influenced by subtropical high-pressure
systems are likely to dry out, those sorts of spatial scales.
When it comes to time scales, we tend to talk in terms of
human-induced climate change on a multiple of decades or
centuries time scale because things are happening at the
same time.
There are still natural variations.
They don't stop j they are human-induced climate
change, but we still have those natural variations.
And the trends that a induced by human effects take a
while to emerge background noise of natural
variability.
This figure just gives you an idea as a schematic view of
some of the the different things we need to
consider when global climate system.
A lot of people, when t think of climate,
they just think of what happens right near
the surface of the earth where they live.
But even though this i simplified schematic,
you can see there are a large number of factors.
The ocean, for one, e not given a large area on this
figure, has a huge climate.
You can t really as kind of the big dog
in the atmosphere as the twitchy tail when it comes to
climate in regards to inertia and time scales and all that.
But it's a atmosphere and the ocean.
We need to think a surface.
We need to think about ice in the cryosphere and ice sheets
and sea ice, and we need to think about natural variations
like volcanic activity.
When large volcanoes blow their top,
they can put small aerosols, small particles up into the
stratosphere which causes the stratosphere to warm but the
surface to cool but just for a couple years.
We also have f surface need to think about the
biosphere.
We are not just thinking about the physics of the climate
system anymore, but we need to think about the way life
interacts with it because if we're interested in how carbon
moves through the system, the carbon cycle,
we need to think about biosphere: How vegetation will
respond, how vegetation respond,
how soil microbes how plankton will respond in
the ocean.
So there's a number of factors and some of those are listed
here.
It's a simple figure but gives you an idea of the complexity
of the system.
What it means is t these years when I
been at NOAA's Geographical Fluid Dynamics Laboratory here
in Princeton working on developing and analyzing global
climate models, I do so as part of a team that allows folks
from a whole lot of scientific backgrounds.
A lot of us are m or oceanographers,
but you can read list.
And I worked w these fields who have
contributed to global climate models.
It's a big complex s we need to integrate
information from a large number of fields.
So, if you have a t get more into the details later
-- but we just basic question you could ask,
compared to other science,
if we have questions about how the climate system works,
how it responds, perform an experiment on the
global climate system.
Some years ago I was giving a talk in middle school and it
became clear to me that a lot of the students have a view of
scientists who work in laboratories that, well,
here is something we can't do.
We can't t shrink it,
put it in a flask and perform experiments this way.
That may be the image that some students may have about how
science occurs, but it's not going to work for the earth.
Similarly, w ability to just take the planet
or have a twin planet, change the carbon dioxide levels,
stick it in a turn the knob and come back and
see how it responded.
So, with that c one tests and perform
experiments on the
climate system, we end up using climate models,
numerical approximations to try to describe what's happening on
the planet.
So we take what we know a the way the climate system
works, convert that knowledge into mathematics,
into computer then that becomes our tool.
So these big and c computer models,
these global climate models -- last time I looked we had about
a half million lines of computer codes than the one I'd
been using lately -- that becomes our tool.
And our laboratory -- w perform our work in big,
expensive, super computers order to perform the
experiments.
That's our s real world.
So climate models you can think of as being our virtual earths.
They give us a science-based way to create and test some
hypotheses, such as how will the planet's climate respond to
changes in greenhouse gases and other climate-forcing agents.
Now, I'll get back to this phrase forcing agent later,
but right now you can just think of it as those sorts of
things that when they change on the earth or from the sun will
change the amount of energy and the way it flows to the earth's
climate system causing it to respond.
So our traditional physical climate model,
we take the earth, we divide it up into millions of individual
grid boxes, but they basically -- there is an assemblage.
One global climate model is really a collection of a number
of separate models, a number of separate computer programs that
interact with one another.
It's
going to be a part that refers and simulates the atmospheric
circulation, another one for the ocean circulation.
We also have m programs for sea ice as well as
the land.
And what those arrows here are representing are just how all
these models, in addition to simulating what is occurring in
each one of those, exchanges fluxes of heat, water,
momentum between them because all these
factors play a determining how the climate
system exists and evolves over time.
Now, the factors other than this simplistic schematic that
factor in -- and over time the models continue to get more
complex -- if we want to think about the carbon cycle,
for example, what of carbon dioxide as it moves
through the system, additional components that have
more chemistry and biology in the atmosphere,
in the land and in order to follow how carbon
cycles through the system.
This allows us to a different set of questions that
we wouldn't be the model that only contains
the physics elements.
So, quick summary w when I speak about dynamical
global climate I'm using the word "dynamical"
to distinguish it statistical models -- but
dynamical models, physics,
simulate the energy, momentum,
water and other factors as it moves through the various parts
of the planet's climate system, but it's not just the
atmosphere but also the ocean, the land and
the ice.
It's also important to n that the dynamical global
climate models aren't -- what they are is they're
time-dependent.
That means they m in time.
It's not l where if you change the value
in one cell, double the number for carbon
dioxide, hit just almost instantaneously
updates all the gives you the answer.
This is a time-evolving simulation.
Now, if we do all t calculations all around the
globe and it gives us what simulation of what the climate
system looks like ten from now,
then we do a round of calculations in all
those places physics and we get another ten
minutes from now and minutes after that,
ten minutes after that.
So you can t bunch of snapshots over time,
so time-evolving model.
So the state-of-the-art global dynamical climate models,
we divide the planet into millions of three dimensional
boxes that cells.
The schematic in the lower right-hand side here,
though the boxes nowadays are actually smaller than shown in
that schematic, but you get the idea.
Then in each of those boxes, there's a lot of equations,
a lot of physics and other things going on describing the
factors that explain how the climate system works,
how energy and through the system.
We start with basic things like F=ma, gravity,
conservation of conservation of energy.
There's other equations like the
equation of state for the ocean that describes what the density
of sea water is as a function of temperatures,
salinity and pressure.
There's all these b that are well-known that's at
the core of the model.
But additionally, r how small those boxes may be,
there are things are relevant to the climate
system that are too us to track.
We're i evaporation,
but we can't track each and every water molecule on the
planet.
We can't simulate each and every white cap on the ocean,
but we need to know, we need to estimate,
we need to simulate evaporation is occurring over
one of those grid point in time.
So that is done by what we call a parameterization.
It would be s that would have an equation
that would temperature of the water,
temperature of the air, humidity, wind speed,
the amount of sunlight coming in and coming up with an
estimate of evaporation that would occur at
that point because we need to track water and heat as it goes
through the system.
And increasingly, as I mentioned,
is also going to be describing chemistry and
biology in the model.
So there is a whole lot there.
It's much more than a spreadsheet.
I just kind of ran t quickly.
I'll just suggest here that in the notes and other materials
presented that I'm trying to provide a number of links that
you might go to for either more detail or just a slower pace
presentation.
And one thing I would suggest is checking out the videos at
the URL shown here, the www.gfdl.noaa.gov,
where there's presentation that discusses how
the climate models using the analogy of all those
grid cells as being like Rubik's Cube cells and how
you can focus on one and about the different processes
that would tell us how temperatures might be
increasing or decreasing at point in time at one place.
Anyway, I invite you to check out those videos,
which brings interactive question.
So I'm new to this, s see how it works.
But the basic question that I have here is,
when you showed up here how would you answer this
question: How can scientists deduce what the climate will be
like a century from now if you can't get a decent daily
weather forecast two weeks in advance?
Isn't climate change just too complex?
So we have three choices here.
First one basically says, yeah, you know,
weather forecasts are okay for a few days,
but then they degrade time.
So it follows then t really can't expect that longer
term, multi-decadal outlooks, to be skillful.
The second option j that making projections about
how the global change over several decades is
kind of a different question than what it takes to
make a weather they could both be skillful
because they are different questions.
And the third option is just basically throwing up our hands
and say, you know, when you get right down to it,
neither weather forecasts nor long-term climate projections
really have scientific basis;
they're more guesswork than science.
So now we'll see what t says.
>> okay, your polls are open and I apologize that we have
had to language now because of the
software requirements, but the polls are open and please make
your selections now.
We got about 70 p loaded, 80 percent.
Some people are s their mind.
We will keep it open for a couple more seconds.
Okay, that's a so we are going to close it now
and share.
Okay.
And that's what we have.
Keith, you see that on the screen?
>> I'm not sure right now, but how about I show you what my
answer would be.
I would go for No.
2.
>> Hang on for one second.
I'll just pop it back over to you.
Okay, there you go.
And that's a people selected.
>> okay.
>> That was the majority selection.
>> So there are basically, even though the physics are the
same, there are different enough questions that we can be
skillful in both.
And now I'll t a few reasons why that's the
case.
So a lot o about the distinction between
weather and climate.
If you go to one of N websites,
like the Climate Prediction Center,
you distinction of
the shorter term for S.
For example, 3-7 day forecast, that is a weather forecast.
That's how it's characterized.
We're tracking individual storms, individual cold fronts,
individual high-pressure systems,
and for individual days say the high temperature five
days from now is expected 52 degrees with a low of 34 or
something like that.
So we're looking at individual features and tracking their
evolution across and predicting where they will be.
But once we g term,
we start speaking about things in more statistical or
probabilistic ways.
So when we get to even j 6-10 day forecast that NOAA
Climate Production Center issues,
it ends up looking like seasonal outlook would look
like in that it's pretty statistical in nature.
For example, here's a figure that I downloaded back on
November 22nd, and it's a 6-10 day climate outlook valid for
the five-day period ending today.
And you can see that the forecast or the prediction made
at that time was that for the past few days it was expected
that the areas centered around Pennsylvania would have an 80
percent chance of being below normal in temperature,
which even though I live over here in New Jersey -- >> Yes
and I think that pretty verified -- there was an area
out in the west that was to be warmer,
but there weren't particular numbers given for temperatures
on individual days.
We're talking about more o statistics of the system.
We expect the probability would
be hig temperatures.
And if we look at not a 6-10 day forecast but now a
three-month outlook, one valid for December,
January or February -- that's what DJF stands for -- forecast
near towards the middle of November,
you see a similar look.
Patterns are different, b it's presented the same way.
Now, the way this ends up working is,
if you think of the years and you say that ten
warmest years would qualify above average,
the ten coolest would be below average,
and those ten remaining ones, that's kind of in the middle,
that would be near normal.
And so here we are expressing what the probabilities are
given the information that we have now that this winter --
December, January, February -- on average will be above
average temperatures below or equal chances of it being any
of the above.
So, there are not any s strong indications here.
You might say Texas is looking to be above normal but,
you know, the probability for Texas being above normal in
this forecast is somewhere between 40 and 50 percent,
while those of the dice,
you would say it was 33 percent.
The basic idea is that when we get to talking about climate,
it's more probabilistic in nature.
Climate does not stop a seasonal.
I am going to be about the multi-decadal to
century time scale.
And we also should note h that we can talk about climate
variations over hundreds years to thousands of years to
millions of years, field as helio climate.
So are
these all different or are they essentially all the same?
I mean, after all, the physics involve for whether we're
talking about a two-day forecast or a one-hundred year
outlook, F=ma still holds, the equations that govern
evaporation or the radiant forces that tell us how
ocean currents.
So all those factors are going to be the same whether we apply
them in the short term or long term.
So that's why some people m say,
if the forecast you're making day-to-day
forecasts, maybe you much in the future.
I'm just going to recast this a little bit and say how about we
instead are thinking of weather and climate.
I'll use a little b here and say let's say we
divide things into what I call initial value problems
versus boundary value problems.
So for initial value p we lump together daily weather
forecasts as well as like that 6-10 day or the
three-month forecast or the spring when we hear about
Atlantic hurricane outlooks.
It is an initial value problem in the sense in order to make
the forecast you want to have two things: One,
we want to have a understanding about the way the
system works.
Secondly, you n really good understanding of
what the starting point is based upon observations because
what you're saying given that I know the
way things hypothetically,
let's say I had a perfect model of the way everything is
interacting -- the starting point and then I
can project and
predict how it will evolve over time.
But there is a limit to h long we expect to have skillful
forecasts.
That's why t fades out over time as we go
into longer time scales.
And the reason for that i something that sometimes gets
referred to as the butterfly effect.
You might have heard of that.
You do a web search on butterfly effect,
but I suggest of these other terms I have
like chaos theory otherwise you get some web
pages about not so great science fiction film.
Basically, the butterfly effect or chaos theory was developed
originally described by a great guy, Ed Lorenz,
who made a try to express it as follows:
When it comes to weather,
the flap of a butterfly's wings over Brazil today can determine
whether or not a tornado forms over central Texas a week from
now.
So it is sensitivity to initial conditions.
What this means is f initial value problem,
even if we had that perfect understanding about the way the
system works, there's going to be some things that we just
can't measure.
Knowing that the t about 34 degrees here,
it might not be good enough.
You would need to know a a bunch of decimal places
because those small inaccuracies are enough to grow
over time and cause the system to evolve in ways that you
hadn't predicted, and hence the forecast degrades.
Now, the way i when we're thinking about
climate change or variations over multiple
decades is we call it a force
boundary value problem.
The difference in that is that in order to make a projection
on those time scales, yes, we are interested, again,
in understanding how the system works,
understanding the physics.
And even if we had that perfect model that understood all the
things that went together, then our forecasts,
our projection for is not going to be based upon
where it started -- system some hundred years ago
-- it's going to how those forcing agents change
that I described things that change the amount
of energy that flow system.
And then w interested in how the climate
system responds to that.
So there is a number of things that can be forcing agents:
Change the amount of carbon dioxide in the atmosphere,
changes the flow of infrared energy, that causes changes;
volcanos blow their tops, changes the flow of energy;
the sun variation, 11-year cycles,
changes the flow of energy.
All those things are forcing agents,
and if we can make projections about how some of those are
going to change, we can then, with a good model, predict,
project how the climate will change in those time scales.
Now, it can be a bit of semantics but a lot times you
will find the word "prediction" being used for the short-term
initial value problems and "projections" being used for
the longer term value-value problems.
So, I hope that helps.
If not, here is another
link to a fairly accessible description of the same kind of
thing.
And here is an analogy t have used before and that some
people like, and you can know if it works for you.
So if we think of weather and short-term climate as an
initial value problem, the longer term climate as a
boundary value agree that the initial value
problem is a scale due to butterfly effect,
let's think of it not as weather and climate,
but let's think of it as a lava lamp.
Now, we can treat lava lamps both as an initial value
problem and as a boundary value problem.
So the weather analogy would be,
if we focus on this lava lamp in the middle,
is what's going be these individual blobs.
You can think of them as s or high-pressure systems over
time.
So for the next s time,
can we prediction of what the fate
will be of this blob.
When will it detach?
How quickly will it move upward?
Will it interact with the other blobs as time goes on?
And how about the one that's descending,
how long will that take?
Well, i something about the lava lamp
system, the mineral oil that's clear,
the wax-based substance that's brightly colored;
and if we're bunch of observations inside
here, really figure out on each one of the
points in here is there,
what its temperature is, what its density is,
what its velocity, downward and how it's going,
then we ought to be able take that and put it into a
mathematical model, kind of like the climate model but now
our lava lamp model, and then crunch it in the computer and
it would give us a forecast of what that blob would do over
time.
And it w skillful for some period but
not forever.
You would not be able to start that computer program and
expect it to simulate so that if we came back here tomorrow
at this time that we would be able to predict just where the
blobs were.
That's an initial value problem.
But if you know how l work,
the system being a bulb in the bottom,
say a 25 watt bulb, produces heat that causes these
blobs to get warmer dense and causes them to rise
and they cool up at the they come back down in a
process known as convection.
So what if we change that boundary condition,
what if we change bulb to a 40 watt bulb,
now we are adding more heat.
We have not changed a about the substances inside,
but we're changing one boundary value problems.
That's more like longer term climate.
Now, in this case, w going to be interested in
saying what happens to the system when I change the bulb.
We're not interested in the individual blobs.
We want to know how the climate of the lava lamp will change:
Will the convection speed up?
If so, how much?
Will the blobs get bigger o smaller?
Will they become m or more random or chaotic?
There's a whole bunch o things.
And so it's the s how the system is going to
change.
That is what
our model would then predict and that would be analogous to
the boundary value problem for longer term climate.
So you could have a mod-uletic forecast,
how an individual blob to work and that could be
skillful but not what it's going to be like two days from
now in terms of blobs, but you could also have knowledge or if
you change from a 25 watt bulb to a 40 watt bulb,
you could say how the climate of the lava
lamp is going to change.
>> Keith, this is Bruce.
We had a question come in.
If you could define just once again what the boundary value
is.
>> So the boundary value, we could consider that,
for our purposes here -- I mean it's a jargon term and I kind
of apologize for that.
It's essentially the only one that's used in the field,
so if you were to Google it, it's what you're going to find.
In that c changes that are going about by
like the something external to the
system.
We are n changing -- we're not changing
the physics of the system.
What we're doing i changing some things that are
going to initiate a the flow of energy.
So on the lava lamp, the boundary condition was the heat
coming in through the surface, and then we're going to see how
the system responds.
In the climate system, it could be any of these factors shown
on this slide.
We could change the a carbon dioxide.
That c boundary values in that carbon
dioxide
change then interacts with the flow of infrared energy causing
temperatures to change in different places.
I hope that helps.
>> Thank you.
>> So if we move on then to what lava lamps have to do with
global climate models instead of a lightbulb,
there is a whole different radiative forcing
agents that I mentioned before, the things that can change the
flow of radiation or energy through the climate system.
They are known w levels of accuracies,
what their net effects are.
If we look up here a line,
you can see carbon dioxide, so the bars that extend to the
right are warming effects.
So carbon dioxide is t largest one if we compared our
atmosphere today to the atmosphere of the middle of the
of the 1700s.
But methane, a gas,
also contributes to warming.
And you can go through the list throughout here.
There is also t people put in the atmosphere
that can cause some Sulphate aerosols,
byproducts of sulfur and combustion,
tend to have a as described by these bars
here.
But also when w incomplete combustion that puts
out soot or black has a net warming effect.
So these are the kind o that climate science tracks,
comes up with estimates.
The length of the bar shows you how much the best estimate is
for warming or cooling effects.
The little stem and whiskers part gives you an idea about
what the levels of certainty are
about each.
And if we sum them t can say relative to 1750,
nowadays there is about watts per square meter of
forcing, extra warming, occurring if we sum together
all the human-induced changes.
Some warming, some cooling but maybe two and a quarter or so
watts per square meter.
So you can think of it roughly as like a nightlight bulb,
every square planet.
But that's the amount of energy imbalance that has occurred due
to the change in the atmospheric composition over
the past couple to people,
although the total arrow bars are somewhat substantial,
ranging from about 1.1 watts per square meter to 3.3.
So those are the factors.
Then we can then go back to our climate models and we can now
perform some of those what-if experiments.
The black line in t is based upon observations,
observations of global surface air temperatures
and we can focus on century.
So in the blue line, this gives you an idea that it is often
characterized that global temperatures in the 20th
century were characterized by warming in the early part of
the century, plateau in the middle and warming at the end.
In this red line represents the results of some climate models
experiments that we ran when we said what if we hold all things
that people had an ability to change, we hold those constant.
We ignore the changes in carbon dioxide and aerosols in that we
keep those at the levels of what they were back in the
middle of
the 1800s, and what we're going to have change in the system
are those natural forces.
By the n talking about changes in solar
output, the 11-year cycle and other subtle long-term changes
in the sun as well as volcanoes that every so often they blow
their tops.
You can see here in the 1800s and then there are others that
now can cause some short-term coolings.
So if we run the m experiment then and say here's
our best understanding of the physics work and if these
were the only forcing that we're changing,
we would get something like the red line.
So we can see up until t 1960s really they had not
separated, but things were consistent with the idea that
the changes that have been observed were consistent,
consistent with actual variations.
But in the last 40, 5 so,
we see this observations have warmed,
but our models tell was only the natural forcings
that we're changing, we expect temperatures to have not
gone up, maybe even little bit.
So that was a of our climate models where we
can take that model, that same physics,
and now we can just of those boundary values,
some of those forcing and say now let's include all
the different forcing agents that we think we know something
about.
So in addition to the s variations of volcanoes,
we're going to include the variations in time of seven
different greenhouse gases, different aerosols, land-use
changes that change the amount of reflectivity and we are
going to run our experiments again.
Again, the black line is the observations.
The red line, the m results, much better agreement.
So, this kind of experiment was done not just at GFDL but in
more than a dozen different research centers around the
world, which is summarized by the results here.
And the basic t is as we perform these
experiments, these experiments,
using our virtual models, our best understanding of the way
the climate system works -- we can't explain the observations,
the black lines if we only include the natural forces,
that in order to get model results that match up or within
the envelope of what we saw in the observations,
whether we talk global values or the open land
or the ocean or the continents,
we need to forces,
the anthropogenic forces, the greenhouse gases and the
aerosols.
So that's one of the w we can kind of use the models
to help answer the questions: Can we say why the
climate is changing.
Which brings us to our second question.
So this is which of t following is not a question
that can be addressed by physical climate science.
So I have four different options here,
and the fifth one is that of these things actually are
part of physical climate science,
with the first one just question about how much would
the surface temperature in the earth if we
double re-equilibrate.
Second one is h carbon dioxide and methane will
humans -- how much add to the atmosphere during
each of the next five decades.
Third one, can cosmic rays from the sun affect the climate?
Fourth, can we learn about past climates by gathering data from
holes we drill into the earth?
So those are the four choices.
We can either pick one that you say can't be really addressed
by physical climate science or just pick No.
5, which would be, no, actually all fit for that.
>> Once again, I apologize for the editing job.
Oh, and the polls are open.
And just so you k the last one it was, in fact,
95 percent of the picked your second choice.
>> Oh, got you.
Then I don't h any of that stuff then.
>> We have a pretty well educated group here.
Okay, we got about 70 percent.
Then we're looking to top it off at 85 to 90.
I will leave it open f another few moments.
Okay, m selections.
Okay, we'll close the polls.
And these are the results.
>> And my answer is No.
2, that how much carbon dioxide and methane humans would add to
the atmosphere during each of the five decades is not a
question that can be addressed by physical climate science.
That is a question about how people are going to behave,
and F=ma doesn't handle that very well.
It is an issue that we're interested in and that informs
us about how the climate drivers
will change, but it's not a question that we can address
itself.
>> That's interesting.
So about 3 percent of our audience -- 35
percent of our selected No.
2.
It's 61 percent selected that all of them could be.
>> So this is where I'll make the distinction sometimes where
it might seem a little subtle but often when we read about
climate matters in the media, everything is inflated: Policy,
inputs, outputs, the climate science.
And that's just one of t I sometimes will try to
describe how we can think about some of these different parts.
So, if you're interested in that aspect about what affects
the greenhouse gas sources, you can do a web search on the
phrase Kaya identity, which is shown here in the bottom named
after a Japanese economist.
But basically if you wanted to know what the total annual
greenhouse gas emissions would be,
either globally or for an individual nation,
the formula isn't too tough.
The factors are what's t population,
not a climate per capita gross domestic
product is, how economic activity is -- you
want to think of it that how energy intensive the
economy is, how much energy is used for each dollar of gross
product, and then the factor F is kind of the greenhouse gas
emissions per unit energy.
So you can think of that as an issue of what source of energy
you use.
You would have much l values if you add all your
energy from nuclear energy instead of burning coal, for
example.
So the different f there,
and while they are related to climate,
they are not a question that can be addressed by climate
science itself.
But one point I do want t from that is that since most of
the major greenhouse human ones,
the anthropogenic ones -- CO2, methane and the like -- stay in
the atmosphere for several years to more than a century,
the climate system really doesn't care where they enter
the atmosphere, which country they came from.
As long as they are t it makes a difference.
So, how do we use t projections, though?
Here's an example of some different climate model
projections for the time period of 2000 to 2100.
You see three d bars.
Each of the different colors represent different
possibilities of what will do,
different emission scenarios, different amounts that would
come out from year by year.
You can just think of them as higher values,
some in the middle and somewhat lower values.
The shaded areas around e represent the uncertainties
because if we run climate models at a dozen or more
different centers,
we're going to get somewhat different answers from each,
even for some about what emissions there will
be.
So there is a difference in how the different models might
project there to be climate sensitivity.
Put it all together, you get a fairly sizable range,
maybe two warming Fahrenheit to about
seven and a
half by 2100.
But we at least s different sources of what we
might classify as here.
What people will do, whether we take the high emissions,
pathway or low there is also some questions
about the details of give emotions pathway just how
much warming there will be.
But all of them are well a what we have seen for the past
30 or 40 years.
So that gets me to the slide.
I'll move a wrap this up.
There's different types of uncertainties in climate change
projections.
The three broad t uncertainties are, first,
what will the future of greenhouse gases be.
That's the question about w will people do.
And these are used as inputs to the climate model;
those determine agents.
The second question is how the source of uncertainties,
how will the climate system respond to changes in
greenhouse gases forcing agents.
So this is what the climate models do: They take those
inputs and then they convert them with all that computation
into creating a how the climate will evolve
over time.
The models are good.
They are getting better.
But no one is s perfect,
and they don't all agree out to the last decimal place.
So there's s possibilities.
And the third source of uncertainty,
one that a lot of interested in,
is you're not necessarily interested in how temperature
precips are going to change, but you want to know how those
climate changes are going to affect the things you care
about: Where you live, how it's going to affect crops or
viruses or polar bears or that.
And those are climate change impacts.
And what happens is t of our global climate model,
so simulations of how the climate change then often get
used as inputs where other researchers then will say how
those projected climate changes might affect the things you
care about.
So, in the interest of t think I will show this but then
won't bother with the that's okay with you.
I just want people to think about the question of what
would you think is the most important thing scientists can
do to make models better.
I will lay out four different options here.
One would be to d using higher spatial
resolution, making the boxes smaller so we can get a
higher resolution view of what's going on.
Other ways to m more complex,
more comprehensive, adding new parts,
better and more detailed representations of physics or
more chemistry, just more comprehensive.
Or you can, just o option, tune an existing model.
Take one, look a doing pretty good and say,
you know, maybe it's a warm or a little too cold in
the artic and so maybe when I consider the number that's in
there for how reflective new snow is when it falls on ice,
I can tweak that a little bit and help compensate for that
bias.
Or fourth, you might just decide what the weak point in
your model is, since the atmosphere
isn't the ocean, and put all your effort in that.
So those a possibilities.
And I will go right to the answer here and just tell you
it is little bit of a trick question,
that there is no single right answer.
Different places, d people,
follow different pathways.
But if you w questions at a meeting with a
bunch of climate a great way to start what would
nicely be debate because there's
different ways of doing it depends a little bit about
what you want your climate model to do.
These are p tools.
We're l questions in a scientific way,
and making projections is just one part of it.
So, in reality, how we i the global climate models is
one way is by group bigger, stronger computers.
You can make the b involves more calculations.
That's a lot of h so much brain-ware.
Or you can rely on k and make the model smarter.
I mentioned about the d components and mentioned a
couple times that important.
If you were coming from another planet and coming to the earth
every 50 years to take the planet's temperature,
you wouldn't be atmosphere so much;
you would be looking to ocean.
The ocean is big, i and it has a very high heat
capacity.
So if we actually look where the additional energy that's
gone to warm the planet over the past half century or so,
most of it
now sits in the ocean.
So that's why we spend a lot of time not just dealing with the
atmosphere but with the ocean as opposed to climate models.
I mentioned resolution.
Here just g about what the sizes of these
boxes are.
When I first showed up at GFDL, here these individual boxes
show the spatial resolution that we had in the 1980s.
You can see North A South America,
but it's pretty course.
As of a few y boxes got a lot smaller
basically because stronger and zoom in a little
bit on the Gulf of Mexico.
Just to give you an i is what the grid resolution was
for the ocean and what became our workforce
model in recent years.
Going back to just a few y ago, it's courser than that.
1990s Florida and Cuba a little tougher to see.
1980s, well, that's the best we could do for the Gulf of
Mexico.
So computer power makes a big difference,
and it doesn't just produce prettier pictures,
but you can see that additional detail we can start
simulating more model that move heat and other
things.
So, in s basically three climate science
questions that answered and addressed in
recent decades that are important to society.
One is, is the planet's climate changing in significant ways?
It's a question of detection.
For that one we largely need to rely on observations.
The second q if the climate is changing --
statement has been that it's unequivocal
that the planet has warmed -- well then we ask that question
of attribution: Can we say what is happening.
In some of those p showed you with the red lines
and the black schematically are simply trying
to show you how we a synthesis of the models and
the observations to those questions to try to
attribute whether it's people, natural or both,
that are contributing to the change.
And the conclusion there is that most of the warming that's
occurred in the past half century can be attributed very
well to human activity.
Or if we want to look at the future,
if you want to make projections,
then we need to rely on global climate models because we don't
have observations of the future.
So again, we have our three-legged stool to help us
move along.
And with that, I'll t for your attention.