*36C3 preroll music*
Herald: ... so I'm looking forward and I hope you are, too. I am looking forward to
be told the difference between.. *laughs* ... we will all be told the
difference between an input model and a climate model, and we are going to be told
this difference by karlabyrinth. There you go.
*applause*
karlabyrinth: Thank you. Hello and welcome, everyone. I would like to see my
slides. Are the slides ...? Ah, OK. *laughs* nice. So welcome, everyone, to my
talk about Climate Modeling - the science behind climate reports. First of all, I
will shortly introduce myself and what I do. I work at the UFZ. That's the
Helmholtz Center for Environmental Research in Leipzig and I work for the ESM
project, which is short for 'advanced earth system' modelling capacity. I am also a PhD
student at the University of Potsdam and I am part of the developer team for the
middle scale hydrologic model, which is an impact model. And I'm also a scientist for
future and an artist. So what this talk is about? This talk is partitioned into three
sections, mainly. First is the introduction where I will introduce some
nomenclature like what is weather, what is climate and what we can say about
predictions. For example, why we can't tell the weather in three years but we can
say something about the climate, and what are climate models. Then the second part
will be the longest, the science behind warming graphs. I will show you a graph
that's typically shown when people tell you about climate change, and I will
explain that graph in detail and what is behind it. The third part would be
installing an impact model to your local PC if there is time. If there is no time I
will skip that. And in the end, there is, as always, a summary and conclusion. So
starting with the introduction. Weather is defined as the physical state of the
atmosphere at a given time whilst climate is averaged weather. Most of the time a
time period of 30 years is taken for that averaging but also other time periods
could be taken. So, while, the main question was, while we
are not able to predict whether at a specific date in a
decade, for example, let's say the 27th of December in 50 years or so. Why does it
still make sense to propose general trends for the climate? That is a question that
often arises when... and I'll answer that. So, first of all, it is about average.
Average cloud coverage gives us information on average reflection. And
average reflection is ... has an impact on the warmth on the earth. And the same is
true for another scenario. For example, average precipitation - meaning rain or snow
and temperature - has an impact on vegetation and vegetation influences the
carbon cycle. And that again influences the warming or cooling and that has an
influence on the ice coverage. And that, again, on the reflection. So there are
lots of processes that are connected to each other and if we know something about
the average of some of these physical state of the atmosphere, we can say
something about the climate trends. So the question is, what is a climate model? And
the AR5 defines a climate model is a numerical representation of the climate
system. The AR5 is a source I will cite quite often. So I have one slide
with the whole citation. It's the fifth IPCC report. IPCC is the
Intergovernmental Panel on Climate Change and the fifth assessment report is, yeah,
so AR5 is the abbreviation for fifth assessment report. But coming back to a
climate model. So a climate model could, for example, be a GCM - a general
circulation model - which is a global climate model that usually consists of an
ocean and atmosphere circulation. An RCM is not a GCM but it's a regional climate
model, meaning a climate model at a limited area, and mainly it has a higher
resolution. And for it is at a limited area, that usually means that there is
some input and output going in because it's not a closed system. And an impact
model again has usually a higher resolution in time and space and it's not
a climate model, but it's for simulating extreme weather events
like floods. So if you want to build a dam or a dike and you want to know how
high this dike or dam should be, then you would usually run an impact model that
gives you information about water height over decades or longer or so. And then
you would decide on the height. So this is the use for impact models. So that's for
the introduction part. Now I come to the main part and I will start with a
question: is it proven? Or with a climate graph. As that, I will show you a graph, a
typical image people would show you when they address climate change. This graph
has an x-axis with a time scale and you see it's reaching far into the future. And
it also has three or four regions and the first region is only in the past.
And the y-axis is the global surface temperature change, meaning how much
degrees in Celsius or in Kelvin, if it's different it's the same, we will have in
future or we had already. And then, you see several lines and different colors and
with the names RCP something. And I will explain all the numbers and everything
about that graph because it's a pretty important graph. So first of all, I will
tell you... no, no. I will tell you something about the numbers and
uncertainties. The uncertainties are the transparent colors behind the lines. I
will tell you something about the representative concentration pathways,
which is short RCP, and so it's reflecting the colors of the lines. I will tell you
something about the source of the graphs. So where does this graph actually come
from? So I will tell you something about the assessment report. And first of all, I
will answer the question: is it proven or is there scientific evidence that we will
face that climate change? So, you will see that graph quite often. First of all, I
took a definition for proof, for scientific evidence, from Wikipedia. The
strength of scientific evidence is generally based on the results of
statistical analysis and the strength of scientific controls.
Meaning, you make an experiment over and over again and you change basically some
influences on the experiments where you want to know that this does not influence
the output. So you can narrow it down and know what is the source of your results
and so you can prove a physical law or something. Yeah, I took this comic from
xkcd because it's a nice... it's somehow connected. So there is a person who pulls
a trigger and then gets struck by a bolt or some something. Something bad happens,
for example climate change. And then, yeah, there are two scenarios. For
example, a person usually would decide, okay, I would not pull the lever again.
But scientists usually or more often would say, okay, maybe: Does that happen every
time if I do so? Because yeah, that's basically how you prove something. That's
experiments. But in case of climate change, even scientists say you shouldn't.
Although it's pretty interesting for us from a scientific perspective. But the
problem is we only have one earth. We cannot do this experiment very often,
except we had a time machine. Then we could go back, but we haven't so we
shouldn't do that experiment. And that's something scientists before, long ago in
1957 said already: "Human beings are now carrying out a large-scale geophysical
experiment of a kind that could not have happened in the past nor be reproduced in
the future." So another question is, yeah, if you ask this question if it is proven
or that it probably is not happening or so to climate deniers, they usually would
tell you: Okay, maybe it's not happening. And the other side would take the position
and ask you, okay, if you stand in front of a road and you want to cross the road
and there's a car approaching very fast, would you cross that road? Because it
could happen that the car stops or makes a U-turn or something. But well, usually it
doesn't. And sadly, we know lots about this experiment, because it's done very
often before. We know something about traffic, and that it's
pretty dangerous. So let's change the factors a little so that we don't know so
much about that situation. Let's say a cube approaches us with a high velocity on
something that is maybe not a road. Would you still cross that something? The answer
is you still probably wouldn't. And why wouldn't you do so although you know
nothing about this situation? Well, you do know something. You know conservation of
the momentum, which is a physical law you know about. So you have a situation, you
know not so much about. You have never had an experience before, but you still are
able to make some assumptions because you know the physical laws behind it. And
that's basically the same we do with, in fact, in the context of climate. So we
have, let's say, just an earth and the sun and the sun has some radiation and that
comes to the earth and gets partially reflected and the earth radiates itself
because it has some temperature. We know something about this sun. We know the
solar insolation. And we know parts of the light is reflected. And the factor that is
reflected is usually called albedo. And so the reflected energy is albedo times the
solar insulation and albedo is something about 30 percent. And we know then that
the light that is absorbed must be all the remaining energy. So the energy of the
surface is 1 minus albedo times the solar insolation. Then knowing Stefan-Boltzmann
law for energy emissions where the temperature goes in to the power of four
and with the Stefan-Boltzmann constant we can actually find find out the surface
temperature which then is derived to -19.5 degrees Celsius. Well, we know, probably
we know, that the earth is much warmer, and that's because our model in this case,
which is maybe a climate model, is far too simple. So we change something about that.
We add atmosphere, and atmosphere has some interesting impact. So atmosphere has some
trace greenhouse gases, for example CO2 but also H2O, ozone,
methane, O2 and nitrous oxide. And these greenhouse gases reflect the radiation of
earth back to earth, partially. Meaning the atmosphere has a transparency and this
transparency we call t is something between 15 percent and 30 percent. So it's
not fixed. And that's another interesting fact. The atmosphere emits energy, which
we call j atmos, and that goes out in space and to earth and the energy that
goes into the atmosphere is 1 minus the transparency times the energy. So we know
two equations. The first is the energy that goes into the atmosphere also goes
out of the atmosphere. The second is that the surface energy of the earth
is the term we had before, 1 minus albedo times the solar
insolation plus the one part of the energy that is reflected by the atmosphere. And
so we have two formulas, two equations with two unknowns and with the Stefan-
Boltzmann law from before we can derive the surface temperature, which is 15
degrees of Celsius. And that actually is not so far from what it actually is. In
2000 it was measured that the surface temperature is 14.5 degrees. So, I did
this for a specific t which is 22.5 percent but when we change that t a little
to, for example, 20 percent, so we add more CO2 because, for example, we would
add a factory that would do carbon emissions. Then the transparency goes down
and the temperature rises to, for example, 16.6 degrees in case of 20 percent. This
is also a very old knowledge. So this is maybe a little much on a slide but it is
still very interesting because it is copied directly from a paper that was
published from Svante Arrhenius in 1896 already. And it's on the influence of
carbon acid in the air upon the temperature of the ground. And carbon acid
is the old term for carbon dioxide. So if we have a look to the percentage... So he
investigated: What if we change carbon dioxide? So what is the impact of
our behavior? Let's say carbon dioxide in our atmosphere would
double, so would increase by a factor of 2, then the average temperature rise in
Leipzig in December, so I choose the region for Leipzig, would be 6.1 degrees.
Well, that's probably a little high, but what we can't see is already that
Arrhenius back then already knew that there is a seasonal impact on
climate... that climate change is seasonal and also spatial. So it is not just one...
not the average temperature is the only interesting knowledge we get. So
Arrhenius said something like the temperature in case of carbon acid doubled
would be around four to six degrees. And the current models predict something like
an increase of two to four degrees for that scenario. So there is maybe overlap
already with that simple model from back then. So, then I come to the question...
a climate model represents physical laws. That's what we learned. Where do the
uncertainties come from? So if we know all the physics laws and we would just
calculate everything with this physics laws, why are there even uncertainties?
And there are some reasons for that. For example, the initial conditions is one
main source of uncertainties, meaning how is the current state of the climate system
now? How fast does something move? Where are the clouds exactly?
And so on. We don't know these precise initial
conditions and therefore errors occure. Second, would be the
resolution of a model. So the temporal and spatial step length, meaning
we can't... always represent our climate system with differential equations
and we approximate everything. We have not the movement of every molecule but we have
some average on cells. And if we increase the resolution then usually the
uncertainties go down. But sometimes they even don't for some question, for some
questions it's better to have a lower resolution. But mostly it's better to have
a higher. Then, truncation, so we have
computational limits. And lack of understanding, for example, clouds. Clouds
are not understood pretty well. And when I read the fifth assessment report, I found
a sentence a little amusing: Climate model... Clouds in climate models
usually tend to rain too early. Yeah, so but if you know all these sources of
uncertainty, why is there no such thing as the one best climate model? Meaning, why
can't we go to the highest resolution and to the best... the best computer we get
and do everything just in the best way and then we would have our best climate model?
And there are some reasons for that. For example, the so-called dynamic core,
including the method for differential equations or something like grids.
For example, if we have a triangular grid or a rectangular grid. On a
rectangular grid we usually can calculate faster but on a triangular grid
we could, for example, increase the resolution locally. That might be
differences. And both have advantages and disadvantages. Also, the parametrization:
parameters in our last slide were for example the t and the albedo which will
probably be not the final parameters because they are derived from other
parameters, but physical laws or something are often represented by rules with
parameters, and these parameters can be estimated. And they can be calibrated with
different error measures. And there this is another reason for
uncertainties and differences in climate models and then their schemes. For
example, there are different formulations of physical processes, for example, that
again, clouds. And last the truncation, again, we can also decide how we limit due
to our lack of computational power. So, yeah, what do we do? We investigate all
the models we have. So there are different climate models that are representing our
climate and we take all the models that match certain conditions - I come to that
later - and we average the output and then we
get a climate prediction and also that uncertainty band you see. So what climate
models do we investigate? They are so- called coordinated GCMS. So climate models
are compared in so-called coupled model intercomparison projects in different
phases. These coupled model intercomparison projects are called CMIP
4, 5 and 6. So there might have been four earlier ones. But currently, for the AR6,
so for the sixth assessment report CMIP6 investigated. And I showed you on the
map the research centers which took part in CMIP6, so which take part in the 6th
assessment report. These research centers are mainly specialized research centers,
university and metereological offices, but generally it's open for any institution to
participate, as long as they follow a protocol for their contribution, where
there are some rules so you cannot just do anything. These institutions
need to produce variables for a set of defined experiments and a historical
simulation from 1850 to present. This blue part is a link, so if you go to my slides
afterwards, you can see these variables you need to reproduce and then you can do
something like this. So we have a graph here again. On the x axis, we see again a
timescale that reaches from 1850 to today. And on the y axis we again see the
temperature anomaly or the temperature difference between.. so, exactly the
temperature difference, so how much the earth has warmed up. We see CMIP3 and CMIP5
compared, which were the models that were investigated for the AR5. So we see a
band. This uncertainty with the yellow and bluish and the background and then we see
these two lines, the blue and the red one from CMIP3 and CMIP5 and then we see the
black one. And that is what actually was observed. And we see that this differs
quite a lot. And that's due to there was only investigated the natural forces,
meaning excluded what the human did. And if we
also put the human forces into it, then it's quite matching. And that is the best
kind of proof we can get. And again, I said we investigate the physical laws and
the physical laws were actually results of scientific experiments. And so, yeah,
there's this kind of proof. And yeah. So maybe a little addition. There are also
other coordinated model intercomparisons projects that are outside of the IPCC and
so the ones that are inside the IPCC where the scientific focus is on a
subtopic, on something like land surface for example (and that's what I do). And
they're also published work outside from IPCC. So back to the graph.
We talked about the part: Is it proven? And I hope I convinced you that it is. And
now I will talk about the sources of the graph. So I talked a lot about the IPCC.
The IPCC, the Intergovernmental Panel on Climate Change, publishes reports. So for
example, the 5th assessment report and what you see here is part of the cover.
But there have been 4 ones before, as the name 5th suggests, the first assessment
report FAR was published in 1990. The second SAR in 1995. Then there was the TAR
and then for the 4th assessment report they changed the name scheme for some
reason to AR4 and then there was AR5, which I'm talking about. The IPCC consists
of several working groups, including Working Group 1 to 3, providing the
assessment reports and I mainly focus on the assessment report from a working group
1, which investigates the scientific aspects of the climate system and climate
change. But there is also a working group investigating on vulnerability and
economic impact. And the third one on the options of limiting greenhouse gas
emissions and others. So I totally show you a history of the climate models. In
something like the 70s, climate models were investigated where there was just an
atmosphere, the sun, rain - clouds were missing - and CO2 emissions. And
I hope you believe that the sun is behind the atmosphere and not in this atmosphere.
In the mid 80s there was prescribed ice added and already clouds and land
surfaces and yeah, you see a nice mountain. But actually in that time the resolution
was so low that the Alps only had one or two grid cells, meaning that was not so
much about land surface, but it was added. And for the first assessment report there
was a swamp ocean added, meaning an ocean was added, but it was had no depth. For
the second assessment report, the ocean got some depth. So it was a normal ocean
with surface circulation and there was added volcano activity and sulphates. For
the third assessment report, there was added... So this is all about which kind of
processes were there in the climate models that were investigated in these assessment
reports. Meaning there were climate models before that already had those processes
included, but they were not investigated in the assessment reports. So this is a
history of which climate models or which processes and climate models were
investigated in assessment reports. And the third assessment report, there was
another circulation edit for the ocean, the overturning circulations. And there
were rivers added, which is interesting because I do something with rivers and
there were aerosols added and a carbon cycle, meaning that the carbon that goes
into the atmosphere also goes out. But yeah, not everything, or that half-time is
not so good. For the AR5, er 4, there was chemistry added in the atmosphere and
interactive vegetation, and for the AR5 there was ozone added and biomass burning
emissions. And there is a history of processes, but there is also a history of
computer modeling that might be really interesting. It started more or less in
1904 with Vilhelm Bjerknes, who found equations that could be solved to obtain
future states of the atmosphere. And he thought about that maybe these equations
are really hard to solve and that task should be split and
distributed to many people. So he basically mentioned a human computer and
then Lewis Fry Richardson came in 1922 and did actually calculate all this.
This did a six hour forecast solving the equations by hand, alone. And 42 days user
time, meaning he himself calculated 42 days on it. But those 42 days were
distributed over two years in total. So he was a little behind the weather, only to
find out that it didn't give the correct answer. *audience laughs* That was long
forgotten. But people said, yea, that's not quite practical. We cannot do that.
But then computers came. In 1950, the first successful weather model was run on
a computer called ENIAC, and in 1950, weather predictions were run twice a day
on an IBM 701. Nowadays we use supercomputers much larger and there's a
whole list and rank and I will shortly introduce JEWELS to you: the Jülich Wizard
for European Leadership Science. That's a supercomputer in Jülich and I would have
shown you a picture, but you are not allowed, you are not simply allowed to
take pictures on that campus. But since super computers are fancy shiny cupboards
anyway, I thought this is OK. So we have these cupboards that look at shiny covers
and then this covers their blades and each blade is called a standard node and
consists of, in case of JEWELS, 2 times 24 cores with 2.7 GHz and it's hyper-
threaded, meaning you can actually run 96 threads or processes on one of these
nodes. And these notes have 12 times 8 GB of memory. And that's not quite much if
you want to run a climate model but I'll come to that a little later. And in
fact, in case of JEWELS, you have like three rows of five of these cupboards or
something. And so there are in total 2271 standard nodes, 240 large memory nodes and
56 accelerated nodes having something like GPUs. And I tell you about JEWELS, not
because it's the fastest, actually, it's maybe the 30th,
not even because it's the fastest in Germany - it was when it was built but
that's a while ago - but I told you about that because JEWELS provides actually
computing budget for the ESM project, the Advanced Earth System Modelling Capacity.
And so there are actually earth system models run on that machine. So what I told
you before, there is not so much memory on each node.
So what you need to do is you need to cut down your problem and distribute them over
the nodes. And then there needs to be some communication. So usually if the task
is so simple, you can cut down your grid and put a number of grid cells to each
node. And then there's communication between the nodes on the boundaries to
solve the differential equations. Talking about grids, I would talk about the
resolution. Also, again, a history of resolution of the climate models. For the
1st assessment report, the region resolution was 500 kilometers times 500
kilometers. And as I said before, you see these two yellow yellowish cells in the
middle that are the Alps. For the second assessment report, the resolution already
doubled or halved, depends on how you want to phrase it. For the TAR, it was 180
kilometers for AR4, it was 120 kilometers. For the AR5, it's a
little bit a different section I show you. And also, I show you two resolutions.
There's a resolution for the higher models, which is 87, for example, 87.5
kilometers. And for the very high resolution, 30 kilometers. And that's
because climate models are not just one model, but they are different kinds of
models that are coupled. And each model has its own resolution. So it's more or
less like something like this. So we have a model for ice, we have a model for
atmosphere, for ocean and for terrestrial. And this is coupled. So they all sent
their data to a coupler or something. And that set was there as an input to the
other model. So this is more or less like how climate models look.
And each of the models, again, has several layers. For example, the terrestrial layer
has a ground water part and the atmosphere. And so there's some input from
the atmosphere to the soil and plant system. And then there's some water that
is sinking into the groundwater and then coming out to the rivers. And yeah. So
then we have the runoff. So meaning rivers get water. And then if you have a look to
rivers and want to parallelize rivers, then it's not so easy because we have a
source somewhere and the water has to go from the source or something that happens
at the source has an impact to the sink, meaning this has to communicate all the
way along to the sink. And that's where I come in. I actually do. So I show you the
Danube, which you probably know better with the name Donau. At a resolution of
five kilometers and basically cut down the Danube into sub river domains. And we
need. If we parallelize these we need to calculate the subriver domains that are
farther away from the sink first and it uses that in the first graph a little. So
the grayish areas are calculated first and then it goes down farther to the sink. So
just to tell you about what I do. So now we come back to the main question. So we
answered where the sources of the graphs come from. Now we answer the questions:
What is a representative concentration pathway? Meaning what we all did before
was more or less telling how we get to that black line in the first section. And
now we concentrate on the colored part where we have more graphs than one. So the
working group 1 of the IPCC generally tests the selection of coupled models, that
is what I told you before, matching specific conditions and investigates the
output assuming different emission scenarios. Meaning we have a couple of
climate models that are somehow different, for example in their grid. And then we
have input data. The input scenarios would be, for example, the first one where we
just do business as usual and don't reduce carbon emissions.
The second would be we start with our way we do it today, but we would slowly change
to renewable energy. And the third one would be a scenario where we do it
spontaneously now or so. And that is an input scenario that we put into the
systems and then we get out a model output, that says something about the future. So
there is a black line that says, OK, this was our history until today. And from that
on, we have three scenarios and they are represented upper to lower. So the upper,
upper and right line represents the way where we do nothing or so. So this is
basically what we do with scenarios. And the RCPs, Representative Concentration
Pathways are scenarios that include time series of emissions and concentrations of
the full suite of greenhouse gases and aerosols and chemical active gases as well
as land use and land cover. So that is another graph from the AR5 and it shows
again in the X-axis the years, it's the same timescale as before, but on the Y-
axis we now have the rate of forcing, that is basically having this impact on our
climate. And so each of the RCP scenarios has some kind of equivalent in
radiative forcing. Yeah. So we have a 4 of these scenarios.
The data for the RCP scenarios is coordinated by again the input4MIPS: input
datasets for model and intercomparison projects that I told you before. And most
of it is freely available and I gave you the link. So if you want to run your own
climate model and test it with these input, you can find it there. And now I
will explain the last part. The numbers and uncertainties.
So first of all, again, to the graph from before the numbers behind the RCP refer to
the radiative forcing at the end of the modeling period of 2100. Meaning if you
follow one of these lines, for example, the red one to where it crosses the 2100
line, then the number there is 8.5. So RCP8.5 is the name for this RCP scenario.
But then the numbers of these different sections are the numbers of models used
for this scenario in this time period. Yes. So as I said, there are lots of
models intercompared and we even have different models for the different time
periods. So until 2100 there are 39 models for the RCP8.5. And of all the all the
rest, there are 12. And you see this little gap, this line break at 2100. And
that is caused by the change of numbers of models that took took part in this
project. And another interesting thing that we see here, and maybe the most
important, is we have quite huge model uncertainties. So if we compare all the
models, there's a huge band where we can't exactly say, OK, it's like this or that.
But this band is still... About human uncertainties are more important, than this
model uncertainties. We see tiny overlap, but mainly we can say how will the human
behave derives our future. And that there will be this climate change we are talking
about. So that was the main part about this three parts. And it's also it is also
the most important part. Now, I could probably show you how you can install an
impact model to your local PC, but probably I will have maybe something like
three minutes left. So we'll switch to the conclusion. And yeah, maybe if it's
arising as a question, I can do it. So what have we learned? Weather is the
physical state of the atmosphere at a given time, while climate is average weather
over 30 years. A climate model as a numerical representation of the climate
system. And we learned that the main uncertainty is the way we solve a
differential equations. I would probably have told you what a differential equation
is in particular, but that would have taken maybe another lecture. Climate
change is not proven throughout repeating one real experiment over and over again.
So there is only one earth it is said. But models simulate our
past climate pretty, well based on physical laws that were proven in real
experiments. And then maybe the most important message. Human behavior
is the primary source of climate change. Therefore, we talk about projections and
not predictions. Meaning if we wanted to predict the climate, then we needed to
simulate all human minds. And what we will decide on future. But we don't. That would
be another talk again. We take what humans will decide in future as an input
scenario, and with these input scenarios we create different output scenarios. So
with different inputs scenarios, we get these different output scenarios. Where we
can tell, OK, when we behave like that, this is the output. And human behavior
scenarios dominate model uncertainties, meaning the question is what do we want?
And if you go to a demonstration, the answer is usually climate justice. And I
think that's a good answer. Thank you.
*postroll music*
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