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Yes.
Give a warm applause to Dr.
Jonathan Dongas.
He is the co-lead
of the flagship project coupon
at the Potsdam Institute for Climate
Impact Research and all information
about it. He will provide
and he will introduce a lot more
information for you.
Thanks.
So I'm in a lucky position that my
colleagues in
two talks already gave quite a nice
introduction to climate
change and climate
impacts and what can be done about it,
maybe vegan, becoming vegan.
And so I can I can build on that
and talk a bit about the um,
what we see as as a bit of the
frontier of of assisted modeling
and that some of the key challenges
and I try to create
connections to
things that are maybe of interest
for people here at different places.
So well again, to
to to to emphasize this again,
this challenge of of global
warming here from a paleoclimate
perspective. So this starts 20000
years ago in the late,
in the maximum
of the last glaciation where we had about
four degrees cooler climate than today
between three and four.
And the members were aren't there right?
And then there was the slow warming of
about three degrees, which took
ten thousand years.
It's actually quite slow.
Then we have.
There were 10000 years, nearly of
of a nearly very steady climate, that's
the Holocene, the blue curve, right?
That's where human civilization
prospered, developed, where humans
developed agriculture technologies,
the industrial revolution took place.
And then suddenly there's this kink
as very comparably,
very fast warming there.
That's the industrial revolution driven
by human anthropogenic emissions
of greenhouse gases and land use change
mainly.
This is all from paleoclimate data from
ice cores and other sources.
Now, these had crude data.
These are measurements, and they
confirm the earlier sources
here. There's still this quick
warming, and it's really important to
mention that it's not only the amplitude
of warming that's important to you.
We have about one degree
of warming in this in the threat curve
already compared to these three degrees
of warming since the maximum
off the last Ice Age.
But what is also very important is the
rate of warming. You know, it's it's 100
times faster than what's
happened from the last Ice Age to
the Holocene.
This anthropogenic warming 100 times
faster, and that's actually what creates
a lot of problems for adaptation.
Then if we go further into the future,
this gets even more extreme, right?
This is one of the,
um, quite extreme greenhouse
gas emissions in areas, which is actually
a business as usual scenario.
So that's what is
projected to happen when we just continue
with business as usual emitting.
And this goes up to three
degrees or four degrees.
So we've seen that before as well.
So again, 100 times faster.
Very extreme.
So what does this doing this is actually
bringing us to a new geological
age, which has been 20 Anthropocene
where social dynamics,
social technological dynamics actually
has become the dominant process in the
Earth's system. The Earth system is very
complex.
Lots of different processes to acting on
global scales, on low cascades, regional
scales, on different temporary scales.
But humans have created these these
networks, which we call also
globalization, right?
Networks of information
flows, networks of trade, networks
of resource flux.
And it's actually these technological
systems, these technological networks
that have created that have enabled
this global warming, this this huge
greenhouse gas emissions.
So it's it's really a social
technological dynamic that
has been driving this so that that's kind
of relating to the title, right?
That's the Anthropocene.
There are some challenges.
Then again, this curve here the
paleoclimate view on global warming the
last 20000 years.
But this also brings in some more
information. One is the
tipping element in
so-called tipping elements in the climate
system. These are parts
of the climate system that have a
nonlinear response to global warming.
So they they can tip, they can
tip like like something that
to standing on a set, they can tip over
if a certain threshold is exceeded and
the threshold is indicated by this
uncertainty ranges staff from yellow
to red. So
it's and a lot of
them actually a lot of these thresholds,
they cluster in a range between
1.5 and two degrees.
That's this. This cluster of tipping
elements possibly switch within the Paris
range. And these include important
ice sheets on the Earth, which could
create huge sea level rise, but
also at peak glaciers
and coral reefs.
And that's actually where this Paris
Agreement temperature range comes from.
1.5 to two degrees has been agreed
as the limit to global warming and the
Paris Climate Agreement two years ago.
And but if
you look at this curve, it shows that
really most of the scenarios, these
RCP scenarios that have been studies,
they they were should
beyond that and that would hit even
these upper clusters of tipping elements,
which are, for example, Amazon rainforest
boreal forest so had in circulation
of the oceans.
And only if you have a very nice
sustainability transformation, just as if
you two point six scenario, you might
avoid these tipping elements to tip.
So that's the picture of of
current climate science,
and this might even intersect and create
tipping cascades.
So this is
from a paper that is just in press.
But now
they are not only climate
change as an issue, but as Ben has
pointed out and others as well also other
sustainability dimensions that humans
have had an impact on, and they
are summarized in the planetary
boundaries concept.
And it's just important to mention that
not only climate change is a problem,
that all of these are a problem and
these different dimensions like biosphere
integrity, destruction and climate
change, but also chemical pollution
or deforestation.
They are all intersecting and they are
taking the system away from something
that has been called a safe operating
space for humanity by people like you and
talks to him with Stefan.
And the challenge for sustainability
transformation is to basically
return to the safe operating space,
which is the only
state of the US system that is known to
be a good state for human societies to
prosper.
And you can then bring
the human social dimension in and asked
well for what our system states
are. Dispensary boundaries not violated.
But we are also certain social
foundations are met.
So they are basic, um,
basic needs
of humans like food, water income,
but also jobs, voice,
gender equality.
So and the safe and just operating
space proposed by Kate Rogers is a
hypothetical space of
Earth system states that allow for
these two things the environmental
sealing and the social foundation to
be met at the same time.
And that's, of course, what
some people call
Earth resilience.
So the question is in which what
are future trajectories of the Earth
system, where the Earth's system
and human societies in it are resilient?
And we are currently on a business
as usual trajectory which takes us
out of such a resilient state clearly
and might take us even to hothouse states
similar to those where the dinosaurs
lived. That's why the T.
Rex was there on earlier slide.
But then the question is, are there
actually non-linear social
transformations that can maintain the
Earth system in a manageable state,
in a state of higher resilience?
So that would be the curve that goes
down, right?
Or are there intrinsic feedbacks and
tipping cascades that take the system up
to such a hot ice free state?
And now the
the problem is really that current
models of global change cannot really
answer such questions in
a in a satisfactory
degree and why this is so I would try to
explain in
the next slide. So this this
is the challenge of of an
integrated system modeling
that through metros collapse
of computer simulations that Ben has
already talked about and the
change that Chen Hooper has has already
outlined in 1999.
And even before that, and
the idea is to look at the
system as a core evolution space of where
they are environmental dimensions of
the biophysical climate system, but also
social dimensions of human societies.
And you can look at trajectories
of the other system in this space.
They are there might be catastrophic
domains, but you don't want to go with
the tipping. Elements are tipping.
They are inaccessible domains.
But you cannot go because they violate,
for example, a basic physical laws,
energy conservation or something like
that. And then but then there might also
be parts of that space
that that are something like a safe
operating space in the sense of the
planetary boundaries or even a safe and
just operating space in the sense of
K-dramas, in the sense of the so-called
Oxfam donut.
And the challenge for such an integrated
whole Earth system analysis is really
to ask, for example, are there even
is there even something like a safe
operating space in the sense?
What does the size of it?
What is its shape?
What is the resilience of societies,
the states within this, this
space, under which conditions does it
exist?
And these are really systems
questions where you need the complex
systems analysis to answer them.
So you have to run huge ensembles of
of complex models.
You have to do uncertainty analysis,
probabilistic things and so on and
so forth, and
even becomes a bit more complex.
So they are different
than. Mentions of such an analysis, which
which are very important and just too
to mention them very briefly, such
new, such a kind of
integrated a system analysis we think
needs to take into account really
the dimension of human agency.
So models have to represent agents
and they cannot.
And their macro dynamics that emerges
from agent behavior, for example, people
making decisions, sharing
information on social networks, what
recourse copycat dynamics emerges from
that that might be relevant, for example,
for climate policy,
then actually taking these networks into
account? Globe what that make up
globalization,
they taking modeling them explicitly.
That is what way a complex
network theory provides a lot of it for
doing that. That's the second point.
These representing these networks in our
net microscopes and
the third point is really to capture
the evolution of
nature and societies in these
models and to really go beyond
what
just doing optimization
calculations like economists often
do with actually good reasons.
But if you want to look at resilience, if
you want to look at these questions like
that's a safe operating space exist at
all, you cannot do it with optimization
models.
So a novel type of model is
needed for doing this type of analysis,
which we would as models.
Such models currently don't exist.
And so we are doing some modest efforts
in the coupon project
to create such models.
And for example,
again, this model should allow to
represent social network dynamics,
opinion formation, social tipping points,
tipping into actions to address questions
like How do actually these climate
tipping elements like the Greenland Ice
Sheet and Arctic Ice Sheet interact with
potential social tipping elements?
What are potential social tipping
elements, for example, opinion formation?
It's not. Who has the
potential to be a tipping element.
Climate policy can be a tipping element
to the divestment movement.
The social movement on divestment from
fossil fuels can be a tipping element
that can be tipping cascades on this
network, and
this is something we have to.
So we have to do that,
and this is just showing
the types of
global change models that are out there,
that the types of the two dominant
types of models that are out there right
now are so-called assisted models and
integrated assessment models.
Both are focusing,
focusing, respectively on the
biophysical dimensions of the Earth
system. These are the Earth system
models. They focus on the climate system
and the
integrated assessment models.
They focused mainly on the socio
metabolic or economic dimensions of the
Earth system.
And then these these new types
of models that we envision should focus
on a process detailed representation
of things going on in socio cultural
domains. Really, again, what is
sociocultural?
This is again, opinion formation,
creation of institutions, dynamics of
organizations, um,
and such things.
And why?
Why do we even think that we can do that
because it seems to be a huge, very
difficult task.
And we've seen that
even though we've come a long way since
the Limits to Growth Report 1972.
I mean, there have been a lot of
developments since then 40 years
past or even more.
Forty five.
And um, there has been
huge progress in computing power since
then, of course. So comprehensive
system modeling is advancing fast
already, and we can exploit this progress
further.
The advances in complex systems theory,
social ecological systems modeling,
social simulation and similar fields
that allow really computer simulations of
certain aspects of social macro dynamics.
So, for example, again, this opinion
formation example, we've done
a study on how smokers have
been marginalized in social networks in
the US through the past 40
years or so, and we can do,
at least on a qualitative level,
quite nice projections
of of social network dynamics, and we can
understand why certain things are
happening the way they are doing.
And this is actually possible through
mostly agent based modeling approaches,
where you simulate the
dynamics of thousands of
simple agents and studied the macro
properties that emerge from
that, for example, at the fraction of
people that have a certain opinion and
how it changes over time.
Then the third point is that big data
on social dynamics is increasingly
available and
in, if you will.
And of course, this is valuable for
science, but you have to be, of course,
very careful about, um,
issues that that can happen when you have
too much data like that.
And in most cases, you don't have it.
That's one of our main problems.
I mean, they are the data are that, but
we cannot get it as scientists.
And the fourth point is they are emerging
research networks that aim at fostering
such inter and transdisciplinary
research. So this is really about
integrating natural and social sciences
to a degree that is much
closer than has been done in
the past.
And this is, of course, very difficult
because everyone speaks different
languages, but they are these networks.
Progress is slow, but
there's some hope.
And now this is a bit more concrete on
what we are doing in
the coupon project, we are developing a
software framework for that, which is
called code coupon code because it's our
core activity at the moment.
So it doesn't really hasn't have a
meaning as an acronym.
So so far, maybe it has at some
point. And the idea is
really to provide a framework to build
such models that has the potential to
represent these, these different spheres
of the system that we've talked about
before. So this is the environmental
sphere than nature, which
we call the biophysical text on which,
where. And then there's this metabolic
sphere and the culture is fear
and the they are different entities
in the models. So different things that
are. And one
thing that is very simple and easy to
understand ourselves.
So these are basically grids said, where
typically a lot of biophysical dynamics
are going on, but also, for example,
agents, individuals, which is a different
type of entity.
Lives can live in a certain cell.
Um, and then there's another type of
entity that we highlight here.
These are social systems.
These are things like aggregate
systems of parts of society, like
a nation state, for example, or a city
that I described more on
the on the macroscopic level by
some aggregate variables, for example,
like production, harvesting, wealth,
capital stocks and so on.
And these different entities are, of
course, interacting.
They are somehow related to each other,
and they are different modeling
approaches like agent based modeling,
adaptive networks, differential
equations, stochastic
equations that we can use to model
the dynamics of these entities.
So they are, um.
This is another
type of another look at that, which is
more like an UML diagrams that just shows
how the different entities
in the model are represented by
bi classes, by objects and
how they can be related to each other.
For example, an individual can live on a
cell or a social
system and owns
a number of individuals so individuals
can belong to social systems and so on.
Then software design, it's
we have a reference implementation in
Python.
It's, um, it's object oriented.
Um, we have we put a lot of effort
in in providing a good documentation
test framework and tool
to allow for parallel simulations on our
cluster because that's super important.
And this is, of course, all, um,
all work in progress in a way because
we have relatively low resources for
doing that.
Um, but it's basically made in
a way that people can really
plug and play, um, their modules
together. So it's a broad based module
resolution that allows for different
levels of involvement of people.
So that can be model users that are just
using modules in a scripting kind
of way. There can be component developers
to develop different parts
to represent different types of assistant
processes, for example, and
there can be framework developers like us
who are really providing the sourcing and
trying to make it more performant, for
example, or to
port it to a different programing,
language or something like that.
And we will be hosted on GitHub.
The link is down there, but
unfortunately, um, we are not
allowed yet to put it on source because
of our administration, but it will be
hopefully open source, um, early
next year.
And um, we we made we
designed this to be very, very flexible
and modular so we can, um,
connect it to other models, for example,
like this land use and vegetation
models that Benny just talked about?
Um, and yeah, we tried to follow
these standards of Open-Source Open
Science here and to enable
all people from these different networks,
but also maybe people from
from this community here to to contribute
to this.
And yeah, this is an example of
how a typical script
looks like that um, runs,
um uh, that's, uh, model
run. So this is just in thirty
lines of code here because we it's, uh,
this this thing is highly modular and
this is just showing how, um,
how simple this can be actually for a
model user.
And this is this is now a bit even
more concrete.
This is, uh, illustrative example the
model that we um
that we put together to to show that
this whole thing works.
And um, so the white
white boxes, here they are model
components that the model component
developer would and would provide.
For example, the global carbon cycle.
So this, uh, that
determines how carbon this, for
example, diffusing between the atmosphere
and the ocean. The whole carbon is going
from the, uh, from the
vegetation into the atmosphere.
For example, this, for example,
entities involved in this carbon cycle
component would be the word.
So the whole planet, which is one entity
and then also said because
photosynthesis and in
general biology happens on cells
and entities are, for
example, also represented, individual
social systems are there.
And then also each of these entities
is involved in different processes.
So just to give one example, if you look
into the social, social learning
component, this is showing how
people imitate each other.
So it's a very typical human behavior to
to look what your friends are doing and
then you want to do it as well.
Many times, um, if you think
it makes sense and then
the individual has this learn
environmentally friendly process
that it participates in.
So this is if you see your friend
is environmentally friendly, maybe you
want to be environmentally friendly, too.
And then we put this all together, and
then this gets a bit complicated.
Again, this looks more and more like the
limits to growth model.
Um, and yeah, you can
look into the paper, uh, for for
details here.
But um yeah, we have we
have, of course, some some results here.
This is just really for, um,
for illustration.
So these are not, um, these are not
predictions of any kind.
They are not even projections,
but they are just illustrations of
of this base of possibilities
of of a system dynamics.
If you assume all the things
that we have assumed, right?
So they are and this is a very legitimate
thing that models have to do as well, or
that we think models are important for
as well to explore the space of
possibilities to really understand how
the world earth system works.
And you don't have to do predictions
to have a good model.
And this is,
for example, we show here trajectories
of the system in the
next 100 years without social processes.
So we have, for example,
the environmental friendliness of
individuals is fixed where
certain subsidies, emission Texas
and fossil fuel events are fixed, right?
So here we have, then these things are
not changing.
We put them into the sociocultural
dimension.
Then they are we on the social metabolic
dimension, on the economic.
I mentioned these are the shares of
different energy
sources that the societies use.
So in this example, we have five social
systems that are running at the same time
each has 100 individuals.
And in this example, for example, you
have a strong increase of biomass
in the beginning, later,
even later, increase of renewable energy
sources and a decrease of
fossil resources, which
is not very fast.
And then the system here on the
biophysical dimension responds
accordingly.
And then if you switch the social
processes on, so these are now really
people can do social learning so they can
learn to be environmentally friendly
from their peers.
That can be a renewable.
Subsidies can be implemented
under certain conditions.
Emissions Texas can be implemented in the
fossil fuel, then can be implemented
basically by certain voting processes
among the individuals.
So we emulate a democratic decision
making process here.
And then we see that all these things
they are, they are switched on quite
fast. And this leads to a rapid
decrease year of the
fossil energy sources and
to a faster increase of the
renewables compared to
the case without social dynamics, right?
So we get a completely different
dynamics, which is, of course, what
you would expect because we change the
model to a large degree.
But this is what this type of modeling is
about to really understand what is
happening when you make plausible
assumptions.
And now, OK, what's the status of this
quote unquote? It is already under
operational use and it
is ready for community integration, and
we have a description paper submitted
actually today.
And uh, the Open-Source
launch of the software is planned for
early 2018.
Now, OK, two, I want to give to
outlook slides for four
types of research questions that I would
find really interesting important to look
at. So
the context of this conference here.
So one is really to look at more into the
competition trade offs of synergies
between these really the two
maybe main
UM transformations going on in
the 21st century.
And one is that the digital
transformation is ongoing rapidly
and is just happening.
And the other one, is this necessary?
Maybe we think necessary social
ecological sustainability transformation.
Um, but there can be
there can be a competition even between
them.
For example, we've seen that before.
There's a large and strongly increasing
energy consumption of digital technology.
So the internet in general is consuming
huge amounts of energy.
Then the blockchain and bitcoin mining
now is becoming is
mentioned a lot as an example, because
bitcoin mining consumes the
same amount of energy as a small country
already, and it's just increasing
exponentially.
Then a bit more on on a more
subtle level, the influence
of digital communication and online
social networks on public opinion,
formation and governance processes
that may be relevant, that are relevant
for sustainability transformation.
So this is really the issue with
debate about fake news and
echo chambers and all these things,
whether they are really there or not,
is, of course, up to debate.
And um, but
this is this is really relevant to study
this more also maybe using these types of
models. And then the last point is
things that are a bit more far off,
maybe, but that people are seriously
concerned about like, um, emerging
general artificial intelligence and then
asking really, what would be the Earth
system consequences of of such,
um, events.
And this is,
of course, a bit. Yeah.
Then maybe even more, um
uh, philosophical idea.
But there's this interesting debate
about the so-called techno sphere.
So this is, um um um,
looking at basically the the complex
of global technological
networks and connected to human societies
and what this actually has to
say about sustainability.
And um, they have.
Peter Hough has written a lot about
that and we've made a small contribution
to that debate. And maybe, um,
the um, this type of modeling
that I talked about might also shed some
light on how human societies
are actually interrelated with these
technological and macro structures that
we've created and what this
means for the really
the viability of sustainable
development in the future.
And also, um,
this actually highlights some, some deep
ethical questions again about what
sustainability actually is and
how how this depends on on
a basic humanistic principles so
that that sustainability is really mostly
about humans and what what
is um, what do you
do with that concept if it's true
that humans don't really have
agency anymore, as some people claim,
like Peter and to some degree?
Right. So these are all open questions.
And now the take home messages,
computer simulation model state for
the dole system, microscopes
and and the big social ecological data
analytics, they are really essential
tools for understanding sustainable
development in the Anthropocene.
We think a new class of world Earth
models is really needed to capture key
aspects of this dynamics.
And really one central
challenge of of this century, I think, is
really how to reconcile the
sustainability and digital
transformations.
And yes,
please contact us, maybe contribute
and tweet.
Well.
You used your time perfectly,
which means that we have just two minutes
for two question, if there are any,
please go to the microphone or via
internet. I see one question here
and one perhaps
there, so we have two questions.
I amazing work, really interesting
talk. Thanks a lot.
So I'm working more in the
Earth system, modeling community and
dare we also have we have the tendency
to make our models more and more complex.
And what we've observed is that as we
make them more complex, the less we
understand them, we do less understand
what's going on. And I've seen that a lot
of your work, you're trying to do the
same, right? You trying to integrate all
the processes that you're interested in.
And then we've observed that a
lot of the uncertainties we know
that we have are coming from from the
parameters. So each time you integrate a
new module into
your interior model, you at some
time, at some point you need to
parameterized some values you don't
know. And for us, it's been very hard
to measure some of the quantities.
And my question would be,
I guess, for you, it's even harder,
right? You.
What are your parameters and how do
you try to estimate them?
Thanks a lot.
Right. So I mean, they of
course, this is this is
always the challenge with modeling.
And um, they are.
Yeah, for example, um, some
some parameters that that are
typical for these types of social
dynamics modeling, they are concerning
rates. For example, what is the rate of
of imitation of of
behaviors or opinions between people?
Compared to the rate of um
of the network dynamics,
the whole faster network changes
due to processes like Homophily, for
example, which is the process that you
would let you up.
Typically, people are more likely to
interact with other people that are
similar to them, um, than
that with people that are different.
And so this is different timescales.
They can be parameters.
And of course, you can
address that by doing, um,
by doing simulations with, um,
on parameters basis model
robustness tests.
One thing that that we do a lot in the
current project is actually to to do
this huge agent based simulations
that that have shown you today.
But we also do conceptual
modeling and Kepler's theoretical
work where we try to really understand
what the models are doing in a more
simplified way. So actually, most of our
work so far has been working
on very simplified models
of such things that we can still
understand mathematically, sometimes even
derive analytical mathematical
approximations, and this works
quite well. But of course, this is this
is always a challenge.
Yeah, I saw
that there is one question from the
internet, so I ask you, please
go afterwards and speak with them.
We don't have time because of the next
speakers. So internet, what is your
question?
Yeah, it's somehow connected.
Um, how does your group, especially
in your scientific community in general,
validate models?
And how do you convince people that
predictions using this model actually
have some validity?
Right. So I um,
I try to I try to address
that during the talk already, but very
briefly, I think the one
thing is that we in this social
dynamics modeling particularly, it's very
difficult to do quantitative predictions.
And it's also why what what we try
to do more is to as we validate
the model it's doing, we're producing
qualitatively what's past developments,
for example, in this study on on the
smoking change in smoking behavior
and how people were marginalized and
social net.
And this.
This one goes to one of your pockets,
maybe with your hoodie or jeans.
If it has
always ways that way,
you know,
we really want to have to keep running
around the front.
OK,
maybe hold up.
The.