And we will now proceed with the next presentation, which was originally thought to be given by
Julia Slingo of the UK Met Office.
But unfortunately, she couldn't make it to the conference.
So Jochen volunteered to present her presentation as well.
And it's titled, Why Revolution and Climate Prediction is Needed and How to Achieve It.
Outcomes from the World Modeling Summit.
Jochen, please.
Yeah, the easy way out for me would be to say, well, all questions, please, direct it towards Julia,
who's back in Reading.
And so, but that would be too easy a cop-out.
The fact is that Julia, like me, was one of the organizers of the Climate Modeling Summit.
And so I take full responsibility also for the things that are stated here, even though I didn't write the transparencies.
So what was the Summit, the World Modeling Summit for Climate Prediction?
It's held about a year ago.
It convened about 120 leading climate scientists.
And the charge to the summit by the sponsor organizations was to define a strategy for how
climate modeling ought to evolve or maybe ought to be revolutionized through the early 21st century
to deal with the impending climate change.
And based on the belief that all political action having to do with adaptation to climate change
and mitigation against further climate change has to be based on climate science, on solid knowledge,
including the uncertainty, and on climate prediction.
And so that really there is an overwhelmingly strong case that the science base needs to be strengthened.
I gave you some of the issues already in my presentation for where we clearly have to improve the quality of the science base
if you want to base decisions on these.
And issues that come up are protection of our cities, for example, against sea level rise.
It's about food security that has become really put back into the focus in the last few years.
But generally, water availability, especially in dry areas, all these are things that are very strongly influenced by climate change,
by climate variability, and that is certainly the starting point for the summit,
that better information will help make better decisions, just as we do rely on good weather forecasts
for many of the decisions we make in the shorter term.
It's important to realize that there is no antagonism between mitigation, the prevention of further climate change and adaptation,
if only because long term, of course, we have to mitigate, we have to reduce greenhouse gas emissions in order to limit the degree of climate change.
But we are already committed to a certain amount of climate change, we cannot prevent it, it has already happened, and more will come.
And so equally important as mitigation on the somewhat shorter term is how do societies prepare for the inevitable climate change
that we are already committed to.
And in particular, the question arises, how can you respond on a local scale, on a regional scale, to climate variability and climate change.
And there, again, one of the major issues is rainfall precipitation, which is one of the weakest parts in the climate models, generally in the climate business.
Also, observations are difficult.
Weather hazards generally, there's, of course, heavy storms, heavy rainfall, floods, and so on.
Also important is to recognize that we can't really separate climate variability and climate change.
Climate change is not something that will happen sort of on a smooth, gradual manner.
If you remember the animation I showed of the increase in temperature, which is a simple example, but still, you saw it was very dynamic, very variable.
And so climate variability is a natural phenomenon.
Think of the El Nino phenomenon, which is a natural phenomenon.
But the question is, will climate variability change in a changing climate?
Conversely, to what extent is it the climate variability that contributes to the change?
The world is warmer in an El Nino year.
If El Nino happens more often, then, of course, the world will be warmer.
And these are questions you want to answer.
And we do not want to keep this in the purely climate scientific domain and realm, but we want to deal with the fact, how does it impact people?
How does it impact societies if there is climate variability and change?
Other questions, of course, we have the how will various policies influence the emissions on the longer time scale?
We want to know, eventually, of the longer time scales, which level of global warming is inevitable, where how can we limit it?
And one of the questions is sort of abrupt changes, sometimes called tipping points.
We want to deal with this. Are there surprises in the climate system?
And we might also be interested in the question whether geoengineering is an option.
Many schemes have been proposed and very few of them have been analyzed to the extent, what are the effective, what are the side effects?
Before we make an ethical or political decision about geoengineering, we ought to know what the implications are of what is being proposed.
So all these require a much better science base than we have available now.
I would argue not better in a sense that we do not know where to go.
In many respects, we know what one should be doing.
But in many respects, we are simply technologically limited, having a lot to do with high performance computing.
Also, I would like to stress how these things hang together.
The key word in climate science now is seamless prediction.
What we mean by that, traditionally, you've been told, we've been told that there is weather here and there's climate there and they have little to do with each other, little if anything.
Weather is what happens out to two weeks.
Climate is everything that is 30 years and longer.
The weather, the limitation of the weather definition to two weeks has to do with chaos in the atmosphere, chaos in the mathematical sense that weather forecasting is fundamentally limited to two weeks.
And climate traditionally was a 30-year average of weather and, of course, there's a huge gap in scale between the two weeks and 30 years.
But what if you are interested, if it's June, and you want to know whether there will be an El Nino in December?
Then you are right in the gap between those two definitions.
And if you want to know whether California is hit by torrential rainfalls, you don't really care about the fact that the classical definition of weather ends at two weeks.
And the classical definition of climate starts at 30 years.
You want to know what's happening, never mind the definition.
And this has emerged as a clear consensus that there is no gap in the time scales, that the prediction problem is a seamless one that we have now.
Well, we look at the past for good reason.
We look at today, especially for extreme rainfall events.
We want to know whether we can advance.
It's where the deterministic weather forecast operates.
But we want to try predictions over months and seasons.
These cannot be deterministic predictions anymore.
We cannot expect to predict a single weather event, a single rainfall event, but some statistical properties over seasons, over the years and up to decades.
And of course, the longer you go here, the different then you have different planning aspects that come into play from sort of immediate planning or maybe planning for emergencies for today's morning.
Outer resource planning and general management, outer infrastructure planning, if you have predictions over decades.
So there is really a continuum of time scales which you would like to predict.
What that implies is that also you cannot simply say, well, everything having to do with weather variability and weather events doesn't interest me if I'm interested in climate.
You have to take into account the fact that there are weather events and the variability and so on.
Also, when you think of longer term and what that means more and more than in order to make good predictions, longer time in the future, you must be able to represent weather events faithfully in your models.
And that means, you know, to represent them, at least you have to have the spatial resolution necessary to represent small scale events.
And again, there we come together.
But also, again, we have the drivers of HBC requirement.
A few examples.
This series is a good depiction of whether in the atmosphere a cloud cover.
This is actually not from observations.
This is from a simulation done by colleagues in the UK on the Earth simulator at a spatial resolution of 60 kilometers.
So much higher than the IPCC runs of the last generation.
So you begin, you see those filaments in the clouds, a fairly small scale, which you couldn't possibly see if you had a coarse resolution climate model.
We have weather in the ocean.
This is sea surface temperature in the Gulf Stream.
So here is the Gulf Stream at Cape Hadras, where it leaves the coast in the Atlantic.
And you, again, see filamental structures, very small scale structures in the ocean.
And I showed you the animation before.
This is sort of a typical low resolution 100 kilometer run of the order of the, as it was used in the latest IPCC report.
And now as we go to a successively higher resolution, quarter degree, 25 kilometers, 12th of a degree, so less than 10 kilometers.
And if you compare this, you begin to see similarities in the structure and the size of the structure.
It's at this resolution where it is not trivial any longer to say, well, this is an unrealistic model.
You probably would have to look very carefully in order to falsify the model, or maybe you would fail to falsify the model.
You would see, actually, this model at this resolution gets a lot of the observed properties of the ocean right.
So you have the weather in the ocean resolved.
And as I argued before, this is what you need to resolve in order to get the high kinetic energy simulated in the ocean.
Again, this is a driver of the, comes from the resolution.
This is Julia's depiction of the complexity issue in Earth System Modeling, so beyond the purely physical system.
For example, you have atmospheric chemistry and aerosols interacting with atmospheric physics and dynamics.
To the non-metrologist, when the atmospheric scientists speak of physics, it's more like radiation and clouds.
And when they speak of dynamics, they mean the fluid dynamics.
Decades ago, they invented this funny separation in terms between physics and dynamics.
So basically, these are the physical processes.
But the atmosphere interacts with the land.
I talked about the carbon cycle.
There is interaction of carbon cycle and nitrogen cycles, for example, with the fertilizers that adds nitrogen, influences the carbon cycle.
And of course, we have the whole issue of ocean physics and sea ice.
So again, this is a driver from the complexity that I also mentioned before.
One of the questions that people really need to know in the future in a changing climate is how rainfall will change.
This is from the latest IPCC report.
It's a multimodal ensemble.
So every quantity at every point in space and time was averaged over the ensemble of different models.
And so this average is what is shown here.
By the way, we found many instances where the average over all the models that I used gives a better simulation than every individual model.
We don't quite understand why.
And Miles may have a few things to say on that also in the following talk.
But this is what was done prominently, this figure prominently in the latest IPCC report.
And we see here for the two seasons, December, January, February, June, July, August, the change in precipitation of the world assimilated by the multimodal ensemble.
And you see blue wetter in high latitudes, wetter in the tropics and drier in the subtropics.
Now, if you compare this to the current pattern of rainfall, you would find pretty much the same pattern.
More relatively strong rainfall in high latitudes in the tropics.
Of course, much, much stronger rainfall in the tropics generally and relatively little rainfall in the in the subtropics where the desert belts are.
What this means is that the models agree quite some extent that where it is dry already today,
it will be drier in the future and where it is wet already today, it will be wetter in the future.
And in particular, if you already have too much water today, you will have even more of what you do not need in the future.
And you have too little water today, you will have even less of what you so desperately need in the future.
That is what's coming out of the climate models of the IPCC.
But the uncertainty is large.
There is some agreement.
The stippling actually shows where 90 or 95 percent of the models agree.
But the magnitude is still very, very uncertain.
We know that.
So we need significantly improved representation of the changes in rainfall as we expected in the future climate.
Here are some impressions of the reason why it's difficult.
Rainfalls and clouds, of course, clouds have very small scale features in part.
These are very difficult to simulate again.
And some of the problem is shown here.
The difficulty with clouds and rainfall is that we have processes across an enormous range of scales.
These are only the well, mostly the smaller scales and a thousand kilometers.
But we start with nanometers.
This is a logarithmic scale.
So the chemistry, the kinetics happening there, we have the chemistry leading rise to condensation nuclei that you need for cloud formation.
And then you have, as you go, so the condensation, they coaculate and they give rise to cloud formation.
You have to look at the evolution of water droplets as they grow in size, as the rain falls out and you have a vast range of turbulence there.
And so overall, there's nowhere is there a scale gap.
And so strictly speaking, you would have to deal with the whole range of scales in one.
Of course, that is completely possible to do explicitly.
So you have to make a cut and say, this is all I can resolve explicitly.
And you have to find expressions, parameterizations for the smaller scale processes.
And this is what makes what makes atmospheric science so challenging, that you always have to deal with these parameterizations and these are uncertain.
But apart from that, apart from the ever always needed parameterization, there are some issues where we ask, what can we resolve?
What do we need to resolve?
Top is observed in the optical band from the satellite, satellite imagery, possibly little enhanced showing cloud cover.
And the bottom is the visualization of cloud cover as shown in a high resolution atmospheric model.
And the point I want to make here is that if you run very high resolution atmospheric models as this one, this is not the standard climate resolution.
This is a specialized run.
It gets quite difficult to distinguish between the two.
And again, it would be quite hard to falsify the scale distribution, the size distribution of the cloud patterns that you see here in the simulation as compared to the real world, to the observations.
So what this says is resolution, high resolution will not solve every problem.
You will still have a parameterization problem for the unresolved scales, but it will solve some problems.
It makes some aspects of our simulations a lot more realistic than we had before.
So to the challenges that we have, sort of from the processes, it's the multiscale nature and the complexity, the various different compartments interacting.
So that's, if you wish, the pure science problem that we as climate scientists would love to focus on.
But alas, it's not to be.
Maybe for this part of this audience, it's a good news that if you want to have more powerful HPC systems, we will have to learn how to use them.
It was said in no uncertain terms this morning that the demands on software development are enormous.
And I would say we as a field, we are only now beginning to face explicitly this challenge of dealing with the massive apparel architectures.
So whether we like it or not, we have to bring those two aspects of the development, the computing science, the HPC research together with the pure climate science.
How to exploit the Earth observation data?
Earth observation is for us a shorthand for satellite observations to get to which it gives global coverage, enormous data streams, enormous richness.
And we have to find ways to use these to systematically evaluate our models, but also to combine the observations with our model information to define initial conditions for climate prediction, which goes under data assimilation and has its very own mathematical, technical and software challenges.
Now, it's clear that a lot of this requires brainpower and people.
And one question is, how do we train more people into our field?
How do we increase intellectual capacity, generally speaking, off the field?
And so we are talking about a big challenge.
The numbers are clear, petaflops, petabytes, order 100 person years for the software development, say for the parallelization and data assimilation, where I think those 100 years are more on the optimistic than the pessimistic side and again, the scientific expertise.
So it's a big problem, both technologically, but also intellectually and in terms of the workforce that we need to have.
And this led, the size of the problem, the size of the challenge, led us, the Climate Modeling Summit, to ask, well, can a single country deal with this?
Or is it both from the investment needed and from the number of people you would need beyond the capability of a single country and maybe better coordination worldwide is needed to do this?
And so this is what led to the recommendation of the summit.
I will let you read it, but maybe pick a few things that we believe that it is time to start something that you could call the Climate Prediction Project as an internationally coordinated enterprise of climate modeling together with high performance computing.
Very important, this is not to supplant or replace any of the existing efforts.
This will work only if it is in addition to pool resources in a way, well, to add resources and to pool the added resources.
For example, to bring people together to deal with the climate prediction research in a more coordinated way.
And the most immediate and visible aspect of it would be what we call the World Climate Research Facility for Climate Prediction, one or several of them.
If you said only one, it would probably be politically unpalatable to quite some countries, maybe several of them.
But certainly to have a facility at a scale that no single country would be either willing to fund currently or would be able to staff with the available person power.
So in that sense, pool resources that we can also bring people together.
There are examples for facilities like that. The most famous is CERN in high energy physics, of course.
In our field, it's possible on a smaller scale, the European Center for Medium-Range Weather Forecasting.
And what the summit collectively believed is that the quality of climate prediction, therefore the service to society, will be significantly enhanced if you could manage to launch a climate
prediction research facility of this style. Thank you for your attention.
Thank you very much, Johan. I think we can take one question, right, since you're a co-author, so.
Questions, please. So you're all convinced now, there's a question.
You said that now you are technologically limited in your simulations. Is there also a physical limit, like, or is your dream that one day you can actually simulate every molecule in the atmosphere, or is there something before that where you can say, okay, physics now stops simulating more accurately?
I don't think we will ever be able to simulate every molecule, nor do I believe that this will be necessary. I mean, there are very fundamental intellectual challenges unsolved ones, and those have to do with those parameterizations that for hundreds of years and maybe forever, there will always be unresolved scales, so we have to deal with the unresolved scales, and that is a large intellectual challenge, and some of them are not known yet.
So we will have those, too. But one reason why I call it technologically limited is really where I ask A, what is the most, where does it hurt the most, where does the limitation hurt the most, and also when I ask what is needed and have to have maybe a breakthrough and where is the qualitative change.
Now, on there, I'm less worried about intellectual challenges, because there are people interested in climate science, maybe not enough, but intellectual challenges will be tackled no matter what, because there will be people who are curious and who just want to solve a scientific problem, but the technological challenge, which would have to do with a facility of the size that we need, which would basically say, let us take the largest HPC system that can be usefully
exploited today and dedicate that to climate research. That is a political decision, and that is one that I find much, much harder to realize, this will not happen by itself. So in that sense, I see the difference in the magnitude of the challenge. One will happen by more ordinary channels, it can be enhanced, it can be strengthened, the intellectual enterprise, but the other will not happen by itself. We don't push very hard, it will not happen, despite the urgency with which
countries have realized the climate problem must be tackled. Okay, thank you very much. I think this is worth an applause again, because he volunteered to do two presentations. While Miles is cabled, I would like to introduce you to Dr. Miles L. He's the joint head of the Climate Dynamics Group in the Department of Physics at Oxford