convene. Good afternoon, everybody, and I want to go to the second part of our climate
session now. In fact, what you saw before is an insight into that what drives the climate
community, their scientific work. I think in the afternoon we will have a little bit
different focus more also on the future challenges, what is the right architecture, for example,
or a climate computing, not only computer or HPC system, but what is the right architecture
also in which the community should interact? So do we need, for example, grid-based infrastructure?
Do we need cloud computing? Or what is the right mixture between capacity and capability
computing? Where should the multi-modal, multi-ensemble runs be done? Where should the high-resolution
models be run? So a lot of questions will be focused. And a specific issue in Europe,
starting from the European network for our system modeling, Ines, will be brought to
you by our next speaker, Cévie Jusson. She is director de Rucherche in Santo Nacional
de Rucherche scientific at C2PR Simon Laplace. And her recent topics, where she was engaged,
the Ines network, which I mentioned already, where she is also chair of the Ines board,
but also she was engaged in a very big paleoclimate modeling intercomparison project, and will
give you some very interesting insight now into the climate modeling in the European
HPC environment. So when the micro is fixed.
Okay. So good afternoon, everybody. So let's use that. I can try to use the microphone.
So it's a bit difficult to talk after all the presentations where a lot of arguments
were already presented. So I'll try to show things a bit differently. Basically to give
you a bit more insight of the way the community works. And then first at the international
level and then more at the European level to see how this can be related to HPC.
So let's start, as was already shown by several, by predecessors there. So climate modeling
is clearly a societal issue. It's not only a science issue to understand the processes
and how the system works, but also to help predict what can happen in the future. So
here basically you have, let me, do we see something with a, not really. I don't, yes,
that's it. So starting with scenarios of concentration of CO2, we run the various climate models
to do predictions of the evolution of the future temperature at the global scale. Here
is the version which is part of the figure that was shown by Mais, but which is the part
which is really using the global climate atmosphere ocean models. And here you have what it can
look like when you look more at the regional scale for high scenarios. The one which is
the A2 or a low emission scenario showing the great difference of course it has for
the impact on climate. All those science which is derived on those climate changes rely on
the simulations which are done for the IPCC assessment. So IPCC, Intergovernmental Panel
on Climate Change. Basically the curves I've shown come from the coupled atmosphere ocean
models. Those models include representation of the atmosphere, ocean, sea ice, and land,
exchanging water and energy. But beyond that, more and more we introduce the carbon cycle
and that interacts with the system. We emit CO2 and half of this CO2 is absorbed by the
system, the ocean and land. And the problem is that with climate changing, these CO2 things
change also and tend to be reduced according to the simulations we are producing on this
coupled atmosphere ocean carbon cycle. It's typically including these CO2 feedback that
we produce the higher range of estimates for the global temperature arise from the fact
that according with this carbon feedback we have more uncertainty. So adding complexity
in the system does not necessarily reduce the range. It tends to increase the range
because you add more processes and that they differ from one model to the other. What is
not fully systematically included right now is the interaction with aerosols and chemistry
and that will be certainly done more systematically in the future simulations. All these correspond
to what we call the Earth System models and from time to time we use either climate models
or Earth System models but that's basically those models that are used for climate projections.
Typically one example of these models too which is the one from IPSL which is one of
the groups developing such models. You see again similar in a different way but the boxes
are already shown but each individual box is one code and developed by different researchers
so the objective is to couple the overall and if we look for example for the IPSL we
counted it's about 80 scientists that are involved in the development of those Earth
System models because you need to couple to include several aspects of the system from
the physics to the biology to the chemistry for the land, for the atmosphere and for the
ocean. In those 80 scientists it's about 15 software engineers and we estimate the users
of the results of those models to be about 200. So it gives you for one of those Earth
System models the amount of effort which is behind these models. I tend to estimate your
home said 500 manures, I tend to say it's the order of southern manures. It's clearly
long term commitment and we can't change the code like this when changing the machines
for example we need to have long term use of those models and there's a lot of knowledge
which is behind those models. So climate modeling we have basically at the base of this is the
model development with process studies, parametrization developed with a lot of disease of problems
like cloud, not the cloud for the real clouds to be produced. Multicomponent coupling also
questions more adapted corresponding more to HPC which is also to adapt and optimize
these codes for different platforms. When you see the curves for IPCC it's important
to realize the amount of work which is behind IPCC. Behind those assessments there's a lot
of work on benchmarking. Participating to these exercises means strong international effort,
a lot of climatic test cases to be run. It can be for past climate situations or for
the actual climate. Evaluation of the different components, there are simulations that evaluate
the carbon, that evaluate the aerosols, the chemistry, the physical part of the system.
And then the projections for future climate which is really the emerged part of the icebergs
and a lot of work is behind all the overall. To perform that of course there's a lot of
HPC requirements. If we look, for example, at the next IPCC assessment which is planned
2013, the World Climate Research Program is setting what we call CIMIP 5 for coupled modeling
intercomparison program or project which will be composed of two types of experiments, near
term experiments with very high resolution but which are more devoted to studying the
capability of the system for climate prediction or longer term experiments which are devoted
to look at what we can say for the 21th century. Here I'm not going to describe all but this
is typically the number associated to the longer terms on the scale of the 21th century.
This is typically the kind of all the experiments which are recommended. In the core you have
mandatory experiments, then you have highly recommended experiments. In the overall it's
a very strong burden on the scientific community. There is a strict timeframe. Simulations must
be done before the end of 2010. There is a large number of experiments of the order of
50 or even more. Someone mentioned 5,000 years. I have 6,000 5,000 years of experiments.
500 simulated years or even more. It involves more than 20 modeling groups. It's really
a strong international effort that has a strong constraint on the availability of computing
power. Many of the centers that run those climate models have just done some upgrades
of the system to be able to sustain and to provide computing power for those experiments
at the case for the KRS but also in Italy with CMCC in France, for example. For IPSL
we have had some dedicated time just for the IPCC exercises which is about three nodes
of 48 processes of Essex 9 just dedicated to these and for fully dedicated over two years
to be able to perform this. It gives you a measure of the involvement and the requirements
associated with these IPCC assessments. There is also an issue for data distribution and
that was mentioned also by Balaji, the predecessor, which is that we want to have distribution
of data integrated in a system with core nodes which will have a selected set of data and
he mentioned two in Europe, two in the U.S. at least. There will be also modeling nodes
so much more data distributed staying where they are produced in the computing centers
producing those simulations and that will be able to provide even more data for all
the users like the regional climate modelers that need input for their models.
If we look at the past IPCC report, what we call AR4, there was 16 modeling groups and
23 models that were run for this exercise and if we look in Europe we had seven of those
23 models that were run and basically in UK, France, Germany and Norway. For the next IPCC
report we expect more modeling groups to be involved with newcomers like Italy, now they
have a couple global climate models and also a new consortium which is called EC Earth
which is based, which uses the ECMWF so the reading model for weather forecast but they
use it in the climate mode and with several countries involved like Netherlands, Sweden.
So if we look at the challenges for the system modeling or climate modeling community, if
we want to improve our understanding and prediction of future climate change we have to account
for their complexity of the earth system, that was already also mentioned previously,
but also to better produce, better investigate, better tools to investigate the climate on
the regional scales, we have to enhance interactions with the impact community so we have a lot
of challenges to fulfill and in the background we have this growing international dimension
of climate research which is really growing as time goes on.
We see that with the fact that we have to provide assessments there is really a strong
need to have quite strict evaluation set and this is probably, most probably something
which is quite unique compared to other domains using simulation. To do that we need both
to improve our research infrastructure both on the model side and on the high performance
computers. In your hope we have another challenge which is to help integrate, sorry I have really
problems with, it's also to help integrate the new countries that want to, which are
new to the subject and that want to develop models or to use existing models and to have,
and to help them and have access to the tools already existing.
This need for infrastructure for climate modelling was already emphasised previously, there was
a first in your hope, a concerted action which was called Roklaivar in 1998 which really
emphasised the need for a better integration of European modelling effort with respect
to human potential, hardware and software and the emphasis was on the development of
collaboration on the need for European climate computing facility for the exchange of software
and model results. Similar conclusions are also pushed at the international level by
the World Climate Research Programme with a need to go towards a common climate and
our system modelling infrastructure. We also heard from the World Modelling Summit which
also pushed the same idea. So we have been pushing this idea and we are really now in
the step where we have to really go forward and improve the situation.
In Europe we have developed a network which is called INES, a network for a system modelling
that was first launched by Guy Brasseur when he was at the Max Planck here in Hamburg and
we have about 50 groups from academic, public and industrial world which are interested
to discuss strategy and to accelerate progress in climate and our system modelling and understanding.
We have set several EU projects and just to mention one which is PRISM which was really
the first idea to help develop altogether some common infrastructure that led to the
Coppola OASIS which is now quite used. We have a board, a scientific board that I have
that I am chairing. In recently what we have set is to use the tools that we have with
the ECFP7 which is to develop an integrated infrastructure project which is called EASINES
infrastructure for INES which helps us gather about 18 partners from climate centres, computing
centres, data centres with expertise on computing, expertise on climate change impacts and that
allows us to gather the six main system models in Europe which are two in France, Domito
France and IPSL, one in Italy which is CMCC, one in Germany around Cosmos, the European
Consomme which is E-CRS and in UK the Hadley model and we have collaboration with US group
PCMDI which is really strongly involved in developing the international standards.
The idea is to complement the HPC so it's not the HPC part, it's the earth system models
or the model part or the model data part with the objective to help the integration of the
European system modelling community. The idea is typically to really develop our strategy,
where we want to go, where we are now and where we want to go in the coming ten years,
ease the exchange of expertise, develop some training, develop portal with model documentation
so that people can really have access to what exists at the European level. If we want
to integrate the new commerce, new countries that are new to the subject, it's important
that at least they have access to the information and to what exists at the European level.
We have a second objective which is to help the development of the models with some services
on common tools and model components. Up to now it's relatively limited on an ocean
model NEMO, on the Coupler Oasis, on the post-processing software CDO but the idea is that in the future
we may go towards a more extensive service around the models.
Help also, all this effort on the evaluation requires a lot of effort by this community
but so we have really to help develop common tools to help this evaluation and help the
dissemination of international standards. We also have, and I will elaborate a little
bit more on this because it is more related to the topic of today, to help prepare for
eye and simulations and help make the interface with PRACE and DSAR2 as European project and
help the improvement of model performance and I will elaborate a little bit more on
this topic. The last point is also to help foster the use of these model results for
climate change impact and the dissemination of the model results, the core and modeling
nodes that were already mentioned and the interface with decision makers and users.
This infrastructure project would not exist without the link with other projects. It's
really to do the infrastructure part, so the basis on which we have other projects developing,
like Metaphor that was already mentioned, which is the metadata format, with a science
project that accompany the work on climate change, like Ansar-Boltz, that is now nearly
finished, combined, which is a new one, which is on climate change simulation and the development
of models, like Eclipse, which is on model evaluation, or all the projects that are linked
on impact studies and of course the HPC, which is for us an important part, which is the
provision of computing time.
To prepare for I&M simulations, first we have been, this community, the Ines community,
has been pushing for high world class or at least European scale world class machine for
climate since 2001, and we have participated, for example, to the press scientific case,
to define our needs, why we need high performance computing facility. Let's typically, all this
work that was done within the press scientific case and that started before it was completed.
Launched by the PRAISE group led to things that you have already heard and that I summarize
on this slide, with questions, issues like the regional impacts of climate change, which
need increase of model resolution, improvement of model physics, the roles, what are the
roles of the components of the earth system with the fact that we need to include all
the carbon cycle, the chemistry, the aerosols, the ice, and it's not finished, we have the
permafrost, which is quite an issue now, whether it might increase the level, further increase
the level of greenhouse gases. How can we reduce, quantify, at least quantify
in certainties like ensemble experiments or model benchmarking, so the evaluation of models
and that was mentioned previously, and also the fact that there might be some possible
surprises and it's important also to do some long-term experiments to see whether there
might be some big jump in the climate on longer time scale. All this lead to increasing a
need of increasing computing power, that was already mentioned, I'm not going to give again
the numbers, of course the most demanding being the resolution and being the number
of experiment. Interestingly, if we look in the past, we went from gigaflops to teraflop,
mainly by increasing the complexity of the system and mainly by increasing the time lengths
of the experiments. A little bit, how much? Okay, thank you. And a little bit by increasing
the resolution, but that has been a bit less the focus up to now, but it seems to be more
and more something we are looking for. Since now, we can at least reach the minimum time
length of the experiments. A typical experiment needs to be of the order of 100 year or several
100 year, and 100 year must be run in about one month's time, or otherwise in the next
century, if you have the results quite late, you can't really benefit and do the science
that goes together. For the scientific case, we tried to emphasize, if we reach 100 teraflops
sustained, for example, or one petaflop or 100 petaflop, what can we solve? This is not
an easy question, but it's a bit different than saying we, if we increase resolution,
we need a factor of solvent, if we increase complexity, a factor of 10, and all that.
We have most probably to go a bit more along those lines, which is to be able to say what
can we solve, what can be the big gaps if we reach such amount of computing time. This
needs to be typically updated, and we also need to clarify what could go on a world class
machine compared to a national facility. On the easiness project, so it's really to update
this place scientific case, so what is our strategy, what are our needs, define our specific
requirements, among those it's clear that the input output, the IO, is quite an issue
in our field, and the amount of storage is quite limiting. We contribute to benchmarks,
and we already have some in the PRACE project, and hence help the interface with the large
infrastructure like DAISA and PRACE, and we have already been identified as a virtual
community within DAISA, although we still have to understand exactly what this means
in practice. We also have to improve and help improve model performance on different
and test on different computer architecture, test portability, test performance, improve
the IO, and prepare for future architectures. I must confess that we are not fully ready
to really have a strong activity on the preparation for future activity architecture, and it remains
more a kind of networking activity right now, but this is an issue which is growing,
and we're in Europe, we are not that much in advance, and we have really to foster this
at the European level. And we have set an HPC task force with people like Jean-Claude
Andre, Raina Boudic, who is there, Marielle Spuchel, Santo Nieuw, Navarra.
So to conclude, climate change is clear that HPC plays a key role in the information we
can provide to answer societal needs and questions. Then behind that, there's a need to have a
common climate modeling infrastructure, and this need is felt more and more by the community,
which comprises both the IO performance computing aspect and the infrastructural models and
data. The need for world class, and one of the questions we have is how much goes to
a world class, how much needs to stay at the national level, and it's clear that in the
community there are still debates of the way of going to these. There's no ambiguity that
we need high performance computing and more power. How do we distribute it, and we have
a mix of capability and capacity needs, which needs to be where we need to understand how
we best fit those needs. And infrastructural models and data, that's typically what I intended
to show you during this talk. It's clear that in the other hall, it's a challenge at the
different levels, which national, European, and international, and that we are really,
I don't know whether HPC needs a revolution, but at least the organization of the climate
modeling community is really in a revolution state. Thank you.