The Distributed Arctic Sea Ice Model

[anonymous]

Here is a relevant NASA information pool: https://modelingguru.nasa.gov/

[Jim Hunt]

An alternative take on potential hardware platforms:

http://goparallel.sourceforge.net/vendors-accelerating-hpc-cores-per-buck/

All the tier-one server vendors–from IBM to HP to Silicon Graphics (SGI)–are offering Xeon Phi powered models, but Super Micro Computer, Inc. (San Jose, Calif.) has perhaps the widest range of options in its line up.

Super Micro offers single-, double-, triple- and quad-height (1U, 2U, 3U and 4U, respectively) rack mountable models, as well as a tower for supercomputer-caliber workstations.

Four different single-height models support the installation of one-, two- or three-coprocessor cards for 60, 120 or 180 Xeon Phi cores per unit, in addition to either single- or dual-sockets for its Xeon E5 main processor(s).

[Artful Dodger]

An alternative take on potential hardware platforms:

Hi Jim,

Have you considered a PS2 or PlayStation 3 cluster? You'll never bet the MIPS/$ rating, and you can start building the cluster with donated 5+ year old hardware. 



In fact, there might be some hobbyists/amateurs out there already with a PS2/3 Cluster just looking for a good science problem to chew... 

[Jim Hunt]

Hi Lodger,

Yes indeed, since a PS3 cluster is already on the "hardware shopping list". Roadrunner:

Includes 12,000 modified versions of the processor originally designed for the Sony Playstation 3, and 92km (57 miles) of fibre optic cable, housed in 288 refrigerator-sized cases.

I guess we should add both Intel and Sony to the "sponsor shopping list"?

[Artful Dodger]

I guess we should add both Intel and Sony to the "sponsor shopping list"?

Thanks, Jim (esp. for the quote, the SMF search tool is weak )

I haven't built a mini-cluster in a while, but at that time the PS3 was being 'bricked' by Sony, and certain ATI graphic cards were becoming even more competitive in MIPS/$ from a GPU Cluster. 

Still, unless you see an ongoing need for CPU time, finding free MIPS on existing community clusters may be the best way to go.

[Jim Hunt]

Kim has stated elsewhere that he has been "involved in collaborative open source software development projects many times".

For Kim and anyone else with similar experience - Would you be happy using the Google Code feature request / bug tracking system?

https://code.google.com/p/dasim/issues/list

If not perhaps you can suggest a more comprehensive alternative instead? By way of example, the Los Alamos CICE team use Trac.

P.S. Amended following Kim's subsequent comments!

[Ice Cool Kim]

Kim is Mr

[Artful Dodger]

Hi Jim,

Friend of the ASI Blog 'Seke Rob' has not registered yet here at the 'Forum, but he is involved with several large distributed computing projects.

I've sent him a PM inviting him to visit this thread, and to contribute his thoughts.

Cheers,
Lodger

[Ice Cool Kim]

Hadley Centre Technical Note 91

Arctic sea ice extent has declined at an annual rate of over 4% per decade since
satellite records began in 1979. This rate is faster in the summer season and
there is evidence that the rate of loss has increased over the latter half of the
satellite period. There is also evidence that the ice has thinned at a rate of
approximately 60cm per decade. However, the heating required to melt the ice at
this rate is very small - just 1 W/m2 representing only 2% of the magnitude of the
seasonal cycle, implying that observing and modelling the mechanisms
underlying these changes will be challenging.

That means that a model which is able to model the true radiative processes with an uncertainty of +/- 2% would produce somewhere between twice the actual decadal melting variation and nothing at all.

ie an uncertainty of 100% in the result. Clearly such a level of uncertainty would not be much use. Even to be roughly informative it would need to be about an order of magnitude more accurate than that, ie +/- 0.2 % .

How realistically do you expect to be able to achieve that level in a model? What do you imagine the uncertainty of current models to be in completely and accurately modelling the entire earth's climate?

In view of  those figures from the Met Office modelling group, perhaps we can now better understand the relevance of the comment I made in the other thread:

I actually think trying to model the Earth from first principals is a bit , shall we say, ambitious.

Thinking that current GCMs are capable of prediction, is foolishness.

[Ice Cool Kim]

Kim has suggested elsewhere that he has been "involved in collaborative open source software development projects many times".

I did not "suggest" I stated clearly that I had. The innuendo is unwarranted.

re the uncertainty problem:

Years ago I worked on a model to estimate the cross-channel interference between two microwave communications beams between ground stations and satellites when there was precipitation in the common volume of the two beams.

Done on a limited scale, this is very similar to what is involved in climate modelling.


When we did a check against real data I was amazed to find that we were within 10%. I'd estimated that the uncertainty in the end result would be about +/-100%.

That does not mean that I was wrong. That was the uncertainty.

The fact that the result was much closer than that is not contradictory. There could have been two or more inaccuracies that fortuitously cancelled. That one test does not mean the model was accurate to within 10%.

This is the point I also made about GCM estimates of global temps.

Two parameters that are given values that are too strong, but act in opposing directions, can produce hind-casts that are reasonably close over the calibration period without that making the model correct.

Giving too much weight to both CO2 and volcanoes works for periods where there is mix of both.

It fails when there is a period without one or other forcing, as is now becoming apparent in the post Pinatubo period.

[Jim Hunt]

Lodger - OK - Thanks very much.

Kim - The "innuendo" as you call it was unintentional. I was merely trying to provide some context for a question that "appeared out of nowhere" on this thread.

Given that you have been involved in such projects many times, do you have any strong views and/or firm favourites when it comes to bug squishing procedures?

Decades ago I worked on a model for simulating physical processes on the "supercomputers" of the day, which "is very similar to what is involved in climate modelling." These days, however,  I refer to myself as "an engineer" rather than "a scientist".  If "a model" is useful in the real world I'm content with that.

[Ice Cool Kim]

" on the supercomputers of the day... " ,  about the equivalent of your Rasp Pi today then

If "a model" is useful in the real world I'm content with that.

The corollary being you have to prove it is "useful" before you can say it is useful and be content to use it.

That stage seems to have been skipped in climate policy. The current process seems to be to use the models to redesign global policy ... until they are proved wrong.

[anonymous]

Here a link to a model with a medium complexity and resolution. It's pleasing because installation takes less than 5 minutes (linux/mac) and simple simulations run really fast. Also it is easy to design experiments, no sea ice, no Himalaya, bigger earth, other orbit, etc. It helped me a lot to understand the fundamental concepts. The sea ice modul is nicely structured and even non Fortran experts should get a clue. MPI is supported. See below screenshots of configuration and dash before start. Forum, Docu, Papers.

[Ice Cool Kim]

That's interesting. I may test it when I have a bit of free time.

[Jim Hunt]

Hi Arctic.io,

Here a link to a model with a medium complexity and resolution.

Thanks very much for that, and it is indeed most interesting. Having had a spare moment I can confirm that the "Planet Simulator" builds and runs very easily on a Raspbian equipped Raspberry Pi, although the process does take somewhat longer than five minutes.  It seems to rebuild most of itself each time you run it though?

More from me when I've found another spare moment to read the manual and/or the forum!

[anonymous]

... on a Raspbian equipped Raspberry Pi, although the process does take somewhat longer than five minutes.  It seems to rebuild most of itself each time you run it though?

Here is a guy running Linux on a 8bit microcontroller, I think it'll take weeks to install Planet Simulator. 

And yes, for every run the model compiles and let's say creates a new program with all variables hard coded. But this is usual optimization. It even creates a new folder for that, so you can have a bunch of experiments and pause and run them as (and where) you like.

Have you found the 'touch Expert' trick? In case filenames haven't changed this table helps to map them onto surface parameters.

Code: [Select]

file name abbr. unit variable name
N032.surf.0129.sra sg m²/s² surface geopotential orography
N032.surf.0169.sra tsa K mm surface temperature accumulated
N032.surf.0172.sra lsm fract. land sea mask
N032.surf.0173.sra z0 m roughness length
N032.surf.0174.sra alb fract. mm albedo (surface background albedo)
N032.surf.0199.sra vegc fract. mm fractional vegetation
N032.surf.0200.sra lai mm leaf area index
N032.surf.0210.sra sic % mm sea ice cover
N032.surf.0212.sra vegf fract. forest ratio
N032.surf.0229.sra mrfc m maximum soil water holding (field) capacity
N032.surf.0232.sra glac fract. glacier fraction
N032.surf.1730.sra z0t m roughness length due to topography
N032.surf.1731.sra z0v m roughness length due to vegetation and land use
N032.surf.1740.sra albs fract. bare soil albedo
N032.surf.1741.sra albv fract. albedo due to vegetation


[Jim Hunt]

Have you found the 'touch Expert' trick?

I have now. I wondered what you meant at first, but then I discovered that I needed to edit a bit of source code to persuade the Planet Simulator to run on a small cluster of Raspberry Pis. The whole process is documented here:

http://econnexus.org/plasim

Amongst other things I was wondering why the "Sea Ice" box was greyed out, and perusing most.c made the answer abundantly clear! Sadly it seems even with the box ticked no visualisation of sea ice is available. 

I'd be most grateful to hear any further thoughts you may have on this little experiment.

[anonymous]

... to persuade the Planet Simulator to run on a small cluster of Raspberry Pis. The whole process is documented here:

http://econnexus.org/plasim

Wow, that's an impressive step forward within a small time frame! I'm not sure I can be of any further help, I have only vague memories of the week I spent with plasim. I'm sure you have found the user and the reference manual. Two links from my bookmarks: http://www.bgc-jena.mpg.de/bgc-theory/index.php/Support/PlaSim and http://www.dgf.uchile.cl/rene/PLASIM/ google translation works good on the latter. You might get live sea ice visualization via gui.cfg, in any case via postprocessor and netcdf files. Panoply has a good GUI and can transform netcdf into many other formats including video files(avi).

The thing is, once you got Planet Simulator running, you'll discover literally a planet full of possibilities. From that point, everybody chooses his unique path and makes different experiences. I think, that's why googling this subject is only of little help. But contact Edilbert at the forum: http://www.mi.uni-hamburg.de/Planet-Simul.5892.0.html?&L=3%27%22 If you can raise his attention, you'll get the best support achievable.

[Artful Dodger]

Hi fellows,

I think 'Planet Simulator' deserves its own thread, so it's not buried here among the main topic.

Who would like to start this new thread? Jim, or Arcticio?

Thanks! 

[Jim Hunt]

Hi Lodger,

I think 'Planet Simulator' deserves its own thread, so it's not buried here among the main topic.

No sooner said than done!

The University of Hamburg's "Planet Simulator"

[anonymous]

Found a great essay about the history of climate models. It starts with the time 'computers' were merely build by scientists using vast amount of papers and pencils, moves on describing the era they consisted of glowing tubes and nicely ends earlier this year. The author explains very well, how one layer of complexity was build on the next one and which critics emerged from the imperfections.

General Circulation Models of Climate

The website and the essay is based on the book written by Spencer Weart: The Discovery of Global Warming

[Jim Hunt]

Hi arcticio,

Found a great essay about the history of climate models

Thanks for those most interesting links.

Some more links to potentially relevant threads here on the ASIF itself:

What do the GCM models get wrong about sea ice?

and

On the importance of conserving mass in sea ice models

In the latter Messrs Moon and Wettlaufer state that:

We describe how a long standing approach used in the thermodynamic modeling of sea ice fails to conserve mass

.

Sounds like a fairly fundamental problem?

[Jim Hunt]

Why trust climate models? According to Ars Technica "It's a matter of Simple Science"!

It's possible that shrinking sea ice in the Arctic could increase snowfall over Siberia, pushing the jet stream southward, creating summer high pressures in Europe that allow India’s monsoon rains to linger, and on it goes… It's hard to examine those connections in the real world, but it's much easier to see how things play out in a climate model.

The article explains the inner workings of climate models, with the emphasis on the Community Earth System Model. Here's one of the graphics:



Courtesy of Kate Alexander and Steve Easterbrook

Those links are well worth exploring too. By way of conclusion, another quote from the article:

What is so remarkable about these climate models is that it really shows how much we know about the physics and chemistry of the atmosphere, because they’re ultimately driven by one thing—that is, the Sun. So you start with these equations, and you start these equations with a world that has no moisture in the atmosphere that just has seeds on land but has no trees anywhere, that has an ocean that has a constant temperature and a constant amount of salt in it, and it has no sea ice, and all you do is turn it on. [Flick on] the Sun, and you see this model predict a system that looks so much like the real world. It predicts storm tracks where they should be, it predicts ocean circulation where it should be, it grows trees where it should, it grows a carbon cycle—it really is remarkable.

[Jim Hunt]

According to Wired:

Alex Ramirez wants to build a supercomputer that’s six times as powerful as Tianhe-2, the Chinese machine that’s ranked as the world’s most powerful. That’s an incredibly ambitious goal, but here’s the surprising part: He wants to build it using the sort of chips you typically find in mobile phones and tablets.

More at: http://montblanc-project.eu/project/introduction

[Jim Hunt]

Elizabeth Hunke's presentation to the Sea Ice Prediction Network about sea ice models in general and CICE 5 in particular:



The slides from the webinar are available at:

http://www.arcus.org/sipn/meetings/webinars/archive

[AndrewB]

Jim,
I am aware I am kind of resurrecting this thread, but just wanted to note two things:

• A crowd-funded, collaboratively developed, Open Source climate model is always a Good Idea (tm) imho.
• The significant caveat here, according to my limited expertise in the subject, is that parallel climate simulation algorithms require high bandwidth and low latency interconnects between computing nodes. Hence most are run in batch mode on supercomputers, and do not lend themselves to a distributed environment.

Note that some (many?) climate models are Open Source and their source code is freely available for academic purposes. But honestly, downloading this kind of source code which would take months to run on a desktop PC doesn't seem very practical to me.
So I guess we'll always have to keep counting on large, expensive supercomputer resources for running the latest, most sophisticated climate simulations, including sea ice models.

[Jim Hunt]

I am aware I am kind of resurrecting this thread

No problem!

A crowd-funded, collaboratively developed, Open Source climate model is always a Good Idea (tm) imho.

I won't argue!

The significant caveat here, according to my limited expertise in the subject, is that parallel climate simulation algorithms require high bandwidth and low latency interconnects between computing nodes. Hence most are run in batch mode on supercomputers, and do not lend themselves to a distributed environment.

True enough. Fortran + MPI seems to be order of the day. Perhaps that needs to change?

Note that some (many?) climate models are Open Source and their source code is freely available for academic purposes. But honestly, downloading this kind of source code which would take months to run on a desktop PC doesn't seem very practical to me.

By way of experiment, a cluster of Raspberry Pi Mark 1Bs running a "medium complexity" climate model:

http://econnexus.org/projects/the-distributed-arctic-sea-ice-model/raspberry-pi-planet-simulator-cluster/

Fortran + MPI!

[misfratz]

Since it is, today, eighteen months since I left the Hadley Centre, I'm going to out myself now as a former climate modeller.

What are you interested in doing that is different to what is being done by the supercomputer-using modelling centres?

Is your initial aim just to recreate what they have done so that you can understand it better, and work from there?

Bearing in mind the fast pace of development it is possible to do a lot without a supercomputer, if you are prepared not to be operating at the latest high resolutions.

[Jim Hunt]

I'm going to out myself now as a former climate modeller.

Intriguing! Which bits of the climate did you used to work on?

What are you interested in doing that is different to what is being done by the supercomputer-using modelling centres?

Modelling sea ice better! Going back to early 2013 it seemed that existing models didn't do terribly well in the "new Arctic". Thinner sea ice, More fragmented and more mobile than in the "old Arctic". Plus my personal hobby horse, open water in the Arctic basin allowing the generation of significant swells.

Is your initial aim just to recreate what they have done so that you can understand it better, and work from there?

Perhaps the University of Hamburg's Planet Simulator covers that base?

Bearing in mind the fast pace of development it is possible to do a lot without a supercomputer, if you are prepared not to be operating at the latest high resolutions.

The "distributed" bit involved conjecturing whether it would be feasible to parallelise climate models in general and sea ice models in particular such that millions of mobile phones and/or Raspberry Pis could be persuaded to act like a supercomputer. As Andrew pointed out above:

Climate simulation algorithms require high bandwidth and low latency interconnects between computing nodes.

Unless of course you know different!

[misfratz]

I used to work on the atmosphere model. In a coupled model a lot of the problems in the atmosphere model that I tried to reduce were caused by errors in the sea-surface temperature distribution, which were caused by tropical convection.

It's hard to tell whether the climate models struggle with Arctic Sea Ice because they are too conservative (because they have to have enough sea-ice in the control), or because the atmospheric forcing is wrong. If it is the atmospheric forcing then one is in the realms of trying to devise a better way to parametrise convection, which is not for the faint-hearted.

It is true that the different parts of the simulation need to communicate with each other, so you would struggle to distribute a single simulation over the internet. What you could do, if you had a compact enough model, is to run it lots of times with different settings, as done by climateprediction.net

One of the most obvious ways to cut the computational cost down is to not have an atmosphere model. This then reduces the risk that you will have to try to become an expert in tropical convection to fix your errors. Then you could probably run a sea-ice model with a reasonably good resolution.

There are a couple of things that you could think of doing with a stand-alone sea-ice model. You could try to recreate the past using atmospheric forcing from a reanalysis, and then see how changing the sea-ice model affects its accuracy. Or you could create a simple statistical "weather" model to force a sea-ice forecast with a realistic range of different weather patterns, running the forecast many times with different weather.

[Jim Hunt]

There's a shiny new Wikipedia article on the CICE sea ice model:

https://en.wikipedia.org/wiki/CICE_(sea_ice_model)

[Jim Hunt]

P.S. For new readers the econnexus.org domain quoted above has lapsed, and I don't seem to be able to edit posts at this distance in time. Try econnexus.org.uk instead. By way of example:

http://econnexus.org.uk/projects/the-distributed-arctic-sea-ice-model/sea-ice-pie/

[oren]

Thanks Jim.
Note the ")" needs to be edited into the URL for Wiki above.

[Jim Hunt]

Thanks Jim.
Note the ")" needs to be edited into the URL for Wiki above.

I'm not allowed to modify my own old posts Oren.

Perhaps the moderator is permitted to do so?

[kassy]

The moderator has permission but can´t fix it.

If i correct the link and paste it here it is exactly what Jim posted. If i then click on it it opens the same way. The forum software does not consider the ")" to be part of a link.

[oren]

There's a shiny new Wikipedia article on the CICE sea ice model:

https://en.wikipedia.org/wiki/CICE_(sea_ice_model)

A. It works when enclosed with URL tags as above.
B. I didn't notice it was a post from 2018, and didn't think of quoting it to fix.