Importance of Natural Resources

UQx DENIAL101x 4.4.6.1 From the experts: Climate models


Lunt: A good question is what actually is
a climate model, so a climate model is a piece of, or the usual use of the word for climate
model, is a piece of computer code, a list of instructions to a computer that encapsulate
our very best understanding of the way that the atmosphere of our planet and the ocean
work in a physical sense. Pitman: The basic underpinning laws that climate
models are built from includes the basic Newton’s laws of motion, conservation energy, conservation
mass. Basic physical principles that physicists discovered a very long time ago. Meissner: All these models share lots of characteristics
and, of course, the physics of the models are very very similar because they are all
based on the fundamental same equations of fluid dynamics. It’s the same equation that
we knew that equation since hundreds of years. Lunt: And actually you can write down the
very fundamental equations on a single piece of paper. Solving them is a lot harder, and
that’s actually what climate model does. Pitman: All a climate model is is a million
lines of computer code running on a really really big computer system. Lunt: The way that climate models work is
that the divide the world up into a series of boxes, so it’s very like LEGO if you like. Meissner: Basically a model is like if you
would construct your world out of little LEGO blocks, and it’s basically the size of the
LEGO blocks. You can buy a real expensive LEGO Star Wars ship, my son has those, very
big expensive, takes the poor parents two days to build them and they have lots of detail
or you can buy a little car for a 3 year old, which doesn’t have that much detail but is
made out of big big blocks. Lunt: So you can imagine sort of building
up LEGO in that, each one of those LEGO blocks perhaps represents a box in which the climate
model has a value for temperature, has a value for the amount of air or water within that
box, it has a value for how fast the air or water within that box is moving, how much
moisture is contained within it, if it’s the atmosphere. So you imagine you’ve got this
kind of matrix, if you like, surrounding the world of these boxes that go up into the atmosphere,
down in the ocean. Now the model can’t actually tell you anything about the climate on a scale
that is smaller than one of these grid boxes. The very highest resolution models are perhaps
10s of kilometers, but most models you’re talking hundred of kilometers, so there are
lots of processes in the atmosphere in particular that actually occur in reality on a much smaller
scale than that. For example, clouds themselves are much smaller than the size of one of these
grid boxes and so we have to make approximations to have some of those processes work. Pitman: You build parametisations or representations
of processes, which need to be resolved at scales we can’t explicitly model in the climate
models, things like clouds, things like convection in the atmosphere, things like eddies in the
ocean, things like land-surface processes. Lunt: The first weather forecast that was
carried out on a computer that I’m aware of was carried out by a guy called Charney [Jule
Gregory Charney] and actually he did a 24 hour weather forecast. It took him 24 hours
to do that forecast, so it wasn’t particularly useful. It turned out that if you, there is
some archived photography of what that machine actually looked like, and actually it looked
very similar to a modern day supercomputer. It’s about the size of a room, it’s got lots
of leads everywhere, and it’s got a few people looking around, technicians looking after
it. Actually it looks very similar to a modern day super computer, but actually if you work
out, it turns out that the amount of computer power in that first supercomputer that did
that first weather forecast in the 70s, your mobile phone is probably about 30,000 times
more powerful than that supercomputer. A modern day supercomputer is about 30,000 times more
powerful than your mobile phone, so there are many orders of magnitude. That gives you
a flavour of how supercomputing has moved on from the 70s just today, just in 40 years
or so. Pitman: There’s probably no parts of a climate
model, of a modern climate model, that still reflect what was done in the 70s. Almost everything
has been rebuilt or rewritten, I think. The resolution has increased from something around
700 by 500 kilometers to 100 by 100 kilometers. The detail in the vertical has increased dramatically.
Oceans have been properly and fully coupled. The land surface has been completely revised
to incorporate a whole suite of processes. Sea ice models have improved dramatically.
Cloud parametisations have improved. We’ve resolved most of the water vapour feedback
problem. It’s like asking what’s the relationship between a Formula 1 Grand Prix car in 2014
compared to 1970. And the answer is there probably isn’t a single widget that’s shared. Lunt: The resolution is getting higher. In
other words, the boxes are getting smaller and smaller, but the computer power is also
increasing. We’re able to simulate sort of the same amount of time, if you like, with
one of these models. Pitman: One of our grand challenges in climate
models is to dramatically improve the spatial detail that climate models use and that’s
really a computational problem. We just need bigger supercomputers to really resolve the
detail of those things. The things that climate models struggle to capture well would include
some extreme events. They struggle with the location of the storm tracks. They struggle
with the detail of cloud fields. They struggle with some major challenges we don’t represent
at all – the processes which might trigger abrupt climate change, so methane release
or permafrost melt. Lunt: A climate model is never going to be
able to completely reproduce the weather of the last 200 years; however, when you average
together all of these weather events, what you end up with is climate. What we think
is if we also run lots of climate model simulations as well as and compare the average of those
with the average of many years, for example, of observed weather, we can get quite a good
comparison between a climate of a model and the climate of the real world and compare
those. That is a fundamental test that we can use to test our climate models. Pitman: If you ask what do the climate models
struggle to represent in terms of the simulation of whether it will warm for doubling of CO2,
my answer would be nothing at all because they do that really well. If you ask what
the climate models struggle to predict at the scale of a region and its response to
doubling of CO2 in terms of rainfall, lots of things .They don’t get the details for
clouds, the convection, the rainfall processes, the detailed synoptics blocking a whole range
of things because the spatial resolution that we use for climate models is probably too
coarse to capture a a lot of kinds of those key phenomena. There’s a whole range of reasons
why we’re confident in the skill of climate models for the problems that they were designed
for. First of all, they are built upon physical principles, and those physical principles
are known unless of course Newton was stupid, which I don’t think he was. So we have basic
fundamental theory, not sourced from climate science but sourced from basic chemistry,
basic physics, basic biology and applied mathematics that says the core of climate models is sound.
Secondly, of course, they’re used routinely in other applications like weather forecasting,
and so we effectively can evaluate a lot of our science routinely and weather forecasting
is becoming increasingly accurate irrespective of what some of your listeners might think.
If they actually write a diary of a 5 day forecast and check off how those 5 day forecasts
evolve they’ll find that they are shockingly accurate nowadays. That’s the second test.
Thirdly, we routinely test our models against observations over the last century and earlier
and they do extremely well in that respect. Finally, we can test our models against perturbations,
so we can, for instance, simulate a volcanic eruption, for example, and check that the
climate models respond appropriately to what a volcano does to the atmosphere. There are
multiple lines of evidence and they all point to the climate models being reliable for what
they were designed. There’s a lot of myths out there on what we think or how we build
our understanding of what will happen in the future. There are many lines of evidence that
are used to understand how climate might change in the future. If you could take the climate
models away, we would still be just as worried about the future climate. Climate models merely
inform and embellish and colour and flavour to the future of the climate, the projections
of future climate, but we would be just as worried based on theory than data. Climate
models are one strand of evidence for future climate change, but by no means do they underpin
our concerns. What underpins our concerns are physics.


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