Hello, and welcome to the Physics World Weekly
podcast. I'm Hamish Johnston.
In this episode, we explore 2 related areas
of physics that are very close to my
heart,
thermodynamics
and statistical
physics.
Coming up a bit later in the podcast,
we hear from 2 meteorologists
who explain why an intense storm
last year
may have affected the taste of tea brewed
in Southern England.
But first, I'm in conversation with 2 physicists
working at the cutting edge of Statistical Physics.
1 studies systems that are far from equilibrium,
including active matter,
and the other uses Statistical Physics to study
machine learning and artificial intelligence.
Both are associated with the Journal of Statistical
Mechanics,
Theory and Experiment
or J stat,
which is celebrating
its 20th anniversary.
The journal is a multidisciplinary
peer reviewed
international journal
created by the International School For Advanced Studies
or CISA
and IOP Publishing.
JSTAC covers all aspects of statistical physics,
including
experimental
work that impacts on the subject.
My guests are Leticia
Cugliandolo,
who is Jstat's chief
scientific
director,
and Marc Maillard,
who has just stepped down from that role.
Marc joins me down the line from Bocconi
University
in Milan,
where he's professor
of theoretical physics.
And Leticia
joins us from Paris, where she is Professor
of Physics at Sorbonne
University.
Mark and Leticia,
welcome to the podcast.
So before we talk about the journal, let's
find a bit more about your research.
Leticia,
what research problems are capturing your imagination at
the moment?
And how are you using statistical mechanics
to solve them?
So, my main interests are in out of
equilibrium statistical physics.
So, what this means is that systems,
that are not able to reach stationary conditions,
where equilibrium standard statistical physics ideas apply.
And more concretely, two examples of which I'm
working nowadays
are active matter
and integrable systems.
So let me say a couple of words
about both of them. So active matter, they
are systems of, many
constituents.
They could be animals. They could be, of
different scales.
So,
I don't know, birds,
fish,
cells,
bacteria.
And, they are all living together,
interacting.
But also, they undergo motion
that they can do because of
internal motors or
the capability of getting
injection of energy from the surroundings.
So the combined aspect of interactions between them
and these propels, self propels, sometimes it's called
motion,
makes them be out of equilibrium
and have, you know, special collective features.
So similarly to what we see in statistical
physics, they are phase transitions that they are
in the goal. For example, between solid like,
liquid like phases.
And,
although they look like, you know, the standard
ones, they have special features that are
due
to
their
microscopic
dynamics. So this is one kind of system
in which I'm interested in. Another is the
kind of integrable
classical problems, as I said. So
usually,
in standard physical systems,
there are a few,
constant of motions
where they are isolated from the environment, from
the surroundings,
a few quantities are conserved. For example, energy
or angular momentum.
But in integrable systems, the number of these
constants of motion
is the same as the number of degrees
of freedom in the problem. So this makes
them very special. The dynamics is special.
So
the the the most important question in the
context of, integrable systems that people are asking
nowadays
is what happens when you have
an infinite number of constants of motion that
constrain the dynamics?
And whether the long time behavior of the
system,
for example, the behavior
of,
microscopic observables
can be described with a statistical
measure.
So more precisely, whether the ergodicity hypothesis that
we are all used to in the standard
statistical physics,
applies in some modified version to the integrable
problems. That is to say, whether
time averages of observables
become equal in the long term run to
statistical averages calculated with
adequate,
measure.
Okay. And, Mark, what are you focused on
at the moment when it comes to statistical
mechanics?
Well, at the moment, I am applying I'm
using statistical physics in order to study problems
of machine learning, problem of artificial intelligence.
Maybe to explain why because the connection is
not so obvious, I can step back a
little bit. I mean, I I started, in
the in the eighties working on the statistical
physics of disordered systems, what they are called
spin glasses, systems in which you have a
lot of interacting elements,
but they are not all the same.
And so we had to develop a whole
new set of methods in order to handle
these kind of systems and to understand
their emerging properties, what happens when there are
many such individual elements. That's the essence of
statistical physics. And
and once these methods were developed, it turned
out that they could be used in other
contexts, like for instance, in
information
theory, in which instead of having spins or
atoms that interact, we have bits of information.
And in recent time, really,
the
the the thing that has been the topic
that has been interesting me most
is machine learning
with the the the the new,
the new era
of artificial intelligence, which started 12 years ago,
basically.
And, the idea is the following,
when you use a deep network
and you train a deep a deep network
in order to do a certain task, like
analyzing an image, for instance,
This deep network, it consists actually
of a lot many, many, many tunable parameters.
And these tunable parameters,
there are billions of them, actually,
they are like elementary variables.
And you have to tune them in order
to in order for the network to be
able to achieve a certain task. For instance,
face recognition. It has to be able to
recognize the faces.
And how you tune them? You tune them
by training it on a database. You have
a database of faces. You have said what
are the faces which are there. And and
from that, you have to find the right
set of 1,000,000,000 parameters. So, you know, if
you have a safe at home which has
10 buttons,
the number of combination is already very large.
So if you have you have to imagine
that it is a safe with 1,000,000
1,000,000,000
buttons. So it becomes incredibly large, and and
and you reach the the regime in which
there are so many variables to be tuned
that you can you can start to use
the the methods of statistical mechanics in order
to understand that. So the idea is to
see
depending on the database, depending on the task
that you want to realize,
how
how is the training going? How is the
error that you do? How does it depend
on the amount of training or the amount
of data?
Things like that. How are scaling laws? You
you find that there are some scaling laws
that tell you that the error that you
do decays with a certain power or with
the size of the network, the size of
the data.
These these are all elements which actually have
triggered
the
the mode the motion of of of the
whole field toward go going to larger and
larger machines
trained with more and more data, as we
have seen with in recent years with large
language models, for instance.
So it has become a field in which
statistical physics
can can be used, can be
brought to bear, and and and and try
to,
analyze and find new mechanisms,
at work in these in these problems.
Oh, that's really interesting. So statistical mechanics as
applied to,
something very practical like machine learning or artificial
intelligence,
or as Leticia points out, active matter, which,
I suppose, you know, also plays
an important role in our lives.
So, Mark, it's the 20th anniversary of J
STAT. Can you tell us a bit about
the journal?
And and what sort of research,
is published in it?
Well, the the the journal was created 20
years ago with the idea of having a
a journal which is really,
offering a service to the community of statistical
physicists, let's say. And, so it should be
it has been always run by
professional statistical physicist, and, I think that it
plays an important role for the community.
Originally, when it was launched, it was more,
oriented to the core of statistical physics, which
is more like,
on one hand, stochastic processes, but also integrable
systems,
etcetera. The the the real historical core of
statistical physics. But then gradually, there was there
was some opening. And in particular,
when when I I took the the the
job of a chief scientific director that was
in 2010,
we reorganized the journal in order to open
it to
very different directions,
directions which include
biophysics,
which include,
information theory, which include,
aspects of protein folding,
which include
aspects of, econophysics.
I mean, so now the the drone has
taken this shape, which is, which is that
it has all the topics of the core,
but also the topics at the interfaces.
And, and in the last, 12 years or
so, the interfaces have been growing quite substantially
because these are fields in which, it turns
out that the ideas of conceptual apparatus
and also some of the techniques of statistical
physics can can be, quite interesting, as as
Leticia was mentioning, active matter,
or I was mentioning,
AI. All these are kind of new frontiers
for statistical physics.
And so the journal is is organized
in in this way. It is it really
I think it it does reflect quite well
the breadth of interest of people who are
professional researchers
in statistical physics in the world, which have
all these broad spectrum of activity,
often with interdisciplinary
research at the frontier with other communities, with
communities of biologists,
communities of
engineers and mathematicians,
and all kinds of other communities.
I see. And and Leticia,
Mark has has passed the torch, the JSTAT
torch onto you.
What are your future plans for the journal?
What what are the hot topics
in statistical physics today? I mean, both you
and Mark have mentioned a few of them.
And and what can we expect over the
next 20 years?
So, I just want to support what, Mark
has said because,
it's very important that statistical physics is a
field of physics that applies to many other
fields of research.
And it has developed the tools that allow
us to try to understand at least
problems in
interfaces. Like, mathematical
physics, biological
problems, you know, artificial intelligence,
neural networks.
All these problems for which we can help
with our knowledge and with our
analytical and numerical tools,
to to to better understand what's going on.
And try also to to predict new phenomena
and, to to to guide in a sense,
the people who are more, you know, inside
those fields to tell them, well, you know,
there are these other questions that come from
our,
milieu, from our knowledge that could be of
interest and that perhaps you have not thought
about them,
because you're not used to ask these kind
of questions. So I think that,
this is
certainly
the journal and the field itself will go
into this direction of
interchanges,
exchanges, collaborations
with people in other fields of research.
Something that is important about this journal is
this the fact that it's a journal which
is
administered and, you know, handled and
controlled by
by by scientists, by physicists. So the editorial
board is made by
scientists who are active and who are, you
know, contributing to the field and very willing
to collaborate
and, to obtain referee reports and referees who
are also interested
in the questions that are being discussed. So,
something I always liked about this journal is
that the exchange with the referees is very
fruitful, very constructive.
And, that the papers submitted
get better because of this exchange.
And, this is very important and something to
be to be stressed.
Well, that's great. I mean, it it sounds
to me I mean, you can both tell
me if I'm, you know, sort of in
left field with this one. But,
you know, the the the world is just
becoming much more complicated.
And you know, isn't statistical physics very good
at at sort of simplifying
complicated systems? You know, it sounds to me
that,
you know, there's all this talk about quantum
as the, you know, the sort of physics
technology of the future. But, maybe we should
be looking at statistical mechanics to help us
deal with,
well, I suppose,
the world as it becomes much more complicated.
Maybe yeah. I think it's it's a very
good remark.
Often when I start when I when I
give some kind of a general
public talk,
on, on on my work,
I start by saying most of people are
aware of of 2 frontiers
of physics, which is
the very large frontier, the universe and cosmology,
etcetera, and the very small and the quantum
world. But most people are not so much
aware
of, the frontier of the infinitely complex. That
is what what are the new properties that
emerge when you have many constituents
interacting.
And this has been around for some time.
It has puzzled,
already starting from
from,
from Gipps and and, or the creators of
statistical physics.
But, but it is, more and more,
relevant for modern science because
the more we go, the more we are
able to study, but also experimentally,
systems which are actually very complex, which include
a a lot of constituents which interact.
And and there, you need to you need
to bring some kind of modeling simplicity.
And this approach of modeling, of trying to
find what are the crucial ingredients, what are
the crucial elements, what are their relations.
Dropping maybe a lot of other aspects with
but because they are less relevant,
this is very much in the state of
mind, let's say, of statistical physics. And I
think it's a very modern science.
And, Leticia, I mean, one field that I,
you know, springs to mind when when you
mention active matter
is medicine. I mean, I would imagine that
understanding
active systems
could be crucial
in in many types of medicine. I mean,
understanding cancer
to begin with or infections,
that sort of thing. I mean, it sounds
like statistical mechanics,
while probably is and will play a very
important role in the future in in medicine.
Well, in the context of cancer, there are
many people who have been studying the the
growth of tumors, for example, from a statistical
physics point of view. So there is this,
kind of
field in statistical physics, which is called coarsening
phenomenon or growth phenomena.
And,
one of the possible applications, and certainly very
interesting,
is the
issue about, you know, how tumors grow. And
eventually, how one could one stop them from
growing. So this is one possible application in
that direction.
And, another one which is
linked to the field of active matter.
Which is sort of unexpected if you wish.
But it's,
appearing
in that context is the the motion of,
groups of people. So for example, collective motion.
And, for example, I was in a conference
focused on active matter. And there was a
full session,
which was focusing on, you know, for example,
if you have a concert and you have
a lot of people. And like,
how
should you organize yourselves to allow them to
leave the place if there is some emergency.
And, in a way which is controlled and
that not, you know, they don't
kill each other
just trying to escape. So,
issues about where should you put obstacles.
Actually, the the the point is that you
should put obstacles that organize the,
escape,
but in an efficient way. Right? So these
kind of questions are asked in the context
of, statistical physics. And, with experiments,
with with pedestrians, actual pedestrians moving, with observations
in these big, concerts that take place in
Europe during the summer, for example.
And, well, with simulations, of course, American methods.
Well, that's great. Thanks, Mark and Leticia. And,
you know, we look forward to reading papers,
about those topics in JSTAT
in the future.
And if, if you've never read JSTAT,
you can find it on IOPscience,
which is, the website of IOP Publishing.
So Mark and Leticia, thanks so much for
coming on the podcast. Thank you. Thank you
very much.
In autumn 2023,
a large storm called Kieran
rolled in off the Atlantic coast,
crossing Ireland and the UK
before striking continental
Europe.
Wind gusts in excess of 200 kilometers per
hour were measured on the Breton coast.
As well as causing widespread damage,
the deaths of 21 people
have been linked to the storm.
One hallmark of such storms is a drop
in air pressure,
which reduces the boiling temperature of water.
This temperature is important in tea drinking countries
like the UK and Ireland,
where the best tea is said to be
brewed with very hot water.
Concerned about the quality of their nation's tea
during such storms,
meteorologists
in the UK
measured the boiling point of water
as Kieran passed across the country.
Here is Physics World's Margaret Harris in conversation
with 2 of those researchers.
So my guests today are the meteorologists Giles
Harrison and Caleb Miller, both of Reading University
here in the UK,
and they're here to talk about the surprising
physics consequences of an an unusual weather event
that hit Reading last November.
Hello, Giles. Hello, Caleb. Welcome to the podcast.
Hi. Hello.
So first off, what was this weather event?
Well, this was a a very,
deep depression. So, Atlantic storm,
which came over the UK
on the 2nd November.
And the the lowest pressure parts of the
center of the storm
tracked up the channel and, then went, sort
of
north across East Anglia. So it really went
across
many of the populated parts
of the
UK and associated with the weather system like
that is heavy rain
and, strong winds.
But the particular feature of this one was
that its central pressure was really very low,
particularly low for a storm, in November.
Now this particular storm had a name. It
was storm Kiran. Is that right?
Yeah. So what's this what distinguishes a storm
like Kiran from the hurricanes and typhoons and
cyclones that can some strike other parts of
the world?
So the sort of storms that we we
get in the UK,
are less intense,
and they show,
lower winds, and they are,
we're we're thinking when you think about hurricanes,
then they're very,
substantial,
weather systems.
In the mid latitudes,
there are still deep central pressures,
but they don't have the same elements that
the, sort of equatorial tropical storms have.
And so we experience
rainfall and,
high winds, which are associated to them with
them.
But the practice recently has been to name
these storms. And,
the ones that are severe, then they can
be named
by different MET services, confusingly.
But there's a MET service that will name
them, and it's often the MET service where
they're first encountered.
And so this one was named, Storm Kieran.
From Ireland, I think. Yeah. So before we
go any further, it's probably a good idea
to acknowledge that Storm Kieran, even though it
wasn't as big as the the storms you
get in the tropical regions of the earth,
it was a genuinely harmful event. But your
research focused on a less serious consequence. What
was that?
Do you want do you wanna talk about
that? So,
so, yeah, one of the things you already
mentioned, Giles, is this is a it's quite
an unusual event,
in terms of how low this pressure
was for for a place like, the UK.
And, so we kind of saw an opportunity
there to make some measurements. And I know
Giles had already, done some measurements a couple
of years before during a very high pressure
system
of, of the boiling temperature
of water.
And so, so we decided to go ahead
and do that again with a very similar
setup,
during this storm in order to actually measure
how the boiling temperature of water changes
because of this low pressure. And how much
does it change by? How much are we
talking of a change in temperature?
Yeah. So it's a it's a few degrees
Celsius here. So I think the lowest temperature
that we got down to was just below
98 degrees, so 97
and a bit
compared to, of course, the usual temperature of
a 100 or so.
And
and this is a physics podcast, so many
of our listeners will already be familiar with
the idea that liquids boil lower temperatures when
the pressure is low. But maybe you could
just remind us why that is.
Yeah.
So, obviously,
like you mentioned,
the temperature
which the liquid boils does change based on
the on the pressure.
And the reason for that is that boiling
happens when the, the vapor pressure of the
liquid
is equal to kind of the ambient pressure
around that. And so, the vapor pressure,
of the liquid is dependent on temperature. So,
yeah, that's why that changes.
And to go and probe a little bit
deeper, you know, Giles, could you tell us
why storms are associated with low pressure? What
what is the association between those two things?
Well, the reason that storms
are active is that when we've got a
region of low pressure, then there's a pressure
gradient
around it, changing pressure with distance.
And that change in pressure with distance causes
winds. And so people are used to seeing
weather charts. And when there's a lot
of isobars, that's lines of constant pressure
on a weather chart,
then
there's a intense pressure gradient and the winds
are quite intense.
And when there's quite a lot of spacing
between
isobars,
then the winds are are much lighter. So
associated with a deep change in in pressure,
a very rapid reduction in pressure over a
short distance,
then there will be winds and storms. So
these things are all associated. I mean, they're
they're rotating systems,
and they are generating
some
intense,
pressure gradients and associated winds with it. So
this is disturbed weather. That that's what it
is. It it's it's due to to changes,
in pressure over fairly rapid times.
Yeah. And, of course, on top of that,
then the the low pressure also results in
kind of rising air. And as that happens,
the air cools and and can form into
clouds and and then precipitation. So that's why
you get a lot of rain as well
with these kinds of storms.
Okay. So we've got low pressure area. We've
got, therefore, a reduced boiling point of of
water, and that has some consequences
for what is, in fact, Britain's favorite drink.
I think we should probably step back a
little bit from the consequences
and to say that we we did think
more broadly about what the consequences
of a change
would be. Now people clearly live at,
altitude
and at pressures, therefore, that are different from
the standard pressures with boiling points that are
different. And
that clearly isn't a problem.
But what is different in the circumstances we're
describing is that, of course, this is a
transient effect. Some days,
the pressure is different to other days and
the boiling point is different to other days.
In other words, it might not be something
that you've adjusted to. It might be something
where one day is different to another.
And so could we see a consequence
of that was really our question.
And, the first thing we looked at was
was there a difference in electricity consumption,
and power consumption due to taking more or
less energy and high or low pressure to,
to boil a kettle.
And, of course, you can't see that because,
really, the major thing that's happening with a
storm is that it's colder, windier, wetter. People's
behavior across the region changes,
and that dominates the energy consumption. So that
was a needle in the haystack. But we
thought we should we should just look for
it. And so then we got to think,
well, what is it that you might respond
to and what might you sense in response
to these really small changes? You know, can
you detect a change of a degree
reliably? Well, I mean, if you have a
fever, those temperature changes are things that you
do notice, but that's probably for a different
reason other than just temperature.
But is there something physiologically that you might
be responding to?
And taste is very sensitive.
And,
perhaps there's some response
people might have to a change in taste
where something is linked to that temperature. And,
of course, we we move on to think
about actually hot drinks. And we know that
tea is a drink that's quite sensitive to
the temperature that it's made at.
And a lot of customs
and energy have gone into,
you know, do people warm the pot before
they make the tea? Do they use tea
bags? Do they use,
leaf tea?
A terrible amount of paraphernalia and cultural baggage
with making tea, which we really don't want
to get into.
But it may be that the temperature effect
is part of that.
And it turns out that there is some
research that suggests that different temperatures are better
for different t's.
And so our small change in temperature, and
we were looking at a couple of degrees
in the low pressure and a couple of
degrees in the high pressure,
Perhaps where you might sense that is through
taste.
And so,
we just mentioned this towards the end of
our papers
that that might be how it's sensed, but
people have picked up on that very much.
And so the idea that we are somehow
evilly destroying
British tea is, the villains that we're now
painted as, I think. Of course, it's not
you that the villains is. It's the storm,
of course.
Well, meteorologists are always responsible for the weather,
I'm afraid. So it's not as simple as
saying that there is a a moral distance
here.
I think there's an implication somehow. But, yes,
clearly, the storm did it, and, Kieran was
not innocent from that point of view. But,
we have
mentioned that. And and and, of course, you
know, tea and weather are very characteristic
national discussion points. So, it may well be
something that people have particularly looked at.
When you were setting up this experiment, how
did you decide to do it? What what
was involved in measuring this this pressure drop
and corresponding that to a temperature drop in
brewing tea?
Yeah. No. It was, I came in in
the morning and had an email from Giles
saying you know, suggesting it might be a
good idea to go and make some measurements.
I thought, yeah, that's a good idea, and
went ahead and did that. And
it was quite a simple setup that we
used. It's probably something that a lot of
people could even potentially do at home. So
what I did was I used sort of
a standard,
electric kettle
and filled that up with,
the one kind of unusual thing that we
use is deionized water, which is water that's
been purified, have kind of all the contaminants
taken out of it that might affect the
boiling point.
So I filled that up with deionized water
and then just set it boiling.
And then once it was boiling, I put
in a a thermometer. And we have pretty
good thermometers in our department, but you could
really use any thermometer.
Yeah. And then once it was boiling, left
that in there.
The tricky part, of course, is keeping the
kettle boiling long enough to make the temperature
measurements. You have to kind of hold down
the, the trigger to keep it keep it
boiling for longer than it usually would. But
after,
you know, a few moments of that, you
kind of get a good reading of what
the temperature is and then just kind of
note that down.
I mean,
the way we did the experiment
was,
that we just suddenly realized it in the
morning. This is an opportunity. And in atmospheric
science, opportunities come along and you grab them.
That's what happens. You're not not planning a
lab experiment. It might sound that ours is
a casual set of apparatus. But, in fact,
there's apparatus we use a few years ago.
And
in atmospheric science, you kinda go after environmental
science in general. You might have to go
after opportunities when they come. And although we
should have known it was gonna be a
low pressure, we haven't quite thought this bit
through.
Yeah. After taking a few measurements over a
few hours,
just sent out an email to the to
our department who were already having a conversation
about,
the exciting low pressure system that was coming
through. And, just seemed to be a lot
of interest from people around the department about
about this pressure effect on temperature, even though
it's a fairly
well known sort of thing in in thermodynamics.
Just to actually see that happen in in
real life is is quite exciting. And to
see it make such a tangible,
sort of effect on an everyday
apparatus as well, like a tea kettle. Yeah.
I thought it sounded a little bit like
a a tea room discussion in a department
that sort of got went beyond its its
origins.
No. No. I I can see that, but
it's mean, it was literally about this problem
of the, you need to seize the opportunity
because the low pressure is not gonna last
very long. And so if you don't go
and do it now,
then it's gonna be gone. And,
for me, I I suppose I wanted it
for my lectures.
And,
it's nice when you I mean, I give
lectures on measurements in in meteorology and
pressure and temperature are 2 of the topics
that you have to deal with, of course.
And so just to have some new stuff
and a new result and just, you know,
just add something to the to the lectures.
So that's really why I wanted it, but
but in fact, many people seem to be
interested in it beyond that.
It seems like this might be, you know,
a really good citizen science type experiment for
for people to to try boiling their kettles
and, you know, do a taste test of
their tea in a storm or in a
very nice weather. You know, have you got
any plans to expand this in the future?
Well, a few a few years ago, I
did try,
a sort of trial citizen science experiment, and
it worked quite well at the high pressure
end because the high pressure was there for
many days. And so you could draw people's
attention to it.
But, what I couldn't do was the low
pressure end because,
in storm Kieran was through in a few
hours.
And so and maybe the few hours in
the middle of the night.
It happened that Kieran was around breakfast time,
which is why we made this link with
tea at breakfast, and people were probably brewing
at that time.
But,
it seemed more difficult to do it at
the low pressure end because, you gotta alert
people, and it's it's less convenient to measure
at the low pressure than the high pressure
end. So as I don't think there's an
effect at the high,
pressure end, then it's less obvious how to
do it. But, of course, yes, you could
formulate this as a citizen science experiment where
you ask people to rate their tea in
some way.
And,
we we've done it before in the solar
eclipse in 2015 where we make,
forms where they were fairly easy and unambiguous
to fill out. People describe the cloud
and and the wind conditions and the temperature
conditions. And we we learned things about how
to get people to enter things in a
way that isn't ambiguous.
So you set it up. We've got a
taste scale, and we ask people to do
it at certain times.
And maybe we do that a few times.
And maybe we could answer the question whether
the tea was was better or worse, but
I suspect quite a distribution of outcomes from
an experiment like that.
I suppose this also could be something that's
kind of a maybe a science education sort
of a project that maybe a family could
do as well if they're going on holiday
to a location that's at a higher elevation.
And,
they could try making some tea and and
measure the temperature of the water they've boiled,
see if they notice any difference. Could be
a useful
way of of talking about some of those
effects,
and then trying that into talking about storms
as well.
I did want to add something to that,
which is that
you shouldn't be playing with kettles in, just
a random kind of way. They they you
know, the steam is dangerous. The hot water
is dangerous. So you put a thermometer down
this bag and you stand back, and you're
well away from what's happening there. So, it's
it's something that needs to be thought through
a little bit and how it's done before
everyone starts putting random thermometers. And it needs
to be a thermometer that's able to read
above a 100 degrees Celsius as well. So
they're fairly unusual.
So it's not necessarily a straightforward experiment,
to do at home for those reasons, I
think. And I suppose also climate change must
be having an impact on this because I
I think we've been seeing in past few
years, we had a lot of storms, not
just Kieran, but but several named storms in
the UK last winter. You know, what sort
of effect would you say that this increasing
frequency of storms is likely to have on
these effects you've you've found?
Well, I think that there will be more
storms, more and more frequent storms seem seem
a possibility,
and also that we've been getting higher pressures,
and possibly lower pressures. I mean, we just
don't have enough data to really say there's
a a trend there, but perhaps these are
becoming more frequent things.
Giles and Caleb, thank you both very much.
It's been a pleasure to have you on
the podcast.
Thanks for asking us. Thank you.
I'm afraid that's all the time we have
for this week's podcast.
Thanks to Caleb Miller,
Giles Harrison,
Leticia
Cugliandolo,
Mark Mezard,
and Margaret Harris for joining me today.
And a special thanks to our producer, Fred
Isles.
We'll be back again next week with a
special podcast
produced in collaboration with IPEM,
the Institute of Physics and Engineering
in Medicine.
See you then.