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Weather vs Climate – Why the Difference Matters · Talking Science

0:00
Welcome to today's Deep Dive. You know, I was
0:02
reading through our sources for this one and
0:04
I came across this fascinating sort of paradox
0:07
about weather forecasting. Oh, right. The one
0:10
about the meteorologists. Yeah, exactly. Basically,
0:13
if a meteorologist misses just a single solitary
0:16
gust of wind in their data today, like maybe
0:20
just a tiny breeze rustling through a valley
0:22
in the Andes that, you know, a sensor didn't
0:24
pick up, their forecast for a month from now
0:26
isn't just a little bit wrong. It is. mathematically
0:30
completely useless. Right. Which is wild to think
0:33
about. It is because the physics of our atmosphere
0:36
just dictate that predicting the specific weather
0:38
like several weeks out is literally impossible.
0:41
But, and this is the big question for today's
0:44
deep dive, how can scientists stand on a stage
0:47
and claim to know exactly what the Earth's climate
0:49
will look like in the year 2070? I mean, it sounds
0:52
like a total contradiction, doesn't it? Yeah.
0:54
It basically sounds like they're just throwing
0:55
darts blindfolded and asking you to trust the
0:57
results. Exactly. Which perfectly explains why,
1:01
you know, the moment the temperature drops below
1:03
freezing in November, you will inevitably hear
1:06
someone at a bus stop or maybe at a family dinner
1:08
just shivering and saying, it's bloody freezing
1:11
today, whatever happened to global warming? We've
1:14
all heard it. We've all thought it, honestly.
1:16
Right. And honestly, when you are standing there
1:18
freezing your toes off, it feels like a knockout
1:20
argument. I mean, you've been told the planet
1:23
is boiling, yet you literally can't feel your
1:25
toes. The immediate physical reality just completely
1:28
overrides the abstract scientific projection.
1:33
Well, yeah, because we process the world through
1:34
what is right in front of us. I mean, when the
1:37
immediate data like ice on your windshield conflicts
1:40
with the long term claim of a warming planet.
1:44
our brains just kind of short circuit. Yeah,
1:45
it's a massive disconnect. But our mission today
1:47
is to untangle that exact confusion, right? Exactly.
1:51
Because that tension, that exact contradiction
1:53
you just set up about the meteorologist missing
1:56
a gust of wind is actually the key to unlocking
1:59
this whole debate. By the time we wrap up this
2:02
deep dive, you're going to have a mental toolkit
2:04
and frankly, a few really brilliant analogies
2:07
from our sources to make sure you never confuse
2:10
weather and climate again. Which I think we really
2:13
need. Because the confusion just stems from a
2:15
fundamental misunderstanding of what scientists
2:18
are actually measuring when they talk about the
2:20
future. So let's just dig straight into that
2:23
impossibility of predicting the weather. Yeah,
2:25
let's start there. Because I think a lot of people
2:27
assume meteorology is just a... Like if we just
2:31
had better satellites or faster supercomputers
2:34
or AI, we could easily predict the rain three
2:36
months in advance. Right. Like it's just a technology
2:38
problem. Exactly. But we can't. And according
2:40
to the sources, it has literally nothing to do
2:43
with technology. It has to do with chaos. Chaos
2:46
in the strict scientific sense. Yeah. Because
2:49
the atmosphere is basically the ultimate chaotic
2:51
system. Yeah. And. But a chaotic system is one
2:55
that is just exquisitely, fiercely sensitive
2:58
to microscopic differences in its starting conditions.
3:00
Which brings us to the 1960s, right? The Edward
3:03
Lorenz story. Yes, exactly. So Edward Lorenz
3:05
was this mathematician and meteorologist, and
3:08
he was running an early computer simulation of
3:10
the weather. And one day, he wanted to rerun
3:13
a specific sequence. But instead of entering
3:15
the full starting number, which was 0 .506127,
3:19
he just typed in 0 .506. Oh, wow. Wow. So he
3:23
just chopped off the very end of the decimal.
3:24
Right. He rounded off a fraction of a fraction.
3:26
A difference of less than one part in a thousand.
3:29
I mean, you'd think the weather output would
3:31
be maybe, I don't know, a fraction of a degree
3:33
different a few days later in the simulation.
3:35
You would think so. But instead, the simulation
3:37
produced a completely radically different weather
3:40
pattern because that tiny rounding error didn't
3:43
just persist, it amplified. It multiplied. Exactly.
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In a chaotic fluid like the atmosphere, a microscopic
3:49
error today doubles tomorrow. Then it redoubles.
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Then it redoubles again. The system just multiplies
3:55
uncertainty without any limit. And that's the
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actual science behind the butterfly effect, isn't
4:00
it? I mean, we've all seen those sci -fi movies
4:02
where a butterfly flapping its wings in Brazil
4:05
causes a tornado in Texas. Right, which always
4:09
makes it sound like the butterfly has some sort
4:10
of magical superpower. Yeah, exactly. But from
4:13
what we're looking at here, the butterfly is
4:15
just a metaphor for unmeasured data. Like, the
4:18
flap of the wing creates a tiny, tiny change
4:20
in air pressure. And because the system is chaotic,
4:23
that microscopic change gets folded into the
4:25
atmospheric math, doubles, redoubles, and, you
4:28
know, a month later, the entire global weather
4:30
map is totally unrecognizable from what it would
4:33
have been without that one butterfly. Precisely.
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Because we can never measure every butterfly
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or, you know, every exhalation of a deer or every
4:41
shifting leaf on the planet, our starting data
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will always be slightly imperfect. Therefore,
4:46
a specific weather forecast a month out is provably
4:49
impossible. OK, hold on. I want to stop you right
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there. Because if the atmosphere is a giant compounding
4:56
error multiplier, then doesn't that prove the
4:59
skeptics completely right? What do you mean?
5:01
Well, if you cannot tell me if it's going to
5:03
rain next Thursday because the errors have multiplied
5:06
into infinity, then predicting the temperature
5:09
in the year 2070 is just pure mathematical fiction.
5:12
It's a brilliant question. And it would be fiction
5:15
if scientists were actually trying to predict
5:16
the weather. on, say, January 15th, 2070. But
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they aren't. They're doing something entirely
5:23
different. OK, so what are they doing? Well,
5:25
to see the difference, let's establish the basic
5:27
definitions. Weather is immediate and local.
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Is it raining in Melbourne this afternoon? Will
5:32
it be windy in Perth tomorrow? Climate, on the
5:35
other hand, is the long -term pattern. The classic
5:38
rule of thumb is climate is what you expect,
5:40
weather is what you get. Climate is what you
5:42
expect, weather is what you get. I like that.
5:44
It's a golden rule. And to really visualize this,
5:47
I want you to picture a person walking a dog
5:49
up a long, steady hill. Okay, I have the picture.
5:52
Just a person and a dog on a hill. Right. But
5:55
the dog is on one of those really long, stretchy,
5:59
retractable leashes. Now, the person is walking
6:01
at a very steady, deliberate pace, straight up
6:04
the incline. That person represents the long
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-term climate, the underlying trend. But the
6:10
dog... The dog represents the daily weather.
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And the dog is just going absolutely everywhere.
6:15
Everywhere. I mean, it darts ahead to the end
6:16
of the leash, then it spots a squirrel and lunges
6:19
to the left. It sniffs a bush on the right. It
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gets distracted by some wrapper on the ground
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and runs completely backward as fast as it can
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downhill. Just total chaos. Exactly. At any given
6:30
second, that dog's movements are chaotic and
6:33
unpredictable. Okay, let's unpack this for a
6:36
second because this ties right back to our intro.
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If I'm looking through a telescope and I'm zoomed
6:40
in super tight and I only watch the dog for like
6:42
five seconds, I might see it sprinting downhill.
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Right. And I would logically conclude, oh, this
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walk is heading downhill. I mean, I'm looking
6:49
at the freezing Tuesday morning. I'm experiencing
6:51
the cold snap. I'm watching the dog run downhill.
6:53
And I'm entirely blind to the person holding
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the leash. That is the perfect visualization
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of the category error people make. Saying, it's
7:01
freezing today, where is global warming, is a
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statement about the dog. Global warming is a
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statement about the person. They are linked.
7:09
I mean, the dog can only run so far from the
7:11
human, but tracking one specific movement of
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the dog tells you absolutely nothing about the
7:16
elevation of the hill they're climbing. Let's
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apply that to the physical atmosphere for a second,
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because when we experience that dog running downhill,
7:23
a brutal cold snap in an era of overall warming,
7:27
what is physically happening with the heat? Because,
7:30
I mean, the heat didn't just vanish into thin
7:32
air. No, it didn't. The atmosphere is essentially
7:34
this massive circulating fluid. It's constantly
7:37
churning to balance out temperature differences
7:40
between the equator and the poles. Weather is
7:42
really just the mechanism that moves that heat
7:44
around. So it's basically a mixing bowl. A giant
7:47
thermal mixing. Bowl. Yeah. So if you are freezing
7:50
in Melbourne or New York or London, it's often
7:53
because the jet stream, which is this band of
7:56
fast moving air that usually keeps cold Arctic
7:58
air penned up at the poles, ahead of buckled.
8:01
It gets wavy, right? Exactly. It gets wavy. And
8:03
a deep trough of that cold air just spills south
8:07
over your city. But, and this is a really fascinating
8:10
detail from the text, the atmosphere is a closed
8:12
system. If a massive block of cold air pushes
8:16
south, It has to displace something. It's like
8:19
pushing your hand into a bathtub. The water has
8:21
to go somewhere else. Right. The warm air that
8:23
was previously sitting over your city gets shoved
8:26
north to replace the cold air that just left
8:29
the Arctic. So while you're shivering at the
8:31
bus stop, completely convinced the planet is
8:33
cooling, a monitoring station up in the Arctic
8:36
Circle is recording unseasonably, terrifyingly
8:39
warm temperatures. Which is why taking the temperature
8:42
of your own backyard is entirely meaningless
8:44
when discussing the climate. are experiencing
8:46
local distribution, not global energy retention.
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The only way to see the person walking up the
8:52
hill is to average out the dog's movements across
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the entire surface of the globe over decades.
8:57
But OK, I understand the visual of the dog and
9:01
the walker, but I really want to push back on
9:03
the mathematics of it for a second. Because if
9:06
the individual data points, like the daily weather
9:08
events, are governed by chaotic compounding errors,
9:11
how can you possibly average them out into something
9:14
reliable? I see where you're going with this.
9:17
Right. Because if every piece of data is essentially
9:19
just random noise after two weeks, shouldn't
9:22
an average of random noise just be, well, more
9:25
random noise? It's a fair question. To answer
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that, we have to look at how statistics actually
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constrain chaos. Let me hand you an imaginary
9:35
coin. If I ask you to flip that coin and predict
9:38
whether it'll land on heads or tails, can you
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do it? Obviously not. I mean, it's a 50 -50 shot.
9:44
The physics of my thumb hitting the coin, the
9:45
air resistance, the height of the flip. It's
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a micro chaotic system. Right. The single flip
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is unpredictable. It is the weather. Now, what
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if I want you to flip that exact same coin 10
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,000 times and tell me how many heads you're
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going to get? Out of 10 ,000. Well, I can guarantee
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it will be incredibly close to 5 ,000. But how
10:01
can you possibly know that? You've just admitted
10:04
that you can't predict a single flip. So how
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can you predict 10 ,000? Because the underlying
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physics of the coin, the fact that it's evenly
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weighted with two sides, creates a boundary.
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The individual flip is free to be whatever it
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wants within that boundary. But over a large
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enough sample, the physical reality of the coin
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dictates the average. The physical reality dictates
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the average. That right there is the exact definition
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of climate. Weather is the single coin flip.
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Climate is the behavior of 10 ,000 flips. We
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don't need to predict flip number 4 ,321 to know
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the total average. Just like we don't need to
10:39
predict a thunderstorm in August of 2070 to know
10:42
the global average temperature. That makes so
10:44
much sense. Let's take that concept out of statistics
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and put it into a physical environment to make
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it even more relatable. Think about a pot of
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water sitting on a stove. Okay, a pot on a stove.
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You turn the burner on. As the water starts to
10:56
heat up, it begins to churn. Currents form. Now.
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If I ask you to track the exact path of one single
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churning bubble rising from the bottom of that
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pot, you couldn't do it. It gets bumped by other
11:08
bubbles caught in microcurrents. Its path is
11:11
completely chaotic. Right, the bubble is the
11:13
dog on the leash. Exactly. But if I reach out
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and turn the dial on the stove from medium to
11:17
high... I don't need to calculate the trajectory
11:20
of a single bubble to know with absolute certainty
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that the average temperature of the entire pot
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of water is going to rise. Because you change
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the boundary conditions. You change the total
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amount of energy entering the system, and the
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climate operates on that exact same thermodynamic
11:35
principle. It's just basic physics. Yes. While
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the day -to -day weather is driven by chaotic
11:40
atmospheric fluid dynamics, the long -term climate
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is driven by massive, steady physical drivers.
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And the biggest driver is the balance between
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the energy we receive from the sun and the energy
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the Earth radiates back out into space. And this
11:54
is where greenhouse gases come in, isn't it?
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They are basically the dial on the stove. They
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are the dial. Gases like carbon dioxide and methane
12:01
are transparent to the shortwave radiation coming
12:04
in from the sun. So the sunlight passes right
12:07
through them, hits the Earth, and warms it up.
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The Earth then tries to shed that heat by radiating
12:13
it back out as long -wave infrared radiation.
12:15
But the greenhouse gases block it. Right. They
12:18
are not transparent to infrared. They absorb
12:20
it and radiate it back down. So the energy comes
12:23
in easily, but it really struggles to get out.
12:26
We are thickening the blanket. We are trapping
12:27
more energy in the system. And that is not a
12:29
chaotic process. That is... basic immovable thermodynamics.
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When you turn up the dial on the stove, the pot
12:36
gets hotter. You don't need a perfectly functioning
12:38
weather model to understand that a planet retaining
12:40
more solar energy will, on average, have a higher
12:43
global temperature. The whole predictability
12:45
paradox just vanishes entirely. So scientists
12:48
are utterly blind to the dog and completely focused
12:51
on the walker. Both things are true at the exact
12:53
same time. Okay, so the dial on the stove has
12:56
been turned up. The average temperature of the
12:58
whole system is rising. Let's ground this in
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everyday reality for a moment. Because I think
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another trap people fall into is imagining that
13:06
a warming climate means a smooth, uniform increase.
13:10
Oh, definitely. Like people think if the average
13:12
goes up, winter just gets abolished and every
13:15
Tuesday is slightly warmer than the Tuesday before
13:17
it. Which leads right back to the bus stop confusion.
13:20
If it snows heavily, people assume the dial on
13:23
the stove must be broken. But climate change
13:26
isn't a linear progression of hotter individual
13:28
days. It is a shift in the probabilities of what
13:31
kind of weather you are going to experience.
13:34
To visualize this, I want you to picture a bell
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curve. Okay, like a standard statistical distribution,
13:39
a big sloping hill of data. Yes. Think of that
13:42
bell curve as containing all the possible weather
13:45
days for your city. The vast majority of the
13:47
days sit right in the bulky, tall middle of the
13:50
curve. Those are your average, unremarkable days.
13:53
And the sides of the curve just taper off into
13:55
those long, thin tails. Exactly. The left tail
13:59
contains your extreme cold days. The right tail
14:03
contains your extreme hot days. Under normal
14:06
conditions, the weather randomly pulls from this
14:09
curve. You get a lot of days from the middle
14:11
and very occasionally a day from one of the extreme
14:14
tails. So what physically happens to that bell
14:18
curve when you turn up the dial on the stove?
14:20
The entire curve, the whole bulky middle and
14:23
both tails, slides to the right. It slides toward
14:26
the hotter end of the temperature spectrum. Which
14:28
explains everything we've been talking about.
14:30
Because sliding the curve to the right doesn't
14:32
magically chop off the cold tail on the left.
14:35
The cold tail is still there. It is absolutely
14:37
still there. You can still reach into the curve
14:39
and pull out a freezing morning. the dog can
14:41
still run downhill sometimes. Right. But the
14:44
odds have fundamentally shifted. Because the
14:46
curve moved, the temperatures that used to sit
14:48
in the extreme heat tail on the far right are
14:50
now sitting closer to the bulky middle. They
14:52
become much more common. And more alarmingly,
14:55
the very edge of that right tail gets pushed
14:58
out into empty space. Into completely uncharted
15:00
territory. Yeah, it gets pushed into brand new
15:03
temperature zones that the previous climate literally
15:06
never reached. We hit new scorching extremes
15:09
we've never seen before. It is exactly like playing
15:12
a game with loaded dice. Imagine you have a pair
15:15
of standard dice. You can roll anything from
15:18
a 2 to a 12. Now imagine someone tampers with
15:21
them. They weight the dice heavily toward the
15:24
higher numbers. Maybe they even paint a seven
15:26
on a couple of the faces. Okay, but if I roll
15:29
those loaded dice, I can still roll a two. A
15:31
two is physically possible. But if you roll a
15:33
two, you wouldn't jump up and declare, aha, the
15:36
dice aren't loaded. You know they are loaded
15:39
because over 50 rolls, you're rolling sevens,
15:41
eights, and twelves at a highly unnatural elevated
15:44
frequency. Climate change is a load and die.
15:47
A single cold snap is just a roll of a two. It's
15:49
entirely expected within the system. Exactly.
15:52
The proof of the loading is in the frequency
15:54
of the heat waves, the intensity of the storms,
15:56
the breaking of historical records year after
15:58
year. It's all about probabilities. So let's
16:00
bring this all together. Because armed with this
16:03
mental toolkit we've built here, you know, the
16:05
chaotic dog and the steady walker, the unpredictable
16:08
coin flips averaging out, the thermodynamics
16:11
of the stove and the loaded dice. can instantly
16:15
spot the massive logical fallacy driving so much
16:19
of public debate. It really is just a simple
16:21
category error. People are holding up a piece
16:23
of microdata and trying to invalidate a macro
16:26
trend. Saying it snowed today, therefore global
16:29
warming is fake is entirely indistinguishable
16:32
from saying the dog just ran backward, therefore
16:35
the hill doesn't exist. And honestly, it is vital
16:37
to point out that this logical fallacy cuts both
16:40
ways. Oh, absolutely. If someone points to a
16:42
single brutal heat wave in July and says, see,
16:45
this proves climate change is real. They are
16:48
making the exact same category error. A single
16:50
heat wave is also just weather. It is just the
16:53
dog running quickly uphill. Right. Now a scientist
16:55
will tell you that a heat wave is exactly the
16:57
type of weather you expect more frequently when
16:59
you load the dice. But honest science does not
17:01
build its case on one hot summer. No. The evidence
17:05
for the warming planet doesn't care about your
17:06
hot July or your freezing November. The evidence
17:10
rests entirely on the decades -long global trend.
17:13
The steady, relentless figure walking up the
17:16
incline measured across thousands of data points
17:19
over decades. The inability of a supercomputer
17:22
to tell you if it will rain three weeks from
17:24
Tuesday isn't a failure of climate science. It's
17:27
just the reality of chaotic fluid dynamics. The
17:31
weather is what you get on any random day. The
17:34
climate is what you logically expect based on
17:37
the physics of the system. So the next time someone
17:39
points out the window at a cold day, you know,
17:41
hoping to settle a massive, complex scientific
17:43
debate with what's happening in their driveway,
17:45
you will know exactly what is happening. Their
17:47
brain is watching the dog. Exactly. They are
17:49
fixated on the chaotic, short -term noise, and
17:52
they are completely ignoring the underlying physics.
17:54
The dog runs everywhere, but the person keeps
17:57
walking uphill. If you want to understand the
17:59
future, you have to look past the dog. Keep your
18:02
eye on the person walking uphill. Look past the
18:04
dog. It's such a clarifying way to view the world.
18:07
And as we wrap up this deep dive, I actually
18:10
want to invite you to take that exact same clarifying
18:13
lens and turn it away from the atmosphere and
18:16
point it at your own life. Oh, that's an interesting
18:18
thought. Because the human brain's vulnerability
18:21
to this category error, our tendency to be, you
18:25
know, hijacked by immediate chaotic feedback
18:28
while completely missing the underlying truth.
18:31
trend doesn't just ruin conversations about the
18:33
climate. It actively sabotages us in our daily
18:36
lives. It really does. Think about it. Where
18:38
else in your life are you currently obsessing
18:40
over the darting dog and completely missing the
18:43
person steadily walking uphill? Maybe you've
18:45
been investing in the stock market for years,
18:47
but the market has a terrible, brutal two week
18:49
dip. The dog sprints downhill. Do you panic,
18:52
sell everything and abandon the strategy, forgetting
18:55
that the long term historical trend of the market
18:57
is a steady climb? People do it all the time.
18:59
Or maybe you are trying to learn a new skill
19:01
or you've started a new diet or a new career
19:04
path. You are putting in the work. You're changing
19:06
your underlying habits. You are essentially turning
19:09
up the dial on the stove, but you step on the
19:12
scale one morning or you bomb one. It's so easy
19:22
to be tricked by the short -term weather. We
19:24
are biologically wired to react to the weather
19:27
in our lives, the immediate cold snaps, the sudden
19:29
thunderstorms. But true progress, whether it's
19:33
in atmospheric science, wealth building, or personal
19:35
growth, is always about the climate. It is about
19:37
the long -term averages, the underlying habits,
19:39
the steady direction of the walk. Well said.
19:42
So this week, when things inevitably get chaotic,
19:45
take a breath. Ask yourself, am I reacting to
19:48
the dog or am I trapping the walker? Thanks for
19:52
joining us on this deep dive. We will catch you
19:54
on the next one.