If you felt like 2025 was a year where every
conversation, every headline, and maybe even
your insurance premium was just dominated by
extreme weather, you weren't wrong. Not at all.
It felt like weather wasn't just something we
predicted anymore. It was, I don't know, it was
this persistent, unavoidable reality. that was
just reshaping everything. I think that's a perfect
way to put it. It was a year that really forced
a shift in perspective. And what's crucial for
us to get our heads around right away is that
despite natural processes that should have acted
as a kind of break on global heat. Right, these
natural cooling cycles. Exactly, cycles we've
historically relied upon. Despite those, global
heat records still remained wildly persistently
above the baseline we've known. So we're looking
back at a year of Well, undeniable severity.
Undeniable severity, but also, and this is critical,
a year of these unprecedented technological leaps
in how we actually track these relentless extremes.
Welcome to Meteorology Matters, the podcast that
dives deep into the science, chaos, and stories
behind the weather that shapes our world. This
show was created by meteorologist Rob Jones.
Now let's get into today's episode of Meteorology
Matters. So let's start this deep exploration
by looking back at the sheer scale of what happened
globally in 2025. It's the universal question
at the end of every year, isn't it? Was it a
bad year for extreme weather? It is. And based
on all the research, all the analysis we've seen,
the answer is tragically. an unequivocal yes
again. That yes feels almost too simple. Oh,
it's deceptive in its simplicity because it needs
so much context. When researchers finally tallied
the numbers, 2025 was, believe it or not, globally
slightly cooler than the record -shattering year
of 2024. It's a tiny dip. Tiny dip? But here's
the critical point, the one that tells you everything
about the state of our climate. Despite that
dip, 2025 was still far, far hotter than almost
any other year on record before 2023. That fact
alone just confirms we are operating from a dramatically
new baseline. That slight cooling feels like
a, well, a meteorological contradiction. If the
Earth takes a tiny step back on temperature,
why didn't they give us more widespread relief?
Because we're dealing with this massive underlying
human -driven force that is just overwhelming
natural variability. The really surprising detail,
meteorologically speaking, is that this persistent
extreme heat happened even while the Pacific
was experiencing weak La Nina conditions. OK,
so let's just quickly clarify La Nina for everyone
listening. It's the opposite of El Nino. Right.
El Nino is this recurring natural thing, a phenomenon
characterized by unusually cool sea surface temperatures
across the central and eastern equatorial Pacific.
And that has a big impact. A huge atmospheric
impact. Historically, that cooling effect tends
to pull the average global temperature down slightly
for that year. It's one of nature's biggest breaks
on global heating. So the fact that 2025 was
still historically incredibly hot, even with
La Niña trying to hit the brakes, that means
the human driven warming signal is now so strong
it could just neutralize one of nature's biggest
cooling systems. Exactly. The sheer strength
of anthropogenic warming, that's the warming
caused by human activity, is just laid bare by
this paradox. It means the heat that drove all
those destructive extremes across every continent
wasn't just some random spike. It was the new
normal. It was the climate system operating at
a dramatically elevated level. This isn't some
distant theoretical threat anymore. It is our
present undeniable reality. And the scale of
the impact really supports that conclusion. I
mean, researchers didn't just look at temperature
anomalies. They analyzed 22 of the most significant
extreme weather events globally. A terrible mosaic,
really? It really was. Catastrophic heatwaves,
intense floods, devastating storms, persistent
droughts, and these uncontrollable wildfires
that just claimed countless lives, destroyed
infrastructure, and crippled communities. And
every story embedded in those events is tied
directly to something that sounds clinically
small. It's called incremental warming. We're
talking about that slow, steady, upward creep
of global average temperatures, fractions of
a degree, that have these disproportionately
massive real -world impacts. To put that in perspective
for everyone, we often talk about the 2015 Paris
Agreement as this kind of benchmark moment. So
how much warming have we actually seen just in
the decades since that agreement was signed?
Since 2015, global warming has increased by 0
.3 degrees Celsius. 0 .3. Just sounds so tiny.
It sounds tiny. It sounds scientifically insignificant.
But that fraction represents a massive escalation
of risk that has fundamentally changed the odds
of extreme weather. That number, .3 degrees C,
is so abstract to most people. What does that
tiny incremental warming actually mean in terms
of daily life, you know, for you and me just
walking outside? It's the difference between
a harsh summer and a catastrophic one. That seemingly
small rise has already resulted in extreme heat
becoming significantly more frequent across the
entire globe, adding on average 11 extra hot
days per year. 11 extra days? Think about that.
That's nearly two full weeks of additional potentially
dangerous, life -threatening heat every single
year for the whole planet. 11 extra hot days
on average. That statistic doesn't just sound
chilling it. I mean, it means a fundamental restructuring
of what a normal year even looks like. It does.
And it means the type of heat is changing too.
It's not just the dry, you know, high temperature
heat we worry about. It's the increasingly frequent
wet bulb heat events. Right. Where the humidity
is the real killer. That's the one. Wet bulb
temperature measures heat and humidity combined.
And when it rises past a certain point, the human
body literally cannot cool itself by sweating.
An increase of 0 .3 degrees C pushes regions,
especially near the equator, closer and closer
to those critical, lethal, wet bulb thresholds.
So those 11 extra days are just much more dangerous
than they would have been in the past. Far more
dangerous. And the effects just ripple out from
there. Agriculture is strained because crops
have specific temperature tolerances. You see
mass crop failures, reduced yield. And our infrastructure.
Our infrastructure, which was designed for a
climate that doesn't exist anymore, just starts
to buckle. You have asphalt roads melting, rail
lines warping, electricity grids struggling because
the demand for cooling spikes exponentially with
just a few extra degrees. So the warming makes
heat waves longer and they happen more often.
So there's no time to recover. Exactly. Nature
can't recover. Human systems can't recover. There's
no break. And the change in probability for specific
Local events is even more alarming than that
global average. Oh, absolutely. Okay for specific
well -documented heat waves We've seen in previous
years like that extreme heat that devastated
parts of the Amazon or the severe events that
hit West African nations like Burkina Faso and
Mali. Researchers found that the probability
of the specific high intensity events happening
again has increased almost 10 times since 2015.
10 times more likely. 10 times. Every single
fraction of a degree matters because it shifts
the odds exponentially toward disaster. It makes
events that were once rare anomalies, you know,
once in a century events, now disturbingly routine.
That analysis just drives home the urgency for,
well, for both mitigation and adaptation. We're
getting very close to the limits of what society
can actually absorb. And when we talk about who
absorbs that cost, we have to look beyond the
generalized impact. Extreme weather does not
distribute its effects equally. No, it consistently
and devastatingly hits those who are already
marginalized and vulnerable, the hardest. We
saw some particularly detailed research that
illustrated this profound inequity across regions
of the global south in 2025. Yes, there was a
study focused on South Sudan, which provided
a powerful, really granular example of how these
climate hazards intersect with preexisting social
structures. What do they find? They found that
women in South Sudan are disproportionately affected
by extreme heat, mainly because of their concentration
in informal heat exposed work. Can you paint
a clearer picture of what that informal work
actually looks like on the ground? We're talking
about essential economic activity that often
provides zero shelter. Things like subsistence
agriculture, spending hours tending fields under
intense sun, or jobs like street vending, where
the only place to do business is directly exposed
to peak daytime heat. And these are jobs that
are critical for family. Absolutely critical,
but they offer zero mitigation against severe
heat. So it's not just the exposure to the heat
itself, and during a heat wave, that work gets
even harder. It intensifies. Family members are
more likely to fall ill, so their own exposure
to dangerous conditions increases while their
mobility and ability to earn a living shrinks.
And the impact spills into the next generation.
Extreme heat forces, widespread school closures,
which immediately interrupts the education of
girls more severely than boys. perpetuating the
cycle of vulnerability. Exactly. What's equally
important and something the analysis really pointed
out is that this inequity extends right into
the scientific process itself. It's not just
that the impact is unequal. It's the evidence
we have to study those impacts. It creates a
kind of scientific blind spot. That is a critical
finding. Many studies that focused on severe
weather in the global south in 2025, specifically
heavy rainfall, they ran into the same problem
over and over again. Which one? Significant gaps.
in localized on -the -ground observational data.
And even worse, the studies had to rely heavily
on global climate models that were fundamentally
developed, calibrated, and optimized, primarily
for the conditions and infrastructure of the
global north. So we're trying to understand the
hyperlocal effects of massive rainfall in a place
like South Sudan using models that were optimized
for, what, Kansas or Germany? In effect, yes.
The scientific foundation is uneven. When you
lack that granular, reliable local data, and
your predictive tools are biased toward different
atmospheric conditions and terrains, it severely
hinders your ability to draw confident, actionable
conclusions. And that cripples local planning
where it's needed most. It absolutely cripples
it, in the regions already experiencing the most
immediate and severe consequences. Addressing
this is just as vital as reducing emissions.
We need investment in closing these scientific
data gaps globally so marginalized communities
can build genuinely effective data -driven resilience.
That sets a very intense, very sobering stage
for 2025. The overall warming picture is relentless
and the human cost is so unequally distributed.
So let's pivot now from that persistent global
heat to a specific terrifying example of catastrophic
weather. the Atlantic hurricane season. This
was a year where the early forecast predicting
a highly active season, well, they proved devastatingly
accurate. The season was undeniably busy. Overall,
we saw 13 named storms, which is a busy count,
and frighteningly, three of those intensified
into category five hurricanes. Three cat fives?
Three. This puts 2025 among the most intense
seasons on record, just based on that peak strength.
There was one small silver lining, for the U
.S. coastline at least. For the first time in
a decade, a hurricane did not make landfall on
American soil. That localized good fortune unfortunately
did not extend to our neighbors in the Caribbean.
We have to zero in on the most destructive storm
of the year, Hurricane Melissa. A truly devastating
storm. It was a monster, a category five that
achieved maximum intensity, slamming Jamaica
in late October with sustained winds of 185 miles
per hour. It devastated communities and, tragically,
resulted in dozens of deaths. Hurricane Melissa,
in its sheer intensity, it really highlighted
a painful lesson that ties right back to our
discussion on adaptation in this rapidly warming
world. It showed the clear limits of preparedness.
We talk constantly about the need for adaptation
building better seawalls, improving warning systems
and forcing building codes, and those are all
absolutely essential for preventing massive loss
of life from less intense storms. But Melissa
was different. Melissa made it painfully clear.
When a storm of that maximum intensity strikes
a small island nation like Jamaica, even relatively
high levels of preparedness simply cannot prevent
catastrophic losses and infrastructure annihilation.
The physical force is just too great. Which emphasizes
that forecasting alone isn't the solution, but
it is that critical first step. And this is where
the story gets really compelling, shifting from
the tragedy of the storm to the technological
leaps happening in the background. Yes. A week
before Melissa made landfall, forecasters were
in agony. The traditional forecast models were
just disagreeing widely on the storm's eventual
track. One model had it moving north toward the
Bahamas, another had it tracking directly into
the Gulf of Mexico. This uncertainty, that common
agonizing cone of uncertainty, is a huge challenge
when you're issuing life -saving warnings. The
pressure on the human forecasters must have been
just immense trying to synthesize all those competing
possibilities. It was. But amidst all that confusion,
one specific model delivered an outlier forecast
that proved to be exactly right. One model about
it. It predicted both Melissa's precise track
into Jamaica and, crucially, it forecast its
Category 5 intensity. That model was Google's
DeepMind AI -based hurricane model. A perfect
call in a chaotic life or death situation. That
kind of endorsement for an AI model must have
been impossible for the meteorological community
to ignore. It was a watershed moment. Former
officials from the National Hurricane Center
later analyzed the 2025 performance of all the
available guidance. They went on record to call
the Google DeepMind model the best guidance we
saw this year. Wow. They noted its stunning accuracy,
especially when you compare it directly to the
results from the long -standing traditional physics
-based systems. That endorsement suggests AI
isn't just a useful addition anymore. It might
be fundamentally changing how meteorology even
works. Let's really unpack the core difference
for the listener, because these models operate
on entirely separate philosophies, don't they?
They really do. You can think of it in terms
of philosophy. Traditional weather models like
the standard global forecast system, the GFS
developed by the National Oceanic and Atmospheric
Administration, or NOAA, they rely on these complex
first principle equations. These models are like
the atmosphere as a physics textbook. They calculate
how wind, moisture, and heat move and interact
using established scientific laws. They're trying
to predict the future based on pure continuous
mathematical calculation. If an additional physics
-based model is solving this massive complex
equation, how does an AI model, like Google DeepMind,
approach the same problem? It approaches it like
a detective studying a crime scene archive. AI
models don't necessarily know physics in that
traditional sense. Instead, they focus intensely
on historical patterns. They're trained on decades,
even centuries of past weather data, looking
for subtle, nonlinear relationships that a human
or even a pure physics model might miss. They
just ask, based on thousands of similar atmospheric
setups in history, what was the most probable
outcome? That pattern -based approach explains
why AI has... for a while now, been pretty good
at track forecasting. That's essentially a large
-scale pattern recognition problem. Exactly.
Storm tracks are governed by these massive, predictable
atmospheric steering currents. That's why AI
could always see the path well. But historically,
AI models always struggled with intensity. And
why is intensity how strong a storm will be?
So much harder to predict than where it's going.
Because intensity is governed by these small,
chaotic, localized processes. Things like eyewall
replacement cycles, internal storm dynamics,
specific wind shear values. Things happening
on a much smaller scale. Right, and they're notoriously
difficult to capture in generalized data. But
the Google DeepMind model overcame this. It incorporated
highly specific historical data detailing precisely
how past hurricanes developed and intensified
under all these various conditions. And that
allowed it to forecast maximum intensity with
the accuracy we saw with Melissa. This shift,
though, while it's delivering better forecasts,
it introduces a new and kind of challenging dynamic
for experienced human forecasters. It's this
critique called the black box problem. It's a
genuine operational concern. When a forecaster
uses a physics -based model, they can look at
the data outputs, the pressure gradients, the
wind shear fields, and they can trace the precise
physical mechanism that led to the prediction.
They know why the storm is being steered northwest.
But the AI model doesn't offer that same transparency.
Not always. Because the AI models focus on these
subtle, learned historical correlations, the
process can feel opaque. A black box. Massive
amounts of data go in, a highly accurate forecast
comes out, but the specific, understandable mechanism
it used to connect the input to the output might
be invisible to the human. But if the forecasters
don't know why the AI made a choice... Let's
say the AI forecasts a radical path that their
trusted GFS physics model completely disagrees
with. How can they trust the AI enough to override
the physics? Isn't that a massive potentially
catastrophic liability when human lives are at
stake. That is the essential challenge of integrating
AI. It requires a massive leap of faith in the
underlying statistical accuracy of the AI, even
without the comfort of that physical traceability.
And forecasters are inherently skeptical, and
they should be because they have to be accountable
for their warnings. The only way you build that
trust is through repeated, proven performance,
like the perfect call on Hurricane Melissa. It
sounds like the human element, the essential
judgment of the experienced meteorologist, is
actually becoming even more critical, not less.
Now they have to decide which computer outlier
to trust. Absolutely. The consensus is clear.
AI will not replace the long -standing physics
-based models or the essential judgment of experienced
forecasters, but it's clearly going to be a key
foundational component that provides an alternative
perspective and critical time savings. It's not
a replacement. It's a powerful new tool in the
toolbox. Exactly. And the practical benefit for
you, the listener, is really simple. As our coastlines
become more populated, and as these storms become
more intense, we desperately need more time.
More time to get people out of the way, to secure
property, to activate infrastructure. Forecasts
further and further into the future become vital,
and AI is delivering that crucial time advantage.
And this innovation moved beyond the experimental
phase really fast. We're now transitioning from
that successful proof of concept stage, like
Google DeepMind's stellar performance, to large
-scale operational use deployed by major government
agencies. Like the National Oceanic and Atmospheric
Administration. or NOAA. NOAA has now deployed
a groundbreaking new suite of operational AI
-driven global weather prediction models as of
December 2025. This feels like a profound strategic
leap forward for American weather model innovation.
It's a signal that the U .S. weather infrastructure
is fully committing to the AI revolution. It
really is. The mission statement from NOAA was
clear and multifaceted. They're aiming to provide
improved accuracy for large -scale weather and
tropical tracks, and to deliver that guidance
much faster and at a dramatically lower cost
than the current traditional models allow. The
cost and speed element is the often overlooked
secret weapon of AI modeling, isn't it? What
is the most transformative feature of these new
NOAA AI models compared to the GFS running on
those massive supercomputers? It is without question
computational efficiency. We cannot overstate
this. These AI models use a tiny fraction of
the computational resources of their traditional
physics -based counterparts. This isn't just
about saving money. It's a game changer because
it allows NOAA to run models more often at higher
resolution and to run many more members in their
ensemble systems. It basically democratizes access
to high quality modeling. Let's look at the specific
models that NOAA has rolled out. We can start
with the artificial intelligence global forecast
system. or AI GFS. This is the single deterministic
forecast model. Right, so the AI GFS is designed
to generate a single forecast that's comparable
in quality to the traditional physics -based
GFS. And on the performance side, NOAA found
an immediate gain. improved forecast skill for
many large -scale atmospheric features, and a
significant measurable reduction in tropical
cyclone track errors, especially at longer lead
times. So just like DeepMind, the AI is delivering
superior guidance on where the storm will go.
That's right. Now let's talk about the efficiency,
because these numbers are just astonishing. They
redefine what's possible. A single 16 -day forecast
from the AI GFS uses only 0 .3 % of the computing
resources required for the traditional GFS. 0
.3%. Less than half of 1 % of the power. It's
incredible. And the time savings are just as
vital. It finishes in approximately 40 minutes.
40 minutes, compared to a traditional GFS run
that can take hours and hours. Exactly. That
dramatically reduced latency means forecasters
get critical data much, much faster. Speed means
earlier warnings, and earlier warnings mean lives
saved. That speed advantage is huge. But we did
note there was one initial area for improvement
in this first version, V1 .0. Yes. The initial
testing confirmed that prevailing challenge in
AI. Version 1 .0 did show a slight degradation
in tropical cyclone intensity forecasts compared
to the traditional physics -based model. Still
the hardest nut to crack. It is. But the speed
gains and the track improvements were so significant
that they deployed it operationally immediately,
with the understanding that future versions will
focus heavily on boosting that intensity accuracy.
Okay, next up is the really essential component
of modern forecasting. The ensemble system. NOAA
deployed the AIG -FS, the artificial intelligence
global ensemble forecast system. Before we get
into the AI version, just remind us, what is
the core role of an ensemble system? An ensemble
system is maybe the most crucial tool forecasters
have for managing uncertainty. Instead of running
the model just once, you run it many times. In
Noah's case, 31 times. And each run starts with
slightly different initial atmospheric conditions.
So you get a range of possibilities. You get
a fan of possibilities, a range of probable outcomes,
rather than one single confident solution. It
helps meteorologists and decision makers understand
the overall forecast uncertainty and figure out
the highest risk scenarios. And how did the AI
based AGFS perform against the traditional global
ensemble forecast system, the GEFS. The AI version's
forecast skill is comparable to the traditional
ensemble system, so the quality is maintained,
but it provides a huge benefit. It extends reliable
forecast skill by an additional 18 to 24 hours.
A full day. An enormous gain in lead time. And
again, the efficiency is astounding. It requires
only 9 % of the computing resources of the traditional
GEFS. Those savings and time gains are phenomenal.
But the real innovation breakthrough, the thing
that makes NOAA's deployment a global first,
is the HGEFS, the hybrid GEFS. This sounds like
the ultimate answer to that black box problem
we were talking about. This is where the physics
and the patterns marry. It acknowledges that
neither system is perfect on its own. The HGEFS
is a pioneering 62 -member grand ensemble. Okay,
what does that mean? It's created by combining
the 31 members of the physical GEFS ensemble
with the 31 members of the AI -based AIGF. So
the hybrid model is essentially letting the physics
tell us the rules of the atmosphere. while the
AI uses the patterns of history to refine those
rules. All in one gigantic, robust package. That's
a perfect way to describe it. By combining two
fundamentally different modeling systems, one
based purely on physical equations, one based
on learned historical patterns, the HGEFS creates
an ensemble that's twice the size, making it
much more robust. And crucially, because the
two systems fail for different reasons, combining
them more effectively represents the full spectrum
of forecast. uncertainty. And the result of that
blending, did it actually work to create better
forecasts? It consistently worked. The HGEFS
consistently outperforms both the AI -only system
and the physics -only system across most major
verification metrics. It is a first -of -its
-kind operational approach globally, and it confirms
that the future of forecasting is hybrid. It's
physics -informed by history. All run with maximum
computational efficiency. And this entire suite,
the AIGFS, AGSS and HGEFS. This wasn't just whipped
up overnight. This is the culmination of a huge
collaborative effort. A massive undertaking.
It resulted from something called Project EGL,
which was a collaborative initiative involving
various elements of NOAA, the National Weather
Service, its Oceanic and Atmospheric Research
Labs, the Environmental Modeling Center, and
the Earth Prediction Innovation Center. So they
leveraged work from the private sector too. They
leveraged foundational academic and private industry
work, specifically Google DeepMind's Breakthrough
Graphcast model, and then they fine -tuned it
using NOAA's own massive global data assimilation
system data. So they took the best of theoretical
private industry innovation and married it with
the essential operational necessities and the
vast real -time data flow of the National Oceanic
and Atmospheric Administration. That is how you
achieve a quantum leap in forecasting capability.
The administrator of NOAA summed it up perfectly.
This is a new paradigm for providing improved
accuracy, faster delivery of forecast products,
and at a dramatically lower operational cost.
The pace of innovation is finally accelerating
to meet the pace of climate change. As we wrap
up this deep dive into 2025, let's work all back
together into a single narrative. From the persistent,
overwhelming heat, despite the natural cooling
efforts of La Nina, which showed us the relentless
nature of human driven warming. To the profound
social inequities highlighted in regions like
South Sudan, 2025 really showed us the current
harsh reality of a planet that is fundamentally
warmer and more volatile. And the devastating
power of Hurricane Melissa in the Gulf of Mexico
region was the perfect, terrifying case study
of that. It reminded us that the ultimate devastation
is still possible and that even the best preparation
has its limits when a storm achieves that kind
of maximum intensity. But the rapid pace of innovation,
evidenced by both the success of Google DeepMind
and the operational deployment of NOAA's hybrid
AI models, it suggests that while the weather
is becoming undeniably more extreme, our ability
to see it coming and faster is reaching astonishing
new heights. It really is. It's offering that
extra vital 18 to 24 hours of warning time. It's
a race against the clock. As the climate changes
and volatility increases, our tools for prediction
have to evolve even faster just to keep pace.
I want to leave you with this final provocative
thought. While forecast accuracy is dramatically
improving and we celebrate every single hour
of extra lead time, Hurricane Melissa reminded
us that adaptation, no matter how good, has hard
limits. Knowing a category 5 is coming earlier
is absolutely vital for saving lives, but what
truly prevents catastrophe? Is it only forecasting?
Or is it fundamentally reducing the fuel for
these storms, addressing the root cause of the
warming that makes them so powerful in the first
place? That's the question we have to grapple
with moving forward. If you want to keep exploring
the science, chaos, and stories behind the weather,
please like, follow, comment, and rate the Meteorology
Matters podcast. You can also follow meteorologist
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the Meteorology Matters podcast playlist. You've
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by meteorologist Rob Jones. Thanks for listening,
and remember, meteorology always matters.