Occasional Thoughts on Climate Change

[TheClimateChanger]

Incredible! Downloaded data from SERCC (observations from March 1-April 14, forecast values through April 19) reveals nearly 80 long-term threaded stations are in the midst of their warmest spring on record, calculated by average daily high temperature. Led by Huntington, West Virginia, where the first 50 days of spring has seen a mean high temperature of 72.8F, an astounding 12.1F above the 1991-2020 mean. Again, that's a 50-day average!

[GaWx]

31 minutes ago, TheClimateChanger said:

Incredible! Downloaded data from SERCC (observations from March 1-April 14, forecast values through April 19) reveals nearly 80 long-term threaded stations are in the midst of their warmest spring on record, calculated by average daily high temperature. Led by Huntington, West Virginia, where the first 50 days of spring has seen a mean high temperature of 72.8F, an astounding 12.1F above the 1991-2020 mean. Again, that's a 50-day average!

Have you changed from the “Global Warmer” to the “US Warmer”?  
 The US has only 2% of the world’s surface area and only 6% of the world’s land surface area and that’s including Alaska. This is the same argument used against those talking about how hot the US was in the 1930s summers.

[Typhoon Tip]

I found this to be an interesting article...

https://phys.org/news/2026-04-climate-decline-hot-cold-extreme.html   (https://link.springer.com/article/10.1007/s00704-026-06200-3)

Dr. John R. Christy, Alabama State Climatologist (retired); professor of atmospheric and Earth science at The University of Alabama in Huntsville

I'm curious if this may ever be corroborated by other's findings. 

**Also ... bear in mind the study is regarding "extremes"    It doesn't address the longer term climate averages rising. Those are created by the daily stuffing, the vast majority of which are not extreme, per se.  

I can see a pathway toward reducing the "extremeness" of extreme events, while the bulk temperatures results are warming over time.  It would come from increasing the water vapor content.  This is basic Meteorology: in order to store more water vapor requires more energy.  Raising the global energy level ( planetary energy imbalance ) by way of packing green house gases faster than the background geological processes can compensate, raises temperature.  GW incarnate.  This provides the necessary energy to evaporate more water and keep it in the atmosphere.  

I would be really keenly interested in a DP comparison variation of Dr Christy's findings, if the lowering extreme magnitudes might also have correlative relationship with any rises in integrated Dew Point (relative to sigma levels) since 1899.   The idea being, as DP ambience becomes more and dense over time, it has a modulating impact on ambient temperature.  This is actually rather low-bar atmospheric thermodynamics. The magnitudes of extremes of hot and cold are modulated less due to the thermodynamics involved with the energy needed to keep water in vapor form.  When the ambient cold sourcing has high WV content it's just not going to be as cold, because it's holding more thermal latency.  Contrasting, as the DP (temp and water vapor integral) rises the temp always comes down; that is because therms are "borrowed" from the kinetic temperature in order to keep the water in gaseous form.   98/80 has equivalent energy to 115/68 ..etc. 

The shortened version is a 'trade off' so to speak.  Is the extremeness of extreme events trading magnitude for high WV content. 

[TheClimateChanger]

5 hours ago, Typhoon Tip said:

I found this to be an interesting article...

https://phys.org/news/2026-04-climate-decline-hot-cold-extreme.html   (https://link.springer.com/article/10.1007/s00704-026-06200-3)

Dr. John R. Christy, Alabama State Climatologist (retired); professor of atmospheric and Earth science at The University of Alabama in Huntsville

I'm curious if this may ever be corroborated by other's findings. 

**Also ... bear in mind the study is regarding "extremes"    It doesn't address the longer term climate averages rising. Those are created by the daily stuffing, the vast majority of which are not extreme, per se.  

I can see a pathway toward reducing the "extremeness" of extreme events, while the bulk temperatures results are warming over time.  It would come from increasing the water vapor content.  This is basic Meteorology: in order to store more water vapor requires more energy.  Raising the global energy level ( planetary energy imbalance ) by way of packing green house gases faster than the background geological processes can compensate, raises temperature.  GW incarnate.  This provides the necessary energy to evaporate more water and keep it in the atmosphere.  

I would be really keenly interested in a DP comparison variation of Dr Christy's findings, if the lowering extreme magnitudes might also have correlative relationship with any rises in integrated Dew Point (relative to sigma levels) since 1899.   The idea being, as DP ambience becomes more and dense over time, it has a modulating impact on ambient temperature.  This is actually rather low-bar atmospheric thermodynamics. The magnitudes of extremes of hot and cold are modulated less due to the thermodynamics involved with the energy needed to keep water in vapor form.  When the ambient cold sourcing has high WV content it's just not going to be as cold, because it's holding more thermal latency.  Contrasting, as the DP (temp and water vapor integral) rises the temp always comes down; that is because therms are "borrowed" from the kinetic temperature in order to keep the water in gaseous form.   98/80 has equivalent energy to 115/68 ..etc. 

The shortened version is a 'trade off' so to speak.  Is the extremeness of extreme events trading magnitude for high WV content. 

I’ve read through the Christy & Spencer paper and I think it’s worth discussing, but a lot of the conclusions depend heavily on how the analysis is set up. First, it’s U.S.-only, which already limits how broadly you can interpret it. The U.S. is a small, noisy region with a lot of land-use influence, and it’s not necessarily representative of global behavior. Second, they lean on minimally adjusted station data, which is not a neutral choice. One big issue there is time of observation bias (TOBs)—earlier stations often used afternoon observation times, which can effectively double-count hot days across two calendar days. That tends to inflate extreme heat in the early part of the record relative to today, when observations are mostly taken in the morning. If you don’t correct for that, you’re giving early decades a built-in advantage in heat extremes.

On top of that, the way “extremes” are counted matters a lot. If you’re relying on record-setting events and not handling ties properly (or only counting first occurrences), later decades are inherently disadvantaged. Early in the record, everything is a “new record.” Later on, even in a warmer climate, you’re more likely to tie or narrowly exceed previous values. If ties aren’t treated equally, you can manufacture a downward trend in “new extremes” even if the underlying distribution is shifting warmer.

Another big limitation is that the analysis focuses almost entirely on summer high extremes and winter low extremes, which is a pretty narrow slice of the climate system. It doesn’t really tell you anything about extreme warmth in winter, spring, or fall—which is where a lot of the warming signal actually shows up—or about the broader shift in temperatures. Summer daytime highs (Tmax) are one of the least responsive variables in many parts of the U.S., especially in the East.

And this is where land-use changes become critical. The eastern and central U.S. have undergone significant reforestation over the past century after being heavily clear-cut, which increases evapotranspiration and tends to suppress daytime highs. At the same time, the Midwest has seen an explosion in corn and soybean agriculture, and those crops transpire enormous amounts of water—far more than the historical landscape. That effectively adds a massive, artificial cooling mechanism during the growing season. So you’ve got large parts of the country where land surface changes are actively dampening summer heat extremes, even as the broader climate warms. Meanwhile, more arid regions in the West—where you don’t have that same vegetation-driven cooling—show increasing heat extremes, which is more in line with expectations.

On top of that, the 1930s Dust Bowl looms large in any long-term U.S. extremes analysis. That period featured extreme heat driven in large part by land degradation and drought—largely human-influenced surface conditions. If your methodology already favors early extremes (via TOBs and record-counting asymmetry), and you anchor the dataset with the most extreme decade in the record for partly non-climatic reasons, you’re stacking the deck toward showing a long-term decline.

There are also the usual issues with long-term station data—station moves, instrument changes, siting differences, etc. Homogenization is meant to deal with those. Skipping it doesn’t remove bias, it just means you’re accepting a different set of biases, many of which tend to inflate earlier extremes relative to modern ones.

To be fair, the paper is very likely correct that cold extremes are declining—that’s one of the most consistent and physically well-understood signals in the observational record. It’s also true that regional trends (especially in the U.S.) can look different due to land-use effects. But the headline claim about declining heat extremes is much more questionable and looks heavily dependent on methodology. At a minimum, this analysis is incomplete—it’s looking at a narrow set of metrics that are least likely to show strong warming signals and then generalizing from them. If you actually want to understand how extremes are changing, you need to look at the full temperature distribution, across all seasons, with methods that account for known observational biases and major land surface changes.

 

 

 

[GaWx]

31 minutes ago, TheClimateChanger said:

I’ve read through the Christy & Spencer paper and I think it’s worth discussing, but a lot of the conclusions depend heavily on how the analysis is set up. First, it’s U.S.-only, which already limits how broadly you can interpret it. The U.S. is a small, noisy region with a lot of land-use influence, and it’s not necessarily representative of global behavior.

You’re saying how this being U.S. only limits how broadly you can interpret it. But at the same time, many of your recent posts ITT have been U.S. only! You wouldn’t even consider the intense cold in Canada in March. You’re not being consistent.

[TheClimateChanger]

6 minutes ago, GaWx said:

You’re saying how this being U.S. only limits how broadly you can interpret it. But at the same time, many of your recent posts ITT have been U.S. only! You wouldn’t even consider the intense cold in Canada in March. You’re not being consistent.

That’s not inconsistent at all—those are two completely different contexts. When I post here, I’m discussing weather in the U.S. on a U.S.-focused forum. That’s the scope of the discussion, and everyone does the same thing. But when you’re evaluating a scientific paper making broader claims about climate behavior, scope absolutely matters. If a study is U.S.-only, that’s an important limitation that needs to be acknowledged before drawing bigger conclusions.

On the Canada point, focusing on a cold spell there would actually be the cherry-pick. Short-term regional cold anomalies happen all the time, even in a warming world. The broader context right now is near-record global warmth, so isolating one region’s cold period doesn’t really tell you much about the overall climate signal.

So the distinction is pretty simple:

• Talking about regional weather → it’s perfectly fine to focus on the U.S.
• Evaluating climate claims in a paper → you have to be clear about geographic limits and not overextend them

Those aren’t contradictory standards—they’re just applying the right frame to the right situation.

[TheClimateChanger]

I was looking at some data for Pittsburgh and, with GPT’s help, calculated the annual and monthly trends for periods starting in 1960, 1970, 1980, 1990, and 2000 (all using May–April data).

For the first four start dates, warming is pretty insensitive to the choice of starting point — it comes out to roughly ~6°F per century in each case. However, starting in 2000, the trend is nearly double that rate (see table of trends below).

Start Year	Period	Trend (°F/decade)	Trend (°F/century)
1960	1960–2026	+0.59	+5.9
1970	1970–2026	+0.60	+6.0
1980	1980–2026	+0.62	+6.2
1990	1990–2026	+0.58	+5.8
2000	2000–2026	+1.14	+11.4

The obvious caveat is the shorter time window. I intentionally didn’t go any shorter than 2000 because it quickly becomes too noisy to draw meaningful conclusions.

With that in mind, I wanted to get a sense of what the climate might look like 50 years from now. The table/graphic shows a range:

• Low end: continuation of the long-term trend (1960–present)
• High end: continuation of the more accelerated warming seen since 2000

This shouldn’t be interpreted as a true “high-end forecast.” If anything, one could argue warming may continue to accelerate as CO₂ increases and feedbacks come into play. This is simply a trend-based extrapolation, not a model projection.

A couple interesting takeaways:

• Winter shows the largest absolute changes
• Summer warms less in °F, but still shifts meaningfully given its low variability
• November consistently stands out as a relative laggard

Curious what others think, especially regarding the seasonal differences and whether similar patterns show up in nearby regions.

A couple quick notes on the table:

• “Recent” refers to the most recent 7 years (May 2019 through April 2026), so it should be thought of as a snapshot of the current climate rather than a formal 30-year normal.
• The 2070s range is not a forecast — it’s simply an extrapolation of observed trends:
  • Low end = continuation of the long-term (~1960–present) trend
  • High end = continuation of the more accelerated warming seen since ~2000
• Importantly, this should not be interpreted as a true upper bound. If anything, actual warming could end up higher than shown here if the recent acceleration continues or increases due to rising CO₂ and amplifying feedbacks. This is just a simple trend-based framework to give a sense of scale.

[TheClimateChanger]

Here are the trends since 1960 by month, sorted May to April.

Month	Trend (°F/decade)	Trend (°F/century)
May	+0.60	+6.0
Jun	+0.47	+4.7
Jul	+0.56	+5.6
Aug	+0.45	+4.5
Sep	+0.54	+5.4
Oct	+0.42	+4.2
Nov	+0.22	+2.2
Dec	+0.92	+9.2
Jan	+0.68	+6.8
Feb	+0.94	+9.4
Mar	+0.60	+6.0
Apr	+0.74	+7.4

[TheClimateChanger]

Just now, TheClimateChanger said:

Here are the trends since 1960 by month, sorted May to April.

Month	Trend (°F/decade)	Trend (°F/century)
May	+0.60	+6.0
Jun	+0.47	+4.7
Jul	+0.56	+5.6
Aug	+0.45	+4.5
Sep	+0.54	+5.4
Oct	+0.42	+4.2
Nov	+0.22	+2.2
Dec	+0.92	+9.2
Jan	+0.68	+6.8
Feb	+0.94	+9.4
Mar	+0.60	+6.0
Apr	+0.74	+7.4

One angle I don’t see discussed much is how “feels like” temperatures are changing relative to actual air temperature.

We usually focus on raw temperature trends, but that’s not necessarily what people experience. In winter, wind chill matters; in summer, humidity does. So I pulled PIT data back to 1960 and looked at mean “feels like” temperatures (wind chill in winter, heat index in summer) alongside the air temperature trends.

A couple of interesting contrasts:

• January:
  Mean “feels like” temperature is increasing at ~10–11°F per century, versus about ~6–7°F per century for air temperature.
  So winters aren’t just warming — they’re becoming less harsh even faster than the thermometer suggests.

• July:
  The heat index trend is also higher than the air temperature trend (by roughly ~1°F/century on average).
  But that actually understates the real effect.

  Nighttime heat indices are typically equal to the air temperature (they’re not additive until you get into ~80°F+ conditions), so averaging over the full day mutes the signal.

  That implies that daytime heat indices are likely increasing on the order of ~2–3°F/century in addition to the air temperature trend.