Pressure Causes Temperature? It’s Time to Climb Down from “Mount Stupid”

May 28th, 2026

There was a recent weeks-long exchange of emails between many climate people — professional and amateur — regarding the idea that air pressure (in combination with absorbed solar energy) is what causes temperature. There were insults launched at those who refused to believe what a certain physics-trained person says should be a revolution in our understanding of planetary temperatures. That person even managed to get a paper published in a journal that (in my opinion) used reviewers who were in over their heads on the subject.

The whole ordeal makes me think of the Dunning-Kruger Effect, which is the tendency for people who start to understand a complex subject to overestimate their level of understanding. This then leads to a point of peak overconfidence (climbing “Mount Stupid”), which then gradually relaxes as more is learned and the person increasingly realizes that the subject is more complicated and nuanced than they originally thought.

I claim that the person in question who thinks [pressure + absorbed solar energy = temperature] is still stuck on Mount Stupid.

The reason I bring this up again (I’ve preached on it before) is that many have been misled into believing the “theory”. As a result, I have spent many years responding to questions from the public (including science-savvy citizens) regarding the issue. Many have been convinced by the “theory”, and have joined the proponent of the theory on Mount Stupid.

After lurking in the weeks-long email discussion, I finally responded with the following summary of the issue. I have removed the person’s name to protect the not-so-innocent.

SUBJECT: Where <NAME REDACTED> Is Right… and Where <PRONOUN REDACTED> Is Wrong

All:

After working in meteorology and then climate during my 40+ year career, I think I can offer some insight into the issues being discussed in these emails. Like <NAME REDACTED>, I have always been skeptical of what I have been told until I could fully understand an issue for myself.

I’m sure the following explanations will be of help to many of you. (I suspect <NAME REDACTED> is too invested in theories to change <PRONOUN REDACTED> mind.) Many of the concepts are not trivial, and I will admit it wasn’t until many years after all of my education (PhD Meteorology) that I finally understood a few of them, because they were not taught in school. Dick Lindzen helped me in this in the early climate research years.

Most of what follows is fundamental atmospheric thermodynamics, and I question whether <NAME REDACTED> really did take a university-level Atmospheric Thermodynamics course. If he did, I’d like to know where.

And if <PRONOUN REDACTED> shows me <PRONOUN REDACTED> grades, I’ll show <PRONOUN REDACTED> mine.

A THOUGHT EXPERIMENT

Imagine you could suddenly dump an extra 1 atmosphere of air on top of the existing atmosphere, what would happen to air temperature in the 1 ATM below? Just as <NAME REDACTED> would predict, the temperature of the original atmosphere below would increase greatly through adiabatic compression.

But what would happen NEXT?….

The high [hot] atmospheric temperatures in the lower atmosphere would then be far out of energy balance compared to what existed before. The result would be cooling of all of that air that was heated through adiabatic (or nearly so) compression (work done on the lower atmosphere) until a new state of energy equilibrium was reached. The energy loss would be through infrared radiation of the hotter air.

In fact is it always ENERGY BALANCE that determines temperature, through the 1st Law of Thermodynamics. A change in temperature is proportional to the rate of energy input minus the energy output (which includes any work done in the process).

In contrast, the Ideal Gas Law (PV=nRT) cannot tell you what the temperature “should be”. It only says how the variables P, V, and T are interrelated during the process of re-equilibration and in the final equilibrium state. What <NAME REDACTED> misses in <PRONOUN REDACTED> theory is the “n” part of the equation (the number of moles, or mass… which is in the density form of the equation, P = rho RT). In my hypothetical 2-atmosphere thought experiment, as the lower atmosphere cools to reach a new state of energy equilibrium with the solar input, the decreasing temperature causes an increase in the air density (“shrinkage”), and the pressure remains the same… even while the temperature is changing.

Specifically, following the 1st Law, the internal temperature of a volume of atmosphere exposed to an energy INPUT will increase until the temperature-dependent energy OUTPUT processes equal the rate of energy gain. This is true of every physical system… the atmosphere, a pot of water on the stove, a car’s engine, the human body, the interior of the sun, etc. That energy equilibrium is what determines the final temperature. (In the real atmosphere, there are constant energy imbalances and thus changes in temperature; Trenberth’s global-average energy balance diagram is only useful to gain a conceptual understanding of the relative role of the major energy flows in the global-average climate system.)

THE IDEAL GAS LAW

Again, the Ideal Gas Law equation (PV=nRT) cannot tell you what the temperature of a gas should be, only energy flows in and out can do that. The gas law just tells you how the P, n, and T are interrelated for a given volume (V) of air. Yes, <NAME REDACTED>, on short time scales, ascending air cools and descending air warms, but if all of that motion was to stop, energy flow processes would then determine what the final temperature would be… not what the air pressure is.

For a given surface air pressure, a huge range of temperatures is possible, and that huge range is all due to energy flow processes. Again, if the near-surface air temperature over the whole planet is much higher than local energy flow processes can support, the temperature falls, and the air’s volume shrinks (or density, rho, increases, according to the equivalent Ideal Gas Law equation, P=rhoRT). The surface air pressure remains the same because the total mass of the atmosphere is unchanged.

SO WHY MIGHT THERE BE A CLOSE RELATIONSHIP BETWEEN DIFFERENT PLANET’S LOWER ATMOSPHERIC TEMPERATURE AND PRESSURE?

I haven’t studied the atmospheres of other planets, because I don’t care. Even if those other planets did not exist, they are not necessary for understanding our own atmosphere. But if indeed <NAME REDACTED> is correct about a close statistical relationship between different planets’ surface air pressure and temperature, after adjusting for solar input, then I suspect it’s because the more atmosphere there is, the more greenhouse gases there are.

On the subject of GHGs, I’ve forgotten… does <NAME REDACTED> believe that air absorbs and emits IR energy? Because the greenhouse effect is a necessary consequence of that absorption/emission. Energetically, the GHE is a radiative insulator. It’s analogous to adding insulation to a heated building’s walls in winter. For a given energy input into the building, the air temperature inside will rise, and the outside of the walls will experience a temperature fall. This is exactly what the GHE does to the atmospheric temperature profile (in an energetic sense.. clearly involving radiation rather than conduction as a heat transfer mechanism).

If <NAME REDACTED> doesn’t believe air absorbs IR energy, how does <PRONOUN REDACTED> explain all of the thousands of spectroscopic measurements of CO2, water vapor, and methane as a function of temperature and pressure? And if <PRONOUN REDACTED> does believe the atmosphere absorbs and emits IR energy, then <PRONOUN REDACTED> must also believe in a greenhouse effect, because it is a necessary consequence…. the greenhouse effect in planetary atmospheres always causes warming of the lower atmosphere and cooling of the upper atmosphere.

(BTW, it is a common misconception that air which absorbs IR energy immediately loses that energy through emission of IR. Not true. Look up the “kinetic theory of gases” and related concepts. When CO2 or H2O vapor molecules absorb IR photons they extremely rapidly lose their extra energy to other air molecules through collisions. This happens much faster [by a factor of ~50,000] than the time it takes to re-emit energy through IR photons. This is how IR absorption immediately leads to “thermalization” [a term I hate].

Furthermore, it is crucial to understand that since IR absorption is largely independent of temperature, but IR loss is VERY dependent upon temperature, almost all air in the atmosphere is in a continual state of IR energy imbalance. Much of that imbalance is what [is balanced by] convective overturning.

WHAT IS THE ROLE OF THE ADIABATIC LAPSE RATE?

The adiabatic lapse rate in the troposphere (9.8 deg C per km without moisture condensation) is the RESULT OF convective overturning. If condensation of moisture is involved in updrafts, then the lapse rate is lower. Like the Ideal Gas Law, it doesn’t tell you what the temperature “should” be. It just tells you how the temperature of an air parcel changes during ascent or descent, if there is no energy gain or loss (“a-diabatic”). [But there are energy gains and losses occurring everywhere, all the time, and those determine what the absolute temperature will be — not pressure.]

HOW DOES THE GREENHOUSE EFFECT PLAY INTO THE LAPSE RATE?

This is a very interesting subject. It is something that even many atmospheric scientists and climate researchers don’t really understand. The combination of solar heating of the surface and IR absorption and emission by the surface and atmosphere ALONE, WITHOUT ANY CONVECTIVE OVERTURNING would result in an extremely steep tropospheric lapse rate, with very high surface temperatures and exceedingly cold upper tropospheric temperatures. This was first demonstrated by Manabe & Strickler (1964), and it’s called the “pure radiative equilibrium” case. It is sort of what makes the term “greenhouse effect” technically correct; like a real greenhouse inhibiting convective heat loss [because it has a roof], the greenhouse effect is, by definition, what happens WITHOUT the resulting convective overturning.

But in the real world, convective overturning is the RESPONSE to this GHE destabilization! So, that 33 deg. GHE warming people talk about? That’s not the GHE. It’s the GHE + CONVECTION. Without convection, that 33 deg. C figure would be more like 65 or 75 deg. C. Which then leads to another fascinating question…

WHAT WOULD HAPPEN IF THE ATMOSPHERE DID NOT ABSORB AND EMIT IR ENERGY?

Imagine a cold planetary atmosphere with no energy input. Then, turn on the sun. Solar heating of the surface would warm the atmosphere through convective overturning. But the [deep] atmosphere would have no way to shed that energy to cool in the presence of all of that energy input. The temperature of the [deep] atmosphere would then continue to rise until it had the same temperature as the surface, through its entire depth. Long before that process finished convective overturning would have stopped, because the atmosphere would be too stable to support convection. The atmosphere would eventually become isothermal (or nearly so, since there might be some planetary scale overturning between the tropics and the poles, due to their different rates of solar input), with the same temperature as the surface. Interestingly, as a result all weather activity would cease. All clouds would probably disappear, resulting in higher temperatures. Any [remaining] circulation systems would have a planetary scale, because the horizontal scale of those systems are related to the lapse rate (through the “Rossby radius of deformation”), which is also why the stratosphere only has planetary-scale circulations.

-Roy

UAH v6.1 Global Temperature Update for April, 2026: +0.39 deg. C

May 7th, 2026

This month I’m adding plots for USA48 and Canada, too.

The Version 6.1 global average lower tropospheric temperature (LT) anomaly for April, 2026 was +0.39 deg. C departure from the 1991-2020 mean, which remains statistically unchanged for 4 months now.

The Version 6.1 global area-averaged linear temperature trend (January 1979 through April 2026) remains at +0.16 deg/ C/decade (+0.22 C/decade over land, +0.13 C/decade over oceans).

The following table lists various regional Version 6.1 LT departures from the 30-year (1991-2020) average for the last 28 months (record highs are in red). Note I’ve added Canada to the table this month, by request (although WordPress won’t allow me to add September 2024 for some reason). The warmest April in Canada was in 2010 (+2.61 deg. C), while the warmest anomaly out of all months was in January 1981 (+3.75 deg. C).

YearMonGlobeNHemSHemTropicUS48ArcticAust.Can.
2024Jan+0.80+1.02+0.57+1.20-0.19+0.40+1.12+0.97
2024Feb+0.88+0.94+0.81+1.16+1.31+0.85+1.16+2.45
2024Mar+0.88+0.96+0.80+1.25+0.22+1.05+1.34+1.12
2024Apr+0.94+1.12+0.76+1.15+0.86+0.88+0.54+1.39
2024May+0.77+0.77+0.78+1.20+0.04+0.20+0.52+0.67
2024June+0.69+0.78+0.60+0.85+1.36+0.63+0.91+0.19
2024July+0.73+0.86+0.61+0.96+0.44+0.56-0.07+1.15
2024Aug+0.75+0.81+0.69+0.74+0.40+0.88+1.75+1.36
2024Sep+0.81+1.04+0.58+0.82+1.31+1.48+0.98
2024Oct+0.75+0.89+0.60+0.63+1.89+0.81+1.09+0.89
2024Nov+0.64+0.87+0.40+0.53+1.11+0.79+1.00+1.61
2024Dec+0.61+0.75+0.47+0.52+1.41+1.12+1.54+1.65
2025Jan+0.45+0.70+0.21+0.24-1.07+0.74+0.48+1.04
2025Feb+0.50+0.55+0.45+0.26+1.03+2.10+0.87-0.35
2025Mar+0.57+0.73+0.41+0.40+1.24+1.23+1.20+0.80
2025Apr+0.61+0.76+0.46+0.36+0.81+0.85+1.21+0.45
2025May+0.50+0.45+0.55+0.30+0.15+0.75+0.98+0.81
2025June+0.48+0.48+0.47+0.30+0.80+0.05+0.39-0.22
2025July+0.36+0.49+0.23+0.45+0.32+0.40+0.53-0.23
2025Aug+0.39+0.39+0.39+0.16-0.06+0.82+0.11+0.62
2025Sep+0.53+0.56+0.49+0.35+0.38+0.77+0.30+2.44
2025Oct+0.53+0.52+0.55+0.24+1.12+1.42+1.67+2.59
2025Nov+0.43+0.59+0.27+0.24+1.32+0.78+0.36+1.47
2025Dec+0.30+0.45+0.15+0.19+2.10+0.32+0.37-1.86
2026Jan+0.35+0.51+0.19+0.09+0.30+1.40+0.95+1.17
2026Feb+0.39+0.54+0.23+0.03+1.91-0.48+0.73+0.32
2026Mar+0.38+0.33+0.42+0.07+3.74-0.48+1.14-3.17
2026Apr+0.39+0.43+0.34+0.23+1.20+0.30+0.70-0.89
YearMonGlobeNHemSHemTropicUS48ArcticAust.Can.

Time Series Plots for USA48 and Canada

Starting this month I will include time series graphs for USA48 and Canada, in addition to the usual global plot. Note that for the previous month (March) the record warmth in USA48 (+3.74 deg. C) was in stark contrast to the coldest March in Canada in the 48-year satellite record (-3.17 deg. C).

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly map for April, 2026 and a more detailed analysis by John Christy, should be available within the next several days here.

The monthly anomalies for various regions for the four deep layers we monitor from satellites will be available in the next several days at the following locations:

Lower Troposphere

Mid-Troposphere

Tropopause

Lower Stratosphere

The Urban Heat Island and Urban Cool Island: A Few Examples for U.S. Major Metropolitan Areas

April 23rd, 2026

I’ve been spending recent months applying our novel methodology of quantifying the urban heat island (UHI) effect on surface air temperature, now using Landsat-based Impervious Surface (IS) cover fraction as a proxy for urbanization. This is an adaptation of our published research using population density (PD) as a proxy for urbanization, in which we showed that about 60% of the U.S. warming trend since the late 1800s in urban and suburban areas could be attributed to increases in population density. We used non-homogenized (raw) GHCN temperature data in that study; it remains unknown to what extent homogenization procedures implemented by NOAA, Berkeley BEST, et al. have removed this spurious warming effect.

One important aspect of the population density-based research was that the UHI effect on U.S. warming trends largely disappeared after about 1960. We used population density for that study because there are global gridpoint datasets of PD at approximately 10 km spatial resolution going back centuries. So, it was a data availability choice.

But the more physically direct proxy for urbanization in the context of the UHI effect is how much of the surface is covered by impervious surfaces (mainly roads, parking lots, buildings, etc). There are now Landsat-based datasets of IS coverage over the U.S. at high spatial resolution (~30 m) but only since 1985 when Landsat data quality was sufficient for such retrievals. This post addresses some results using those IS data. Here’s an example of IS data for the NYC area in 2024:

Fig. 1. Landsat-based impervious surface (IS) cover fraction for the New York City area based upon 2024 data. (Source: https://www.mrlc.gov/viewer/).

Specifically, I’m going through the top major Metropolitan Statistical Areas (MSAs) ranked by total population to quantify the average summertime (JJA) UHI impact on daily maximum temperature (Tmax) and minimum temperature (Tmin). I’m computing these effects separately for excessively hot days (~97th percentile) versus non-excessively hot days, which is yielding some interesting results. The analyses are based upon all available GHCN daily data during the summers of 1985 through 2025 within 40 to 100 km of the approximate centroidal location of the major metropolitan areas.

The Surprising (to me) Impact of Elevation, Nighttime Watering, and Daytime Ocean- and Lake-Breezes

Elevation

One thing I enjoy about analyzing large datasets is when I find something that surprises me… even when it shouldn’t have surprised me. The first effect was elevation. We all know that temperature decreases with height in the troposphere. This is why other UHI studies have required urban thermometer locations to be at elevations not very different from the rural locations. The “gold standard” requirement has been no more than 10 m or 30 m difference in elevation.

The problem with this standard is that it greatly restricts the number of available GHCN stations being analyzed. Since the UHI effect is often not much more different from station-specific biases due to other factors, one needs as many stations as possible to beat down the noise and extract the UHI signal. I have been using a rather loose 100 m to 250 m, but I gradually realized this was causing a bias in the results.

Why a bias, rather than just elevation difference-related noise? As I went down the list of the top U.S. metropolitan areas I realized that virtually all of them have something in common: they are at average elevations lower than the surrounding rural areas. This makes sense historically since major cities were originally developed next to major water bodies to factilitate transportation: the ocean, major rivers, and large lakes, which are all at lower elevations than their surroundings. This means that a portion of what we perceive to be the urban heat island effect is often due to differences in elevation. Sometimes there isn’t a major water body (e.g. Las Vegas), but for several practical reasons cities are seldom built in the mountains; they are instead in the low-lands.

So, I implemented a multiple regression procedure to separate out the impact of elevation from impervious surface cover in my calculations. This allows me to use all available stations, no matter their elevation, which helps to beat down the noise from other, non-UHI effects on measured air temperatures.

Nighttime Watering

I also found that most of the western U.S. cities have curious UHI effects, expecially during excessively hot days. Most of the U.S. West is characterized by summertime drought as a persistent feature of the weather there. I am now pretty sure that in many of these cases the curious results are due to nighttime watering of vegetation, which increases during excessively hot days.

Ocean and Lake Breezes

Several major cities experience significant daytime ocean breezes (e.g. Los Angeles) or lake breezes (e.g. Chicago). This acts against the urban heat island warming. As we will see, in the case of Los Angeles the cooling sea breeze almost totally dominates over any UHI warming.

Some Major Metropolitan Area Results

My methodology uses all available GHCN station pairs available on each summer day for the years 1985 through 2025. For each station pair, I compute the temperature differences (Tmax and Tmin, separately), as well as the differences in 1×1 km average impervious surface coverage centered on those station locations (I also looked at 2×2, 5×5, and 10×10 km results). This is done for all station pairs within 40 km to 100 km (city-dependent) of the approximate centroid of the MSA being considered (in the case of NYC, I chose Central Park). I then group all of these station-paired data into 7 classes of 2-station average IS coverage, which allows me to examine any nonlinearities in the UHI-vs-IS relationships. For each class, I regress the temperature differences against the IS differences to get an average dT/dIS (regression slope) value. These 7 slopes are then integrated across IS to arrive at curves of UHI temperature impact versus IS.

An important feature of the method is that I don’t have to categorize a station as “rural” or “urban”, as most other UHI studies have done. As seen in Fig. 1 (above) there is a continuum of urbanization as quantified by IS coverage from 0% (wilderness) to 100% (complete coverage by roads, parking lots, buildings, etc.)

New York City-Newark-Jersey City MSA

The New York-Newark-Jersey City MSA is the most populous in the U.S., with 6% of the U.S. population residing there. Fig. 2 shows the resulting average UHI effects across this MSA on Tmax and Tmin, and for excessively-hot days vs. not excessively hot days.

Fig. 2. Calculated UHI air temperature dependence on 1×1 km impervious surface coverage for the New York City-Newark-Jersey City metropolitan statistical area (MSA) based upon all GHCN station pairs within 60 km of Central Park. The regression-derived temperature lapse rate adjustments used to correct for station elevation differences are listed, as are the 7-class average correlaion coefficients and regression t-statistics. There were a total of 943,907 daily station pairs analyzed for the non-excessive heat days, and 34,469 daily station pairs for the excessive heat days.

(It is important to point out that these results should not be interpreted as necessarily representing inner-city NYC vs. surrounding rural areas. They are the average results for all available station pairs found within 60 km of Central Park, thus are for stations generally not in downtown NYC. Instead, they provide an average picture of how urbanization affects air temperatures, on average, across the entire metropolitan region.)

The first thing we see in Fig. 2 is that the UHI warming effects are much larger on Tmin than on Tmax, which many others have found.

Secondly, we see that excessively hot days have a somewhat stronger UHI warming effect at the most urbanized locations (largest IS values). But for Tmax on non-excessively hot days there is evidence of the “urban cool island” effect, which others have studied and published results on. This is a natural consequence of impervious surfaces conducting heat down into the sub-surface compared to natural land (and vegetation) surfaces, which causes a time lag in the diurnal temperature response.

Los Angeles-Long Beach-Anaheim MSA

We need only go to the 2nd most populous MSA (Los Angeles) to see that the temperature changes in urban areas are not always due to warming from urbanization. This is shown in Fig. 3.

Fig. 3. As in Fig. 2, but for all GHCN station pairs within 40 km of downtown Los Angeles.

In this case we see a huge daytime cooling effect on Tmax in urban areas, which I assume is due to the persistent daytime sea breeze in the LA basin during summer. The effect is also seen to a lesser extent in Tmin for excessively hot days. I don’t know whether this is due to stronger and more persistent sea breezes on excessively hot days, or due to more nighttime watering of vegetation during those days, or some combination of both.

At this point you might be wondering, how can the hottest days have cooler urban temperatures? This is where I have to explain how I classify “excessively hot days”. Because there are so many GHCN stations within 40 km of downtown LA, there are days when some stations exceed their 97th percentile hottest temperature and other stations do not. So how do we decide which days are “excessively hot” for the metropolitan region as a whole? I calculate for each date in the summers of 1985-2025 how many stations exceed their 97th percentile threshold. I then compute the average daily temperature across those stations. For LA, it turns out at least 12 stations exceeding their 97th percentile temperature threshold are required in order for approximately 3% of the dates to be categorized as “excessively hot”, thus providing a 97th percentile threshold for the whole MSA region. I then use that 12-station minimum, applied to Tmax (not Tmin), to decide which dates are “excessively hot”.

I am finding that most of the major cities in the western U.S. have reduced UHI heating (and like LA, even cooling) during daytime and nighttime on excessively hot days. In many cases I believe this is due to watering of vegetation, which for every city I have checked, Grok says that city has more water usage during the nighttime hours on excessively hot days. For example, here are the results for Portland-Vancouver-Hillsboro, the 24th most populous MSA in the U.S; note how the fairly strong UHI warming effect on Tmax and Tmin is reduced on the hottest days, especially at night when most watering occurs:

Fig. 4. As in Fig. 2, but for all station pairs within 60 km of downtown Portland, Oregon.

For the bottom curve in Fig. 4 (nighttime Portland temperatures on excessively hot dates), one might even imagine the maximum cooling effect from more watering is in the suburbs (IS less than 20-30%), but then switching to warming in the most urban areas (IS over 50%), presumably due to differences in areal coverage by vegetation being watered.

I am through about two dozen of the 50 most populous metropolitan areas I want to include results for as part of a paper we are preparing for submission to the journal Urban Climate. Since those 50 MSAs include over 50% of the U.S. population, chances are good your city or town will also be included.

UAH v6.1 Global Temperature Update for March, 2026: +0.38 deg. C

April 3rd, 2026

March 2026 was record-warm for the Lower 48.

The Version 6.1 global average lower tropospheric temperature (LT) anomaly for March, 2026 was +0.38 deg. C departure from the 1991-2020 mean, statistically unchanged from the February, 2026 value of +0.39 deg. C.

The Version 6.1 global area-averaged linear temperature trend (January 1979 through March 2026) remains at +0.16 deg/ C/decade (+0.22 C/decade over land, +0.13 C/decade over oceans).

The following table lists various regional Version 6.1 LT departures from the 30-year (1991-2020) average for the last 27 months (record highs are in red).

YEARMOGLOBENHEMSHEMTROPICUSA48ARCTICAUST
2024Jan+0.80+1.02+0.57+1.20-0.19+0.40+1.12
2024Feb+0.88+0.94+0.81+1.16+1.31+0.85+1.16
2024Mar+0.88+0.96+0.80+1.25+0.22+1.05+1.34
2024Apr+0.94+1.12+0.76+1.15+0.86+0.88+0.54
2024May+0.77+0.77+0.78+1.20+0.04+0.20+0.52
2024June+0.69+0.78+0.60+0.85+1.36+0.63+0.91
2024July+0.73+0.86+0.61+0.96+0.44+0.56-0.07
2024Aug+0.75+0.81+0.69+0.74+0.40+0.88+1.75
2024Sep+0.81+1.04+0.58+0.82+1.31+1.48+0.98
2024Oct+0.75+0.89+0.60+0.63+1.89+0.81+1.09
2024Nov+0.64+0.87+0.40+0.53+1.11+0.79+1.00
2024Dec+0.61+0.75+0.47+0.52+1.41+1.12+1.54
2025Jan+0.45+0.70+0.21+0.24-1.07+0.74+0.48
2025Feb+0.50+0.55+0.45+0.26+1.03+2.10+0.87
2025Mar+0.57+0.73+0.41+0.40+1.24+1.23+1.20
2025Apr+0.61+0.76+0.46+0.36+0.81+0.85+1.21
2025May+0.50+0.45+0.55+0.30+0.15+0.75+0.98
2025June+0.48+0.48+0.47+0.30+0.80+0.05+0.39
2025July+0.36+0.49+0.23+0.45+0.32+0.40+0.53
2025Aug+0.39+0.39+0.39+0.16-0.06+0.82+0.11
2025Sep+0.53+0.56+0.49+0.35+0.38+0.77+0.30
2025Oct+0.53+0.52+0.55+0.24+1.12+1.42+1.67
2025Nov+0.43+0.59+0.27+0.24+1.32+0.78+0.36
2025Dec+0.30+0.45+0.15+0.19+2.10+0.32+0.37
2026Jan+0.35+0.51+0.19+0.09+0.30+1.40+0.95
2026Feb+0.39+0.54+0.23+0.03+1.91-0.48+0.73
2026Mar+0.38+0.33+0.42+0.07+3.74-0.48+1.14
YEARMOGLOBENHEMSHEMTROPICUSA48ARCTICAUST

Record Warmth in the Contiguous U.S. (Lower 48)

For the Lower 48, the March 2026 temperature anomaly was easily the record warmest of all months in the 47+ year satellite record: +3.7 deg. C above average for all Marches. Second place goes to March 2012, with +2.2 deg. C above the mean, while 3rd place goes to December 2025 at +2.1 deg. C.

Interestingly, December through April are periods of large variability for the Lower 48. All 6 of the warmest months (in terms of departures from normal) since 1979 occurred in December through April. Furthermore, all 8 of the coldest months occurred in December through April.

————————-

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly map for March, 2026 and a more detailed analysis by John Christy, should be available within the next several days here.

The monthly anomalies for various regions for the four deep layers we monitor from satellites will be available in the next several days at the following locations:

Lower Troposphere

Mid-Troposphere

Tropopause

Lower Stratosphere

March 2026 Satellite Temperatures: Record Warmth in U.S., But Uneventful for the Northern Hemisphere

April 3rd, 2026

I’m told by John Christy that there has been considerable discussion amongst the state climatologists about March temperatures in the U.S. setting a new record. If true, the media will no doubt lecture us on how this is evidence for global warming. (Why do we never hear about cool months being evidence against global warming?)

It is human nature to think the weather we experience has some sort of global significance. But look at NOAA’s best estimate of March 2026 temperature departures from “normal” (1991-2020 average) over North America (below). Yeah, the U.S. was unusually warm. But what about all the unusual chill over the northern parts of North America? Alaska and most of Canada were below normal.

Fig. 1. NOAA Climate Data Assimilation System (CDAS) surface air temperature departures from the 1991-2020 average for March, 2026 (courtesy of WeatherBell.com).

UAH Satellite Lower Tropospheric Temperatures for March 2026

As part of our monthly global temperature updates (posted separately) here are the March temperature departures from normal for the lower troposphere, 1979 through 2026 in the Lower 48 (top panel of Fig. 2). Last month was clearly the warmest in the 48-year satellite temperature record.

Fig. 2. March satellite-based lower tropospheric temperature departures from the 1991-2020 average during the period 1979-2026, for (top) the contiguous 48 U.S. states, and (bottom) Northern Hemisphere land areas. All quantities are area-weighted averages.

But when we examine the bottom panel in Fig. 2 we see that, averaged over all land areas of the Northern Hemisphere (including Canada and Alaska), March 2026 was uneventful, and was even cooler than 2024 and 2025. In fact, 2026 was right on the long-term trend line.

The message here is that the unusual (and likely record) warmth of March 2026 in the U.S. was largely due to normal month-to-month weather variations, while the large-scale climate signal shows March was a continuation of the slow (and largely benign, and possibly even beneficial) warming trend we have been experiencing in recent decades.

UAH v6.1 Global Temperature Update for February, 2026: +0.39 deg. C

March 3rd, 2026

The Version 6.1 global average lower tropospheric temperature (LT) anomaly for February, 2026 was +0.39 deg. C departure from the 1991-2020 mean, up a little from the January, 2026 value of +0.35 deg. C.

The Version 6.1 global area-averaged linear temperature trend (January 1979 through February 2026) remains at +0.16 deg/ C/decade (+0.22 C/decade over land, +0.13 C/decade over oceans).

The following table lists various regional Version 6.1 LT departures from the 30-year (1991-2020) average for the last 26 months (record highs are in red).

YEARMOGLOBENHEM.SHEM.TROPICUSA48ARCTICAUST
2024Jan+0.80+1.02+0.57+1.20-0.19+0.40+1.12
2024Feb+0.88+0.94+0.81+1.16+1.31+0.85+1.16
2024Mar+0.88+0.96+0.80+1.25+0.22+1.05+1.34
2024Apr+0.94+1.12+0.76+1.15+0.86+0.88+0.54
2024May+0.77+0.77+0.78+1.20+0.04+0.20+0.52
2024June+0.69+0.78+0.60+0.85+1.36+0.63+0.91
2024July+0.73+0.86+0.61+0.96+0.44+0.56-0.07
2024Aug+0.75+0.81+0.69+0.74+0.40+0.88+1.75
2024Sep+0.81+1.04+0.58+0.82+1.31+1.48+0.98
2024Oct+0.75+0.89+0.60+0.63+1.89+0.81+1.09
2024Nov+0.64+0.87+0.40+0.53+1.11+0.79+1.00
2024Dec+0.61+0.75+0.47+0.52+1.41+1.12+1.54
2025Jan+0.45+0.70+0.21+0.24-1.07+0.74+0.48
2025Feb+0.50+0.55+0.45+0.26+1.03+2.10+0.87
2025Mar+0.57+0.73+0.41+0.40+1.24+1.23+1.20
2025Apr+0.61+0.76+0.46+0.36+0.81+0.85+1.21
2025May+0.50+0.45+0.55+0.30+0.15+0.75+0.98
2025June+0.48+0.48+0.47+0.30+0.80+0.05+0.39
2025July+0.36+0.49+0.23+0.45+0.32+0.40+0.53
2025Aug+0.39+0.39+0.39+0.16-0.06+0.82+0.11
2025Sep+0.53+0.56+0.49+0.35+0.38+0.77+0.30
2025Oct+0.53+0.52+0.55+0.24+1.12+1.42+1.67
2025Nov+0.43+0.59+0.27+0.24+1.32+0.78+0.36
2025Dec+0.30+0.45+0.15+0.19+2.10+0.32+0.37
2026Jan+0.35+0.51+0.19+0.09+0.30+1.40+0.95
2026Feb+0.39+0.54+0.23+0.03+1.91-0.48+0.73

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly map for February, 2026 and a more detailed analysis by John Christy, should be available within the next several days here.

The monthly anomalies for various regions for the four deep layers we monitor from satellites will be available in the next several days at the following locations:

Lower Troposphere

Mid-Troposphere

Tropopause

Lower Stratospher

UAH v6.1 Global Temperature Update for January, 2026: +0.35 deg. C

February 3rd, 2026

The Version 6.1 global average lower tropospheric temperature (LT) anomaly for January, 2026 was +0.35 deg. C departure from the 1991-2020 mean, up a little from the December, 2025 value of +0.30 deg. C.

The Version 6.1 global area-averaged linear temperature trend (January 1979 through January 2026) remains at +0.16 deg/ C/decade (+0.22 C/decade over land, +0.13 C/decade over oceans).

The following table lists various regional Version 6.1 LT departures from the 30-year (1991-2020) average for the last 25 months (record highs are in red).

YEARMOGLOBENHEM.SHEM.TROPICUSA48ARCTICAUST
2024Jan+0.80+1.02+0.57+1.20-0.19+0.40+1.12
2024Feb+0.88+0.94+0.81+1.16+1.31+0.85+1.16
2024Mar+0.88+0.96+0.80+1.25+0.22+1.05+1.34
2024Apr+0.94+1.12+0.76+1.15+0.86+0.88+0.54
2024May+0.77+0.77+0.78+1.20+0.04+0.20+0.52
2024June+0.69+0.78+0.60+0.85+1.36+0.63+0.91
2024July+0.73+0.86+0.61+0.96+0.44+0.56-0.07
2024Aug+0.75+0.81+0.69+0.74+0.40+0.88+1.75
2024Sep+0.81+1.04+0.58+0.82+1.31+1.48+0.98
2024Oct+0.75+0.89+0.60+0.63+1.89+0.81+1.09
2024Nov+0.64+0.87+0.40+0.53+1.11+0.79+1.00
2024Dec+0.61+0.75+0.47+0.52+1.41+1.12+1.54
2025Jan+0.45+0.70+0.21+0.24-1.07+0.74+0.48
2025Feb+0.50+0.55+0.45+0.26+1.03+2.10+0.87
2025Mar+0.57+0.73+0.41+0.40+1.24+1.23+1.20
2025Apr+0.61+0.76+0.46+0.36+0.81+0.85+1.21
2025May+0.50+0.45+0.55+0.30+0.15+0.75+0.98
2025June+0.48+0.48+0.47+0.30+0.80+0.05+0.39
2025July+0.36+0.49+0.23+0.45+0.32+0.40+0.53
2025Aug+0.39+0.39+0.39+0.16-0.06+0.82+0.11
2025Sep+0.53+0.56+0.49+0.35+0.38+0.77+0.30
2025Oct+0.53+0.52+0.55+0.24+1.12+1.42+1.67
2025Nov+0.43+0.59+0.27+0.24+1.32+0.78+0.36
2025Dec+0.30+0.45+0.15+0.19+2.10+0.32+0.37
2026Jan+0.35+0.52+0.19+0.09+0.30+1.40+0.95

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly map for January, 2026 and a more detailed analysis by John Christy, should be available within the next several days here.

The monthly anomalies for various regions for the four deep layers we monitor from satellites will be available in the next several days at the following locations:

Lower Troposphere

Mid-Troposphere

Tropopause

Lower Stratosphere

March, 2000: “Snowfalls are now just a thing of the past”

January 25th, 2026

…posted without comment:

The Independent, March 2000: “Snowfalls are now just a thing of the past

In Unrelated News: I’m Back Into Astrophotography

January 19th, 2026

I got sucked back in when I learned about the ZWO ASIAir controller that “simplifies” some of the tasks that kept me from improving my telescope skills, so the telescope just sat for several years.

But the learning curve was still pretty steep. I now have an autofocuser, a guide scope and camera, and it took me forever to get the autoguiding to work (which I had to make myself understand and use because my new telescope mount has a periodic error in the gears that makes little star streaks back and forth).

Anyway, after I practiced enough in my suburban, moderately light-polluted backyard with some pretty good results, last night I took the rig out to a dark sky location on Alabama’s Lake Guntersville. This is the result: 4.25 hours of 5-minute images processed in Pixinsight and stretched and color-enhanced in Adobe Camera Raw. I was blown away… click on it to do some pixel-peeping.

Tropical Tropospheric Temperature Trends, 1979-2025: The Epic Climate Model Failure Continues

January 13th, 2026

As a follow-on to my recent post regarding global surface air temperature trends (1979-2025) and how they compare to climate models, this is an update on a similar comparison for tropical tropospheric temperature trends, courtesy of tabulations made by John Christy. It also represents an update to my popular “epic fail” blog post from 2013.

As most of you know, climate models suggest that the strongest warming response the climate system has to increasing anthropogenic greenhouse gas (GHG) emissions (mainly CO2 from fossil fuel burning) is in the tropical upper troposphere. This produces the model-anticipated “tropical hotspot”.

While the deep oceans represent the largest reservoir of heat energy storage in the climate system during warming, that signal is exceedingly small (hundredths of a degree C per decade) and so its uncertainty is rather large from an observational standpoint. In contrast, the tropical upper troposphere has the largest temperature response in climate models (up to 0.5 deg. C per decade).

This shown in the following plot of the decadal temperature trends from 39 climate models (red bars) compared to observations gathered from radiosondes (weather balloons); satellites; and global data reanalyses (which use all kinds of available meteorological data).

The sonde trend bar in the above plot (green) is the average of 3 datasets (radiosonde coverage of the tropics is very sparse); the reanalysis trend (black) is from 2 datasets, and the satellite trend (blue) is the average of 3 datasets. Out of all types of observational data, only the satellites provide complete coverage of the tropics.

Amazingly, all 39 climate models exhibit larger warming trends than all three classes of observational data.

Time Series, 1979-2025

If we compare the average model warming to the observations in individual years, we get the following time series (note that complete reanalysis data for 2025 are not yet available); color coding remains the same as in the previous plot:

The unusually warm year of 2024 really stands out (likely due to a decrease in cloud cover letting in more sunlight), but in 2025 the satellites and radiosondes show a “return to trend”. Of course, what happens in the future is anyone’s guess.

“So What? No One Lives In the Tropical Troposphere”

What is going on that might explain these discrepancies, not only between the models and the observations, but even between the various models themselves? And why should we care, since no one lives up in the tropical troposphere, anyway?

Well, the same argument can be made about the deep oceans (no one lives there), yet they are pointed to by many climate researchers as the most important “barometer” of the positive global energy imbalance of the climate system caused by increasing GHGs (and maybe by natural processes… who knows?).

The excessive warming of the tropical troposphere is no doubt related to inadequacies in how the models handle convective overturning in the tropics, that is, organized thunderstorm activity that transports heat from the surface upward. That “deep moist convection” redistributes not only heat energy, but clouds and water vapor, both of which have profound impacts on tropical tropospheric temperature. While moistening of the lowest layer of the troposphere in response to warming no doubt contributes to positive water vapor feedback, precipitation microphysics governs how much water vapor resides in the rest of the troposphere, and as we demonstrated almost 30 years ago, that leads to large uncertainties in total water vapor feedback.

My personal opinion has always been that the lack of tropical warming is because positive water vapor feedback, the primary positive feedback that amplifies warming in climate models, is too strong. Climate models actually support this interpretation because it has long been known that those models with the strongest “hotspot” in the upper troposphere tend to have the largest positive water vapor feedback.

Will Climate Models Ever Be “Fixed”?

I find it ironic that climate models are claimed to be based upon fundamental “physical principles”. If that were true, then all models would have the same climate sensitivity to increasing GHGs.

But they don’t.

Climate models range over a factor of three in climate sensitivity, a disparity that has remained for over 30 years of the climate modeling enterprise. And the main reason for that disparity is inter-model differences in the moist convective processes (clouds and water vapor) which cause positive feedbacks in the models.

Maybe if the modelers figured out why their handling of moist convection is flawed, models would then produce warming more in line with observations, and more in line with each other.

Much of global warming alarmism arises from scientific publications biased toward (1) the models that produce the most warming, and (2) the excessive GHG increases (“SSP scenarios“) they assume for the most dire climate change projections. Those scenarios are now known to be excessive compared to observed rates of global GHG emissions (and to the reviewer of our DOE report who said this conclusion was in error because I didn’t account for land use changes, no, I removed land use changes from the SSP scenarios… it was an apples-to-apples comparison).

Finally, I don’t want to make it sound like I’m against climate modeling. I am definitely not. I just think the models, as a tool for energy policy guidance, have been misused.