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).

YEAR	MO	GLOBE	NHEM	SHEM	TROPIC	USA48	ARCTIC	AUST
2024	Jan	+0.80	+1.02	+0.57	+1.20	-0.19	+0.40	+1.12
2024	Feb	+0.88	+0.94	+0.81	+1.16	+1.31	+0.85	+1.16
2024	Mar	+0.88	+0.96	+0.80	+1.25	+0.22	+1.05	+1.34
2024	Apr	+0.94	+1.12	+0.76	+1.15	+0.86	+0.88	+0.54
2024	May	+0.77	+0.77	+0.78	+1.20	+0.04	+0.20	+0.52
2024	June	+0.69	+0.78	+0.60	+0.85	+1.36	+0.63	+0.91
2024	July	+0.73	+0.86	+0.61	+0.96	+0.44	+0.56	-0.07
2024	Aug	+0.75	+0.81	+0.69	+0.74	+0.40	+0.88	+1.75
2024	Sep	+0.81	+1.04	+0.58	+0.82	+1.31	+1.48	+0.98
2024	Oct	+0.75	+0.89	+0.60	+0.63	+1.89	+0.81	+1.09
2024	Nov	+0.64	+0.87	+0.40	+0.53	+1.11	+0.79	+1.00
2024	Dec	+0.61	+0.75	+0.47	+0.52	+1.41	+1.12	+1.54
2025	Jan	+0.45	+0.70	+0.21	+0.24	-1.07	+0.74	+0.48
2025	Feb	+0.50	+0.55	+0.45	+0.26	+1.03	+2.10	+0.87
2025	Mar	+0.57	+0.73	+0.41	+0.40	+1.24	+1.23	+1.20
2025	Apr	+0.61	+0.76	+0.46	+0.36	+0.81	+0.85	+1.21
2025	May	+0.50	+0.45	+0.55	+0.30	+0.15	+0.75	+0.98
2025	June	+0.48	+0.48	+0.47	+0.30	+0.80	+0.05	+0.39
2025	July	+0.36	+0.49	+0.23	+0.45	+0.32	+0.40	+0.53
2025	Aug	+0.39	+0.39	+0.39	+0.16	-0.06	+0.82	+0.11
2025	Sep	+0.53	+0.56	+0.49	+0.35	+0.38	+0.77	+0.30
2025	Oct	+0.53	+0.52	+0.55	+0.24	+1.12	+1.42	+1.67
2025	Nov	+0.43	+0.59	+0.27	+0.24	+1.32	+0.78	+0.36
2025	Dec	+0.30	+0.45	+0.15	+0.19	+2.10	+0.32	+0.37
2026	Jan	+0.35	+0.51	+0.19	+0.09	+0.30	+1.40	+0.95
2026	Feb	+0.39	+0.54	+0.23	+0.03	+1.91	-0.48	+0.73
2026	Mar	+0.38	+0.33	+0.42	+0.07	+3.74	-0.48	+1.14
YEAR	MO	GLOBE	NHEM	SHEM	TROPIC	USA48	ARCTIC	AUST

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

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.

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.

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.

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).

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2024	Jan	+0.80	+1.02	+0.57	+1.20	-0.19	+0.40	+1.12
2024	Feb	+0.88	+0.94	+0.81	+1.16	+1.31	+0.85	+1.16
2024	Mar	+0.88	+0.96	+0.80	+1.25	+0.22	+1.05	+1.34
2024	Apr	+0.94	+1.12	+0.76	+1.15	+0.86	+0.88	+0.54
2024	May	+0.77	+0.77	+0.78	+1.20	+0.04	+0.20	+0.52
2024	June	+0.69	+0.78	+0.60	+0.85	+1.36	+0.63	+0.91
2024	July	+0.73	+0.86	+0.61	+0.96	+0.44	+0.56	-0.07
2024	Aug	+0.75	+0.81	+0.69	+0.74	+0.40	+0.88	+1.75
2024	Sep	+0.81	+1.04	+0.58	+0.82	+1.31	+1.48	+0.98
2024	Oct	+0.75	+0.89	+0.60	+0.63	+1.89	+0.81	+1.09
2024	Nov	+0.64	+0.87	+0.40	+0.53	+1.11	+0.79	+1.00
2024	Dec	+0.61	+0.75	+0.47	+0.52	+1.41	+1.12	+1.54
2025	Jan	+0.45	+0.70	+0.21	+0.24	-1.07	+0.74	+0.48
2025	Feb	+0.50	+0.55	+0.45	+0.26	+1.03	+2.10	+0.87
2025	Mar	+0.57	+0.73	+0.41	+0.40	+1.24	+1.23	+1.20
2025	Apr	+0.61	+0.76	+0.46	+0.36	+0.81	+0.85	+1.21
2025	May	+0.50	+0.45	+0.55	+0.30	+0.15	+0.75	+0.98
2025	June	+0.48	+0.48	+0.47	+0.30	+0.80	+0.05	+0.39
2025	July	+0.36	+0.49	+0.23	+0.45	+0.32	+0.40	+0.53
2025	Aug	+0.39	+0.39	+0.39	+0.16	-0.06	+0.82	+0.11
2025	Sep	+0.53	+0.56	+0.49	+0.35	+0.38	+0.77	+0.30
2025	Oct	+0.53	+0.52	+0.55	+0.24	+1.12	+1.42	+1.67
2025	Nov	+0.43	+0.59	+0.27	+0.24	+1.32	+0.78	+0.36
2025	Dec	+0.30	+0.45	+0.15	+0.19	+2.10	+0.32	+0.37
2026	Jan	+0.35	+0.51	+0.19	+0.09	+0.30	+1.40	+0.95
2026	Feb	+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

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).

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2024	Jan	+0.80	+1.02	+0.57	+1.20	-0.19	+0.40	+1.12
2024	Feb	+0.88	+0.94	+0.81	+1.16	+1.31	+0.85	+1.16
2024	Mar	+0.88	+0.96	+0.80	+1.25	+0.22	+1.05	+1.34
2024	Apr	+0.94	+1.12	+0.76	+1.15	+0.86	+0.88	+0.54
2024	May	+0.77	+0.77	+0.78	+1.20	+0.04	+0.20	+0.52
2024	June	+0.69	+0.78	+0.60	+0.85	+1.36	+0.63	+0.91
2024	July	+0.73	+0.86	+0.61	+0.96	+0.44	+0.56	-0.07
2024	Aug	+0.75	+0.81	+0.69	+0.74	+0.40	+0.88	+1.75
2024	Sep	+0.81	+1.04	+0.58	+0.82	+1.31	+1.48	+0.98
2024	Oct	+0.75	+0.89	+0.60	+0.63	+1.89	+0.81	+1.09
2024	Nov	+0.64	+0.87	+0.40	+0.53	+1.11	+0.79	+1.00
2024	Dec	+0.61	+0.75	+0.47	+0.52	+1.41	+1.12	+1.54
2025	Jan	+0.45	+0.70	+0.21	+0.24	-1.07	+0.74	+0.48
2025	Feb	+0.50	+0.55	+0.45	+0.26	+1.03	+2.10	+0.87
2025	Mar	+0.57	+0.73	+0.41	+0.40	+1.24	+1.23	+1.20
2025	Apr	+0.61	+0.76	+0.46	+0.36	+0.81	+0.85	+1.21
2025	May	+0.50	+0.45	+0.55	+0.30	+0.15	+0.75	+0.98
2025	June	+0.48	+0.48	+0.47	+0.30	+0.80	+0.05	+0.39
2025	July	+0.36	+0.49	+0.23	+0.45	+0.32	+0.40	+0.53
2025	Aug	+0.39	+0.39	+0.39	+0.16	-0.06	+0.82	+0.11
2025	Sep	+0.53	+0.56	+0.49	+0.35	+0.38	+0.77	+0.30
2025	Oct	+0.53	+0.52	+0.55	+0.24	+1.12	+1.42	+1.67
2025	Nov	+0.43	+0.59	+0.27	+0.24	+1.32	+0.78	+0.36
2025	Dec	+0.30	+0.45	+0.15	+0.19	+2.10	+0.32	+0.37
2026	Jan	+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

…posted without comment:

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

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.

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.

This is just a short update regarding how global surface air temperature (Tsfc) trends are tracking 34 CMIP6 climate models through 2025. The following plot shows the Tsfc trends, 1979-2025, ranked from the warmest to the coolest.

“Observations” is an average of 4 datasets: HadCRUT5, NOAAGlobalTemp Version 6 (now featuring AI, of course), ERA5 (a reanalysis dataset), and the Berkeley 1×1 deg. dataset, which produces a trend identical to HadCRUT5 (+0.205 C/decade).

I consider reanalyses to be in the class of “observations” since they are constrained to match, in some average sense, the measurements made from the surface, weather balloons, global commercial aircraft, satellites, and the kitchen sink.

The observations moved up one place in the rankings since the last time I made one of these plots, mainly due to an anomalously warm 2024.

2025 was the 2nd warmest year (a distant 2nd behind 2024) in the 47-year satellite record

The Version 6.1 global average lower tropospheric temperature (LT) anomaly for December, 2025 was +0.30 deg. C departure from the 1991-2020 mean, down from the November, 2025 value of +0.43 deg. C. (In the following plot note that the 13-month centered-average trace [red curve] has now been updated after several months of not being updated).

The Version 6.1 global area-averaged linear temperature trend (January 1979 through December 2025) remains at +0.16 deg/ C/decade (+0.22 C/decade over land, +0.13 C/decade over oceans).

2025 Ended the Year as a Distant 2nd Warmest Behind 2024

The following plot shows the ranking of the 47 years in the UAH satellite temperature record, from the warmest year (2024) to the coolest (1985). As can be seen, 2024 really was an anomalously warm year, more than can be attributed to El Nino alone.

The next plot shows how our UAH LT yearly anomalies compare to those posted on the WeatherBell website (subscription required) for the surface air temperatures from NOAA’s Climate Data Assimilation System (CDAS). There is pretty good correspondence between the two datasets, with LT having warm outliers during major El Ninos (especially 1987, 1998, 2010, and 2024). This behavior is due to extra heating of the troposphere (which LT measures) during El Nino by enhanced deep moist convection in the tropics when the tropical Pacific Ocean surface warms from reduced upwelling of cold water from below, an effect exaggerated by the several-month lag of tropospheric warming behind surface warming during El Nino:

The following table lists various regional Version 6.1 LT departures from the 30-year (1991-2020) average for the last 24 months (record highs are in red).

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2024	Jan	+0.80	+1.02	+0.57	+1.20	-0.19	+0.40	+1.12
2024	Feb	+0.88	+0.94	+0.81	+1.16	+1.31	+0.85	+1.16
2024	Mar	+0.88	+0.96	+0.80	+1.25	+0.22	+1.05	+1.34
2024	Apr	+0.94	+1.12	+0.76	+1.15	+0.86	+0.88	+0.54
2024	May	+0.77	+0.77	+0.78	+1.20	+0.04	+0.20	+0.52
2024	June	+0.69	+0.78	+0.60	+0.85	+1.36	+0.63	+0.91
2024	July	+0.73	+0.86	+0.61	+0.96	+0.44	+0.56	-0.07
2024	Aug	+0.75	+0.81	+0.69	+0.74	+0.40	+0.88	+1.75
2024	Sep	+0.81	+1.04	+0.58	+0.82	+1.31	+1.48	+0.98
2024	Oct	+0.75	+0.89	+0.60	+0.63	+1.89	+0.81	+1.09
2024	Nov	+0.64	+0.87	+0.40	+0.53	+1.11	+0.79	+1.00
2024	Dec	+0.61	+0.75	+0.47	+0.52	+1.41	+1.12	+1.54
2025	Jan	+0.45	+0.70	+0.21	+0.24	-1.07	+0.74	+0.48
2025	Feb	+0.50	+0.55	+0.45	+0.26	+1.03	+2.10	+0.87
2025	Mar	+0.57	+0.73	+0.41	+0.40	+1.24	+1.23	+1.20
2025	Apr	+0.61	+0.76	+0.46	+0.36	+0.81	+0.85	+1.21
2025	May	+0.50	+0.45	+0.55	+0.30	+0.15	+0.75	+0.98
2025	June	+0.48	+0.48	+0.47	+0.30	+0.80	+0.05	+0.39
2025	July	+0.36	+0.49	+0.23	+0.45	+0.32	+0.40	+0.53
2025	Aug	+0.39	+0.39	+0.39	+0.16	-0.06	+0.82	+0.11
2025	Sep	+0.53	+0.56	+0.49	+0.35	+0.38	+0.77	+0.30
2025	Oct	+0.53	+0.52	+0.55	+0.24	+1.12	+1.42	+1.67
2025	Nov	+0.43	+0.59	+0.27	+0.24	+1.32	+0.78	+0.36
2025	Dec	+0.30	+0.45	+0.15	+0.19	+2.10	+0.32	+0.38

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly map for December, 2025 as well as a global map of the 2025 anomalies 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

UPDATE: Criticism of the Following Post from The Daily Sceptic

Due to the holidays, I just now saw the post at The Daily Sceptic criticizing my support of the UK Met Office methodology for combining temperature monitoring stations’ data. In retrospect, I should have made it clear that my comments that follow only address the UKMO method for combining stations of various lengths of record, how they “replace” closed stations with surrounding stations, and the use of urbanization-influenced stations. I do not mean to suggest that there are no other time-dependent changes (say, in instrumentation types) that could cause spurious warming in the record. Nor do I claim there are no time-dependent increases in urban heat island spurious biases in the record. I only address (1) the fact that a closed station being replaced with a surrounding station does not necessarily cause problems with long-term monitoring; (2) that the UKMO creation of a fine (1×1 km) grid of UK temperatures from extremely sparse data does not seem to cause spurious trends in the record, and (3) just because a station is poorly-sited doesn’t mean it can’t be used for long-term monitoring. I agree that there are other issues I did not address which impact long-term trends in the UK (or elsewhere). In other words, I was not trying to be comprehensive in my analysis.

SUMMARY

1. My analysis of UK daily high (Tmax) and low (Tmin) temperatures during 1960-2025 using a station relative bias removal method produces UK-average summer temperature variations essentially identical to the very different UKMO methodology.
2. In both my and the UKMO analysis, 1976 (not 2025) was the hottest summer in daily high temperatures (Tmax), with 2025 taking 3rd or 4th place; the “record” hot year of 2025 was due to nightly low temperatures (Tmin) being anomalously warm.
3. The average of the three hottest daytime temperatures in each summer month put the summer of 2025 in 4th place since 1960, behind 1976, 1995, and 2022 (which were essentially identical).

There has been criticism of the UK Met Office’s methodology for monitoring long-term changes in UK-average temperatures, starting with Tallbloke’s (Ray Sanders’) blog post on 31 October 2024. A major criticism that Tallbloke has is the fact that most UK stations do not meet the World Meteorological Organization (WMO) criteria for a good climate monitoring station. The UKMO doesn’t actually use the WMO quality classification system, but their own 4-tiered system. Another criticism is that many UK stations have closed in recent years, and so those stations are, in effect, estimated (“fabricated”?) from surrounding stations.

No Station is Perfect

On the subject of which WMO (or UKMO) class of station is suitable for long-term climate monitoring, I think it’s important to note that a station could be placed in a non-natural, anomalously warm urban environment, but as long as that environment stays the same over time, it can probably still be used for climate change monitoring.

For example, the urban heat island (UHI) effect of London was described over 200 years ago by Luke Howard. Even if London is significantly warmer than the surrounding rural areas, it might be that there has been little additional UHI warming since then, and so a downtown London weather station might be adequate for monitoring large-scale climate change, since I have no reason to believe that (say) 1 deg. C of large-scale warming will lead to city warming substantially different from 1 deg. C.

On the additional subject of replacing a closed station with estimates from surrounding stations (which NOAA also does because so many of their UNHCN stations in the U.S. have closed, a process that has also been criticized), I believe it is a little disingenuous to claim those data are “fabricated”. Rather than continuing the closed station record with estimates from surrounding stations, one could just use the surrounding stations, which is the same thing.

The UKMO’s Fancy High-Resolution Mapping of UK Temperatures

The Met Office divides the UK land mass into tiny (1×1 km) grid cells, and the temperature in each one is estimated from the nearest station(s) using average, regression-based adjustments for elevation, latitude, longitude, terrain shape, coastal proximity, and land use variations. The result is a seemingly complete coverage of UK for the purpose of temperature monitoring:

And I get why this is done: the UKMO primary mission is to provide daily weather monitoring and forecasts, and given limited station data providing actual measurements, their system provides useful temperature estimates in areas far removed from actual weather stations.

Of course, all of this high-resolution fanciness must be anchored by actual measurements, and in the daily Global Historical Climate Network (GHCNd) database, only ~100 stations exist across the UK in recent years. (There were very few GHCNd stations before 1960, so I will address temperature change only since then here). This means only 1 in ~2,400 UK grid cells has an actual temperature monitoring station in the GHCNd dataset, which is the dataset all global temperature monitoring efforts rely upon. While the UKMO might have access to somewhat more stations than are included in the GHCNd dataset, my point will remain valid.

Nevertheless, this doesn’t mean that long-term climate change can’t be monitored with the existing station network. What complicates matters is that stations come and go over time, and this can introduce biases that change over time and corrupt long-term estimates of temperature change. How one accounts for, and adjusts for, these changes is not a settled matter.

Removing Relative Biases Between Stations

From what I’ve been able to glean, the UKMO does not actually calculate and remove relative biases between stations. Instead, they use the above-described strategy to evaluate how station temperatures vary with latitude, longitude, elevation, proximity to the coast, land use (e.g. urbanization), etc., then apply regression-based techniques to estimate temperatures on the 1×1 km grid. This has no doubt involved considerable effort, and having done similar kinds of data analysis myself, it’s a complex task.

A simpler way of monitoring climate change is to assume that long-term (in the current example, 65 years) warming trends that actually exist in nature are pretty uniform across the UK. If this assumption holds, we can just take whatever stations exist over time, no matter where they are located or what their local microclimate-induced biases are, and quantify how the temperatures at each one varies over time, and then average all of those variations together. This methodology is somewhat similar to that of Hansen and Lebedeff, 1987, as well as our UAH satellite global temperature dataset.

In my implementation of this relative bias removal method, I start with the stations having the longest periods of record. In the UK, only 3 stations have had continuous records in all 126 years from 1900 to 2025: CET Central England, Armagh, and Stornoway Airport. (Only 31% of the UK stations had periods of record at least half as long, 63+ years). I average those 3 stations together. Then, I take the station(s) with the next-longest record (Oxford, 124 years), compute the average difference with the original series, and add it to the series to make a new 4-station average. This is done sequentially for all (148) stations in the UK since 1900 that have at least 2 years of record, going down the list from the longest periods of record to the shortest. Again, since there were few stations before 1960, the following plots cover variations since 1960.

Amazingly, the result using this simple relative bias removal method produces yearly summer-average temperatures (average of daily high [Tmax] and daily low [Tmin]) that are nearly identical to the much fancier UKMO methodology:

In this plot (as well as the others, below) for display purposes I have removed a small (~1-2 deg C) temperature offset due to my use of the original 3 stations for an absolute temperature baseline, whereas the UKMO uses their gridded estimate of the entire area of the UK. The linear trends in the above plot are essentially identical, at +0.27 C/decade.

But that record high did not exist for the daytime high temperatures. As seen in the next plot, 2025 was very similar to several years since 1995, while 1976 holds the record for hottest summertime daily high temperatures:

So, where did the 2025 record come from? It was due to the nighttime temperatures being so warm (although I don’t see how 53 deg. F is is insufferably warm). This was true in both analyses of the station data:

Finally, since I am analyzing daily temperature data, I can compute the average of the three hottest daytime temperatures in each summer month, which produces this:

For this statistic we see that the record is a 3-way tie between 1976, 1995, and 2022. We also see a stronger warming trend (+0.40 C/decade vs ~+0.26 C/decade in all-days average Tmax and Tmin). I suspect this is due to more Saharan air intrusions in recent decades, which are the primary cause of excessively hot days in the UK.

Conclusions

Despite criticisms of the UKMO data and methods for computing UK-average temperatures, I find that a simple bias-removal method of combining all available UK stations produces essentially identical results to the much more complex UKMO methodology. It should provide some vindication for the UKMO methodology in the context of climate temperature trend monitoring.

The record hot summer of 2025 in the UK was in the nightly minimum temperatures, not in the daytime maximum temperatures. This is true in both my analysis and that of the UKMO.

Finally, neither my nor the UKMO method accounts for possible changes in stations over time, such as an increasing urban heat island (UHI) effect at some stations. Based upon our work on this in recent years I suspect this effect since 1960 would be small, but I don’t know that for sure.