The Version 6 global average lower tropospheric temperature (LT) anomaly for February 2023 was +0.08 deg. C departure from the 1991-2020 mean. This is up from the January 2023 anomaly of -0.04 deg. C.

The linear warming trend since January, 1979 remains at +0.13 C/decade (+0.11 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).

Various regional LT departures from the 30-year (1991-2020) average for the last 14 months are:

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2022	Jan	+0.03	+0.06	-0.00	-0.23	-0.13	+0.68	+0.10
2022	Feb	-0.00	+0.01	-0.01	-0.24	-0.04	-0.30	-0.50
2022	Mar	+0.15	+0.27	+0.03	-0.07	+0.22	+0.74	+0.02
2022	Apr	+0.26	+0.35	+0.18	-0.04	-0.26	+0.45	+0.61
2022	May	+0.17	+0.25	+0.10	+0.01	+0.59	+0.23	+0.20
2022	Jun	+0.06	+0.08	+0.05	-0.36	+0.46	+0.33	+0.11
2022	Jul	+0.36	+0.37	+0.35	+0.13	+0.84	+0.55	+0.65
2022	Aug	+0.28	+0.31	+0.24	-0.03	+0.60	+0.50	-0.00
2022	Sep	+0.24	+0.43	+0.06	+0.03	+0.88	+0.69	-0.28
2022	Oct	+0.32	+0.43	+0.21	+0.04	+0.16	+0.93	+0.04
2022	Nov	+0.17	+0.21	+0.13	-0.16	-0.51	+0.51	-0.56
2022	Dec	+0.05	+0.13	-0.03	-0.35	-0.21	+0.80	-0.38
2023	Jan	-0.04	+0.05	-0.14	-0.38	+0.12	-0.12	-0.50
2023	Feb	+0.08	+0.17	0.00	-0.11	+0.68	-0.24	-0.12

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for February, 2023 should be available within the next several days here.

The global and regional monthly anomalies for the various atmospheric layers we monitor should be available in the next few days at the following locations:

Lower Troposphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt

Mid-Troposphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tmt/uahncdc_mt_6.0.txt

Tropopause:

http://vortex.nsstc.uah.edu/data/msu/v6.0/ttp/uahncdc_tp_6.0.txt

Lower Stratosphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tls/uahncdc_ls_6.0.txt

In Part I I showed the Landsat satellite-based measurements of urbanization around the Global Historical Climate Network (GHCN) land temperature-monitoring stations. Virtually all of the GHCN stations have experienced growth in the coverage of human settlement “built-up” (BU) structures.

As an example of this growth, here is the 40-year change in BU values (which range from 0 to 100%) at 1 km spatial resolution over the Southeast United States.

How has this change in urbanization been expressed at the GHCN stations distributed around the world? Fig. 2 shows how urbanization has increased on average across 19,885 GHCN stations from 20N to 82.5N latitude, at various spatial averaging resolutions of the data.

NONE of the 19,885 GHCN stations experienced negative growth, which is not that surprising since that would require a removal of human settlement structures over time. In all of the analysis that follows, I will be using the 21×21 km averages of BU centered on the GHCN station locations.

So, what effect does urbanization measured in this manner have on GHCN temperatures? And, especially, on temperature trends used for monitoring global warming?

While we all know that urban areas are warmer than rural areas, especially at night and during the summer, does an increase in urbanization lead to spurious warming at the GHCN stations that experienced growth (which is the majority of them)?

And, even if it did, does the homogenization procedure NOAA uses to correct for spurious temperature effects remove (even partially) urban heat island (UHI) effects on reported temperature trends?

John Christy and I have been examining these questions by comparing the GHCN temperature dataset (both unadjusted and adjusted [homogenized] versions) to these Landsat-based measurements of human settlement structures, which I will just call “urbanization”.

Here’s what I’m finding so far.

The Strongest UHI Warming with Urbanization Growth Occurs at Nearly-Rural Stations

As Oke (1973) and others have demonstrated, the urban heat island effect is strongly nonlinear, with (for example) a 2% increase in urbanization at rural sites producing much more warming than a 2% increase at an urban site. This means that a climate monitoring dataset using mostly-rural stations is not immune from spurious warming from creeping urbanization, unless there has been absolutely zero growth.

For example, Fig. 3 shows the sensitivity of GHCN (absolute) temperatures to increasing urbanization in various classes of urbanization, based upon well over 1 million station pairs separated by less than 150 km.

By far the greatest sensitivity to a change in urbanization in Fig. 3 is in the 0-2% (nearly rural) category. We also see in Fig. 3 that the homogenization procedure used by NOAA reduces this effect by only 9% averaged across all seasons, and by even less (2.1%) in the summer season.

If we integrate the sensitivities in Fig. 3 from 0 to 100% urbanization, we get the total UHI effect on temperature (Fig. 4).

The temperature data used here is the average of the daily maximum and minimum temperatures ([Tmax+Tmin]/2), and since almost all of the urban heat island effect is in Tmin, the temperature scale in Fig. 4 would be nearly doubled for the Tmin UHI effect.

The black curve in Fig. 4 is a square-root relationship, which seems to match the data reasonable well for most of the GHCN stations (which are generally less than 30% urbanized). But this is not nearly as non-linear as the 4th root relationship Oke (1973) calculated for some eastern Canadian stations, using population data as a measure of urbanization.

But what I have shown so far is based upon spatial information (the difference between closely-spaced stations). It does not tell us whether, or by how much, spurious warming exists in the GHCN temperature trends. To examine this question, next I looked at how the NOAA homogenization procedure changed station trends as a function of how fast the station environment has become more urbanized.

NOAA’s homogenization produces a change in most of the station temperature trends. If I compute the average homogenization-induced change in trends in various categories of station growth in urbanization, we should see a negative trend adjustment associated with positive urbanization growth, right?

But just the opposite happens.

First let’s examine what happens at stations with no growth in urbanization. In Fig. 5 we see that the 881 stations with no trend in urbanization during 1975-2014 have an average 0.011 C/decade warmer trend in the adjusted (homogenized) data than in the unadjusted data. This, by itself, is entirely possible since there are time-of-observation (“Tobs”) adjustments made to the data, adjustments for station moves, instrumentation types, etc.

So, let’s assume that value at zero growth in Fig. 5 represents what we should expect for the NON-urbanization related adjustments to GHCN trends. As we move to the right from zero urbanization growth in Fig. 5, stations with increasing growth in urbanization should have downward adjustments in their temperature trends, but instead we see, for all classes of growth in urbanization, UPWARD adjustments instead!

Thus, it appears that NOAA’s homogenization procedure is spuriously warming station temperature trends (on average) when it should be cooling them. I don’t know how to conclude any different.

Why are the NOAA adjusments going in the wrong direction? I don’t know.

To say the least, I find these results… curious.

OK, so how big is this spurious warming effect on land temperature trends in the GHCN dataset?

Before you jump to the conclusion that GHCN temperature trends have too much spurious warming to be relied upon for monitoring global warming, what I have shown does not tell us by just how much the land-average temperature trends are biased upward. I will address that in Part III.

My very preliminary calculations so far (using the UHI curves in Fig. 4 applied to the 21×21 km urbanization growth curve in Fig. 2) suggest the UHI warming averaged over all stations is about 10-20% of the GHCN trends. Small, but not insignificant. But that could change as I dig deeper into the issue.

The Version 6 global average lower tropospheric temperature (LT) anomaly for January 2023 was -0.04 deg. C departure from the 1991-2020 mean. This is down from the December 2022 anomaly of +0.05 deg. C.

The linear warming trend since January, 1979 now stands at +0.13 C/decade (+0.11 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).

Various regional LT departures from the 30-year (1991-2020) average for the last 13 months are:

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2022	Jan	+0.03	+0.06	-0.00	-0.23	-0.13	+0.68	+0.10
2022	Feb	-0.00	+0.01	-0.01	-0.24	-0.04	-0.30	-0.50
2022	Mar	+0.15	+0.27	+0.03	-0.07	+0.22	+0.74	+0.02
2022	Apr	+0.26	+0.35	+0.18	-0.04	-0.26	+0.45	+0.61
2022	May	+0.17	+0.25	+0.10	+0.01	+0.59	+0.23	+0.20
2022	Jun	+0.06	+0.08	+0.05	-0.36	+0.46	+0.33	+0.11
2022	Jul	+0.36	+0.37	+0.35	+0.13	+0.84	+0.55	+0.65
2022	Aug	+0.28	+0.31	+0.24	-0.03	+0.60	+0.50	-0.00
2022	Sep	+0.24	+0.43	+0.06	+0.03	+0.88	+0.69	-0.28
2022	Oct	+0.32	+0.43	+0.21	+0.04	+0.16	+0.93	+0.04
2022	Nov	+0.17	+0.21	+0.13	-0.16	-0.51	+0.51	-0.56
2022	Dec	+0.05	+0.13	-0.03	-0.35	-0.21	+0.80	-0.38
2023	Jan	-0.04	+0.05	-0.14	-0.38	+0.12	-0.12	-0.50

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for January, 2023 should be available within the next several days here.

The global and regional monthly anomalies for the various atmospheric layers we monitor should be available in the next few days at the following locations:

Lower Troposphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt

Mid-Troposphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tmt/uahncdc_mt_6.0.txt

Tropopause:

http://vortex.nsstc.uah.edu/data/msu/v6.0/ttp/uahncdc_tp_6.0.txt

Lower Stratosphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tls/uahncdc_ls_6.0.txt

I’ve previously posted a variety of articles (e.g. here and here) where I address the evidence that land surface temperature trends from existing homogenized datasets have some level of spurious warming due to urban heat island (UHI) effects. While it is widely believed that homogenization techniques remove UHI effects on trends, this is unlikely because UHI effects on trends are largely indistinguishable from global warming. Current homogenization techniques can remove abrupt changes in station data, but cannot correct for any sources of slowly-increasing spurious warming.

Anthony Watts has approached this problem for the U.S. temperature monitoring stations by physically visiting the sites and documenting the exposure of the thermometers to spurious heat sources (active and passive), and comparing trends from well-sited instruments to trends from poorly sited instruments. He found that stations with good siting characteristics showed, on average, cooler temperature trends than both the poorly-sited locations and the official “adjusted” temperature data from NOAA.

I’ve taken a different approach by using global datasets of population density and, more recently, analysis of high-resolution Landsat satellite based measurements of Global Human Settlements “Built-Up” areas. I have also started analyzing weather station data (mostly from airports) which have hourly time resolution, instead of the usual daily maximum and minimum temperature data (Tmax, Tmin) measurements that make up current global land temperature datasets. The hourly data stations are, unfortunately, fewer in number but have the advantage of better maintenance since they support aviation safety and allow examination of how UHI effects vary throughout the day and night.

In this two-part series, I’m going to look at the latest official global GHCN thermometer (Tmax, Tmin) dataset (Version 4) to see if there is evidence of spurious warming from increasing urbanization effects over time. In the latest GHCN dataset version Tmax and Tmin are no longer provided separately, only their average (Tavg) is available.

Based upon what I’ve seen so far, I’m convinced that there is spurious warming remaining in the GHCN-based temperature data. The only question is, how much? That will be addressed in Part II.

The issue is important (obviously) because if observed warming trends have been overstated, then any deductions about the sensitivity of the climate system to anthropogenic greenhouse gas emissions are also overstated. (Here I am not going to go into the possibility that some portion of recent warming is due to natural effects, that’s a very different discussion for another day).

What I am going to show is based upon the global stations in the GHCN monthly dataset (downloaded January, 2023) which had sufficient data to produce at least 45 years of July data during the 50 year period, 1973-2022. The start years of 1973 is chosen for two reasons: (1) it’s when the separate dataset with hourly time resolution I’m analyzing had a large increase in the number of digitized records (remember, weather recording used to be a manual process onto paper forms, which someone has to digitize), and (2) the global Landsat-based urbanization data starts in 1975, which is close enough to 1973.

Because the Landsat measurements of urbanization are very high resolution, one must decide what spatial resolution should be used to relate to potential UHI effects. I have (somewhat arbitrarily) chosen averaging grid sizes of 3×3 km, 9×9 km, 21×21 km, and 45 x 45 km. In the global dataset I am getting the best results with the 21 x 21 km averaging of the urbanization data, and all results here will be shown for that resolution.

The resulting distribution of 4,232 stations (Fig. 1) shows that only a few countries have good coverage, especially the United States, Russia, Japan, and many European countries. Africa is poorly represented, as is most of South America.

I’ve analyzed the corresponding Landsat-based urban settlement diagnoses for all of these stations, which is shown in Fig. 2. That dataset covers a 40 year period, from 1975 to 2014. Here I’ve plotted the 40-year average level of urbanization versus the 40-year trend in urbanization.

There are a few important and interesting things to note from Fig. 2.

1. Few GHCN station locations are truly rural: 13.2% are less than 5% urbanized, while 68.4% are less than 10% urbanized.
2. Virtually all station locations have experienced an increase in building, and none have decreased (which would require a net destruction of buildings, returning the land to its natural state).
3. Greatest growth has been in areas not completely rural and not already heavily urbanized (see the curve fitted to the data). That is, very rural locations stay rural, and heavily urbanized locations have little room to grow anyway.

One might think that since the majority of stations are less than 10% urbanized that UHI effects should be negligible. But the seminal study by Oke (1973) showed that UHI warming is non-linear, with the most rapid warming occurring at the lowest population densities, with an eventual saturation of the warming at high population densities. I have previously showed evidence supporting this based upon updated global population density data that the greatest rate of spurious warming (comparing neighboring stations with differing populations) occurs at the lowest population densities. It remains to be seen whether this is also true of “built-up” measurements of human settlements (buildings rather than population density).

Average Urbanization or Urbanization Growth?

One interesting question is whether it is the trend in urbanization (growing amounts of infrastructure), or just the average urbanization that has the largest impact on temperature trends? Obviously, growth will have an impact. But what about towns and cities where there have been no increases in building, but still have had growth in energy use (which generates waste heat)? As people increasingly move from rural areas to cities, the population density can increase much faster than the number of buildings, as people live in smaller spaces and apartment and office buildings grow vertically without increasing their footprint on the landscape. There are also increases in wealth, automobile usage, economic productivity and consumption, air conditioning, etc., all of which can cause more waste heat production without an increase in population or urbanization.

In Part II I will examine how GHCN station temperature trends relate to station urbanization for a variety of countries, in both the raw (unadjusted) temperature data and in the homogenized (adjusted) data, and also look at how growth in urbanization compares to average urbanization.

December of 2022 finished the year with a global tropospheric temperature anomaly of +0.05 deg. C above the 1991-2020 average, which was down from the November value of +0.17 deg. C.

The average anomaly for the year was +0.174 deg. C, making 2022 the 7th warmest year of the 44+ year global satellite record, which started in late 1978. Continuing La Nina conditions in the Pacific Ocean have helped to reduce global-average temperatures for the last two years. The 10 warmest years were:

• #1 2016 +0.389
• #2 2020 +0.358
• #3 1998 +0.347
• #4 2019 +0.304
• #5 2017 +0.267
• #6 2010 +0.193
• #7 2022 +0.174
• #8 2021 +0.138
• #9 2015 +0.138
• #10 2018 +0.090

The linear warming trend since January, 1979 continues at +0.13 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).

Various regional LT departures from the 30-year (1991-2020) average for the last 24 months are:

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2021	Jan	+0.13	+0.34	-0.09	-0.08	+0.36	+0.50	-0.52
2021	Feb	+0.20	+0.32	+0.08	-0.14	-0.65	+0.07	-0.27
2021	Mar	-0.00	+0.13	-0.13	-0.28	+0.60	-0.78	-0.79
2021	Apr	-0.05	+0.06	-0.15	-0.27	-0.01	+0.02	+0.29
2021	May	+0.08	+0.14	+0.03	+0.07	-0.41	-0.04	+0.02
2021	Jun	-0.01	+0.31	-0.32	-0.14	+1.44	+0.64	-0.76
2021	Jul	+0.20	+0.34	+0.07	+0.13	+0.58	+0.43	+0.80
2021	Aug	+0.17	+0.27	+0.08	+0.07	+0.33	+0.83	-0.02
2021	Sep	+0.26	+0.19	+0.33	+0.09	+0.67	+0.02	+0.37
2021	Oct	+0.37	+0.46	+0.28	+0.33	+0.84	+0.64	+0.07
2021	Nov	+0.09	+0.12	+0.06	+0.14	+0.50	-0.42	-0.29
2021	Dec	+0.21	+0.27	+0.15	+0.04	+1.63	+0.01	-0.06
2022	Jan	+0.03	+0.06	-0.00	-0.23	-0.13	+0.68	+0.10
2022	Feb	-0.00	+0.01	-0.02	-0.24	-0.04	-0.30	-0.50
2022	Mar	+0.15	+0.27	+0.02	-0.07	+0.22	+0.74	+0.02
2022	Apr	+0.26	+0.35	+0.18	-0.04	-0.26	+0.45	+0.61
2022	May	+0.17	+0.25	+0.10	+0.01	+0.59	+0.23	+0.19
2022	Jun	+0.06	+0.08	+0.04	-0.36	+0.46	+0.33	+0.11
2022	Jul	+0.36	+0.37	+0.35	+0.13	+0.84	+0.56	+0.65
2022	Aug	+0.28	+0.32	+0.24	-0.03	+0.60	+0.50	-0.00
2022	Sep	+0.24	+0.43	+0.06	+0.03	+0.88	+0.69	-0.28
2022	Oct	+0.32	+0.43	+0.21	+0.04	+0.16	+0.93	+0.04
2022	Nov	+0.17	+0.21	+0.12	-0.16	-0.51	+0.51	-0.56
2022	Dec	+0.05	+0.13	-0.03	-0.35	-0.21	+0.80	-0.38

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for December, 2022 should be available within the next several days here.

The global and regional monthly anomalies for the various atmospheric layers we monitor should be available in the next few days at the following locations:

Lower Troposphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt

Mid-Troposphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tmt/uahncdc_mt_6.0.txt

Tropopause:

http://vortex.nsstc.uah.edu/data/msu/v6.0/ttp/uahncdc_tp_6.0.txt

Lower Stratosphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tls/uahncdc_ls_6.0.txt

… if you haven’t had a comment approved here before, I will need to approve your first one. Then your comments should be approved automatically after that. Sometimes I get busy and won’t check for several days, but I will try to check once or twice a day.

In response to reviewers’ comments on a paper John Christy and I submitted regarding the impact of El Nino and La Nina on climate sensitivity estimates, I decided to change the focus enough to require a total re-write of the paper.

The paper now addresses the question: If we take all of the various surface and sub-surface temperature datasets and their differing estimates of warming over the last 50 years, what does it imply for climate sensitivity?

The trouble with estimating climate sensitivity from observational data is that, even if the temperature observations were globally complete and error-free, you still have to know pretty accurately what the “forcing” was that caused the temperature change.

(Yes, I know some of you don’t like the forcing-feedback paradigm of climate change. Feel free to ignore this post if it bothers you.)

As a reminder, all temperature change in an object or system is due to an imbalance between rates of energy gained and energy lost, and the global warming hypothesis begins with the assumption that the climate system is naturally in a state of energy balance. Yes, I know (and agree) that this assumption cannot be demonstrated to be strictly true, as events like the Medieval Warm Period and Little Ice Age can attest.

But for the purpose of demonstration, let’s assume it’s true in today’s climate system, and that the only thing causing recent warming is anthropogenic greenhouse gas emission (mainly CO2). Does the current rate of warming suggest (as we are told) that a global warming disaster is upon us? I think this is an important question to address, separate from the question of whether some of the recent warming is natural (which would make AGW even less of a problem).

Lewis and Curry (most recently in 2018) addressed the ECS question in a similar manner by comparing temperatures and radiative forcing estimates between the late 1800s and early 2000s, and got answers somewhere in the range of 1.5 to 1.8 deg. C of eventual warming from a doubling of the pre-industrial CO2 concentration (2XCO2). These estimates are considerably lower than what the IPCC claims from (mostly) climate model projections.

Our approach is somewhat different from Lewis & Curry. First, we use only data from the most recent 50 years (1970-2021), which is the period of most rapid growth in CO2-caused forcing, the period of most rapid temperature rise, and about as far back as one can go and talk with any confidence about ocean heat content (a very important variable in climate sensitivity estimates).

Secondly, our model is time-dependent, with monthly time resolution, allowing us to examine (for instance) the recent acceleration in deep ocean temperature (ocean heat content) rise.

In contrast to Lewis & Curry and differencing two time periods’ averages separated by 100+ years, our approach is to use a time-dependent model of vertical energy flows, which I have blogged on before. It is run at monthly time resolution, so allows examination of such issues as the recent acceleration of the increase in oceanic heat content (OHC).

In response to reviewers comments, I extended the domain from non-ice covered (60N-60S) oceans to global coverage (including land), as well as borehole-based estimates of deep-land warming trends (I believe a first for this kind of work). The model remains a 1D model of temperature departures from assumed energy equilibrium, within three layers, shown schematically in Fig. 1.

One thing I learned along the way is that, even though borehole temperatures suggest warming extending to almost 200 m depth (the cause of which seems to extent back several centuries), modern Earth System Models (ESMs) have embedded land models that extend to only 10 m depth or so.

Another thing I learned (in the course of responding to reviewers comments) is that the assumed history of radiative forcing has a pretty large effect on diagnosed climate sensitivity. I have been using the RCP6 radiative forcing scenario from the previous (AR5) IPCC report, but in response to reviewers’ suggestions I am now emphasizing the SSP245 scenario from the most recent (AR6) report.

I run all of the model simulations with either one or the other radiative forcing dataset, initialized in 1765 (a common starting point for ESMs). All results below are from the most recent (SSP245) effective radiative forcing scenario preferred by the IPCC (which, it turns out, actually produces lower ECS estimates).

The Model Experiments

In addition to the assumption that the radiative forcing scenarios are a relatively accurate representation of what has been causing climate change since 1765, there is also the assumption that our temperature datasets are sufficiently accurate to compute ECS values.

So, taking those on faith, let’s forge ahead…

I ran the model with thousands of combinations of heat transfer coefficients between model layers and the net feedback parameter (which determines ECS) to get 1970-2021 temperature trends within certain ranges.

For land surface temperature trends I used 5 “different” land datasets: CRUTem5 (+0.277 C/decade), GISS 250 km (+0.306 C/decade), NCDC v3.2.1 (+0.298 C/decade), GHCN/CAMS (+0.348 C/decade), and Berkeley 1 deg. (+0.280 C/decade).

For global average sea surface temperature I used HadCRUT5 (+0.153 C/decade), Cowtan & Way (HadCRUT4, +0.148 C/decade), and Berkeley 1 deg. (+0.162 C/decade).

For the deep ocean, I used Cheng et al. 0-2000m global average ocean temperature (+0.0269 C/decade), and Cheng’s estimate of the 2000-3688m deep-deep-ocean warming, which amounts to a (very uncertain) +0.01 total warming over the last 40 years. The model must produce the surface trends within the range represented by those datasets, and produce 0-2000 m trends within +/-20% of the Cheng deep-ocean dataset trends.

Since deep-ocean heat storage is such an important constraint on ECS, in Fig. 3 I show the 1D model run that best fits the 0-2000m temperature trend of +0.0269 C/decade over the period 1970-2021.

Finally, the storage of heat in the land surface is usually ignored in such efforts. As mentioned above, climate models have embedded land surface models that extend to only 10 m depth. Yet, borehole temperature profiles have been analyzed that suggest warming up to 200 m in depth (Fig. 4).

This great depth, in turn, suggests that there has been a multi-century warming trend occurring, even in the early 20th Century, which the IPCC ignores and which suggests a natural source for long-term climate change. Any natural source of warming, if ignored, leads to inflated estimates of ECS and of the importance of increasing CO2 in climate change projections.

I used the black curve (bottom panel of Fig. 4) to estimate that the near-surface layer is warming 2.5 times faster than the 0-100 m layer, and 25 times faster than the 100-200 m layer. In my 1D model simulations, I required this amount of deep-land heat storage (analogous to the deep-ocean heat storage computations, but requiring weaker heat transfer coefficients for land and different volumetric heat capacities).

The distributions of diagnosed ECS values I get over land and ocean are shown in Fig. 5.

The final, global average ECS from the central estimates in Fig. 5 is 2.09 deg. C. Again, this is somewhat higher than the 1.5 to 1.8 deg. C obtained by Lewis & Curry, but part of this is due to larger estimates of ocean and land heat storage used here, and I would suspect that our use of only the most recent 50 years of data has some impact as well.

Conclusions

I’ve used a 1D time-dependent model of temperature departures from assumed energy equilibrium to address the question: Given the various estimates of surface and sub-surface warming over the last 50 years, what do they suggest for the sensitivity of the climate system to a doubling of atmospheric CO2?

Using the most recent estimates of effective radiative forcing from Annex III in the latest IPCC report (AR6), the observational data suggest lower climate sensitivities (ECS) than promoted by the IPCC with a central estimate of +2.09 deg C. for the global average. This is at the bottom end of the latest IPCC (AR6) likely range of 2.0 to 4.5 deg. C.

I believe this is still likely an upper bound for ECS, for the following reasons.

1. Borehole temperatures suggest there has been a long-term warming trend, at least up into the early 20th Century. Ignoring this (whatever its cause) will lead to inflated estimates of ECS.
2. I still believe that some portion of the land temperature datasets has been contaminated by long-term increases in Urban Heat Island effects, which are indistinguishable from climatic warming in homogenization schemes.

Sorry for the late posting of the global temperature update, I’ve been busy responding to reviewers of one of our papers for publication.

The Version 6 global average lower tropospheric temperature (LT) anomaly for November 2022 was +0.17 deg. C departure from the 1991-2020 mean. This is down from the October anomaly of +0.32 deg. C

The linear warming trend since January, 1979 now stands at +0.13 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.18 C/decade over global-averaged land).

Various regional LT departures from the 30-year (1991-2020) average for the last 22 months are:

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2021	Jan	+0.13	+0.34	-0.09	-0.08	+0.36	+0.50	-0.52
2021	Feb	+0.20	+0.32	+0.08	-0.14	-0.65	+0.07	-0.27
2021	Mar	-0.00	+0.13	-0.13	-0.28	+0.60	-0.78	-0.79
2021	Apr	-0.05	+0.06	-0.15	-0.27	-0.01	+0.02	+0.29
2021	May	+0.08	+0.14	+0.03	+0.07	-0.41	-0.04	+0.02
2021	Jun	-0.01	+0.31	-0.32	-0.14	+1.44	+0.64	-0.76
2021	Jul	+0.20	+0.34	+0.07	+0.13	+0.58	+0.43	+0.80
2021	Aug	+0.17	+0.27	+0.08	+0.07	+0.33	+0.83	-0.02
2021	Sep	+0.26	+0.19	+0.33	+0.09	+0.67	+0.02	+0.37
2021	Oct	+0.37	+0.46	+0.28	+0.33	+0.84	+0.64	+0.07
2021	Nov	+0.09	+0.12	+0.06	+0.14	+0.50	-0.42	-0.29
2021	Dec	+0.21	+0.27	+0.15	+0.04	+1.63	+0.01	-0.06
2022	Jan	+0.03	+0.06	-0.00	-0.23	-0.13	+0.68	+0.10
2022	Feb	-0.00	+0.01	-0.02	-0.24	-0.04	-0.30	-0.50
2022	Mar	+0.15	+0.27	+0.02	-0.07	+0.22	+0.74	+0.02
2022	Apr	+0.26	+0.35	+0.18	-0.04	-0.26	+0.45	+0.61
2022	May	+0.17	+0.25	+0.10	+0.01	+0.59	+0.23	+0.19
2022	Jun	+0.06	+0.08	+0.04	-0.36	+0.46	+0.33	+0.11
2022	Jul	+0.36	+0.37	+0.35	+0.13	+0.84	+0.56	+0.65
2022	Aug	+0.28	+0.32	+0.24	-0.03	+0.60	+0.50	-0.00
2022	Sep	+0.24	+0.43	+0.06	+0.03	+0.88	+0.69	-0.28
2022	Oct	+0.32	+0.43	+0.21	+0.04	+0.16	+0.93	+0.04
2022	Nov	+0.17	+0.21	+0.12	-0.16	-0.51	+0.51	-0.56

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for November, 2022 should be available within the next several days here.

The global and regional monthly anomalies for the various atmospheric layers we monitor should be available in the next few days at the following locations:

Lower Troposphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
Mid-Troposphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tmt/uahncdc_mt_6.0.txt
Tropopause:

http://vortex.nsstc.uah.edu/data/msu/v6.0/ttp/uahncdc_tp_6.0.txt
Lower Stratosphere:

http://vortex.nsstc.uah.edu/data/msu/v6.0/tls/uahncdc_ls_6.0.txt

It has been brought to my attention that I owe Willis Eschenbach an apology, based upon a comment I made on my blog:

“I’ve previously commented on Willis thermostat hypothesis of climate system regulation, which Willis never mentioned was originally put forth by Ramanathan and Collins in a 1991 Nature article.”

Some have interpreted my words as implying that Willis knew of the previously published Thermostat Hypothesis and chose not to reveal it, which would suggest plagiarism. That was not my intention, and I apologize to Willis if my comment made it look that way.

Summary

Comparison of rural with urban temperature monitoring sites across Canada during the summers of 1978-2022 shows the expected average nighttime warm bias in urban areas, with a weaker daytime effect. When applied to the Landsat imagery-based diagnoses of increased urbanization over time, 20% of the temperature trends in a small region encompassing Calgary and Edmonton are found to be due to increasing urbanization. Calgary leads the list of Canadian cities with increased urbanization, with an estimated 50% of the nighttime warming trends across 10 Canadian mostly-metro areas attributable to increased urbanization, and 20% of the daytime warming trends.

Introduction

This is part of my continuing investigation of the degree to which land-based temperature datasets are producing warming trends exaggerated by increasing urbanization (the urban heat island effect, UHI). Current “homogenization” techniques for thermometer data adjustment do not explicitly attempt to correct urban trends to match rural trends, although I would expect that they do perform this function if most of the stations are rural. Instead, they amount to statistical “consensus-building” exercises where the majority wins. So, if most of the stations are affected by increasing UHI effects, to varying degrees, these are not forced to match the rural stations. Instead, the reverse occurs. For example, in the U.S. the Watts et al. analysis of station data showed that the U.S. homogenized dataset (USHCN) produced temperature trends as large as those produced by the stations with the worst siting in terms of spurious heat sources. They further found that use of only well-sited thermometer locations leads to substantial reductions in temperature trends compared to the widely used homogenized dataset.

I consider homogenization to be a black-box approach that does not address the spurious warming in thermometer records resulting from widespread urbanization over time. My approach has been different: Document the absolute temperature differences between station pairs and relate that to some independent measure of urbanization difference. The Landsat-based global dataset of “built-up” areas (which I will loosely refer as measures of urbanization) offers the opportunity to correct for urbanization in thermometer data extending back to the 1970s (when the Landsat series of satellite started).

My main region of focus to start has been the southeast U.S., partly because my co-researcher, John Christy, is the Alabama state climatologist, and I am partly funded through that office. But I am also examining other regions. So far, I’ve done some preliminary analysis for the UK, France, Australia, China, and Canada. Here I will show some initial results for Canada.

The first step is to quantify, from closely-spaced stations, the difference in monthly-average temperatures between more-urban and more-rural sites. The temperature dataset I am using is the Global Hourly Integrated Surface Database (ISD), archived on a continuing basis at NOAA/NCEI. The data are dominated by operational hourly (or 3-hourly) observations made to support aviation at airports around the world. They are mostly (but not entirely) independent of the maximum and minimum (Tmax and Tmin) measurements that make up other widely-used and homogenized global temperature datasets. The advantages of the ISD dataset is the hourly time resolution, allowing more thorough investigation of day vs. night effects, and better instrumentation and maintenance for aviation safety support. A disadvantage is that there are not as many stations in the dataset compared to the Tmax/Tmin datasets.

As I discussed in my last post on the subject, a critical component to my method is the relatively recent high-resolution (1 km) global dataset of urbanization derived from the Landsat satellites since 1975 as part of the EU’s Global Human Settlement (GHS) project. This allows me to compare neighboring stations to quantify how much urban warmth is associated with differences in urbanization as diagnosed from Landsat imagery of “built-up” structures.

Urban vs. Rural Summertime Temperatures in Canada

Canada is a mostly-rural country, with widely scattered temperature monitoring stations. Most of the population (where most of the thermometers are) is clustered along the coasts and especially along the U.S. border. There are relatively few airports compared to the size of the country which limits how many rural-vs-urban match-ups I can make.

For 150 km maximum space between station pairs, as well as a few other tests for inclusion (e.g. less than 300 m elevation difference between stations), Fig. 1 shows the differences in average temperature and area-average Landsat-based urbanization values for (a) 09 UTC (late night) and (b) 21 UTC (afternoon). These times were chosen to approximate the times of minimum and maximum temperatures (Tmin and Tmax) which make up other global temperature datasets, so I can do a comparison to them.

As other studies have documented, the UHI effect on temperature is larger at night, when solar energy absorbed into the ground by pavement (which has high thermal conductivity compared to soil or vegetation) is released into the air and is trapped over the city by the stability of the nocturnal boundary layer and weaker winds compared to daytime. For this limited set of Canadian station pairs the UHI warm bias is 0.21 deg. C per 10% urbanization during the day, and 0.35 deg. C per 10 % at night.

Next, if we apply these relationships to the monthly temperature and urbanization data at ~70 individual stations scattered across Canada, we get some idea of how much increasing urbanization has affected temperature trends. (NOTE: the relationships in Fig. 1 only apply in an average sense, and so it is not known how well they apply to the individual stations in the tables below.)

Across approximately 70 Canadian stations, the 10 stations with the largest diagnosed spurious warming trends (1978-2022) are listed below. Note that the raw trends have considerable variability, some of which is likely not weather- or climate-related (changes in instrumentation, siting, etc.). Table 1 has the nighttime results, which Table 2 is for daytime.

TABLE 1: Most Urbanized Nighttime Temperature Trends (1978-2022)

Location	Raw Temp. Trend	De-urbanized Trend	Urban Trend Component
Calgary Intl. Arpt.	+0.33 C/decade	+0.16 C/decade	+0.17 C/decade
Ottawa Intl. Arpt.	+0.07 C/decade	-0.08 C/decade	+0.14 C/decade
Windsor	+0.20 C/decade	+0.08 C/decade	+0.11 C/decade
Montreal/Trudeau Intl.	+0.47 C/decade	+0.36 C/decade	+0.10 C/decade
Edmonton Intl. Arpt.	+0.10 C/decade	0.00 C/decade	+0.10 C/decade
Saskatoon Intl. Arpt.	+0.03 C/decade	-0.04 C/decade	+0.07 C/decade
Abbotsford	+0.48 C/decade	+0.41 C/decade	+0.07 C/decade
Regina Intl.	-0.11 C/decade	-0.17 C/decade	+0.06 C/decade
Grande Prairie	+0.07 C/decade	+0.02 C/decade	+0.05 C/decade
St. Johns Intl. Arpt.	+0.31 C/decade	+0.27 C/decade	+0.04 C/decade
10-STN AVERAGE	+0.19 C/decade	+0.10 C/decade	+0.09 C/decade

Calgary, Ottawa, Windsor, Montreal, and Edmonton are the five station locations with the greatest rate of increased urbanization since the 1970s as measured by Landsat, and therefore the greatest rate of spurious warming since 1978 (the earliest for which I have complete hourly temperature data). Averaged across the 10 highest-growth locations, 48% of the average warming trend is estimated to be due to urbanization alone.

Table 2 shows the corresponding results for summer afternoon temperatures, which from Fig. 1 we know have weaker UHI effects than nighttime temperatures.

TABLE 2: Most Urbanized Afternoon Temperature Trends (1978-2022)

Location	Raw Temp. Trend	De-urbanized Trend	Urban Trend Component
Calgary Intl. Arpt.	+0.26 C/decade	+0.16 C/decade	+0.11 C/decade
Ottawa Intl. Arpt.	+0.27 C/decade	+0.19 C/decade	+0.09 C/decade
Windsor	+0.27 C/decade	+0.20 C/decade	+0.07 C/decade
Montreal/Trudeau Intl.	+0.35 C/decade	+0.28 C/decade	+0.06 C/decade
Edmonton Intl. Arpt.	+0.42 C/decade	0.36 C/decade	+0.06 C/decade
Saskatoon Intl. Arpt.	+0.18 C/decade	+0.13 C/decade	+0.04 C/decade
Abbotsford	+0.45 C/decade	+0.40 C/decade	+0.04 C/decade
Regina Intl.	+0.08 C/decade	+0.04 C/decade	+0.04 C/decade
Grande Prairie	+0.19 C/decade	+0.16 C/decade	+0.03 C/decade
St. Johns Intl. Arpt.	+0.31 C/decade	+0.28 C/decade	+0.03 C/decade
10-STN AVERAGE	+0.28 C/decade	+0.22 C/decade	+0.06 C/decade

For the top 10 most increasingly urbanized stations in Table 2, the average reduction in the observed afternoon warming trends is 20%, compared to 48% for the nighttime trends.

Comparison to the CRUTem5 Data in SE Alberta

How do the results in Table 1 affect widely-reported warming trends averaged across Canada? Given that Canada is mostly rural with only sparse measurements, that would be difficult to determine from the available data. But there is no question that the public’s consciousness regarding climate change issues is heavily influenced by conditions where they live, and most people live in urbanized areas.

As a single sanity test of the use of these mostly airport-based measurements of temperature for climate monitoring, I examined the region of southeast Alberta bounded by the latitude/longitudes of 50-55N and 110-115W, which includes Calgary and Edmonton. The comparison area is determined by the IPCC-sanctioned CRUTem5 temperature dataset, which reports average data on a 5 deg. latitude/longitude grid.

There are 4 stations in my dataset in this region, and averaging the 4 stations’ raw temperature data produces a trend (Fig. 2) essentially identical to that produced by the CRUTem5 dataset, which has extensive homogenization methods and (presumably) many more stations (which are often limited in their periods of record, and so must be pieced together). This high level of agreement is at least partly fortuitous.

Applying the urbanization corrections from Fig. 1 (large for Calgary and Edmonton, tiny for Cold Lake and Red Deer) lead to an average reduction of 20% in the area-average temperature trend. This supports my claim that homogenization procedures applied to global Tmax/Tmin datasets have not adjusted urban trends to rural trends, but instead represent a “voting” adjustment where a dataset dominated by stations with increasing urbanization will mostly retain the trend characteristics of the UHI-contaminated locations.

Conclusions

Canadian cities show a substantial urban heat island effect in the summer, especially at night, and Landsat-based estimates of increased urbanization suggest that this has caused a spurious warming component of reported temperature trends, at least for locations experiencing increased urbanization. A limited comparison in Alberta suggests there remains an urban warming bias in the CRUTem5 dataset, consistent with my previous postings on the subject and work done by others.

The issue is important because rational energy policy should be based upon reality, not perception. To the extent that global warming estimates are exaggerated, so will be energy policy decisions. As it is, there is evidence (e.g. here) that the climate models used to guide policy produce more warming than observed, especially in the summer when excess heat is of concern. If that observed warming is even less than being reported, then the climate models become increasingly irrelevant to energy policy decisions.