The Version 6 global average lower tropospheric temperature (LT) anomaly for April 2023 was +0.18 deg. C departure from the 1991-2020 mean. This is down slightly from the March 2023 anomaly of +0.20 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 16 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
2023
Mar
+0.20
+0.23
+0.16
-0.14
-1.44
+0.17
+0.40
2023
Apr
+0.18
+0.11
+0.25
-0.03
-0.38
+0.53
+0.21
The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for April, 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:
In my last post (Part IV) I showed how urbanization (as measured by population density) affects GHCN monthly-average Tmax and Tmin near-surface air temperatures during the warm season in the Northern Hemisphere. We are utilizing a technique that recognizes rural thermometer sites can experience large spurious warming with very small increases in population density, as has been known for over 50 years.
The urban heat island (UHI) effects on Tmin averaged 3.5 times as large as on Tmax, an unsurprising result and qualitatively consistent with previous studies. Also, I showed that the homogenization procedure NOAA uses to adjust the Tmax and Tmin temperatures caused greater UHI effects compared to raw (unadjusted) data, a result I cannot explain.
Again I will emphasize that these UHI warming results are based upon spatial comparisons between neighboring stations, and do not say anything quantitative about how much urbanization effects have spuriously warmed long-term temperature trends over land. That is indeed the goal of our study, but we have not reached that point in the analysis yet.
Here in Part V of my series on UHI I just want to show the difference between U.S. and non-U.S. stations, in this cased for adjusted (homogenized) Tmin data. This is shown in the following two plots, which are the same except the second plot has a logarithmic scale in population density.
The non-U.S. stations have a more rapid rise in UHI warming at very low population densities than the U.S. stations do, but less rapid warming at high population densities. Possible reasons for this include country differences in thermometer siting and differences in waste heat generation. I’m sure you can think of other possible reasons.
As can be seen most (over 80%) of the GHCN 2-station matchups come from the U.S. Other countries have considerably fewer 2-station matchups, for example Canada (7.8% of the Northern Hemisphere total), Japan (4.7%), Turkey (2.8%), South Korea (1.3%), and China (1.1%). These low totals are not necessarily due to a lack of stations, but to a lack of station pairs within 150 km and 300 m elevation of each other needed for my current method of analysis.
This is part 4 of my series on quantifying Urban Heat Island (UHI) effects on surface air temperatures as reported in the monthly GHCN datasets produced by NOAA.
In previous posts I showed results based upon monthly-average Tavg, which is the average of of daily maximum (Tmax) and minimum (Tmin) temperatures. Since late 2019, NOAA produces monthly average datasets for only Tavg, but since there are large differences in the UHI effects between Tmin and Tmax (urban warming is much larger at night than during the day, thus affecting Tmin more), John Christy wanted me to compute results for the older Tmax and Tmin datasets archived by NOAA.
As I have discussed previously, our computations of UHI are, I believe, rather novel since we do not classify stations as urban or rural. That is how most researchers have approached the problem. But as I have mentioned before, UHI warming occurs much more rapidly at very low population densities (PD) than it does at high population densities for the same population increase. As a result, a small population increase at a rural station can produce the same spurious warming as a large population increase at an urban station. This means that previous published results showing little difference between rural and urban trends did not actually demonstrate that homogenization methods actually remove UHI effects from temperature trends.
Instead of classifying stations as either rural or urban, we use regression to compute the slope of temperature-vs-population density in many sub-intervals of 2-station pair average population density, from near-zero PD to very high PD values. Then we integrate these regression slopes through the full range of population densities.
Since NOAA’s GHCN Tmax and Tmin dataset (v3) does not have nearly as many stations as their newer (v4) Tavg dataset, I have combined the 2-station matchups for May, June, and July rather than showing results for an individual month. I have used all matchups every ten years from 1880, 1890, 1900,… 2010 that are within 150 km and 300 m elevation of each other. All land stations from 20N to 80N latitude are included. I have computed results for both the unadjusted data as well as the adjusted (homogenized) data.
The results (below) show that the total UHI effect in summer for highly-populated stations averages 3.5 times as large in Tmin as it does in Tmax. Each curve is based upon approximately 300,000 monthly 2-station matchups.
The nonlinearity of the relationship is, as other investigators have found, very strong.
Note that the UHI effect shows up more strongly in the adjusted GHCN data than in the unadjusted data. I cannot explain this. It is not because of the weeding out of bad temperature data, because that only affects regression coefficients if noise is reduced in the independent variable (2-station population density differences), and not in the dependent variable (2-station temperature differences). The 2-station PD differences do not change between the raw and adjusted GHCN data.
As I have mentioned before, the above results do not tell us the extent to which GHCN temperature trends have been affected by urbanization effects. SPOILER ALERT: My preliminary work on this suggests UHI effects are rather large between 1880 and 1980 or so, then become quite small compared to observed temperature trends. But it must be remembered that here we are using population density as a proxy for UHI, which is not necessarily optimum. It is possible for UHI effects to increase as prosperity increases for a population density that remains the same.
This is an update of my CO2 budget model that explains yearly Mauna Loa atmospheric CO2 concentrations since 1959 with three main processes:
an anthropogenic source term, primarily from burning of fossil fuels
a constant yearly CO2 sink (removal) rate of 2.05% of the atmospheric “excess” over 295 ppm
an ENSO term that increases atmospheric CO2 during El Nino years and decreases it during La Nina years
The CO2 Budget Model
I described the CO2 budget model here. The most important new insight gained was that the model showed that the CO2 sink rate has not been declining as has been claimed by carbon cycle modelers after one adjusts for the history of El Nino and La Nina activity.
If the sink rate was really declining, that means the climate system is becoming less able to remove “excess” CO2 from the atmosphere, and future climate change will be (of course) worse than we thought. But I showed the declining sink rate was just an artifact of the history of El Nino and La Nina activity, as shown in the following figure (updated through 2022).
The model also showed how the eruption of Mt. Pinatubo caused a large increase in rate of removal of CO2 from the atmosphere (not a new finding) due to enhanced photosynthesis from more diffuse sunlight. This contradicts the popular perception that volcanoes are a major source of atmospheric CO2.
I attempted to get the results published in Geophysical Research Letters, and was conditionally accepted after one review. But the editor wanted more reviewers, which he found, who then rejected the paper. The model is straightforward, physically consistent, and agrees with the observed Mauna Loa CO2 record, as shown in the following plot.
2022 Update: CO2 continues to Rise Despite Renewable Energy Transition
As I have pointed out before, the global economic downturn from COVID had no measurable impact on the Mauna Loa record of atmospheric CO2, and that is not surprising given the large year-to-year variations in natural sources and sinks of CO2. Atmospheric CO2 concentrations continue to rise, mainly due to emissions from China and India whose economies are rapidly growing.
The following plot zooms in on the 2010-2035 period and shows the Mauna Loa CO2 rise compared to my budget model forced with 3 scenarios from the Energy Information Administration (blue lines), and also compared to the RCP scenarios used by the IPCC in the CMIP5 climate model intercomparison project.
The observations are tracking below the RCP8.5 scenario, which assumes unrealistically high CO2 emissions, yet remains the basis for widespread claims of a “climate crisis”. The observations are running a little above my model for the last 2 years, and only time will tell if this trend continues.
But clearly the international efforts to reduce CO2 emissions are having no obvious impact. This is unsurprising since global energy demand continues to grow faster than new sources of renewable energy can make up the difference.
As I spend more time working on a research project, the more time I have to reflect on things that others have simply assumed to be true. And in the process I sometimes have an epiphany than clarifies my thinking on a subject.
As I continue to investigate how to quantify urban heat island (UHI) effects for the purpose of determining the extent to which land surface temperature trends have been spuriously inflated by urbanization effects, there is one recurring theme I find has not been handled well in previously published papers on the subject. I’ve mentioned it before, but it’s so important, it deserves its own (brief) blog post.
It has to do with the common assumption that “urban” thermometer sites experience spurious warming over time, while “rural” sites do not.
Obviously, at any given point in time urban environments are warmer than rural environments, especially at night. And urbanization has increased around temperature monitoring sites over the last 50 to 100 years (and longer). Yet, a number of studies over the years have curiously found that urban and rural sites have very similar temperature trends. This has led investigators to conclude that temperature datasets such as the Global Historical Climate Network (GHCN), especially after “homogenization”, is largely free of spurious warming effects from urbanization.
But the conclusion is wrong…all it shows is that temperature trends between rural and urban sites are similar… not that those trends are unaffected by urbanization effects.
Instead, studies have demonstrated that the greatest rate of warming as population increases is for nearly-rural sites, not urban. The one-fourth power relationship found by Oke (1973) and others (and which I am also finding in GHCN data in the summer) means that a population density increase from 1 to 10 persons per sq. km (both “rural”) produces more warming than an urban site going from 1,000 to 1,700 persons per sq. km.
Thus, “rural” sites cannot be assumed to be immune to spurious warming from urbanization. This means that studies that have compared “rural” to “urban” temperature trends haven’t really proved anything.
The mistake people have made is to assume that just because urban locations are warmer than rural locations at any given time that they then have a much larger spurious warming impact on trends over time. That is simply not true.
The Version 6 global average lower tropospheric temperature (LT) anomaly for March 2023 was +0.20 deg. C departure from the 1991-2020 mean. This is up from the February 2023 anomaly of +0.08 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 15 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
2023
Mar
+0.20
+0.23
+0.16
-0.14
-1.44
+0.17
+0.40
The USA48 region had the 2nd coldest March in the 45-year satellite record, 1.44 deg. C below the 30-year normal. The coldest March was in 1981, at 1.91 deg. C below normal.
The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for March, 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:
This is the third in my (never-ending, it appears) series on measuring the effect of Urban Heat Islands (UHI) on land surface temperature trends.
In Parts I and II I emphasized the Landsat-based “built-up” structure dataset as a proxy for urbanization, which I’m sure we will continue to examine as part of our Department of Energy grant to examine (mostly) satellite-based methods and datasets for testing climate models and their predictions of global warming.
Much of the original research on the UHI effect (e.g. T.R. Oke, 1973 and later) related warming to the total population of towns and cities. Since population datasets extend back in time much further than the satellite period, they can provide information on the UHI effect going back well before 1900. In the last few weeks I’ve taken a detour from using the Landsat-based diagnoses of human settlement built-up structures as a proxy for urbanization, to population density (PD). Along the way I’ve had to investigate issues related to low correlations, and linear regression (specifically, regression dilution). I decided not to cover that here because it’s a little too technical.
The deeper I dig into this project, the more I learn.
Urbanization Effects from 1880 to 2015
I have a lot of results I could show, but I think I will introduce just one plot that should be of interest. Using tens (in the early years) to hundreds of thousands of 2-station pairs of temperature differences and PD differences, I sort those from the smallest to largest 2-station average PD. Then I perform regressions in separate PD intervals (12 to 19 of them) to get the change in temperature with population density (dT/dPD). These coefficients are, in effect, tangents to the non-linear function relating PD to the UHI warming effect. The data shown below are from the month of June in 20-year intervals from 1880 to 2015, in the latitude band 20N to 80N.
Then, by summing those regression coefficients up (integrating them, in calculus terms) from zero PD to the maximum 2-station average PD value, I construct curves of PD vs. UHI effect. I have looked at quite a few published UHI papers, and I cannot find a similar approach to the UHI problem.
Fig. 1. UHI warming (deg. C) curves as a function of 10×10 km population density at GHCN temperature monitoring sites, in five different 20-year periods from 1880 to 2015. Population density (PD, persons per sq. km) and temperature data are from the month of June every 10 years, and all station pairs within 150 km of each other, and within 300 m elevation of each other, are included. PD data com from the HYDE 3.2 dataset, which is on a ~10×10 km global grid.
I have to admit, the results in Fig. 1 are not what I expected. They show the total UHI effect being stronger in the late 19th Century, and weakening somewhat since then. (Remember, because these results are based upon 2-station differences, these are spatial relationships, that is, for the 1880, 1890, 1900 period there is a greater temperature difference between rural and heavily populated locations than in later decades.)
I do not have a ready explanation for this, and ideas are welcome.
If the results were reversed, I would guess it is due to larger errors in early population estimates, since errors in the independent variable (PD) reduces the regression slope (dT/dPD) below the “true” relationship (regression dilution). But just the opposite is happening. And, it cannot be due to much lower numbers of stations in the early periods because that leads to only noise in regression coefficients, not systematic bias.
Some Thoughts
From reading the literature, I think this is rather novel approach that avoids a common problem: the usual separation of stations into “rural” versus “urban” categories. Because the curves in Fig. 1 are non-linear, a nearly rural station will experience much more warming from a given increase in population than will very urban site. Thus, previous investigations that found little difference in temperature trends between urban and rural sites don’t really prove anything. My methodology avoids that problem by constructing curves that start at zero population density (truly rural conditions).
Eventually, all of this will lead to an estimation of how much of the land warming (say, since 1880) has been spurious due to the Urban Heat Island effect. As I have mentioned previously, I don’t believe it will be large. But it needs to be documented.
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:
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.
Fig. 1. The 40-year change in urbanization over the Southeast U.S. between 1975 and 2014.
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.
Fig. 2. Average forty-year change (1975 to 2014) in Landsat-based urbanization (BU) values over 19,885 GHCN stations from 20N to 82.5N at five different averaging scales of the 1 km BU 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.
Fig. 3. Computed bin-average change in temperature with change in urbanization (BU), in 2-station BU average bins of 0-2%, 2-5%, 5-10%, 10-20%, 20-30%, 30-40%, 40-50%, 50-60%, 60-70%, and 70-100%, for four seasons and all GHCN stations in the 30N-70N latitude band. Solid lines are for adjusted (homogenized) GHCN data, and dashed lines are for unadjusted data.
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).
Fig. 4. Seasonal average UHI effects across all GHCN stations between 30N and 70N by integrating the dT/dBU values in Fig. 3 from 0% to 100%, for adjusted (homogenized) temperature data (solid) and unadjusted data (dashed). The black curve is a power law relationship with temperature increasing as the square root of urbanization.
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.
Fig. 5. GHCN station temperature trend adjustments from the homogenization procedure inexplicably increase the station temperature trends as growth in urbanization occurs, rather than decrease them as would be expected if NOAA’s homogenization procedure was removing spurious warming from urban heat island effects.
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:
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