The Version 6 global average lower tropospheric temperature (LT) anomaly for May 2023 was +0.37 deg. C departure from the 1991-2020 mean. This is up from the April 2023 anomaly of +0.18 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 17 months are:

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for May, 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

We hear that a new El Nino forming in the Pacific Ocean is likely to push global-average temperatures to new record highs in 2023.

Setting aside the fact that we have no idea if current temperatures are warmer than during the Medieval Warm Period of ~1,000 years ago, I have to ask…

So what?

Doing something about global warming depends a lot on how much we are asked to pay to fix it. If it was cheap and practical, we would have already transitioned to renewable energy sources.

It also depends upon just how much global warming we have experienced, and whether it is enough to be concerned with. For the global oceans, the climate models enlisted to scare us in a steady stream of alarmist news reports over-predict ocean warming by a factor of 2. In America’s heartland during the summer, the discrepancy is a factor of 6(!). So, clearly, public concern is being inflated by factually incorrect information.

What Temperature do Americans Choose?

When it comes to life in these United States, roughly 50% of U.S. residents have at least a moderate worry about climate change and global warming. As mentioned above, I believe this is largely due to their response to what is reported by the news media, which is routinely exaggerated.

An interesting question that the late Dr. Pat Michaels asked about 25 years ago is, what temperature do Americans choose to live with? We have a large country with a wide range of climates, from frigid winters to tropical year-round, so there is considerable choice of what climate we decide to live in.

Dr. Michaels pointed out (most recently in 2013) that over the years, Americans tend to migrate to warmer climates. Some of us might claim to be concerned about global warming, but we increasingly choose to live where it’s warmer. I’ve updated those calculations to 2022, and the results are the same:

The blue curve is the usual area-averaged temperatures for the Lower 48, while the orange curve is the state population-weighted average. While the area average temperatures have warmed modestly over the last century, the temperatures where people choose to live have increased by twice that amount. (The possibility that Urban Heat Island effects have spuriously warmed these NOAA-reported temperatures is part of a research project we have been involved in).

Some might claim that the migration to states with warmer temperatures has more to do with economic opportunity than with temperature. But who creates economic opportunity? People. And where do people choose to live? Where the weather is warmer.

There’s a reason why people are flocking to Texas and Florida, and not to the Dakotas or Maine. Ultimately, it’s due to the climate. So, while some of us like to think we are Saving the Earth by buying a Tesla, our migration habits are telling a different story.

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:

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:

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 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:

1. an anthropogenic source term, primarily from burning of fossil fuels
2. a constant yearly CO2 sink (removal) rate of 2.05% of the atmospheric “excess” over 295 ppm
3. 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:

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:

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

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.

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:

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