SUMMARY: The Urban Heat Island (UHI) is shown to have affected U.S. temperature trends in the official NOAA 1,218-station USHCN dataset. I argue that, based upon the importance of quality temperature trend calculations to national energy policy, a new dataset not dependent upon the USHCN Tmax/Tmin observations is required. I find that regression analysis applied to the ISD hourly weather data (mostly from airports)  between many stations’ temperature trends and local population density (as a UHI proxy) can be used to remove the average spurious warming trend component due to UHI. Use of the hourly station data provides a mostly USHCN-independent measure of the U.S. warming trend, without the need for uncertain time-of-observation adjustments. The resulting 311-station average U.S. trend (1973-2020), after removal of the UHI-related spurios trend component, is about +0.13 deg. C/decade, which is only 50%  the USHCN trend of +0.26 C/decade. Regard station data quality, variability among the raw USHCN station trends is 60% greater than among the trends computed from the hourly data, suggesting the USHCN raw data are of a poorer quality. It is recommended that an de-urbanization of trends should be applied to the hourly data (mostly from airports) to achieve a more accurate record of temperature trends in land regions like the U.S. that have a sufficient number of temperature data to make the UHI-vs-trend correction.

The Urban Heat Island: Average vs. Trend Effects

In the last 50 years (1970-2020) the population of the U.S. has increased by a whopping 58%. More people means more infrastructure, more energy consumption (and waste heat production), and even if the population did not increase, our increasing standard of living leads to a variety of increases in manufacturing and consumption, with more businesses, parking lots, air conditioning, etc.

As T.R. Oke showed in 1973 (and many others since), the UHI has a substantial effect on the surface temperatures in populated regions, up to several degrees C. The extra warmth comes from both waste heat and replacements of cooler vegetated surfaces with impervious and easily heated hard surfaces. The effects can occur on many spatial scales: a heat pump placed too close to the thermometer (a microclimate effect) or a large city with outward-spreading suburbs (a mesoscale effect).

In the last 20 years (2000 to 2020) the increase in population has been largely in the urban areas, with no average increase in rural areas. Fig. 1 shows this for 311 hourly weather station locations that have relatively complete weather data since 1973.

Fig. 1. U.S. population increases around hourly weather stations have been in the more populated areas (except for mostly densely populated ones), with no increase in rural areas.

This might argue for only using rural data for temperature trend monitoring. The downside is that there are relatively few station locations which have population densities less than, say, 20 persons per sq. km., and so the coverage of the United States would be pretty sparse.

What would be nice is that if the UHI effect could be removed on a regional basis based upon how the average warming trends increase with population density. (Again, this is not removal of the average difference in temperature between rural and urban areas, but the removal of spurious temperature trends due to UHI effects).

But does such a relationship even exist?

UHI Effects on the USHCN Temperature Trends (1973-2020)

The most-cited surface temperature dataset for monitoring global warming trends in the U.S. is the U.S. Historical Climatology Network (USHCN). The dataset has a fixed set of 1,218 stations which have records extending back over 100 years. Because most of the stations’ data consist of daily maximum and minimum temperatures (Tmax and Tmin) measured at a single time daily, and that time of observation (TOBs) changed around 1960 from the late afternoon to the early morning (discussion here), there was a TOBs-related temperature bias that occurred, which is somewhat uncertain in magnitude but still must be adjusted for.

NOAA makes available both the raw unadjusted, and adjusted (TOBs & spatial ‘homogenization’) data. The following plot (Fig. 2) shows how both of the datasets’ station temperature trends are correlated with the population density, which should not be the case if UHI effects have been removed from the trends.

Fig.2. USHCN station temperature trends are correlated with population density, which should not be the case if the Urban Heat Island effect on trends has been removed.

Any UHI effect on temperature trends would be difficult to remove through NOAA’s homogenization procedure alone. This is because, if all stations in a small area, both urban and rural, are spuriously warming from UHI effects, then that signal would not be removed because it is also what is expected for global warming. ‘Homogenization’ adjustments can theoretically make the rural and urban trends look the same, but that does not mean the UHI effect has been removed.

Instead, one must examine the data in a manner like that in Fig. 2, which reveals that even the adjusted USHCN data (red dots) still have about a 30% overestimate of U.S. station-average trends (1973-2020) if we extrapolate a regression relationship (red dashed line, 2nd order polynomial fit) to zero population density. Such an analysis, however, requires many stations (thus large areas) to measure the average effect. It is not clear just how many stations are required to obtain a robust signal. The greater the number of stations needed, the larger the regional area required.

U.S. Hourly Temperature Data as an Alternative to USHCN

There are many weather stations in the U.S. which are (mostly) not included in the USHCN set of 1,218 stations. These are the operational  hourly weather stations operated by NWS, FAA, and other agencies, and which provide most of the data the National Weather Service reports to you. The data are included in the multi-agency Integrated Surface Database (ISD) archive.

The data archive is quite large, since it has (up to) hourly resolution data (higher with ‘special’ observations during changing weather) and many weather variables (temperature, dewpoint, wind, air pressure, precipitation) for many thousands of stations around the world. Many of the stations (at least in the U.S.) are at airports.

In the U.S., most of these measurements and their reporting are automated now, with the AWOS and ASOS systems.

This map shows all of the stations in the archive, although many of these will not have full records for whatever decades of time are of interest.

Fig. 3. Locations of ISD surface weather data quality-controlled and stored at NOAA.

The advantage of these data, at least in the United States, is that the equipment is maintained on a regular basis. When I worked summers at a National Weather Service office in Michigan, there was a full-time ‘met-tech’ who maintained and adjusted all of the weather-measuring equipment. 

Since the observations are taken (nominally) at the top of the hour, there is no uncertain TOBs adjustment necessary as with the USHCN daily Tmax/Tmin data.

The average population density environment is markedly different between the ISD (‘hourly’) stations and the USHCN stations, as is shown in Fig. 4.

Fig. 4. The dependence of U.S. weather station population density on averaging area is markedly different between 1,218 USHCN and 311 high-quality ISD (‘hourly’) stations, mainly due to the measurement of the hourly data at “uninhabited” airports to support aviation safety.

In Fig. 4 we see that the population density in the immediate vicinity of the ISD stations averages only 100 people in the immediate 1 sq. km area since no one ‘lives’ at the airport, but then increases substantially with averaging area since airports exist to serve population centers.

In contrast, the USHCN stations have their highest population density right in the vicinity of the weather station (over 400 persons in the first sq. km), which then drops off with distance away from the station location.

How such differences affect the magnitude of UHI-dependent spurious warming trends is unknown at this point.

UHI Effects on the Hourly Temperature Data

I have analyzed the U.S. ISD data for the lower-48 states for the period 1973-2020. (Why 1973? Because many of the early records were on paper, and at hourly time resolution, that represents a lot of manual digitizing. Apparently, 1973 is as far back as many of those stations data were digitized and archived).

To begin with, I am averaging only the 00 UTC and 12 UTC temperatures (approximately the times of maximum and minimum temperatures in the United States). I required those twice-daily measurements to be reported on at least 20 days in order for a month to be considered for inclusion, and then at least 10 of 12 months from a station to have good data for a year of that station’s data to be stored.

Then, for temperature trend analysis, I required that 90% of the years 1973-2020 to have data, including the first 2 years (1973, 1974) and the last 2 years (2019-2020), since end years can have large effects on trend calculations.

The resulting 311 stations have an 8.7% commonality with the 1,218 USHCN stations. That is, only 8.7% of the (mostly-airport) stations are also included in the 1,218-station USHCN database, so the two datasets are mostly (but not entirely) independent.

I then plotted the Fig. 2 equivalent for the ISD stations (Fig. 5).

Fig. 5. As in Fig. 2, but for the ISD (mostly airport) station trends for the average of daily 00 and 12 UTC temperatures. Where the regression lines intercept the zero population axis is an estimate of the U.S. temperature trend during 1973-2020 with spurious UHI trend effects removed.

We can see for the linear fit to the data, extrapolation of the line to zero population density gives a 311-station average warming trend of +0.13 deg. C/decade.

Significantly, this is only 50% of the USHCN 1,218-station official TOBs-adjusted, homogenized average trend of +0.26 C/decade.

It is also significant that this 50% reduction in the official U.S temperature trend is very close to what Anthony Watts and co-workers obtained in their 2015 analysis using the very best-sited USHCN stations.

I also include the polynomial fit in Fig. 5, since my use of the fourth root of the population density is not meant to perfectly capture the nonlinearity of the UHI effect, and some nonlinearity can be expected to remain. In that case, the extrapolated warming trend at zero population density is close to zero. But for the purpose of the current discussion, I will conservatively use the linear fit in Fig. 5. (The logarithm of the population density is sometimes also used, but is not well behaved as the population approaches zero.)

Evidence that the raw ISD station trends are of higher quality than those from UHCN is in the standard deviation of those trends:

Std. Dev. of 1,218 USHCN (raw) trends = +0.205 deg. C/decade

Std. Dev. of 311 ISD (‘hourly’) trends = +0.128 deg. C/decade

Thus, the variation in the USHCN raw trends is 60% greater than the variation in the hourly station trends, suggesting the airport trends have fewer time-changing spurious temperature influences than do the USHCN station trends.

Conclusions

For the period 1973-2020:

1. The USHCN homogenized data still have spurious warming influences related to urban heat island (UHI) effects. This has exaggerated the global warming trend for the U.S. as a whole. The magnitude of that spurious component is uncertain due to the black-box nature of the ‘homogenization’ procedure applied to the raw data.
2. An alternative analysis of U.S. temperature trends from a mostly independent dataset from airports suggests that the U.S. UHI-adjusted average warming trend (+0.13 deg. C/decade) might be only 50% of the official USHCN station-average trend (+0.26 deg. C/decade).
3. The raw USHCN trends have 60% more variability than the raw airport trends, suggesting higher quality of the routinely maintained airport weather data.

Future Work

This is an extension of work I started about 8 years ago, but never finished. John Christy and I are discussing using results based upon this methodology to make a new U.S. surface temperature dataset which would be updated monthly.

I have only outlined the very basics above. One can perform similar calculations in sub-regions (I find the western U.S. results to be similar to the eastern U.S. results). Also, the results would probably have a seasonal dependence in which case that should be calculated by calendar month.

Of course, the methodology could also be applied to other countries.

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for January, 2021 was +0.12 deg. C, down a little from the December, 2020 value of +0.15 deg. C. NOTE: We have changed the 30-year averaging period from which we compute anomalies to 1991-2020, from the old period 1981-2010. This change does not affect the temperature trends.

The linear warming trend since January, 1979 remains at +0.14 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 13 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
2020 01  0.42  0.44  0.41  0.52  0.57 -0.22  0.41
2020 02  0.59  0.74  0.45  0.63  0.17 -0.27  0.20
2020 03  0.35  0.42  0.28  0.53  0.81 -0.96 -0.04
2020 04  0.26  0.26  0.25  0.35 -0.70  0.63  0.78
2020 05  0.42  0.43  0.41  0.53  0.07  0.83 -0.20
2020 06  0.30  0.29  0.30  0.31  0.26  0.54  0.97
2020 07  0.31  0.31  0.31  0.28  0.44  0.26  0.26
2020 08  0.30  0.34  0.26  0.45  0.35  0.30  0.25
2020 09  0.40  0.41  0.39  0.29  0.69  0.24  0.64
2020 10  0.38  0.53  0.22  0.24  0.86  0.94 -0.01
2020 11  0.40  0.52  0.27  0.17  1.45  1.09  1.28
2020 12  0.15  0.08  0.22 -0.07  0.29  0.43  0.13
2021 01  0.12  0.34 -0.09 -0.08  0.36  0.49 -0.52

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for January, 2021 should be available within the next few 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

Several lines of evidence suggest observed warming trends are not nearly as large as what you have been told.

It’s been almost eight years since I posted results on my analysis of the global Integrated Surface Database (ISD) temperature data. Despite finding evidence that urbanization effects on temperature measurements have not been removed from official land temperature datasets, I still refer people to the official products (e.g. from NOAA GHCN, HadCRUT, etc.). This is because I never published any results from my analysis.

But I’ve started thinking again about the question, Just how much warming has there been in recent decades (say, the last 50 years)? The climate models suggest that this should have been the period of most rapid warming, due to ever-increasing atmospheric CO2 combined with a reduction in aerosol pollution. Since those models are the basis for proposed changes in energy policy, it is important that the observations to which they are compared be trustworthy.

A Review of the Diagnosed Urban Heat Island Effect

The official datasets of land surface temperature are (we are told) already adjusted for Urban Heat Island (UHI) effects. But as far as I know, it has never been demonstrated that the spurious warming from urban effects have been removed. Making temperature trends be the same independent of urbanization does NOT mean urban warming effects have been removed. It could be that spurious warming has simply been spread around to the non-affected stations.

Back in 2010 I quantified the Urban Heat Island (UHI) effect, based upon the difference in absolute temperatures between closely-spaced neighboring stations having different population densities (PD). The ISD temperature data are not max/min (as in GHCN), but data taken hourly, with the longest-record stations reporting at just the 6-hourly synoptic times (00, 06, 12, 18 UTC). Because there were many more stations added to the global dataset in 1973, all of my analyses started then.

By using many station pairs from low to high population densities, I constructed the cumulative UHI effect as a function of population density. Here are the results from global data in the year 2000:

Fig. 1. Diagnosed average Urban Heat Island warming in 2000 from over 11,000 closely spaced station pairs having different population densities.

As can be seen, the largest warming effect with a change in population density occurs at the lowest population densities (not a new finding), with the most total warming at the highest population densities.

The Effect of Population Density on U.S. Station Temperature Trends

In 2012 I experimented with methods to removed the observed UHI effect in the raw ISD 6-hourly data using population density as a proxy. As you can see in the second of the two graphs below, the highest population density stations had ~0.25 C/decade warming trend, with a reduced warming trend as population density was reduced:

Fig. 2. U.S. surface temperature trends as a function of local population density at the station locations: top (raw), bottom (averages into 4 groups).

Significantly, extrapolating to zero population density would give essentially no warming in the United States during 1973-2011. As we shall see (below) official temperature datasets say this period had a substantial warming trend, consistent with the warming in the highest population density locations.

How can one explain this result other than, at least for the period 1973-2011, (1) spurious warming occurred at the higher population density stations, and (2) the evidence supports essentially no warming if there were no people (zero population density) to modify the microclimate around thermometer sites?

I am not claiming there has been no global warming (whatever the cause). I am claiming that there is evidence of spurious warming in thermometer data which must be removed.

Next, we will examine how well that effect has been removed.

How Does this Compare to the ‘Official’ Temperature Trends?

Since I performed these analyses almost 10 years ago, the ‘official’ temperature datasets have been adjusted several times. For the same period I analyzed 8-10 years years ago, look at how some of these datasets have increased the temperature trends (I used only CRUTem3 back then):

Fig. 3. U.S. surface temperature trend from different datasets.

The CRUTem3 data produce a trend reasonably close to the raw, unadjusted 6-hourly ISD-based data (the correlation of the two datasets’ monthly anomaly time series was 0.994). Note that the latest USHCN data in the above graph has the most warming, at +0.26 C/decade.

Note that this is about the same as the trend I get with the stations having the highest (rather than lowest) population density. Anthony Watts reported qualitatively similar results using different data back in 2015.

How in the world can the warming result from NOAA be reconciled with the (possible zero warming) results in Fig. 2? NOAA uses a complex homogenization procedure to make its adjustments, but it seems to me the the results in Fig. 2 suggest that their procedures might be causing spurious warming trends in the data. I am not the first to point this out; others have made the same claims over the years. I am simply showing additional quantitative evidence.

I don’t see how it can be a change in instrumentation, since both rural and urban stations changed over the decades from liquid-in-glass thermometers in Stevenson screens, to digital thermistors in small hygrothermometer enclosures, to the new automated ASOS measurement systems.

Conclusion

It seems to me that there remains considerable uncertainty in just how much the U.S. has warmed in recent decades, even among the established, official, ‘homogenized’ datasets. This has a direct impact on the “validation” of climate models relied upon by the new Biden Administration for establishing energy policy.

I would not be surprised if such problems exist in global land temperature datasets in addition to the U.S.

I’m not claiming I know how much it has (or hasn’t) warmed. Instead, I’m saying I am still very suspicious of existing official land temperature datasets.

Joe Biden’s administration has made climate change one of its top priorities. Photographer: Doug Mills/The New York Times/Bloomberg

In what appears to be a never-ending string of ineffective efforts to force the public to use expensive, unreliable, intermittent, and not-widely-deployable renewable energy, the Biden Administration is issuing an executive order that (among other things) directs federal agencies to end fossil fuel subsidies.

Personally, I would not mind if all federal subsidies were ended, since all that subsidies do is put the government, rather than the consumer, in charge of what you spend your money on.

But federal subsidies on fossil fuels represent less that 3% of the revenues of the fossil fuel industry. This action will have essentially no impact on an economy that still runs on fossil fuels. That 3% will be voluntarily paid by the consumer, just directly rather than through subsidies.

In contrast, renewables currently enjoy 25 times the level of subsides per unit of energy produced as do fossil fuels, and the market penetration of EVs is still only 1.2%. One can see that massive government meddling in the energy market is the only way that people will — at least for the foreseeable future — “choose” renewables over fossil fuels.

So, while environmentalists might applaud Biden’s decision, the effect on the energy markets will be barely measurable, if at all.

You see, when it comes to global warming, modern environmentalism depends upon feelings over facts. Even if all CO2 emissions in the U.S. were to end, the impact on global temperatures by 2100 would be small. This is because the U.S. now produces less than 15% of the global total greenhouse gas emissions. The same is true if all countries abide by their commitments under the Paris Climate Agreement, which makes Biden’s rejoining that Agreement rather pointless. The effect of Paris is calculated to be a 0.2 deg. C reduction in warming by 2100, which is too small to measure over the next 80 years with temperature monitoring technologies currently in place.

Even the godfather of modern global warming alarmism, NASA’s James Hansen says the Paris Agreement is ineffective and a “fraud”, and that only massive taxation of (i.e. punishment for) using fossil fuels will make much difference.

To show just how much CO2 emissions will have to decrease to affect the atmospheric CO2 concentration, just look at what happened (or didn’t happen) last year. The U.S. Energy Information Agency (EIA) estimates that the economic downturn in 2020 produced only an 11% reduction in fossil fuel use. The resulting change in atmospheric CO2 concentration was unmeasurable:

The 11% reduction in global CO2 emissions in 2020 had no measurable impact on atmospheric CO2 concentrations at Mauna Loa, Hawaii.

Furthermore, while we nibble around the edges of the “carbon pollution” problem, China’s CO2 emissions continue to grow.

The U.S. has led the way in reducing CO2 emissions, mainly through a market-driven switch from coal to natural gas in recent years, China’s emissions continue to grow.

And while the “social cost of carbon” continues to be advanced as the justification for reducing CO2 emissions, no one wants to talk about the social benefits. For example, Nature loves the stuff. It is estimated that global agricultural productivity has increased by $3.5 Trillion from the extra CO2 in the atmosphere. It is well known that excessive cold kills far more people than excessive heat. There is no evidence that recent, modest global warming has caused a global-average increase in severe weather.

The claims by China that they will become “carbon neutral” by 2060 is just political posturing. One thing I have learned about China in recent decades is that their political culture is to say anything necessary to nominally appease other countries, and then do just the opposite if it suits their national interests. With over four times the population of the U.S., one can see why they would not want the U.S. (or any other county) dictating their behavior, especially as they continue to lift millions out of poverty.

Not unless the Biden Administration pushes for a massive increase in the taxation of fossil fuels, and then embraces either nuclear plant construction or widespread wind and solar projects to service a huge fleet of electric vehicles (currently at 1.2% of U.S. market penetration) will there be any substantial move away from fossil fuels.

Anything less will only falsely assuage fears rather than address facts.

As part of my Jan. 19 presentation for Friends of Science about there being no climate emergency, I also examined surface temperature in Canada to see how much warming there has been compared to climate models.

Canada has huge year-to-year variability in temperatures due to its strong continental climate. So, to examine how observed surface temperature trends compare to climate model simulations, you need many of those simulations, each of which exhibits its own large variability.

I examined the most recent 30-year period (1991-2020), using a total of 108 CMIP5 simulations from approximately 20 different climate models, and computed land-surface trends over the latitude bounds of 51N to 70N, and longitude bounds 60W to 130W, which approximately covers Canada. For observations, I used the same lat/lon bounds and the CRUTem5 dataset, which is heavily relied upon by the UN IPCC and world governments. All data were downloaded from the KNMI Climate Explorer.

First let’s examine the annual average temperature departures from the 1981-2010 average, for the average of the 108 model simulations compared to the observations. We see that Canada has been warming at only 50% the rate of the average of the CMIP5 models; the linear trends are +0.23 C/decade and +0.49 C/decade, respectively. Note that in 7 of the last 8 years, the observations have been below the average of the models.

Fig. 1. Yearly temperature departures 1991-2020 from the 1981-2010 mean in Canada in observations (blue) versus the average of 108 CMIP5 climate model simulations (red). The +/-1 standard deviation bars indicate the variability among the 108 individual model simulations.

Next, I show the individual models’ trends compared to the observed trends, with a histogram of the ranked values from the least warming to the most warming, 1991-2020.

Fig. 2. Ranked Canada surface temperature trends (1991-2020) for the 108 model simulations and the observations.

Note that the 93.5% of the model simulations have warmer temperature trends than the observations exhibit.

These results from Canada are generally consistent with the results I have found in the Midwest U.S. in the summertime, where the CMIP5 models warm, on average, 4 times faster than the observations (since 1970), and 6 times faster in a limited number of the newer CMIP6 model simulations.

Implications

The Paris Climate Accords, among other national and international efforts to reduce greenhouse gas emissions, assume warming estimates which are approximately the average of the various climate models. Thus, these results impact directly on those proposed energy policy decisions.

As you might be aware, proponents of those climate models often emphasize the general agreement between the models and observations over a long period of time, say since 1900.

But this is misleading.

We would expect little anthropogenic global warming signal to emerge from the noise of natural climate variability until (approximately) the 1980s. This is for 2 reasons: There was little CO2 emitted up through the 1970s, and even as the emissions rose after the 1940s the cooling effect of anthropogenic SO2 emissions was canceling out much of that warming. This is widely agreed to by climate modelers as well.

Thus, to really get a good signal of global warming — in both observations and models — we should be examining temperature trends since approximately the 1980s. That is, only in the decades since the 1980s should we be seeing a robust signal of anthropogenic warming against the background of natural variability, and without the confusion (and uncertainty) in large SO2 emissions in the mid-20th century.

And as each year passes now, the warming signal should grow slightly stronger.

I continue to contend that climate models are now producing at least twice as much warming as they should, probably due to an equilibrium climate sensitivity which is about 2X too high in the climate models. Given that the average CMIP6 climate sensitivity is even larger than in CMIP5 — approaching 4 deg. C — it will be interesting to see if the divergence between models and observations (which began around the turn of the century) will continue into the future.

On Tuesday evening, January 19, at 8 p.m. CST there will be a 30 minute livestream presentation where I cover the most important reasons why there is no climate emergency. I just reviewed the video and I am very satisfied with it.

In only 1/2 hour I cover what I consider to be the most important science issues, the disinformation campaign that spreads climate hysteria, some of the harm that will be caused by forcing expensive and unreliable renewable energy upon humanity, and the benefits of more CO2 in the atmosphere.

You can go to the FoS website for more information. The tickets are $15, and I will be doing a live Q&A after the event.

Yesterday, the New York Times and other media outlets repeated the falsehood that I am a climate “denier”.

I usually ignore such potentially libelous statements, otherwise I’d be defending myself every week.

So, to set the record straight, here’s what I believe… I’ll let you decide whether I’m a climate “denier”.

1. I believe the climate system has warmed (we produce one of the global datasets that shows just that, which is widely used in the climate community), and that CO2 emissions from fossil fuel burning contributes to that warming. I’ve said this for many years.
2. I believe future warming from a doubling of atmospheric CO2 would be somewhere in the range of 1.5 to 2 deg. C, which is actually within the range of expected warming the UN Intergovernmental Panel on Climate Change (IPCC) has advanced for 30 years now. (It could be less than this, but we simply don’t know).

As people who frequent this blog well know, I have held these views for many years. I routinely take other skeptics to task for believing such things as “there is no greenhouse effect”, or “it’s impossible for a cold atmosphere to make the Earth’s surface even warmer”.

So, Why Is Roy Spencer Called a Climate Denier?

In the case of global warming, alarmists apparently insist that you must believe that global warming is a “crisis” or an “emergency”, or else you will be thrown under the bus.

They claim we must embrace expensive (and ineffective) sources of alternative energy. But, like Bjorn Lomborg (who actually believes the alarmist predictions of future warming) and many others, I believe it will be much worse for humanity if we abandon fossil fuels before alternative technologies are abundant, affordable, and practical.

Human flourishing requires access to affordable energy, which is required for almost all human activities. It is immoral to deny fossil-fueled electricity to the world’s poor, and its replacement in even the richest countries still destroys prosperity, especially for the poor.

For believing these things, I am declared evil, apparently on par with a Holocaust denier (thus the rhetoric).

Here’s some of that rhetoric from the Daily Kos yesterday, which covered the firing of White House skeptical scientists Dr. David Legates and Dr. Ryan Maue (emphasis added):

“The bundle of boring and basic denial myths compiled to appease the deadly denial of the Trump administration was published first, it appears at least, by U-Alabama Huntsville’s Dr. Roy Spencer, who contributed a chapter. His post about the flyers was then bounced around the deniersphere, where the same audiences who gobble up unhinged conspiracies about voter fraud or satan-worshipping Democrats can eagerly read the climate denial versions of those violent fantasies.”

This is apparently what happens when you take frustrated creative writers and give them jobs as journalists.

Given recent political events it appears there is now a renewed efforts to have dissenting voices silenced through “cancel culture”, removal of websites, public ridicule, censorship, etc.

Unity in our country will, apparently, be achieved, because once dissenting voices are silenced, “unity” is all that is left.

Dr. David Legates has been Fired by White House OSTP Director and Trump Science Advisor, Kelvin Droegemeier

President Donald Trump has been sympathetic with the climate skeptics’ position, which is that there is no climate crisis, and that all currently proposed solutions to the “crisis” are economically harmful to the U.S. specifically, and to humanity in general.

Today I have learned that Dr. David Legates, who had been brought to the Office of Science and Technology Policy to represent the skeptical position in the Trump Administration, has been fired by OSTP Director and Trump Science Advisor, Dr. Kelvin Droegemeier.

The event that likely precipitated this is the invitation by Dr. Legates for about a dozen of us to write brochures that we all had hoped would become part of the official records of the Trump White House. We produced those brochures (no funding was involved), and they were formatted and published by OSTP, but not placed on the WH website. My understanding is that David Legates followed protocols during this process.

So What Happened?

What follows is my opinion. I believe that Droegemeier (like many in the administration with hopes of maintaining a bureaucratic career in the new Biden Administration) has turned against the President for political purposes and professional gain. If Kelvin Droegemeier wishes to dispute this, let him… and let’s see who the new Science Advisor/OSTP Director is in the new (Biden) Administration.

I would also like to know if President Trump approved of his decision to fire Legates.

In the meantime, we have been told to remove links to the brochures, which is the prerogative of the OSTP Director since they have the White House seal on them.

But their content will live on elsewhere, as will Dr. Droegemeier’s decision.

The Version 6.0 global average lower tropospheric temperature (LT) anomaly for December, 2020 was +0.27 deg. C, down substantially from the November, 2020 value of +0.53 deg. C.For comparison, the CDAS global surface temperature anomaly for the last 30 days at Weatherbell.com was +0.31 deg. C.

2020 ended as the 2nd warmest year in the 42-year satellite tropospheric temperature record at +0.49 deg. C, behind the 2016 value of +0.53 deg. C.

Cooling in December was largest over land, with 1-month drop of 0.60 deg. C, which is the 6th largest drop out of 504 months. This is likely the result of the La Nina now in progress.

The linear warming trend since January, 1979 remains at +0.14 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 (1981-2010) average for the last 24 months are:

YEAR MO GLOBE NHEM. SHEM. TROPIC USA48 ARCTIC AUST 
 2019 01 +0.38 +0.35 +0.41 +0.36 +0.53 -0.14 +1.14
 2019 02 +0.37 +0.46 +0.28 +0.43 -0.03 +1.05 +0.05
 2019 03 +0.34 +0.44 +0.25 +0.41 -0.55 +0.97 +0.58
 2019 04 +0.44 +0.38 +0.51 +0.54 +0.49 +0.93 +0.91
 2019 05 +0.32 +0.29 +0.35 +0.39 -0.61 +0.99 +0.38
 2019 06 +0.47 +0.42 +0.52 +0.64 -0.64 +0.91 +0.35
 2019 07 +0.38 +0.32 +0.44 +0.45 +0.10 +0.34 +0.87
 2019 08 +0.38 +0.38 +0.39 +0.42 +0.17 +0.44 +0.23
 2019 09 +0.61 +0.64 +0.59 +0.60 +1.13 +0.75 +0.57
 2019 10 +0.46 +0.64 +0.27 +0.30 -0.04 +1.00 +0.49
 2019 11 +0.55 +0.56 +0.54 +0.55 +0.21 +0.56 +0.37
 2019 12 +0.56 +0.61 +0.50 +0.58 +0.92 +0.66 +0.94
 2020 01 +0.56 +0.60 +0.53 +0.61 +0.73 +0.12 +0.65
 2020 02 +0.75 +0.96 +0.55 +0.76 +0.38 +0.02 +0.30
 2020 03 +0.47 +0.61 +0.34 +0.63 +1.08 -0.72 +0.16
 2020 04 +0.38 +0.43 +0.34 +0.45 -0.59 +1.03 +0.97
 2020 05 +0.54 +0.60 +0.49 +0.66 +0.17 +1.15 -0.15
 2020 06 +0.43 +0.45 +0.41 +0.46 +0.37 +0.80 +1.20
 2020 07 +0.44 +0.45 +0.42 +0.46 +0.55 +0.39 +0.66
 2020 08 +0.43 +0.47 +0.38 +0.59 +0.41 +0.47 +0.49
 2020 09 +0.57 +0.58 +0.56 +0.46 +0.96 +0.48 +0.92
 2020 10 +0.54 +0.71 +0.37 +0.37 +1.09 +1.23 +0.24
 2020 11 +0.53 +0.67 +0.39 +0.29 +1.56 +1.38 +1.41
 2020 12 +0.27 +0.22 +0.32 +0.05 +0.56 +0.59 +0.23

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for December, 2020 should be available within the next few 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

As part of a DOE contract John Christy and I have, we are using satellite data to examine climate model behavior. One of the problems I’ve been interested in is the effect of El Nino and La Nina (ENSO) on our understanding of human-caused climate change. A variety of ENSO records show multi-decadal variations in this activity, and it has even showed up in multi-millennial runs of a GFDL climate model.

Since El Nino produces global average warmth, and La Nina produces global average coolness, I have been using our 1D forcing feedback model of ocean temperatures (published by Spencer & Braswell, 2014) to examine how the historical record of ENSO variations can be included, by using the CERES satellite-observed co-variations of top-of-atmosphere (TOA) radiative flux with ENSO.

I’ve updated that model to match the 20 years of CERES data (March 2000-March 2020). I have also extended the ENSO record back to 1525 with the Braganza et al. (2009) multi-proxy ENSO reconstruction data. I intercalibrated it with the Multivariate ENSO Index (MEI) data up though the present, and further extended into mid-2021 based upon the latest NOAA ENSO forecast. The Cheng et al. temperature data reconstruction for the 0-2000m layer is also used to calibrate the model adjustable coefficients.

I had been working on an extensive blog post with all of the details of how the model works and how ENSO is represented in it, which was far too detailed. So, I am instead going to just show you some results, after a brief model description.

1D Forcing-Feedback Model Description

The model assumes an initial state of energy equilibrium, and computes the temperature response to changes in radiative equilibrium of the global ocean-atmosphere system using the CMIP5 global radiative forcings (since 1765), along with our calculations of ENSO-related forcings. The model time step is 1 month.

The model has a mixed layer of adjustable depth (50 m gave optimum model behavior compared to observations), a second layer extending to 2,000m depth, and a third layer extending to the global-average ocean bottom depth of 3,688 m. Energy is transferred between ocean layers proportional to their difference in departures from equilibrium (zero temperature anomaly). The proportionality constant(s) have the same units as climate feedback parameters (W m-2 K-1), and are analogous to the heat transfer coefficient. A transfer coefficient of 0.2 W m-2 K-1 for the bottom layer produced 0.01 deg. C of net deep ocean warming (below 2000m) over the last several decades which Cheng et al. mentioned there is some limited evidence for.

The ENSO related forcings are both radiative (shortwave and longwave), as well as non-radiative (enhanced energy transferred from the mixed layer to deep ocean during La Nina, and the opposite during El Nino). These are discussed more in our 2014 paper. The appropriate coefficients are adjusted to get the best model match to CERES-observed behavior compared to the MEIv2 data (2000-2020), observed SST variations, and observed deep-ocean temperature variations. The full 500-year ENSO record is a combination of the Braganza et al. (2009) year data interpolated to monthly, the MEI-extended, MEI, and MEIv2 data, all intercalibrated. The Braganza ENSO record has a zero mean over its full period, 1525-1982.

Results

The following plot shows the 1D model-generated global average (60N-60S) mixed layer temperature variations after the model has been tuned to match the observed sea surface temperature temperature trend (1880-2020) and the 0-2000m deep-ocean temperature trend (Cheng et al., 2017 analysis data).

Fig. 1. 1D model temperature variations for the global oceans (60N-60S) to 50 m depth, compared to observations.

Note that the specified net radiative feedback parameter in the model corresponds to an equilibrium climate sensitivity of 1.91 deg. C. If the model was forced to match the SST observations during 1979-2020, the ECS was 2.3 deg. C. Variations from these values also occurred if I used HadSST1 or HadSST4 data to optimize the model parameters.

The ECS result also heavily depends upon the accuracy of the 0-2000 meter ocean temperature measurements, shown next.

Fig. 2. 1D model temperature changes for the 0-2000m layer since 1940, and compared to observations.

The 1D model was optimized to match the 0-2000m temperature trend only since 1995, but we see in Fig. 2 that the limited data available back to 1940 also shows a reasonably good match.

Finally, here’s what the full 500 year model results look like. Again, the CMIP5 forcings begin only in 1765 (I assume zero before that), while the combined ENSO dataset begins in 1525.

Fig. 3. Model results extended back to 1525 with the proxy ENSO forcings, and since 1765 with CMIP5 radiative forcings.

Discussion

The simple 1D model is meant to explain a variety of temperature-related observations with a physically-based model with only a small number of assumptions. All of those assumptions can be faulted in one way or another, of course.

But the monthly correlation of 0.93 between the model and observed SST variations, 1979-2020, is very good (0.94 for 1940-2020) for it being such a simple model. Again, our primary purpose was to examine how observed ENSO activity affects our interpretation of warming trends in terms of human causation.

For example, ENSO can then be turned off in the model to see how it affects our interpretation of (and causes of) temperature trends over various time periods. Or, one can examine the affect of assuming some level of non-equilibrium of the climate system at the model initialization time.

If nothing else, the results in Fig. 3 might give us some idea of the ENSO-related SST variations for 300-400 years before anthropogenic forcings became significant, and how those variations affected temperature trends on various time scales. For if those naturally-induced temperature trend variations existed before, then they still exist today.