2023 Was the Warmest Year In the 45-Year Satellite Record

The Version 6 global average lower tropospheric temperature (LT) anomaly for December, 2023 was +0.83 deg. C departure from the 1991-2020 mean, down from the November, 2023 anomaly of +0.91 deg. C.

The 2023 annual average global LT anomaly was +0.51 deg. C above the 1991-2020 mean, easily making 2023 the warmest of the 45-year satellite record. The next-warmest year was +0.39 deg. C in 2016. The following plot shows all 45 years ranked from the warmest to coolest.

The linear warming trend since January, 1979 still stands at +0.14 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.19 C/decade over global-averaged land).

It might be partly coincidence, but the +0.51 deg. C number for 2023 from satellites is the same as the surface air temperature estimate from the NOAA/NCEP/NCAR Climate Data Assimilation System (CDAS). Note that the CDAS estimate is only partly based upon actual surface air temperature observations… it represents a physically consistent model-based estimate using a wide variety of data sources (surface observations, commercial aircraft, weather balloons, satellites, etc.). [UPDATE: it appears the CDAS anomalies are not relative to the 1991-2020 base period… I recomputed them, and the CDAS anomaly appears to be +0.45 deg. C, not +0.51 deg. C]:

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

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2022	Jan	+0.03	+0.07	+0.00	-0.23	-0.12	+0.68	+0.10
2022	Feb	+0.00	+0.02	-0.01	-0.24	-0.04	-0.30	-0.49
2022	Mar	+0.16	+0.28	+0.03	-0.07	+0.23	+0.74	+0.03
2022	Apr	+0.27	+0.35	+0.18	-0.04	-0.25	+0.45	+0.61
2022	May	+0.18	+0.25	+0.10	+0.02	+0.60	+0.23	+0.20
2022	Jun	+0.07	+0.08	+0.05	-0.36	+0.47	+0.33	+0.11
2022	Jul	+0.36	+0.37	+0.35	+0.13	+0.85	+0.56	+0.65
2022	Aug	+0.28	+0.32	+0.25	-0.03	+0.60	+0.51	+0.00
2022	Sep	+0.25	+0.43	+0.06	+0.03	+0.88	+0.69	-0.28
2022	Oct	+0.32	+0.44	+0.21	+0.05	+0.17	+0.94	+0.05
2022	Nov	+0.17	+0.21	+0.13	-0.16	-0.50	+0.52	-0.56
2022	Dec	+0.05	+0.13	-0.02	-0.34	-0.20	+0.80	-0.38
2023	Jan	-0.04	+0.05	-0.13	-0.38	+0.12	-0.12	-0.50
2023	Feb	+0.09	+0.17	+0.00	-0.10	+0.68	-0.24	-0.11
2023	Mar	+0.20	+0.24	+0.17	-0.13	-1.43	+0.17	+0.40
2023	Apr	+0.18	+0.11	+0.26	-0.03	-0.37	+0.53	+0.21
2023	May	+0.37	+0.30	+0.44	+0.40	+0.57	+0.66	-0.09
2023	June	+0.38	+0.47	+0.29	+0.55	-0.35	+0.45	+0.07
2023	July	+0.64	+0.73	+0.56	+0.88	+0.53	+0.91	+1.44
2023	Aug	+0.70	+0.88	+0.51	+0.86	+0.94	+1.54	+1.25
2023	Sep	+0.90	+0.94	+0.86	+0.93	+0.40	+1.13	+1.17
2023	Oct	+0.93	+1.02	+0.83	+1.00	+0.99	+0.92	+0.63
2023	Nov	+0.91	+1.01	+0.82	+1.03	+0.65	+1.16	+0.42
2023	Dec	+0.83	+0.93	+0.73	+1.08	+1.26	+0.26	+0.85

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for December, 2023, and a more detailed analysis by John Christy, should be available within the next several days here.

The monthly anomalies for various regions for the four deep layers we monitor from satellites will be available in the next several days:

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

The Version 6 global average lower tropospheric temperature (LT) anomaly for November, 2023 was +0.91 deg. C departure from the 1991-2020 mean, statistically unchanged from the October, 2023 anomaly of +0.93 deg. C.

The linear warming trend since January, 1979 still stands at +0.14 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.19 C/decade over global-averaged land).

Various regional LT departures from the 30-year (1991-2020) average for the last 23 months are:

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2022	Jan	+0.03	+0.07	-0.00	-0.23	-0.12	+0.68	+0.10
2022	Feb	-0.00	+0.01	-0.01	-0.24	-0.04	-0.30	-0.49
2022	Mar	+0.15	+0.28	+0.03	-0.07	+0.23	+0.74	+0.03
2022	Apr	+0.27	+0.35	+0.18	-0.04	-0.25	+0.45	+0.61
2022	May	+0.18	+0.25	+0.10	+0.01	+0.60	+0.23	+0.20
2022	Jun	+0.06	+0.08	+0.05	-0.36	+0.47	+0.33	+0.11
2022	Jul	+0.36	+0.37	+0.35	+0.13	+0.84	+0.56	+0.65
2022	Aug	+0.28	+0.32	+0.24	-0.03	+0.60	+0.51	-0.00
2022	Sep	+0.25	+0.43	+0.06	+0.03	+0.88	+0.69	-0.28
2022	Oct	+0.32	+0.43	+0.21	+0.05	+0.16	+0.94	+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	+0.09	+0.17	0.00	-0.11	+0.68	-0.24	-0.11
2023	Mar	+0.20	+0.24	+0.16	-0.13	-1.44	+0.17	+0.40
2023	Apr	+0.18	+0.11	+0.25	-0.03	-0.38	+0.53	+0.21
2023	May	+0.37	+0.30	+0.44	+0.39	+0.57	+0.66	-0.09
2023	June	+0.38	+0.47	+0.29	+0.55	-0.35	+0.45	+0.06
2023	July	+0.64	+0.73	+0.56	+0.87	+0.53	+0.91	+1.44
2023	Aug	+0.70	+0.88	+0.51	+0.86	+0.94	+1.54	+1.25
2023	Sep	+0.90	+0.94	+0.86	+0.93	+0.40	+1.13	+1.17
2023	Oct	+0.93	+1.02	+0.83	+1.00	+0.99	+0.92	+0.62
2023	Nov	+0.91	+1.01	+0.82	+1.03	+0.65	+1.16	+0.42

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for November, 2023 and a more detailed analysis by John Christy, should be available within the next several days here.

Lower troposphere:

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

Middle 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

The urban heat island (UHI) was first described by Luke Howard in 1833 for London, England. Urban area air temperatures are almost always warmer than their rural surroundings, especially at night. Thus, the average human experiences warmer temperatures than they would if they lived in wilderness conditions.

This has nothing to do with global warming, and would occur even if there was no long-term ‘global warming’. In fact, since over 50% of the Earth’s population now lives in urban areas (expected to increase to nearly 70% by 2045), the temperatures humans actually experience would continue to break high temperature records even without climate change. For reference, the following plot shows the increase in global population between 1800 and 2023.

Our new global gridded UHI dataset allows one to compute just how much warmth (vs wilderness conditions) the average person experiences merely because most people live where human settlements cause localized warming. The following plot shows my computed ‘demographic warmth’ (during June, July, and August) experienced by the average human and how it has changed since 1800. For comparison I’ve also plotted the area-average temperature departures from the 1885-1984 average of the land portion of the HadCRUT4 thermometer dataset.

What can one conclude from this plot? At a minimum it shows humans choose to live under warmer conditions just by living in densely populated areas — and increasingly so. I will leave it up to the reader to decide if it shows anything beyond that. Note that this does not include the effect of (for instance) the migration of the U.S. population from colder to warmer latitudes, which would show an additional source of demographic warming. The warming shown by the red curve is only for urban effects relative to wilderness conditions at the same location.

Now, don’t be confused about what this means regarding the UHI impact on the thermometer measurements — that’s a different subject. All this shows is an metric of human-centric experienced warmth, not a thermometer-centric estimate of how much warming from the thermometer network can be attributed to UHI effects. The UHI effect on air temperature is due to a variety of processes associated with human settlements, such as replacement of vegetation with buildings and impervious surfaces and generation of waste heat that change the daily energy budget of those locations. Our UHI dataset simply approximates all of those processes using population density as a proxy, a choice made for us by the fact that it is the best (and possibly only) long-term dataset that exists to analyze the UHI problem.

Since few people who visit here will actually download and analyze data, I present some imagery of the new Urban Heat Island (UHI) dataset we have developed, at their full (~9×9 km or better) spatial resolution.

A Review: The Method

(Skip this section if you just want to see the pretty pictures, below).

To review, the dataset is based upon over 13 million station-pairs of monthly average air temperature measurements at closely-spaced GHCN stations between 1880 and 2023. It quantifies the average *spatial* relationship between 2-station differences in temperature and population density (basically, quantifying the common observation that urban locations are warmer than suburban, which are in turn warmer than rural). The quantitative relationships are then applied to a global population density dataset extending back through time.

The quantitative relationships between temperature and population are almost the same whether I use GHCN raw or adjusted (homogenized) data, with the homogenized data producing a somewhat stronger UHI signal. They are also roughly the same whether I used data from 1880-1920, or 1960-1980; for this global dataset, all years (1880 through 2023) are used together to derive the quantitative relationships.

I use six classes of station-pair average population density to construct the (nonlinear) relationship between population density and the UHI effect on air temperature. To make the UHI dataset, I apply these equations (derived separately in 7 latitude bands and 4 seasons) to global gridded population density data since 1800.

As I previously announced, our paper submitted for publication on the method showed that UHI warming in the U.S. since 1895 is 57% of the GHCN warming trend averaged over all suburban and urban stations. But because most of the U.S. GHCN stations that go into the CONUS area average are rural, the UHI warming trend area averaged across all GHCN stations is only 20% of that computed from GHCN data. Thus, there is evidence that GHCN warming trends for the U.S. as a whole have been inflated somewhat (20% or so) by the urban heat island effect, but by a much larger fraction at urban station locations. The UHI contamination of the area average trends could be larger than this, since we do not account for some regions possibly having increased levels of UHI contamination as prosperity increases (more buildings, pavement, vehicles, air conditioning, and other waste heat sources) increases but population remains the same.

Some Dataset Examples

Here are some examples of the UHI dataset for several regions, showing the estimated total UHI effect on air temperature in the years 1850 and 2023 (I have files every 10 years from 1800 to 1950, then yearly thereafter). By “total UHI effect” I mean how much warmer the locations are compared to wilderness (zero population density) conditions. I emphasize the warm season months, which is when the UHI effect is strongest.

Remember, these quantitative relationships hold for the *average* of all GHCN stations in 7 separate latitude bands. It is unknown how accurate they are at individual locations depicted in the following imagery.

First let’s start with a global image for April, 2023 that Danny Braswell put together for me using mapping software, for April of 2023 (click on the image for higher resolution… and if you dare, here is a super-duper-hi-res version):

And here are some regional images using my crude Excel “mapping” (no map outlines):

In my next post I will probably do some graphs of just how many people in the world live in various levels of elevated temperature just because the global population is increasingly urbanized. Over 50% of the population now lives in urban areas, and that fraction is supposed to approach 70% by 2045. This summer we have seen how the media reports on temperature records being broken for various cities and they usually conflate urban warmth with global warming even through such record-breaking warmth would increasingly occur even with no global warming.

Again, all of the ArcGIS format (ASCII grid) files are located here (public permissions now fixed).

As a follow-on to our paper submitted on a new method for calculating the multi-station average urban heat island (UHI) effect on air temperature, I’ve extended that initial U.S.-based study of summertime UHI effects to global land areas in all seasons and produced a global gridded dataset, currently covering the period 1800 to 2023 (every 10 years from 1800 to 1950, then yearly after 1950).

It is based upon over 13 million station-pair measurements of inter-station differences in GHCN station temperatures and population density over the period 1880-2023. I’ve computed the average UHI warming as a function of population density in seven latitude bands and four seasons in each latitude band. “Temperature” here is based upon the GHCN dataset monthly Tavg near-surface air temperature data (the average of daily Tmax and Tmin). I used the “adjusted” (homogenized, not “raw”) GHCN data because the UHI effect (curiously) is usually stronger in the adjusted data.

Since UHI effects on air temperature are mostly at night, the results I get using Tavg will overestimate the UHI effect on daily high temperatures and underestimate the effect on daily low temperatures.

This then allows me to apply the GHCN-vs-population density relationships to global historical grids of population density (which extend back many centuries) for every month and every year since as early as I choose. The monthly resolution is meant to capture the seasonal effects on UHI (typically stronger in summer than winter). Since the population density dataset time resolution is every ten years (if I start in, say, 1800) and then it is yearly starting in 1950, I have produced the UHI dataset with the same yearly time resolution.

As an example of what one can do with the data, here is a global plot of the difference in July UHI warming between 1800 and 2023, where I have averaged the 1/12 deg spatial resolution data to 1/2 deg resolution for ease of plotting in Excel (I do not have a GIS system):

If I take the 100 locations with the largest amount of UHI warming between 1800 and 2023 and average their UHI temperatures together, I get the following:

Note that by 1800 there was 0.15 deg. C of average warming across these 100 cities since some of them are very old and already had large population densities by 1800. Also, these 100 “locations” are after averaging 1/12 deg. to 1/2 degree resolution, so each location is an average of 36 original resolution gridpoints. My point is that these are *large* heavily-urbanized locations, and the temperature signals would be stronger if I had used the 100 greatest UHI locations at original resolution.

Again, to summarize, these UHI estimates are not based upon temperature information specific to the year in question, but upon population density information for that year. The temperature information, which is spatial (differences between nearby stations), comes from global GHCN station data between 1880 and 2023. I then apply the GHCN-derived spatial relationships between population density and air temperature during 1880-2023 to those population density estimates in any year. The monthly time resolution is to capture the average seasonal variation in the UHI effect in the GHCN data (typically stronger in summer than winter); the population data does not have monthly time resolution.

In most latitude bands and seasons, the relationship is strongly nonlinear, so the UHI effect does not scale linearly with population density. The UHI effect increases rather rapidly with population above wilderness conditions, then much more slowly in urban conditions.

It must be remembered that these gridpoint estimates are based upon the average statistical relationships derived across thousands of stations in latitude bands; it is unknown how accurate they are for specific cities and towns. I don’t know yet how finely I can regionalize these regression-based estimates of the UHI effect, it requires a large number (many thousands) of station pairs to get good statistical signals. I can do the U.S. separately since it has so many stations, but I did not do that here. For now, we will see how the seven latitude bands work.

I’m making the dataset publicly available since there is too much data for me to investigate by myself. One could, for example, examine the growth over time of the UHI effect in specific metro regions, such as Houston, and compare that to NOAA’s actual temperature measurements in Houston, to get an estimate of how much of the reported warming trend is due to the UHI effect. But you would have to download my data files (which are rather large, about 117 MB for a single month and year, a total of 125 GB of data for all years and months). The location of the files is:

https://www.nsstc.uah.edu/public/roy.spencer

You will be able to identify them by name.

The format is ASCII grid and is exactly the same as the HYDE version 3.3 population density files (available here) I used (ArcGIS format). Each file has six header records, then a grid of real numbers with dimension 4320 x 2160 (longitude x latitude, at 1/12 deg. resolution).

Time for Willis to get to work.

The Version 6 global average lower tropospheric temperature (LT) anomaly for October, 2023 was +0.93 deg. C departure from the 1991-2020 mean. This is slightly above the September, 2023 anomaly of +0.90 deg. C, and establishes a new monthly high temperature anomaly record since satellite temperature monitoring began in December, 1978.

The linear warming trend since January, 1979 still stands at +0.14 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.19 C/decade over global-averaged land).

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

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2022	Jan	+0.03	+0.07	-0.00	-0.23	-0.12	+0.68	+0.10
2022	Feb	-0.00	+0.01	-0.01	-0.24	-0.04	-0.30	-0.49
2022	Mar	+0.15	+0.28	+0.03	-0.07	+0.23	+0.74	+0.03
2022	Apr	+0.27	+0.35	+0.18	-0.04	-0.25	+0.45	+0.61
2022	May	+0.18	+0.25	+0.10	+0.01	+0.60	+0.23	+0.20
2022	Jun	+0.06	+0.08	+0.05	-0.36	+0.47	+0.33	+0.11
2022	Jul	+0.36	+0.37	+0.35	+0.13	+0.84	+0.56	+0.65
2022	Aug	+0.28	+0.32	+0.24	-0.03	+0.60	+0.51	-0.00
2022	Sep	+0.25	+0.43	+0.06	+0.03	+0.88	+0.69	-0.28
2022	Oct	+0.32	+0.43	+0.21	+0.05	+0.16	+0.94	+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	+0.09	+0.17	0.00	-0.11	+0.68	-0.24	-0.11
2023	Mar	+0.20	+0.24	+0.16	-0.13	-1.44	+0.17	+0.40
2023	Apr	+0.18	+0.11	+0.25	-0.03	-0.38	+0.53	+0.21
2023	May	+0.37	+0.30	+0.44	+0.39	+0.57	+0.66	-0.09
2023	June	+0.38	+0.47	+0.29	+0.55	-0.35	+0.45	+0.06
2023	July	+0.64	+0.73	+0.56	+0.87	+0.53	+0.91	+1.44
2023	Aug	+0.70	+0.88	+0.51	+0.86	+0.94	+1.54	+1.25
2023	Sep	+0.90	+0.94	+0.86	+0.93	+0.40	+1.13	+1.17
2023	Oct	+0.93	+1.02	+0.83	+1.00	+0.99	+0.92	+0.62

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for October, 2023 and a more detailed analysis by John Christy, should be available within the next several days here.

Lower troposphere:

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

Middle 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

After years of dabbling in this issue, John Christy and I have finally submitted a paper to Journal of Applied Meteorology and Climatology entitled, “Urban Heat Island Effects in U.S. Summer Surface Temperature Data, 1880-2015“.

I feel pretty good about what we’ve done using the GHCN data. We demonstrate that, not only do the homogenized (“adjusted”) dataset not correct for the effect of the urban heat island (UHI) on temperature trends, the adjusted data appear to have even stronger UHI signatures than in the raw (unadjusted) data. This is true of both trends at stations (where there are nearby rural and non-rural stations… you can’t blindly average all of the stations in the U.S.), and it’s true of the spatial differences between closely-space stations in the same months and years.

The bottom line is that an estimated 22% of the U.S. warming trend, 1895 to 2023, is due to localized UHI effects.

And the effect is much larger in urban locations. Out of 4 categories of urbanization based upon population density (0.1 to 10, 10-100, 100-1,000, and >1,000 persons per sq. km), the top 2 categories show the UHI temperature trend to be 57% of the reported homogenized GHCN temperature trend. So, as one might expect, a large part of urban (and even suburban) warming since 1895 is due to UHI effects. This impacts how we should be discussing recent “record hot” temperatures at cities. Some of those would likely not be records if UHI effects were taken into account.

Yet, those are the temperatures a majority of the population experiences. My point is, such increasing warmth cannot be wholly blamed on climate change.

One of the things I struggled with was how to deal with stations having sporadic records. I’ve always wondered if one could use year-over-year changes instead of the usual annual-cycle-an-anomaly calculations, and it turns out you can, and with extremely high accuracy. (John Christy says he did it many years ago for a sparse African temperature dataset). This greatly simplifies data processing, and you can use all stations that have at least 2 years of data.

Now to see if the peer review process deep-sixes the paper. I’m optimistic.

I’m not a statistician, and I am hoping someone out there can tell me where I’m wrong in the assertion represented by the above title. Or, if you know someone expert in statistics, please forward this post to them.

In regression analysis we use statistics to estimate the strength of the relationship between two variables, say X and Y.

Standard least-squares linear regression estimates the strength of the relationship (regression slope “m”) in the equation:

Y = mX + b, where b is the Y-intercept.

In the simplest case of Y = X, we can put in a set of normally distributed random numbers for X in Excel, and the relationship looks like this:

Now, in the real world, our measurements are typically noisy, with a variety of errors in measurement, or variations not due, directly or indirectly, to correlated behavior between X and Y. Importantly, standard least squares regression estimation assumes all of these errors are in Y, and not in X. This issue is seldom addressed by people doing regression analysis.

If we next add an error component to the Y variations, we get this:

In this case, a fairly accurate regression coefficient is obtained (1.003 vs. the true value of 1.000), and if you do many simulations with different noise seeds, you will find the diagnosed slope averages out to 1.000.

But, if there is also noise in the X variable, a low bias in the regression coefficient appears, and this is called “regression attenuation” or “regression dilution”:

This becomes a problem in practical applications because it means that the strength of a relationship diagnosed through regression will be underestimated to the extent that there are errors (or noise) in the X variable. This issue has been described (and “errors in variables” methods for treatment have been advanced) most widely in the medical literature, say in quantifying the relationship between human sodium levels and high blood pressure or heart disease. But the problem will exist in any field of research to the extent that the X measurements are noisy.

One can vary the relative amounts of noise in X and in Y to see just how much the regression slope is reduced. When this is done, the following relationship emerges, where the vertical axis is the regression attenuation coefficient (the ratio of the diagnosed slope to the true slope) and the horizontal axis is how much relative noise is in the X variations:

What you see here is that if you know how much of the X variations are due to noise/errors, then you know how much of a low bias you have in the diagnosed regression coefficient. For example, if noise in X is 20% the size of the signals in X, the underestimate of the regression coefficient is only 4%. But if the noise is the same size as the signal, then the regression slope is underestimated by about 50%.

Noise in Y Doesn’t Matter

But what the 3 different colored curves show is that for Y noise levels ranging from 1% of the Y signal, to 10 times the Y signal (a factor of 1,000 range in the Y noise), there is no effect on the regression slope (except to make its estimate more noisy when the Y noise is very large).

There is a commonly used technique for estimating the regression slope called Deming regression, and it assumes a known ratio between noise in Y versus noise in X. But I don’t see how the noise in Y has any impact on regression attenuation. All one needs is an estimate of the relative amount of noise in X, and then the regression attenuation follows the above curve(s).

Anyway, I hope someone can point out errors in what I have described, and why Deming regression should be used even though my analysis suggests regression attenuation has no dependence on errors in Y.

Why Am I Asking?

This impacts our analysis of the urban heat island (UHI) where we have hundreds of thousands of station pairs where we are relating their temperature difference to their difference in population density. At very low population densities, the correlation coefficients become very small (less than 0.1, so R2 less than 0.01), yet the regression coefficients are quite large, and — apparently — virtually unaffected by attenuation, because virtually all of the noise is in the temperature differences (Y) and not the population difference data (X).

With the approaching El Nino superimposed upon a long-term warming trend, many high temperature records were established in September, 2023.

(Now updated with the usual tabular values).

The Version 6 global average lower tropospheric temperature (LT) anomaly for September, 2023 was +0.90 deg. C departure from the 1991-2020 mean. This is above the August 2023 anomaly of +0.70 deg. C, and establishes a new monthly high temperature record since satellite temperature monitoring began in December, 1978.

The linear warming trend since January, 1979 still stands at +0.14 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.19 C/decade over global-averaged land).

Regional High Temperature Records for September, 2023

From our global gridpoint dataset generated every month, there are 27 regional averages we routinely monitor. So many of these regions saw record high temperature anomaly values (departures from seasonal norms) in September, 2023 that it’s easier to just list all of the regions and show how September ranked out of the 538 month satellite record:

Globe: #1

Global land: #1

Global ocean: #1

N. Hemisphere: #2

N. Hemisphere land: #1

N. Hemisphere ocean: #4

S. Hemisphere: #1

S. Hemisphere land: #1

S. Hemisphere ocean: #1

Tropics: #6

Tropical land: #2

Tropical ocean: #8

N. Extratropics: #2

N. Extratropical land: #1

N. Extratropical ocean: #4

S. Extratropics: #1

S. Extratropical land: #1

S. Extratropical ocean: #1

Arctic: #11

Arctic land: 7th

Arctic ocean: 65th

Antarctic: 15th

Antarctic land: 26th

Antarctic ocean: 14th

USA48: 144th

USA49: 148th

Australia: 12th

Various regional LT departures from the 30-year (1991-2020) average for the last 21 months are:

YEAR	MO	GLOBE	NHEM.	SHEM.	TROPIC	USA48	ARCTIC	AUST
2022	Jan	+0.03	+0.07	-0.00	-0.23	-0.12	+0.68	+0.10
2022	Feb	-0.00	+0.01	-0.01	-0.24	-0.04	-0.30	-0.49
2022	Mar	+0.15	+0.28	+0.03	-0.07	+0.23	+0.74	+0.03
2022	Apr	+0.27	+0.35	+0.18	-0.04	-0.25	+0.45	+0.61
2022	May	+0.18	+0.25	+0.10	+0.01	+0.60	+0.23	+0.20
2022	Jun	+0.06	+0.08	+0.05	-0.36	+0.47	+0.33	+0.11
2022	Jul	+0.36	+0.37	+0.35	+0.13	+0.84	+0.56	+0.65
2022	Aug	+0.28	+0.32	+0.24	-0.03	+0.60	+0.51	-0.00
2022	Sep	+0.25	+0.43	+0.06	+0.03	+0.88	+0.69	-0.28
2022	Oct	+0.32	+0.43	+0.21	+0.05	+0.16	+0.94	+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	+0.09	+0.17	0.00	-0.11	+0.68	-0.24	-0.11
2023	Mar	+0.20	+0.24	+0.16	-0.13	-1.44	+0.17	+0.40
2023	Apr	+0.18	+0.11	+0.25	-0.03	-0.38	+0.53	+0.21
2023	May	+0.37	+0.30	+0.44	+0.39	+0.57	+0.66	-0.09
2023	June	+0.38	+0.47	+0.29	+0.55	-0.35	+0.45	+0.06
2023	July	+0.64	+0.73	+0.56	+0.87	+0.53	+0.91	+1.44
2023	Aug	+0.70	+0.88	+0.51	+0.86	+0.94	+1.54	+1.25
2023	Sep	+0.90	+0.94	+0.86	+0.93	+0.40	+1.13	+1.17

The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for September, 2023 and a more detailed analysis by John Christy, should be available within the next several days here.

Lower troposphere:

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

Middle troposphere:

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

Tropopause:

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

Lower Stratosphere:

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

If we assume ALL *observed* warming of the deep oceans and land since 1970 has been due to humans, we get an effective climate sensitivity to a doubling of atmospheric CO2 of around 1.9 deg. C. This is considerably lower than the official *theoretical* model-based IPCC range of 2.5 to 4.0 deg. C. Here’s the Phys.org news blurb from this morning.