Much of the global climate science community would have you believe that Earth’s atmosphere is heating at a catastrophic rate – and that human-caused climate change is the root cause. In this article, I present evidence that pours cold water on the anthropogenic-global-boiling narrative while deepening our understanding of a complicated subject.
Over the past decade, I have had countless discussions with people on social media over a broad range of subjects related to climate science, and I have learned a great deal from these engagements and my research regarding the idea of an average global air temperature (GAT).
Whereas in March 2024, I produced for BIG Media readers an in-depth introduction to the topic of how air temperatures have evolved over the Holocene Interglacial, this article focuses on the 19th century to present.
I have heard many times from likeminded scientists that an average GAT is as meaningful as the average phone number in a telephone book. I always chuckle when I hear this analogy, but I am not entirely of the same mindset on its limited utility.
The entry point to this discussion is to highlight the historical distribution of land-based measurement stations in the mid-19th century, as well as the lack of geographical coherence in the changes in GAT since the late-19th century.
Figure 1 – Weather station distribution in 1850, together with zonal latitudinal and global average changes in air temperatures from the late-19th century to 1980.
Figure 1 shows the Global Historical Climatological Networks weather station locations as of 1850, and an illustration from Hansen et al showing both the zonal latitudinal and average GAT changes since 1880.
The Center for Environmental Research and Earth Sciences (CERES) station distribution map for 1850, shows that the vast majority of surface stations were located in urban centres (red) in Europe. Further, CERES demonstrated, using 50-year increments, that global coverage was not provided until the mid-20th century.
This is very important to bear in mind, as changes in GAT are always referenced relative to the mid- to late-19th century.
This suggests that European stations offer a higher-merit representation of the changes in surface air temperatures over the last two centuries than does the GAT anomaly. Note that an anomaly is a fancy word for a difference relative to a constant value.
In this case, the GAT anomaly uses the baseline average air temperature over 1951 to 1980; thus, negative (positive) values represent temperatures that are lower (higher) than the baseline.
Anomalies are commonly used in environmental science when attempting to compare how time series vary, when the absolute values of said time series are significantly different (e.g., tropics versus polar regions).
Given Figure 1 shows a high density of surface stations in Europe during the mid-19th century, it makes sense to examine this record.
Figure 2 – Central European air temperature anomaly versus the Antarctic ice core proxy temperature anomaly (y-axis is anomaly divided by the standard deviation).
One of the best studies that I have seen using European temperature records is a 2014 publication by H-J Ludecke et al (Figure 2).
Figure 2 includes data from both air temperatures for central Europe from 1750 to 2010 and proxy air temperatures from Antarctic ice-core records.
H-J Ludecke et al plot both data series as a z-score (i.e., anomaly / σ), which is used in time series analysis and is a unitless parameter due to the z-score being derived by dividing the anomaly time series by the standard deviation (σ) of the time series (i.e., celsius/celsius).
As the σ is a constant, the variability is indicative of the anomaly in the numerator.
Note that proxy temperatures in environmental science are derived from isotopic ratios, which in the case of ice, is the ratio of both oxygen-16 & 18 (O18 & O16) , as well as hydrogen (H1) & deuterium (H2). As the melting point of H2O is a function of is molecular weight, the ratio of heavier H2O to lighter H2O will vary with temperature and thus forms the basis of how a proxy temperature can be calculated through mass spectrometry.
H-J Ludecke et al argue that both Antarctic proxy temperatures and European air temperatures show a similar time dependence over most of the 19th and 20th centuries.
Specifically, we see that temperatures in both Europe and the Antarctic declined over the 19th century and rose over much of the 20th century. Both time series exhibit similar multi-decadal variability over both centuries, such as the peak around 1860 to 1880, and again around 1920 to 1950.
Thus, I argue that perpetrating GAT anomalies that start at the low point in Figure 2, effectively creates a false understanding of history.
Next, I will demonstrate that the over-reliance of the GAT anomaly works to create a false impression that warming since the late-19th century has been geographically coherent. In reality, GAT is statistically biased by the extra-tropical Northern Hemisphere.
Hansen et al’s 1981 study shown in Figure 1 is a perfect illustration of this bias. Compare the zonal latitudinal temperature anomalies to the GAT anomaly, and the similarity between GAT and the extra-tropical northern latitude air temperature anomaly is especially apparent.
To further complete this picture, I have included Figures 3a and 3b.
Figure 3a – GISS GAT anomaly as a function of latitude and time since the late 19th century.
Note that the grey zones in Figure 3a, along both polar latitudes, indicate the absence of data, and the baseline average used to create this anomaly map was the 1960-1980 global average. The colour coding shows that shades of blue correspond to surface air temperature colder than the baseline, while shades of red are higher.
Figure 3b plots the absolute change by latitude, using much the same data as represented in Figure 3a, and it quantifies the strong bias to warming in the Arctic zone (>60 degrees North) and the middle latitudes of the Northern Hemisphere.
Figure 3b – Surface air temperature anomaly by latitude since the late 19th century.
While I would enjoy doing a deep dive into the numerous hypotheses in the literature on why warming is so strongly biased to the higher latitudes of the Northern Hemisphere, suffice to say that explanations invoke natural (i.e., non-anthropogenic) mechanisms.
Next, I use publicly available radiosonode (aka weather balloon) data obtained from the National Oceanic and Atmospheric Administration (NOAA) through its NCEP program’s dashboard and will highlight the latitudinal dependence of the monthly average near surface (925 mb) air temperature over the Seasonal Cycle.
This exercise will demonstrate the role of orbital mechanics in dictating how the near-surface air temperature evolves across the Earth over the seasonal cycle as the planet completes its annual transit around our G2V main sequence star (aka the Sun).
Figure 4 – Seasonal dependence of near surface air temperature by latitude.
Figure 4 shows that the peak seasonal temperature lags the point in time over the season cycle that the Sun’s vertical position relative to the horizon (aka zenith angle) reaches an annual maximum. In the Northern Hemisphere, the annual peak temperature lags its summer solstice (June 21) by three to four weeks, as it does in the Southern Hemisphere (December 21).
Similarly, two peak temperatures are observed in the tropics, and these, too, lag spring and fall equinox.
Note that on a monthly average basis within the seasonal cycle, the Northern Hemisphere exhibits the largest change in near-surface air temperature.
This observation is extremely important when we then compare the seasonal cycle of the near-surface air temperature of the extra-tropical (ET) Northern and Southern Hemisphere to the global average, as shown in Figure 5.
On a monthly basis over the seasonal cycle, the GAT average again shows a statistical bias to the Northern Hemisphere.
Figure 5 – Seasonal dependence of the global average air temperature compared on a monthly basis to the extra-tropical (ET) Northern and Southern Hemisphere.
The last focus area is the story told by modern satellites equipped to measure the thermal temperature of oxygen molecules through microwave sounding techniques. Since oxygen emits microwave radiation (post-absorption) at intensities proportional to its temperature, these measurements allow for the creation of temperature profiles at various altitudes.
While satellite measurements have only been available since the late 1970s, what they lack in terms of length of records, they make up for in global coverage and precision.
In the closing portion of this article, I highlight evidence that GAT is a function of what is called Walker Circulation.
So far, I have used annual average surface station data (e.g., Figures 3a and 3b) and monthly seasonal average radiosonode data (e.g., Figures 4 and 5).
I will now use what is called seasonal detrending techniques to examine changes in air temperature that develop on an inter-annual basis. Seasonal detrending is simply a 12-month rolling average and is a technique that acts as a high-pass filter to smoothen the seasonal cycle from the data (i.e., summer to winter).
This technique is important in highlighting changes in atmospheric temperature that develop in response to and at the frequencies of the El Nino Southern Oscillation (ENSO).
Figure 6 compares the University of Alabama at Huntsville (UAH) average GAT (green) against the Pacific Trade Wind Speed (blue) over the time frame of 2000-2025. Both time series have been filtered with a 12-month rolling average.
Note that as trade winds are a vector quantity, a negative value means the winds are originating in the east and moving to the west.
Therefore, when trade winds trend more negative (positive), it means they are accelerating (decelerating), and the Pacific tropics are cooling (warming) and eventually entering La Nina (El Nino) conditions.
The lagging of the UAH average GAT relative to the Pacific trade winds is seen when comparing the offset between peaks in both time series. This lag is the hallmark of a causative relationship.
The lag we observe is like ripples in a pond after a stone is dropped in its centre (tropics); it takes time for the wave (heat) to propagate across the pond (Earth).
Figure 6 – Satellite-measured average global air temperatures versus radiosonode-measured Pacific trade winds (all seasonally detrended).
A close examination of Figure 6 shows that ENSO variations between El Nino and La Nina states is the control knob on the inter-annual variabilities seen in the seasonally detrended monthly averaged GAT anomaly over the 21st century.
This physical relationship is due to the first-order influence of the Pacific trade winds on variations in Walker Circulation in the equatorial Pacific climate zone, which involve whole-scale atmospheric-circulation-induced changes in both cloud coverage and thermal energy storage in the thermohaline across the global heat engine (aka Pacific tropics).
It is increasingly understood that a sudden deceleration of the trade winds and the slowing of Walker Circulation in the Pacific tropics, give rise to increased absorption of sunshine as cloud cover retracts in the eastern Pacific tropics, which in turn results in warming sea surface temperatures (aka El Ninos).
The reverse of this pattern results in a return to a neutral or La Nina/ENSO state.
Figure 7 is a select figure from a 2024 study that demonstrates this relationship between sudden reductions in low-altitude (aka marine stratiform) clouds in the eastern tropical Pacific and along northern mid-latitudes that was observed by satellites during the 2023 to 2024 El Nino event.
Figure 7 – 2024 publication demonstrating ENSO’s controlling influence on the planetary albedo (reflectivity).
Note that we do not understand the physics causing these irregularities in Walker Circulation in the Pacific tropics, nor how these tropical irregularities affect with bias the higher latitudes of the Northern Hemisphere.
What we do know is that this relationship behaves analogously to dominoes, with the Pacific tropics leading and the Northern Hemisphere following as if it is teleconnected to ENSO’s influence on Walker Circulation.
In closing, I hope that my representation challenges your understanding of a familiar but often misunderstood topic, while providing an alternative way of looking at this thing called the average global air temperature.
(Joseph Fournier – BIG Media Ltd. 2025)
