AI Proves Its Mettle in Reconstruction of Antarctic Temperatures

In a 2025 post, I described the largely unsuccessful attempt of an AI to spearhead research in climate science. Now, however, another AI appears to have succeeded in the more technical task of accurately reconstructing surface air temperatures across Antarctica – something that standard temperature datasets have been unable to achieve. The work is reported in a recent paper by a team of Chinese researchers.

The figure below illustrates the geographical distribution of available observational data in Antarctica from 1979 to 2023. The data comes from a number of manned and automatic weather stations, together with meteorological observations over the ocean collected from ships and buoys. As can be seen, the majority of observations are in coastal or near-coastal regions, precluding full spatial coverage of the continent.

To overcome this shortfall, the limited station observations have traditionally been interpolated using various reanalyses. But it’s difficult for reanalysis datasets to capture complex spatial patterns, say the researchers, and such datasets often contain significant uncertainties. Moreover, deriving surface air temperatures from reanalysis datasets depends in part on model simulations rather than actual instrumental measurements.

In the light of these limitations to an accurate reconstruction of Antarctic temperatures, the Chinese research team has applied deep learning methods. This approach has already been utilized successfully to reconstruct Arctic temperatures.

Antarctic temperatures were reconstructed using daily surface air temperature data from the various sources depicted in the figure above. Daily average temperatures were calculated from observations made at 3-hour intervals for some sources, 1-hour intervals for others. Training data for the deep learning model was provided by surface air temperatures from the three reanalysis datasets that showed the best agreement with observed temperatures.

The training data, which covered the period from 1979 to 2005, totaled 29,211 daily temperature samples. Using reanalysis data from different time periods, such as 1995 to 2021, as the training set had little impact on the temperature reconstruction. Validation of the training data and testing of the reconstructed temperature set employed reanalysis data from 2006 to 2012 and 2013 to 2018, respectively.

Testing of the reconstructed Antarctic temperatures was conducted for three specific days in 2015: January 1, July 1 and November 1. For these days, the reconstructed temperatures were found to be highly correlated with their reanalysis counterparts, with spatial correlation coefficients >0.99. The researchers say this correlation shows that the trained deep learning model is capable of accurately reproducing Antarctic surface air temperatures, even with the limited observational data available.

Just how different the reconstructed surface temperatures are from global observational temperature datasets for Antarctica is depicted in the next figure. The figure shows linear trends in annual Antarctic surface air temperatures from 1979 to 2023, measured in degrees Celsius per decade. The datasets are: (a) this reconstruction; (b) Berkeley Earth; (c) ERA reanalysis; (d) NOAAGlobalTemp5.1; (e) GISTEMPv4; and (f) HadCRUT5.

You can see that none of the standard datasets exhibit the pronounced cooling trend in East Antarctica in (a), something that was inferred earlier from the ERA reanalysis dataset by a different group of Chinese researchers. Nevertheless, all datasets show warming in the Antarctic Peninsula (on the left of the continent in the maps above).

Differing from the annual trends is the pattern for the summer months (November to April) only, presented in the figure below for the period from 1989 to 2022. Although the cooling trend still dominates in East Antarctica, warming is no longer prominent in the Peninsula but is found in West Antarctica and the southern portion of East Antarctica.

East Antarctica actually experienced a summer heat wave in 2022, when the temperature soared to -10.1 degrees Celsius (13.8 degrees Fahrenheit) at the Concordia weather station, located at the 4 o’clock position from the South Pole, on March 18. This balmy reading was the highest recorded hourly temperature at that station since its establishment in 1996, and 20 degrees Celsius (36 degrees Fahrenheit) above the previous March record high there. Remarkably, the temperature remained above that record for three consecutive days, including nighttime.

But Antarctica is nothing if not unpredictable. Despite the 2022 heat wave, the mercury dropped to -51.2 degrees Celsius (-60.2 degrees Fahrenheit) on January 31, 2023. This marked the lowest January temperature recorded anywhere in Antarctica since the first meteorological observations there in 1956. Two days earlier on January 29, the nearby Vostok station, about 400 km (250) miles closer to the South Pole, registered a low temperature of -48.7 degrees Celsius (-55.7 degrees Fahrenheit), that location’s lowest January temperature since 1957.

Such swings from record highs to record lows remain a puzzle, but the present reconstruction at least helps to characterize long-term trends.

Next: The Atlantic “Cold Blob” – Cause for Alarm or Just a Curiosity?

AI Tries Its Hand at Climate Science

Much-ballyhooed AI (artificial intelligence), which is rapidly making inroads in many areas, has found its way into scientific publishing with the recent appearance of a peer-reviewed study featuring AI Grok 3 beta as the lead author. The 2025 paper, titled “A Critical Reassessment of the Anthropogenic CO₂-Global Warming Hypothesis,” disputes the narrative that global warming is largely driven by human emissions of CO₂.

Grok 3 beta, who wrote its own press release, spearheaded the research but needed critical guidance from four human coauthors. While the AI on its own was able to identify some of the relevant papers in the scientific literature, it fell short by missing a number of others and had to rely on its human colleagues to fill the gaps – and even they overlooked the work of one important researcher.

The study essentially reviews the conclusions of the IPCC (Intergovernmental Panel on Climate Change) regarding the global carbon cycle, computer climate models, and solar variability. The paper’s subtitle is “Empirical Evidence Contradicts IPCC Models and Solar Forcing Assumptions.”

On the carbon cycle, shown in the figure below, the study challenges the claim made in the IPCC’s AR6 (Sixth Assessment Report) that the effective residence time of CO₂ in the atmosphere is more than 100 years. The claim has been questioned by several authors, including Greek civil engineer Demetris Koutsoyiannis who, in a 2024 paper, estimated a residence time as short as 3.5 to 4 years.

The Koutsoyiannis paper simply divides the atmospheric CO₂ level, which was 416.4 ppm (parts per million) or 887 GtC (gigatonnes of carbon) in 2020, by the total flow of CO₂ into the atmosphere of approximately 230 GtC per year. However, this analysis only accounts for the so-called fast carbon cycle – the rapid movement of CO₂ between living organisms, the atmosphere and the oceans – but ignores the slow carbon cycle, in which CO₂ is incorporated into long-lived vegetation such as tree bark, or the deep ocean, or limestone and other rock.

Notable among other authors who share the beliefs of Koutsoyiannis are atmospheric scientists Hermann Harde and the late Murray Salby. Their work (see, for example, here) is acknowledged by Grok 3 beta, but only because the AI was reprimanded by its coauthors for omitting any mention of Harde or Salby papers from its first draft.

Another major omission the first time around was the extensive work of U.S. astrophysicist Willie Soon, one of the coauthors, on solar contributions to global warming. I’ll touch on Soon’s research below.

But more egregious than any of these omissions is Grok 3 beta’s failure to include any papers of physicist David Andrews, who has been highly critical of Harde and Salby.

Andrews (see here, here and here) distinguishes between the fast and slow carbon cycles, and points out that large, natural, two-way exchanges of carbon occur between the atmosphere, the oceans and land biomass. The fluxes each way exceed the one-way flux into the atmosphere of anthropogenic CO₂ from fossil fuels. Andrews also discusses errors in the interpretation of radiocarbon data in the Harde and Salby papers.

As for Koutsoyiannis, I discussed in a 2023 post how an earlier study of his is unable to explain 88% of the increase in atmospheric CO₂ during global warming of approximately 1 degree Celsius (1.8 degrees Fahrenheit) since 1880, in terms of CO₂ outgassing from the oceans.

Turning to climate models, Grok 3 beta gets it essentially right. As I’ve explained in numerous previous posts, many climate models run too hot, greatly exaggerating future global warming; only a small number come close to actual measurements. And a whole ensemble of models is unable to reproduce observed sea surface temperature trends in the Pacific and Southern Oceans since 1979.

Grok 3 beta correctly states that “model runs consistently fail to replicate observed temperature trajectories and sea ice extent trends, exhibiting correlations (R²) near zero when compared to unadjusted records.” On the subject of adjustments to raw temperature data, the paper also duly notes that such adjustments are contentious, especially the use of homogenization techniques which can introduce systematic errors into temperature data.

Finally, the AI rightfully draws attention to the IPCC’s questionable reliance on a single reconstruction of past solar output or TSI (total solar irradiance), to support its narrative of overwhelmingly human-caused global warming with essentially no contribution from the sun. As I described in another 2023 post, Soon and a team of coauthors have shown that a number of alternative TSI reconstructions, in which the sun plays a much larger role, can explain observed warming just as well as the IPCC’s chosen reconstruction.

So how well has Grok 3 beta mastered climate science? In this case, its paper is a mixed bag worthy of barely a passing grade.

Next: Climate Model Track Record Improves Slightly: Paused Arctic Sea Ice Loss Predicted Correctly