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Study: 70% of Warming Caused by Sun, Depending on Which Dataset Used

A study published on 28 August 2023 titled “The Detection and Attribution of Northern Hemisphere Land Surface Warming (1850–2018) in Terms of Human and Natural Factors: Challenges of Inadequate Data” by W. Soon, R. Connolly, M. Connolly et al argues that the claims of the 6th Assessment Report (6AR) published by the Intergovernmental Panel on Climate Change (IPCC) may not be entirely true.


The 6AR claims that of the 1.09ºC rise in global surface temperature, human-induced warming is 1.07ºC and therefore concludes that climate change is “overwhelmingly due to human influence”.


There is also the issue of “urbanization bias” since urban areas are merely a fraction of the global land area but much of the data are taken from urban areas with potential “warming biases from the growth of urban heat islands around weather stations”. 6AR claims that urbanization bias is small at <10%.


This study is 36 pages in total with the main text at about 29 pages. The remaining pages are references.


The authors first discuss a few other studies and their findings. Their methods are essentially one of “hindcasts” modeled by applying solar, volcanic and anthropogenic component “forcings”.


The authors basically conduct the same analysis and over half the paper is dedicated to discussing the specifics of the method like what datasets to use. They also conducted their analysis for “urban and rural” and “rural-only” for comparison.


In short, depending on which solar dataset is applied, the conclusions can be very different. AR6 used “Solar #1” only whereas this study applied both “Solar #1” and “Solar #2” datasets. The authors found that “Solar #2” is overall a better fit even if it doesn’t explain everything.

The statistics associated with the Solar #1 fits are much weaker than that for Solar #2. Indeed, using the shorter 1900–2018 period for fitting, the linear relationship is not statistically significant for the rural-only record (p > 0.05) and barely statistically significant for the rural and urban record (p = 0.049). The fits for volcanic activity are quite weak, and they are not statistically significant for the rural and urban records either (p > 0.05).

More specifically:

Solar #2 can explain more than 70% of the long-term warming (0.62ºC/century) and 65% of the AR6 comparison metric. It also captures quite a bit of the multidecadal variability in addition to the overall linear trend—Figure 7b. The results are even better using the fittings based on the shorter 1900–2018 period with 76% of the 1850–2018 warming and 71% of the AR6 metric…
Moreover, when we consider the shorter-term periods, the Solar #2 fits can explain the early-20th-century warming and the mid-20th-century cooling much better than the anthropogenic fits, i.e., the anthropogenic fits can only explain about 20% of both trends while the Solar #2 fit explain more than twice the observed warming and cooling over these intervals.

As for the difference between “urban and rural” and “rural-only”, the massive difference in itself indicates at least some urbanization bias.

…the long-term 1850–2018 linear warming trend of the “rural-only” estimate (0.55ºC/century) is only 62% of that of the “rural and urban” estimate (0.89ºC/century).

To reiterate:

…simply substituting an alternative solar forcing dataset to that considered by AR6’s climate model hindcasts can substantially increase the amount of the 1850–2018 warming that can be explained in terms of natural forcing from 21% to 70% of the long-term warming implied by the “rural and urban” series and 87% of the “rural-only” temperature series.

Figure 7: The results of fitting (a–d) the “rural and urban” or (e–h) the “rural-only” temperature records (indicated by thick black lines) using only one component (using ordinary least squares linear regression) over the 1850–2018 period. The best fits for each individual component are indicated in each panel with colored circles joined by a dotted line. (a,e) show the best fits for Solar #1; (b,f) show the best fits for Solar #2; (c,g) show the best fits for volcanic; (d,h) show the best fits for the net anthropogenic forcing.
Figure 7: The results of fitting (a–d) the “rural and urban” or (e–h) the “rural-only” temperature records (indicated by thick black lines) using only one component (using ordinary least squares linear regression) over the 1850–2018 period. The best fits for each individual component are indicated in each panel with colored circles joined by a dotted line. (a,e) show the best fits for Solar #1; (b,f) show the best fits for Solar #2; (c,g) show the best fits for volcanic; (d,h) show the best fits for the net anthropogenic forcing.
 

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