{"id":350328,"date":"2024-11-06T16:35:33","date_gmt":"2024-11-06T15:35:33","guid":{"rendered":"https:\/\/climatescience.press\/?p=350328"},"modified":"2024-11-06T16:35:35","modified_gmt":"2024-11-06T15:35:35","slug":"natural-climate-change-factors","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=350328","title":{"rendered":"Natural Climate Change Factors"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

<\/p>\n\n\n\n

From Watts Up With That?<\/a><\/p>\n\n\n\n

By Andy May<\/a><\/p>\n\n\n\n

\u201cConsensus\u201d scientists do not believe that solar variability, internal climate variability (in this model simplified to the ~67-year stadium wave), or volcanism influence net global warming or climate change since 1750, yet considerable evidence exists that these factors have an impact. I\u2019ve previously built a model of the HadCRUT5<\/a> global average temperature (see here<\/a>) from seven known climate and solar cycles using multiple regression and was reasonably successful.<\/p>\n\n\n\n

In this attempt, I utilize the six best documented solar\/climate cycles in listed Table 1 and the stadium wave climate-only interior variability cycle to create a multiple regression model of HadCRUT5.<\/p>\n\n\n

\n
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Table 1. Solar and climate proxies and measurements used in this model.<\/figcaption><\/figure>\n<\/div>\n\n\n

When using many series to build a multiple regression model one is always confronted with von Neuman\u2019s joke<\/a> that with four arbitrary parameters he can fit an elephant. Further, all these series are serially correlated<\/a>, which weakens the computed statistics of the resulting fit, such as R2<\/sup>. However, none of these series are \u201carbitrary parameters.\u201d Except for the stadium wave and Log2_CO2<\/sub> they are observed solar and climate cycles that share the same period and are in phase with one another. All are well supported with multiple lines of evidence. Thus, they are quite constrained and not arbitrary.<\/p>\n\n\n\n

While they are not arbitrary in the von Neuman sense, they are not independent of one another. Probably the solar dynamo is behind all of them, but the mechanism Sun \u2192 climate is not understood for any of them except possibly for the Hale Cycle and the Barycenter. The solar dynamo is quite complex, especially in the longer term.<\/p>\n\n\n\n

The stadium wave has no associated solar cycle and seems to be wholly internal variability, it has a period of roughly 67 years and correlates well with global average temperature (May & Crok, 2024)<\/a>. Internal climate variability is poorly understood and simply choosing the stadium wave to represent it is probably a vast oversimplification, but it is the best I can do. For a good up-to-date discussion of the components of internal variability I recommend Marcia Wyatt\u2019s excellent report<\/a> on circulation patterns (Wyatt M. , Circulation Patterns: Atmospheric and Oceanic, 2020). The report is paywalled, but a slightly different earlier version of the same report can be downloaded here<\/a>.<\/p>\n\n\n\n

The whole point of including the stadium wave is to account for the variable lag in emitting received solar radiation to space. Most solar radiation is received in the tropics, more than they can emit to space. As a result, some of the radiation received in the tropics must be transported to higher latitudes which emit more energy than they receive, especially in the winter months. Atmospheric and ocean circulation patterns change the timing of this tropics-to-pole energy transfer. A longer time to emit absorbed solar energy causes the planet to warm and a shorter time causes it to cool. Earth is never in thermal equilibrium except briefly by coincidence and the thermal energy residence time in the ocean\/atmosphere climate system is constantly changing but seems to have roughly a 67-year cycle. The 67-year cycle overlies a longer secular trend of change, that longer trend could be due to solar cycles or CO2<\/sub> or both. See figure 2<\/a> in May & Crok<\/a> as an example.<\/p>\n\n\n\n

There are no measurements from space or in the atmosphere that support the hypothesis that additional CO2<\/sub> (man-made or otherwise) will cause global warming, but there are lab measurements, correlations, and climate models that support the hypothesis. CO2<\/sub> has some effect, but how much is anyone\u2019s guess at this point. We try and use multiple regression to derive a value below.<\/p>\n\n\n\n

Critical Solar and Cycles<\/h1>\n\n\n\n

In this section we briefly describe each of the six well documented solar cycles listed in Table 1 that have a demonstrated impact on Earth\u2019s climate and provide references for them.<\/p>\n\n\n\n

Bray\/Hallstatt Cycle<\/h2>\n\n\n\n

Roger Bray used glacial advance and retreat records to identify an approximately 2400-year climate cycle originally called the \u201cHallstatt\u201d cycle (Bray, 1968). Although originally discovered by its effect on Earth\u2019s climate there is an associated solar cycle that is in phase with the climate cycle and has the same period as discussed here<\/a>. A more technical discussion of the solar Bray\/Hallstatt Cycle is presented by Ilya Usoskin et al. here<\/a> (Usoskin, Gallet, Lopes, Kovaltsov, & Hulot, 2016). A low in the Bray Cycle played a role in creating the very cold period from about 1650-1715AD during the Little Ice Age.<\/p>\n\n\n\n

Eddy Cycle<\/h2>\n\n\n\n

The roughly 1000-year Eddy Cycle was named by Jos\u00e9 A. Abreu and colleagues (Abreu, Beer, & Ferriz-Mas, 2010). They note that John Eddy identified and documented periods of very low solar activity that corresponded with colder climates on Earth, like the Little Ice Age (Eddy, 1976). The 1000-year climate cycle can be seen by combining the Medieval Warm Period (roughly 800 to 1250AD with the Little Ice Age (roughly 1300-1850AD). Solar proxies find a 1000-year solar cycle that is in phase with the climate cycle as pointed out by John Eddy, see figure 5 in (Eddy, 1976). More on the Eddy Cycle can be seen in Javier Vin\u00f3s\u2019 book (Vin\u00f3s, 2022), Chapter 8, page 123 here<\/a>. The powerful Bray Cycle and the Eddy Cycle both had lows between 1470 and 1680 which undoubtably contributed to that extremely cold time during the Little Ice Age. For more on the Little Ice Age see (May & Crok, 2024) and here<\/a>.<\/p>\n\n\n\n

De Vries Cycle<\/h2>\n\n\n\n

The De Vries Cycle is also often called the Suess Cycle after Hans Suess (Suess, 1955) and (Sonett & Suess, 1984). Like most cycles it was first discovered in climate proxies, especially tree ring records, and it has a period of 193-209 years. The matching solar cycle is probably related to a beat period between the fundamental Hale Solar Cycle and the rosette-like motion of the Sun around the solar system barycenter (Stefani, Horstmann, Klevs, Mamatsashvili, & Weier, 2023).<\/p>\n\n\n\n

Feynman Cycle<\/h2>\n\n\n\n

It was always recognized that some sort of solar cycle existed with a period between 50 and 150 years and the mysterious poorly defined cycle was usually called the Gleissberg Cycle. Meanwhile a climate cycle with a length of about 100 years was also observed. Eventually Joan Feynman nailed down what is now called the ~100-year Feynman Solar Cycle in 2014 (Vin\u00f3s, 2022, p. 129) and (Feynman & Ruzmaikin, 2014). We use a period of 105 years for the Feynman Cycle. The Feynman climate and solar cycles have the same period and are in phase.<\/p>\n\n\n\n

Hale Cycle<\/h2>\n\n\n\n

The 22.14-year Hale Cycle is a very prominent solar cycle that is formed by a 22-year fundamental period of solar magnetic activity. It encapsulates two solar cycles that are marked by reversals of the solar magnetic field. Thus, one Hale cycle sees a reversal of the magnetic field and then a return to the original polarity. The Hale Cycle is closely related to the 22-year southwestern U.S. drought cycle (Mitchell, Stockton, & Meko, 1979).<\/p>\n\n\n\n

Solar Barycenter rosette<\/h2>\n\n\n\n

The Sun tracks a complicated rosette-like motion about the solar system barycenter that takes 19.86 years (Stefani, Horstmann, Klevs, Mamatsashvili, & Weier, 2023). Any climate cycle associated with this is buried, or possibly shared, with the Hale Cycle.<\/p>\n\n\n\n

Other Natural Climate Cycles<\/h1>\n\n\n\n

Stadium Wave<\/h2>\n\n\n\n

What we call the Stadium Wave Cycle or oscillation is a very strong global climate cycle that has a period of about 67 years (Wyatt & Curry, Role for Eurasian Arctic shelf sea ice in a secularly varying hemispheric climate signal during the 20th century, 2014) and (Wyatt M. G., 2012c). It is composed of many climate oscillations that propagate in an organized fashion across the Northern Hemisphere and affect the climate of much of the globe. It is not clear if this oscillation is related to a solar cycle.<\/p>\n\n\n\n

CO2<\/sub><\/h2>\n\n\n\n

CO2<\/sub> as a factor that influences climate was first properly described by Svante Arrhenius in his book Worlds in the Making<\/em> (Arrhenius S. , 1908), but he also discusses it in an earlier paper (Arrhenius S. , 1896). Even before Arrhenius\u2019 attempt to quantify the atmospheric CO2<\/sub> concentration\u2019s impact on the climate, the idea that CO2<\/sub> can affect Earth\u2019s climate was also discussed, although in a less quantitative way, by Fourier (Fourier, 1824), Tyndall (Tyndall J. , 1861) and (Tyndall J. , 1859), and Langley (Langley, 1884). None of these earlier writers suggested that atmospheric CO2<\/sub> concentration \u201ccontrolled the climate,\u201d as the IPCC proposes in AR6 and previous reports (IPCC, 2021, p. 179), (Lacis, Hansen, Russell, Oinas, & Jonas, 2013), and (Lacis, Schmidt, Rind, & Ruedy, 2010). They also did not believe that human CO2<\/sub> and other greenhouse gas emissions were responsible for almost all the warming Earth has experienced since 1750 as claimed by the IPCC in AR6, page 961, figure 7.7.<\/p>\n\n\n\n

None-the-less, CO2<\/sub> and other greenhouse gas concentrations probably do influence the average atmospheric temperature, however the idea that they somehow \u201ccontrol\u201d the global average surface temperature and climate change is highly doubtful.<\/p>\n\n\n\n

Figure 1 is a plot of all the series discussed in this post.<\/p>\n\n\n\n

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Figure 1. The time series discussed in this post.<\/figcaption><\/figure>\n\n\n\n

Discussion<\/h1>\n\n\n\n

If we use the top seven series listed in table one, that is everything but CO2<\/sub>, and regress against HadCRUT5 we get the result shown in orange in figure 2. If we then regress everything, including CO2<\/sub>, we get the blue dashed line in figure 2. The two lines are nearly identical.<\/p>\n\n\n

\n
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Figure 2. The faint gray line is HadCRUT5, the orange line is a regression against it using only the solar and climate cycles, the blue dashed line uses the same cycles, but adds the Log, base 2, of CO2<\/sub>.<\/figcaption><\/figure>\n<\/div>\n\n\n

Now, that is interesting! The fit with the climate\/solar cycles only and the fit with CO2<\/sub>\u00a0and the same cycles are nearly identical. We need more information to make sense of this. Below are the statistics of the two regressions.<\/p>\n\n\n

\n
\"\"
Table 2. Statistics of the regression with all the solar\/climate cycles, but no CO2<\/sub>. Adjusted R2<\/sup>\u00a0= 0.8523.<\/figcaption><\/figure>\n<\/div>\n\n\n

Table 2 shows the statistics of the regression with no CO2<\/sub>. The derived coefficients are on the left, and the mean and standard deviation of each series are shown and then the normalized coefficients. We can see that the Bray and Eddy Cycles are the most important series, and they slightly oppose one another since they have different signs. All the \u201cP\u201d values are good. The standard errors for the Bray and Eddy Cycles are a bit high, but that may be because their coefficients have opposite signs.<\/p>\n\n\n\n

In Table 3 we show the statistics for the regression that contains CO2<\/sub>.<\/p>\n\n\n

\n
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Table 3. The regression statistics when CO2<\/sub>\u00a0is added. Adjusted R2<\/sup>\u00a0= 0.8548<\/figcaption><\/figure>\n<\/div>\n\n\n

Table 3 contains a lot of new and valuable information. In Table 2 the normalized coefficients show that the Bray and Eddy Cycles dominated the regression, but in Table 3 it is CO2<\/sub>, by a long way and the P values for both Bray and Eddy have become unacceptable. CO2<\/sub>\u00a0has essentially replaced the strong Bray and Eddy Cycles and knocked them out. Removing those two cycles results in the De Vries cycle becoming unacceptable with a P value of 0.37, so I also removed it, and the result is shown in Table 4.<\/p>\n\n\n

\n
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Table 4. The regression statistics when CO2<\/sub>\u00a0is added. Adjusted R2<\/sup>\u00a0= 0.8548<\/figcaption><\/figure>\n<\/div>\n\n\n

In Table 4 all the P values are acceptable and CO2<\/sub> dominates the regression. For all practical purposes the adjusted R2<\/sup> from all three regressions are identical at about 0.85. The \u201cadjusted R2<\/sup>\u201d is corrected for the number of predictor variables and the number of observations. In all these cases the regular R2<\/sup> is nearly identical to the adjusted R2<\/sup>.<\/p>\n\n\n\n

Figure 3 compares the no CO2<\/sub> case to the case described in Table 4.<\/p>\n\n\n

\n
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Figure 3. Comparing the all-cycles\/no CO2<\/sub>\u00a0regression in blue to the CO2<\/sub>\u00a0and no Bray, Eddy or de Vries cycles in orange. Statistics for the orange regression are given in Table 4.<\/figcaption><\/figure>\n<\/div>\n\n\n

Conclusions<\/h1>\n\n\n\n

As usual in climate science (and statistical analysis) you can take away whatever you like from this analysis. Statistics and climate science are similar in this way, you can always generate a lot of discussion around either of them and still know nothing.<\/p>\n\n\n\n

CO2<\/sub> makes no difference if all the climate\/solar cycles are used, but CO2<\/sub> can replace the most powerful Bray, Eddy, and de Vries solar\/climate cycles. Objectively, one could point out that solar cycles (including the Milankovitch cycles) have driven climate change as far back as we can trace them with proxies and historical records, so why would we think CO2<\/sub> is driving climate if adding it to the regression makes no difference?<\/p>\n\n\n\n

On the other hand, CO2<\/sub> can replace the very powerful Bray, Eddy, and de Vries cycles and get the same result. As usual, this analysis just shows we have no clue what drives climate change, but isn\u2019t that what we\u2019ve been saying all this time?<\/p>\n\n\n\n

One can look at these plots and statistics and conclude that the impact of CO2<\/sub> on climate is zero, or they can conclude it is 100%. This study is inconclusive by itself. However, it also shows that well-known solar cycles, combined with internal variability can explain recent global warming; CO2<\/sub> is not required to explain it.<\/p>\n\n\n\n

As with all purely statistical studies, this regression model is unsuitable to use in forecasting or hindcasting, the large coefficients in tables 2 to 4 show that. The value of the study is only to demonstrate that CO2<\/sub> is not necessary to explain recent warming.<\/p>\n\n\n\n

This post looks at the problem \u201cdoes CO2<\/sub> control global warming?\u201d from a statistical perspective. To see how experts in climate science look at it from an atmospheric physics perspective it is worth reviewing the excellent 2014 American Physical Society workshop on climate change hosted by Steve Koonin. It is discussed and summarized here<\/a>.<\/p>\n\n\n\n

This post is the result of many email conversations with Charlie May, who contributed substantially to the ideas and models presented herein.<\/em><\/p>\n\n\n\n

Download the bibliography <\/em>here<\/em><\/a><\/p>\n\n\n\n

Download the Regression statistics <\/em>here<\/em><\/a>.<\/em><\/p>\n\n\n\n

Download the data used and the plots <\/em>here<\/em><\/a>.<\/em><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

\u201cConsensus\u201d scientists do not believe that solar variability, internal climate variability (in this model simplified to the ~67-year stadium wave), or volcanism influence net global warming or climate change since 1750, yet considerable evidence exists that these factors have an impact. I\u2019ve previously built a model of the\u00a0HadCRUT5\u00a0global average temperature (see\u00a0here) from seven known climate and solar cycles using multiple regression and was reasonably successful.<\/p>\n","protected":false},"author":121246920,"featured_media":350346,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_crdt_document":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[1],"tags":[691827130,691818056,691818087,691831351],"class_list":{"0":"post-350328","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","6":"hentry","7":"category-uncategorized","8":"tag-carbon-dioxide-co2","9":"tag-climate-change","10":"tag-global-warming","11":"tag-solar-climate-cycles","13":"fallback-thumbnail"},"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/11\/00Screenshot-2024-11-06-163438.png?fit=1152%2C758&ssl=1","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paxLW1-1t8s","jetpack-related-posts":[{"id":383684,"url":"https:\/\/climatescience.press\/?p=383684","url_meta":{"origin":350328,"position":0},"title":"Climate Oscillations 1: The Regression","author":"uwe.roland.gross","date":"19\/06\/2025","format":false,"excerpt":"I did a regression analysis to see how the twelve oscillations (14 in the 1978 regression) I looked at correlated to the HadCRUT5 global mean surface temperature (GMST). GMST is not a very good indicator of climate or climate change, but it is a commonly used yardstick of climate model\u2026","rel":"","context":"In \"Climate change\"","block_context":{"text":"Climate change","link":"https:\/\/climatescience.press\/?tag=climate-change"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/06\/ChatGPT-Image-8.-Juni-2025-18_50_51-2.png?fit=1200%2C800&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/06\/ChatGPT-Image-8.-Juni-2025-18_50_51-2.png?fit=1200%2C800&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/06\/ChatGPT-Image-8.-Juni-2025-18_50_51-2.png?fit=1200%2C800&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/06\/ChatGPT-Image-8.-Juni-2025-18_50_51-2.png?fit=1200%2C800&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/06\/ChatGPT-Image-8.-Juni-2025-18_50_51-2.png?fit=1200%2C800&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":393316,"url":"https:\/\/climatescience.press\/?p=393316","url_meta":{"origin":350328,"position":1},"title":"Climate Oscillations 12: The Causes & Significance","author":"uwe.roland.gross","date":"06\/08\/2025","format":false,"excerpt":"While internal variability may play a role in our observed oscillations, it is possible that gravitational forces and changes in solar output provide the pacing of the oscillations. Since all climate oscillations clearly influence the others through a mechanism named \u201cteleconnections,\u201d if the pacing of a few of the oscillations\u2026","rel":"","context":"In \"astronomical periods\"","block_context":{"text":"astronomical periods","link":"https:\/\/climatescience.press\/?tag=astronomical-periods"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/08\/0AQNPLSWo_KzxXKVbW7IbL7_vsFYRwpeEDr7n4wOji7EYEkkB1n0lKGSzzfQRN21EEW2YTvQtJVQSWUfh7fwAwOb_zqmvvqK2jdNxixoG7mgswXaDvyZS-6qY2mUTFO5a-1.jpeg?fit=1200%2C1200&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/08\/0AQNPLSWo_KzxXKVbW7IbL7_vsFYRwpeEDr7n4wOji7EYEkkB1n0lKGSzzfQRN21EEW2YTvQtJVQSWUfh7fwAwOb_zqmvvqK2jdNxixoG7mgswXaDvyZS-6qY2mUTFO5a-1.jpeg?fit=1200%2C1200&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/08\/0AQNPLSWo_KzxXKVbW7IbL7_vsFYRwpeEDr7n4wOji7EYEkkB1n0lKGSzzfQRN21EEW2YTvQtJVQSWUfh7fwAwOb_zqmvvqK2jdNxixoG7mgswXaDvyZS-6qY2mUTFO5a-1.jpeg?fit=1200%2C1200&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/08\/0AQNPLSWo_KzxXKVbW7IbL7_vsFYRwpeEDr7n4wOji7EYEkkB1n0lKGSzzfQRN21EEW2YTvQtJVQSWUfh7fwAwOb_zqmvvqK2jdNxixoG7mgswXaDvyZS-6qY2mUTFO5a-1.jpeg?fit=1200%2C1200&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/08\/0AQNPLSWo_KzxXKVbW7IbL7_vsFYRwpeEDr7n4wOji7EYEkkB1n0lKGSzzfQRN21EEW2YTvQtJVQSWUfh7fwAwOb_zqmvvqK2jdNxixoG7mgswXaDvyZS-6qY2mUTFO5a-1.jpeg?fit=1200%2C1200&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":305733,"url":"https:\/\/climatescience.press\/?p=305733","url_meta":{"origin":350328,"position":2},"title":"Climate Model Bias 3: Solar Input","author":"uwe.roland.gross","date":"04\/03\/2024","format":false,"excerpt":"In\u00a0part 2\u00a0we discussed the IPCC hypothesis of climate change that assumes humans and our greenhouse gas emissions and land use choices are the climate change \u201ccontrol knob.\u201d\u00a0This hypothesis underpins their attempts to model Earth\u2019s climate. But the model output fails to match many critical observations and in some cases the\u2026","rel":"","context":"In \"Climate change\"","block_context":{"text":"Climate change","link":"https:\/\/climatescience.press\/?tag=climate-change"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/03\/image-42.png?fit=1200%2C872&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/03\/image-42.png?fit=1200%2C872&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/03\/image-42.png?fit=1200%2C872&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/03\/image-42.png?fit=1200%2C872&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/03\/image-42.png?fit=1200%2C872&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":332239,"url":"https:\/\/climatescience.press\/?p=332239","url_meta":{"origin":350328,"position":3},"title":"Climatists Deny Natural Warming\u00a0Factors","author":"uwe.roland.gross","date":"10\/06\/2024","format":false,"excerpt":"After a recent contretemps at Climate Etc. with CO2 warmists, I was again reminded how insistent are zero carbon zealots to deny multiple natural climate factors, in order to attribute all modern warming to humans burning hydrocarbons. A large part of this blindness comes from constraints dictated by the IPCC\u2026","rel":"","context":"In \"AGW (Anthropogenic Global Warming)\"","block_context":{"text":"AGW (Anthropogenic Global Warming)","link":"https:\/\/climatescience.press\/?tag=agw-anthropogenic-global-warming"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/06\/0wp4555644.jpg?fit=1200%2C675&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/06\/0wp4555644.jpg?fit=1200%2C675&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/06\/0wp4555644.jpg?fit=1200%2C675&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/06\/0wp4555644.jpg?fit=1200%2C675&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/06\/0wp4555644.jpg?fit=1200%2C675&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":385343,"url":"https:\/\/climatescience.press\/?p=385343","url_meta":{"origin":350328,"position":4},"title":"Scafetta: Climate Models Have\u00a0Issues","author":"uwe.roland.gross","date":"27\/06\/2025","format":false,"excerpt":"The Coupled Model Intercomparison Project (CMIP) global climate models\u00a0(GCMs) assess\u00a0that nearly\u00a0100% of global surface warming\u00a0observed\u00a0between 1850\u20131900 and 2011\u20132020 is attributable to\u00a0anthropogenic drivers like\u00a0greenhouse gas emissions.\u00a0These models\u00a0also generate future climate projections based on shared socioeconomic pathways (SSPs), aiding in risk assessment and the development of costly \u201cNet-Zero\u201d climate mitigation strategies.","rel":"","context":"In \"Climate change\"","block_context":{"text":"Climate change","link":"https:\/\/climatescience.press\/?tag=climate-change"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0-CMIP6-climate-models.jpeg?fit=1200%2C900&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0-CMIP6-climate-models.jpeg?fit=1200%2C900&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0-CMIP6-climate-models.jpeg?fit=1200%2C900&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0-CMIP6-climate-models.jpeg?fit=1200%2C900&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2023\/06\/0-CMIP6-climate-models.jpeg?fit=1200%2C900&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":419369,"url":"https:\/\/climatescience.press\/?p=419369","url_meta":{"origin":350328,"position":5},"title":"Climate Change Perceptions","author":"uwe.roland.gross","date":"01\/01\/2026","format":false,"excerpt":"The Atlantic Multidecadal Oscillation (AMO), also referred to as Atlantic Multidecadal Variability (AMV), is a natural, large-scale climate pattern characterized by long-term fluctuations in sea surface temperatures (SSTs) in the North Atlantic Ocean. It alternates between warm (positive) and cool (negative) phases, each typically lasting 20\u201340 years, with a full\u2026","rel":"","context":"In \"Atlantic Multidecadal Oscillation (AMO)\"","block_context":{"text":"Atlantic Multidecadal Oscillation (AMO)","link":"https:\/\/climatescience.press\/?tag=atlantic-multidecadal-oscillation-amo"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/01\/0Screenshot-2026-01-01-181353.png?fit=1200%2C600&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/01\/0Screenshot-2026-01-01-181353.png?fit=1200%2C600&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/01\/0Screenshot-2026-01-01-181353.png?fit=1200%2C600&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/01\/0Screenshot-2026-01-01-181353.png?fit=1200%2C600&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2026\/01\/0Screenshot-2026-01-01-181353.png?fit=1200%2C600&ssl=1&resize=1050%2C600 3x"},"classes":[]}],"_links":{"self":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/350328","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/users\/121246920"}],"replies":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=350328"}],"version-history":[{"count":8,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/350328\/revisions"}],"predecessor-version":[{"id":350347,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/350328\/revisions\/350347"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/media\/350346"}],"wp:attachment":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=350328"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=350328"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=350328"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}