Tag: surface temperatures

Truths about Coral Bleaching: CO2 Warming vs Reduced Clouds Cover?

The coral that now form our modern reefs evolved 240 million years ago during the Age of Dinosaurs when coral formed a marvelously fluid symbiotic relationship with symbiodinium algae. The new algae symbionts absorbed CO2 and produced sugars via photosynthesis for the coral. In turn coral respiration produced CO2 that supported the algaeโ€™s photosynthetic production. This symbiotic relationship evolved when CO2 was 4 times above todayโ€™s concentrations and global temperatures were several degrees higher.

Jim Steele

The coral that now form our modern reefs evolved 240 million years ago during the Age of Dinosaurs when coral formed a marvelously fluid symbiotic relationship with symbiodinium algae. The new algae symbionts absorbed CO2 and produced sugars via photosynthesis for the coral. In turn coral respiration produced CO2 that supported the algaeโ€™s photosynthetic production. This symbiotic relationship evolved when CO2 was 4 times above todayโ€™s concentrations and global temperatures were several degrees higher. Takashima (2006) reports the mid-Cretaceous earth โ€œsurface temperatures were more than 14ยฐC higher than today,โ€ a time when dinosaurs roamed Antarctica. Atlantic warm pool sea surface temperatures reached a maximum of 42ยฐC (107ยฐF).

That evolutionary history explains why shallow-water coral are now restricted to the warmest ocean waters. Graphic A shows the location of todayโ€™s coral. Graphic B shows the oceans regional temperatures. Corals are most abundant in the warmest waters of the Indian Ocean and Western Pacific Warm Pool, also known as the Coral Triangle. There, sea surface temperatures (SST) reach 28-30ยฐC (82-86ยฐF), sometime a little higher.

In contrast, the eastern tropical Pacific, experiences cooler SSTs due to upwelling typically in the range of 20ยฐC (68ยฐF). With the eastern tropical Pacific 8-10ยฐC (14-18ยฐF) cooler than the tropical western Pacific, there coral reefs are scarce and restricted to much smaller Atlantic and eastern Pacific warm pool regions. So why are todayโ€™s warm temperatures blamed for coral bleaching? Why does CNN report bleaching as catastrophic?

o survive 240 million years of extreme environmental change that required a fluid symbiosis. More recently during glacial maximums sea levels fell by 120 meters, killing all the shallow water coral reefs. As the ice melted, sea levels then rose 10 times faster than sea level rise today. Such rapid sea level changes greatly altered the sunlight reaching the surviving coral and the amount of nourishment the algae could produce.

Increased sunlight produces excessive dangerous Reactive Oxygen Species (ROS) like hydrogen peroxide and superoxides during the light reactions of photosynthesis that produce highly charge electrons and singlet oxygen by splitting water. Normally algae have several methods of neutralizing those dangerous ROS molecules but a rapid increase in sunlight, and thus ROS, sometimes overwhelms their protective systems. Thus, many corals threatened by increasing ROS, have adopted the survival strategy of ejecting their mal-adapted algae (i.e. bleaching). Coral can re-absorb those algae later when the extreme light conditions subside or absorb new symbiodinium algae adapted to the higher light conditions.

Today the major factor affecting solar insolation is changing cloud cover associated El Nino-La Nina oscillations. As seen in graphic C, during La Nina-like conditions heavy cloud cover reduces solar insolation over the coral triangle. During an El Nino, that heavy cloud cover shifts eastward causing greater solar heating around the Great Barrier Reef. The El Niรฑo events of 1982-83, 1998, 2010, and 2015-2016 are all associated with massive coral bleaching.

Great Barrier Reef bleaching can also happen during a La Nina, but only when a high-pressure weather system sits over the reef and locally reduces cloud cover. Either way, reduced cloud cover both increases surface temperatures and overwhelms the algaeโ€™s photosystems by increasing ROS production. That prompted some scientists to argue that surface warming alone, greater than 1ยฐC degree, will cause bleaching and that fit their climate crisis narratives. Alarmists like Fred Pearce on Yale 360 fear mongered global warming threatens coral writing, โ€œThe coral reefs of the tropics have looked doomedโ€ โ€ฆ โ€œSome experts say they will be gone by mid-century, the first great ecosystem casualty of the climate emergency.โ€

However, the science of coral bleaching now reports it is changes in the sunโ€™s insolation that primarily drives bleaching by increasing ROS production during photosynthesis along with solar heating.

If global warming alone is causing coral bleaching and rising coral deaths, we would expect a steady decline in coral cover as CO2 rises. However, changes in coral cover have oscillated as we would expect from the El Nino oscillation. After recent El Nino-related massive bleaching events impacted nearly 90% of Australiaโ€™s corals, in 2022 the Australian Institute of Marine Science (AIMS) reported the highest levels of coral cover across two-thirds of the Great Barrier Reef (GBR) in over 36 years (graphic D).

Sleep well! Corals are not threatened by rising CO2.

Earthโ€™s Surface Temperatures: Made in the Shade

Everyone knows when out in the sun and you start to overheat, just find some shade. In contrast to common sense, climate alarmists ignore the effects of shade from trees and clouds to create a narrative of a CO2 induced global warming climate crisis. Its a scam the globalists push claiming they can โ€œsaveโ€ you if, you let them enslave you!

From JimSteele

Everyone knows when out in the sun and you start to overheat, just find some shade. In contrast to common sense, climate alarmists ignore the effects of shade from trees and clouds to create a narrative of a CO2 induced global warming climate crisis. Its a scam the globalists push claiming they can โ€œsaveโ€ you if, you let them enslave you!

Fear not! Scientific field measurements validate your common sense. Surface temperatures can be 30F cooler in the shade. Urban heat islands, as measured in Atlanta GA, accordingly show much higher local temperatures where trees have been replaced and shading is lost. But to dishonestly create a global warming crisis they average hot urbanized temperatures with naturally cooler temperatures in naturally shaded vegetated areas.

Furthermore, scientific cloud research continues to show how changes in the shade from clouds affects our climate. The recent research from the Mediterranean region show high percentage of cloud cover coincided with cooler temperatures of the Little Ice Age, and declining cloud cover coincides with a century of warming. Don’t let globalist climate fear mongers cloud your common sense.

Detecting Climate Scientistsโ€™ Dishonest Fearmongering Loss of Ocean Oxygen

All humans and animals require oxygen to live. So, to evoke public fear, headlines often claimed global warming will cause ocean life to suffocate. They evoke the true but misleading factoid that warmer water holds less oxygen. Thus, rising CO2 will cause the ocean to hold less oxygen leading to suffocation. Dishonestly, they ignore more important dynamics controlling oxygen.

All humans and animals require oxygen to live. So, to evoke public fear, headlines often claimed global warming will cause ocean life to suffocate. They evoke the true but misleading factoid that warmer water holds less oxygen. Thus, rising CO2 will cause the ocean to hold less oxygen leading to suffocation. Dishonestly, they ignore more important dynamics controlling oxygen.

As illustrated (graphic A), ocean surface temperatures reach 25ยฐC to 30ยฐC (red) and rapidly cool to just 5ยฐC (blue) at 1000-meter depths. Yet in contrast to climate crisis narratives, oxygen levels (graphic B) are highest in the warm surface layers and then become severely depleted to their lowest levels at cold 1000-meter depths. Temperature is simply not the oxygen control knob.

The high concentrations of oxygen in the upper ocean layers are produced when photosynthesizing phytoplankton split water molecules. In fact, most scientists have determined that not only do phytoplankton provide oxygen for ocean life, but 50% of all the oxygen we breathe is produced by ocean phytoplankton thatโ€™s then released into the atmosphere.

The organic matter produced in the oceanโ€™s sunlit surface layers gradually sinks. As the organic matter is consumed, oxygen is consumed and CO2 is released. Virtually all that organic matter is consumed by the 1000-meter depths, explaining why oxygen reaches its lowest levels by 1000 meters. All that sinking matter feeds the abundant marine life in what is known as the twilight zone, the 200 to 1000-meters depths that lack of light for photosynthesis. Scientists now estimate that 90% of the oceansโ€™ biomass lives and feeds in the twilight zone, fed by organic matter produced by photosynthesis in the warmest surface layer.

It has been proven that the increase in CO2 concentrations has increased photosynthesis, which benefits all marine life in the twilight zone. All photosynthesizing plants and algae depend on one key enzyme known as RUBISCO (graphic C). RUBISCO evolved during the earthโ€™s earliest history when the atmosphere consisted over 6000 ppm CO2 and less than 2% oxygen. Thus, RUBISCO has the ability to incorporate either oxygen, which causes detrimental photorespiration, or incorporate carbon dioxide, which makes the sugars that are the building blocks for all organic tissue. The ratio of oxygen to carbon dioxide determines photosynthesis efficiency. More oxygen or less carbon dioxide depresses photosynthesis.

During the late Carboniferous and Permian periods 250 to 300 million years ago, oxygen reached its highest concentrations and carbon dioxide reached its lowest. As a result, Tropical Rainforests collapsed and there was a phytoplankton blackout driving the greatest extinctions in earthโ€™s history.

So, the truth is: more CO2 enables more ocean photosynthesis, increases organic matter to feed marine life, while producing more oxygen that all animals need to live. But that is not the science the climate crisis grifters want you to understand.

There is indeed observed increases in oceans oxygen minimum zones. The decrease in oxygen is driven by the increase in primary production due to increased upwelling since the end of the Little Ice Age. Upwelling raises oxygen depleted and nutrient enriched deep to the surface triggering increased photosynthesis and organic matter production in the warm surface layers. That further depletes oxygen when increasing organic matter is digested as it sinks back to lower levels. Attached are two illustrations. The top illustrates the oxygen minimum zones are located where upwelling is greatest from the 2015 paper by Moffitt Paleoceanographic Insights on Recent Oxygen Minimum Zone Expansion: Lessons for Modern Oceanography The bottom illlustration shows how upwelling and productivity has increased since the Little ICe Age from Chavez (2011) Marine Primary Production in Relation to Climate Variability and Change

Modeling HadCRUT5 with CO2 and without CO2


From Watts Up With That?

By Andy May

I hate statistics, as many of you know. Some people think statistics and/or statistical models that meet standard statistical criteria are facts. The IPCC can be like that. They statistically model global surface temperatures with models of volcanic and anthropogenic forcing and compare the model to one with only volcanic forcing. Then they turn to us, with a straight face, and say the comparison shows anthropogenic forcing is driving all the warming. What about solar? Oh, they considered that they say, the Sun makes no difference, see their chart in figure 1 from AR6.[1] Solar is assumed to be zero and volcanism is small, thus the model assumes all recent warming is due to humans, then draws the same conclusion in a perfect example of circular reasoning. But what if the solar forcing is not zero? What difference does that make?

Figure 1. The IPCC AR6 assumed forces affecting global surface warming translated to degrees C. From AR6 WG1, page 961.

Numerous papers have been published that show the Sun could have more impact on global temperatures and climate change than assumed by the IPCC.[2] We must remember that statistical models are not evidence or theories, they arenโ€™t even proper hypotheses. They are just a tool to test the validity of ideas and a hypothesis might come out of a statistical model, but proof never will. If a model repeatedly predicts the future accurately, then it is evidence the hypothesis is correct, it isnโ€™t proof. The IPCC presents their statistical climate model with the plots shown in figure 2.

Figure 2 is quite busy, but what it says, in brief, is that they assume that natural warming (heavy green line) is zero, which makes, under their assumptions, all warming due to human activities. The WG1 AR6 report is 2,391 pages long, but figure 2, modified slightly from what they display on page 441, really encapsulates everything it proposes. The rest is filler.

Figure 2. The IPCC model shows their greenhouse gas warming hypothesis with this graph. This is after IPCC AR6 WG1 figure 3.9b (page 441). The vertical axis is the temperature anomaly relative to 1850-1900.

There are numerous problems with figure 2, but we will focus on the comparison between the anthropogenic + natural models, in orange, and the observations in black. First of all, the orange is not one model, but the average of many selected models. The range of model calculations (5 to 95th percentile) is shown with light orange shading. The range is quite large, if they had confidence in their models wouldnโ€™t they choose the best one and use it? If they donโ€™t trust the models, why try to use them as evidence that the Sun has no influence, and all the warming is due to human activities? Why use the models to confidently predict a man-made climate catastrophe? AR6 WGII Summary for Policymakers (p 12-20) reports high confidence in many future catastrophes based on model results. Why high confidence, if the models are so imprecise, that they must be averaged? Second, they use thick lines to try and obscure the differences between the black and orange lines, but the differences are significant, especially between 1935 and 1976 and 1980 to 2000. The model average between 1920 and 1960 looks almost hand-drawn because it is so straight relative to rising temperatures until 1944 and falling temperatures afterward.

So, letโ€™s take a different approach. The classical paleoclimate literature, pre-IPCC, mostly thought that solar variability dominated climate change.[3] Over time the study of the cosmogenic isotopes 14C[4] in tree rings and 10Be[5] in ice cores has led to accepted proxy records of the Sunโ€™s output that go back thousands of years (see the discussion of Carbon-14 and Beryllium-10 here).[6] These isotopes are created in the atmosphere when galactic cosmic rays make it through the solar magnetic field and impact the atmosphere. When solar output is high, its magnetic field is stronger than when it is low. Thus, low concentrations of 14C[7] and 10Be[8] suggest a strong solar output and vice versa. Since 1700 sunspot records provide a more accurate view of solar activity.[9]

Studies of 14C, 10Be, and sunspot records have uncovered four major long-term solar cycles. These are the Hallstatt (or Bray) cycle of about 2,400 years,[10] the Eddy Cycle of about 1,000 years,[11] the de Vries (or Suess) cycle of about 210 years, Feynman (or Gleissberg) cycle of about 105 years,[12] and the Pentadecadal cycle of about 50 years.[13] All the cycle periods are approximate, further, they may vary over geological time.[14] Some may not like my use of the term โ€œcycles,โ€ since our understanding of the cycle periods and the strength or power of each cycle is poor. Perhaps the term oscillation would be better but understand that I fully appreciate how poorly we understand these cycles and use the term only for convenience and not necessarily according to the precise definition of the word.

The Sun is a dynamo and generates a magnetic field that controls the variations in its output over time. Such a dynamo will have cycles, we have shown they exist and affect Earthโ€™s climate, but the details are sketchy. What astrophysicists and paleoclimatologists have done is observe the Sun and solar impacts on Earthโ€™s climate and recognized in-phase patterns of both solar activity and climate impacts. We discuss these observed (but only approximate) patterns in the post and correlate them to HadCRUT5. Cycles are also observed in other stars that are like our Sun.[15]

There are also shorter periods of solar variability, like the sunspot cycle which has a varying period and asymmetrical shape that averages about 11 years.[16] Finally, we have the ENSO cycle, also with a varying period, that is driven, in part, by solar activity.[17] To cover the shorter solar cycles we include the SILSO sunspot record[18] and the ERSST Niรฑo 3.4 (ENSO) record from KNMI.[19]

If we ignore the IPCC assumption that solar activity has played no role in climate change since 1750, as suggested in figure 1, it is possible to investigate the correlation of these well-established cycles or oscillations and one of the global surface temperature records used in AR6, the HadCRUT5[20] record. Unfortunately, the HadCRUT5 global surface temperature record only goes back to 1850, but it is an instrumental record, and preferable to proxies. The data used to build HadCRUT5 is poor prior to 1958,[21] so we will also investigate the even shorter period of more accurate data from 1958 to 2023.

We used statistical multiple regression to see how well these cycles and data can predict HadCRUT5. We understand going in, that even if we can build a multiple regression model with a high R2 (Coefficient of determination or the square of the correlation coefficient), we havenโ€™t proven anything. We also understand that while global average surface temperature is an important metric of climate change, it is not the only important metric. Other metrics, such as mid-latitude wind speed and direction, as well as surface temperature trends at the poles, and in the tropics (especially in the middle troposphere[22]) are also important. The purpose of this post is simply to show that the IPCCโ€™s choice to characterize the correlation of the trends in the logarithm of CO2 concentration and global average surface temperature as โ€œproofโ€ or โ€œevidenceโ€ that CO2 and other human greenhouse gas emissions drive climate change is not very solid. In fact, it is probably wrong. Other reasonable correlations are possible, and arguably better.

Figure 3 is a plot of the independent or predictor variables used in our regression study. They have been normalized to scales of -3 to +3 by dividing the larger variables (Log(CO2) and sunspots) by their mean to better compare the variables to one another. In addition, we divided the sunspot number by its standard deviation to help make it comparable in scale to other variables.

Figure 3. The input series used in this multiple regression study. The y axis scale is an index, and the curves cannot be compared quantitatively.

Unfortunately, our period is too short to properly evaluate some of the stronger climate cycles, like the Hallstatt (light blue) and Eddy (orange) cycles. These two cycles bottomed in the Little Ice Age and their periods are so long they almost appear as straight lines, but they are increasing like the HadCRUT5 record. The logarithm of CO2[23] is also nearly a straight line, and very slightly increasing. The CO2 data are interpolated yearly averages to avoid the seasonal wiggles.

The ENSO 3.4, sunspots (SN Norm), and CO2 (Log CO2 Norm) records used in the study are from well-known datasets.[24] The longer-term solar cycles are created using a sinusoid function[25] of the form:

  • Cycle (t) = cos(2ฯ€ft โ€“ offset)

Where the cosine argument is in radians, f=frequency, t=time, and the offset is used to align the sine wave with assumed cycle lows (cold periods) from Ilya Usoskin[26] and Joan Feynman.[27] For more on this transform, used in Fourier analysis, see David Evansโ€™ paper here.[28] These lows are not precise and must be estimated from the available data. The actual values used, and the precise functions are in the supplementary materials which are linked at the end of this post.

The Multiple Regression Model

I performed a number of regressions with the variables plotted in figure 3 and various subsets of them. In every case where I could tell, the statistically most important single variable, judging from AIC,[29] sum of squares, and R2, was the logarithm of CO2. However, all the variables were significant, and CO2 compared to the impact of all the others combined was small, as we will see. AIC ranks the input predictors for the 1958 case rank as follows: Log_CO2, Nino_3_4, Hallstatt, Eddy, Pentadecadal, sunspots, and finally de Vries. AIC is based on the sum of squares, so it can be problematic in autocorrelated series[30] like these. The plots below give you feel for the relative importance of the main variables, which is hard (maybe impossible) to calculate statistically with any precision, mainly due to the brief period of our instrumental data and the long periods of the important solar cycles. The next four plots are for the whole instrumental record, 1850 to 2023. Figure 4 includes all the variables in the study.

Figure 4. A model with all series, including log(CO2). The fine gray line is the monthly HadCRUT5 data, and the blue line is smoothed with an 11-year moving average. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990. The orange line is the model.

Figure 5 uses all the variables except Log_CO2. In both figures the blue line is the smoothed HadCRUT5 record, and the fine gray line is the monthly HadCRUT5 data. The orange line is the model. We can see that Log_CO2 visually adds little to the match between observations and the model. Significant improvement is visible around 1940, otherwise the two models are about the same.

Figure 5. Plot of the regression with log(CO2) removed from the list of predictors. The R2 drops to 0.84 and there is noticeable deterioration in the fit between 1935 and 1947. The fine gray line and the blue line are as before. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 6 compares the model that uses Log_CO2 to the model that only uses the solar related variables. The two models are similar. The only noticeable differences are before 1940 when CO2 was supposedly not very important. It is possible that the differences are due to data quality. As we will see, the data prior to 1958 was lower in quality than the data after that date.

Figure 5. Plot of the regression with log(CO2) removed from the list of predictors. The R2 drops to 0.84 and there is noticeable deterioration in the fit between 1935 and 1947. The fine gray line and the blue line are as before. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 6 compares the model that uses Log_CO2 to the model that only uses the solar related variables. The two models are similar. The only noticeable differences are before 1940 when CO2 was supposedly not very important. It is possible that the differences are due to data quality. As we will see, the data prior to 1958 was lower in quality than the data after that date.

Figure 6. A comparison of the โ€œno CO2โ€ versus โ€œwith CO2โ€ models. All other predictors are in both models. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

In figure 7 we model HadCRUT5 with only CO2. While the R2 is 0.8 and the model generally follows HadCRUT5, the model lacks the granularity and detail that is apparent in figures 5 and 6. The IPCC calls the granularity natural variability and dismisses it as statistical โ€œnoiseโ€ that is random. Notice the P-value doesnโ€™t change, the P-value is of little use in models like this that have a lot of observations and produce good matches. It is not a good measure of model quality.

Figure 7. HadCRUT5 is modeled with only CO2. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Next, we repeat the above four plots using a new model that only uses the data between 1958 and the present day. This is the largest period possible with good data. To get another upward step change in data quality we would need to move to 2005 when the ARGO array became sufficiently large to produce better data on ocean temperatures than we can get from ships. But only 17 years of good ocean data is not long enough to judge the influence of the longer solar cycles.

Figure 8 shows a good visual match between observations and a model with all the variables. It also has an R2 of 0.9, which would be impressive if the variables were independent and not autocorrelated. The mismatch between 1992 and 1995 is probably due to the Pinatubo eruption in 1991, which was not incorporated into this model.

Figure 8. A model with all predictors, including CO2, from 1958 to the present day. The Pinatubo eruption is identified. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 9. A model from 1958 with all predictors except CO2. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 9 is the model with all variables except for CO2. The match is still good, but there are differences in detail suggesting that adding CO2 makes a difference. The large difference just after 1992 is probably due to the influence of the Mt. Pinatubo eruption in the summer of 1991. The effect of the eruption lasted several years. With the exception of the Pinatubo eruption, the model is almost as good as the model that includes CO2, at least visually.

Figure 10. Models with and without CO2 over the 1958 to present period. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 10 compares the models with and without CO2 directly, and except for the period right around the Mt. Pinatubo eruption, the match is excellent. Iโ€™m not saying that Pinatubo had an effect before it erupted, just that the large impact of the eruption on the HadCRUT5 record (see Figure 11) could have distorted the two regressions differently in that period. Possibly the addition of CO2 makes a small difference, but it isnโ€™t apparent in this plot anywhere except around the eruption.

Figure 11. A model with only CO2 as a predictor from 1958 to the present. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

Figure 11 shows a model using only the logarithm of CO2, there is a general correspondence of temperature and CO2, but a great deal of detail is missing that we see in the other models. We can argue that the variation of the HadCRUT5 record around the orange model in figure 11 is not random noise if it can be modeled with solar cycles.

A word on statistics

The risk in evaluating regression statistics of models of autocorrelated series is most easily seen by considering that any two monotonically increasing time series, for example CO2 and temperature since 1850, will appear to correlate, even if they are unrelated. This is why I often hate statistics, too often statistical measures of fit, like R2, or computed statistical probabilities are used to gaslight readers into believing something that isnโ€™t true. Your first judgment of a correlation should be made with a plot of the data versus the model, second should be a plot of the residuals. Are the residuals evenly dispersed about zero, or do they have a trend? All the residual plots for the top models in this post are trendless, as they should be.

The main point is to trust your eyes, not statistical measures of the fits, they are secondary. Sometimes the obvious is correct. To illustrate this point, I used a stepwise regression to order the models. To generate these four models, I removed the top variable (according to its AIC) and reran the regression with the remaining variables until the visual model did not match HadCRUT5 very well. The procedure suggests the most important variables are Log_CO2, Hallstatt, and Eddy. The four acceptable stepwise regression models are plotted in figure 12.

Figure 12. The four best forward stepwise regression models. The vertical scale is the HadCRUT5 temperature anomaly in degrees C, relative to 1961-1990.

The first stepwise model (All) chose the variables listed in the figure. The variables are listed in order of importance according to their AIC scores. The best models, visually, are โ€œAllโ€ and โ€œno CO2,โ€ and it is hard to tell the difference between the two. Notice that when CO2 was removed from the selection list, more variables were chosen.

After Hallstatt is removed, the list of chosen variables shrinks, but the model visually degraded a lot. Once Eddy was removed the model becomes very poor. The top variable, by AIC, is Log_CO2, but when Log_CO2 is removed from the model (the green curve) the match to HadCRUT5 is still good. Other models were also evaluated in this fashion, but these three are the best.

The variables that came out consistently on the bottom, according to AIC, were the Pentadecadal cycle and sunspots. However, removing these variables always caused the model to visually deteriorate unacceptably. Thus, AIC, while useful, is not a good sole criterion for the value of variables or models. Always look at the plots.

Conclusions

There are several logical conclusions from this study.

  1. A successful model can be built using only solar cycles, ENSO, and the sunspot record.
  2. Adding CO2 to the model described in (1) above adds a little to the fit, mostly in short intervals, like from 1935 to 1940 and in the middle 1990s around the Pinatubo eruption.
  3. Standard statistical measures, like AIC, R2 or the P test, cannot be used as the sole measure of the success of the model. Evaluating the plots is critical.

This study shows that solar variability, at least statistically, correlates to HadCRUT5 at least as well as CO2. Since HadCRUT5 is one of the main global average surface temperature records used by the IPCC to measure climate change, their conclusion, as stated in the AR6 Technical Summary is:

โ€œTaken together with numerous formal attribution studies across an even broader range of indicators and theoretical understanding, this underpins the unequivocal attribution of observed warming of the atmosphere, ocean, and land to human influence.โ€AR6 TS, page 63, emphasis added.

This is incorrect, and the result of their unsupported assertion that the Sun has no influence on climate. They should seriously investigate the influence of solar variability on climate change. I expected to have to deal with lagged solar effects on climate in this study going in. Possible multi-year lags between solar events and related climatic effects are mentioned in many papers (example here, other examples are cited in Eichler, et al.), but the observation/model matches in this post were all achieved with no lags.

I would like to thank Charley May and David Evans for their help with this post, although if there are any errors, they are mine alone.

Download the bibliography here.

Download the supplementary material here. You will find the R code to create all the models and Excel code to make the main models, not all the R models can be made in Excel. To run the models in Excel you will need to the โ€œAnalysis ToolPakโ€ and the โ€œSolverโ€ Add-in. These are found under File/Options/Add-ins.

  1. (IPCC, 2021, p. 961) โ†‘
  2. See especially: (Connolly et al., 2021), (Hoyt & Schatten, 1997), (Soon, Connolly, & Connolly, 2015), (Usoskin I. , 2017), (Usoskin, Gallet, Lopes, Kovaltsov, & Hulot, 2016), (Scafetta N. , 2023), (Vahrenholt & Lรผning, 2015), and (Judge, Egeland, & Henry, 2020). โ†‘
  3. (Hoyt & Schatten, 1997) and (Bray, 1968) โ†‘
  4. 14C is the Carbon-14 isotope, except for nuclear bombs it is only created in the atmosphere by galactic cosmic rays, which increase when the Sun is less active. It has been used a proxy for solar activity for many decades. It is stored in tree rings, which provide a convenient and accurate date for each 14C concentration. (Cain & Suess, 1976) and (Cain W. , 1975). โ†‘
  5. 10Be is an isotope of Beryllium that is created by cosmic rays and is also inversely correlated with solar activity. It is stored in ice cores. (Beer, Blinov, Bonani, & al., 1990). โ†‘
  6. (Beer, Blinov, Bonani, & al., 1990) and (Hoyt & Schatten, 1997, p. 174) โ†‘
  7. (Bray, 1968) โ†‘
  8. (Delaygue & Bard, 2011) โ†‘
  9. https://www.sidc.be/SILSO/datafiles โ†‘
  10. (Bray, 1968) โ†‘
  11. (Abreu, Beer, & Ferriz-Mas, 2010) โ†‘
  12. Joan Feynman studied this centennial cycle and the pentadecadal cycle for many years. She called it the Gleissberg cycle, but since many have used the name Feynman cycle, we continue with that name here (Feynman & Ruzmaikin, 2014). See also (Peristykh & Damon, 2003). โ†‘
  13. The Pentadecadal cycle was first recognized by Rudolf Wolf in 1862 (Peristykh & Damon, 2003). He recognized that two or three high cycles were often followed by two or three low cycles. More formal recognition of the cycle was made by (Feynman & Ruzmaikin, 2014) and (Clilverd, Clarke, Ulich, Rishbeth, & Jarvis, 2006). โ†‘
  14. (Peristykh & Damon, 2003) โ†‘
  15. (Judge, Egeland, & Henry, 2020) and (Baliunas, et al., 1995) โ†‘
  16. (Peristykh & Damon, 2003) โ†‘
  17. (Roy, 2014) โ†‘
  18. https://www.sidc.be/SILSO/datafiles โ†‘
  19. https://climexp.knmi.nl/getindices.cgi?WMO=NCDCData/ersst_nino3.4a&STATION=NINO3.4&TYPE=i&id=someone@somewhere โ†‘
  20. https://www.metoffice.gov.uk/hadobs/hadcrut5/ โ†‘
  21. 1958 was the International Geophysical Year (IGY), which led to gathering much higher quality climate and climate-related data. It is notable that the late S. Fred Singer was one of the organizers of this project and that it was organized in James Van Allenโ€™s living room in 1950. According to Van Allen, it was his wifeโ€™s (Abigail) chocolate cake that sealed the deal that day. (Korsmo, 2007). โ†‘
  22. (McKitrick & Christy, 2018) and (McKitrick & Christy, 2020) โ†‘
  23. CO2 concentration varies as the logarithm to the base 2 with temperature, which means as CO2 doubles, temperature increases linearly. As CO2 concentration increases, its effect on surface temperature decreases. (Romps, Seeley, & Edman, 2022) and (Wijngaarden & Happer, 2020) โ†‘
  24. ENSO 3.4 is from ERSST, which only goes back to 1854. 1850 through 1853 are filled in with the Webb, 2022 ONI. The sunspot number is from SILSO, and the CO2 concentration data are from NASA and NOAA. The CO2 record is interpolated yearly averages to avoid the seasonal changes. โ†‘
  25. (Evans, 2013) โ†‘
  26. (Usoskin, Gallet, Lopes, Kovaltsov, & Hulot, 2016) and (Usoskin I. , 2017) โ†‘
  27. (Feynman & Ruzmaikin, 2014) โ†‘
  28. (Evans, 2013) โ†‘
  29. AIC stands for Akaike Information Criterion. It estimates the information lost by using the regression model in the place of the measurements. Like R2, it is based on the sum of squares and is susceptible to inflation (making variables and models look better than they actually are) due to autocorrelation. The Wikipedia article on this metric is helpful, see here. The lower the AIC value the better the model. โ†‘
  30. All the input time series used in these multiple regression models are autocorrelated, which simply means each value in the series is highly dependent on its previous values not independent of one another as required by the rules of regression. This artificially inflates the statistical measures often used to evaluate the quality of a regression, such as the R2 value shown in some of the plots. โ†‘

New Study Finds Most Of Antarctica Has Cooled By Over 1ยฐC Since 1999โ€ฆW. Antarctica Cooled 1.8ยฐC

During the second half of the twentieth century, the West Antarctic Ice Sheet (WAIS) has undergone significant warming at more than twice the global mean and thus is regarded as one of the most rapidly warming regions on Earth. However, a reversal of this trend was observed in the 1990s, resulting in regional cooling. In particular, during 1999โ€“2018, the observed annual average surface air temperature had decreased at a statistically significant rate, with the strongest cooling in austral spring.

From NoTricksZone

Byย Kenneth Richardย onย 6. November 2023

Significant 21st century cooling in the Central Pacific, Eastern Pacific, and nearly all of Antarctica โ€œimplies substantial uncertainties in future temperature projections of CMIP6 models.โ€ โ€“ Zhang et al., 2023

New research indicates West Antarcticaโ€™s mean annual surface temperatures cooled by more than -1.8ยฐC (-0.93ยฐC per decade) from 1999-2018. In spring, the West Antarctic Ice Sheet (WAIS) cooling rate reached -1.84ยฐC per decade.

Not only has the WAIS undergone significant cooling in the last two decades, most of the continent also cooled by more than 1ยฐC. See, for example, the ~1ยฐC per decade cooling trend for East Antarctica (2000 to 2018) shown in Fig. ES1.

Of 28 CMIP6 models, none captured a cooling trend โ€“ especially of this amplitude โ€“ for this region. This modeling failure โ€œimplies substantial uncertainties in future temperature projections of CMIP6 models.โ€

Image Source:ย Zhang et al., 2023

The post-1999 cooling trend has not just been confined to Antarctica. Sea surface temperatures (SSTs) in the Eastern and Central Pacific (south of 25ยฐN) also cooled from 1999-2018 relative to 1979-1997. This cooling encompasses nearly half of the Southern Hemisphereโ€™s SSTs.

Image Source:ย Zhang et al., 2023

The 1999-2018 mean annual surface temperature cooling of the Antarctic continent and nearly half of the Southern Hemisphereโ€™s SSTs do not support the claims that surface warming is driven by human emissions of greenhouse gases (GHGs). After all, if the widespread cooling cannot be explained by the increase in GHG forcing, why would the same concentrations of GHGs explain the areas with warming temperatures?

Study investigates primary drivers of observed warming: challenges of inadequateย data

The study, entitledย The Detection and Attribution of Northern Hemisphere Land Surface Warming (1850โ€“2018) in Terms of Human and Natural Factors: Challenges of Inadequate Dataย (August 2023), has 40 authors, some of whom are regular contributors to the โ€˜climate debateโ€™ both in published papers and elsewhere. It takes a critical look at recent IPCC reports and summaries, especially the quality or otherwise of some of the data used to support its assertions. It suggests ways some of these issues could/should be addressed. Below is the abstract and the closing summary.

From Tallbloke’s Talkshop

September 18, 2023 byย oldbrew

The study, entitled The Detection and Attribution of Northern Hemisphere Land Surface Warming (1850โ€“2018) in Terms of Human and Natural Factors: Challenges of Inadequate Data (August 2023), has 40 authors, some of whom are regular contributors to the โ€˜climate debateโ€™ both in published papers and elsewhere. It takes a critical look at recent IPCC reports and summaries, especially the quality or otherwise of some of the data used to support its assertions. It suggests ways some of these issues could/should be addressed. Below is the abstract and the closing summary.
โ€“ โ€“ โ€“
Abstract
A statistical analysis was applied to Northern Hemisphere land surface temperatures (1850โ€“2018) to try to identify the main drivers of the observed warming since the mid-19th century.

Two different temperature estimates were consideredโ€”a rural and urban blend (that matches almost exactly with most current estimates) and a rural-only estimate. The rural and urban blend indicates a long-term warming of 0.89 ยฐC/century since 1850, while the rural-only indicates 0.55 ยฐC/century.

This contradicts a common assumption that current thermometer-based global temperature indices are relatively unaffected by urban warming biases.

Three main climatic drivers were considered, following the approaches adopted by the Intergovernmental Panel on Climate Change (IPCC)โ€™s recent 6th Assessment Report (AR6): two natural forcings (solar and volcanic) and the composite โ€œall anthropogenic forcings combinedโ€ time series recommended by IPCC AR6. The volcanic time series was that recommended by IPCC AR6.

Two alternative solar forcing datasets were contrasted. One was the Total Solar Irradiance (TSI) time series that was recommended by IPCC AR6. The other TSI time series was apparently overlooked by IPCC AR6.

It was found that altering the temperature estimate and/or the choice of solar forcing dataset resulted in very different conclusions as to the primary drivers of the observed warming.

Our analysis focused on the Northern Hemispheric land component of global surface temperatures since this is the most data-rich component. It reveals that important challenges remain for the broader detection and attribution problem of global warming:
(1) urbanization bias remains a substantial problem for the global land temperature data;
(2) it is still unclear which (if any) of the many TSI time series in the literature are accurate estimates of past TSI;
(3) the scientific community is not yet in a position to confidently establish whether the warming since 1850 is mostly human-caused, mostly natural, or some combination.

Suggestions for how these scientific challenges might be resolved are offered.
. . .
In summary, to resolve the causes of the climate changes since the 19th century more satisfactorily, we encourage more research into the following:
1. Better quantification of the contribution of urbanization bias to current global temperature estimates.
2. Improving temperature homogenization techniques to minimize urban blending and more accurately correct for other non-climatic biases.
3. Establishing which (if any) of the current TSI datasets are most reliable. We see this as involving two distinct periods: the satellite era and the pre-satellite era. We propose that further satellite missions could help improve the former, while more sun-like star projects could help improve the latter.
4. Consideration of the possibility that current estimates of the anthropogenic contribution to recent climate change might be too high.
5. Natural climate change drivers other than TSI and volcanic activity.

Full studyย here.

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