Oceans Make Climate | Science Matters

September 2025 Ocean SST Cooling

The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

The ocean covers 71% of the globe and drives average temperatures;
SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
A major El Nino was the dominant climate feature in recent years.

Previously I used HadSST3 for these reports, but Hadley Centre has made HadSST4 the priority, and v.3 will no longer be updated. I’ve grown weary of waiting each month for HadSST4 updates, so the July and August reports were based on data from OISST2.1. This dataset uses the same in situ sources as HadSST along with satellite indicators. Now however, the US government is shut down and updates to climate datasets are likely to be delayed. Reminds of what hospitals do when their budgets are slashed: They close the Maternity Ward to get public attention.

So this September report is based again on HadSST 4, but with a twist. The data is slightly different in the new version, 4.2.0.0 replacing 4.1.1.0. Product page is here.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST 4.2 starting in 2015 through September 2025. A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016, followed by rising temperatures in 2023 and 2024 and cooling in 2025.

Note that in 2015-2016 the Tropics and SH peaked in between two summer NH spikes. That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period. A small warming was driven by NH summer peaks in 2021-22, but offset by cooling in SH and the tropics, By January 2023 the global anomaly was again below the mean.

Then in 2023-24 came an event resembling 2015-16 with a Tropical spike and two NH spikes alongside, all higher than 2015-16. There was also a coinciding rise in SH, and the Global anomaly was pulled up to 1.1°C in 2023, ~0.3° higher than the 2015 peak. Then NH started down autumn 2023, followed by Tropics and SH descending 2024 to the present. During 2 years of cooling in SH and the Tropics, the Global anomaly came back down, led by Tropics cooling from its 1.3°C peak 2024/01, down to 0.6C in September this year. Note the smaller peak in NH in July 2025 now declining along with SH and the Global anomaly cooler as well. Presently the Global anomaly is only slightly above the mean for this period

Comment:

The climatists have seized on this unusual warming as proof their Zero Carbon agenda is needed, without addressing how impossible it would be for CO2 warming the air to raise ocean temperatures. It is the ocean that warms the air, not the other way around. Recently Steven Koonin had this to say about the phonomenon confirmed in the graph above:

El Nino is a phenomenon in the climate system that happens once every four or five years. Heat builds up in the equatorial Pacific to the west of Indonesia and so on. Then when enough of it builds up it surges across the Pacific and changes the currents and the winds. As it surges toward South America it was discovered and named in the 19th century It iswell understood at this point that the phenomenon has nothing to do with CO2.

Now people talk about changes in that phenomena as a result of CO2 but it’s there in the climate system already and when it happens it influences weather all over the world. We feel it when it gets rainier in Southern California for example. So for the last 3 years we have been in the opposite of an El Nino, a La Nina, part of the reason people think the West Coast has been in drought.

It has now shifted in the last months to an El Nino condition that warms the globe and is thought to contribute to this Spike we have seen. But there are other contributions as well. One of the most surprising ones is that back in January of 2022 an enormous underwater volcano went off in Tonga and it put up a lot of water vapor into the upper atmosphere. It increased the upper atmosphere of water vapor by about 10 percent, and that’s a warming effect, and it may be that is contributing to why the spike is so high.

A longer view of SSTs

To enlarge, open image in new tab.

The graph above is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations. Previous posts focused on the rise and fall of the last El Nino starting in 2015. This post adds a longer view, encompassing the significant 1998 El Nino and since. The color schemes are retained for Global, Tropics, NH and SH anomalies. Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July. 1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino.

The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2.

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8, a lower peak warming in 2009-10, following by cooling in 2011-12. Again SSTs are average 2013-14.

Now a different pattern appears. The Tropics cooled sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off. But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average. In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16. NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peaks came in 2019 and 2020, only this time the Tropics and SH were offsetting rather adding to the warming. (Note: these are high anomalies on top of the highest absolute temps in the NH.) Since 2014 SH has played a moderating role, offsetting the NH warming pulses. After September 2020 temps dropped off down until February 2021. In 2021-22 there were again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, then in October to January 2023 by deeper cooling in NH and Tropics.

Then in 2023 the Tropics flipped from below to well above average, while NH produced a summer peak extending into September higher than any previous year. Despite El Nino driving the Tropics January 2024 anomaly higher than 1998 and 2016 peaks, following months cooled in all regions, and the Tropics continued cooling in April, May and June along with SH dropping. After July and August NH warming again pulled the global anomaly higher, September through January 2025 resumed cooling in all regions, continuing February through April 2025, with little change in May,June and July despite upward bumps in NH. Now temps in all regions are cooling August and September 2025.

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH. The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before. After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years.

Contemporary AMO Observations

Through January 2023 I depended on the Kaplan AMO Index (not smoothed, not detrended) for N. Atlantic observations. But it is no longer being updated, and NOAA says they don’t know its future. So I find that ERSSTv5 AMO dataset has current data. It differs from Kaplan, which reported average absolute temps measured in N. Atlantic. “ERSST5 AMO follows Trenberth and Shea (2006) proposal to use the NA region EQ-60°N, 0°-80°W and subtract the global rise of SST 60°S-60°N to obtain a measure of the internal variability, arguing that the effect of external forcing on the North Atlantic should be similar to the effect on the other oceans.” So the values represent SST anomaly differences between the N. Atlantic and the Global ocean.

The chart above confirms what Kaplan also showed. As August is the hottest month for the N. Atlantic, its variability, high and low, drives the annual results for this basin. Note also the peaks in 2010, lows after 2014, and a rise in 2021. Then in 2023 the peak reached 1.4C before declining to 0.9 last month. An annual chart below is informative:

Note the difference between blue/green years, beige/brown, and purple/red years. 2010, 2021, 2022 all peaked strongly in August or September. 1998 and 2007 were mildly warm. 2016 and 2018 were matching or cooler than the global average. 2023 started out slightly warm, then rose steadily to an extraordinary peak in July. August to October were only slightly lower, but by December cooled by ~0.4C.

Then in 2024 the AMO anomaly started higher than any previous year, then leveled off for two months declining slightly into April. Remarkably, May showed an upward leap putting this on a higher track than 2023, and rising slightly higher in June. In July, August and September 2024 the anomaly declined, and despite a small rise in October, ended close to where it began. Note 2025 started much lower than the previous year and headed sharply downward, well below the previous two years, then since April through September aligning with 2010.

The pattern suggests the ocean may be demonstrating a stairstep pattern like that we have also seen in HadCRUT4.

The rose line is the average anomaly 1982-1996 inclusive, value 0.18. The orange line the average 1982-2025, value 0.41 also for the period 1997-2012. The red line is 2015-2025, value 0.69. As noted above, these rising stages are driven by the combined warming in the Tropics and NH, including both Pacific and Atlantic basins.

Curiosity: Solar Coincidence?

The news about our current solar cycle 25 is that the solar activity is hitting peak numbers now and higher than expected 1-2 years in the future. As livescience put it: Solar maximum could hit us harder and sooner than we thought. How dangerous will the sun’s chaotic peak be? Some charts from spaceweatherlive look familar to these sea surface temperature charts.

Summary

The oceans are driving the warming this century. SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.” The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect. The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up? And is the sun adding forcing to this process?

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

About Sea Surface Temperatures

Posted on October 12 by Ron Clutz

Background from NOAA Climate.gov

Q: What’s the temperature of water at the ocean’s surface?
A: Colors on the map show the temperature of water right at the ocean’s surface. The darkest blue shows the coldest water: floating sea ice is usually present in these areas. Lighter shades of blue show temperatures of up to 80°F. White and orange areas show where surface temperatures are higher than 80°F, warm enough to fuel tropical cyclones or hurricanes.

Q: Where do these measurements come from?
A: Satellite instruments measure sea surface temperature—often abbreviated as SST—by checking how much energy comes off the ocean at different wavelengths. Computer programs merge sea surface temperatures from ships and buoys with the satellite data, and incorporate information from maps of sea ice. To produce the daily maps, programs invoke mathematical filters to combine and smooth data from all three sources.

Q: Why do these data matter?
A: While heat energy is stored and mixed throughout the depth of the ocean, the temperature of water right at the sea’s surface—where the ocean is in direct contact with the atmosphere—plays a significant role in weather and short-term climate. Where sea surface temperatures are high, relatively large amounts of heat energy and moisture enter the atmosphere, sometimes producing powerful, drenching storms downwind. Conversely, lower sea surface temperatures mean less evaporation. Global patterns of sea surface temperatures are an important factor for weather forecasts and climate outlooks.

Q: How did you produce these snapshots?
A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA’s Climate Data Records Program produces the Opitimum Interpolated Sea Surface Temperature files. To produce our images, we run a set of scripts that access the source files, re-project them into desired projections at various sizes, and output them with a custom color bar.

With the federal government shutdown, dataset updates are uncertain, but OISST is current and shows how presently the ocean is cooling down from it’s 2024 high temperatures.

Note: Daily SST Ocean Temperature Graphic, 1982-2025

Use the options below to generate graphics of daily sea surface temperatures since 1982 using data from NOAA’s Optimum Interpolation Sea Surface Temperature (OISST) v2.1 dataset. [The chart above defines Global as 60°N to 60°S.] These graphics will update daily, or as data becomes available on the Climate Reanalyzer website. Note the most recent two weeks of data are considered preliminary. Specific information about the data can be found here.

My Comment:

The chart shows how 2025 is tracking ~half a degree F cooler than 2024. That may not seem significant, except that the ocean covers 71% of the Earth’s surface, and any SST warming is reported in numbers of zettajoules. This is explained at the EPA website Climate Change Indicators: Ocean Heat:

The top 700 meters of the ocean contain 63% of the ocean’s heat content. The data shows that ocean layer has warmed about a zettajoule (1×10^22 joules) yearly since 1990.

For reference, an increase of 1 unit on this graph (1 × 10^22 joules) is equal to approximately 17 times the total amount of energy used by all the people on Earth in a year (based on a total global energy supply of 606 exajoules in the year 2019, which equates to 6.06 × 10^20 joules).

It appears that presently we may be about to lose that amount of energy through oceanic cooling. And the sun could be to blame:

Scare du jour Marine Heat Waves

Posted on October 8 by Ron Clutz

If you watch legacy media, you must also be wondering after seeing all the current headlines about Marine Heat Waves raising the ocean to its boiling point.

Ocean heatwaves are breaking Earth’s hidden climate engine, Science Daily

The Pacific Ocean is overheated, making fall feel like summer, CBC

The ‘blob’ is back — except this time it stretches across the entire north Pacific, CNN

Record marine heatwaves may signal a permanent shift in the oceans, New Scientist

Global warming drives a threefold increase in persistence and 1 °C rise in intensity of marine heatwaves, PNAS

Etc., etc. etc.

The last one is the paper driving this recent clamor over Ocean SSTs Marcos et al. 2025 From the abstract:

We determine that global warming is responsible for nearly half of these extreme events and that, on a global average, it has led to a three-fold increase in the number of days per year that the oceans experience extreme surface heat conditions. We also show that global warming is responsible for an increase of 1 °C in the maximum intensity of the events. Our findings highlight the detrimental role that human-induced global warming plays on marine heatwaves. This study supports the need for mitigation and adaptation strategies to address these threats to marine ecosystems.

The coordinated media reports are exposed by all of them containing virtually the same claim:

As climate change causes our planet to warm, marine heatwaves are
becoming more frequent, more intense, and longer lasting.

Animation shows locations of moderate to severe MHWs mid-month 2025 January to October. A marine heatwave is defined as one where the measured temperature is within 10% of the maximum values observed (i.e., above the Threshold (90th quantile) , for at least 5 consecutive days. For this, the intensity is compared to the difference between the climatological mean and the 90th percentile value (threshold). A marine heatwave intensity between 1 and 2 times this difference corresponds to a heatwave of moderate category; between 2 and 3 times, to a strong category; between 3 and 4 times, to a severe category; and a difference greater than 4 times corresponds to an extreme category.

First some background context on the phenomena (in italics with my bolds).

Background from perplexity.ai How Do Warm and Cool Ocean Blobs Circulate?

Warm and cool ocean blobs circulate through distinct oceanic and atmospheric processes, often linked to major currents and atmospheric patterns.

Warm ocean blobs, such as the “warm blob” in the northeast Pacific, form due to atmospheric circulation changes triggered by factors like Arctic warming. This leads to a high-pressure system over the region, weakening westerly winds and reducing ocean heat loss, causing surface waters to warm and creating persistent warm anomalies. The formation of these warm blobs involves a feedback loop between weakened winds, reduced ocean-atmosphere heat exchange, and ocean circulation, which retains heat in the mixed layer of the ocean.

Cool ocean blobs, like the North Atlantic “cold blob,” are influenced by weakening of the Atlantic Meridional Overturning Circulation (AMOC). This circulation moves warm, salty water northward, which cools, sinks, and then the cooler deep water travels southward in a conveyor-belt style flow. The cold blob forms when excess freshwater from ice melt dilutes the salty water, reducing its density and sinking ability, weakening this circulation and causing cooler surface water to persist. This cooling also affects the atmosphere by reducing water vapor, which decreases greenhouse effect locally and amplifies the cold anomaly, creating a coupled ocean-atmosphere feedback loop.

In summary, warm and cool ocean blobs circulate through a combination of ocean current dynamics and atmospheric interactions. Warm blobs form where atmospheric changes reduce ocean heat loss and circulation shifts retain heat, while cool blobs occur where circulation weakens, allowing cooler, less dense waters to persist and affect atmospheric conditions as well.

Then a summary of the issues undermining the alarmists’ claim.

From perplexity.ai What are reasons to doubt climate change is increasing marine heatwaves?

There are several reasons to doubt that climate change is definitively increasing the frequency, intensity, duration, and spatial extent of marine heatwaves, based on some ongoing scientific debates and uncertainties.

Natural Variability and Other Factors

♦ Marine heatwaves are influenced by natural climate variability, such as El Niño, Pacific Decadal Oscillation (PDO), and other oceanic and atmospheric processes. These phenomena can cause fluctuations in sea surface temperatures independent of long-term climate change, leading to periods of warmer ocean conditions that may be mistaken for climate-driven trends.

♦ Some studies emphasize the role of internal ocean variability, which can cause significant short-term temperature anomalies without requiring a direct link to anthropogenic climate change.

Complexity of Attribution

♦ The attribution of marine heatwave trends specifically to climate change involves complex modeling and statistical analysis, which can have uncertainties. Certain models suggest that long-term temperature increases are the primary driver, but the contribution of natural variability remains significant and sometimes difficult to separate clearly from climate signals.

♦ Regional differences and localized oceanic processes can obscure the global patterns, leading some scientists to argue that not all observed phenomena are directly attributable to climate change, particularly in areas with strong natural variability.

Limitations of Climate Models

♦ Climate models predicting future marine heatwave conditions depend heavily on assumptions about greenhouse gas emissions and other factors. These models often have limitations in resolution and in capturing small-scale processes, which could lead to overestimations or underestimations of climate change impacts.

Data Gaps and Uncertainties

♦ Although current observations show increasing trends in marine heatwaves, data gaps exist, especially in remote or deep-sea regions, making comprehensive global assessments challenging. These gaps contribute to uncertainty regarding the full extent and causality of observed changes.

♦ The precise long-term ecological impacts and possible adaptation or resilience mechanisms of marine ecosystems also remain uncertain, complicating the understanding of climate change’s role versus natural variability.

Summary

While a considerable body of evidence supports the role of climate change in increasing marine heatwaves, skepticism persists due to the influence of natural variability, model limitations, regional differences, and data gaps. These factors suggest that attribution is complex, and ongoing research continues to refine our understanding of the relative contributions of human influences and natural climate fluctuations.

Finally, a discussion of a specific example revealing flawed methods supposedly connecting CO2 emissions to marine heatwaves.

Much Ado About Marine Heat Waves

The promotion of this scare was published in 2022 at Nature by Barkhordarian et al. Recent marine heatwaves in the North Pacific warming pool can be attributed to rising atmospheric levels of greenhouse gases. This post will unpack the reasons to distrust this paper and its claims. First the Abstract of the subject and their declared findings in italics with my bolds.

Abstract

Over the last decade, the northeast Pacific experienced marine heatwaves that caused devastating marine ecological impacts with socioeconomic implications. Here we use two different attribution methods and show that forcing by elevated greenhouse gases levels has virtually certainly caused the multi-year persistent 2019–2021 marine heatwave. There is less than 1% chance that the 2019–2021 event with ~3 years duration and 1.6 ∘C intensity could have happened in the absence of greenhouse gases forcing. We further discover that the recent marine heatwaves are co-located with a systematically-forced outstanding warming pool, which we attribute to forcing by elevated greenhouse gases levels and the recent industrial aerosol-load decrease. The here-detected Pacific long-term warming pool is associated with a strengthening ridge of high-pressure system, which has recently emerged from the natural variability of climate system, indicating that they will provide favorable conditions over the northeast Pacific for even more severe marine heatwave events in the future.

Background on Ocean Warm Pools

Wang and Enfield study is The Tropical Western Hemisphere Warm Pool Abstract in italics with my bolds.

Abstract

The Western Hemisphere warm pool (WHWP) of water warmer than 28.5°C extends from the eastern North Pacific to the Gulf of Mexico and the Caribbean, and at its peak, overlaps with the tropical North Atlantic. It has a large seasonal cycle and its interannual fluctuations of area and intensity are significant. Surface heat fluxes warm the WHWP through the boreal spring to an annual maximum of SST and areal extent in the late summer/early fall, associated with eastern North Pacific and Atlantic hurricane activities and rainfall from northern South America to the southern tier of the United States. SST and area anomalies occur at high temperatures where small changes can have a large impact on tropical convection. Observations suggest that a positive ocean-atmosphere feedback operating through longwave radiation and associated cloudiness is responsible for the WHWP SST anomalies. Associated with an increase in SST anomalies is a decrease in atmospheric sea level pressure.

Chou and Chou published On the Regulation of the Pacific Warm Pool Temperature:

Abstract

Analyses of data on clouds, winds, and surface heat fluxes show that the transient behavior of basin-wide large-scale circulation has a significant influence on the warm pool sea surface temperature (SST). Trade winds converge to regions of the highest SST in the equatorial western Pacific. The reduced evaporative cooling due to weakened winds exceeds the reduced solar heating due to enhanced cloudiness. The result is a maximum surface heating in the strong convective and high SST regions. The maximum surface heating in strong convective regions is interrupted by transient atmospheric and oceanic circulation. Regions of high SST and low-level convergence follow the Sun. As the Sun moves away from a convective region, the strong trade winds set in, and the evaporative cooling enhances, resulting in a net cooling of the surface. We conclude that the evaporative cooling associated with the seasonal and interannual variations of trade winds is one of the major factors that modulate the SST distribution of the Pacific warm pool.

Comment:

So these are but two examples of oceanographic studies describing natural factors driving the rise and fall of Pacific warm pools. Yet the Nature paper claims rising CO2 from fossil fuels is the causal factor, waving away natural processes. Skeptical responses were already lodged upon the first incidence of the North Pacific marine heat wave, the “Blob” much discussed by west coast US meteorologists. One of the most outspoken against the global warming attributionists has been Cliff Mass of Seattle and University of Washington. Writing in 2014 and 2015, he observed the rise and fall of the warming blob and then posted a critique of attribution attempts at his blog. For example, Media Miscommunication about the Blob. Excerpts in italics with my bolds.

Blob Media Misinformation

One of the most depressing things for scientists is to see the media misinform the public about an important issue.

During the past few days, an unfortunate example occurred regarding the warm water pool that formed over a year ago in the middle of the north Pacific, a.k.a., the blob. Let me show how this communication failure occurred, with various media outlets messed things up in various ways.

The stimulant for the nationwide coverage of the Blob was a very nice paper published by Nick Bond (UW scientist and State Climatologist), Meghan Cronin, Howard Freeland, and Nathan Mantua in Geophysical Research Letters.

This publication described the origin of the Blob, showing that it was the result of persistent ridging (high pressure) over the Pacific. The high pressure, and associated light winds, resulted in less vertical mixing of the upper layer of the ocean; with less mixing of subsurface cold water to the surface. Furthermore, the high pressure reduced horizontal movement of colder water from the north. Straightforward and convincing work.

The inaccurate press release then led to a media frenzy, with the story going viral. And unfortunately, many of the media got it wrong.

There were two failure modes. In one, the headline was wrong, but the internal story was correct. . . In the second failure mode, the story itself was essentially flawed, with most claiming that the Blob off of western North America was the cause of the anomalous circulation (big ridge over West Coast, trough over the eastern U.S.). (The truth: the Blob was the RESULT of the anomalous circulations.) That the Blob CAUSED the California drought or the cold wave in the eastern U.S. These deceptive stories were found in major outlets around the country, including the Washington Post, NBC News, and others.

Blob Returns, Attribution Misinformation

When the Blob returned 2020-2021, Cliff Mass had cause to again lament how the public is misled. This time misdirection instigated by activist scientists using flawed methods. His post Miscommunication in Recent Climate Attribution Studies. Excerpts in italics with my bolds.

This attribution report, and most media stories that covered it, suggested a central role for global warming for the heatwave. As demonstrated in my previous blog, their narrative simply does not hold up to careful examination.

This blog will explain why their basic framing and approach is problematic, leading readers (and most of the media) to incorrect conclusions.

For the heatwave, the attribution folks only examine the statistics of temperatures hitting the record highs (108F in Seattle), but avoid looking at the statistics of temperature exceeding 100F, or even the record highs (like 103F in Seattle). There is a reason they don’t do that. It would tell a dramatically different (and less persuasive) story.

In the attribution studies, the main technology for determining changed odds of extreme weather is to use global climate models. First, they run the models with greenhouse gas forcing (which produces more extreme precipitation and temperature), and then they run the models again without increased greenhouse gases concentrations. By comparing the statistics of the two sets of simulations, they attempt to determine how the odds of extreme precipitation or temperature change.

Unfortunately, there are serious flaws in their approach: climate models fail to produce sufficient natural variability (they underplay the black swans) and their global climate models don’t have enough resolution to correctly simulate critical intense, local precipitation features (from mountain enhancement to thunderstorms). On top of that, they generally use unrealistic greenhouse gas emissions in their models (too much, often using the RCP8.5 extreme emissions scenario) And there is more, but you get the message. ( I am weather/climate modeler, by the way, and know the model deficiencies intimately.)

Vaunted Fingerprinting Attribution Is Statistically Unsound

From Barkhordarian et al.

Unlike previous studies which have focused on linking the SST patterns in the North Pacific to changes in the oceanic circulation and the extratropical/tropical teleconnections2,12,17,18,20,24,26, we here perform two different statistical attribution methodologies in order to identify the human fingerprint in Northeast Pacific SST changes both on multidecadal timescale (changes of mean SST) and on extreme SST events on daily timescale (Marine Heatwaves). Evidence that anthropogenic forcing has altered the base state (long-term changes of mean SST) over the northeast Pacific, which is characterized by strong low-frequency SST fluctuations, would increase confidence in the attribution of MHWs27, since rising mean SST is the dominant driver of increasing MHW frequency and intensity, outweighing changes due to temperature variability1,2.

In this study, we provide a quantitative assessment of whether GHG forcing, the main component of anthropogenic forcings, was necessary for the North Pacific high-impact MHWs (the Blob-like SST anomalies) to occur, and whether it is a sufficient cause for such events to continue to repeatedly occur in the future. With these purposes, we use two high-resolution observed SST datasets, along with harnessing two initial-condition large ensembles of coupled general circulation models (CESM1-LE28,29 with 35 members, and MPI-GE30 with 100 members). These large ensembles can provide better estimates of an individual model’s internal variability and response to external forcing31,32, and facilitate the explicit consideration of stochastic uncertainty in attribution results33. We also use multiple single-forcing experiments from the Detection and Attribution Model Intercomparision Project (DAMIP34) component of Coupled Model Intercomparison Project phase 6 (CMIP635).

From Barkhordarian et al. References

The IPCC’s attribution methodology is fundamentally flawed

The central paper underpinning the attribution analysis was assessed and found unreliable by statistician Ross McKitrick’s published evaluation. Excerpts in italics with my bolds.

One day after the IPCC released the AR6 I published a paper in Climate Dynamics showing that their “Optimal Fingerprinting” methodology on which they have long relied for attributing climate change to greenhouse gases is seriously flawed and its results are unreliable and largely meaningless. Some of the errors would be obvious to anyone trained in regression analysis, and the fact that they went unnoticed for 20 years despite the method being so heavily used does not reflect well on climatology as an empirical discipline.

My paper is a critique of “Checking for model consistency in optimal fingerprinting” by Myles Allen and Simon Tett, which was published in Climate Dynamics in 1999 and to which I refer as AT99. Their attribution methodology was instantly embraced and promoted by the IPCC in the 2001 Third Assessment Report (coincident with their embrace and promotion of the Mann hockey stick). The IPCC promotion continues today: see AR6 Section 3.2.1. It has been used in dozens and possibly hundreds of studies over the years. Wherever you begin in the Optimal Fingerprinting literature (example), all paths lead back to AT99, often via Allen and Stott (2003). So its errors and deficiencies matter acutely.

Abstract

Allen and Tett (1999, herein AT99) introduced a Generalized Least Squares (GLS) regression methodology for decomposing patterns of climate change for attribution purposes and proposed the “Residual Consistency Test” (RCT) to check the GLS specification. Their methodology has been widely used and highly influential ever since, in part because subsequent authors have relied upon their claim that their GLS model satisfies the conditions of the Gauss-Markov (GM) Theorem, thereby yielding unbiased and efficient estimators.

But AT99:

stated the GM Theorem incorrectly, omitting a critical condition altogether,
their GLS method cannot satisfy the GM conditions, and
their variance estimator is inconsistent by construction.
Additionally, they did not formally state the null hypothesis of the RCT nor
identify which of the GM conditions it tests, nor
did they prove its distribution and critical values, rendering it uninformative as a specification test.

The continuing influence of AT99 two decades later means these issues should be corrected. I identify 6 conditions needing to be shown for the AT99 method to be valid.”

In Conclusion, McKitrick:

One point I make is that the assumption that an estimator of C provides a valid estimate of the error covariances means the AT99 method cannot be used to test a null hypothesis that greenhouse gases have no effect on the climate. Why not? Because an elementary principle of hypothesis testing is that the distribution of a test statistic under the assumption that the null hypothesis is true cannot be conditional on the null hypothesis being false. The use of a climate model to generate the homoscedasticity weights requires the researcher to assume the weights are a true representation of climate processes and dynamics.

The climate model embeds the assumption that
greenhouse gases have a significant climate impact.

Or, equivalently, that natural processes alone cannot generate a large class of observed events in the climate, whereas greenhouse gases can. It is therefore not possible to use the climate model-generated weights to construct a test of the assumption that natural processes alone could generate the class of observed events in the climate.

August 2025 Ocean SSTs: NH Warms Slightly

Posted on September 20 by Ron Clutz

The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

The ocean covers 71% of the globe and drives average temperatures;
SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
A major El Nino was the dominant climate feature in recent years.

Previously I used HadSST3 for these reports, but Hadley Centre has made HadSST4 the priority, and v.3 will no longer be updated. I’ve grown weary of waiting each month for HadSST4 updates, so this report is based on data from OISST2.1. This dataset uses the same in situ sources as HadSST along with satellite indicators. Importantly, it produces daily anomalies from baseline period 1991-2020. The data is available at Climate Reanalyzer (here). Product guide is (here). The charts and analysis below is produced from the current data.

The Current Context

The chart below shows SST monthly anomalies as reported in OISST2.1 starting in 2015 through August 2025. A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016, followed by rising temperatures in 2023 and 2024 and cooling in 2025.

Then in 2023-24 came an event resembling 2015-16 with a Tropical spike and two NH spikes alongside, all higher than 2015-16. There was also a coinciding rise in SH, and the Global anomaly was pulled up to 0.6°C in 2023, ~0.2° higher than the 2015 peak. Then NH started down autumn 2023, followed by Tropics and SH descending 2024 to the present. During 2 years of cooling in SH and the Tropics, the Global anomaly came back down, led by Tropics cooling the last 12 months from its 0.9°C peak last August, down to 0.3C in August this year. Small changes in NH and SH offset each other, leaving the global anomaly the same.

Comment:

A longer view of SSTs

To enlarge, open image in new tab.

Contemporary AMO Observations

The pattern suggests the ocean may be demonstrating a stairstep pattern like that we have also seen in HadCRUT4.

The rose line is the average anomaly 1982-1996 inclusive, value -0.25. The orange line the average 1982-2025, value -0.014 also for the period 1997-2012. The red line is 2015-2025, value 0.32. As noted above, these rising stages are driven by the combined warming in the Tropics and NH, including both Pacific and Atlantic basins.

Curiosity: Solar Coincidence?

Summary

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

July 2025 Ocean SSTs: NH Warms Slightly

Posted on August 20 by Ron Clutz

The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

The ocean covers 71% of the globe and drives average temperatures;
SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
A major El Nino was the dominant climate feature in recent years.

Previously I used HadSST3 for these reports, but Hadley Centre has made HadSST4 the priority, and v.3 will no longer be updated. I’ve grown weary of waiting each month for HadSST4 updates, so this report is based on data from OISST2.1. This dataset uses the same in situ sources as HadSST along with satellite indicators. Importantly, it produces daily anomalies from baseline period 1982-2010. The data is available at Climate Reanalyzer (here). Product guide is (here). The charts and analysis below is produced from the current data.

The Current Context

The chart below shows SST monthly anomalies as reported in OISST2.1 starting in 2015 through July 2025. A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016, followed by rising temperatures in 2023 and 2024 and cooling in 2025.

Then in 2023-24 came an event resembling 2015-16 with a Tropical spike and two NH spikes alongside, all higher than 2015-16. There was also a coinciding rise in SH, and the Global anomaly was pulled up to 0.8°C in 2023, ~0.2° higher than the 2015 peak. Then NH started down autumn 2023, followed by Tropics and SH descending 2024 to the present. During 2 years of cooling in SH and the Tropics, the Global anomaly came back down, led by NH cooling the last 12 months from its 1.0°C peak last August, down to 0.5C in April this year.. Further declines in Tropics and SH offset NH warming in May and June, and now in July 2025 a slight upward bump in Global anomaly over 0.6°C.

Comment:

A longer view of SSTs

To enlarge, open image in new tab.

Contemporary AMO Observations

The chart above confirms what Kaplan also showed. As August is the hottest month for the N. Atlantic, its variability, high and low, drives the annual results for this basin. Note also the peaks in 2010, lows after 2014, and a rise in 2021. Then in 2023 the peak was holding at 1.4C before declining. An annual chart below is informative:

The pattern suggests the ocean may be demonstrating a stairstep pattern like that we have also seen in HadCRUT4.

The rose line is the average anomaly 1982-1996 inclusive, value -0.10. The orange line the average 1982-2025, value 0.13, also for the period 1997-2012. The red line is 2015-2025, value 0.46. As noted above, these rising stages are driven by the combined warming in the Tropics and NH, including both Pacific and Atlantic basins.

Curiosity: Solar Coincidence?

Summary

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

June 2025 Ocean SSTs: NH Warms, SH Cools

Posted on July 17 by Ron Clutz

The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

The ocean covers 71% of the globe and drives average temperatures;
SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
A major El Nino was the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source. Previously I used HadSST3 for these reports, but Hadley Centre has made HadSST4 the priority, and v.3 will no longer be updated. HadSST4 is the same as v.3, except that the older data from ship water intake was re-estimated to be generally lower temperatures than shown in v.3. The effect is that v.4 has lower average anomalies for the baseline period 1961-1990, thereby showing higher current anomalies than v.3. This analysis concerns more recent time periods and depends on very similar differentials as those from v.3 despite higher absolute anomaly values in v.4. More on what distinguishes HadSST3 and 4 from other SST products at the end. The user guide for the current version HadSST4.1.1.0 is here. The charts and analysis below is produced from the current data.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through June 2025. A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016, followed by rising temperatures in 2023 and 2024 and cooling in 2025.

Then in 2023-24 came an event resembling 2015-16 with a Tropical spike and two NH spikes alongside, all higher than 2015-16. There was also a coinciding rise in SH, and the Global anomaly was pulled up to 1.1°C last year, ~0.3° higher than the 2015 peak. Then NH started down autumn 2023, followed by Tropics and SH descending 2024 to the present. After 12 months of cooling in SH and the Tropics, the Global anomaly came back down, led by NH cooling the last 8 months from its 1.3C peak in August, down to 0.8C in March and April. Remarkably, April 2025 SST anomalies in all regions and globally are the coolest since March 2023. May showed little change in the Global anomaly, while in June declines in SH along with the Tropics mostly offset an upward bump in NH.

Comment:

A longer view of SSTs

To enlarge, open image in new tab.

Contemporary AMO Observations

The chart above confirms what Kaplan also showed. As August is the hottest month for the N. Atlantic, its variability, high and low, drives the annual results for this basin. Note also the peaks in 2010, lows after 2014, and a rise in 2021. Then in 2023 the peak was holding at 1.4C before declining. An annual chart below is informative:

Then in 2024 the AMO anomaly started higher than any previous year, then leveled off for two months declining slightly into April. Remarkably, May showed an upward leap putting this on a higher track than 2023, and rising slightly higher in June. In July, August and September 2024 the anomaly declined, and despite a small rise in October, ended close to where it began. Note 2025 started much lower than the previous year and is headed sharply downward, well below the previous two years, now in May and June aligning with 2010.

The pattern suggests the ocean may be demonstrating a stairstep pattern like that we have also seen in HadCRUT4.

The purple line is the average anomaly 1980-1996 inclusive, value 0.17. The orange line the average 1980-2024, value 0.4, also for the period 1997-2012. The red line is 2013-2024, value 0.67. As noted above, these rising stages are driven by the combined warming in the Tropics and NH, including both Pacific and Atlantic basins.

Curiosity: Solar Coincidence?

Summary

Footnote: Why Rely on HadSST4

HadSST is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST4 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

May 2025 Two Years of Ocean Cooling Persists

Posted on June 19 by Ron Clutz

The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

The ocean covers 71% of the globe and drives average temperatures;
SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
A major El Nino was the dominant climate feature in recent years.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through May 2025. A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016, followed by rising temperatures in 2023 and 2024.

Then in 2023-24 came an event resembling 2015-16 with a Tropical spike and two NH spikes alongside, all higher than 2015-16. There was also a coinciding rise in SH, and the Global anomaly was pulled up to 1.1°C last year, ~0.3° higher than the 2015 peak. Then NH started down autumn 2023, followed by Tropics and SH descending 2024 to the present. After 12 months of cooling in SH and the Tropics, the Global anomaly came back down, led by NH cooling the last 8 months from its 1.3C peak in August, down to 0.8C in March and April. Remarkably, April 2025 SST anomalies in all regions and globally are the coolest since March 2023. May shows little change in the Global anomaly, with a SH decline offsetting an upward bump in NH.

Comment:

A longer view of SSTs

To enlarge image, open in new tab.

Contemporary AMO Observations

The chart above confirms what Kaplan also showed. As August is the hottest month for the N. Atlantic, its variability, high and low, drives the annual results for this basin. Note also the peaks in 2010, lows after 2014, and a rise in 2021. Then in 2023 the peak was holding at 1.4C before declining. An annual chart below is informative:

Then in 2024 the AMO anomaly started higher than any previous year, then leveled off for two months declining slightly into April. Remarkably, May showed an upward leap putting this on a higher track than 2023, and rising slightly higher in June. In July, August and September 2024 the anomaly declined, and despite a small rise in October, ended close to where it began. Note 2025 started much lower than the previous year and is headed sharply downward, well below the previous two years, now in May aligning with 2010.

The pattern suggests the ocean may be demonstrating a stairstep pattern like that we have also seen in HadCRUT4.

The purple line is the average anomaly 1980-1996 inclusive, value 0.17. The orange line the average 1980-2024, value 0.38, also for the period 1997-2012. The red line is 2013-2024, value 0.67. As noted above, these rising stages are driven by the combined warming in the Tropics and NH, including both Pacific and Atlantic basins.

Curiosity: Solar Coincidence?

Summary

Footnote: Why Rely on HadSST4

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

April 2025 Two Years Ocean Warming Gone

Posted on May 23, 2025 by Ron Clutz

The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

The ocean covers 71% of the globe and drives average temperatures;
SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
A major El Nino was the dominant climate feature in recent years.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through April 2025. A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016, followed by rising temperatures in 2023 and 2024.

Then in 2023-24 came an event resembling 2015-16 with a Tropical spike and two NH spikes alongside, all higher than 2015-16. There was also a coinciding rise in SH, and the Global anomaly was pulled up to 1.1°C last year, ~0.3° higher than the 2015 peak. Then NH started down autumn 2023, followed by Tropics and SH descending 2024 to the present. After 12 months of cooling in SH and the Tropics, the Global anomaly came back down, led by NH cooling the last 8 months from its 1.3C peak in August, down to 0.8C in March and April. With some recent warming in the Tropics and SH, all regions are now close together nearly at the global anomaly, less than 0.1C higher than the average for this period.

Remarkably, April 2025 SST anomalies in all regions and globally are the coolest since March 2023.

Comment:

A longer view of SSTs

The graph below is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations. Previous posts focused on the rise and fall of the last El Nino starting in 2015. This post adds a longer view, encompassing the significant 1998 El Nino and since. The color schemes are retained for Global, Tropics, NH and SH anomalies. Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July.

To enlarge image, open in new tab.

Contemporary AMO Observations

The chart above confirms what Kaplan also showed. As August is the hottest month for the N. Atlantic, its variability, high and low, drives the annual results for this basin. Note also the peaks in 2010, lows after 2014, and a rise in 2021. Then in 2023 the peak was holding at 1.4C before declining. An annual chart below is informative:

Then in 2024 the AMO anomaly started higher than any previous year, then leveled off for two months declining slightly into April. Remarkably, May showed an upward leap putting this on a higher track than 2023, and rising slightly higher in June. In July, August and September 2024 the anomaly declined, and despite a small rise in October, ended close to where it began. Note 2025 started much lower than the previous year and is headed sharply downward, well below the previous two years.

The pattern suggests the ocean may be demonstrating a stairstep pattern like that we have also seen in HadCRUT4.

Curiosity: Solar Coincidence?

Summary

Footnote: Why Rely on HadSST4

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

Solar Activity Linked to Ocean Cycles

Posted on February 20, 2025 by Ron Clutz

Solar energy accumulates massively in the ocean and is variably released during circulation events.

Thanks to Franklin Isaac Ormaza-González alerting me to this paper Did Schwabe cycles 19–24 influence the ENSO events, PDO, and AMO indexes in the Pacific and Atlantic Oceans? by Ormaza-González, Espinoza-Celi and Roa-López, all from ESPOL Polytechnic University, Ecuador. Why is this important? Because warming in the modern era is closely tied to El Niño and La Niña events (ENSO). For example,

The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4. This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C. Previously the 1997-98 El Nino produced a plateau increase of 0.4C. Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

As shown in the synopsis below, the paper analyzes multiple oceanic oscillations during the years 1954 to 2019 in order to compare with solar cycles of sunspots 19 through 24 occurring during that time frame. The title is stated as a question, and the conclusion provides this answer (in italics with my bolds).

Finally, did Schwabe cycles 19–24 influence the ENSO events, PDO, and AMO indexes in the Pacific and Atlantic Oceans? Yes, it has been found a wide range correlation coefficient from 0.100 to about 0.500 statistically significant (p < 0.05) with lag times from few months to over 2 years between the Schwabe cycles and the ocean indices chosen here. These results could be a potential source to improve predictive skills for the understanding of ENSO, PDO and AMO interannual and decadal fluctuations. Better predictive models are imperative given that El Niño or La Niña has vast impacts on lives, property, and economic activity around the globe, especially when dramatic peaks of El Niño occur. The new cycle 25 has started and could have a major oceanic swing follow suit, and the next El Niño would be in around 2023–2024 according to historical events and results presented here.

Given that the paper was drafted before submitting in February 2022, and publication in October that year, the forecast of a 2023-24 El Nino was confirmed in a remarkable way.

To enlarge, open image in new tab.

The cyan line represents SST anomalies in the Tropics and shows the major El Ninos, 2015-16, 2019-20 and 2023-24. Note all three events included pairs of major NH summer warming peaks. The synopsis below consists of excerpts in italics with my bolds to present the broad strokes of the analyses and findings. (Note: The paper includes detailed analyses and many references to supporting studies, and interested readers can access them by linking there.)

Context

The surface-subsurface layers of the ocean that interact with the lower atmosphere alternately release and absorb heat energy. The work of Zhou and Tung (2010) reported the impact of the TSI on global SST over 150 years, finding signals of cooling and warming SSTs at the valley and peak of the SS cycles. Schlesinger and Ramankutty (1994) report a global cycle of 65–70 years for SST that is affected by greenhouse anthropogenic gases, sulphate aerosols and/or El Niño events, but they did not imply any external forcing such as the SS. There have been other studies on how solar radiation variability could affect temperature; recently, Cheke et al. (2021) have studied those solar cycles of SS that would affect the El Nino Southern Oscillation (ENSO) indexes.

There are well known oceanic events that show periodicity with low or high frequencies: 25–30 and 3–7 years, respectively. These include the Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and Interdecadal Pacific Oscillation (IPO), as well as El Niño or La Niña. During El Niño events, the surface and subsurface lose energy to the atmosphere and the opposite occurs during La Niña; these events have a periodicity of 3–7 years. The Interdecadal oscillations have a series of impacts; e.g., the PDO gives rise to teleconnections between the tropic and mid-latitudes, and the effects include:

1) ocean heat content,
2) the lower and higher levels of the trophic chain including small pelagic fisheries (tuna and sardines);
3) biogeochemical air-sea CO2 fluxes;
4) the frequency of La Niña/El Niño.

The interactions between decadal oscillations PDO/IPO and AMO may also affect ocean heat content. All these low and high frequency oceanographic events have a direct impact on local, regional, and global climate patterns, and there is growing evidence from many studies that the driving source of energy is the sun.

Thus, whatever affects the solar irradiation falling on the surface of the oceans, including volcanic eruptions (Fang et al., 2020), and cloudiness for example, it would affect the gain or loss of heat content of the oceans. The cited works tried to find the physical reasons for these connections, but they remained unknown or difficult to explain.

The work reported here investigates how fluctuations of sunspots over time (1954–2019) may cross-correlate with low and high frequency oceanic events such as the sea surface temperature (SST), anomalies (SSTA), Oceanographic El Niño Index (ONI), Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI) in the central and east equatorial Pacific Ocean; and PDO, as well as on the AMO in the North Pacific and Atlantic basins. The hypothesis is that even small variations of the TSI can be reflected in these tele-connected indexes.

Discussion

Fig. 1. Behaviour of monthly counts of SS, ONI, MEI, PDO and AMO. The Indexes start at t = 0, 12, 24 and 36 months (panels a, b, c, and d respectively). The SS series starts at t = 0 in the four panels. The left vertical axis gives the values for the Indexes, and SS counts at the right vertical scale. The end of each Schwabe cycle is marked by vertical dashed lines.

Maxima in the PDO, AMO, ONI, and MEI series were offset by 0, 12, 24 and 36 months (Fig. 1, panels a, b, c, and d respectively), with the SS series starts at t = 0 in the four panels. It has been reported that the lag times for responses of some Indexes to SS cycles (SS) are around 12–36 months (see fig. 1 of Hassan et al., 2016), and Fang et al. (2020) have reported that ENSO responds with a 2–3 years of lag time after a major volcanic eruption. From 1954 to the present time, each sunspot cycle from 19 to 24 has occurred with a period of around 11 years (Hathaway, 2015), which is slightly less than the 11.2 years reported by Dicke (1978). The highest SS activity is seen in cycle 19 with around 250 SS/month, followed by <150, and at cycle 21 around 200, before decreasing steadily over cycles 22 to 24 to just over 100 SS/month. Cycle 24 is the lowest contemporary value of SS activity that is comparable only to cycles 12–15 (around 1880–1930) and is the lowest in the last 200 years (Clette et al., 2014).

Fig. 12. Sunspots monthly counts curves per cycle. Red and blue lines represent El Niño and La Niña events. Note that Cycle 24 finished on December 2019 (National Weather Service, 2020).

The SSTA in El Niño 1 + 2 region cross-correlated with SS many times, especially during descending phases of all cycles except SS 22 with cc-ρ up 0.389 (SS 24) and main lag times from 5 to 13 months. The SS cycles (20 and 24) during cold phase PDO showed alternate cross-correlation reaching a maximum 0.389 and negative −0.314 (p < 0.05). During the ascending phase in El Niño 1 + 2 region (blue bars, Fig. 5a) the cc-ρ peaked at 0.393 (p < 0.05). In the cycles 19 and 24 the highest cc-ρ were found, −0.460 and 0.394 (p < 0.05) respectively. These coefficients coincided with the largest (over 2 years) and most intense (<−1.5C) La Niña during 1954–1955, and 2010–2012 (Fig. 12).

It must be noticed that during cycle 21 two big events El Niño (1983–1985) and La Niña (1984–1985) were registered as well as in cycles 23 and 24 with coefficients just around 0.2. The highest coefficients would mean an influence up to 21.2% and 15.5% of the SS on the SSTAs in El Niño 3.4 region. These results would suggest the cross-correlations are stronger in El Niño 3.4 region due to the less dispersing oceanographic-meteorological conditions than in El Niño 1 + 2 region. Also, these findings would suggest that during the cold phase of PDOs (see NOAA, 2016), the cc-ρ in El Niño 3.4 region tends to be higher, as the solar energy reaching the ocean surface increases as the cloudiness tends to decrease significantly during prolonged periods around or over in El Niño 3.4 region (Porch et al., 2006).

The sun cycle 19 is the most intense since the last 100 years, the contrary is the cycle 24 (NWS, 2021). In general, the ascending phase of the SS cycles takes a shorter time than descending phase, therefore the slope of the curve is steeper (Fig. 12); then the increasing change of the TSI influences in a clearer way the studied indexes. It seems that during the ascending phases, El Niño events are prone to develop as TSI increases (as well as UV radiation does, NWS, 2021), while during plunging SS phases, when the TSI tends to diminish (see Formula (1)), could lead to La Niña events, like the 2020–2022 occurrence (Ormaza-González, 2021).

Most of the La Niña events occur during the descending phase or just when approaching or leaving the valley or minimum SS counts (Fig. 12) when the TSI decreases and reaches the minimum (Scafetta et al., 2019). La Niña 2020–2022 is a good example, the lowest SS counts (<2 counts/months) occurred during extended periods when reaching the valley of the SS 24. The valley of SS 24 has had an extended period of close to 3 years, during which there have been weeks and months without sunspots, before the SS 25 started in December 2020.

The weakest sunspot cycle (SS 24) over the last 100 years (NWS, 2021) has had four La Niña events: 2007–2009, 2010–2012, 2016–2017, and 2020–2022 (Fig. 12), it is the only cycle with that number of La Niña events.

Conclusions

Over the studied period 1954–2019, sunspot numbers decreased from a monthly maximum between 225 (SS 21) to a minimum around 20–25 (SS 24). The SS 24 had 913 days without SS counts until December 2019 (Burud et al., 2021), being this cycle the weakest since 1755; and the SS 25 will probably be weaker than or like SS 24 (Ineson et al., 2014; Chowdhury et al., 2021; NASA, 2021a, NASA, 2021b). Thus, the Earth has been receiving slightly decreasing solar energy over this almost 7-decade period.

On the ocean surface the influence of sunspots could chiefly be due to UV energy fluctuation (Ineson et al., 2014) as this radiation penetrates down to 75–100 m depth in the water column (Smyth, 2011). van Loon et al. (2007) suggested that even though SS cycles produce weak changes on the Total Solar Irradiation (TSI) of about 0.07% (Gray et al., 2010), these can still produce decadal and millennial impacts on global thermohaline circulation (Bond et al., 2001; Gray et al., 2016).

The ONI Index showed to be poorly cross-correlated with cc-ρ values <0.100, only twice approached to −0.200. On the other hand, the MEI registered around ±0.200 through all cycles and predominant lag times within 12 months. The SOI showed cross-correlations with SS cycles (19–21, and) averaging a coefficient of 0.200 with lags times range of 9–34 months. The SOI temporal behaviour has also been associated with SS and it could enhance or affect the oceanographic Indexes of the equatorial Pacific (Higginson et al., 2004). [The Multivariate ENSO Index does not only consider the SST Anomaly but also sea-level pressure and other variables.]

The MEI index could have been influenced from 7.3% up to 23%. The MEI correlated in all ascending and descending phases of SS cycles. The SOI had similar cross-correlation coherence to those oceanographic indexes during ascending and descending phases. These results would provide evidence on how SS affects the studied Indexes during the ascending/descending phases of their cycles. In some cycles, the impact will be stronger and in other weaker depending on intensity and behaviour in time of the cycle.

Finally, did Schwabe cycles 19–24 influence the ENSO events, PDO, and AMO indexes in the Pacific and Atlantic Oceans? Yes, it has been found a wide range correlation coefficient from 0.100 to about 0.500 statistically significant (p < 0.05) with lag times from few months to over 2 years between the Schwabe cycles and the ocean indices chosen here. These results could be a potential source to improve predictive skills for the understanding of ENSO, PDO and AMO interannual and decadal fluctuations. Better predictive models are imperative given that El Niño or La Niña has vast impacts on lives, property, and economic activity around the globe, especially when dramatic peaks of El Niño occur. The new cycle 25 has started and could have a major oceanic swing follow suit, and the next El Niño would be in around 2023–2024 according to historical events and results presented here.

No, Grist, MSN, et al: CO2 Is Not Making Oceans Boil

Posted on February 3, 2025 by Ron Clutz

The Climate Crisis media network is announcing a new claim that rising CO2 is causing recent ocean warming, proving it’s dangerous and must be curtailed. Examples in the last few days include these:

Finally, an answer to why Earth’s oceans have been on a record hot streak Grist

Ocean warming 4 times faster than in 1980s — and likely to accelerate in coming decades MSN

News spotlight: Fossil fuels behind extreme ocean temperatures, study says. Conservation International

Ocean temperature rise accelerating as greenhouse gas levels keep rising UK Natural History Museum

The surface of our oceans is now warming four times faster than it was in the late 1980s The Independent UK

Oceans Are Warming Four Times Faster as Earth Traps More Energy Bloomberg Law News

All this hype deriving from one study,
and ignoring the facts falsifying that narrative.

Fact: Historically, ocean natural oscillations drive observed global warming.

The long record of previous warmings prior to the satellite record shows that the entire rise of 0.8C since 1947 is due to oceanic, not human activity.

The animation is an update of a previous analysis from Dr. Murry Salby. These graphs use Hadcrut4 and include the 2016 El Nino warming event. The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4. This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C. Previously the 1997-98 El Nino produced a plateau increase of 0.4C. Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

Importantly, the theory of human-caused global warming asserts that increasing CO2 in the atmosphere changes the baseline and causes systemic warming in our climate. On the contrary, all of the warming since 1947 was episodic, coming from three brief events associated with oceanic cycles.

Fact: Recent rise in SST was driven by ENSO and N. Atlantic Anomalies.

And now in 2024 we have seen an amazing episode with a temperature spike driven by ocean warming in all regions, along with rising NH land temperatures, now dropping below its peak.

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through December 2024. A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016, followed by rising temperatures in 2023 and 2024.

To enlarge, open image in new tab.

Now in 2023-24 came an event resembling 2015-16 with a Tropical spike and two NH spikes alongside, all higher than 2015-16. There was also a coinciding rise in SH, and the Global anomaly was pulled up to 1.1°C last year, ~0.3° higher than the 2015 peak. Then NH started down autumn 2023, followed by Tropics and SH descending 2024 to the present. After 10 months of cooling in SH and the Tropics, the Global anomaly came back down, led by NH cooling the last 4 months from its peak in August. It’s now about 0.1C higher than the average for this period. Note that the Tropical anomaly has cooled from 1.29C in 2024/01 to 0.66C as of 2024/12.

Fact: Empirical measurements show ocean warms the air, not the other way around.

One can read convoluted explanations about how rising CO2 in the atmosphere can cause land surface heating which is then transported over the ocean and causes higher SST. But the interface between ocean and air is well described and measured. Not surprisingly it is the warmer ocean water sending heat into the atmosphere, and not the other way around.

The graph displays measures of heat flux in the sub-tropics during a 21-day period in November. Shortwave solar energy shown above in green labeled radiative is stored in the upper 200 meters of the ocean. The upper panel shows the rise in SST (Sea Surface Temperature) due to net incoming energy. The yellow shows latent heat cooling the ocean, (lowering SST) and transferring heat upward, driving convection. [From An Investigation of Turbulent Heat Exchange in the Subtropics by James B. Edson]

As we see in the graphs ocean circulations change sea surface temperatures which then cause global land and sea temperatures to change. Thus, oceans make climate by making temperature changes.

Fact: On all time scales, from last month’s observations to ice core datasets spanning millennia, temperature changes first and CO2 changes follow.

Previously I have demonstrated that changes in atmospheric CO2 levels follow changes in Global Mean Temperatures (GMT) as shown by satellite measurements from University of Alabama at Huntsville (UAH). That background post is included in the posting referenced later below.

My curiosity was piqued by the remarkable GMT spike starting in January 2023 and rising to a peak in April 2024, and then declining afterward. I also became aware that UAH has recalibrated their dataset due to a satellite drift that can no longer be corrected. The values since 2020 have shifted slightly in version 6.1, as shown in my recent report Ocean Leads Cooling UAH December 2024.

I tested the premise that temperature changes are predictive of changes in atmospheric CO2 concentrations. The chart above shows the two monthly datasets: CO2 levels in blue reported at Mauna Loa, and Global temperature anomalies in purple reported by UAHv6.1, both through December 2024. Would such a sharp increase in temperature be reflected in rising CO2 levels, according to the successful mathematical forecasting model? Would CO2 levels decline as temperatures dropped following the peak?

The answer is yes: that temperature spike resulted
in a corresponding CO2 spike as expected.
And lower CO2 levels followed the temperature decline.

Above are UAH temperature anomalies compared to CO2 monthly changes year over year.

Changes in monthly CO2 synchronize with temperature fluctuations, which for UAH are anomalies now referenced to the 1991-2020 period. CO2 differentials are calculated for the present month by subtracting the value for the same month in the previous year (for example December 2024 minus December 2023). Temp anomalies are calculated by comparing the present month with the baseline month. Note the recent CO2 upward spike and drop following the temperature spike and drop.

Summary

Changes in CO2 follow changes in global temperatures on all time scales, from last month’s observations to ice core datasets spanning millennia. Since CO2 is the lagging variable, it cannot logically be the cause of temperature, the leading variable. It is folly to imagine that by reducing human emissions of CO2, we can change global temperatures, which are obviously driven by other factors.

12/2024 Update–As Temperature Changes, CO2 Follows

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