Climate Science

Top Climate Model Improved to Show ENSO Skill

Posted on by Ron Clutz

Previous posts (linked at end) discuss how the climate model from RAS (Russian Academy of Science) has evolved through several versions. The interest arose because of its greater ability to replicate the past temperature history. The model is part of the CMIP program which is now going the next step to CMIP7, and is one of the first to test with a new climate simulation. Improvements to the latest model, INMCM60, show an enhanced ability to replicate ENSO oscillations in the Pacific ocean, which have significant climate impacts world wide.

This news comes by way of a new paper published in the Russian Journal of Numerical Analysis and Mathematical Modelling February 2024.  The title is ENSO phase locking, asymmetry and predictability in the INMCM Earth system model,  Seleznev et al. (2024) Excerpts in italics with my bolds and images from the article.

Abstract:

Advanced numerical climate models are known to exhibit biases in simulating some features of El Niño–Southern Oscillation (ENSO) which is a key mode of inter-annual climate variability. In this study we analyze how two fundamental features of observed ENSO – asymmetry between hot and cold states and phase-locking to the annual cycle – are reflected in two different versions of the INMCM Earth system model (state-of-the-art Earth system model participating in the Coupled Model Intercomparison Project).

We identify the above ENSO features using the conventional empirical orthogonal functions (EOF) analysis which is applied to both observed and simulated upper ocean heat content (OHC) data in the tropical Pacific. We obtain that the observed tropical Pacific OHC variability is described well by two leading EOF-modes which roughly reflect the fundamental recharge-discharge mechanism of ENSO. These modes exhibit strong seasonal cycles associated with ENSO phase locking while the revealed nonlinear dependencies between amplitudes of these cycles reflect ENSO asymmetry.

We also assess and compare predictability of observed and simulated ENSO based on linear inverse modeling. We find that the improved INMCM6 model has significant benefits in simulating described features of observed ENSO as compared with the previous INMCM5 model. The improvements of the INMCM6 model providing such benefits arediscussed. We argue that proper cloud parametrization scheme is crucial for accurate simulation of ENSO dynamics with numerical climate models

Introduction

El Niño–Southern Oscillation (ENSO) is the most prominent mode of inter-annual climate variability which originates in the tropical Pacific, but has a global impact [41]. Accurately simulating ENSO is still a challenging task for global climate modelers [3,5,15,25]. In the comprehensive study [35] large-ensemble climate model simulations provided by the Coupled Model Intercomparison Project phases 5 (CMIP5)and 6 (CMIP6) were analyzed. It was found that the CMIP6 models significantly outperform those fromCMIP5 for 8 out of 24 ENSO-relevant metrics, especially regarding the simulation of ENSO spatial patterns, diversity and teleconnections. Nevertheless, some important aspects of the observed ENSO are still not satisfactorily simulated by the most of state-of-the-art models [7,38,49]. In this study we are aimed at examination of how two such aspects – ENSO asymmetry and ENSO phase-locking to the annual cycle –are reflected in the INMCM Earth system model [44, 45].

The asymmetry between hot (El Nino) and cold (La Nina) states is a fundamental feature in the observed ENSO occurrences [39]. El Niño events are often stronger than La Niña events, while the latter ones tend to be more persistent [10]. Such an asymmetry is generally attributed to nonlinear feedbacks between sea surface temperatures (SSTs), thermocline and winds in the tropical Pacific [2,19,28]. The alternative conceptions highlight the role of tropical instability waves [1] and fast atmospheric processes associated with irregular zonal wind anomalies [24]. ENSO phase-locking is identified as the tendency of ENSO-events to peak in boreal winter.

Several studies [11,17,34] argue that the phase-locking is associated with seasonal changes in thermocline depth, ocean upwelling velocity, and cloud feedback processes. These processes collectively contribute to the coupling strength modulation between ocean and atmosphere, which, in the context of conceptual ENSO models [4,18], provides seasonal modulation of stability (in the sense of decay rate) of the “ENSO oscillator”. Another theory [20,42] supposes the phase-locking results from nonlinear interactions between the seasonal forcing and the inherent ENSO cycle. Both the asymmetry and phaselocking effects are typically captured by low-dimensional data-driven ENSO models [14, 21, 26, 29, 37].

In this work we identify the ENSO features discussed above via the analysis of upper ocean heat content (OHC) variability in the the tropical Pacific. The recent study [37] analyzed high-resolution reanalysis dataset of the tropical Pacific (10N – 10S, 120E – 80W) OHC anomalies in the 0–300 m depth layer using the standard empirical orthogonal function (EOF) decomposition [16]. It was found that observed OHC variability is effectively captured by two leading EOFs, which roughly describe the fundamental recharge-discharge mechanism of ENSO [18]. The time series of the corresponding principal components (PCs) demonstrate strong seasonal cycles, reflecting ENSO phase-locking, while the revealed inter-annual nonlinear dependencies between these cycles can be associated with ENSO asymmetry [37].

Here we apply similar analysis to the OHC data simulated by two different versions of INMCM Earth system model. The first is the INMCM5 model [45] from CMIP6, and the second is the perspective INMCM6 [44] model with improved parameterization of clouds, large-scale condensation and aerosols. Along with the traditional EOF decomposition we invoke the linear inverse modeling to assess and compare predictability of ENSO from observed and simulated data.

The paper is organized as follows. Sect. 2 describes the datasets we analyze: OHC reanalysis dataset and OHC data obtained from the ensemble simulations of global climate with two versions of INMCM model. Data preparation, including separation of the forced and internal variability, is also discussed. The ensemble EOF analysis is represented, which is used for identifying the meaningful processes contributing to observed and simulated ENSO dynamics. Sect. 3 presents the results we obtain in analyzing both observed and simulated OHC data. In Sect. 4 we summarize and discuss the obtained results, particularly regarding the significant benefits of new version of INMCM model (INMCM6) in simulating key features of observed ENSO.

Fig. 1: Two leading EOFs of the observed tropical Pacific upper ocean heat content (OHC) variability

Fig. 2: Two leading EOFs of the INMCM5 ensemble of tropical Pacific upper ocean heat content simulations

Fig. 3: The same as in Fig. 2 but for INMCM6 model simulations

The corresponding spatial patterns in Fig. 1 have clear interpretation. The first contributes to the central and eastern tropical Pacific, where most significant variations of sea surface temperature (SST) during El Niño/La Nina events occur [9]. The second predominates mainly in the western tropical Pacific and can be associated with the OHC accumulation and discharge before and during the El Niño events [48].

What we can see from Fig. 2 is that the two leading EOFs of OHC variability simulated by the INMCM5 model do not correspond to the observed ones. The corresponding time series and spatial patterns exhibit smaller-scale features, as compared to those we obtain from the reanalysys data, indicating their noisier spatio-temporal nature.

The two leading EOFs of the improved INMCM6 model (Fig. 3), by contrast, capture well both the spatial and temporal features of observed EOFs. In the next section we focus on furtheranalysis of these EOFs assuming that they contain the most meaningful information about ENSO dynamics.

Discussion

In this study we have analyzed how two different versions of the INMCM model [44,45] (state-of-the-art Earth system model participating in the Coupled Model Intercomparison Project, CMIP) simulate some features of El Niño–Southern Oscillation (ENSO) which is a key mode of the global climate. We identified the ENSO features via the EOF analysis applied to both observed and simulated upper ocean heat content(OHC) variability in the the tropical Pacific. It was found that the observed tropical Pacific OHC variability is captured well by two leading modes (EOFs) which reflect the fundamental recharge-discharge mechanism of ENSO involving a recharge and discharge of OHC along the equator caused by a disequilibrium between zonal winds and zonal mean thermocline depth. These modes are phase-shifted and exhibit the strong seasonal cycles associated with ENSO phase locking. The inter-annual dependencies between amplitudes of the revealed ESNO seasonal cycles are strongly nonlinear which reflects the asymmetry between hot (ElNino) and cold (La Nina) states of observed ENSO. We found that the INMCM5 model (the previous version of the INMCM model from CMIP6) poorly reproduces the leading modes of observed ENSO and reflect neither the observed ENSO phase locking nor asymmetry. At the same time, the perspective INMCM6 model demonstrates significant improvement in simulating these key features of observed ENSO. The analysis of ENSO predictability based on linear inverse modeling indicates that the improved INMCM6 model reflects well the ENSO spring predictability barrier and therefore could potentially have an advantage in long range weather prediction as compared with the INMCM5.

Such benefits of the new version of the INMCM model (INMCM6) in simulating observed ENSO dynamics can be provided by using more relevant parametrization of sub-grid scale processes. Particularly, the difference in the amplitude of OHC anomaly associated with ENSO between INMCM5 and INMCM6 shown in Fig.2-3 can be explained mainly by the difference in cloud parameterization in these models. In short, in INMCM5 El-Nino event leads to increase of middle and low clouds over central and eastern Pacific that leads to cooling because of decrease in surface incoming shortwave radiation.

While decrease in low clouds and increase in high clouds in INMCM6 over El-Nino region during positive phase of ENSO lead to further upper ocean warming [43]. This is consistent with the recent study [36] which argued that erroneous cloud feedback arising from a dominant contribution of low-level clouds may lead to heat flux feedback bias in the tropical Pacific, which play a key role in ENSO dynamics. Fast decrease in OHC in central Pacific after El-Nino maximum in INMCM6 can probably occur because of too shallow mixed layer in equatorial Pacific in the model, that leads to fast surface cooling after renewal of upwelling and further increase of tradewinds. Summarizing the above we can conclude that proper cloud parameterization scheme is crucial for accurate simulation of observed ENSO with numerical climate models.

Background on INMCM6

New 2023 INMCM RAS Climate Model First Results

The INMCM60 model, like the previous INMCM48 [1], consists of three major components: atmospheric dynamics, aerosol evolution, and ocean dynamics. The atmospheric component incorporates a land model including surface, vegetation, and soil. The oceanic component also encompasses a sea-ice evolution model. Both versions in the atmosphere have a spatial 2° × 1° longitude-by-latitude resolution and 21 vertical levels up to 10 hPa. In the ocean, the resolution is 1° × 0.5° and 40 levels.

The following changes have been introduced into the model compared to INMCM48.

Parameterization of clouds and large-scale condensation is identical to that described in [4], except that tuning parameters of this parameterization differ from any of the versions outlined in [3], being, however, closest to version 4. The main difference from it is that the cloud water flux rating boundary-layer clouds is estimated not only for reasons of boundary-layer turbulence development, but also from the condition of moist instability, which, under deep convection, results in fewer clouds in the boundary layer and more in the upper troposphere. The equilibrium sensitivity of such a version to a doubling of atmospheric СО2 is about 3.3 K.

The aerosol scheme has also been updated by including a change in the calculation of natural emissions of sulfate aerosol [5] and wet scavenging, as well as the influence of aerosol concentration on the cloud droplet radius, i.e., the first indirect effect [6]. Numerical values of the constants, however, were taken to be a little different from those used in [5]. Additionally, the improved scheme of snow evolution taking into account refreezing and the calculation of the snow albedo [7] were introduced to the model. The calculation of universal functions in the atmospheric boundary layer in stable stratification has also been changed: in the latest model version, such functions assume turbulence at even large gradient Richardson numbers [8].

 

El Nino Keeps Ocean Warm January 2024

Posted on 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;
  • Major El Ninos have been 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 HadSST4 is here.

The Current Context

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

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.  

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. After an uptick in December, temps in January 2023 dropped everywhere, strongest in NH, with the Global anomaly further below the mean since 2015.

Then came El Nino as shown by the upward spike in the Tropics since January, the anomaly nearly tripling from 0.38C to 1.09C.  In September 2023, all regions rose, especially NH up from 0.70C to 1.41C, pulling up the global anomaly to a new high for this period. But then in October anomalies in all regions started dropping down bringing down the Global anomaly.  By December, NH cooled to 1.1C and the Global anomaly down to 0.94C from its peak of 1.10C, despite slight warming in SH and Tropics.

Now in January both Tropics and SH rose, resulting Global Anomaly going higher. Tropics anomaly reached a new peak of 1.29C. Note that all ocean regions are now higher than 01/2016, the previous peak.

Comment:

The climatists have seized on this unusual warming as proof of their Zero Carbon agenda, 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 is well 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.  

Now in 2023 the Tropics flipped from below to well above average, while NH has produced a summer peak extending into September higher than any previous year. In fact, October and now November are showing that this number is likely the crest, despite El Nino driving the Tropics anomaly close to 1998 and 2015 peaks.

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 data through October.  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 varibility, high and low, drives the annual results for this basin.  Note also the peaks in 2010, lows after 2014, and a rise in 2021. Now in 2023 the peak was holding at 1.4C before decling.  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. January 2024 is unchanged from the previous month.

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 0.18.  The orange line the average 1980-202306, value 0.38, also for the period 1997-2012. The red line is 2013-202306, value 0.64. 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?

Space weather impacts the ionosphere in this animation. Credits: NASA/GSFC/CIL/Krystofer Kim

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

 

 

Climatists Mistake Means for Ends

Posted on by Ron Clutz

Roy Gilbert exposes the fundamental mistaken thinking regarded global warming/climate change.  His Spectator Australia article is Conceptual Error in Climate Change Analysis.  H/T John Ray  Excerpts in italics with my bolds and added images.

It is often said that the ‘science is in on climate change’. Is it? We should always adhere to the principle of the ‘working hypothesis’ and have an open mind on scientific questions no matter how well-recognised the researchers are. In the study of science, there is always the chance new information can come along to cause a rethink.

A common error in problem-solving and policy development is to confuse
a technical strategy for a desired client outcome.

Our Climate Change Minister could be accused of this. Reducing emissions is a ‘strategy’, not the fundamental desired client outcome. With the mission ‘to reduce carbon emissions’ by increasing renewable energy, the way to assess performance is to concentrate on measuring emission reduction, and then to follow this up with how quickly the renewables are built and their cost (wind farms, solar panels, transmission lines).

Instead of the current strategy-driven mission, a fundamental client outcome statement would be: ‘To protect against, and where possible, prevent damage from extreme off-trend fluctuations in climate.’ How would you go about managing your program using this mission statement?

First, you gather accurate temperature, rainfall, and weather measurements. They are the valid and fundamental ‘outcome’ measures – not data on CO2 emissions. If there is an undeniable and dangerous increase in temperature and rainfall, more cyclones, and a clear and unabated rise in sea level, then the possible cause must be thoroughly identified. Depending on the answer, you would adopt appropriate mitigation strategies, or strategies that adapt to weather patterns and temperature levels.

Another principle of problem-solving is to map out the total picture and not be driven by ideology. The Climate Change Minister should consider possible causes other than human-induced emissions. It was announced in April 2023 that coronal cones 20 times larger than Earth have been discovered and may cause a massive outburst of energy from the sun. What could be the implications for our planet? Ask solar physicists.

Chief scientist in applied helio-physics at John Hopkins, Ian Cohen, has suggested that solar storms could take out satellites, cut power and shut down the internet. In 1972 a solar storm caused 4,000 magnetically sensitive mines in water off Vietnam to detonate. Earth is said to be entering a period of peak activity as part of an eleven-year cycle. It is suggested this potentially could be more violent than the solar cycles of the past three decades. Now that would be something for climate scientists to really worry about…

With respect to the world’s temperature, there are several sources that claim to present the precise figure. One says the 2023 average global temperature was 1.45c above the 1950-90 average. Another says since 1880, Earth’s temperature has increased by 0.08c. Another says during the last 50 years the increase is 0.13c. To the unscientific mind, these temperatures do not appear to be verging on catastrophic boiling us all to death.

As of 2024, data on natural changes in temperature, rainfall, and sea level
do not show any statistically significant difference to historical records.

There are respected scientists who question the current climate orthodoxy. Physicist Prof. William Happer of Princeton University and Prof. Richard Lindzen, Earth, Atmospheric and Planetary Sciences at MIT have argued science demonstrates there is no climate-related risk caused by fossil fuels and CO2, and that 600 million years of CO2 and temperature data contradicts the theory that high levels of CO2 will cause catastrophic global warming. They state reliable scientific theories come from validating theoretical predictions with observations, not consensus, peer review, government opinion, or manipulated data.

In July 2023, the International Monetary Fund cancelled a planned talk on climate change by 2022 Nobel physicist John Clauser when they learned he had stated publicly: ‘I can confidently say there is no real climate crisis, and that climate change does not cause extreme weather events. The IPCC is one of the worst sources of dangerous disinformation.’  Clauser pointed out that the US Environmental Protection Authority has charts that show a heatwave Index going back to 1895, showing heatwaves were more common before the 1960s and especially in the 1930s.

In addition to these physicists, there are eminent Australian geologists who challenge the CO2 cause theory. Emeritus Prof. Ian Pilmer of the University of Melbourne, and Prof. Michael Asten of Monash University, have argued that throughout the history of the planet, there have been long periods of major change in climate due to natural forces. This would indicate recent human-based emissions may not be the important factor that we have been led to believe.

With respect to measuring emissions (nitrous oxide and methane), there is an expectation that the Intergovernmental Panel on Climate Change would have collected accurate data. Then one reads an independent 2023 report of these greenhouse gas emissions from farm dams in Australia’s irrigation regions, that the measurements had been massively over-estimated by the IPCC by 4 to 5 per cent.

To add further confusion to the issue, a 2023 research paper submitted to the European Physical Journal Plus claimed climate science has become ‘highly politicised’. Italian scientists analysed long-term data on heat, droughts, floods, hurricanes, tornadoes, and ecosystem productivity, and found no clear trend of extreme events. The statements by these scientists would appear worthy of examination. Unfortunately, comments to the publisher by other climate scientists caused the withdrawal of the article.

If activists are correct, and if temperatures and rainfall start to show a significant increase without any influence from natural factors such as the sun or outer atmospheric disturbances, the second ‘outcome’ mission opens your mind to several strategies that could be compared against each other on cost and effectiveness – renewables, outer space satellites capturing solar energy and transmitting to Earth, small nuclear, carbon capture, examine possibility of amalgamating carbon and turning it into a useful product, lower emission coal-fired power stations, hydro, hydrogen fuel cells, a scientific search for a predator for carbon other than trees (or the planting of more trees), and so on.

A valid client ‘outcome’ statement encourages you not to jump to a conclusion
in the initial stages of critical thinking about the cause of any global warming.

If you make a mistake at that point, there are significant productivity implications. Governments could waste a significant amount of money (a catastrophic amount) on a less than optimum strategy. Rather than relying almost entirely on climate scientists who concentrate on carbon emissions, a politician with a mind focused on validity could bring together an inter-disciplinary team – climate scientists, nuclear physicists, solar physicists, atmospheric physicists, examine the moon’s behaviour, plant technologists, oceanographers, geologists, volcanologists, botanists, bushfire specialists and so on. Has any national government followed this approach? Has any Minister for Energy, in any country, expanded their vision beyond their own narrow ideology is a potential danger to their country…?

There are very obvious reasons why some politicians and many rich investors in renewable energy would oppose a serious questioning of the renewable strategy and switching to nuclear instead. If small nuclear was introduced – as is being done in many countries – it would make current renewable energy strategies redundant. That would mean all the billions of dollars spent on wind and solar would have been a waste of money. We wouldn’t need them. Admitting that would be far too embarrassing for any ideological politician and far too financially damaging to any rich wind farm investor obtaining government grants.

If the Sun is found to be the fundamental cause of the problem (variations in energy output, massive infrequent solar flares, and/or variations in distance between Earth and Sun), or if there is a slight tilting of the Earth on its axis, or the Moon changes position, or even disturbance further out in our solar system, you would evaluate adaptation strategies.

It seemed reasonable for some people to assume the vast flooding in 2022 could be attributed to human-induced climate change. There is however, a different possibility … nature. Environment analyst Graham Lloyd explained.

‘The meteorological processes at play are well understood. Three consecutive La Nina weather patterns have left the eastern seaboard soaked and prone to flooding. Triple La Ninas have happened four times in the Bureau of Meteorology’s 120-year record … The Southern Annular Mode is a climate driver that can influence rainfall and temperature. Although wet, the latest BoM figures show that 2022 was the ninth wettest year on record (not the wettest).’

fWhen the above material, stressing the need to examine the total picture in any critical thinking, was shown to a high school Principal, to a high school science teacher and to an environmental engineer, they were all surprised and quite critical that one would want to show this to students. Annoyed actually. One was emphatic…

‘Why waste the students’ time having them look at irrelevant issues?
We KNOW what the problem is. It is CO2 emissions.
And we KNOW what the solution is. It is 100 per cent renewables.’

My answer to them was:

‘The difference between you and me, is that you want to tell the students WHAT to think. I want to teach them HOW to think. I want them to understand insightful thinking. Not to be indoctrinated’.  You can be the judge as to who is on the right track.

See Also

Answers Before Climate Action

 

World of CO2 Infographics 2024 Update

Posted on by Ron Clutz

Update February 13, 2024

Many of my posts include some high quality infographics produced by a colleague, Raymond Inauen.  This update is because due to other pressing time demands, Raymond has discontinued the website he set up to host the infographics. Below is an overview to the content, followed by links to the PDF files now hosted at this blog. The infographic PDFs can be downloaded at no charge with no restrictions on use. 

World of CO2 Infographics January 2023

This post is to announce that Raymond Inauen of RIC-Communications has a website up for the public to access a series of infographics regarding CO2 and climate science.  The Website content is:

The World of CO2

Readers will be aware of previous posts on the four themes to be discovered.  Raymond introduces this resource in this way:

WELCO₂ME

Would you like to learn more about CO₂ so you can have informed conversations about climate policy and future energy investments? Or would you rather pass judgment on CO₂ after learning about the basics? Then this is the website for you.

There are 29 infographic images that can be downloaded in four PDF files.  Thanks again, Raymond for your interest and efforts to make essential scientific information available to one and all. PDF links are in red.

The+World+of+CO2 CO2 charts

Example (#8 of 14)

 

The+World+of+Climate+Change Charts

Example (#5 of 6)

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UAH January 2024: Ocean Warm, Land Cooling

Posted on by Ron Clutz

The post below updates the UAH record of air temperatures over land and ocean. Each month and year exposes again the growing disconnect between the real world and the Zero Carbon zealots.  It is as though the anti-hydrocarbon band wagon hopes to drown out the data contradicting their justification for the Great Energy Transition.  Yes, there has been warming from an El Nino buildup coincidental with North Atlantic warming, but no basis to blame it on CO2.  

As an overview consider how recent rapid cooling  completely overcame the warming from the last 3 El Ninos (1998, 2010 and 2016).  The UAH record shows that the effects of the last one were gone as of April 2021, again in November 2021, and in February and June 2022  At year end 2022 and continuing into 2023 global temp anomaly matched or went lower than average since 1995, an ENSO neutral year. (UAH baseline is now 1991-2020). Now we have an usual El Nino warming spike of uncertain cause, but unrelated to steadily rising CO2.

For reference I added an overlay of CO2 annual concentrations as measured at Mauna Loa.  While temperatures fluctuated up and down ending flat, CO2 went up steadily by ~60 ppm, a 15% increase.

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

gmt-warming-events

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. And now in 2023 we are seeing an amazing episode with a temperature spike driven by ocean air warming in all regions, with some cooling the last two months. 

Update August 3, 2021

Chris Schoeneveld has produced a similar graph to the animation above, with a temperature series combining HadCRUT4 and UAH6. H/T WUWT

image-8

 

mc_wh_gas_web20210423124932

See Also Worst Threat: Greenhouse Gas or Quiet Sun?

January 2024 El Nino Spikes Higher While Land Cools 

banner-blog

With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  While you will hear a lot about 2020-21 temperatures matching 2016 as the highest ever, that spin ignores how fast the cooling set in.  The UAH data analyzed below shows that warming from the last El Nino had fully dissipated with chilly temperatures in all regions. After a warming blip in 2022, land and ocean temps dropped again with 2023 starting below the mean since 1995.  Spring and Summer 2023 saw a series of warmings, continuing into October, but with cooling since. 

UAH has updated their tlt (temperatures in lower troposphere) dataset for January 2024. Posts on their reading of ocean air temps this month preceded updated records from HadSST4.  I last posted on SSTs using HadSST4 Ocean Warming Spike Recedes December 2023.  This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

Sometimes air temps over land diverge from ocean air changes.  November 2023 was notable for a dichotomy between Ocean and Land air temperatures in UAH dataset. Remarkably a new high for Ocean air temps appeared with warming in all regions, while Land air temps dropped with cooling in all regions.  As a result the Global Ocean and Land anomaly result remained little changed. Now again in January 2024, ocean temps went higher driven by El Nino and NH, while all land regions cooled except for Tropics.

Note:  UAH has shifted their baseline from 1981-2010 to 1991-2020 beginning with January 2021.  In the charts below, the trends and fluctuations remain the same but the anomaly values change with the baseline reference shift.

Presently sea surface temperatures (SST) are the best available indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.  Eventually we will likely have reliable means of recording water temperatures at depth.

Recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  Thus cooling oceans portend cooling land air temperatures to follow.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

After a change in priorities, updates are now exclusive to HadSST4.  For comparison we can also look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for January.  The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above. Recently there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the revised and current dataset.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. There is the additional feature that ocean air temps avoid Urban Heat Islands (UHI).  The graph below shows monthly anomalies for ocean air temps since January 2015.

Note 2020 was warmed mainly by a spike in February in all regions, and secondarily by an October spike in NH alone. In 2021, SH and the Tropics both pulled the Global anomaly down to a new low in April. Then SH and Tropics upward spikes, along with NH warming brought Global temps to a peak in October.  That warmth was gone as November 2021 ocean temps plummeted everywhere. After an upward bump 01/2022 temps reversed and plunged downward in June.  After an upward spike in July, ocean air everywhere cooled in August and also in September.   

After sharp cooling everywhere in January 2023, all regions were into negative territory. Note the Tropics matched the lowest value, but since have spiked sharply upward +1.7C, with the largest increases in April to July, and continuing through adding to a new high January 2024. NH also spiked upward to a new high, while Global ocean rise was more modest due to slight SH cooling.  

Land Air Temperatures Tracking in Seesaw Pattern

We sometimes overlook that in climate temperature records, while the oceans are measured directly with SSTs, land temps are measured only indirectly.  The land temperature records at surface stations sample air temps at 2 meters above ground.  UAH gives tlt anomalies for air over land separately from ocean air temps.  The graph updated for December is below.

Here we have fresh evidence of the greater volatility of the Land temperatures, along with extraordinary departures by SH land.  Land temps are dominated by NH with a 2021 spike in January,  then dropping before rising in the summer to peak in October 2021. As with the ocean air temps, all that was erased in November with a sharp cooling everywhere.  After a summer 2022 NH spike, land temps dropped everywhere, and in January, further cooling in SH and Tropics offset by an uptick in NH. 

Remarkably, in 2023, SH land air anomaly shot up 2.1C, from  -0.6C in January to +1.5 in September, then dropped sharply, now down to 0.6 in January 2024.  NH land temps have also dropped 0.3C down to 1.0C, resulting in Global land temps cooling to 0.9C, matching the peak in Feb. 2016. Land in the Tropics was unchanged in January, down slightly from its October peak.

The Bigger Picture UAH Global Since 1980

The chart shows monthly Global anomalies starting 01/1980 to present.  The average monthly anomaly is -0.05, for this period of more than four decades.  The graph shows the 1998 El Nino after which the mean resumed, and again after the smaller 2010 event. The 2016 El Nino matched 1998 peak and in addition NH after effects lasted longer, followed by the NH warming 2019-20.   An upward bump in 2021 was reversed with temps having returned close to the mean as of 2/2022.  March and April brought warmer Global temps, later reversed

With the sharp drops in Nov., Dec. and January 2023 temps, there was no increase over 1980. Now in 2023 the buildup to the October/November peak exceeded the sharp April peak of the El Nino 1998 event. It also surpassed the February peak in 2016.  December and January are down slightly, but where it goes from here, up or down further, remains to be seen.

The graph reminds of another chart showing the abrupt ejection of humid air from Hunga Tonga eruption.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, nearly 1C lower than the 2016 peak.  Since the ocean has 1000 times the heat capacity as the atmosphere, that cooling is a significant driving force.  TLT measures started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern. Despite the three El Ninos, their warming has not persisted prior to 2023, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

Ocean Warming Spike Recedes December 2023

Posted on 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;
  • Major El Ninos have been 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 HadSST4 is here.

The Current Context

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

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.  

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. After an uptick in December, temps in January 2023 dropped everywhere, strongest in NH, with the Global anomaly further below the mean since 2015.

Then came El Nino as shown by the upward spike in the Tropics since January, the anomaly nearly tripling from 0.38C to 1.09C.  In September 2023, all regions rose, especially NH up from 0.70C to 1.41C, pulling up the global anomaly to a new high for this period. But then in October anomalies in all regions started dropping down bringing down the Global anomaly.  In December, NH cooled to 1.1C and the Global anomaly down to 0.94C from its peak of 1.10C, despite slight warming in SH and Tropics.

Comment:

The climatists have seized on this unusual warming as proof of their Zero Carbon agenda, 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 is well 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

Open image in a new tab to enlarge.

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.  

Now in 2023 the Tropics flipped from below to well above average, while NH has produced a summer peak extending into September higher than any previous year. In fact, October and now November are showing that this number is likely the crest, despite El Nino driving the Tropics anomaly close to 1998 and 2015 peaks.

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 data through October.  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 varibility, high and low, drives the annual results for this basin.  Note also the peaks in 2010, lows after 2014, and a rise in 2021. Now in 2023 the peak was holding at 1.4C before decling.  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.

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 0.18.  The orange line the average 1980-202306, value 0.38, also for the period 1997-2012. The red line is 2013-202306, value 0.64. 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?

Space weather impacts the ionosphere in this animation. Credits: NASA/GSFC/CIL/Krystofer Kim

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

 

 

UAH December 2023: Cooling Despite El Nino Spike

Posted on by Ron Clutz

The post below updates the UAH record of air temperatures over land and ocean. Each month and year exposes again the growing disconnect between the real world and the Zero Carbon zealots.  It is as though the anti-hydrocarbon band wagon hopes to drown out the data contradicting their justification for the Great Energy Transition.  Yes, there has been warming from an El Nino buildup coincidental with North Atlantic warming, but no basis to blame it on CO2.  

As an overview consider how recent rapid cooling  completely overcame the warming from the last 3 El Ninos (1998, 2010 and 2016).  The UAH record shows that the effects of the last one were gone as of April 2021, again in November 2021, and in February and June 2022  At year end 2022 and continuing into 2023 global temp anomaly matched or went lower than average since 1995, an ENSO neutral year. (UAH baseline is now 1991-2020). Now we have an usual El Nino warming spike of uncertain cause, but unrelated to steadily rising CO2.

For reference I added an overlay of CO2 annual concentrations as measured at Mauna Loa.  While temperatures fluctuated up and down ending flat, CO2 went up steadily by ~60 ppm, a 15% increase.

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

gmt-warming-events

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. And now in 2023 we are seeing an amazing episode with a temperature spike driven by ocean air warming in all regions, with some cooling the last two months. 

Update August 3, 2021

Chris Schoeneveld has produced a similar graph to the animation above, with a temperature series combining HadCRUT4 and UAH6. H/T WUWT

image-8

 

mc_wh_gas_web20210423124932

See Also Worst Threat: Greenhouse Gas or Quiet Sun?

December 2023 Cooling Despite El Nino Spike

banner-blog

With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  While you will hear a lot about 2020-21 temperatures matching 2016 as the highest ever, that spin ignores how fast the cooling set in.  The UAH data analyzed below shows that warming from the last El Nino had fully dissipated with chilly temperatures in all regions. After a warming blip in 2022, land and ocean temps dropped again with 2023 starting below the mean since 1995.  Spring and Summer 2023 saw a series of warmings, continuing into October, but with cooling since. 

UAH has updated their tlt (temperatures in lower troposphere) dataset for December 2023. Posts on their reading of ocean air temps this month preceded updated records from HadSST4.  I last posted on SSTs using HadSST4 November 2023 Ocean Warmth Persists Due to Tropics This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

Sometimes air temps over land diverge from ocean air changes.  November 2023 was notable for a dichotomy between Ocean and Land air temperatures in UAH dataset. Remarkably a new high for Ocean air temps appeared with warming in all regions, while Land air temps dropped with cooling in all regions.  As a result the Global Ocean and Land anomaly result remained little changed. Now in December, all regions cooled except for Tropics.

Note:  UAH has shifted their baseline from 1981-2010 to 1991-2020 beginning with January 2021.  In the charts below, the trends and fluctuations remain the same but the anomaly values change with the baseline reference shift.

Presently sea surface temperatures (SST) are the best available indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.  Eventually we will likely have reliable means of recording water temperatures at depth.

Recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  Thus cooling oceans portend cooling land air temperatures to follow.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

After a change in priorities, updates are now exclusive to HadSST4.  For comparison we can also look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for December.  The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above. Recently there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the revised and current dataset.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. There is the additional feature that ocean air temps avoid Urban Heat Islands (UHI).  The graph below shows monthly anomalies for ocean air temps since January 2015.

Note 2020 was warmed mainly by a spike in February in all regions, and secondarily by an October spike in NH alone. In 2021, SH and the Tropics both pulled the Global anomaly down to a new low in April. Then SH and Tropics upward spikes, along with NH warming brought Global temps to a peak in October.  That warmth was gone as November 2021 ocean temps plummeted everywhere. After an upward bump 01/2022 temps reversed and plunged downward in June.  After an upward spike in July, ocean air everywhere cooled in August and also in September.   

After sharp cooling everywhere in January 2023, all regions were into negative territory. Note the Tropics matched the lowest value, but since have spiked sharply upward +1.5C, with the largest increases in April to July, and continuing through to December 2023.  But now both SH and NH have dropped in December, pulling the global average back down despite the El Nino spike in the Tropics.

Land Air Temperatures Tracking in Seesaw Pattern

We sometimes overlook that in climate temperature records, while the oceans are measured directly with SSTs, land temps are measured only indirectly.  The land temperature records at surface stations sample air temps at 2 meters above ground.  UAH gives tlt anomalies for air over land separately from ocean air temps.  The graph updated for December is below.

Here we have fresh evidence of the greater volatility of the Land temperatures, along with extraordinary departures by SH land.  Land temps are dominated by NH with a 2021 spike in January,  then dropping before rising in the summer to peak in October 2021. As with the ocean air temps, all that was erased in November with a sharp cooling everywhere.  After a summer 2022 NH spike, land temps dropped everywhere, and in January, further cooling in SH and Tropics offset by an uptick in NH. 

Remarkably, in 2023, SH land air anomaly shot up 2.1C, from  -0.6C in January to +1.5 in October, then dropped to 0.7 in November, 0.76 in December.  Tropical land temps are up 1.6 since January and NH Land air temps rose 0.9, mostly since May.  Now in December SH land air temps are little changed while the drop in NH brought the Global anomaly down despite another uptick in Tropical land temps. 

The Bigger Picture UAH Global Since 1980

The chart shows monthly Global anomalies starting 01/1980 to present.  The average monthly anomaly is -0.05, for this period of more than four decades.  The graph shows the 1998 El Nino after which the mean resumed, and again after the smaller 2010 event. The 2016 El Nino matched 1998 peak and in addition NH after effects lasted longer, followed by the NH warming 2019-20.   An upward bump in 2021 was reversed with temps having returned close to the mean as of 2/2022.  March and April brought warmer Global temps, later reversed

With the sharp drops in Nov., Dec. and January 2023 temps, there was no increase over 1980. Now in 2023 the buildup to the October/November peak exceeded the sharp April peak of the El Nino 1998 event. It also surpassed the February peak in 2016.  December is down slightly, but where it goes from here, up or down further, remains to be seen.

The graph reminds of another chart showing the abrupt ejection of humid air from Hunga Tonga eruption.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, nearly 1C lower than the 2016 peak.  Since the ocean has 1000 times the heat capacity as the atmosphere, that cooling is a significant driving force.  TLT measures started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern. Despite the three El Ninos, their warming has not persisted prior to 2023, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

2024 Oceanic Climate Warming At Work

Posted on by Ron Clutz

David Wojick describes how ocean cycles create warming blips in global temperature records in his concise, plain language CFACT article Big temperature spike may lead to small temperature rise.  Excerpts in italics with my bolds and added images.

The recent big temperature spike has the climate alarmists all excited, pulling out all the hyperbolic stops as it were. The warming is huge they say. Their favorite descriptor — unprecedented — appears frequently.

Which makes it all very funny, since we had exactly this same situation not that long ago. What is most interesting is what happened next back then, because in my view it is likely to happen again. Let me explain.

For what follows you need to be looking at the UAH temperature record, which is here:

First of all compare the ongoing spike now with the 1998 spike. They are virtually identical as far as the short term temperature increase is concerned, roughly 1.0 degrees C. So as spikes go there is nothing unprecedented.

Yes the tip of the now spike is at a higher temperature that the 1998 spike and this is where is gets very interesting. The base of the now spike is warmer than the base was in 1998. This is because there has been a little bit of warming since then.

But all of that warming has occurred in two specific steps up, each following a super El Niño. After the 1998 spike the temperature oscillated around a constant value that was warmer than before the spike but there was no additional warming until the 2016 super El Niño spike came along. Then after that spike it was again warmer but with no warming.

All the warming in the entire record occurs in just two steps with no warming in between. For the record I first pointed out this step pattern six years ago, when there was just one clear step, the 1998. See No CO2 warming for the last 40 years

At the time we were wondering if this step pattern would repeat with the 2016 super El Niño and by golly it did.

So now the question is will we get another little step up in average temperature from the ongoing spike? My bet is it will so, Of course I am prepared to be wrong but it is still very likely.  But the basic point from six years ago remains.

There is no evidence of any warming due to the ongoing steady CO2
increase in this entire 45 year record. None whatsoever as it is
all clearly to do with the periodic occurrence of super El Niños.

The likely explanation also seems pretty simple. There is residual energy in the atmosphere left over from each spike. So the total energy goes up with each step.

Note that the energy in the spike does not come from the El Niño. An El Niño is simply a lack of cold water upwelling. Without that cold water the ocean surface layer gets a lot warmer from the incoming solar energy. Some of that energy goes into the atmosphere creating the big spike. That some of it would then hang around does not seem surprising. There is no reason why the La Niña that follows each super El Niño should remove all recently added energy.

Here is my conclusion from six years ago: “But in no case is there any evidence of CO2 induced warming here, nor of any human-caused warming for that matter. These causes would produce a relatively steady warming over time, not the single episodic warming that we clearly see here. In particular, to my knowledge there is no known way that the gradual CO2 increase could have caused this giant El Nino-La Nina cycle.

Thus the little warming that there is in the last 40 years appears to be more or less entirely natural. In any normal science this result would be sufficient to invalidate the hypothesis that the increasing CO2 concentration is causing global warming.”

Nothing has changed. The hypothesis of anthropogenic global warming
is falsified by simple observation. Science is like that, or should be.

Addendum:

The stairstep warming also appears in HadSST4 global ocean temperature dataset, with the suggestion that a new plateau may be in place.

Since Hadcrut4 (ocean + land) goes back early in the 20th century, we can see the same pattern from an earlier analysis updated to today. Going back to 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. And now in 2023 we are seeing an amazing episode with a temperature spike driven by ocean air warming in all regions.

Footnote:

As David stated and diagramed so well, ENSO (El Nino Southern Oscillation) ocean cycle has driven this contemporary warming of atmospheric temperatures.  But we should also note how the Northern Atlantic has contributed to this effect, both in 2016 and currently.

To enlarge open image in new tab.

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.  Then in 2023 we see the Tropical peaking from El Nino at the same time as the remarkable NH spike, raising the Global ocean anomaly to a new high.

Additional evidence for North Atlantic warming comes from the AMO index (Atlantic Multi-decadal Oscillation).  ERSSTv5 AMO dataset uses the NA region EQ-60°N, 0°-80°W and subtracts the global rise of SST 60°S-60°N to obtain a measure of the internal variability of NA. So the values represent SST anomaly differences between the N. Atlantic and the Global ocean.

The chart shows the outlier 2023 spike peaking in the North Atlantic in July, persisting through October, before dropping November and December. Note how much higher are these anomalies compared to 2016 in purple.  Note also that August typically has the highest NA ocean temperatures, so these anomalies are on top of the highest actual temperatures recorded.

It remains to be seen how long this warming will persist, and what will be the longer term effect, but as David explained, it all has nothing to do with CO2.

Temps Cause CO2 Changes, Not the Reverse. 2024 Update

Posted on by Ron Clutz

This post is about proving that CO2 changes in response to temperature changes, not the other way around, as is often claimed.  In order to do  that we need two datasets: one for measurements of changes in atmospheric CO2 concentrations over time and one for estimates of Global Mean Temperature changes over time.

Climate science is unsettling because past data are not fixed, but change later on.  I ran into this previously and now again in 2021 and 2022 when I set out to update an analysis done in 2014 by Jeremy Shiers (discussed in a previous post reprinted at the end).  Jeremy provided a spreadsheet in his essay Murray Salby Showed CO2 Follows Temperature Now You Can Too posted in January 2014. I downloaded his spreadsheet intending to bring the analysis up to the present to see if the results hold up.  The two sources of data were:

Temperature anomalies from RSS here:  http://www.remss.com/missions/amsu

CO2 monthly levels from NOAA (Mauna Loa): https://www.esrl.noaa.gov/gmd/ccgg/trends/data.html

Changes in CO2 (ΔCO2)

Uploading the CO2 dataset showed that many numbers had changed (why?).

The blue line shows annual observed differences in monthly values year over year, e.g. June 2020 minus June 2019 etc.  The first 12 months (1979) provide the observed starting values from which differentials are calculated.  The orange line shows those CO2 values changed slightly in the 2020 dataset vs. the 2014 dataset, on average +0.035 ppm.  But there is no pattern or trend added, and deviations vary randomly between + and -.  So last year I took the 2020 dataset to replace the older one for updating the analysis.

Now I find the NOAA dataset starting in 2021 has almost completely new values due to a method shift in February 2021, requiring a recalibration of all previous measurements.  The new picture of ΔCO2 is graphed below.

The method shift is reported at a NOAA Global Monitoring Laboratory webpage, Carbon Dioxide (CO2) WMO Scale, with a justification for the difference between X2007 results and the new results from X2019 now in force.  The orange line shows that the shift has resulted in higher values, especially early on and a general slightly increasing trend over time.  However, these are small variations at the decimal level on values 340 and above.  Further, the graph shows that yearly differentials month by month are virtually the same as before.  Thus I redid the analysis with the new values.

Global Temperature Anomalies (ΔTemp)

The other time series was the record of global temperature anomalies according to RSS. The current RSS dataset is not at all the same as the past.

Here we see some seriously unsettling science at work.  The purple line is RSS in 2014, and the blue is RSS as of 2020.  Some further increases appear in the gold 2022 rss dataset. The red line shows alterations from the old to the new.  There is a slight cooling of the data in the beginning years, then the three versions mostly match until 1997, when systematic warming enters the record.  From 1997/5 to 2003/12 the average anomaly increases by 0.04C.  After 2004/1 to 2012/8 the average increase is 0.15C.  At the end from 2012/9 to 2013/12, the average anomaly was higher by 0.21. The 2022 version added slight warming over 2020 values.

RSS continues that accelerated warming to the present, but it cannot be trusted.  And who knows what the numbers will be a few years down the line?  As Dr. Ole Humlum said some years ago (regarding Gistemp): “It should however be noted, that a temperature record which keeps on changing the past hardly can qualify as being correct.”

Given the above manipulations, I went instead to the other satellite dataset UAH version 6. UAH has also made a shift by changing its baseline from 1981-2010 to 1991-2020.  This resulted in systematically reducing the anomaly values, but did not alter the pattern of variation over time.  For comparison, here are the two records with measurements through December 2023.

Comparing UAH temperature anomalies to NOAA CO2 changes.

Here 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.  As stated above, CO2 differentials are calculated for the present month by subtracting the value for the same month in the previous year (for example June 2022 minus June 2021).   Temp anomalies are calculated by comparing the present month with the baseline month.

The final proof that CO2 follows temperature due to stimulation of natural CO2 reservoirs is demonstrated by the ability to calculate CO2 levels since 1979 with a simple mathematical formula:

For each subsequent year, the co2 level for each month was generated

CO2  this month this year = a + b × Temp this month this year  + CO2 this month last year

Jeremy used Python to estimate a and b, but I used his spreadsheet to guess values that place for comparison the observed and calculated CO2 levels on top of each other.

In the chart calculated CO2 levels correlate with observed CO2 levels at 0.9986 out of 1.0000.  This mathematical generation of CO2 atmospheric levels is only possible if they are driven by temperature-dependent natural sources, and not by human emissions which are small in comparison, rise steadily and monotonically.

Comment:  UAH dataset reported a sharp warming spike starting mid year, with causes speculated but not proven.  In any case, that surprising peak has not yet driven CO2 higher, though it might,  but only if it persists despite the likely cooling already under way.

Previous Post:  What Causes Rising Atmospheric CO2?

nasa_carbon_cycle_2008-1

This post is prompted by a recent exchange with those reasserting the “consensus” view attributing all additional atmospheric CO2 to humans burning fossil fuels.

The IPCC doctrine which has long been promoted goes as follows. We have a number over here for monthly fossil fuel CO2 emissions, and a number over there for monthly atmospheric CO2. We don’t have good numbers for the rest of it-oceans, soils, biosphere–though rough estimates are orders of magnitude higher, dwarfing human CO2.  So we ignore nature and assume it is always a sink, explaining the difference between the two numbers we do have. Easy peasy, science settled.

What about the fact that nature continues to absorb about half of human emissions, even while FF CO2 increased by 60% over the last 2 decades? What about the fact that in 2020 FF CO2 declined significantly with no discernable impact on rising atmospheric CO2?

These and other issues are raised by Murray Salby and others who conclude that it is not that simple, and the science is not settled. And so these dissenters must be cancelled lest the narrative be weakened.

The non-IPCC paradigm is that atmospheric CO2 levels are a function of two very different fluxes. FF CO2 changes rapidly and increases steadily, while Natural CO2 changes slowly over time, and fluctuates up and down from temperature changes. The implications are that human CO2 is a simple addition, while natural CO2 comes from the integral of previous fluctuations.  Jeremy Shiers has a series of posts at his blog clarifying this paradigm. See Increasing CO2 Raises Global Temperature Or Does Increasing Temperature Raise CO2 Excerpts in italics with my bolds.

The following graph which shows the change in CO2 levels (rather than the levels directly) makes this much clearer.

Note the vertical scale refers to the first differential of the CO2 level not the level itself. The graph depicts that change rate in ppm per year.

There are big swings in the amount of CO2 emitted. Taking the mean as 1.6 ppmv/year (at a guess) there are +/- swings of around 1.2 nearly +/- 100%.

And, surprise surprise, the change in net emissions of CO2 is very strongly correlated with changes in global temperature.

This clearly indicates the net amount of CO2 emitted in any one year is directly linked to global mean temperature in that year.

For any given year the amount of CO2 in the atmosphere will be the sum of

  • all the net annual emissions of CO2
  • in all previous years.

For each year the net annual emission of CO2 is proportional to the annual global mean temperature.

This means the amount of CO2 in the atmosphere will be related to the sum of temperatures in previous years.

So CO2 levels are not directly related to the current temperature but the integral of temperature over previous years.

The following graph again shows observed levels of CO2 and global temperatures but also has calculated levels of CO2 based on sum of previous years temperatures (dotted blue line).

Summary:

The massive fluxes from natural sources dominate the flow of CO2 through the atmosphere.  Human CO2 from burning fossil fuels is around 4% of the annual addition from all sources. Even if rising CO2 could cause rising temperatures (no evidence, only claims), reducing our emissions would have little impact.

Addendum:

Roland Van den Broek made the valid point in his comments below that any two data sets generally trending positive will show a high degree of correlation, not proving any causation.  Certainly, UAH reports rising GMA (Global Mean Anomalies) and MLO reports rising CO2.  Note however that Δ GMA predicts Δ CO2 with a correlation of 0.9986.  For comparison, I generated GMA from CO2 differentials, resulting in a lower correlation of 0.6030.  I conclude that Δ CO2 ⇒ Δ GMA is spurious, while Δ GMA ⇒ Δ CO2 is real.

Resources
For a possible explanation of natural warming and CO2 emissions see Little Ice Age Warming Recovery May be Over
Resources:

CO2 Fluxes, Sources and Sinks

Who to Blame for Rising CO2?

Fearless Physics from Dr. Salby

In this video presentation, Dr. Salby provides the evidence, math and charts supporting the non-IPCC paradigm.

Footnote:  As CO2 concentrations rose, BP shows Fossil Fuel consumption slumped in 2020, Then Recovered

See also 2022 Update: Fossil Fuels ≠ Global Warming

November 2023 Ocean Warmth Persists Due to Tropics

Posted on 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;
  • Major El Ninos have been 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 HadSST4 is here.

The Current Context

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

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.  In 2021 the summer NH summer spike was joined by warming in the Tropics but offset by a drop in SH SSTs, which raised the Global anomaly slightly over the mean.

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. After an uptick in December, temps in January 2023 dropped everywhere, strongest in NH, with the Global anomaly further below the mean since 2015.

Now comes El Nino as shown by the upward spike in the Tropics since January, the anomaly nearly tripling from 0.38C to 1.07C.  In August 2023, all regions rose, especially NH up from 0.70C to 1.37C, pulling up the global anomaly to a new high for this period. September showed a new peak for NH at 1.41, but then in October anomalies in all regions have dropped down 0.1C bringing down the Global anomaly.  In November, NH added cooling, offset by slight warming in SH.  Tropical ocean temps rose to nearly match 2015 in November, but the Global anomaly changed little and remained lower than the September peak.

Comment:

The climatists have seized on this unusual warming as proof of their Zero Carbon agenda, 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 is well 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.  

Now in 2023 the Tropics flipped from below to well above average, while NH has produced a summer peak extending into September higher than any previous year. In fact, October and now November are showing that this number is likely the crest, despite El Nino driving the Tropics anomaly close to 1998 and 2015 peaks.

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 data through October.  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 varibility, high and low, drives the annual results for this basin.  Note also the peaks in 2010, lows after 2014, and a rise in 2021. Now in 2023 the peak is holding at 1.4C.  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 now November cooled by ~0.3C.

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 0.18.  The orange line the average 1980-202306, value 0.38, also for the period 1997-2012. The red line is 2013-202306, value 0.64. 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?

Space weather impacts the ionosphere in this animation. Credits: NASA/GSFC/CIL/Krystofer Kim

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