Climate Science

UAH September 2024: NH Land Up, SH Land Down

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, unrelated to steadily rising CO2 and now moderating.

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

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

image-8

 

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See Also Worst Threat: Greenhouse Gas or Quiet Sun?

September 2024 NH Land Spike Warms More than SH Land Drops

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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 heard 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, followed by cooling. 

UAH has updated their tlt (temperatures in lower troposphere) dataset for September 2024. Posts on their reading of ocean air temps this month are ahead of the update from HadSST4.  I posted last month on SSTs using NH Pulls Oceans Warmer August 2024.  These posts have 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. In July 2024 all oceans were unchanged except for Tropical warming, while all land regions rose slightly. Then in August we saw a warming leap in SH land, slight Land cooling elsewhere, a dip in Tropical Ocean temp and slightly elsewhere. Now in September a dramatic drop in SH land, overcome by a greater NH land increase. End result is an upward bump in Global anomaly.

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 changed 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 September.  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. Then there is a remarkable spiking of Tropical ocean temps from -0.4 up to + 1.3 in January 2024.  The rise was matched by other regions in 2024 January through May, before dropping lower. Now in September comes an upward bump in NH and Tropics pulling up Global anomaly.

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 September 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 to 0.6 in January 2024, matching the SH peak in 2016. Then through May 2024 NH and Tropical land temps spiked to new highs, before dropping down.  Now in August SH land spiked up, then reversed down in September.  NH and Tropics bumped upward, and Global land anomaly rose slightly

The Bigger Picture UAH Global Since 1980

The chart shows monthly Global anomalies starting 01/1980 to present.  The average monthly anomaly is -0.04, 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. Then 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. In 2024 March and April took the Global anomaly to a new peak of 1.05C.  The cool down started with May dropping to 0.90C, and in June a further decline to 0.80C.  The warming level rose again and has persisted through September.

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 HadSST4, but are now showing the same pattern. Despite the three El Ninos, their warming had not persisted prior to 2023, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

NH Pulls Oceans Warmer August 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 August 2024.  A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016, followed by rising temperatures in 2023 and 2024.

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 2023, 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. 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.

In January 2024 both Tropics and SH rose, resulting in Global Anomaly going higher. Since then Tropics have cooled from a  peak of 1.29C down to 0.84C.  SH also dropped down from 0.89C to 0.65C. NH lost ~0.4C as of March 2024, but has risen 0.2C over April to June. Despite that upward NH bump, the Global SST anomaly cooled further. 

In July there was a warming uptick in all regions, and now in August a new high in NH, along with the Tropics, bringing the global anomaly up to almost match August 2023. We have now three distinct warmings: the El Nino driven peak in 2025-16, the lesser peak in 2019-20 and now a stronger warming event in 2023-24.

Comment:

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

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

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

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

A longer view of SSTs

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

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

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

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

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

Then in 2023 the Tropics flipped from below to well above average, while NH produced a summer peak extending into September higher than any previous year.  Despite El Nino driving the Tropics January 2024 anomaly higher than 1998 and 2016 peaks, following months cooled in all regions, and the Tropics continued cooling in April, May and June along with SH dropping, suggesting that the peak might be reached, though now in July and August NH warming has again pulled the global anomaly higher.

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

Contemporary AMO Observations

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

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

Now in 2024 the AMO anomaly started higher than any previous year, then leveled off for two months declining slightly into April.  Remarkably, May shows an upward leap putting this on a higher track than 2023, and rising slightly higher in June.  In July and August 2024 the anomaly declined and is now lower than the peak reached in 2023.

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 1980-202404, value 0.39, also for the period 1997-2012. The red line is 2013-202404, value 0.66. As noted above, these rising stages are driven by the combined warming in the Tropics and NH, including both Pacific and Atlantic basins.

See Also:

2024 El Nino Collapsing

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.

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USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

 

 

Fatal Flaw Discredits IPCC Science

Posted on by Ron Clutz

By way of John Ray comes this Spectator Australia article A basic flaw in IPCC science.  Excerpts in italics with my bolds and added images.

Detailed research is underway that threatens to undermine the foundations of the climate science promoted by the IPCC since its First Assessment Report in 1992. The research is re-examining the rural and urban temperature records in the Northern Hemisphere that are the foundation for the IPCC’s estimates of global warming since 1850. The research team has been led by Dr Willie Soon (a Malaysian solar astrophysicist associated with the Smithsonian Institute for many years) and two highly qualified Irish academics – Dr Michael Connolly and his son Dr Ronan Connolly. They have formed a climate research group CERES-SCIENCE. Their detailed research will be a challenge for the IPCC 7th Assessment Report due to be released in 2029 as their research results challenge the very foundations of IPCC science.

The climate warming trend published by the IPCC is a continually updated graph based on the temperature records of Northern Hemisphere land surface temperature stations dating from the mid 19th Century. The latest IPCC 2021 report uses data for the period 1850-2018. The IPCC’s selection of Northern Hemisphere land surface temperature records is not in question and is justifiable. The Northern Hemisphere records provide the best database for this period. The Southern Hemisphere land temperature records are not that extensive and are sparse for the 19th and early 20th Century. It is generally agreed that the urban temperature data is significantly warmer than the rural data in the same region because of an urban warming bias. This bias is due to night-time surface radiation of the daytime solar radiation absorbed by concrete and bitumen. Such radiation leads to higher urban night-time temperatures than say in the nearby countryside. The IPCC acknowledges such a warming bias but alleges the increased effect is only 10 per cent and therefore does not significantly distort its published global warming trend lines.


Since 2018, Dr Soon and his partners have analysed the data from rural and urban temperature recording stations in China, the USA, the Arctic, and Ireland. The number of stations with reliable temperature records in these areas increased from very few in the mid-19th Century to around 4,000 in the 1970s before decreasing to around 2,000 by the 1990s. The rural temperature recording stations with good records peaked at 400 and are presently around 200.

Their analysis of individual stations needs to account for any variation in their exposure to the Sun due to changes in their location, OR shadowing due to the construction of nearby buildings, OR nearby vegetation growth. The analysis of rural temperature stations is further complicated as over time many are encroached by nearby cities. Consequently, the data from such stations needs to be shifted at certain dates from the rural temperature database to either an intermediate database or to a full urban database. Consequently, an accurate analysis of the temperature records of each recording station is a time-consuming task.


This new analysis of 4,000 temperature recording stations in China, the USA, the Arctic, and Ireland shows a warming trend of 0.89ºC per century in the urban stations that is 1.61 times higher that a warming trend of 0.55ºC per century in the rural stations. This difference is far more significant than the 10 per cent divergence between urban and rural stations alleged in the IPCC reports; a divergence explained by a potential flaw in the IPCC’s methodology. The IPCC uses a technique called homogenisation that averages the rural and urban temperatures in a particular region. This method distorts the rural temperature records as over 75 per cent of the temperature records used in this homogenisation methodology are urban stations. So, a methodology that attempts to statistically identify and correct some biases that may be in the raw data, in effect, leads to an urban blending of the rural dataset. This result is biased as it downgrades the actual values of each rural temperature station. In contrast, Dr Soon and his coworkers avoided homogenisation so the temperature trends they identify for each rural region are accurate as the rural data are not distorted by the readings from nearby urban stations.


The rural temperature trend measured by this new research is 0.55ºC per century and it indicates the Earth has warmed 0.9ºC since 1850. In contrast, the urban temperature trend measured by this new research is 0.89ºC per century and indicates a much higher warming of 1.5ºC since 1850. Consequently, a distorted urban warming trend has been used by the IPCC to quantify the warming of the whole of the Earth since 1850. The exaggeration is significant as the urban temperature record database used by the IPCC only represents the temperatures on 3-4 per cent of the Earth’s land surface area; an area less than 2 per cent of the Earth’s total surface area. During the next few years, Dr Willie Soon and his research team are currently analysing the meta-history of 800 European temperature recording stations. When this is done their research will be based on very significant database of Northern Hemisphere rural and urban temperature records from China, the USA, the Arctic, Ireland, and Europe.

This new research has unveiled another flaw in the IPCC‘s temperature narrative as trend lines in its revised temperature datasets are different from those published by the IPCC. For example, the rural records now show a marked warming trend in the 1930s and 1940s while there is only a slight warming trend in the IPCC dataset. The most significant difference is the existence of a marked cooling period in the rural dataset for the 1960s and 1970s that is almost absent in the IPCC’s urban dataset. This later divergence upsets the common narrative that rising carbon dioxide levels control modern warming trends. For, if carbon dioxide levels are the driver of modern warming, how can a higher rate of increasing carbon dioxide levels exist within a cooling period in the 1960s and 1970s while a lower increasing rate of carbon dioxide levels coincides with an earlier warming interval in the 1930s and 1940s? Or, in other words, how can carbon dioxide levels increasing at 1.7 parts per million per decade cause a distinct warming period in the 1930s and 1940s while a larger increasing rate of 10.63 parts per million per decade is associated with a distinct cooling period in the 1960s and 1970s! Consequently, the research of Willie Soon and his coworkers is discrediting, not only the higher rate of global warming trends specified in IPCC Reports, but also the theory that rising carbon dioxide levels explain modern warming trends; a lynchpin of IPCC science for the last 25 years.

Willie Soon and his coworkers maintain that climate scientists need to consider other possible explanations for recent global warming. Willie Soon and his coworkers point to the Sun, but the IPCC maintains that variations in Total Solar Irradiance (TSI) are over eons and not over shorter periods such as the last few centuries. For that reason, the IPCC point to changes in greenhouse gases as the most obvious explanation for global warming since 1850. In contrast, Willie Soon and his coworkers maintain there can be short-term changes in solar activity and, for example, refer to a period of no sunspot activity that coincided with the Little Ice Age in the 17th Century. They also point out there is still no agreed average figure for Total Solar Irradiance (TSI) despite 30 years of measurements taken by various satellites. Consequently, they contend research in this area is not settled.

The CERES-SCIENCE research project pioneered by Dr Willie Soon and the father-son Connolly team has questioned the validity of the high global warming trends for the 1850-present period that have been published by the IPCC since its first report in 1992. The research also queries the IPCC narrative that rising greenhouse gas concentrations, particularly carbon dioxide, are the primary driver of global warming since 1850. That narrative has been the foundation of IPCC climate science for the last 40 years. It will be interesting to see how the IPCC’s 7th Assessment Report in 2029 treats this new research that questions the very basis of IPCC’s climate science.

The paper is The Detection and Attribution of Northern Hemisphere Land Surface Warming (1850–2018) in Terms of Human and Natural Factors: Challenges of Inadequate Data. 

Abstract

A statistical analysis was applied to Northern Hemisphere land surface temperatures (1850–2018) to try to identify the main drivers of the observed warming since the mid-19th century. Two different temperature estimates were considered—a rural and urban blend (that matches almost exactly with most current estimates) and a rural-only estimate. The rural and urban blend indicates a long-term warming of 0.89 °C/century since 1850, while the rural-only indicates 0.55 °C/century. This contradicts a common assumption that current thermometer-based global temperature indices are relatively unaffected by urban warming biases.

Three main climatic drivers were considered, following the approaches adopted by the Intergovernmental Panel on Climate Change (IPCC)’s recent 6th Assessment Report (AR6): two natural forcings (solar and volcanic) and the composite “all anthropogenic forcings combined” time series recommended by IPCC AR6. The volcanic time series was that recommended by IPCC AR6. Two alternative solar forcing datasets were contrasted. One was the Total Solar Irradiance (TSI) time series that was recommended by IPCC AR6. The other TSI time series was apparently overlooked by IPCC AR6. It was found that altering the temperature estimate and/or the choice of solar forcing dataset resulted in very different conclusions as to the primary drivers of the observed warming.

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

Climate Policies Built on CO2 Deceptions

Posted on by Ron Clutz

From response by James Matkin  Former Deputy Minister at Government of British Columbia to Quora question What are the criticisms of the Canadian federal carbon pricing system implemented under the Trudeau government’s “Pan-Canadian Framework” on climate change?  Excerpts in italics with my bolds.

The big lie is that Canada needs more carbon dioxide, not less, which is the intent of the carbon tax. Also, there is no carbon pricing system; rather, there is a carbon dioxide pricing system. Carbon is not the equivalent of CO2.

We need less carbon pollution from coal and more
carbon dioxide for our health and well-being.

This mistaken terminology deceives the public as it portends pollution. Amazingly, US President Obama, Trudeau, and Kamala Harris falsely call CO2 carbon pollution.

CO2 is used to save premature babies in incubators.

CO2 is the primary gas for fire extinguishers.

The solid form of CO2 is dry ice not carbon.
Likewise the solid form of H2O is ice not hydrogen.

More CO2 has enormous benefits for crops and our health. It also greens deserts and saves fresh water by spurring tree growth.

If CO2 was a problem why does the commercial greenhouse industry infuse up to 1500 ppm 7/24 to increase plant growth?

Canadians pay an imposing Carbon Tax to save the planet, while the rest of North America has no such Carbon Tax. If you look at economics, the Carbon Tax lowered our standard of living and did nothing for Climate Change.

Canada is a foolish outlier that punishes citizens with a carbon tax to harm plant growth, hospital surgeries, and water retention and cause inflation making the poor poorer.

Canadian emissions of CO2 by fossil fuels are only < 5% of natural
CO2 sources which are too tiny to matter if CO2 mattered to the climate, which it doesn’t.

The driving forces of climate change are natural
not human emissions of CO2

The UN claim that human industry Co2 emissions are causing runaway global warming that will end in catastrophe if not arrested with renewables and carbon taxes was only a thought experiment without any physical observation. Think about this – the UN said there was no historical precedent for the rapid rise in temperatures after industrialization therefore increased CO2 must be the culprit. This is both false logic and untrue. A raft of studies about temperature variability from ocean currents or changes in solar cycles evident in rising or falling sunspots easily explains the temperature rise.

Today with the benefit of hindsight there never was any fast rising temperatures needing explanation. In fact temperature’s rise of less than 1 °C over the past 140 years and now falling 0.4 °C in the past three years means no global warming now or in the past.

Because the climate changes over a long period of time no one living or dead has actually looked out the window and observed climate change. You see weather and it may be a heat wave, snow storm or record rainfall, but one weather event is never a new pattern of changing weather because that must be a statistical analysis.

Beware “Fact Checking” by Innuendo

Posted on by Ron Clutz

Kip Hansen gives the game away in his Climate Realism article Illogically Facts —’Fact-Checking’ by Innuendo.  Excerpts in italics with my bolds and added images.

The latest fad in all kinds of activism to attack one’s ideological opponents via “fact checking”.    We see this in politics and all the modern controversies, including, of course, Climate Science.

Almost none of the “fact checking sites” and “fact checking organizations” actually check facts.  And, if they accidentally find themselves checking what we would all agree is a fact, and not just an opinion or point of view, invariably it is checked against an contrary opinion, a different point of view or an alternative fact.

The resulting fact check report depends on the purposes of the fact check.  Some are done to confirm that “our guy” or “our team” is proved to be correct, or that the opposition is proved to be wrong, lying or misinformation.  When a fact is found to be different in any way from the desired fact, even the tiniest way, the original being checked is labelled a falsehood, or worse, an intentional lie. (or conversely, other people are lying about our fact!).   Nobody likes a liar, so this sort of fake fact checking accomplishes two goals – it casts doubt on the not-favored fact supposedly being checked and smears an ideological opponent as a liar.  One stone – two birds.

While not entirely new on the fact-checking scene, an AI-enhanced effort has popped to the surface of the roiling seas of controversy: Logically Facts.  “Logically Facts is part of Meta’s Third Party Fact-Checking Program (3PFC) and works with TikTok in Europe. We have been a verified signatory of the International Fact-Checking Network (IFCN) since 2020 and are a member of the Misinformation Combat Alliance (MCA) in India and the European Digital Media Observatory (EDMO) in Europe.” [ source ]   Meta? “Meta Platforms…is the undisputed leader in social media. The technology company owns three of the four biggest platforms by monthly active users (Facebook, WhatsApp, and Instagram).” “Meta’s social networks are known as its Family of Apps (FoA). As of the fourth quarter of 2023, they attracted almost four billion users per month.”   And TikTok?  It has over a billion users.

I’m doubting that one can add up the 4 billion and the 1 billion to make 5 billion users of META and TikTok combined, but in any case, that’s a huge percentage of humanity any way one looks at it.

And who is providing fact-checking to those billion of people?  Logically Facts [LF].

And what kind of fact-checking does LF do?  Let’s look at an example that will deal with something very familiar with readers here:  Climate Science Denial.

The definition put forward by the Wiki is:

“Climate change denial (also global warming denial) is a form of science denial characterized by rejecting, refusing to acknowledge, disputing, or fighting the scientific consensus on climate change.”

Other popular definitions of climate change denial include: attacks on solutions, questioning official climate change science and/or the climate movement itself.

If I had all the time left to me in this world, I could do a deep, deep dive into the Fact-Checking Industry.  But, being limited, let’s look, together, at one single “analysis” article from Logically Facts:

‘Pseudoscience, no crisis’: How fake experts are fueling climate change denial

This article is a fascinating study in “fake-fact-checking by innuendo”. 

As we go through the article, sampling its claims, I’ll alert you to any check of an actual fact – don’t hold your breath.   If you wish to be pro-active, read the LF piece first, and you’ll have a better handle on what they are doing.

The lede in their piece is this:

“Would you seek dental advice from an ophthalmologist? The answer is obvious. Yet, on social media, self-proclaimed ‘experts’ with little to no relevant knowledge of climate science are influencing public opinion.” 

The two editors of this “analysis” are listed as Shreyashi Roy [MA in Mass Communications and a BA in English Literature] and Nitish Rampal [ … based out of New Delhi and has …. a keen interest in sports, politics, and tech.]  The author is said to be [more on “said to be” in a minute…] Anurag Baruah [MA in English Language and a certificate in Environmental Journalism: Storytelling earned online from the Thompson Founation.]

Why do you say “said to be”, Mr. Hansen?  If you had read the LF piece, as I suggested, you would see that it reads as if it was “written” by an AI Large Language Model, followed by editing for sense and sensibility by a human, probably, Mr. Baruah, followed by further editing by Roy and Rampal.

The lede is itself an illogic.  First it speaks of medical/dental advice, pointing out, quite rightly, that they are different specializations.  But then complains that unnamed so-called self-proclaimed experts who LF claims “have little to no relevant knowledge of climate science” are influencing public opinion.   Since these persons are so-far unnamed, LF’s AI, author and subsequent editors could not possibly know what their level of knowledge about climate science might be.

Who exactly are they smearing here?

The first is:

“One such ‘expert,’ Steve Milloy, a prominent voice on social media platform X (formerly Twitter), described a NASA Climate post (archive) about the impact of climate change on our seas as a “lie” on June 26, 2024.”

It is absolutely true that Milloy, who is well-known to be an “in-your-face” and “slightly over the-top” critic of all things science that he considers poorly done, being over-hyped, or otherwise falling into his category of “Junk Science”, posted on X the item claimed. 

LF , its AI, author and editors make no effort to check what fact/facts
Milloy was calling a lie, or to check NASA’s facts in any way whatever.

You see, Milloy calling any claim from NASA “a lie” would be an a priori case of Climate Denial: he is refuting or refusing to accept some point of official climate science.

Who is Steve Milloy? 

Steve Milloy is a Board Member & Senior Policy Fellow of the Energy and Environment Legal Institute, author of seven books and over 600 articles/columns published in major newspapers, magazines and internet outlets.  He has testified by request before the U.S. Congress many times, including on risk assessment and Superfund issues.  He is an Adjunct Fellow of the National Center for Public Policy Research.

“He holds a B.A. in Natural Sciences, Johns Hopkins University; Master of Health Sciences (Biostatistics), Johns Hopkins University School of Hygiene and Public Health; Juris Doctorate, University of Baltimore; and Master of Laws (Securities regulation) from the Georgetown University Law Center.”

It seems that many consider Mr. Milloy to be an expert in many things.

And the evidence for LF’s dismissal of Milloy as a “self-proclaimed expert”  having “little to no relevant knowledge of climate science”?  The Guardian, co-founder of the climate crisis propaganda outfit Covering Climate Now, said “JunkScience.com, has been called “the main entrepôt for almost every kind of climate-change denial”” and after a link listing Milloy’s degrees, pooh-poohed him for “lacking formal training in climate science.”  Well, a BA in Natural Sciences might count for something. And a law degree is not nothing. The last link which gives clear evidence that Milloy is a well-recognized expert and it is obvious that the LF AI, author, and editors either did not read the contents of the link or simply chose to ignore it.

Incredibly, LF’s next target is “… John Clauser, a 2022 Nobel Prize winner in physics, claimed that no climate crisis exists and that climate science is “pseudoscience.” Clauser’s Nobel Prize lent weight to his statements, but he has never published a peer-reviewed paper on climate change.“

LF’s evidence against Clauser is The Washington Post in an article attacking not just Clauser, but a long list of major physicists who do not support the IPCC consensus on climate change:  Willie Soon (including the lie that Soon’s work was financed by fossil fuel companies) , Steve Koonin, Dick Lindzen and Will Happer.   The Post article fails to discuss any of the reasons these esteemed, world-class physicists are not consensus-supporting club members. 

Their non-conforming is their crime.  No facts are checked.

LF reinforces the attack on world-renown physicists with a quote from Professor Bill McGuire:  “Such fake experts are dangerous and, in my opinion, incredibly irresponsible—Nobel Prize or not. A physicist denying anthropogenic climate change is actually denying the well-established physical properties of carbon dioxide, which is simply absurd.”

McGuire, is not a physicist and is not a climate scientist, but has a PhD in Geology and is a volcanologist and an IPCC contributor.   He also could be seen as “lacking formal training in climate science.”

But, McGuire has a point, which LF, its AI and its human editors seem to miss, the very basis of the CO2 Global Warming hypothesis is based on physics, not based on what is today called “climate science”. Thus, the physicists are the true experts . (and not the volcanologists….)

LF then launches into the gratuitous comparison of “fake experts” in the anti-tobacco fight, alludes to oil industry ties, and then snaps right to John Cook.

John Cook, a world leader in attacking Climate Change Denial, is not a climate scientist.  He is not a geologist, not an atmospheric scientist, not an oceanic scientist, not a physicist, not even a volcanologist.   He  “earned his PhD in Cognitive Science at the University of Western Australia in 2016”.

The rest of the Logically Facts fake-analysis is basically a re-writing of some of Cook’s anti-Climate Denialists screeds.  Maybe/probably resulting from an AI large language model trained on pro-consensus climate materials.  Logically Facts is specifically and openly an AI-based effort.

LF proceeds to attack a series of persons, not their ideas, one after another:  Tony Heller, Dr. Judith Curry, Patrick Moore and Bjørn Lomborg.

The expertise of these individuals in their respective fields
are either ignored or brushed over.

Curry is a world renowned climate scientist, former chair of the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology.  Curry is the author the book on Thermodynamics of Atmospheres and Oceans, another book on Thermodynamics, Kinetics, and Microphysics of Clouds, and the marvelous groundbreaking Climate Uncertainty and Risk: Rethinking Our Response.  Google scholar returns over 10,000 references to a search of “Dr. Judith Curry climate”.

Lomborg is a socio-economist with an impressive record, a best selling author and a leading expert on issues of energy dependence, value for money spent on international anti-poverty and public health efforts, etc.   Richard Tol, is mention negatively for daring to doubt the “97% consensus”, with no mention of his qualifications as a Professor of Economics and a Professor of the Economics of Climate Change.

Bottom Line:

Logically Facts is a Large Language Model-type AI, supplemented by writers and editors meant to clean-up the mess returned by this chat-bot type AI.    Thus, it is entirely incapable to making any value judgements between repeated slander, enforced consensus views, the prevailing biases of scientific fields and actual facts.  Further, any LLM-based AI is incapable of Critical Thinking and drawing logical conclusions.

In short, Logically Facts is Illogical.

Defence offered by Facebook in Stossel defamation lawsuit.

Data Say Summer 2024 Not So Hot

Posted on by Ron Clutz

For sure you’ve seen the headlines declaring 2024 likely to be the Hottest year ever.  If you’re like me, your response is: That’s not the way it’s going down where I live.  Fortunately there is a website that allows anyone to check their personal experience with the weather station data nearby.  weatherspark.com provides data summaries for you to judge what’s going on in weather history where you live.  In my case a modern weather station is a few miles away Summer 2024 Weather History at Montréal–Mirabel International Airport  The story about Summer 2024 is evident below in charts and graphs from this site.  There’s a map that allows you to find your locale.

The daily average high (red line) and low (blue line) temperature, with 25th to 75th and 10th to 90th percentile bands. The thin dotted lines are the corresponding average perceived temperatures.

First, consider above the norms for Summer from the period 1980 to 2016.

Then, there’s Summer 2024 compared to the normal observations.

The daily range of reported temperatures (gray bars) and 24-hour highs (red ticks) and lows (blue ticks), placed over the daily average high (faint red line) and low (faint blue line) temperature, with 25th to 75th and 10th to 90th percentile bands.

The graph shows Summer had some warm days, some cool days and overall was pretty normal.  But since climate is more than temperature, consider cloudiness.

Wow!  Most of the summer was cloudy, which in summer means blocking the warming sun from hitting the surface.   And with all those clouds, let’s look at precipitation:

So, in the observations out of 92 summer days, there were 56 days when it rained, including 11 days of thunderstorms with heavy rainfall. Given what we know about the hydrology cycles, that means a lot of heat removed upward from the surface.

So the implications for Summer temperatures in my locale.

There you have it before your eyes. Mostly warm days for the
three summer months, with exactly eleven hot afternoons (>30°C).
Otherwise comfortable and cool, and no hot
afternoons in September.

Summary:

Claims of hottest this or that month or year are based on averages of averages of temperatures, which in principle is an intrinsic quality and distinctive to a locale.  The claim involves selecting some places and time periods where warming appears, while ignoring other places where it has been cooling.

Remember:  They want you to panic.  Before doing so, check out what the data says in your neck of the woods.  For example, NOAA declared that “July 2024 was the warmest ever recorded for the globe.”

Methane Madness Strikes Again

Posted on by Ron Clutz

The latest comes from Australia by way of John Ray at his blog Methane cuts on track for 2030 emissions goal.  Excerpts in italics with my bolds and added images.

Australia’s methane emissions have decreased over the past two decades, according to a new report by a leading global carbon research group.

While the world’s methane emissions grew by 20 per cent, meaning two thirds of methane in the atmosphere is from human activity, Australasia and Europe emitted lower levels of the gas.

It puts Australia in relatively good stead, compared to 150 other signatories, to meet its non-binding commitments to the Global Methane Pledge, which aims to cut methane emissions by 30 per cent by the end of the decade.

The findings were revealed in the fourth global methane budget, published by the Global Carbon Project, with contributions from 66 research institutions around the world, including the CSIRO.

According to the report, agriculture contributed 40 per cent of global methane emissions from human activities, followed by the fossil fuel sector (34 per cent), solid waste and waste­water (19 per cent), and biomass and biofuel burning (7 per cent).

Pep Canadell, CSIRO executive director for the Global Carbon Project, said government policies and a smaller national sheep flock were the primary reasons for the lower methane emissions in Australasia.

“We have seen higher growth rates for methane over the past three years, from 2020 to 2022, with a record high in 2021. This increase means methane concentrations in the atmosphere are 2.6 times higher than pre-­industrial (1750) levels,” Dr Canadell said.

The primary source of methane emissions in the agriculture sector is from the breakdown of plant matter in the stomachs of sheep and cattle.

It has led to controversial calls from some circles for less red meat consumption, outraging the livestock industry, which has lowered its net greenhouse gas emissions by 78 per cent since 2005 and is funding research into methane reduction.

Last week, the government agency advising Anthony Albanese on climate change suggested Australians could eat less red meat to help reduce emissions. And the government’s official dietary guidelines will be amended to incorporate the impact of certain foods on climate change.

There is ongoing disagreement among scientists and policymakers about whether there should be a distinction between biogenic methane emitted by livestock, which already exists in a balanced cycle in plants and soil and the atmosphere, and methane emitted from sources stored deep underground for millennia.

“The frustration is that methane, despite its source, gets lumped into one bag,” Cattle Australia vice-president Adam Coffey said. “Enteric methane from livestock is categorically different to methane from coal-seam gas or mining-related fossil fuels that has been dug up from where it’s been stored for millennia and is new to the atmosphere.

“Why are we ignoring what modern climate science is telling us, which is these emissions are inherently different?”  Mr Coffey said the methane budget report showed the intense focus on the domestic industry’s environmental credent­ials was overhyped.

“I think it’s based mainly on ideology and activism,” Mr Coffey said.

This concern about methane is nonsense.
Water vapour blocks all the frequencies that methane does
so the presence of methane adds nothing

Technical Background

Methane alarm is one of the moles continually popping up in the media Climate Whack-A-Mole game. An antidote to methane madness is now available to those inquiring minds who want to know reality without the hype.

Methane and Climate is a paper by W. A. van Wijngaarden (Department of Physics and Astronomy, York University, Canada) and W. Happer (Department of Physics, Princeton University, USA) published at CO2 Coalition November 22, 2019. Below is a summary of the more detailed publication. Excerpts in italics with my bolds.

Overview

Atmospheric methane (CH4) contributes to the radiative forcing of Earth’s atmosphere. Radiative forcing is the difference in the net upward thermal radiation from the Earth through a transparent atmosphere and radiation through an otherwise identical atmosphere with greenhouse gases. Radiative forcing, normally specified in units of W m−2 , depends on latitude, longitude and altitude, but it is often quoted for a representative temperate latitude, and for the altitude of the tropopause, or for the top of the atmosphere.

For current concentrations of greenhouse gases, the radiative forcing at the tropopause, per added CH4 molecule, is about 30 times larger than the forcing per added carbon-dioxide (CO2) molecule. This is due to the heavy saturation of the absorption band of the abundant greenhouse gas, CO2. But the rate of increase of CO2 molecules, about 2.3 ppm/year (ppm = part per million by mole), is about 300 times larger than the rate of increase of CH4 molecules, which has been around 0.0076 ppm/year since the year 2008.

So the contribution of methane to the annual increase in forcing is one tenth (30/300) that of carbon dioxide. The net forcing increase from CH4 and CO2 increases is about 0.05 W m−2 year−1 . Other things being equal, this will cause a temperature increase of about 0.012 C year−1 . Proposals to place harsh restrictions on methane emissions because of warming fears are not justified by facts.

The paper is focused on the greenhouse effects of atmospheric methane, since there have recently been proposals to put harsh restrictions on any human activities that release methane. The basic radiation-transfer physics outlined in this paper gives no support to the idea that greenhouse gases like methane, CH4, carbon dioxide, CO2 or nitrous oxide, N2O are contributing to a climate crisis. Given the huge benefits of more CO2 to agriculture, to forestry, and to primary photosynthetic productivity in general, more CO2 is almost certainly benefitting the world. And radiative effects of CH4 and N2O, another greenhouse gas produced by human activities, are so small that they are irrelevant to climate.

Transmission of shortwave solar irradiation and long wavelength radiation from the Earth’s surface through atmosphere, as permitted by Rohde [2]. Note absorption wavelengths of CH4 and N2O are already covered by H2O and CO2.

Radiative Properties of Earth Atmosphere

On the left of Fig. 2 we have indicated the three most important atmospheric layers for radiative heat transfer. The lowest atmospheric layer is the troposphere, where parcels of air, warmed by contact with the solar-heated surface, float upward, much like hot-air balloons. As they expand into the surrounding air, the parcels do work at the expense of internal thermal energy. This causes the parcels to cool with increasing altitude, since heat flow in or out of parcels is usually slow compared to the velocities of ascent of descent.

Figure 2: Left. A standard atmospheric temperature profile[9], T = T (z). The surface temperature is T (0) = 288.7 K . Right. Standard concentrations[10], C {i} = N {i}/N for greenhouse molecules versus altitude z. The total number density of atmospheric molecules is N . At sea level the concentrations are 7750 ppm of H2O, 1.8 ppm of CH4 and 0.32 ppm of N2O. The O3 concentration peaks at 7.8 ppm at an altitude of 35 km, and the CO2 concentration was approximated by 400 ppm at all altitudes. The data is based on experimental observations.

If the parcels consisted of dry air, the cooling rate would be 9.8 C km−1 the dry adiabatic lapse rate[12]. But rising air has usually picked up water vapor from the land or ocean. The condensation of water vapor to droplets of liquid or to ice crystallites in clouds, releases so much latent heat that the lapse rates are less than 9.8 C km−1 in the lower troposphere. A representative lapse rate for mid latitudes is dT/dz = 6.5 K km−1 as shown in Fig. 2.

The tropospheric lapse rate is familiar to vacationers who leave hot areas near sea level for cool vacation homes at higher altitudesin the mountains. On average, the temperature lapse rates are small enough to keep the troposphere buoyantly stable[13]. Tropospheric air parcels that are displaced in altitude will oscillate up and down around their original position with periods of a few minutes. However, at any given time, large regions of the troposphere (particularly in the tropics) are unstable to moist convection because of exceptionally large temperature lapse rates.

The vertical radiation flux Z, which is discussed below, can change rapidly in the troposphere and stratosphere. There can be a further small change of Z in the mesosphere. Changes in Z above the mesopause are small enough to be neglected, so we will often refer to the mesopause as “the top of the atmosphere” (TOA), with respect to radiation transfer. As shown in Fig. 2, the most abundant greenhouse gas at the surface is water vapor, H2O. However, the concentration of water vapor drops by a factor of a thousand or more between the surface and the tropopause. This is because of condensation of water vapor into clouds and eventual removal by precipitation. Carbon dioxide, CO2, the most abundant greenhouse gas after water vapor, is also the most uniformly mixed because of its chemical stability. Methane, the main topic of this discussion is much less abundant than CO2 and it has somewhat higher concentrations in the troposphere than in the stratosphere where it is oxidized by OH radicals and ozone, O3. The oxidation of methane[8] is the main source of the stratospheric water vapor shown in Fig. 2.

Future Forcings of CH4 and CO2

Methane levels in Earth’s atmosphere are slowly increasing.  If the current rate of increase, about 0.007 ppm/year for the past decade or so, were to continue unchanged it would take about 270 years to double the current concentration of C {i} = 1.8 ppm. But, as one can see from Fig.7, methane levels have stopped increasing for years at a time, so it is hard to be confident about future concentrations. Methane concentrations may never double, but if they do, WH[1] show that this would only increase the forcing by 0.8 W m−2. This is a tiny fraction of representative total forcings at midlatitudes of about 140 W m−2 at the tropopause and 120 W m−2 at the top of the atmosphere.

Figure 9: Projected mid-latitude forcing increments at the tropopause from continued increases of CO2 and CH4 at the rates of Fig. 7 and Fig. 8 for the next 50 years. The projected forcings are very small, especially for methane, compared to the current tropospheric forcing of 137 W m−2.

The per-molecule forcings P {i} of (13) and (14) have been used with the column density Nˆ of (12) and the concentration increase rates dC¯{i}/dt, noted in Fig. 7 and Fig. 8, to evaluate the future forcing (15), which is plotted in Fig. 9. Even after 50 years, the forcing increments from increased concentrations of methane (∆F = 0.23 W m−2), or the roughly ten times larger forcing from increased carbon dioxide (∆F = 2.2 W m−2) are very small compared to the total forcing, ∆F = 137 W m−2, shown in Fig. 3. The reason that the per-molecule forcing of methane is some 30 times larger than that of carbon dioxide for current concentrations is “saturation” of the absorption bands. The current density of CO2 molecules is some 200 times greater than that of CH4 molecules, so the absorption bands of CO2 are much more saturated than those of CH4. In the dilute“optically thin” limit, WH[1] show that the tropospheric forcing power per molecule is P {i} = 0.15 × 10−22 W for CH4, and P {i} = 2.73 × 10−22 W for CO2. Each CO2 molecule in the dilute limit causes about 5 times more forcing increase than an additional molecule of CH4, which is only a ”super greenhouse gas” because there is so little in the atmosphere, compared to CO2.

Methane Summary

Natural gas is 75% Methane (CH4) which burns cleanly to carbon dioxide and water. Methane is eagerly sought after as fuel for electric power plants because of its ease of transport and because it produces the least carbon dioxide for the most power. Also cars can be powered with compressed natural gas (CNG) for short distances.

In many countries CNG has been widely distributed as the main home heating fuel. As a consequence, in the past methane has leaked to the atmosphere in large quantities, now firmly controlled. Grazing animals also produce methane in their complicated stomachs and methane escapes from rice paddies and peat bogs like the Siberian permafrost.

It is thought that methane is a very potent greenhouse gas because it absorbs some infrared wavelengths 7 times more effectively than CO2, molecule for molecule, and by weight even 20 times. As we have seen previously, this also means that within a distance of metres, its effect has saturated, and further transmission of heat occurs by convection and conduction rather than by radiation.

Note that when H20 is present in the lower troposphere, there are few photons left for CH4 to absorb:

Even if the IPCC radiative greenhouse theory were true, methane occurs only in minute quantities in air, 1.8ppm versus CO2 of 390ppm. By weight, CH4 is only 5.24Gt versus CO2 3140Gt (on this assumption). If it truly were twenty times more potent, it would amount to an equivalent of 105Gt CO2 or one thirtieth that of CO2. A doubling in methane would thus have no noticeable effect on world temperature.

However, the factor of 20 is entirely misleading because absorption is proportional to the number of molecules (=volume), so the factor of 7 (7.3) is correct and 20 is wrong. With this in mind, the perceived threat from methane becomes even less.

Further still, methane has been rising from 1.6ppm to 1.8ppm in 30 years (1980-2010), assuming that it has not stopped rising, this amounts to a doubling in 2-3 centuries. In other words, methane can never have any measurable effect on temperature, even if the IPCC radiative cooling theory were right.

Because only a small fraction in the rise of methane in air can be attributed to farm animals, it is ludicrous to worry about this aspect or to try to farm with smaller emissions of methane, or to tax it or to trade credits.

The fact that methane in air has been leveling off in the past two decades, even though we do not know why, implies that it plays absolutely no role as a greenhouse gas.  (From Sea Friends (here):

More information at The Methane Misconceptions by Dr. Wilson Flood (UK) here.

Climatists Aim Forks at Our Food Supply

How Damaging Are Math Models? Three Strikes Against Them

Posted on by Ron Clutz

Tomas Fürst explains the dangers in believing models are reality in his Brownstone article Mathematical Models Are Weapons of Mass Destruction.  Excerpts in italics with my bolds and added images.

Great Wealth Destroyed in Mortgage Crisis by Trusting a Financial Model

In 2007, the total value of an exotic form of financial insurance called Credit Default Swap (CDS) reached $67 trillion. This number exceeded the global GDP in that year by about fifteen percent. In other words – someone in the financial markets made a bet greater than the value of everything produced in the world that year.

What were the guys on Wall Street betting on? If certain boxes of financial pyrotechnics called Collateralized Debt Obligations (CDOs) are going to explode. Betting an amount larger than the world requires a significant degree of certainty on the part of the insurance provider.

What was this certainty supported by?

A magic formula called the Gaussian Copula Model. The CDO boxes contained the mortgages of millions of Americans, and the funny-named model estimated the joint probability that holders of any two randomly selected mortgages would both default on the mortgage.

The key ingredient in this magic formula was the gamma coefficient, which used historical data to estimate the correlation between mortgage default rates in different parts of the United States. This correlation was quite small for most of the 20th century because there was little reason why mortgages in Florida should be somehow connected to mortgages in California or Washington.

But in the summer of 2006, real estate prices across the United States began to fall, and millions of people found themselves owing more for their homes than they were currently worth. In this situation, many Americans rationally decided to default on their mortgage. So, the number of delinquent mortgages increased dramatically, all at once, across the country.

The gamma coefficient in the magic formula jumped from negligible values ​​towards one and the boxes of CDOs exploded all at once. The financiers – who bet the entire planet’s GDP on this not happening – all lost.

This entire bet, in which a few speculators lost the entire planet, was based on a mathematical model that its users mistook for reality. The financial losses they caused were unpayable, so the only option was for the state to pay for them. Of course, the states didn’t exactly have an extra global GDP either, so they did what they usually do – they added these unpayable debts to the long list of unpayable debts they had made before. A single formula, which has barely 40 characters in the ASCII code, dramatically increased the total debt of the “developed” world by tens of percent of GDP. It has probably been the most expensive formula in the history of mankind.

Covid Panic and Social Devastation from Following an Epidemic Model

After this fiasco, one would assume people would start paying more attention to the predictions of various mathematical models. In fact, the opposite happened. In the fall of 2019, a virus began to spread from Wuhan, China, which was named SARS-CoV-2 after its older siblings. His older siblings were pretty nasty, so at the beginning of 2020, the whole world went into a panic mode.

If the infection fatality rate of the new virus was comparable to its older siblings, civilization might really collapse. And exactly at this moment, many dubious academic characters emerged around the world with their pet mathematical models and began spewing wild predictions into the public space.

Journalists went through the predictions, unerringly picked out only the most apocalyptic ones, and began to recite them in a dramatic voice to bewildered politicians. In the subsequent “fight against the virus,” any critical discussion about the nature of mathematical models, their assumptions, validation, the risk of overfitting, and especially the quantification of uncertainty was completely lost.

Most of the mathematical models that emerged from academia were more or less complex versions of a naive game called SIR. These three letters stand for Susceptible–Infected–Recovered and come from the beginning of the 20th century, when, thanks to the absence of computers, only the simplest differential equations could be solved. SIR models treat people as colored balls that float in a well-mixed container and bump into each other.

When red (infected) and green (susceptible) balls collide, two reds are produced. Each red (infected) turns black (recovered) after some time and stops noticing the others. And that’s all. The model does not even capture space in any way – there are neither cities nor villages. This completely naive model always produces (at most) one wave of contagion, which subsides over time and disappears forever.

And exactly at this moment, the captains of the coronavirus response made the same mistake as the bankers fifteen years ago: They mistook the model for reality. The “experts” were looking at the model that showed a single wave of infections, but in reality, one wave followed another. Instead of drawing the correct conclusion from this discrepancy between model and reality—that these models are useless—they began to fantasize that reality deviates from the models because of the “effects of the interventions” by which they were “managing” the epidemic. There was talk of “premature relaxation” of the measures and other mostly theological concepts. Understandably, there were many opportunists in academia who rushed forward with fabricated articles about the effect of interventions.

Meanwhile, the virus did its thing, ignoring the mathematical models. Few people noticed, but during the entire epidemic, not a single mathematical model succeeded in predicting (at least approximately) the peak of the current wave or the onset of the next wave.

Unlike Gaussian Copula Models, which – besides having a funny name – worked at least when real estate prices were rising, SIR models had no connection to reality from the very beginning. Later, some of their authors started to retrofit the models to match historical data, thus completely confusing the non-mathematical public, which typically does not distinguish between an ex-post fitted model (where real historical data are nicely matched by adjusting the model parameters) and a true ex-ante prediction for the future. As Yogi Berra would have it: It’s tough to make predictions, especially about the future.

While during the financial crisis, misuse of mathematical models brought mostly economic damage, during the epidemic it was no longer just about money. Based on nonsensical models, all kinds of “measures” were taken that damaged many people’s mental or physical health.

Nevertheless, this global loss of judgment had one positive effect: The awareness of the potential harm of mathematical modelling spread from a few academic offices to wide public circles. While a few years ago the concept of a “mathematical model” was shrouded in religious reverence, after three years of the epidemic, public trust in the ability of “experts” to predict anything went to zero.

Moreover, it wasn’t just the models that failed – a large part of the academic and scientific community also failed. Instead of promoting a cautious and sceptical evidence-based approach, they became cheerleaders for many stupidities the policymakers came forward with. The loss of public trust in the contemporary Science, medicine, and its representatives will probably be the most significant consequence of the epidemic.

Demolishing Modern Civilization Because of Climate Model Predictions

Which brings us to other mathematical models, the consequences of which can be much more destructive than everything we have described so far. These are, of course, climate models. The discussion of “global climate change” can be divided into three parts.

1. The real evolution of temperature on our planet. For the last few decades, we have had reasonably accurate and stable direct measurements from many places on the planet. The further we go into the past, the more we have to rely on various temperature reconstruction methods, and the uncertainty grows. Doubts may also arise as to what temperature is actually the subject of the discussion: Temperature is constantly changing in space and time, and it is very important how the individual measurements are combined into some “global” value. Given that a “global temperature” – however defined – is a manifestation of a complex dynamic system that is far from thermodynamic equilibrium, it is quite impossible for it to be constant. So, there are only two possibilities: At every moment since the formation of planet Earth, “global temperature” was either rising or falling. It is generally agreed that there has been an overall warming during the 20th century, although the geographical differences are significantly greater than is normally acknowledged. A more detailed discussion of this point is not the subject of this essay, as it is not directly related to mathematical models.

2. The hypothesis that increase in CO2 concentration drives increase in global temperature. This is a legitimate scientific hypothesis; however, evidence for the hypothesis involves more mathematical modelling than you might think. Therefore, we will address this point in more detail below.

3. The rationality of the various “measures” that politicians and activists propose to prevent global climate change or at least mitigate its effects. Again, this point is not the focus of this essay, but it is important to note that many of the proposed (and sometimes already implemented) climate change “measures” will have orders of magnitude more dramatic consequences than anything we did during the Covid epidemic. So, with this in mind, let’s see how much mathematical modelling we need to support hypothesis 2.

Yes, they are projecting spending more than 100 Trillion US$.

At first glance, there is no need for models because the mechanism by which CO2 heats the planet has been well understood since Joseph Fourier, who first described it. In elementary school textbooks, we draw a picture of a greenhouse with the sun smiling down on it. Short-wave radiation from the sun passes through the glass, heating the interior of the greenhouse, but long-wave radiation (emitted by the heated interior of the greenhouse) cannot escape through the glass, thus keeping the greenhouse warm. Carbon dioxide, dear children, plays a similar role in our atmosphere as the glass in the greenhouse.

This “explanation,” after which the entire greenhouse effect is named, and which we call the “greenhouse effect for kindergarten,” suffers from a small problem: It is completely wrong. The greenhouse keeps warm for a completely different reason. The glass shell prevents convection – warm air cannot rise and carry the heat away. This fact was experimentally verified already at the beginning of the 20th century by building an identical greenhouse but from a material that is transparent to infrared radiation. The difference in temperatures inside the two greenhouses was negligible.

OK, greenhouses are not warm due to greenhouse effect (to appease various fact-checkers, this fact can be found on Wikipedia). But that doesn’t mean that carbon dioxide doesn’t absorb infrared radiation and doesn’t behave in the atmosphere the way we imagined glass in a greenhouse behaved. Carbon dioxide actually does absorb radiation in several wavelength bands. Water vapor, methane, and other gases also have this property. The greenhouse effect (erroneously named after the greenhouse) is a safely proven experimental fact, and without greenhouse gases, the Earth would be considerably colder.

It follows logically that when the concentration of CO2 in the atmosphere increases, the CO2 molecules will capture even more infrared photons, which will therefore not be able to escape into space, and the temperature of the planet will rise further. Most people are satisfied with this explanation and continue to consider the hypothesis from point 2 above as proven. We call this version of the story the “greenhouse effect for philosophical faculties.”

The important point here is the red line. This is what Earth would radiate to space if you were to double the CO2 concentration from today’s value. Right in the middle of these curves, you can see a gap in spectrum. The gap is caused by CO2 absorbing radiation that would otherwise cool the Earth. If you double the amount of CO2, you don’t double the size of that gap. You just go from the black curve to the red curve, and you can barely see the difference.

The problem is, of course, that there is so much carbon dioxide (and other greenhouse gases) in the atmosphere already that no photon with the appropriate frequency has a chance to escape from the atmosphere without being absorbed and re-emitted many times by some greenhouse gas molecule.

A certain increase in the absorption of infrared radiation induced by higher concentration of CO2 can thus only occur at the edges of the respective absorption bands. With this knowledge – which, of course, is not very widespread among politicians and journalists – it is no longer obvious why an increase in the concentration of CO2 should lead to a rise in temperature.

In reality, however, the situation is even more complicated, and it is therefore necessary to come up with another version of the explanation, which we call the “greenhouse effect for science faculties.” This version for adults reads as follows: The process of absorption and re-emission of photons takes place in all layers of the atmosphere, and the atoms of greenhouse gases “pass” photons from one to another until finally one of the photons emitted somewhere in the upper layer of the atmosphere flies off into space. The concentration of greenhouse gases naturally decreases with increasing altitude. So, when we add a little CO2, the altitude from which photons can already escape into space shifts a little higher. And since the higher we go, the colder it is, the photons there emitted carry away less energy, resulting in more energy remaining in the atmosphere, making the planet warmer.

Note that the original version with the smiling sun above the greenhouse got somewhat more complicated. Some people start scratching their heads at this point and wondering if the above explanation is really that clear. When the concentration of CO2 increases, perhaps “cooler” photons escape to space (because the place of their emission moves higher), but won’t more of them escape (because the radius increases)? Shouldn’t there be more warming in the upper atmosphere? Isn’t the temperature inversion important in this explanation? We know that temperature starts to rise again from about 12 kilometers up. Is it really possible to neglect all convection and precipitation in this explanation? We know that these processes transfer enormous amounts of heat. What about positive and negative feedbacks? And so on and so on.

The more you ask, the more you find that the answers are not directly observable but rely on mathematical models. The models contain a number of experimentally (that is, with some error) measured parameters; for example, the spectrum of light absorption in CO2 (and all other greenhouse gases), its dependence on concentration, or a detailed temperature profile of the atmosphere.

This leads us to a radical statement: The hypothesis that an increase in the concentration of carbon dioxide in the atmosphere drives an increase in global temperature is not supported by any easily and comprehensibly explainable physical reasoning that would be clear to a person with an ordinary university education in a technical or natural science field. This hypothesis is ultimately supported by mathematical modelling that more or less accurately captures some of the many complicated processes in the atmosphere.

Flows and Feedbacks for Climate Models

However, this casts a completely different light on the whole problem. In the context of the dramatic failures of mathematical modelling in the recent past, the “greenhouse effect” deserves much more attention. We heard the claim that “science is settled” many times during the Covid crisis and many predictions that later turned out to be completely absurd were based on “scientific consensus.”

Almost every important scientific discovery began as a lone voice going against the scientific consensus of that time. Consensus in science does not mean much – science is built on careful falsification of hypotheses using properly conducted experiments and properly evaluated data. The number of past instances of scientific consensus is basically equal to the number of past scientific errors.

Mathematical modelling is a good servant but a bad master. The hypothesis of global climate change caused by the increasing concentration of CO2 in the atmosphere is certainly interesting and plausible. However, it is definitely not an experimental fact, and it is most inappropriate to censor an open and honest professional debate on this topic. If it turns out that mathematical models were – once again – wrong, it may be too late to undo the damage caused in the name of “combating” climate change.

Beware getting sucked into any model, climate or otherwise.

Addendum on Chameleon Models

Chameleon Climate Models

Footnote:  Classic Cartoon on Models

 

UAH August 2024: Most Regions Cooler, Offset by SH Land 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, unrelated to steadily rising CO2 and now moderating.

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

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?

August 2024 Most Regions Cooler Offset by SH Land Spike
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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 heard 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, followed by cooling. 

UAH has updated their tlt (temperatures in lower troposphere) dataset for August 2024. Posts on their reading of ocean air temps this month are ahead of the update from HadSST4.  I posted last month on SSTs using HadSST4 Oceans Warming Uptick July 2024. These posts have 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. Last February 2024, both ocean and land air temps went higher driven by SH, while NH and the Tropics cooled slightly, resulting in Global anomaly matching October 2023 peak. Then in March Ocean anomalies cooled while Land anomalies rose everywhere. After a mixed pattern in April, the May anomalies were back down led by a large drop in NH land, and a smaller ocean decline in all regions. In June all Ocean regions dropped down, as well as dips in SH and Tropical land temps. In July all Oceans were unchanged except for Tropical warming, while all land regions rose slightly. Now in August we see a warming leap in SH land, slight Land cooling elsewhere, a dip in Tropical Ocean temp and slightly elswhere. End result is a small upward bump.

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 changed 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 August.  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 of 1.3C January to March 2024.  In April and May that started dropping in all regions.   June showed a sharp decline everywhere, led by the Tropics down 0.5C. The Global anomaly fell to nearly match the September 2023 value. In July, the Tropics rose slightly while SH, NH and the Global Anomaly were unchanged. Now in August a drop in the Tropics, with little NH cooling and Global Ocean anomaly slightly lower.

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 August 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 to 0.6 in January 2024, matching the SH peak in 2016. Then in February and March SH anomaly jumped up nearly 0.7C, and Tropics went up to a new high of 1.5C, pulling up the Global land anomaly to match 10/2023. In April SH dropped sharply back to 0.6C, Tropics cooled very slightly, but NH land jumped up to a new high of 1.5C, pulling up Global land anomaly to its new high of 1.24C.

In May that NH spike started to reverse.  Despite warming in Tropics and SH, the much larger NH land mass pulled the Global land anomaly back down to the February value. In June, sharp drops in SH and Tropics land temps overcame an upward bump in NH, pulling Global land anomaly down to match last December. In July, all land regions rose slightly, and now in August a record spike up to 1.87 and pulling the Global land anomaly up by 0.17°C. Despite this land warming, the Global land and ocean combined anomaly rose only 0.03°C.

The Bigger Picture UAH Global Since 1980

The chart shows monthly Global anomalies starting 01/1980 to present.  The average monthly anomaly is -0.04, 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. Then 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. After March and April took the Global anomaly to a new peak of 1.05C.  The cool down started with May dropping to 0.90C, and in June a further decline to 0.80C.  Despite an uptick to 0.85 in July,   it remains to be seen whether El Nino will weaken or gain strength, and it whether we are past the recent peak.

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 HadSST4, 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.

 

Antidote for Radiation Myopia

Posted on by Ron Clutz

On a previous post a reader queried me about my position.  Taking him to be serious, I prepared a reply with resources that can serve anyone wanting to understand radiative GHG theory and reality.  The key is to escape radiation myopia, that is focusing on radiative energy transfers in earth’s climate system to the exclusion of the other transfers.  Energy in our world moves by conduction, convection and phase changes of H2O in addition to radiation.  And not surprisingly at any place and time, the most active mode is the one with the least resistance.

The post triggering the question was this one:

The Original Sin of GHG Theory

My Reply to Questioner

Thanks for your response. Your inital question sounded trollish, but I take your comment seriously.

Firstly, you said “I’ve never seen anyone outside of the anti-GHG crowd ever talk about “back-radiation”. Actually references to that notion are readily found since it is the primary way global warming/ climate change is explained to the public. Some examples:

“However, GHGs, unlike other atmospheric gases such as oxygen and nitrogen, are opaque to outgoing infrared radiation. As the concentration of GHGs in the atmosphere increases due to human-caused emissions, energy radiated from the surface becomes trapped in the atmosphere, unable to escape the planet. This energy returns to the surface, where it is reabsorbed.” UNEP

“Greenhouses gases are atmospheric gases such as carbon dioxide (CO2), methane (CH4), and water vapor (H2O) that absorb and re-radiate heat, which warms the lower atmosphere and Earth’s surface. This process of absorption and re-radiation of heat is called the greenhouse effect. Although greenhouse gases only make up a small percentage of the atmosphere, small changes in the amount of greenhouse gases can greatly alter the strength of the greenhouse effect, which in turn, affects the Earth’s average temperature and climate. UCBerkeley

“As CO2 soaks up this infrared energy, it vibrates and re-emits the infrared energy back in all directions. About half of that energy goes out into space, and about half of it returns to Earth as heat, contributing to the ‘greenhouse effect.’ ColumbiaU

The favored term now is “re-radiation” and it is central in the narrative everywhere, including among others, NASA, MIT and of course multiple UN agencies. So it is necessary to debunk the notion.

I know as well as you that back- or re-radiation is a caricature, and climate scientists make a different claim, namely raising the ERL which slows the cooling. That theory is also wrong for different empirical reasons. See:

Refresher: GHG Theory and the Tests It Fails

Secondly, the root issue is the abuse of Stefan-Boltzman law to create a fictious downward energy transfer, such as seen in energy balance cartoons, misleading and not funny. The equation calculates the transfer from the difference in temperature between two bodies in thermal contact, it does not attribute thermal radiation to each of them. Full explanation here:

Experimental Proof Nil Warming from GHGs

And regarding the failed energy balance diagrams:

Fatal Flaw in Earth Energy Balance Diagrams

For extra credit and insight, look at a Sabine Hosenfelder video to understand how current GHG theory goes astray. Link includes excerpts and critique.

Sabine’s Video Myopic on GHG Climate Role

Summary

“The Earth, a rocky sphere at a distance from the Sun of ~149.6 million kilometers, where the Solar irradiance comes in at 1361.7 W/m2, with a mean global albedo, mostly from clouds, of 0.3 and with an atmosphere surrounding it containing a gaseous mass held in place by the planet’s gravity, producing a surface pressure of ~1013 mb, with an ocean of H2O covering 71% of its surface and with a rotation time around its own axis of ~24h, boasts an average global surface temperature of +15°C (288K).

Why this specific temperature? Because, with an atmosphere weighing down upon us with the particular pressure that ours exerts, this is the temperature level the surface has to reach and stay at for the global convectional engine to be able to pull enough heat away fast enough from it to be able to balance the particular averaged out energy input from the Sun that we experience.

It’s that simple.”  E. M. Smith

 

See Also

New Wholistic Paradigm of Climate Change