Fossil Fuels ≠ Global Warming Updated

Note: This Analysis was updated with 2019 statistics in the post 2020 Update: Fossil Fuels ≠ Global Warming

Previous posts addressed the claim that fossil fuels are driving global warming. This post updates that analysis with the latest (2016) numbers from BP Statistics and compares World Fossil Fuel Consumption (WFFC) with three estimates of Global Mean Temperature (GMT). More on both these variables below.

WFFC

2016 statistics are now available from BP for international consumption of Primary Energy sources. Statistical Review of World Energy.  2017 numbers should be available this summer.

The reporting categories are:
Oil
Natural Gas
Coal
Nuclear
Hydro
Renewables (other than hydro)

This analysis combines the first three, Oil, Gas, and Coal for total fossil fuel consumption world wide. The chart below shows the patterns for WFFC compared to world consumption of Primary Energy from 1965 through 2016.

WFFC 2016 BP

The graph shows that Primary Energy consumption has grown continuously for 5 decades. Over that period oil, gas and coal (sometimes termed “Thermal”) averaged 90% of PE consumed, ranging from 94% in 1965 to 86% in 2016.  MToe is millions of tons of oil equivalents.

Global Mean Temperatures

Everyone acknowledges that GMT is a fiction since temperature is an intrinsic property of objects, and varies dramatically over time and over the surface of the earth. No place on earth determines “average” temperature for the globe. Yet for the purpose of detecting change in temperature, major climate data sets estimate GMT and report anomalies from it.

UAH record consists of satellite era global temperature estimates for the lower troposphere, a layer of air from 0 to 4km above the surface. HadSST estimates sea surface temperatures from oceans covering 71% of the planet. HADCRUT combines HadSST estimates with records from land stations whose elevations range up to 6km above sea level.

Both GISS LOTI (land and ocean) and HADCRUT4 (land and ocean) use 14.0 Celsius as the climate normal, so I will add that number back into the anomalies. This is done not claiming any validity other than to achieve a reasonable measure of magnitude regarding the observed fluctuations.

No doubt global sea surface temperatures are typically higher than 14C, more like 17 or 18C, and of course warmer in the tropics and colder at higher latitudes. Likewise, the lapse rate in the atmosphere means that air temperatures both from satellites and elevated land stations will range colder than 14C. Still, that climate normal is a generally accepted indicator of GMT.

Correlations of GMT and WFFC

The next graph compares WFFC to GMT estimates over the five decades from 1965 to 2016 from HADCRUT4, which includes HadSST3.

WFFC HadGMT 2016

Over the last five decades the increase in fossil fuel consumption is dramatic and monotonic, steadily increasing by 223% from 3.5B to 11.4 B oil equivalent tons.  Meanwhile the GMT record from Hadcrut shows multiple ups and downs with an accumulated rise of 0.9C over 51 years, 7% of the starting value.

The second graph compares to GMT estimates from UAH6, and HadSST3 for the satellite era from 1979 to 2016, a period of 37 years.

WFFC HadSST UAH 2016

In the satellite era WFFC has increased at a compounded rate of nearly 2% per year, for a total increase of 84% since 1979. At the same time, SST warming amounted to 0.55C, or 3.9% of the starting value.  UAH warming was 0.72, or 5.5% up from 1979.  The temperature compounded rate of change is 0.1% per year, an order of magnitude less.  Even more obvious is the 1998 El Nino peak and flat GMT since.

Summary

The climate alarmist/activist claim is straight forward: Burning fossil fuels makes measured temperatures warmer. The Paris Accord further asserts that by reducing human use of fossil fuels, further warming can be prevented.  Those claims do not bear up under scrutiny.

It is enough for simple minds to see that two time series are both rising and to think that one must be causing the other. But both scientific and legal methods assert causation only when the two variables are both strongly and consistently aligned. The above shows a weak and inconsistent linkage between WFFC and GMT.

Going further back in history shows even weaker correlation between fossil fuels consumption and global temperature estimates:

wfc-vs-sat

Figure 5.1. Comparative dynamics of the World Fuel Consumption (WFC) and Global Surface Air Temperature Anomaly (ΔT), 1861-2000. The thin dashed line represents annual ΔT, the bold line—its 13-year smoothing, and the line constructed from rectangles—WFC (in millions of tons of nominal fuel) (Klyashtorin and Lyubushin, 2003). Source: Frolov et al. 2009

In legal terms, as long as there is another equally or more likely explanation for the set of facts, the claimed causation is unproven. The more likely explanation is that global temperatures vary due to oceanic and solar cycles. The proof is clearly and thoroughly set forward in the post Quantifying Natural Climate Change.

Background context for today’s post is at Claim: Fossil Fuels Cause Global Warming.

What is Global Temperature? Is it warming or cooling?

H/T graeme for asking a good question.

This blog features a monthly update on ocean SST averages from HadSST3 (latest is Oceans Cool Off Previous 3 Years). Graeme added this comment:
I came across this today. Can you comment as your studies seem to show the reverse! Regards, Graeme Weber
https://www.carbonbrief.org/category/science/temperature/global-temperature

While thinking about a concise, yet complete response, I put together this post. This is how I see it, to the best of my knowledge.

The question could be paraphrased in these words: Why are there differences between various graphs that report changes in global temperatures?

The short answer is: The differences arise both from what is measured and how the measurements are processed.

For example, consider HadSST3 as one example and GISTEMP as another. All climate temperature products divide the earth surface into grid cells for analysis. This is necessary because a global average can be biased by some regions being much more heavily sampled, eg. North America or North Atlantic. HadSST takes in measurements only from cells containing ocean, while GISTEMP uses data files from NOAA GHCN v3 (meteorological stations), ERSST v5 (ocean areas), and SCAR (Antarctic stations).

Beyond this, HadSST3 is properly termed a temperature data product, while GISTEMP is a temperature reconstruction product. The distinction goes to how the product team deals with missing data. HadSST3 calculates averages each month from grid cells with sufficient samples of observations, and excludes cells with inadequate samples for the month.

GISTEMP estimates temperature values for cells lacking data by referring to cells that are observed sufficiently. The estimates are a best guess as to what temperatures would have been recorded had there been fully functional sensors operating. This process is called interpolation, resulting in a product combining observations with estimates, ie an admixture of data and guesses.

I rely on HadSST3 because I know their results are based upon observational data. I am doubtful of GISTEMP results because many studies, including some of my own, show that interpolation produces strange and unconvincing results which come to light when you look at changes in the local records themselves.

One disturbing thing is that GISTEMP keeps on changing the past, and always in the direction of adding warming.  What you see today differs from yesterday, and tomorrow who knows?

Roger Andrews does a thorough job analyzing the effects of adjustments upon Surface Air Temperature (SAT) datasets. His article at Energy Matters is Adjusting Measurements to Match the Models – Part 1: Surface Air Temperatures.

Another thing is that temperature patterns are altered so that places that show cooling trends on their own are converted to warming after processing.

Figure 3: Warming vs. cooling at 86 South American stations before and after BEST homogeneity adjustments  This shows results from BEST, another reconstruction product demonstrating how an entire continent is presented differently by means of processing.

Then there is the problem that more and more places are showing estimates rather than observations. Years ago, Dr. McKitrick noticed that the decreasing number of stations reporting coincided with the rising GMT reports last century.   Below is his graph showing the correlation between Global Mean Temperature (Average T) and the number of stations included in the global database. Source: Ross McKitrick, U of Guelph

Ave. T vs. No. Stations

Currently it is clear that a great many places are estimated, and it is even the case that active station records are ignored in favor of estimates.

For these reasons I am skeptical of these land+ocean temperature reconstructions. HadSST3 deals with the ocean in a reasonable way, without inventing data.

When it comes to land surface stations, it is much more reasonable to compute the change derivative for each station (i.e. slope) and average the slopes as an indication of regional, national or global temperature change. This form of Temperature Trend Analysis deals with missing data in the most direct way: by putting unobserved months at a specific station on the trendline of the months that are observed at that station–no infilling, no homogenization.

Several of my studies using this approach are on this blog under the category Temperature Trend Analysis. A guideline to these resources is at Climate Compilation Part I Temperatures

The method of analysis is demonstrated by a post as Temperature Data Review Project-My Submission.which also confirms the problems noted above.

A peer-reviewed example of this way of analyzing climate temperature change is the paper Arctic temperature trends from the early nineteenth century to the present W. A. van Wijngaarden, Theoretical & Applied Climatology (2015) here

Is the globe warming or cooling?

Despite the difficulties depicting temperature changes noted above, we do observe periods of warming and cooling at different times and places.  Interpreting those fluctuations is a matter of context.  For example, consider GISTEMP estimated global warming in the context of the American experience of temperature change during a typical year.

 

Global Ocean Cooling in September

September Sea Surface Temperatures (SSTs) are now available, and we see downward spikes in ocean temps everywhere, led by sharp decreases in the Tropics and SH, reversing the bump upward last month. The Tropical cooling in particular factors into forecasters favoring an unusually late La Nina appearance in coming months.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through September 2017.

The August bump upward was overcome with the Global average matching the lowest level in the chart at February 2015.  September NH temps almost erased a three-month climb; even so 9/2017 is well below the previous two years.  Meanwhile SH and the Tropics are setting new lows for this period.  With current reports from the El Nino 3.4 grid sector, it seems likely October will go even lower, with downward moves across all oceans.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back to its beginning level. Secondly, the Northern Hemisphere added two bumps on the shoulders of Tropical warming, with peaks in August of each year. Also, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.

Note:  Last month someone asked about HadSST calculations, especially as the Global appeared to be a simple average of NH and SH, which would be misleading.  My queries to Met Office received these clarifying responses:

My colleague in the Climate Monitoring and Research team has advised the following:

For HadSST3, we take an area-weighted average of all the grid boxes with data in to calculate the global average. We don’t calculate the two hemispheric series and then average them. In the case of SST, this wouldn’t work because the southern hemisphere ocean area is larger than the northern hemisphere.

The uncertainty that arises from incomplete sampling is estimated and incorporated into the global average SST files. Coverage varies throughout the record with the northern hemisphere being generally better observed, but at other times, coverage is concentrated other places, dictated by where shipping happened to be at those times. Since the mid 2000s drifting buoys have provided a more uniform sampling of the world’s oceans. When we compare to other data sets, we typically compare where both data sets have data which minimizes the coverage problems.

Kind regards,  Misha,  Weather Desk Climate Advisor

Summary

We have seen lots of claims about the temperature records for 2016 and 2015 proving dangerous man made warming.  At least one senator stated that in a confirmation hearing.  Yet HadSST3 data for the last two years show how obvious is the ocean’s governing of global average temperatures.

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

The best context for understanding these two years comes from the world’s sea surface temperatures (SST), for several reasons:

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

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

 

Tropics Lead Ocean Warming in August

August Sea Surface Temperatures (SSTs) are now available, and we see an upward spike in ocean temps everywhere, led by sharp increases in the Tropics and SH, reversing for now the downward trajectory from the previous 12 months.  It seems likely the Tropical warming in particular factored into the active hurricane season peaking this month and next.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through August 2017.

In May despite a slight rise in the Tropics, declines in both hemispheres and globally caused SST cooling to resume after an upward bump in April.  Then in July a large drop showed in both in the Tropics and in SH, declining over 4 months.  The sharp upturn in August in the Tropics is the unusual feature this month, along with SH rising, resulting in a global average matching the previous two Augusts. Meanwhile the NH is peaking in August as in the past two years, but somewhat lower.  Despite the August warming, ENSO has gone below neutral toward La Nina, and no one expects a rise like 2015 in the coming months.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back to its beginning level. Secondly, the Northern Hemisphere added two bumps on the shoulders of Tropical warming, with peaks in August of each year. Also, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.

Note:  Last month someone asked about HadSST calculations, especially as the Global appeared to be a simple average of NH and SH, which would be misleading.  My query to Met Office received this clarifying response:

My colleague in the Climate Monitoring and Research team has advised the following:

For HadSST3, we take an area-weighted average of all the grid boxes with data in to calculate the global average. We don’t calculate the two hemispheric series and then average them. In the case of SST, this wouldn’t work because the southern hemisphere ocean area is larger than the northern hemisphere.

Kind regards,  Misha,  Weather Desk Climate Advisor

Summary

We have seen lots of claims about the temperature records for 2016 and 2015 proving dangerous man made warming.  At least one senator stated that in a confirmation hearing.  Yet HadSST3 data for the last two years show how obvious is the ocean’s governing of global average temperatures.

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

The best context for understanding these two years comes from the world’s sea surface temperatures (SST), for several reasons:

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

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

 

Tropics Lead Ocean Cooling

July Sea Surface Temperatures (SSTs) are now available, and we can see further ocean cooling led by plummeting temps in the  Tropics and SH, continuing the downward trajectory from the previous 12 months.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.

The chart below shows the last two years of SST monthly anomalies as reported in HadSST3 including July 2017.

In May despite a slight rise in the Tropics, declines in both hemispheres and globally caused SST cooling to resume after an upward bump in April.  Now in July a large drop is showing both in the Tropics and in SH, declining the last 4 months.  Meanwhile the NH is peaking in July as usual, but well down from the previous July.  The net of all this is a slightly lower Global anomaly but with likely additional future cooling led by the Tropics and also SH hitting new lows for this period.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back to its beginning level. Secondly, the Northern Hemisphere added two bumps on the shoulders of Tropical warming, with peaks in August of each year. Also, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one. Note that Global anomaly for July 2017 matches closely to April 2015.  However,  SH and the Tropics are lower now and trending down compared to an upward trend in 2015.

We have seen lots of claims about the temperature records for 2016 and 2015 proving dangerous man made warming.  At least one senator stated that in a confirmation hearing.  Yet HadSST3 data for the last two years show how obvious is the ocean’s governing of global average temperatures.

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

The best context for understanding these two years comes from the world’s sea surface temperatures (SST), for several reasons:

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

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

 

How Trustworthy are SSTs?

Roger Andrews as promised has published his analysis of SST (Sea Surface Temperatures) datasets, based on some years of research. The essay is Making the Measurements Match the Models – Part 2: Sea Surface Temperatures and well worth a look.

Some years ago while reading to get up to speed on climate science, I was struck by a Roger Pielke Sr. comment. He said that surface temperatures are serving as a proxy for changes in heat content of the earth climate system, which is the real concern.  And air temperatures are contaminated by fluctuations in water content, such that a degree difference in the humid tropics involves much more additional heat than does the same change in extremely dry polar air.

For those who want to see the math, here it is from the Engineering Toolbox.

The enthalpy of humid air at 25C with specific moisture content x = 0.0203 kg/kg (saturation), can be calculated as 76.9 (kJ/kg). . .The same calculation for moist air at 20C gives a heat capacity of 58.2, so the 5C increase requires 18.7 kj/kg for moist air vs. 5.0 kj/kg for dry air, or a ratio of 1:3.7. Similar ratios apply at all air temperatures above 0C. Subzero air, like that in the Arctic most of the year, shows little difference in heat content between dry or saturated, since cold air doesn’t hold much water vapor. See Arctic Amplication?

One implication is that polar air temperatures lacking moisture are 2-3 times more volatile, leading to the “Arctic Amplication” effect. Even so, a thorough look into weather station records around the Arctic circle undermines fears on that account. See Arctic Warming Unalarming.

The larger point made by Pielke Sr. was that a much better proxy for global warming or cooling is provided by SSTs. Measuring temperature changes in the water itself is a much better idea, giving a more exact indication of changes in heat content. There is also the point that SSTs cover 71% of the planet surface.

Andrews knows well the difficulties in assembling SST datasets, including the bucket era and the engine intake era. He addresses directly the problematic WWII measurements, suggesting they can simply be excluded as bad data without affecting the pattern. He also compares the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) raw global SST series used to generate the global HadSST3 series, which is the most widely cited of the currently-published SST series.

There he finds that prior to 1940, there was systematic warming adjustments making HadSST temps higher than ICOADS. He attributes this to the long-standing belief that Night Marine Air Temperatures (NMATs) should synchronize with SSTs. That assumes that air moisture over the water should be fairly consistent from one location to another, and that marine air would be in thermal equilibrium with the water.

But apparently no studies have proven that assumption. I know of one empirical study of the ocean-air interface which shows considerable fluctuation in both the heat exchange and evaporation rates. See Empirical Evidence: Oceans Make Climate

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

Thanks to Roger’s work on this, we can conclude that SSTs prior to 1950 have issues, but can be encouraged that HadSST3 since then is reasonably consistent with the raw data. And in the future the ARGO record will become long enough for us to follow the trends.

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

Summary

The best context for understanding global temperature effects in recent years 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 features these decades.

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

 

Man Made Warming from Adjusting Data

trends and strings

Roger Andrews does a thorough job analyzing the effects of adjustments upon Surface Air Temperature (SAT) datasets. His article at Energy Matters is Adjusting Measurements to Match the Models – Part 1: Surface Air Temperatures. Excerpts of text and some images are below.  The whole essay is informative and supports his conclusion:

In previous posts and comments I had said that adjustments had added only about 0.2°C of spurious warming to the global SAT record over the last 100 years or so – not enough to make much difference. But after further review it now appears that they may have added as much as 0.4°C.

For example, these graphs show warming of the GISS dataset:

Figure 2: Comparison of “Old” and “Current” GISS meteorological station surface air temperature series, annual anomalies relative to 1950-1990 means

The current GISS series shows about 0.3°C more global warming than the old version, with about 0.2°C more warming in the Northern Hemisphere and about 0.5°C more in the Southern. The added warming trends are almost exactly linear except for the downturns after 2000, which I suspect (although can’t confirm) are a result of attempts to track the global warming “pause”. How did GISS generate all this extra straight-line warming? It did it by replacing the old unadjusted records with “homogeneity-adjusted” versions.

The homogenization operators used by others have had similar impacts, with Berkeley Earth Surface Temperature (BEST) being a case in point. Figure 3, which compares warming gradients measured at 86 South American stations before and after BEST’s homogeneity adjustments (from Reference 1) visually illustrates what a warming-biased operator does at larger scales. Before homogenization 58 of the 86 stations showed overall warming, 28 showed overall cooling and the average warming trend for all stations was 0.54°C/century. After homogenization all 86 stations show warming and the average warming trend increases to 1.09°C/century:

Figure 3: Warming vs. cooling at 86 South American stations before and after BEST homogeneity adjustments

The adjusted “current” GISS series match the global and Northern Hemisphere model trend line gradients almost exactly but overstate warming relative to the models in the Southern (although this has only a minor impact on the global mean because the Southern Hemisphere has a lot less land and therefore contributes less to the global mean than does the Northern). But the unadjusted “old” GISS series, which I independently verified with my own from-scratch reconstructions, consistently show much less warming than the models, confirming that the generally good model/observation match is entirely a result of the homogeneity adjustments applied to the raw SAT records.

 

Summary

In this post I have chosen to combine a large number of individual examples of “data being adjusted to match it to the theory” into one single example that blankets all of the surface air temperature records. The results indicate that warming-biased homogeneity adjustments have resulted in current published series overestimating the amount by which surface air temperatures over land have warmed since 1900 by about 0.4°C (Table 1), and that global surface air temperatures have increased by only about 0.7°C over this period, not by the ~1.1°C shown by the published SAT series.

Land, however, makes up only about 30% of the Earth’s surface. The subject of the next post will be sea surface temperatures in the oceans, which cover the remaining 70%. In it I will document more examples of measurement manipulation malfeasance, but with a twist. Stay tuned.

Footnote:

I have also looked into this issue by analyzing a set of US stations considered to have the highest CRN rating.  The impact of adjustments was similarly evident and in the direction of warming the trends.  See Temperature Data Review Project: My Submission

 

Ocean Cools and Air Temps Follow

June Sea Surface Temperatures (SSTs) are now available, and we can see ocean temps dropping further after a short pause and resuming the downward trajectory from the previous 12 months.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.

The chart below shows the last two years of SST monthly anomalies as reported in HadSST3 including June 2017.

In May despite a slight rise in the Tropics, declines in both hemispheres and globally caused SST cooling to resume after an upward bump in April.  Now in June a large spike upward in NH was overcome by an even larger drop in SH, now three months into a cooling phase. The Tropics also cooled off so the Global anomaly continued to decline.  Presently NH and SH are both changing strongly but in opposite directions.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back to its beginning level. Secondly, the Northern Hemisphere added two bumps on the shoulders of Tropical warming, with peaks in August of each year. Also, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one. Note that June 2017 matches closely to June 2015, with almost the same anomalies for NH, SH and Global.  The Tropics are lower now and trending down compared to an upward trend in 2015.

June satellite measures of air over the land and oceans also shows a sharp drop.  The graph below provides UAH vs.6 TLT (lower troposphere temps) confirming the general impression from SSTs.

In contrast with SST measurements, air temps in the TLT upticked in May with all areas participating in the rise of almost 0.2C.  Then in June SH dropped 0.4C, NH down 0.2C while the Tropics declined slightly. The end result has all areas back to March values except for the Tropics.  June 2017 compares closely with July 2015 but with no signs of an impending El Nino.

We have seen lots of claims about the temperature records for 2016 and 2015 proving dangerous man made warming.  At least one senator stated that in a confirmation hearing.  Yet HadSST3 data for the last two years show how obvious is the ocean’s governing of global average temperatures.

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

The best context for understanding these two years comes from the world’s sea surface temperatures (SST), for several reasons:

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

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

 

Climate Compilation Part I Temperatures

Background

When first investigating the global warming/climate change issue (beginning around the Copenhagen COP in 2008), my interest arose from reading various claims repeated ad nauseum without any other viewpoints expressed. Searching on the web revealed that indeed other researchers had different, sometimes nuanced and sometime outright contradictory findings.

In 2015 while signing up on WordPress to be able to comment on Climate Etc., I was surprised to find that the process left me with my own blogsite. So I began to put up posts of researches I had done, some were data analyses of my own, and others were discussions of analyses done by others. It has always been a niche project intending to provide information and a broader context related to climate science claims, for the sake of others who might be interested but lacked the time or energy to dig in the weeds for the all-important details.

Lately we have a sea change in the discourse around global warming/climate change. The Paris accord and the subsequent US withdrawal from it, along with the tumult around Trump’s presidency, Brexit, the broken electricity grid in Australia, have all shifted the focus from scientific discrepancies to policy questions.

It pleases me that in this current media setting, diverse and skeptical voices are more easily heard by those with inquiring minds who want to know. For example, Master Resource blog provides expert analyses on energy issues such as subsidies and renewables challenges. Other well-known blogs such as WUWT and Notalotofpeopleknowthat are actively addressing exaggerations and bogus claims by activists.

It also looks more likely that we will be treated to an official investigation into the EPA case for CO2 endangerment. Some studies by prominent skeptics are appearing as resources in that context.

So, there are many others rebutting unfounded claims, and less need for me to write such posts. It is also the case that this blog already contains multiple posts on almost all the issues that continue to be raised. This is the first of a series pointing out resources compiled here.

1.Temperature Trend Analysis

This category of posts (title above is link to posts) started some years ago when Dave from California commented on a thread at WUWT:  “I am an actuary not a climate scientist, but it seems to me if you want to know about temperature changes, you should study the changes not the temperatures.”  That rang my bell and suddenly things came together. JR Wakefield studied the change derivatives (slopes) of temperature changes at individual weather stations in Ontario. Lubos Motl did a similar analysis using monthly trends over station lifetimes as a basis for compiling global trends–no anomalies, no adjustments or homogenization.

I termed this technique Temperature Trend Analysis (TTA) and applied it to a set of station records in Kansas and the report was published at WUWT in 2014 with the title Do-It-Yourself Climate Analysis

Richard Mallet and I then collaborated on a study of the 25 best stations in the world (longest continuous records) also published at WUWT as Analyzing Temperature Changes Using World Class Stations. Later on I applied TTA to US stations classified as CRN #1 and then assessed the differences between adjusted and unadjusted datasets. The results were published at No Tricks Zone and then posted as Temperature Data Review Project-My Submission.

Just this week we have a thorough and professional report on the systematic corruption of the land station records by climate authorities:  On the Validity of NOAA, NASA and Hadley CRU Global Average Surface Temperature Data & The Validity of EPA’s CO2 Endangerment Finding  From the Report:

This research sought to validate the current estimates of Global Average Surface Temperature (GAST) using the best available relevant data. The conclusive findings were that the three GAST data sets are not a valid representation of reality. In fact, the magnitude of their historical data adjustments which removed their cyclical temperature patterns are totally inconsistent with published and credible U.S. and other temperature data.  Thus, despite current claims of record setting warming, it is impossible to conclude from the NOAA, NASA and Hadley CRU GAST data sets that recent years have been the warmest ever.

Additional studies included an analysis of Temperatures According to Climate Models

One post provided a visual synopsis why global warming claims are not supported by temperature records. See The Climate Story (Illustrated)

Climate Roller Coaster

Behemoth, Canada’s Wonderland, Ontario   Behemoth’s bright yellow-and-blue steel stands out against the Ontario landscape. At one point the roller coaster, which opened in 2008, drops 230 feet at a 75-degree angle and hits speeds of 77 mph. Its open-air seating gives every rider a front seat to the action.

Roller coasters came to mind when reading recent studies addressing the global warming hiatus this century. For example: The global warming hiatus – a natural product of interactions of a secular warming trend and a multi-decadal variability by Shuai-Lei Yao, Gang Huang, Ren-Guang and WuXia Qu.

Abstract:

The globally-averaged annual combined land and ocean surface temperature (GST) anomaly change features a slowdown in the rate of global warming in the mid-twentieth century and the beginning of the twenty-first century. Here, it is shown that the hiatus in the rate of global warming typically occurs when the internally generated cooling associated with the cool phase of the multi-decadal variability overcomes the secular warming from human-induced forcing.

We provide compelling evidence that the global warming hiatus is a natural product of the interplays between a secular warming tendency due in a large part to the buildup of anthropogenic greenhouse gas concentrations, in particular CO2 concentration, and internally generated cooling by a cool phase of a quasi-60-year oscillatory variability that is closely associated with the Atlantic multi-decadal oscillation (AMO) and the Pacific decadal oscillation (PDO). We further illuminate that the AMO can be considered as a useful indicator and the PDO can be implicated as a harbinger of variations in global annual average surface temperature on multi-decadal timescales.

Our results suggest that the recent observed hiatus in the rate of global warming will very likely extend for several more years due to the cooling phase of the quasi-60-year oscillatory variability superimposed on the secular warming trend.

CO2 sceptics have proposed similar explanations for the global temperature pattern, but were ignored heretofore. For example Syun-Ichi Akasofu,
Two Natural Components of the Recent Climate Change:
(1) The Recovery from the Little Ice Age  (A Possible Cause of Global Warming) and
(2) The Multi-decadal Oscillation  (The Recent Halting of the Warming):

Note that the hypothesis is virtually the same, except for the leap of faith to attribute the secular background rise to CO2, rather than to a steady recovery from the Little Ice Age (LIA).  Finally climate modelers are admitting that natural variability is strong enough to offset warming from any other means.  And by extension the rise in temperatures late last century was due in large measure to a warming natural phase.

(Aside: “Secular” has two main meanings:
a : of or relating to the worldly or temporal secular concerns, not overtly or specifically religious
b : of or relating to a long term of indefinite duration, existing or continuing through ages or centuries
How ironic that some climate scientists use the term “secular” while applying a faith-based attribution.)

Nicola Scafetta is another scientist asserting a solar-lunar cyclical climate pattern based on oscillations within the solar system. More at Scaffetta vs. IPCC: Dueling Climate Theories

cooling-vs-warming-forecasts-scafetta-2017

The Thrill of Riding the Climate Roller Coaster

The original amusement park roller coasters had a single ratcheting up an incline to the top, with gravity pulling the train down to the bottom through a series of curving sine wave peaks and valleys. Newer rides like the one at Wonderland have more than one ratcheting upward to start a new decline. A recent paper explains how this additional excitement operates in our climate system.

Reconciling the signal and noise of atmospheric warming on decadal timescales by Roger N. Jones and James H. Ricketts Victoria University, Melbourne, Australia was published March 16, 2017 in Earth System Dynamics. From the abstract:

Interactions between externally forced and internally generated climate variations on decadal timescales is a major determinant of changing climate risk. Severe testing is applied to observed global and regional surface and satellite temperatures and modelled surface temperatures to determine whether these interactions are independent, as in the traditional signal-to-noise model, or whether they interact, resulting in step-like warming. The multistep bivariate test is used to detect step changes in temperature data. The resulting data are then subject to six tests designed to distinguish between the two statistical hypotheses, hstep and htrend.

record-of-mean-annual-surface-temperature-anomalies-1880-2014-from-the-hadley-centre-and

Figure 1. Record of mean annual surface temperature anomalies 1880–2014 from the Hadley Centre and Climate Research Unit (HadCRU), showing step changes (p < 0.01) and internal trends and shifts taken from the end of one internal trend to the start of the next across a step.

Test 1: since the mid-20th century, most observed warming has taken place in four events: in 1979/80 and 1997/98 at the global scale, 1988/89 in the Northern Hemisphere and 1968–70 in the Southern Hemisphere. Temperature is more step-like than trend-like on a regional basis. Satellite temperature is more step-like than surface temperature. Warming from internal trends is less than 40 % of the total for four of five global records tested (1880–2013/14).

Test 2: correlations between step-change frequency in observations and models (1880–2005) are 0.32 (CMIP3) and 0.34 (CMIP5). For the period 1950–2005, grouping selected events (1963/64, 1968–70, 1976/77, 1979/80, 1987/88 and 1996–98), the correlation increases to 0.78.

Test 3: steps and shifts (steps minus internal trends) from a 107-member climate model ensemble (2006–2095) explain total warming and equilibrium climate sensitivity better than internal trends.

Test 4: in three regions tested, the change between stationary and non-stationary temperatures is step-like and attributable to external forcing.

Test 5: step-like changes are also present in tide gauge observations, rainfall, ocean heat content and related variables.

Test 6: across a selection of tests, a simple stepladder model better represents the internal structures of warming than a simple trend, providing strong evidence that the climate system is exhibiting complex system behaviour on decadal timescales.

This model indicates that in situ warming of the atmosphere does not occur; instead, a store-and-release mechanism from the ocean to the atmosphere is proposed. It is physically plausible and theoretically sound. The presence of step-like – rather than gradual – warming is important information for characterising and managing future climate risk. (my bold)

Summary

The climate roller coaster is thrilling because we can’t see the track ahead for certain. Are we coming off a major peak and heading down into a deep valley? (Scafetta) Or is this a small dip before heading up again? (Yao et al.) Or are we hitting the top of the recovery from 1850 and starting into the next (hopefully little) ice age as signaled by the quiet sun (Akasofu, Abdussamatov)?

Daily sun June 27, 2017 with sunspot 2664.

See Also: Wave Drowns CO2 Warming