Summer “Hothouse” Silliness

This summer’s heat waves are having an unfortunate side effect. Some scientists who should know better are shouting wild claims as though their heads were exploding.  Paleoclimatologists use terms like “Hothouse” Earth and “Icehouse” Earth referring to our planet’s climate shifts over many eons.  One good old-fashioned hot summer is not a transition, or even an harbinger of an “Hothouse” world.  More importantly, the distribution of temperatures in a warmer world is not the hell on earth depicted by these folks who have lost their bearings.

A powerful post by Clive Best describes how earth’s surface temperatures change by means of changing meridional heat transfers. See Meridional Warming.

The key point for me was seeing how the best geological knowledge proves beyond the shadow of a doubt how the earth’s climate profile shifts over time, as presented in the diagram above.  It comes from esteemed paleoclimatologist Christopher Scotese.  His compete evidence and analysis can be reviewed in his article Some thoughts on Global Climate Change: The Transition from Icehouse to Hothouse (here).

In that essay Scotese shows where we are presently in this cycle between icehouse and hothouse.

As of 2015 earth is showing a GMT of 14.4C, compared to pre-industrial GMT of 13.8C.  According to the best geological evidence from millions of years of earth’s history, that puts us leaving the category “Severe Icehouse,” and nearing “Icehouse.”  So, thankfully we are warming up, albeit very slowly.

Moreover, and this is Clive Best’s point, progress toward a warming world means flattening the profile at the higher latitudes, especially the Arctic.  Equatorial locations remain at 23C throughout the millennia, while the gradient decreases in a warmer world.

A previous related post explained what is wrong with averaging temperature anomalies.  See Temperature Misunderstandings

Conclusion:

We have many, many centuries to go before the earth can warm up to the “Greenhouse” profile, let alone get to “Hothouse.”  Regional and local climates at higher latitudes will see slightly warming temperatures and smaller differences from equatorial climates.  These are facts based on solid geological evidence, not opinions or estimates from computer models.

It is still a very cold world, but we are moving in the right direction.  Stay the course.

Meanwhile, keep firing away Clive.

damaged-ship3

 

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Ocean Air Temps Tepid in July

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.  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?

The July update to HadSST3 will appear later this month, but in the meantime we can look at lower troposphere temperatures (TLT) from UAHv6 which are already posted for July. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above.

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 temps since January 2015.

UAH Oceans 201807The anomalies are holding close to the same levels as 2015. In July, both the Tropics and SH rose, while NH rose very slightly, resulting in a small increase in the Global average of air over oceans. Taking a longer view, we can look at the record since 1995, that year being an ENSO neutral year and thus a reasonable starting point for considering the past two decades.  On that basis we can see the plateau in ocean temps is persisting. Since last October all oceans have cooled, with offsetting bumps up and down.

UAHv6 TLT 
Monthly Ocean
Anomalies
Average Since 1995 Ocean 7/2018
Global 0.13 0.21
NH 0.16 0.3
SH 0.11 0.15
Tropics 0.13 0.29

As of July 2018, global ocean temps are slightly higher than June and the average since 1995.  NH remains virtually the same,  while both SH and Tropics rose making the global temp warmer.  Global, NH and SH are matching July temps in 2015, while the Tropics are the lowest July since 2013.

The details of UAH ocean temps are provided below.  The monthly data make for a noisy picture, but seasonal fluxes between January and July are important.

Open image in new tab to enlarge.

The greater volatility of the Tropics is evident, leading the oceans through three major El Nino events during this period.  Note also the flat period between 7/1999 and 7/2009.  The 2010 El Nino was erased by La Nina in 2011 and 2012.  Then the record shows a fairly steady rise peaking in 2016, with strong support from warmer NH anomalies, before returning to the 22-year average.

Summary

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  They started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

Ocean Air Temps Keep Cool

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.  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?

The June update to HadSST3 will appear later this month, but in the meantime we can look at lower troposphere temperatures (TLT) from UAHv6 which are already posted for June. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. The graph below shows monthly anomalies for ocean temps since January 2015.

The anomalies are holding close to the same levels as 2015. In June, both the Tropics and SH rose, while NH declined slightly, resulting in a small increase in the Global average of air over oceans. Taking a longer view, we can look at the record since 1995, that year being an ENSO neutral year and thus a reasonable starting point for considering the past two decades.  On that basis we can see the plateau in ocean temps is persisting. Since last October all oceans have cooled, with offsetting bumps up and down.

UAHv6 TLT 
Monthly Ocean
Anomalies
Average Since 1995 Ocean 6/2018
Global 0.13 0.14
NH 0.16 0.28
SH 0.11 0.03
Tropics 0.12 0.11

As of June 2018, global ocean temps are slightly higher than May and close to the average since 1995.  NH remains higher, but not enough to offset much lower temps in SH and  nearly average Tropics (between 20N and 20S latitudes).  Global ocean air temps are matching the last two March temps, but are the lowest June temps since 2012.  Both NH and SH are the lowest June temps since 2014.

The details of UAH ocean temps are provided below.  The monthly data make for a noisy picture, but seasonal fluxes between January and July are important.

Open image in new tab to enlarge.

The greater volatility of the Tropics is evident, leading the oceans through three major El Nino events during this period.  Note also the flat period between 7/1999 and 7/2009.  The 2010 El Nino was erased by La Nina in 2011 and 2012.  Then the record shows a fairly steady rise peaking in 2016, with strong support from warmer NH anomalies, before returning to the 22-year average.

Summary

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  They started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

2018 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 (2017) 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

2017 statistics are now available from BP for international consumption of Primary Energy sources. 2018 Statistical Review of World Energy. 

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 2017.

WFFC2017

The graph shows that Primary Energy consumption has grown continuously for 5 decades. Over that period oil, gas and coal (sometimes termed “Thermal”) averaged 89% of PE consumed, ranging from 94% in 1965 to 85% in 2017.  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 2017 from HADCRUT4, which includes HadSST3.

WFFC&GMT2017

Over the last five decades the increase in fossil fuel consumption is dramatic and monotonic, steadily increasing by 227% from 3.5B to 11.5B oil equivalent tons.  Meanwhile the GMT record from Hadcrut shows multiple ups and downs with an accumulated rise of 0.9C over 52 years, 6% of the starting value.

The second graph compares to GMT estimates from UAH6, and HadSST3 for the satellite era from 1979 to 2017, a period of 38 years.

WFFC&UAH&HAD2017

In the satellite era WFFC has increased at a compounded rate of nearly 2% per year, for a total increase of 87% since 1979. At the same time, SST warming amounted to 0.44C, or 3.1% of the starting value.  UAH warming was 0.58C, or 4.2% 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.

Cooling Ocean Air Temps

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.  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?

The May update to HadSST3 will appear later this month, but in the meantime we can look at lower troposphere temperatures (TLT) from UAHv6 which are already posted for May. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. The graph below shows monthly anomalies for ocean temps since January 2015.

UAH May2018

Open image in new tab to enlarge.

The anomalies have reached the same levels as 2015.  Taking a longer view, we can look at the record since 1995, that year being an ENSO neutral year and thus a reasonable starting point for considering the past two decades.  On that basis we can see the plateau in ocean temps is persisting. Since last October all oceans have cooled, with upward bumps in Feb. 2018, now erased.

UAHv6 TLT 
Monthly Ocean
Anomalies
Average Since 1995 Ocean 5/2018
Global 0.13 0.09
NH 0.16 0.33
SH 0.11 -0.09
Tropics 0.12 0.02

As of May 2018, global ocean temps are slightly lower than April and below the average since 1995.  NH remains higher, but not enough to offset much lower temps in SH and Tropics (between 20N and 20S latitudes).  Global ocean air temps are now the lowest since April 2015, and SH the lowest since May 2013.

The details of UAH ocean temps are provided below.  The monthly data make for a noisy picture, but seasonal fluxes between January and July are important.

Click on image to enlarge.

The greater volatility of the Tropics is evident, leading the oceans through three major El Nino events during this period.  Note also the flat period between 7/1999 and 7/2009.  The 2010 El Nino was erased by La Nina in 2011 and 2012.  Then the record shows a fairly steady rise peaking in 2016, with strong support from warmer NH anomalies, before returning to the 22-year average.

Summary

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  They started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

Climate Canary? N. America Cooling

Hidden amid reports of recent warmest months and years based on global averages, there is a significant departure in North America. Those of us living in Canada and USA have noticed a distinct cooling, and our impressions are not wrong.

The image above shows how much lower have been April 2018 temperatures. The table below provides the numbers behind the graphs from NOAA State of the Climate.

CONTINENT ANOMALY (1910-2000) TREND (1910-2018) RANK RECORDS
°C °F °C °F (OUT OF 109 YEARS) YEAR(S) °C °F
North America -0.97 -1.75 0.11 0.19 Warmest 94ᵗʰ 2010 2.65 4.77
South America 1.34 2.41 0.13 0.24 Warmest 1ˢᵗ 2018 1.34 2.41
Europe 2.82 5.08 0.14 0.25 Warmest 1ˢᵗ 2018 2.82 5.08
Africa 1.23 2.21 0.12 0.22 Warmest 5ᵗʰ 2016 1.72 3.1
Asia 1.66 2.99 0.18 0.32 Warmest 9ᵗʰ 2016 2.4 4.32
Oceania 2.47 4.45 0.14 0.25 Warmest 2ⁿᵈ 2005 2.54 4.57

The table shows how different was the North American experience: 94th out of 109 years.  But when we look at the first four months of the year, the NA is more in line with the rest of the globe.

 

As the image shows, cooling was more widespread during the first third of 2018, particularly in NA, Northern Europe and Asia, as well as a swath of cooler mid ocean latitudes in the Southern Hemisphere.

CONTINENT ANOMALY (1910-2000) TREND (1910-2018) RANK RECORDS
°C °F °C °F (OUT OF 109 YEARS) YEAR(S) °C °F
North America 0.44 0.79 0.16 0.29 Warmest 44ᵗʰ 2016 2.71 4.88
South America 0.94 1.69 0.13 0.24 Warmest 6ᵗʰ 2016 1.39 2.5
Europe 1.35 2.43 0.13 0.24 Warmest 13ᵗʰ 2014 2.46 4.43
Africa 1.08 1.94 0.1 0.18 Warmest 3ʳᵈ 2010 1.62 2.92
Asia 1.57 2.83 0.19 0.34 Warmest 8ᵗʰ 2002 2.72 4.9
Oceania 1.58 2.84 0.12 0.22 Warmest 1ˢᵗ 2018 1.58 2.84

The table confirms that Europe and Asia are cooler in 2018 than recent years in the decade.

Summary

These data show again that temperature indicators of climate are not global but regional, and even local in their manifestations.  At the continental level there are significant differences.  North America is an outlier, but who is to say whether it is an aberration that will join the rest, or whether it is the trend setter signaling a widespread cooler future.

See Also:  Is This Cold the New Normal?

CanAm Bucks the Trend

Hidden amid reports of recent warmest months and years based on global averages, there is a significant departure in North America. Those of us living in Canada and USA have noticed a distinct cooling, and our impressions are not wrong.

The image above shows how much lower have been April 2018 temperatures. The table below provides the numbers behind the graphs from NOAA State of the Climate.

CONTINENT ANOMALY (1910-2000) TREND (1910-2018) RANK RECORDS
°C °F °C °F (OUT OF 109 YEARS) YEAR(S) °C °F
North America -0.97 -1.75 0.11 0.19 Warmest 94ᵗʰ 2010 2.65 4.77
South America 1.34 2.41 0.13 0.24 Warmest 1ˢᵗ 2018 1.34 2.41
Europe 2.82 5.08 0.14 0.25 Warmest 1ˢᵗ 2018 2.82 5.08
Africa 1.23 2.21 0.12 0.22 Warmest 5ᵗʰ 2016 1.72 3.1
Asia 1.66 2.99 0.18 0.32 Warmest 9ᵗʰ 2016 2.4 4.32
Oceania 2.47 4.45 0.14 0.25 Warmest 2ⁿᵈ 2005 2.54 4.57

The table shows how different was the North American experience: 94th out of 109 years.  But when we look at the first four months of the year, the NA is more in line with the rest of the globe.

 

As the image shows, cooling was more widespread during the first third of 2018, particularly in NA, Northern Europe and Asia, as well as a swath of cooler mid ocean latitudes in the Southern Hemisphere.

CONTINENT ANOMALY (1910-2000) TREND (1910-2018) RANK RECORDS
°C °F °C °F (OUT OF 109 YEARS) YEAR(S) °C °F
North America 0.44 0.79 0.16 0.29 Warmest 44ᵗʰ 2016 2.71 4.88
South America 0.94 1.69 0.13 0.24 Warmest 6ᵗʰ 2016 1.39 2.5
Europe 1.35 2.43 0.13 0.24 Warmest 13ᵗʰ 2014 2.46 4.43
Africa 1.08 1.94 0.1 0.18 Warmest 3ʳᵈ 2010 1.62 2.92
Asia 1.57 2.83 0.19 0.34 Warmest 8ᵗʰ 2002 2.72 4.9
Oceania 1.58 2.84 0.12 0.22 Warmest 1ˢᵗ 2018 1.58 2.84

The table confirms that Europe and Asia are cooler in 2018 than recent years in the decade.

Summary

These data show again that temperature indicators of climate are not global but regional, and even local in their manifestations.  At the continental level there are significant differences.  North America is an outlier, but who is to say whether it is an aberration that will join the rest, or whether it is the trend setter signaling a widespread cooler future.

Plateau in Ocean Air Temps

Years ago, Dr. Roger Pielke Sr. explained why sea surface temperatures (SST) were the best 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.

More recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  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?

The April update to HadSST3 will appear later this month, but in the meantime we can look at lower troposphere temperatures (TLT) from UAHv.6 which are already posted for April. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. The graph below shows monthly anomalies for ocean temps since January 2015.
The anomalies have reached the same levels as 2015.  Taking a longer view, we can look at the record since 1995, that year being an ENSO neutral year and thus a reasonable starting point for considering the past two decades.  On that basis we can see the plateau in ocean temps is persisting. Since last October all oceans have cooled, with upward bumps in Feb. 2018, now erased.

UAHv.6 TLT 
Monthly Ocean Anomalies
Ave. Since 1995 Ocean 4/2018
Global 0.13 0.11
NH 0.16 0.27
SH 0.11 -0.01
Tropics 0.12 -0.1

As of April 2018, global ocean temps are slightly below the average since 1995.  NH remains higher, but not enough to offset much lower temps in SH and Tropics (between 20N and 20S latitudes).

The details of UAH ocean temps are provided below.  The monthly data make for a noisy picture, but seasonal fluxes between January and July are important.

Click on image to enlarge.

The greater volatility of the Tropics is evident, leading the oceans through three major El Nino events during this period.  Note also the flat period between 7/1999 and 7/2009.  The 2010 El Nino was erased by La Nina in 2011 and 2012.  Then the record shows a fairly steady rise peaking in 2016, with strong support from warmer NH anomalies, before returning to the 22-year average.

Summary

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  They started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

Fossil Fuels ≠ Global Warming Updated

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

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.

Source: Real Climate Science

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.