Grid Stability Basics

Charles asked whether I would critique this new nonsense .
Sure , why not .
The highlighted ‘ sciency ’ article purports to create an ‘ open source ’ grid data resource using 17 ‘ synchronous grid ’ European regions to design and test how “ that will help develop new power systems capable of meeting changing demands , such as the move towards renewable energy sources.
” NOPE , this won ’ t help at all .
The body of this silly paper found observationally lower grid frequency variations in larger ‘ isolated ’ European subgrids !
Of course @ @ @ @ @ @ @ trivially true and known for many decades .
The reasons are intuitive and simple .
Larger grids by definition have more generation feeding the grid , so more grid inertia , and at the same time less ‘ instantaneous load variation ’ by simple virtue of the statistical law of large numbers .
Grid inertia is just the frequency stabilization that comes from the kinetic energy of large rotating generator masses .
As grid load ‘ instantly ’ increases , the frequency sags and the generator wants to slow down .
But by @ @ @ @ @ @ @ injects some of its rotational kinetic energy until the driving source ( steam , or natural gas in CCGT ) can be turned up a bit to compensate .
CCGT is particularly good at this .
It runs about 61 % efficient at full load , 60 % efficient at 80 % load , and still 59 % efficient at just 40 % load .
The gas turbine power reaction time is literally about a second no different than a jet engine .
None of this ‘ open source data resource ’ helps @ @ @ @ @ @ @ Basic grid electrical engineering ( EE ) is simple and inescapable concerning renewables— they are uneconomic ( investment always falls when subsidies dry up ) , intermittent ( requiring underutilized backup generation ) , and provide no grid inertia ( which is automatic with big fossil fuel fired generators ) .
ALL BAD .
Irsching Bavaria ‘ s about 10 year old CCGT units 4 ( 375MW ) and 5 ( 860MW ) illustrate the problems that intermittent renewables lacking grid inertia create for any grid .
The owner wants to take them @ @ @ @ @ @ @ run mainly as wind backup rather than grid load .
The German government refuses to make up the difference , yet also refuses to let them shut down .
In the bigger German renewable picture , northern Germany exports surplus wind power to Norway as essentially a giveaway .
That lets Norway throttle back its hydro .
Then , when Germany needs power because wind is insufficient , Norway spools up its hydropower and sells it to Germany at exorbitant rates .
To make matters worse , the German NIMBY crowd won ’ @ @ @ @ @ @ @ needed North-South German transmission interconnect ‘ eyesores ’ —despite wind turbines that are bigger and uglier .
A small amount of renewables in a large stable grid presents no problems–and no need for this new study .
The backup capacity ( spinning reserve ) and grid inertia are already there .
But as renewable penetration grows as a proportion of any stable ‘ synchronous ’ grid , these issues grow and compound .
Depending on grid details ( like how much flexible hydro ) , renewable penetration above about 8-10 % ALWAYS creates @ @ @ @ @ @ @ current grid engineering has been known now for well over 100 years , and nothing ‘ new ’ can change the established maths , physics , and electrical engineering ( EE ) .
There is also some ‘ basic EE stuff ’ this new ‘ open source data resource ’ ignored .
First , their ‘ new ’ grid frequency data is almost always available from the utilities ( grid operators ) , since required to manage the grid .
It is captured both at the generating plants and at the transmission to @ @ @ @ @ @ @ just asked for the utility data rather than gathering their own .
Second , their super duper ‘ proprietary ’ grid frequency detectors for capturing their new open source data can be purchased almost anywhere .
Such detectors are a common electrician ’ s tool .
A very high-end multipurpose ‘ pocket ’ unit is the Extech PQ3450 power quality analyzer , pictured .
Third , their finding that grid frequency sags when demand exceeds supply and rises when supply exceeds demand has been known since Tesla first envisioned AC generation by using @ @ @ @ @ @ @ US is designed to be a stable 60 Hertz , EU 50 Hertz .
Because electric clocks depend on grid frequency , ‘ stable ’ is about plus/minus 0.2Hz in the US .
Just for fun ( because the above is all well trod ground at WUWT and Climate Etc ) , we also provide a simple math/science fun challenge to those at WUWT unfamiliar with AC electrical stuff .
( Thanks to a physics book full of hundreds of such challenges , sadly in Chicago rather than Fort Lauderdale , which I @ @ @ @ @ @ @ Google ‘ facts ’ ) .
It is about right .
Imagine a high voltage AC transmission line between Chicago and New York .
( Hint : the distance does not matter much for this challenge , voltage does some , but is always ‘ high ’ . )
The challenge : how far does a single electron charge travel during its journey along this transmission line ?
Choices : The speed of light ( aka electromagnetic radiation ) is about 299792458 meters/second in a vacuum .
But ‘ electromagnetic force ( emf @ @ @ @ @ @ @ not vacuum ; their metals have electrical resistance ( technically grid AC impedance , a combination of circuit resistance and reactance ) .
Although electrons pushed along by emf have essentially no mass , they still slow down lots in metals .
Skipping a bit of complex math explaining very simple physics , the reason you buy ‘ fat ’ DC audio cables with gold terminals is simple : DC current flows across the entire conductor cross section fairly uniformly .
A fatter cable has uniformly less cross sectional resistance ( DC speaker @ @ @ @ @ @ @ terminals lower connection resistance further since gold doesn ’ t corrode .
The result is truer high frequency pitch amplitudes .
As a side factoid , this same physics is why ‘ fat ’ high voltage DC transmission lines are preferred for long grid distances , like the Germany to Norway undersea interconnects mentioned above .
The ‘ fat ’ AC cable answer is different .
AC current has a conductor skin effect .
Unlike DC current , AC current travels mainly in an annular ‘ skin ’ ring whose thickness depends on @ @ @ @ @ @ @ AC consequences of Maxwell ’ s equations as explained by Feynman ’ s ‘ Lectures on Physics ’ V. 2 chapter 32 , ’ skin effect ’ .
In a ‘ pure ’ copper conductor , that annular skin travel ( the center of the conductor essentially carries no AC current ) is only about 3.2 meters per second .
The much more common ( cheaper , lighter ) aluminum HV transmission conductor is only about 61 % of copper .
So in aluminum high voltage transmission lines electrons travel at best about @ @ @ @ @ @ @ oxidation .
AC is a sine wave varying from 0 to plus emf to 0 to minus emf to 0 at x times a second , ( US 60Hz , EU 50Hz ) .
One cycle is from zero voltage to max emf to zero to opposite polarity max emf back to zero .
The emf back and forth is a nominal US 120 peaks/second , 60 times in each direction .
( Side note : unlike most DC circuits , AC emf is related to , but not the same as , @ @ @ @ @ @ @ AC challenge as simply digital rather than sine wave ( all on or all off emf rather than a varying emf sine wave ) , thereby removing any need for the correct calculus challenge formulation .
Then the most distance a virtually massless electron could travel in its aluminum high voltage skin is about ( 1.95/60 ) 3.2 cm , back and forth and back and forth .
The actual ( sine wave emf calculus ) US answer is less than one centimeter back and forth , and EU less than 2 .
@ @ @ @ @ @ @ a lot of emf except near the peak and trough of the sine wave , slowing down intermediate time things further .
Either answer ( over simplified or correct ) results in not very much electron travel distance ( c ) .
Returning to the main topic of this guest post , despite this new European ‘ grid research ’ : Anywhere , anytime , renewable subsidies get reduced , so does investment in them .
They are provably uneconomic , stand alone .
Renewables are intermittent ; yet do not cover the @ @ @ @ @ @ @ no grid inertia ; yet do not cover the costs of providing frequency stability using massive synchronous condensers .
More research and new ‘ open source ’ data resources concerning fundamentally bad propositions does not improve them .
NOPE !

Guest post by Rud Istvan
Charles asked whether I would critique this new nonsense. Sure, why not.
The highlighted ‘sciency’ article purports to create an ‘open source’ grid data resource using 17 ‘synchronous grid’ European regions to design and test how “that will help develop new power systems capable of meeting changing demands, such as the move towards renewable energy sources.” NOPE, this won’t help at all.
The body of this silly paper found observationally lower grid frequency variations in larger ‘isolated’ European subgrids! Of course larger grids have less frequency variation— something trivially true and known for many decades. The reasons are intuitive and simple. Larger grids by definition have more generation feeding the grid, so more grid inertia, and at the same time less ‘instantaneous load variation’ by simple virtue of the statistical law of large numbers.
Grid inertia is just the frequency stabilization that comes from the kinetic energy of large rotating generator masses. As grid load ‘instantly’ increases, the frequency sags and the generator wants to slow down. But by slowing, it also ‘instantly’ injects some of its rotational kinetic energy until the driving source (steam, or natural gas in CCGT) can be turned up a bit to compensate.
CCGT is particularly good at this. It runs about 61% efficient at full load, 60% efficient at 80% load, and still 59% efficient at just 40% load. The gas turbine power reaction time is literally about a second no different than a jet engine.
None of this ‘open source data resource’ helps ‘the move to renewables’. Basic grid electrical engineering (EE) is simple and inescapable concerning renewables— they are uneconomic (investment always falls when subsidies dry up), intermittent (requiring underutilized backup generation), and provide no grid inertia (which is automatic with big fossil fuel fired generators). ALL BAD.
Irsching Bavaria‘s about 10 year old CCGT units 4 (375MW) and 5 (860MW) illustrate the problems that intermittent renewables lacking grid inertia create for any grid. The owner wants to take them out of service because very unprofitable when run mainly as wind backup rather than grid load. The German government refuses to make up the difference, yet also refuses to let them shut down.
In the bigger German renewable picture, northern Germany exports surplus wind power to Norway as essentially a giveaway. That lets Norway throttle back its hydro. Then, when Germany needs power because wind is insufficient, Norway spools up its hydropower and sells it to Germany at exorbitant rates. To make matters worse, the German NIMBY crowd won’t let the utilities install more much needed North-South German transmission interconnect ‘eyesores’—despite wind turbines that are bigger and uglier.
A small amount of renewables in a large stable grid presents no problems–and no need for this new study. The backup capacity (spinning reserve) and grid inertia are already there. But as renewable penetration grows as a proportion of any stable ‘synchronous’ grid, these issues grow and compound. Depending on grid details (like how much flexible hydro), renewable penetration above about 8-10% ALWAYS creates extra costs and stability problems. Alternating current grid engineering has been known now for well over 100 years, and nothing ‘new’ can change the established maths, physics, and electrical engineering (EE).
There is also some ‘basic EE stuff’ this new ‘open source data resource’ ignored.
First, their ‘new’ grid frequency data is almost always available from the utilities (grid operators), since required to manage the grid. It is captured both at the generating plants and at the transmission to distribution substations. Perhaps they should have just asked for the utility data rather than gathering their own.
Second, their super duper ‘proprietary’ grid frequency detectors for capturing their new open source data can be purchased almost anywhere. Such detectors are a common electrician’s tool. A very high-end multipurpose ‘pocket’ unit is the Extech PQ3450 power quality analyzer, pictured.
Third, their finding that grid frequency sags when demand exceeds supply and rises when supply exceeds demand has been known since Tesla first envisioned AC generation by using complex numbers (a+bi). The US is designed to be a stable 60 Hertz, EU 50 Hertz. Because electric clocks depend on grid frequency, ‘stable’ is about plus/minus 0.2Hz in the US.
Just for fun (because the above is all well trod ground at WUWT and Climate Etc), we also provide a simple math/science fun challenge to those at WUWT unfamiliar with AC electrical stuff. (Thanks to a physics book full of hundreds of such challenges, sadly in Chicago rather than Fort Lauderdale, which I half reconstructed from memory and half from Google ‘facts’). It is about right.
Imagine a high voltage AC transmission line between Chicago and New York. (Hint: the distance does not matter much for this challenge, voltage does some, but is always ‘high’.) The challenge: how far does a single electron charge travel during its journey along this transmission line? Choices:
Kilometers Meters Centimeters
The speed of light (aka electromagnetic radiation) is about 299792458 meters/second in a vacuum. But ‘electromagnetic force (emf) wave guide’ wires are definitely not vacuum; their metals have electrical resistance (technically grid AC impedance, a combination of circuit resistance and reactance). Although electrons pushed along by emf have essentially no mass, they still slow down lots in metals.
Skipping a bit of complex math explaining very simple physics, the reason you buy ‘fat’ DC audio cables with gold terminals is simple: DC current flows across the entire conductor cross section fairly uniformly. A fatter cable has uniformly less cross sectional resistance (DC speaker output is analog), and gold terminals lower connection resistance further since gold doesn’t corrode. The result is truer high frequency pitch amplitudes. As a side factoid, this same physics is why ‘fat’ high voltage DC transmission lines are preferred for long grid distances, like the Germany to Norway undersea interconnects mentioned above.
The ‘fat’ AC cable answer is different. AC current has a conductor skin effect. Unlike DC current, AC current travels mainly in an annular ‘skin’ ring whose thickness depends on frequency (higherèthinner) thanks to interesting AC consequences of Maxwell’s equations as explained by Feynman’s ‘Lectures on Physics’ V. 2 chapter 32, ’skin effect’. In a ‘pure’ copper conductor, that annular skin travel (the center of the conductor essentially carries no AC current) is only about 3.2 meters per second. The much more common (cheaper, lighter) aluminum HV transmission conductor is only about 61% of copper. So in aluminum high voltage transmission lines electrons travel at best about (3.2m/s*0.61) ~1.95 m/s ignoring skin oxidation.
AC is a sine wave varying from 0 to plus emf to 0 to minus emf to 0 at x times a second, (US 60Hz, EU 50Hz). One cycle is from zero voltage to max emf to zero to opposite polarity max emf back to zero. The emf back and forth is a nominal US 120 peaks/second, 60 times in each direction. (Side note: unlike most DC circuits, AC emf is related to, but not the same as, voltage.) For simplicity imagine this AC challenge as simply digital rather than sine wave (all on or all off emf rather than a varying emf sine wave), thereby removing any need for the correct calculus challenge formulation. Then the most distance a virtually massless electron could travel in its aluminum high voltage skin is about (1.95/60) 3.2 cm, back and forth and back and forth. The actual (sine wave emf calculus) US answer is less than one centimeter back and forth, and EU less than 2. That is because there isn’t a lot of emf except near the peak and trough of the sine wave, slowing down intermediate time things further. Either answer (over simplified or correct) results in not very much electron travel distance (c).
Returning to the main topic of this guest post, despite this new European ‘ grid research’:
Anywhere, anytime, renewable subsidies get reduced, so does investment in them. They are provably uneconomic, stand alone.
Renewables are intermittent; yet do not cover the backup costs of intermittency.
Renewables provide no grid inertia; yet do not cover the costs of providing frequency stability using massive synchronous condensers.
More research and new ‘open source’ data resources concerning fundamentally bad propositions does not improve them. NOPE!
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wattsupwiththat--2020-12-13--The Ocean Mixed Layer, SST, and Climate Change

As described in my previous post , the ocean “ mixed layer ” is sandwiched between the very thin “ skin ” layer at the ocean surface and the deep ocean .
The skin layer loses thermal energy ( “ heat ” ) to the atmosphere primarily through evaporation , gains thermal energy from the Sun during the day , and constantly attempts to come to thermal equilibrium with the atmosphere above it .
During the day , with the Sun beating down on the ocean and calm clear conditions , the skin layer might be as thick as ten @ @ @ @ @ @ @ windy conditions , it can be less than a millimeter thick .
The mixed layer is a zone where turbulence , caused by surface currents and wind has so thoroughly mixed the water that temperature , density , and salinity are relatively constant throughout the layer .
Originally , the “ mixed layer depth , ” the name for the base of the layer , was defined as the point where the temperature was 0.5°C different from the surface ( Levitus , 1982 ) .
This was found to be an inadequate definition @ @ @ @ @ @ @ because the temperature there , in certain areas , can be nearly constant to 2,000 meters , but the mixed layer turbulent zone isn ’ t that deep ( Holte & Talley , 2008 ) .
Two of the areas that create problems when picking the mixed layer depth with the 0.5°C criteria are the North Atlantic , between Iceland and Scotland and in the Southern Ocean southwest of Chile .
The areas are noted with light blue boxes in Figure 1 .
Figure 1 .
The North Atlantic and Southern Ocean areas @ @ @ @ @ @ @ difficult because of downwelling surface water during the local winter .
The regions shown in Figure 1 are areas where significant downwelling of surface water to the deep ocean takes place , these are not the only areas where this happens , but these areas often contain nearly constant temperature profiles for the upper 1,000 meters , or even deeper .
Figure 2 shows the average July temperature profile for the Southern Ocean area in Figure 1 .
Figure 2 .
A Southern Ocean July average temperature profile from the blue region shown @ @ @ @ @ @ @ to make the profile is from more than 12 years , centered on 2008 .
Data from NOAA MIMOC .
As explained by James Holte and Lynne Talley ( Holte & Talley , 2008 ) , the deep convection in this part of the Southern Ocean has distorted the temperature profile to such an extent that a simple temperature cutoff can not be used to set the mixed layer depth .
Numerous solutions to the problem have been proposed over the years and these are listed and discussed in their article .
Their @ @ @ @ @ @ @ to define the mixed layer in the NOAA MIMOC dataset discussed below ( Schmidtko , Johnson , & Lyman ) .
The Holte and Talley method is complicated , as are many of the other solutions .
It does not seem that a generally accepted methodology for defining the mixed layer has been found to date .
Depending upon location and season , the mixed layer depth changes .
It is thickest in the local winter in the higher latitudes .
There it can extend to 400 meters below the surface or farther @ @ @ @ @ @ @ and much deeper using the 0.5°C temperature cutoff .
Figure 3 shows a map of the mixed layer depth in January .
Figure 3 .
Ocean mixed layer depth in January using the Holte and Talley logic .
The oranges and reds are 400 to 500 meters .
Data from NOAA MIMOC .
However , the Northern Hemisphere mixed layer thickness thins during the northern summer months and it thickens in the Southern Hemisphere , especially in the Southern Ocean surrounding Antarctica , as shown in Figure 4 .
Figure 4 .
The @ @ @ @ @ @ @ the Holte and Talley logic .
Again , the oranges and reds are 400 to 500 meters .
Data from NOAA MIMOC .
The thicker mixed layer zones always occur in the local winter and reach their peak near 60° latitude as seen in Figure 5 .
Figure 5 .
Average mixed layer thickness by latitude and month .
The thickest mixed layer is reached in the Southern Hemisphere at about 55 degrees south .
In the Northern Hemisphere the peak is reached around 60 degrees .
The mixed layer depth in this @ @ @ @ @ @ @ by Holte and Talley .
Data from NOAA MIMOC .
The significance of the Mixed Layer In our previous post , we emphasized that the mixed layer is in thermal contact with the atmosphere , with a small delay of a few weeks .
It also has about 22 times the heat capacity of the atmosphere , which smooths out the radical changes in atmospheric temperature caused by weather events .
Thus , when looking at climate , which is much longer term than weather , observing the trend in mixed layer temperature @ @ @ @ @ @ @ compare the yearly global average mixed layer temperature from Jamstec , MIMOC and the University of Hamburg to the global sea-surface temperature ( SST ) estimates from the Hadley Climatic Research Unit and NOAA .
These are not anomalies , these are actual measurements , but they are all corrected and gridded .
I have simply averaged the respective global grids .
Figure 6 .
The Jamstec computed global mixed layer temperature is plotted in black .
It is compared to HadSST version 4 , NOAA ’ s ICOADS SST , and NOAA @ @ @ @ @ @ @ and the University of Hamburg ’ s mixed layer temperatures are nearly identical and centered on 2006 , they are plotted as a boxes , which overly one another .
Data is from the respective agencies .
Decent ocean coverage is only available since 2004 , so the years before then are suspect .
In Figure 6 , all data are plotted as yearly averages .
The Hadley CRU temperatures agree well with the Jamstec mixed layer record and , surprisingly , both records show similar declining temperature trends of about two to @ @ @ @ @ @ @ s ICOADS ( International Comprehensive Ocean-Atmosphere Data Set ) SST trend is flat to increasing and over a degree warmer than the other two records .
The number of Jamstec mixed layer observations are plotted in orange ( right scale ) to help us judge the data quality for each year .
Jamstec reached 150,000 observations in 2004 and we held the subjective opinion that this was enough .
The NOAA MIMOC and University of Hamburg mixed layer temperatures are much lower than HadSST and the Jamstec mixed layer temperatures .
Yet , @ @ @ @ @ @ @ centered on 2006 , fall right on top of the ERSST SST record and they are over four degrees lower than HaddSST and Jamstec .
The difference can not be simply where the temperatures are taken .
It can ’ t even be in the data , all these records use nearly the same input raw data , it must be in the corrections and methods .
The various mixed layer temperature estimates are different and so are the SSTs .
Why are two records declining and the rest flat or increasing ?
@ @ @ @ @ @ @ temperature or trend .
Sea surface and mixed layer temperatures should not need to be turned into anomalies unless they are compared to terrestrial temperatures .
They are all taken at approximately the same elevation and in the same medium .
All the datasets are global , with similar input data .
All are gridded to reduce the impact of uneven data distribution .
The grids are different sizes and the gridded areas do differ in their northern and southern limits , but all the grids cover all longitudes .
Table one lists @ @ @ @ @ @ @ , they aren ’ t that different .
Table 1 .
The north and south data limits for each dataset in 2019 .
In Figure 7 we have plotted the HadSST and ERSST anomalies .
How did they get these anomalies from the measurements in Figure 6 ?
The HadSST record is maintained by John Kennedy and his colleagues at the MET Hadley Climatic Research Unit ( Kennedy , Rayner , Atkinson , & Killick , 2019 ) .
They note that their record is different from ERSST and admit it is due @ @ @ @ @ @ @ to the raw data .
Kennedy mentions that SST “ critically contributes to the characterization of Earth ’ s climate.
” We agree .
Kennedy also writes that : One glance at Figure 6 verifies this statement is correct .
Most of Kennedy ’ s 90-page paper catalogues the difficulties of building an accurate SST record .
He notes that even subtle changes in the way SST measurements are made can lead to systemic errors of up to one degree , and this is the estimated 20th century global warming .
We do @ @ @ @ @ @ @ to 2005 is worthwhile .
The ambiguous data sources ( mainly ships and buckets to WWII and ship intake temperatures after ) and the imprecise corrections swamp any potential climate signal .
The data is much better since 2005 , but Figure 6 shows wide differences in compilations from different agencies .
Next , we review the agency definitions of the variables plotted in Figure 6 .
This data was read from a HadSST NetCDF file .
NetCDF files are the way most climate data are delivered , I ’ ve explained how @ @ @ @ @ @ @ high quality free statistical program ) in a previous post .
The variable read from the HadSST file was labeled “ tos , ” it is a 5-degree latitude and longitude grid defined as “ sea water temperature.
” The documentation says it is the ensemble-median sea-surface temperature from HadSST v4 .
The reference given is the paper by John Kennedy already mentioned ( Kennedy , Rayner , Atkinson , & Killick , 2019 ) .
HadSST uses data from ICOADS release 3 , supplemented by drifting buoy data from the Copernicus Marine @ @ @ @ @ @ @ Kennedy mentions the difference between his dataset and ERSST v5 seen clearly in Figure 6 .
The Hadley Centre SST is corrected to a depth of 20 cm .
In Figure 6 we plot yearly global averages of the ERSST v5 NetCDF variable “ sst , ” which is defined as the “ Extended reconstructed sea surface temperature.
” They note that the actual measurement depth varies from 0.2 to 10 m , but all measurements are corrected to the optimum buoy measurement depth of 20 cm , precisely the same reference depth @ @ @ @ @ @ @ , ERSST takes its data from ICOADS release 3 and utilizes Argo float and drifting buoys between 0 and 5 meters to compute SST .
This makes sense , since ERSST agrees with the University of Hamburg dataset and NOAA ’ s MIMOC ( mixed layer ) datasets , which also rely heavily on Argo float data .
As discussed above , SST ( at 20 cm ) and the mixed layer temperature should agree closely with one another almost all the time .
The ERSST anomalies plotted in Figure 7 are from @ @ @ @ @ @ @ ssta to the HadSST reference period of 1961-1990 , from the original reference of 1970-2000 .
The basic reference to ERSST v5 is a paper by Boyin Huang and colleagues ( Huang , et al.
, 2017 ) .
Like Kennedy , Boyin Huang directly addresses the differences between ERSST and HadSST .
Huang believes that the differences are due to the different corrections to the raw data applied by the Hadley Centre and NOAA .
The global average NOAA MIMOC mixed layer “ conservative temperature ” is plotted in Figure 6 as @ @ @ @ @ @ @ line .
It is plotted as one point in 2006 because the MIMOC dataset uses Argo and buoy data over more than 12 years centered on that year .
The global average temperature of all that data is 13.87°C from 0 to 5 meters depth .
Conservative temperature is not the same as SST .
SST is measured , conservative temperature is computed such that it is consistent with the heat content of the water in the mixed layer and takes into account the water salinity and density .
However , we would @ @ @ @ @ @ @ the conservative temperature .
Since the conservative temperature more accurately characterizes the heat content of the mixed layer , it is more useful than SST for climate studies .
The primary reference for this dataset is the already mentioned paper by Schmidtko ( Schmidtko , Johnson , & Lyman ) .
The University of Hamburg dataset is similar to MIMOC in that it is not divided by year , but pools all available data over the past 20 to 40 years to make a high resolution dataset and set of grids of ocean @ @ @ @ @ @ @ MIMOC it relies heavily on Argo and buoy data .
The global average temperature of the upper five meters of the oceans in this data set is 13.88°C , barely different from the MIMOC value .
The NetCDF variable used is “ temperature.
” It is defined as the “ optimally interpolated temperature.
” The documentation states that the zero depth is SST , it is not “ conservative temperature.
” However , the value is nearly identical to conservative temperature .
The main reference for this data set is an Ocean Science @ @ @ @ @ @ @ 2018 ) .
The NOAA ICOADS line in Figure 6 is downloaded from the KNMI Climate Explorer and labeled “ sst.
” The description is : “ Sea Surface Temperature Monthly Mean at Surface.
” ICOADS version 3 data is used in all the other agency datasets described here , but the organization does not do a lot of analysis .
By their own admission they provide a few “ simple gridded monthly summary products.
” Their line is shown in Figure 6 for reference , but it is not a serious analysis @ @ @ @ @ @ @ does help show how imprecise the data is .
The Jamstec MILA GPV product lines up well with HadSST as can be seen in Figure 6 .
This line is from the NetCDF variable “ MLD_TEMP.
” It is the central temperature of the mixed layer .
The temperature is not a true “ conservative temperature , ” but the way it is computed ensures that it will be close to that value .
The reference for this product is a Journal of Oceanography article by Shigeki Hosada and colleagues ( Hosada , @ @ @ @ @ @ @ 2010 ) .
Jamstec uses mostly Argo float and buoy data .
The total temperature spread shown in Figure 6 is over 5.5°C and yet these agencies are starting with essentially the same data .
This is not an attempt to characterize the SST and mixed layer temperature one-hundred years ago , these are attempts to tell us the average ocean surface temperature today .
To put Figure 6 into perspective , the total heat content of our atmosphere is roughly 1.0×1024 Joules , the difference between the HadSST line in Figure 6 @ @ @ @ @ @ @ average mixed layer depth of 60 meters ( from Jamstec ) , is 3.9×1023 Joules or nearly 39 % of the total thermal energy in the atmosphere .
I have no idea whether HadSST ’ s or ERSST ’ s temperatures are correct .
They can not both be correct .
I lean toward the ERSST temperatures since it is hard to have an 18-degree ocean in a 15-degree world , but then why are the HadSST temperatures so high ?
The single most important variable in determining how quickly the world is @ @ @ @ @ @ @ record .
We have better data available since about 2004 , but obviously no agreed method of analyzing it .
When it comes to global warming ( or , perhaps , global cooling ) the best answer is we don ’ t know .
Given that oceans cover 71 % of the Earth ’ s surface and contain 99 % of the heat capacity , the differences in Figure 6 are enormous .
These discrepancies must be resolved , if we are ever to detect climate change , human or natural .
I @ @ @ @ @ @ @ make this post , I think I did it correctly , but I do make mistakes .
For those that want to check my work , you can find my R code here .
None of this is in my new book Politics and Climate Change : A History but buy it anyway .
You can download the bibliography here .

By Andy May
As described in my previous post, the ocean “mixed layer” is sandwiched between the very thin “skin” layer at the ocean surface and the deep ocean. The skin layer loses thermal energy (“heat”) to the atmosphere primarily through evaporation, gains thermal energy from the Sun during the day, and constantly attempts to come to thermal equilibrium with the atmosphere above it. During the day, with the Sun beating down on the ocean and calm clear conditions, the skin layer might be as thick as ten meters. At night, especially under windy conditions, it can be less than a millimeter thick.
The mixed layer is a zone where turbulence, caused by surface currents and wind has so thoroughly mixed the water that temperature, density, and salinity are relatively constant throughout the layer. Originally, the “mixed layer depth,” the name for the base of the layer, was defined as the point where the temperature was 0.5°C different from the surface (Levitus, 1982). This was found to be an inadequate definition near the poles in the winter, because the temperature there, in certain areas, can be nearly constant to 2,000 meters, but the mixed layer turbulent zone isn’t that deep (Holte & Talley, 2008). Two of the areas that create problems when picking the mixed layer depth with the 0.5°C criteria are the North Atlantic, between Iceland and Scotland and in the Southern Ocean southwest of Chile. The areas are noted with light blue boxes in Figure 1.
Figure 1. The North Atlantic and Southern Ocean areas where defining the mixed layer depth is difficult because of downwelling surface water during the local winter.
The regions shown in Figure 1 are areas where significant downwelling of surface water to the deep ocean takes place, these are not the only areas where this happens, but these areas often contain nearly constant temperature profiles for the upper 1,000 meters, or even deeper. Figure 2 shows the average July temperature profile for the Southern Ocean area in Figure 1.
Figure 2. A Southern Ocean July average temperature profile from the blue region shown in Figure 1. The data used to make the profile is from more than 12 years, centered on 2008. Data from NOAA MIMOC.
As explained by James Holte and Lynne Talley (Holte & Talley, 2008), the deep convection in this part of the Southern Ocean has distorted the temperature profile to such an extent that a simple temperature cutoff cannot be used to set the mixed layer depth. Numerous solutions to the problem have been proposed over the years and these are listed and discussed in their article. Their proposed methodology is used by Sunke Schmidtko to define the mixed layer in the NOAA MIMOC dataset discussed below (Schmidtko, Johnson, & Lyman). The Holte and Talley method is complicated, as are many of the other solutions. It does not seem that a generally accepted methodology for defining the mixed layer has been found to date.
Depending upon location and season, the mixed layer depth changes. It is thickest in the local winter in the higher latitudes. There it can extend to 400 meters below the surface or farther using the Holte and Talley logic, and much deeper using the 0.5°C temperature cutoff. Figure 3 shows a map of the mixed layer depth in January.
Figure 3. Ocean mixed layer depth in January using the Holte and Talley logic. The oranges and reds are 400 to 500 meters. Data from NOAA MIMOC.
However, the Northern Hemisphere mixed layer thickness thins during the northern summer months and it thickens in the Southern Hemisphere, especially in the Southern Ocean surrounding Antarctica, as shown in Figure 4.
Figure 4. The ocean mixed layer depth in July using the Holte and Talley logic. Again, the oranges and reds are 400 to 500 meters. Data from NOAA MIMOC.
The thicker mixed layer zones always occur in the local winter and reach their peak near 60° latitude as seen in Figure 5.
Figure 5. Average mixed layer thickness by latitude and month. The thickest mixed layer is reached in the Southern Hemisphere at about 55 degrees south. In the Northern Hemisphere the peak is reached around 60 degrees. The mixed layer depth in this plot is computed using the methodology developed by Holte and Talley. Data from NOAA MIMOC.
The significance of the Mixed Layer
In our previous post, we emphasized that the mixed layer is in thermal contact with the atmosphere, with a small delay of a few weeks. It also has about 22 times the heat capacity of the atmosphere, which smooths out the radical changes in atmospheric temperature caused by weather events. Thus, when looking at climate, which is much longer term than weather, observing the trend in mixed layer temperature seems ideal. In Figure 6 we compare the yearly global average mixed layer temperature from Jamstec, MIMOC and the University of Hamburg to the global sea-surface temperature (SST) estimates from the Hadley Climatic Research Unit and NOAA. These are not anomalies, these are actual measurements, but they are all corrected and gridded. I have simply averaged the respective global grids.
Figure 6. The Jamstec computed global mixed layer temperature is plotted in black. It is compared to HadSST version 4, NOAA’s ICOADS SST, and NOAA’s ERSST. NOAA MIMOC, and the University of Hamburg’s mixed layer temperatures are nearly identical and centered on 2006, they are plotted as a boxes, which overly one another. Data is from the respective agencies.
Decent ocean coverage is only available since 2004, so the years before then are suspect. In Figure 6, all data are plotted as yearly averages. The Hadley CRU temperatures agree well with the Jamstec mixed layer record and, surprisingly, both records show similar declining temperature trends of about two to three degrees per century. NOAA’s ICOADS (International Comprehensive Ocean-Atmosphere Data Set) SST trend is flat to increasing and over a degree warmer than the other two records. The number of Jamstec mixed layer observations are plotted in orange (right scale) to help us judge the data quality for each year. Jamstec reached 150,000 observations in 2004 and we held the subjective opinion that this was enough.
The NOAA MIMOC and University of Hamburg mixed layer temperatures are much lower than HadSST and the Jamstec mixed layer temperatures. Yet, these two multi-year averages, that are centered on 2006, fall right on top of the ERSST SST record and they are over four degrees lower than HaddSST and Jamstec. The difference cannot be simply where the temperatures are taken. It can’t even be in the data, all these records use nearly the same input raw data, it must be in the corrections and methods.
The various mixed layer temperature estimates are different and so are the SSTs. Why are two records declining and the rest flat or increasing? The different estimates do not agree on temperature or trend.
Sea surface and mixed layer temperatures should not need to be turned into anomalies unless they are compared to terrestrial temperatures. They are all taken at approximately the same elevation and in the same medium. All the datasets are global, with similar input data. All are gridded to reduce the impact of uneven data distribution. The grids are different sizes and the gridded areas do differ in their northern and southern limits, but all the grids cover all longitudes. Table one lists the latitude data limits of the grids, they aren’t that different.
Table 1. The north and south data limits for each dataset in 2019.
In Figure 7 we have plotted the HadSST and ERSST anomalies. How did they get these anomalies from the measurements in Figure 6?
Figure 7. HadSST and ERSST version 4 temperature anomalies.
The HadSST record is maintained by John Kennedy and his colleagues at the MET Hadley Climatic Research Unit (Kennedy, Rayner, Atkinson, & Killick, 2019). They note that their record is different from ERSST and admit it is due to the difference in corrections and adjustments to the raw data. Kennedy mentions that SST “critically contributes to the characterization of Earth’s climate.” We agree. Kennedy also writes that:
“One of the largest sources of uncertainty in estimates of global temperature change is that associated with the correction of systematic errors in sea-surface temperature (SST) measurements. Despite recent work to quantify and reduce these errors throughout the historical record, differences between analyses remain larger than can be explained by the estimated uncertainties.” (Kennedy, Rayner, Atkinson, & Killick, 2019)
One glance at Figure 6 verifies this statement is correct. Most of Kennedy’s 90-page paper catalogues the difficulties of building an accurate SST record. He notes that even subtle changes in the way SST measurements are made can lead to systemic errors of up to one degree, and this is the estimated 20th century global warming. We do not believe the SST record from 1850 to 2005 is worthwhile. The ambiguous data sources (mainly ships and buckets to WWII and ship intake temperatures after) and the imprecise corrections swamp any potential climate signal. The data is much better since 2005, but Figure 6 shows wide differences in compilations from different agencies. Next, we review the agency definitions of the variables plotted in Figure 6.
Met Office Hadley Centre HadSST 4 data set.
This data was read from a HadSST NetCDF file. NetCDF files are the way most climate data are delivered, I’ve explained how to read them with R (a high quality free statistical program) in a previous post. The variable read from the HadSST file was labeled “tos,” it is a 5-degree latitude and longitude grid defined as “sea water temperature.” The documentation says it is the ensemble-median sea-surface temperature from HadSST v4. The reference given is the paper by John Kennedy already mentioned (Kennedy, Rayner, Atkinson, & Killick, 2019). HadSST uses data from ICOADS release 3, supplemented by drifting buoy data from the Copernicus Marine Environment Monitoring Service (CMEMS). Kennedy mentions the difference between his dataset and ERSST v5 seen clearly in Figure 6. The Hadley Centre SST is corrected to a depth of 20 cm.
NOAA ERSST v5
In Figure 6 we plot yearly global averages of the ERSST v5 NetCDF variable “sst,” which is defined as the “Extended reconstructed sea surface temperature.” They note that the actual measurement depth varies from 0.2 to 10 m, but all measurements are corrected to the optimum buoy measurement depth of 20 cm, precisely the same reference depth as HadSST. Also, like HadSST, ERSST takes its data from ICOADS release 3 and utilizes Argo float and drifting buoys between 0 and 5 meters to compute SST. This makes sense, since ERSST agrees with the University of Hamburg dataset and NOAA’s MIMOC (mixed layer) datasets, which also rely heavily on Argo float data. As discussed above, SST (at 20 cm) and the mixed layer temperature should agree closely with one another almost all the time. The ERSST anomalies plotted in Figure 7 are from the variable “ssta.” I moved ssta to the HadSST reference period of 1961-1990, from the original reference of 1970-2000. The basic reference to ERSST v5 is a paper by Boyin Huang and colleagues (Huang, et al., 2017).
Like Kennedy, Boyin Huang directly addresses the differences between ERSST and HadSST. Huang believes that the differences are due to the different corrections to the raw data applied by the Hadley Centre and NOAA.
NOAA MIMOC
The global average NOAA MIMOC mixed layer “conservative temperature” is plotted in Figure 6 as a box that falls on the ERSST line. It is plotted as one point in 2006 because the MIMOC dataset uses Argo and buoy data over more than 12 years centered on that year. The global average temperature of all that data is 13.87°C from 0 to 5 meters depth. Conservative temperature is not the same as SST. SST is measured, conservative temperature is computed such that it is consistent with the heat content of the water in the mixed layer and takes into account the water salinity and density. However, we would expect SST to be very close to the conservative temperature. Since the conservative temperature more accurately characterizes the heat content of the mixed layer, it is more useful than SST for climate studies. The primary reference for this dataset is the already mentioned paper by Schmidtko (Schmidtko, Johnson, & Lyman).
University of Hamburg dataset
The University of Hamburg dataset is similar to MIMOC in that it is not divided by year, but pools all available data over the past 20 to 40 years to make a high resolution dataset and set of grids of ocean temperature by depth. Like ERSST and MIMOC it relies heavily on Argo and buoy data. The global average temperature of the upper five meters of the oceans in this data set is 13.88°C, barely different from the MIMOC value. The NetCDF variable used is “temperature.” It is defined as the “optimally interpolated temperature.” The documentation states that the zero depth is SST, it is not “conservative temperature.” However, the value is nearly identical to conservative temperature. The main reference for this data set is an Ocean Science article by Viktor Gouretski (Gouretski, 2018).
NOAA ICOADS
The NOAA ICOADS line in Figure 6 is downloaded from the KNMI Climate Explorer and labeled “sst.” The description is: “Sea Surface Temperature Monthly Mean at Surface.” ICOADS version 3 data is used in all the other agency datasets described here, but the organization does not do a lot of analysis. By their own admission they provide a few “simple gridded monthly summary products.” Their line is shown in Figure 6 for reference, but it is not a serious analysis and probably should be ignored. It does help show how imprecise the data is.
Jamstec ML Temperature
The Jamstec MILA GPV product lines up well with HadSST as can be seen in Figure 6. This line is from the NetCDF variable “MLD_TEMP.” It is the central temperature of the mixed layer. The temperature is not a true “conservative temperature,” but the way it is computed ensures that it will be close to that value. The reference for this product is a Journal of Oceanography article by Shigeki Hosada and colleagues (Hosada, Ohira, Sato, & Suga, 2010). Jamstec uses mostly Argo float and buoy data.
Conclusions
The total temperature spread shown in Figure 6 is over 5.5°C and yet these agencies are starting with essentially the same data. This is not an attempt to characterize the SST and mixed layer temperature one-hundred years ago, these are attempts to tell us the average ocean surface temperature today. To put Figure 6 into perspective, the total heat content of our atmosphere is roughly 1.0×1024 Joules, the difference between the HadSST line in Figure 6 and the ERSST line, assuming an average mixed layer depth of 60 meters (from Jamstec), is 3.9×1023 Joules or nearly 39% of the total thermal energy in the atmosphere.
I have no idea whether HadSST’s or ERSST’s temperatures are correct. They cannot both be correct. I lean toward the ERSST temperatures since it is hard to have an 18-degree ocean in a 15-degree world, but then why are the HadSST temperatures so high? The single most important variable in determining how quickly the world is warming or cooling is the ocean temperature record. We have better data available since about 2004, but obviously no agreed method of analyzing it. When it comes to global warming (or, perhaps, global cooling) the best answer is we don’t know.
Given that oceans cover 71% of the Earth’s surface and contain 99% of the heat capacity, the differences in Figure 6 are enormous. These discrepancies must be resolved, if we are ever to detect climate change, human or natural.
I processed an enormous amount of data to make this post, I think I did it correctly, but I do make mistakes. For those that want to check my work, you can find my R code here.
None of this is in my new book Politics and Climate Change: A History but buy it anyway.
You can download the bibliography here.
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wattsupwiththat--2020-12-13--Where is the Outrage Over Climate and Energy Policy?

The headlines tell the story , and it ’ s not a pretty one .
Climate realists , like me , are losing the climate change debate .
Not because we are wrong .
Factually , we win every time !
But , we are losing the hearts and minds of the people because we have failed to tap into their emotions .
The climate alarmists don ’ t care about the facts .
They beat us down with children , like Greta Thunberg , and lecture us about self-interest and our cowardice in the face of a “ mass @ @ @ @ @ @ @ natural emotions and worst fears by linking climate change to those uncontrollable things we are most afraid to face—hurricanes ( lions ) , wildfires ( tigers ) , and tornadoes ( and bears , oh my ! )
.
• Climate change models have failed to accurately predict the future global average temperature change .
• There is no ideal average temperature for a world where on any given day the temperature could be -50 degrees F in one place and 120 F above zero somewhere else .
( Remember , if you @ @ @ @ @ @ @ comfortable standing with one foot on a block of ice and the other in a fire . )
• Global average temperatures have fluctuated much more and have changed much faster in the geologic past and well before humans started burning carbon-based fuels in significant quantities .
• Weather patterns are much more attributable to cyclical changes in ocean currents than to climate change .
• The use of oil , gas , and coal creates a significantly higher quality of life for billions of people , reduces poverty , provides abundant food @ @ @ @ @ @ @ water .
• There is overwhelming evidence that climate change is neither caused primarily by humans nor an existential threat to mankind or any other species .
Despite all this and more , we are gradually losing the battle for the minds of the people when it comes to the climate change debate .
And we are not just losing the debate at the political level .
We are losing in the board rooms , and not just the woke corporations like Amazon , Nike , Apple , or Google , but in @ @ @ @ @ @ @ companies , the oil & gas industry , and the manufacturers .
We tend to make our case using wonky science that even scientists don ’ t fully understand .
People can ’ t get their heads around our rational explanations , but they darn sure understand fear of events that may affect them directly and personally .
We tend to argue about the adverse macro-economic effects of climate change policy—the loss of millions of jobs , green energy costing trillions of dollars , and the failed goals of wealth redistribution .
These @ @ @ @ @ @ @ , have you ever wondered why the voters do not support Social Security or Medicare reforms , despite the overwhelming macro-economic evidence that both systems will likely be bankrupt within the next decade ?
The answer is fairly simple .
People make decisions based on micro-economics , not macro-economics .
People will choose to protect their personal benefits over the solvency of the system—every time .
Consider these examples of the micro-economic impacts of climate-change policies .
Here in Virginia , Dominion Energy is closing coal-fired power plants in favor of solar and @ @ @ @ @ @ @ renewable energy sources will lead to a $ 1,000 per person per year increase in electric bills by 2030 .
Ask anybody if they are willing to pay a thousand dollars a year when it is not likely to change the average global temperature at all ?
This question brings the issue home , and the answer will much more often be a resounding “ No !
” Ask the same person if they think climate change is a threat and whether we should do something about it , and you will get @ @ @ @ @ @ @ & Climate Initiative , a regional collaboration of 12 Northeast and Mid-Atlantic states plus DC , is proposing a 20-25 % reduction of carbon dioxide emissions for the region .
Their policy of choice is a “ carbon [ dioxide ] tax.
” Recently , Virginia enacted a carbon dioxide tax on utility generation , and the General Assembly will be considering one on transportation fuels that may include a 28 cent per gallon gasoline tax and a 26 cent per gallon diesel tax .
Based on current mileage rates and miles driven @ @ @ @ @ @ @ cost each driver more than $ 1,000 per year !
Once again , I can fairly easily predict the response from most people to the question of whether they are willing to pay another $ 1,000 per year for no material effect on the climate .
People expect their lights and their computer to work when they flip the power switch .
Talk about the potential for rolling brown-outs , or planned black-outs , so that someone else can charge their electric vehicle at the charging station built with tax dollars ( ever @ @ @ @ @ @ @ , and I think you will get a predictable negative response .
I am certain that we can come up with many more examples , but my point is this : let ’ s take the case against climate change down to the personal , micro-economic level .
Remember the charge against George H. W. Bush : “ It ’ s the economy stupid !
” It wasn ’ t that Bush didn ’ t understand that there was a recession ; it was that he failed to recognize how that recession affected @ @ @ @ @ @ @ put it another way , everything in life is political , except politics , that ’ s personal .
When you explain how a policy threatens someone ’ s pocket book , you ’ ll get their attention .
Paul D. Hoffman has been involved in environmental policy making and communications throughout his career .
He has served as State Director for then-Congressman Dick Cheney , Executive Director of the Cody Country Chamber of Commerce just outside Yellowstone National Park , and Deputy Assistant Secretary for Fish and Wildlife and Parks at the @ @ @ @ @ @ @ is currently the publicist for Hope Springs Media and a consultant for Resource Management Strategies .
He has a Bachelor ’ s degree in Economics and Biology from the University of California at San Diego , Revelle College .
He wrote this article for The Cornwall Alliance for the Stewardship of Creation .

by Paul D. Hoffman
The headlines tell the story, and it’s not a pretty one. Climate realists, like me, are losing the climate change debate. Not because we are wrong. Factually, we win every time! But, we are losing the hearts and minds of the people because we have failed to tap into their emotions.
The climate alarmists don’t care about the facts. They beat us down with children, like Greta Thunberg, and lecture us about self-interest and our cowardice in the face of a “mass extinction event.” They play to our natural emotions and worst fears by linking climate change to those uncontrollable things we are most afraid to face—hurricanes (lions), wildfires (tigers), and tornadoes (and bears, oh my!).
Despite these facts:
Climate change models have failed to accurately predict the future global average temperature change.
There is no ideal average temperature for a world where on any given day the temperature could be -50 degrees F in one place and 120 F above zero somewhere else. (Remember, if you live by averages, you would be comfortable standing with one foot on a block of ice and the other in a fire.)
Global average temperatures have fluctuated much more and have changed much faster in the geologic past and well before humans started burning carbon-based fuels in significant quantities.
Weather patterns are much more attributable to cyclical changes in ocean currents than to climate change.
The use of oil, gas, and coal creates a significantly higher quality of life for billions of people, reduces poverty, provides abundant food supplies, and means cleaner air and water.
There is overwhelming evidence that climate change is neither caused primarily by humans nor an existential threat to mankind or any other species.
Despite all this and more, we are gradually losing the battle for the minds of the people when it comes to the climate change debate. And we are not just losing the debate at the political level. We are losing in the board rooms, and not just the woke corporations like Amazon, Nike, Apple, or Google, but in the corporate board rooms of the utility companies, the oil & gas industry, and the manufacturers.
Why?
We tend to make our case using wonky science that even scientists don’t fully understand. People can’t get their heads around our rational explanations, but they darn sure understand fear of events that may affect them directly and personally.
We tend to argue about the adverse macro-economic effects of climate change policy—the loss of millions of jobs, green energy costing trillions of dollars, and the failed goals of wealth redistribution. These effects are real and catastrophic.
However, have you ever wondered why the voters do not support Social Security or Medicare reforms, despite the overwhelming macro-economic evidence that both systems will likely be bankrupt within the next decade? The answer is fairly simple. People make decisions based on micro-economics, not macro-economics. People will choose to protect their personal benefits over the solvency of the system—every time.
Consider these examples of the micro-economic impacts of climate-change policies. Here in Virginia, Dominion Energy is closing coal-fired power plants in favor of solar and wind farms, and this move toward renewable energy sources will lead to a $1,000 per person per year increase in electric bills by 2030.
Ask anybody if they are willing to pay a thousand dollars a year when it is not likely to change the average global temperature at all? This question brings the issue home, and the answer will much more often be a resounding “No!” Ask the same person if they think climate change is a threat and whether we should do something about it, and you will get many more affirmative responses.
The Transportation & Climate Initiative, a regional collaboration of 12 Northeast and Mid-Atlantic states plus DC, is proposing a 20-25% reduction of carbon dioxide emissions for the region. Their policy of choice is a “carbon [dioxide] tax.” Recently, Virginia enacted a carbon dioxide tax on utility generation, and the General Assembly will be considering one on transportation fuels that may include a 28 cent per gallon gasoline tax and a 26 cent per gallon diesel tax. Based on current mileage rates and miles driven per capita, these tax increases could cost each driver more than $1,000 per year! Once again, I can fairly easily predict the response from most people to the question of whether they are willing to pay another $1,000 per year for no material effect on the climate.
People expect their lights and their computer to work when they flip the power switch. Talk about the potential for rolling brown-outs, or planned black-outs, so that someone else can charge their electric vehicle at the charging station built with tax dollars (ever seen a government-built gas station?), and I think you will get a predictable negative response.
I am certain that we can come up with many more examples, but my point is this: let’s take the case against climate change down to the personal, micro-economic level. Remember the charge against George H. W. Bush: “It’s the economy stupid!” It wasn’t that Bush didn’t understand that there was a recession; it was that he failed to recognize how that recession affected people at the personal level.
To put it another way, everything in life is political, except politics, that’s personal. When you explain how a policy threatens someone’s pocket book, you’ll get their attention.
Paul D. Hoffman has been involved in environmental policy making and communications throughout his career. He has served as State Director for then-Congressman Dick Cheney, Executive Director of the Cody Country Chamber of Commerce just outside Yellowstone National Park, and Deputy Assistant Secretary for Fish and Wildlife and Parks at the US Department of the Interior. He is currently the publicist for Hope Springs Media and a consultant for Resource Management Strategies. He has a Bachelor’s degree in Economics and Biology from the University of California at San Diego, Revelle College. He wrote this article for The Cornwall Alliance for the Stewardship of Creation.
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wattsupwiththat--2020-12-14--UN Climate Ambition Summit Falls Flat

UN Climate Ambition Summit Falls Flat

Lots of ambitious promises of action by 2050 , but not a lot of detailed plans .
World is in danger of missing Paris climate target , summit is warned Minister tells more than 80 world leaders that not enough is being done The world is still not on track to fulfil the 2015 Paris climate agreement , the UK ’ s business secretary Alok Sharma warned , after a summit of more than 70 world leaders on the climate crisis ended with few new commitments on greenhouse gas emissions .
He said progress had been made at the Climate @ @ @ @ @ @ @ , the UN and France , marking five years since the Paris accord was adopted .
More than 80 world leaders including China ’ s Xi Jinping , the European commission president Ursula von der Leyen , and Pope Francis urged swifter action on the climate crisis .
But while Xi reaffirmed China ’ s target of net-zero emissions by 2060 , he gave few new details of reductions in the next decade .
India also disappointed observers when the prime minister , Narendra Modi , vowed to “ exceed expectations ” in @ @ @ @ @ @ @ Indian independence in 2047 , but made no pledge on coal production .
The US was not represented , as Donald Trump ’ s administration shunned the meeting , but president-elect Joe Biden issued a statement promising to hold a major event on the climate in his first 100 days in office .
Countries including Australia , Brazil , Russia and Saudi Arabia were excluded from speaking at the summit as they failed to come forward with strong new commitments .

Guest essay by Eric Worrall
Lots of ambitious promises of action by 2050, but not a lot of detailed plans.
World is in danger of missing Paris climate target, summit is warned
Minister tells more than 80 world leaders that not enough is being done
Fiona Harvey
Sun 13 Dec 2020 06.30 AEDT
The world is still not on track to fulfil the 2015 Paris climate agreement, the UK’s business secretary Alok Sharma warned, after a summit of more than 70 world leaders on the climate crisis ended with few new commitments on greenhouse gas emissions.
…
He said progress had been made at the Climate Ambition Summit, co-hosted by the UK, the UN and France, marking five years since the Paris accord was adopted. More than 80 world leaders including China’s Xi Jinping, the European commission president Ursula von der Leyen, and Pope Francis urged swifter action on the climate crisis.
But while Xi reaffirmed China’s target of net-zero emissions by 2060, he gave few new details of reductions in the next decade. India also disappointed observers when the prime minister, Narendra Modi, vowed to “exceed expectations” in curbing carbon dioxide by the centenary of Indian independence in 2047, but made no pledge on coal production.
…
The US was not represented, as Donald Trump’s administration shunned the meeting, but president-elect Joe Biden issued a statement promising to hold a major event on the climate in his first 100 days in office.
…
Countries including Australia, Brazil, Russia and Saudi Arabia were excluded from speaking at the summit as they failed to come forward with strong new commitments.
…


wattsupwiththat--2020-12-13--If you Thought Climate Grief was Strange, Meet the Climate Grief Therapists

If someone is no longer able to function due to their “ pre-traumatic ” climate grief , a plethora of therapists are offering therapies ranging from “ The Great Turning , ” a societal transformation from an industrial , growth , and consumer-based society to a more equitable and sustainable one , to collapse-transcendence ( fostering psycho-social-spiritual-cultural shifts to accept and live through collapse with some composure and stability ) .
From disasters and social disruptions to existential concerns , climate distress groups may not only provide much-needed support to patients but may also help psychiatry ’ s public health @ @ @ @ @ @ @ and complex challenges to the mental health and wellness of individuals and communities .
The need for interventions at a global scale increasingly leads mental health providers to look toward large group interventions .
This article outlines a number of the more popular group approaches .
It informs psychiatrists about the climate distress groups that our patients may access and advances our understanding of these groups ’ methods , which may help psychiatry ’ s own public health response .
The Work That Reconnects ( TWTR ) is an approach developed by Joanna @ @ @ @ @ @ @ as a “ transformative learning process.
” 9 TWTR incorporates aspects of deep ecology , systems theory , eco-psychology , Buddhism , and activism .
… Instead of turning away from the problem , group members reframe and honor their feelings , and then develop fresh perspectives intended to bring about “ The Great Turning , ” a societal transformation from an industrial , growth , and consumer-based society to a more equitable and sustainable one .
The Deep Adaptation Forum was developed by Jem Bendell , PhD , after the publication of @ @ @ @ @ @ @ Map for Navigating Climate Tragedy , ” 12 which went viral .
Deep Adaptation ( DA ) positions itself outside of mainstream work on climate adaptation in that it assumes inevitable societal breakdown and collapse due to climate change and intentionally challenges the idea that hope should be part of climate change adaptation .
DA seeks to develop so-called collapse-readiness ( ie , creating an equitable system for distribution of life essentials such as food , water , energy , and health care ) and collapse-transcendence ( fostering psycho-social-spiritual-cultural shifts to accept and @ @ @ @ @ @ @ stability ) .

Guest essay by Eric Worrall
If someone is no longer able to function due to their “pre-traumatic” climate grief, a plethora of therapists are offering therapies ranging from “The Great Turning,” a societal transformation from an industrial, growth, and consumer-based society to a more equitable and sustainable one, to collapse-transcendence (fostering psycho-social-spiritual-cultural shifts to accept and live through collapse with some composure and stability).
Group Interventions for Climate Change Distress
December 11, 2020
Beth Mark, MD , Janet Lewis, MD
From disasters and social disruptions to existential concerns, climate distress groups may not only provide much-needed support to patients but may also help psychiatry’s public health responses.
The climate crisis presents unique and complex challenges to the mental health and wellness of individuals and communities. The need for interventions at a global scale increasingly leads mental health providers to look toward large group interventions. This article outlines a number of the more popular group approaches. It informs psychiatrists about the climate distress groups that our patients may access and advances our understanding of these groups’ methods, which may help psychiatry’s own public health response.
…
The Work That Reconnects (TWTR) is an approach developed by Joanna Macy, PhD, and is described as a “transformative learning process.”9 TWTR incorporates aspects of deep ecology, systems theory, eco-psychology, Buddhism, and activism. … Instead of turning away from the problem, group members reframe and honor their feelings, and then develop fresh perspectives intended to bring about “The Great Turning,” a societal transformation from an industrial, growth, and consumer-based society to a more equitable and sustainable one.
…
The Deep Adaptation Forum was developed by Jem Bendell, PhD, after the publication of his paper “Deep Adaptation: A Map for Navigating Climate Tragedy,”12 which went viral. Deep Adaptation (DA) positions itself outside of mainstream work on climate adaptation in that it assumes inevitable societal breakdown and collapse due to climate change and intentionally challenges the idea that hope should be part of climate change adaptation.
…
DA seeks to develop so-called collapse-readiness (ie, creating an equitable system for distribution of life essentials such as food, water, energy, and health care) and collapse-transcendence(fostering psycho-social-spiritual-cultural shifts to accept and live through collapse with some composure and stability).
…


wattsupwiththat--2020-12-13--Climate change: Have countries kept their promises?

The UK ’ s BBC ( British Broadcasting Corporation ) has just published an assessment of five “ countries ” and how well they have kept their “ climate ” promises .
The five “ countries ” are the UK , Australia , EU , China , and the Philipinnes .
The article is by Matt McGrath .
Before I continue , please look at the names of the five “ countries ” , and without knowing anything about the data that the BBC used and how they did the assessment , ask yourself which ones the BBC will give @ @ @ @ @ @ @ expectations , knowing that Matt McGrath ’ s assessment would be highly politically charged , was for a Pass for EU and China , a Fail for Australia , and a don ’ t know for UK and Philippines .
Why were Australia and the Philippines included , instead of much more important CO2 emitters like India or Japan ?
The reason for inclusion of Australia is pretty obvious – one purpose of the article was to make Australia look bad ( remember , Australia was excluded from the recent virtual “ climate @ @ @ @ @ @ @ more important than ever for Australia now to look bad ) .
The reason for including the Philippines is less obvious to me , maybe they wanted a lesser country in the list so that Australia wasn ’ t obviously in a different league to the others ( suggestions , anyone ? )
.
Looking at those charts , what can we see ?
1 .
Australia was assessed “ excluding forestry ” .
Why exclude forestry ?
Nothing was excluded for the others .
Well , I think the answer is here @ @ @ @ @ @ @ Australia ’ s total , but Queensland is a large part ] .
Matt McGrath would have been unable to make Australia look so bad if he had looked at the whole picture .
Dishonesty in certain quarters knows no bounds .
It probably took him quite a while to work out how he could fiddle Australia ’ s numbers .
With forestry included , Australia would have been the only one of the five that was well ahead of its 2020 and projected 2030 targets .
2 .
The percentage change and @ @ @ @ @ @ @ 1990 ( ish ) -2019 period shown in the charts were ( approx ) : When you are sea-sick , always remember to go to the leeward ( downwind ) side of the boat , so that you don ’ t get your own back .
The UK and EU have made themselves rather ill with their “ climate ” efforts , but when they look at China ’ s numbers , they must feel like they went to the windward side .
Only it wasn ’ t their own that they got @ @ @ @ @ @ @ of whether the five “ countries ” had kept their promises was : China : “ mostly yes , but with some caveats ” Philippines : “ its actions to date are compatible with keeping warming well below 2C this century ” .
My assessment is that this article is so highly politicised that it is just a sick joke .
There is a perfectly good reason for the USA not to be in the BBC article – they are not in the Paris agreement – but just for interest , their 1990-2019 @ @ @ @ @ @ @ : 0 % ( 0Mt ) – 4,800Mt in 1990 , 5,800Mt in 2007 , 4,800Mt in 2019 ( data from here and here ) .
The BBC article is at https : //www.bbc.com/news/science-environment-55222890 .
I have not checked whether Matt McGrath ’ s numbers are correct .
He gives no sources .
Anyone who believes the projections in the China chart is surely in cloud-cuckoo land .

Guest post by Mike Jonas
The UK’s BBC (British Broadcasting Corporation) has just published an assessment of five “countries” and how well they have kept their “climate” promises. The five “countries” are the UK, Australia, EU, China, and the Philipinnes. The article is by Matt McGrath.
Before I continue, please look at the names of the five “countries”, and without knowing anything about the data that the BBC used and how they did the assessment, ask yourself which ones the BBC will give a “pass” to.
…..
Matt McGrath presents a chart for each “country”:
My expectations, knowing that Matt McGrath’s assessment would be highly politically charged, was for a Pass for EU and China, a Fail for Australia, and a don’t know for UK and Philippines. Why were Australia and the Philippines included, instead of much more important CO2 emitters like India or Japan? The reason for inclusion of Australia is pretty obvious – one purpose of the article was to make Australia look bad (remember, Australia was excluded from the recent virtual “climate” conference, so it’s more important than ever for Australia now to look bad). The reason for including the Philippines is less obvious to me, maybe they wanted a lesser country in the list so that Australia wasn’t obviously in a different league to the others (suggestions, anyone?).
Looking at those charts, what can we see?
1. Australia was assessed “excluding forestry”. Why exclude forestry? Nothing was excluded for the others. Well, I think the answer is here:
[Australia’s] emissions from the land sector decreased 80% between 2005 and 2016 {..} Queensland land sector GHG emissions decreased from 97.7 million tonnes of carbon dioxide equivalent (MtCO2e) in 1990 to 12 MtCO2e in 2016
[I don’t have Australia’s total, but Queensland is a large part]. Matt McGrath would have been unable to make Australia look so bad if he had looked at the whole picture. Dishonesty in certain quarters knows no bounds. It probably took him quite a while to work out how he could fiddle Australia’s numbers. With forestry included, Australia would have been the only one of the five that was well ahead of its 2020 and projected 2030 targets.
2. The percentage change and total change in CO2 emissions over the 1990(ish)-2019 period shown in the charts were (approx):
UK: -45% (-350Mt)
Australia (excluding forestry): +30% (+135Mt)
EU: -30% (-170Mt)
China: +350% (+10,500Mt)
Philippines: +30% (+110Mt)
When you are sea-sick, always remember to go to the leeward (downwind) side of the boat, so that you don’t get your own back. The UK and EU have made themselves rather ill with their “climate” efforts, but when they look at China’s numbers, they must feel like they went to the windward side. Only it wasn’t their own that they got back.
The BBC’s assessment of whether the five “countries” had kept their promises was:
UK: “mostly yes”.
Australia: “not really”.
EU: “mostly yes”.
China: “mostly yes, but with some caveats”
Philippines: “its actions to date are compatible with keeping warming well below 2C this century”.
My assessment is that this article is so highly politicised that it is just a sick joke.
There is a perfectly good reason for the USA not to be in the BBC article – they are not in the Paris agreement – but just for interest, their 1990-2019 numbers are (approx):
USA: 0% (0Mt) – 4,800Mt in 1990, 5,800Mt in 2007, 4,800Mt in 2019 (data from here and here).
The BBC article is at https://www.bbc.com/news/science-environment-55222890. I have not checked whether Matt McGrath’s numbers are correct. He gives no sources. Anyone who believes the projections in the China chart is surely in cloud-cuckoo land.
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