Reykjavik Temperature Adjustments – a comparison

Summary

On 20th February, Paul Homewood made some allegations that the temperature adjustments for Reykjavík were not supported by any known reasons. The analysis was somewhat vague. I have looked into the adjustments by both the GHCN v3 and NASA GISS. The major findings, which support Homewood’s view, are:-

  • The GHCN v3 adjustments appear entirely arbitrary. They do not correspond to the frequent temperature relocations. Much of the period from 1901-1965 is cooled by a full one degree centigrade.
  • Even more arbitrary was the adjustments for the period 1939-1942. In years where there was no anomalous spike in the data, a large cool period was created.
  • Also, despite there being complete raw data, the GHCN adjusters decided to dismiss the data from 1926 and 1946.
  • The NASA GISS homogenisation adjustments were much smaller in magnitude, and to some extent partly offset the GHCN adjustments. The greatest warming was of the 1929-51 period.

The combined impact of the adjustments is to change the storyline from the data, suppressing the early twentieth century warming and massively reducing the mid-century cooling. As a result an impression is created that the significant warming since the 1980s is unprecedented.

 

Analysis of the adjustments

There are a number of data sets to consider. There is the raw data available from 1901 to 2011 at NASA GISS. Nick Stokes has confirmed that this is the same raw data issued by the Iceland Met Office, baring a few roundings. The adjustments made by the Iceland Met Office are unfortunately only available from 1948. Quite separate, is the Global Historical Climatology Network dataset (GHCN v3) from the US National Oceanic and Atmospheric Administration (NOAA) I accessed from NASA GISS, along with the GISS’s own homogenised data used to compile the GISTEMP global temperature anomaly.

The impact of the adjustments from the raw data is as follows

The adjustments by the Icelandic Met Office professionals with a detailed knowledge of the instruments and the local conditions, is quite varied from year-to-year and appears to impose no trend in the data. The impact of GCHN is to massively cool the data prior to 1965. Most years are by about a degree, more than the 0.7oC total twentieth century global average surface temperature increase. The pattern of adjustments has long periods of adjustments that are the same. The major reason could be relocations. Trausti Jonsson, Senior Meteorologist with the Iceland Met Office, has looked at the relocations. He has summarized in the graphic below, along with gaps in the data.

I have matched these relocations with the adjustments.

The relocation dates appear to have no impact on the adjustments. If it does affect the data, the wrong data must be used.

Maybe the adjustments reflect the methods of calculation? Trausti Jonsson says:-

I would again like to make the point that there are two distinct types of adjustments:

1. An absolutely necessary recalculation of the mean because of changes in the observing hours or new information regarding the diurnal cycle of the temperature. For Reykjavík this mainly applies to the period before 1924.

2. Adjustments for relocations. In this case these are mainly based on comparative measurements made before the last relocation in 1973 and supported by comparisons with stations in the vicinity. Most of these are really cosmetic (only 0.1 or 0.2 deg C). There is a rather large adjustment during the 1931 to 1945 period (- 0.4 deg C, see my blog on the matter – you should read it again:http://icelandweather.blog.is/blog/icelandweather/entry/1230185/). 
I am not very comfortable with this large adjustment – it is supposed to be constant throughout the year, but it should probably be seasonally dependent. The location of the station was very bad (on a balcony/rooftop).

So maybe there can be some adjustment prior to 1924, but nothing major after. There is also nothing in the this account, or in the more detailed history, that indicates a reason for the reduction in adjustments in 1917-1925, or the massive increase in negative adjustments in the period 1939-1942.

Further, there is nothing in the local conditions that I can see to then justify GISS imposing an artificial early twentieth century warming period. There are two possible non-data reasons. The first is due to software which homogenizes to the global pattern. The second is human intervention. The adjusters at GISS realised the folks at NOAA had been conspicuously over-zealous in their adjustments, so were trying to restore a bit of credibility to the data story.

 

The change in the Reykjavík data story

When we compare graphs of raw data to adjusted data, it is difficult to see the impact of adjustments on the trends. The average temperatures vary widely from year to year, masking the underlying patterns. As a rough indication I have therefore taken the average temperature anomaly per decade. The decades are as in common usage, so the 1970s is from 1970-1979. The first decade is actually 1901-1909, and for the adjusted data there are some years missing. The decade of 2000-2009 had no adjustments. The average temperature of 5.35oC was set to zero, to become the anomaly.

The warmest decade was the last decade of 2000-2009. Further, both the raw data (black) and the GISS Homogenised data (orange) show the 1930s to be the second warmest decade. However, whilst the raw data shows the 1930s to be just 0.05oC cooler than the 2000s, GISS estimates it to be 0.75oC cooler. The coolest decades are also different. The raw data shows the 1980s to be the coolest decade, whilst GISS shows the 1900s and the 1910s to be about 0.40oC cooler. The GHCN adjustments (green) virtually eliminate the mid-century cooling.

But adjustments still need to be made. Trausti Jonsson believes that the data prior to 1924 needs to be adjusted downwards to allow for biases in the time of day when readings were taken. This would bring the 1900s and the 1910s more into line with the 1980s, along with lowering the 1920s. The leap in temperatures from the 1910s to the 1930s becomes very similar to that from 1980s to the 2000s, instead of half the magnitude in the GHCNv3 data and two-thirds the magnitude in the GISS Homogenised data.

The raw data tell us there were two similar-sized fluctuations in temperature since 1900 of 1920s-1940s and from 1980s-2010s. In between there was a period cooling that almost entirely cancelled out the earlier warming period. The massive warming since the 1980s is not exceptional, though there might be some minor human influence if patterns are replicated elsewhere.

The adjusted data reduces the earlier warming period and the subsequent cooling that bottomed out in the 1980s. Using the GISS Homogenised data we get the impression of unprecedented warming closely aligned to the rise in greenhouse gas levels. As there is no reason for the adjustments from relocations, or from changes to the method of calculation, the adjustments would appear to be made to fit reality to the adjuster’s beliefs about the world.

Kevin Marshall

 

Is there a Homogenisation Bias in Paraguay’s Temperature Data?

Last month Paul Homewood at Notalotofpeopleknowthat looked at the temperature data for Paraguay. His original aim was to explain the GISS claims of 2014 being the hottest year.

One of the regions that has contributed to GISS’ “hottest ever year” is South America, particularly Brazil, Paraguay and the northern part of Argentina. In reality, much of this is fabricated, as they have no stations anywhere near much of this area…

….there does appear to be a warm patch covering Paraguay and its close environs. However, when we look more closely, we find things are not quite as they seem.

In “Massive Tampering With Temperatures In South America“, Homewood looked at the “three genuinely rural stations in Paraguay that are currently operating – Puerto Casado, Mariscal and San Juan.” A few days later in “All Of Paraguay’s Temperature Record Has Been Tampered With“, he looked at remaining six stations.

After identifying that all of the three rural stations currently operational in Paraguay had had huge warming adjustments made to their data since the 1950’s, I tended to assume that they had been homogenised against some of the nearby urban stations. Ones like Asuncion Airport, which shows steady warming since the mid 20thC. When I went back to check the raw data, it turns out all of the urban sites had been tampered with in just the same way as the rural ones.

What Homewood does not do is to check the data behind the graphs, to quantify the extent of the adjustment. This is the aim of the current post.

Warning – This post includes a lot of graphs to explain how I obtained my results.

Homewood uses comparisons of two graphs, which he helpful provides the links to. The raw GHCN data + UHSHCN corrections is available here up until 2011 only. The current after GISS homogeneity adjustment data is available here.

For all nine data sets that I downloaded both the raw and homogenised data. By simple subtraction I found the differences. In any one year, they are mostly the same for each month. But for clarity I selected a single month – October – the month of my wife’s birthday.

For the Encarnacion (27.3 S,55.8 W) data sets the adjustments are as follows.

In 1967 the adjustment was -1.3C, in 1968 +0.1C. There is cooling of the past.

The average adjustments for all nine data sets is as follows.

This pattern is broadly consistent across all data sets. These are the maximum and minimum adjustments.

However, this issue is clouded by the special adjustments required for the Pedro Juan CA data set. The raw data set has been patched from four separate files,

Removing does not affect the average picture.

But does affect the maximum and minimum adjustments. This is shows the consistency in the adjustment pattern.

The data sets are incomplete. Before 1941 there is only one data set – Ascuncion Aero. The count for October each year is as follows.

In recent years there are huge gaps in the data, but for the late 1960s when the massive switch in adjustments took place, there are six or seven pairs of raw and adjusted data.

Paul Homewood’s allegation that the past has been cooled is confirmed. However, it does not give a full understanding of the impact on the reported data. To assist, for the full year mean data, I have created temperature anomalies based on the average anomaly in that year.

The raw data shows a significant cooling of up to 1oC in the late 1960s. If anything there has been over-compensation in the adjustments. Since 1970, any warming in the adjusted data has been through further adjustments.

Is this evidence of a conspiracy to “hide a decline” in Paraguayan temperatures? I think not. My alternative hypothesis is that this decline, consistent over a number of thermometers is unexpected. Anybody looking at just one of these data sets recently, would assume that the step change in 40-year-old data from a distant third world country is bound to be incorrect. (Shub has a valid point) That change goes against the known warming trend for over a century from the global temperature data sets and the near stationary temperatures from 1950-1975. More importantly cooling goes against the “known” major driver of temperature recent change – rises in greenhouse gas levels. Do you trust some likely ropey instrument data, or trust your accumulated knowledge of the world? The clear answer is that the instruments are wrong. Homogenisation is then not to local instruments in the surrounding areas, but to the established expert wisdom of the world. The consequent adjustment cools past temperatures by one degree. The twentieth century warming is enhanced as a consequence of not believing what the instruments are telling you. The problem is that this step change is replicated over a number of stations. Paul Homewood had shown that it probably extends into Bolivia as well.

But what happens if the converse happens? What if there is a step rise in some ropey data set from the 1970s and 1980s? This might be large, but not inconsitent with what is known about the world. It is unlikely to be adjusted downwards. So if there have been local or regional step changes in average temperature over time both up and down, the impact will be to increase the rate of warming if the data analysts believe that the world is warming and human beings are the cause of it.

Further analysis is required to determine the extent of the problem – but not from this unpaid blogger giving up my weekends and evenings.

Kevin Marshall

All first time comments are moderated. Please also use the comments as a point of contact, stating clearly that this is the case and I will not click the publish button, subject to it not being abusive. I welcome other points of view, though may give a robust answer.

DECC’s Dumb Global Calculator Model

On the 28th January 2015, the DECC launched a new policy emissions tool, so everyone can design policies to save the world from dangerous climate change. I thought I would try it out. By simply changing the parameters one-by-one, I found that the model is both massively over-sensitive to small changes in input parameters and is based on British data. From the model, it is possible to entirely eliminate CO2 emissions by 2100 by a combination of three things – reducing the percentage travel in urban areas by car from 43% to 29%; reducing the average size of homes to 95m2 from 110m2 today; and for everyone to go vegetarian.

The DECC website says

Cutting carbon emissions to limit global temperatures to a 2°C rise can be achieved while improving living standards, a new online tool shows.

The world can eat well, travel more, live in more comfortable homes, and meet international carbon reduction commitments according to the Global Calculator tool, a project led by the UK’s Department of Energy and Climate Change and co-funded by Climate-KIC.

Built in collaboration with a number of international organisations from US, China, India and Europe, the calculator is an interactive tool for businesses, NGOs and governments to consider the options for cutting carbon emissions and the trade-offs for energy and land use to 2050.

Energy and Climate Change Secretary Edward Davey said:

“For the first time this Global Calculator shows that everyone in the world can prosper while limiting global temperature rises to 2°C, preventing the most serious impacts of climate change.

“Yet the calculator is also very clear that we must act now to change how we use and generate energy and how we use our land if we are going to achieve this green growth.

“The UK is leading on climate change both at home and abroad. Britain’s global calculator can help the world’s crucial climate debate this year. Along with the many country-based 2050 calculators we pioneered, we are working hard to demonstrate to the global family that climate action benefits people.”

Upon entering the calculator I was presented with some default settings. Starting from a baseline emissions in 2011 of 49.9 GT/CO2e, this would give predicted emissions of 48.5 GT/CO2e in 2050 and 47.9 GT/CO2e in 2100 – virtually unchanged. Cumulative emissions to 2100 would be 5248 GT/CO2e, compared with 3010 GT/CO2e target to give a 50% chance of limiting warming to a 2°C rise. So the game is on to save the world.

I only dealt with the TRAVEL, HOMES and DIET sections on the left.

I went through each of the parameters, noting the results and then resetting back to the baseline.

The TRAVEL section seems to be based on British data, and concentrated on urban people. Extrapolating for the rest of the world seems a bit of a stretch, particularly when over 80% of the world is poorer. I was struck first by changing the mode of travel. If car usage in urban areas fell from 43% to 29%, global emissions from all sources in 2050 would be 13% lower. If car usage in urban areas increased from 43% to 65%, global emissions from all sources in 2050 would be 7% higher. The proportions are wrong (-14% gives -13%, but +22% gives +7%) along with urban travel being too high a proportion of global emissions.

The HOMES section has similar anomalies. Reducing the average home area by 2050 to 95m2 from 110m2 today reduces total global emissions in 2050 by 20%. Independently decreasing average urban house temperature in 2050 from 17oC in Winter & 27oC in Summer, instead of 20oC & 24oC reduces total global emissions in 2050 by 7%. Both seem to be based on British-based data, and highly implausible in a global context.

In the DIET section things get really silly. Cutting the average calorie consumption globally by 10% reduces total global emissions in 2050 by 7%. I never realised that saving the planet required some literal belt tightening. Then we move onto meat consumption. The baseline for 2050 is 220 Kcal per person per day, against the current European average of 281 Kcal. Reducing that to 14 Kcal reduces global emissions from all sources in 2050 by 73%. Alternatively, plugging in the “worst case” 281 Kcal, increases global emissions from all sources in 2050 by 71%. That is, if the world becomes as carnivorous in 2050 as the average European in 2011, global emissions from all sources at 82.7 GT/CO2e will be over six times higher the 13.0 GT/CO2e. For comparison, OECD and Chinese emissions from fossil fuels in 2013 were respectively 10.7 and 10.0 GT/CO2e. It seems it will be nut cutlets all round at the climate talks in Paris later this year. No need for China, India and Germany to scrap all their shiny new coal-fired power stations.

Below is the before and after of the increase in meat consumption.

Things get really interesting if I take the three most sensitive, yet independent, scenarios together. That is, reducing urban car use from 43% to 29% of journeys in 2050; reducing the average home area by 2050 to 95m2 from 110m2; and effectively making a sirloin steak (medium rare) and venison in redcurrant sauce things of the past. Adding them together gives global emissions of -2.8 GT/CO2e in 2050 and -7.1 GT/CO2e in 2100, with cumulative emissions to 2100 of 2111 GT/CO2e. The model does have some combination effect. It gives global emissions of 3.2 GT/CO2e in 2050 and -0.2 GT/CO2e in 2100, with cumulative emissions to 2100 of 2453 GT/CO2e. Below is the screenshot of the combined elements, along with a full table of my results.

It might be great to laugh at the DECC for not sense-checking the outputs of its glitzy bit of software. But it concerns me that it is more than likely the same people who are responsible for this nonsense are also responsible for the glossy plans to cut Britain’s emissions by 80% by 2050 without destroying hundreds of thousands of jobs; eviscerating the countryside; and reducing living standards, especially of the poor. Independent and critical review and audit of DECC output is long overdue.

Kevin Marshall

 

A spreadsheet model is also available, but I used the online tool, with its’ excellent graphics. The calculator is built by a number of organisations.

Why no country should sign up to Climate Mitigation at Paris 2015

The blog “the eco experts“, has produced a map of the countries most likely to survive climate change.

The most populous country with a high risk is India. In fact it has more people than the 50+ nations of Africa, or nearly twice the population of the OECD – the rich nations club. It is determined not to constrain the rapid growth in emissions if it means sacrificing the rapid economic growth that is pulling people out of poverty. Is this sensible when rapidly increasing its emissions create the prospect of dangerous climate change?

Look at the pattern of vulnerability.

Why is Mongolia more vulnerable than Russia or China?

Why is Haiti more vulnerable than Guatemala & El Salvador, which in turn are more vulnerable than Mexico, which in turn is more vulnerable than the USA?

Why are Syria and Iraq more vulnerable than Iran, which in turn is more vulnerable than Saudi Arabia, which is in turn more vulnerable than the UAE?

Why is Madagascar more vulnerable than Tanzania, which in turn is more vulnerable than South Africa, which is in turn more vulnerable than Botswana?

The answer does not lie in the local climate system but in the level of economic development. As with natural extreme weather events, any adverse consequences of climate change will impact on the poorest disproportionately.

In the light of this, should India

  1. Agree to sacrifice economic growth to constrain emissions, having a significant impact on global emissions and maybe encouraging others to do likewise?

    OR

  2. Continue with the high economic growth (and hence emission growth) strategy knowing that if catastrophic climate change is real the population will be better able to cope with it, and if inconsequential they will have sacrificed future generations to a trivial problem?

    OR

  3. Continue with the high economic growth (and hence emission growth) strategy and invest in more accurately identifying the nature and extent of climate change?

Now consider that any Government should be first and foremost responsible for the people of that country. If that can be best progressed by international agreements (such as in trade and keeping global peace) then it is the interests of that country to enter those agreements, and encourage other nations to do likewise. Global peace and globalisation are win-win strategies. But climate change is fundamentally different. It is a prospective future problem, the prospective harms from which are here clearly linked to stage of economic development. Combating the future problem means incurring costs, the biggest of which is economic growth. Technologically, there low-cost solutions are in place, and there is no example of any country aggressively weeding out ineffectual policies. Even if there were effective policies in in theory, for costs to exceed benefits would mean every major country either drastically cutting emissions (e.g. the OECD, China, Russia, Saudi Arabia, South Africa) or drastically constraining future emissions growth (India, Brazil, Indonesia, Vietnam, Thailand, plus dozens of other countries). If some countries fail to sign up then policy countries will be burdened with the certain actual costs of policy AND any residual possible costs of policy. Responsible countries will duck the issue, and, behind the scenes, help scupper the climate talks in Paris 2015.

Kevin Marshall

Veritasium Misinforms on Global Warming

Bishop Hill posts on a You-tube video “13 Misconceptions About Global Warming” from Veritasium (Dr Derek Muller), inviting readers to play a sort of bingo to “spot all the strawmen arguments, cherrypicking, out of date data, and plain old mistakes”. Here is my attempt, restricted to just 13 points.

  1. “Global warming” / “climate change” naming. It might be true that people can deny global warming by pointing to a localized cold weather snap. But it is also true that using the term “climate change” can result in any unusual weather event or short-term trend being blamed on anthropogenic global warming, along with natural global fluctuations. The term “global warming” reminds us that the adverse effects on climate are as a result of rising greenhouse gas levels warming the atmosphere. More importantly the use of the term “global” reminds us those changes in climate due to changes in greenhouse gases is a global issue requiring global solutions. Any mitigation policy that excludes 80% of the global population and two-thirds of global carbon emissions, will not work.

     

  2. Veritasium claims climate change is also about more extreme weather and ocean acidification, not just the average surface temperature is warming. But there is nothing in the greenhouse gas hypothesis that says a rise in temperatures will result in more extreme weather, nor does Veritasium provide the evidence of this happening. At Wattupwiththat there is a page that demonstrates weather is not getting more extreme from a number of different measures.

     

  3. Claim that it has not stopped warming as 13 of the 14 hottest years are in this century. This is a strawman, as there was significant warming in the last quarter of the twentieth century. We would only fail to have hottest years if global average temperatures had taken a sharp step decrease.

     

  4. Claims that taking the satellite data of global temperature anomalies into account shows that warming has not stopped. From Kevin Cowtan’s page (copied by Skeptical Science) we can calculate linear trends. It is the RSS satellite data that shows the longest period of no warming – 18 years from 1997-2014 based on the linear trend. It is just 13 years for GISTEMP and 14 years for HADCRUT4. The other satellite data is UAH, where there is just 6 years of no warming.

     

     

  5. What he is doing is comparing UAH satellite data that only shows the pause from 2009. There is now 35 years of satellite data, with the total recorded trend of 0.48oC. The RSS data shows 0.51oC of warming. The surface thermometer measures vary between 0.59 and 0.63 oC of warming. This is data cherry-picking.

     

  6. There is a claim that climate sensitivity is lower than thought in the 1980s. Not according to Nicholas Lewis, who found that the range of sensitivities is unchanged from the Charney Report 1979 through to AR5 WG1 of Sept-13

     

  7. Claims the central estimate for warming from a doubling of CO2 is 3.0oC of warming. Based on this from 2001 from HADCRUT4 shows no warming there would be 0.30oC of warming, when the trend from HADCRUT4 is zero. In a longer period from 1979 for which we have satellite data, an increase in CO2 from 336.8 to 398.5 ppm (Mauna Loa data) implies an increase in temperatures of 0.72oC – between 1.14 on 1.5 times greater than that measured by the temperature series. Even this is misleading, as there was no warming from 1944 to the late 1970s. In 1944 I estimate that CO2 levels were 308ppm, indicating a total warming in the last 70 years of 1.1oC, respectively 1.7 and 2.1 times greater than the trend in GISTEMP and HADCRUT4.

     

  8. This would appear to contradict this graph, which has no proper labelling showing have 3.0oC of doubling affects temperatures.

    Specifically from 1958 to 1980 CO2 rose from 315 to 339ppm, indicating warming of about 0.31 oC, but there was no warming in the IPCC projections. A rise in CO2 of 315 to 398.5 ppm from 1958 to 2014 would predict 1.0 oC in warming, almost double the actual data and the IPCC projections. Another point is with the “observed temperature”. It is not identified (probably GISTEMP) and ends on the high of 2010.

     

  9. Completely ignores the other greenhouse gases that contribute to warming, such as methane and halocarbons.

     

  10. Claims that sea level rise is another indication of global warming, through thermal expansion. This is not necessarily the case. The average temperature of the ocean is 3.9oC. A rise of to 4.0 oC will have zero expansion. If the rise in sea temperatures is confined to the Arctic or in the deep oceans where temperatures are below 4.0 oC, a rise in temperatures would mean a fall in sea levels. Below I have compiled a graph to show the expansion of a 100metre column of water by 0.1 oC from various starting temperatures.

     

  11. On Arctic Sea ice, is correct in saying that the 40% uptick in the last two years ignores the longer period of data. But in turn, Veritasium ignores evidence pre-satellites that were fluctuations in sea ice. Further, the uptick occurred at precisely the year when previous experts had predicted that summer sea ice cover would disappear. As a consequence, contraction of the sea ice is both less severe and less likely to be linked to human-caused warming than previously thought.

     

  12. Correctly points out that water vapour is the major greenhouse gas, but incorrectly claims to have evidence that water vapour is increasing in the atmosphere. The evidence is from a graphic from a 2007 PNAS paper.

    The evidence from 1900 is the average of 12 models. The confidence intervals are utter rubbish, appearing to be related to the magnitude of the average modelled anomaly. The actual (estimated) data in black does not have a confidence interval. It would appear that this estimated data has a step increase at roughly the time, or slightly before, when the warming stopped in the surface temperature records.

     

  13. Policy justification is totally wrong.

Veritasium says at 5.35

I’m not claiming it’s going to be some sort of crazy catastrophe, but we are going to get more intense storms, more droughts and floods, the oceans will become more acidic, sea levels will rise and my point is it would be better for all species on this planet and probably cheaper for us if we just started reducing emissions now than if we wait and pay the consequences later.

Every economic justification of policy projects “some sort of crazy catastrophe” that human being and other species will not be able to adapt to. Further they project that global emissions reductions will be both effective and relatively costless, which is contradicted by the evidence. But most of all, there is no political proposal in the climate talks that will reduce global emissions in the next twenty years. The proposals may only constrain the rate of increase.

Kevin Marshall

Has NASA distorted the data on global warming?

The Daily Mail has published some nice graphics from NASA on how the Earth’s climate has changed in recent years. The Mail says

Twenty years ago world leaders met for the first ever climate change summit but new figures show that since then the globe has become hotter and weather has become more weird.

Numbers show that carbon dioxide emissions are up, the global temperature has increased, sea levels are rising along with the earth’s population.

The statistics come as more than 190 nations opened talks on Monday at a United Nations global warming conference in Lima, Peru.

Read more: http://www.dailymail.co.uk/news/article-2857093/Hotter-weirder-How-climate-changed-Earth.html#ixzz3KyaTz1j9

Follow us: @MailOnline on Twitter | DailyMail on Facebook

http://www.dailymail.co.uk/news/article-2857093/Hotter-weirder-How-climate-changed-Earth.html

See if anyone can find a reason for the following.

  1. A nice graphic compares the minimum sea ice extent in 1980 with 2012 – nearly three month after the 2014 minimum. Why not use the latest data?

  2. There is a nice graphic showing the rise in global carbon emissions from 1960 to the present. Notice gradient is quite steep until the mid-70s; there is much shallower gradient to around 2000 when the gradient increases. Why do NASA not produce their temperature anomaly graph to show us all how these emissions are heating up the world?

    Data from http://cdiac.ornl.gov/GCP/.

     

  3. There is a simple graphic on sea level rise, derived from the satellite data. Why does the NASA graph start in 1997, when the University of Colorado data, that is available free to download, starts in 1993? http://sealevel.colorado.edu/

     

     

Some Clues

Sea Ice extent

COI | Centre for Ocean and Ice | Danmarks Meteorologiske Institut

Warming trends – GISTEMP & HADCRUT4

The black lines are an approximate fit of the warming trends.

Sea Level Rise

Graph can be obtained from the University of Colorado.

 

NB. This is in response to a post by Steve Goddard on Arctic Sea Ice.

Kevin Marshall

Have 250.000 Spanish jobs been sacrificed for the folly of saving the planet?

Spain is one of the leading countries in Europe for Renewables. In 2013 output broke new records, with renewables accounting for 21.1% of Spanish electricity demand, with wind and hydroelectric power production increasing by 12% and 16%, respectively on 2012.

This is to the detriment of the Spanish economy for three financial reasons.

First is the huge amount now likely being spent on wind power subsidies. In 2013 output from wind farms was about 54GWh, or 12% higher than the 48.5GWh produced in 2012. Assuming an average subsidy of €54MWh (the rate for onshore wind turbines in the UK) that would be €2.9billion in subsidies.

Second, there is the huge amount now likely being spent on solar power. Spain is home to the massive Anadasol Solar Power Station. The three sections are expected to produce 495GWh per year, which at 38% of capacity seems a tad high. This will have a guaranteed price of €270 per megawatt. In the UK, the wholesale price is about £45 or €60 a megawatt. The excess cost (or subsidy) is therefore €210MWh, or €100million a year. At this rate, the total 8.2GWh produced by photovoltaics would have attracted a subsidy of €1.7bn in subsidies.

The combined estimated subsidy is worth €4.6bn is equivalent to 0.3% of GDP. Total subsidies are likely to be much more.

Third is the disastrous foray in solar panels lead to huge amounts of investments in solar schemes. In 2008 there were an estimated 30,000 jobs supported in the boom years. These jobs disappeared with the bust. With this sudden boom, caused by extremely generous subsidies, the quality of the panels was poor and overpriced. Many investors would not have got their money back even if the subsidies had remained. Now they will be saddled in debt, with no income. These borrowing were often state-backed. According to Bloomberg this fund was €24bn at the end of 2011. If some of this has to be written off, then there could be a material impact on deficit reduction plans, and thus the levels of unemployment. Government backing loss-making projects costs jobs.

This claim can be cross-checked. In the same Bloomberg article the Renewable Energy Producers Association (Asociación de productores de energías renovables or APPA) was quoted as saying that the renewables industry sustains about 110,000 Spanish jobs. In 2011 Verso Economics, a Kirkcaldy-based outfit, wrote a report about the effect of renewables jobs in Scotland and the impact on the wider UK. Whilst the report found that the jobs in renewables were largely neutral with Scotland – one job lost in the wider economy for each gained in renewables – in the wider UK economy for each job gained in Scottish renewables 3.7 jobs were lost in the wider UK economy. (report here, and reported at Caledonian Mercury, BBC and Scottish Sceptic) If this were replicated in Spain, the net impact of 110,000 jobs in renewables would be 400,000 jobs less jobs in the wider Spanish economy. Without renewables more than 250,000 people could be in work, or over 1% of the labor force.

Why I call Spain’s attempt to save the planet a folly, are the same reasons for calling Britain’s attempts a folly. Any emissions reductions in Europe will be more than offset by many times over from the emerging economies elsewhere. In reducing emissions, Spain will increase unemployment and reduce growth. But future generations will still bear over 80% of any consequences of warming than if no rich country did anything. In the current situation, I believe that a lot of Spanish people might object to their country being called “rich” anyway.

Update 20/11/14 – minor editing.

Spending Money on Foreign Aid instead of Renewables

On the Discussion at BishopHill, commentator Raff asked people whether the $1.7 trillion spent so far on renewables should have been spent on foreign aid instead. This is an extended version of my reply.

The money spent on renewables has been net harmful by any measure. It has not only failed to even dent global emissions growth, it will also fail even if the elusive global agreement is reached as the country targets do not stack up. So the people of the emissions-reducing countries will bear both the cost of those policies and practically all the costs of the unabated warming as well. The costs of those policies have been well above anything justified in the likes of the Stern Review. There are plenty of British examples at Bishop Hill of costs being higher than expected and (often) solutions being much less effective than planned from Wind, solar, CCS, power transmission, domestic energy saving etc. Consequences have been to create a new category of poverty and make our energy supplies less secure. In Spain the squandering of money has been proportionately greater and likely made a significant impact of the severity of the economic depression.1

The initial justification for foreign aid came out of the Harrod and Domar growth models. Lack of economic growth was due to lack of investment, and poor countries cannot get finance for that necessary investment. Foreign Aid, by bridging the “financing gap“, would create the desired rate of economic growth. William Easterly looked at 40 years of data in his 2002 book “The Elusive Quest for Growth“. Out of over 80 countries, he could find just one – Tunisia – where foreign aid conformed to the theory. That is where increased aid was followed by increased investment which was followed by increased growth. There were plenty examples of where countries received huge amounts of aid relative to GDP over decades and their economies shrank. Easterly graphically confirmed what the late Peter Bauer said over thirty years ago – “Official aid is more likely to retard development than to promote it.

In both constraining CO2 emissions and Foreign Aid the evidence shows that the pursuit of these policies is not just useless, but possibly net harmful. An analogy could be made with a doctor who continues to pursue courses of treatment when the evidence shows that the treatment not only does not work, but has known and harmful side effects. In medicine it is accepted that new treatments should be rigorously tested, and results challenged, before being applied. But a challenge to that doctor’s opinion would be a challenge to his expert authority and moral integrity. In constraining CO2 emissions and promoting foreign aid it is even more so.

Notes

  1. The rationale behind this claim is explored in a separate posting.

Kevin Marshall

The Climate Policy Issue Crystallized

There is a huge amount of nonsense made about how the rich industrialized countries need to cut carbon emissions to save the world from catastrophic global warming. Just about every climate activist group is gearing up to Paris 2015 where at last they feel that world agreement will be reach on restraining the growth of greenhouse gas emissions. Barak Obama will be pushing for a monumental deal in the dying days of his Presidency. There is a graphic that points out, whatever agreement is signed attempts to cut global emissions will be a monumental failure. It comes from the blandly named “Trends in global CO2 emissions: 2013 report” from the PBL Netherlands Environmental Assessment Agency. In the interactive presentation, there is a comparison between the industrialised countries in 1990 and 2012.


In over two decades the emissions of the industrialised countries have fallen slightly, almost entirely due to the large falls in emission in the ex-Warsaw Pact countries consequent on the collapse in the energy-inefficient communist system. In the countries formerly known as the “First World” the emissions have stayed roughly the same. It is the developing countries that account for more than 100% of the emissions increase since 1990. Two-thirds of the entire increase is accounted for by China where in less than a generation emissions quadrupled. Yet still China has half the emissions per capita of United States, Australia or Canada. It emissions growth will slow and stop in the next couple of decades, not because population will peak, or because of any agreement to stop emissions growth. China’s emissions will peak, like with other developed countries, as heavy industry shifts abroad and the country becomes more energy efficient. In the next 30-40 years India is likely to contribute more towards global emissions growth than China. But the “remaining developing countries” is the real elephant in the room. It includes 1050 million people in Africa (excluding South Africa); 185m in South America (excluding Brazil); 182m in Pakistan; 167m in Bangladesh, 98m in Philippines and 90m in Vietnam. The is over 2000 million people, or 30% of the global population that do not currently register on the global emissions scale, but by mid-century could have emissions equivalent to half of the 1990 global emissions. To the end of the century most of the global population increase will be in these countries. As half the countries of the world are in this group any attempt to undermine their potential economic growth through capping emissions would derail any chance of a global agreement.

Hattip Michel of trustyetverify

Kevin Marshall

Ivanpah 392MW Solar Plant a green energy failure even at the planning stage

The Hockey Schtick blog specializes in summarizing scientific papers that have a sceptical leaning. A couple of days ago it posted about the World’s largest solar energy plant applying for a $539million federal grant to help pay off a $1.5 billion federal loan. The Ivanpah solar electric generating plant is owned by Google and renewable energy giant NRG. Google can certainly afford to bear these loses. At the end of 2013 its accounts state that it had Cash, Cash Equivalents & Marketable Securities of $58,717million, $10,000million than the year before.

Technologically the Ivanpah plant sounds impressive. Problem is that in it’s first year of operation it produced one quarter of the projected electricity. As a minor consequence, it was projected to scorch 1,000 birds a year. Instead it is 28,000 in the first year. A three minute summary is at Fox News.

But even at the planning stage there was either no proper business plan presented, or at least no proper scrutiny like a bank would do when making a loan. 1065,000 MWh annually from a 392 MW nameplate is a planned output of 31% of capacity. Even accepting that figure, a $2bn investment with a 20 year payback (zero discount rate) is still nearly $100 MWh. A 10 year payback is much more reasonable. Add maintenance and operating costs easily gets to $200 MWh. A small utility company in Wisconsin buys in extra electricity for $30 MWh. So the planned cost was 6-7 times the wholesale price of electricity.

Maybe this was justified in saving the planet?

The AR4 synthesis report of 2007* said that peer-reviewed estimates of the social costs of carbon from averaged on 2005 $12 per tonne of CO2, but the range from 100 estimates is large (-$3 to $95/tCO2). If we take the bold assumption that the theoretic output of this plant would entirely replace the electricity from a typical coal-fired power station producing 900kg of CO2 per MWh, then the saving is $190t/CO2, or double the very top-end 2005 estimate, or 15 times the average estimate. For some reason, the Social Cost of Carbon is missing from the

Suppose the US was “really serious” about doing its bit to save the planet and tried to cut its CO2 emissions by 80%. In round figures, in 2013 that was 5 billion tonnes of CO2 equivalent (source CDIAC). Using similar schemes, it would cost $760bn a year or 5% of 2013 GDP of $16.8trn. Remember, that is if similar schemes are successful. The Ivanpah solar plant does not look like a success.

 

* For some reason, the Social Cost of Carbon is missing from the AR5 Synthesis Report published on November 1st. I would guess the reason that it has fallen out of favour is that the marginal abatement costs are much larger than the highest estimates, and the cost of doing nothing per tonne of CO2 are about zero.

Kevin Marshall

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