Month: June 2019

Himalayan Glaciers–The Story The BBC Refuse To Tell You

By Paul Homewood

 

 image

Images from Cold War spy satellites have revealed the dramatic extent of ice loss in the Himalayan glaciers.

Scientists compared photographs taken by a US reconnaissance programme with recent spacecraft observations and found that melting in the region has doubled over the last 40 years.

The study shows that since 2000, glaciers heights have been shrinking by an average of 0.5m per year.

The researchers say that climate change is the main cause.

"From this study, we really see the clearest picture yet of how Himalayan glaciers have changed," Joshua Maurer, from Columbia University’s Lamont-Doherty Earth Observatory in New York, told BBC News.

https://www.bbc.co.uk/news/science-environment-48696023

 

As usual the BBC fail to explain the wider picture.

Glaciers worldwide have been retreating since the mid 19thC, which marked the ending of the Little Ice Age. The Himalayas are no exception.

This is what the first IPCC Report had to say in 1990:

 

image_thumb91

https://www.ipcc.ch/report/ar1/wg1/

Note the comment about the period 1920 to 1960.

They add this chart:

image_thumb92

And comment:

 

image_thumb93

 

And:

 

image_thumb94

 

In other words, glacier melt may in large part be due to natural phenomenon, rather than man-made.

The rate of recession since the 19thC has not always been constant, as the IPCC noted:

 

Wood (1988) found that from 1960 to 1980 the number of retreating glaciers decreased This may be related to the relatively cool period in the Northern Hemisphere over much of this time (Figure 7 10)

 

In other words, the fact that the rate of retreat seems to have speeded up in the Himalayas in recent years is of little significance, at least for such a short period of time.

Moreover recent studies have found that many glaciers in the Himalayas have actually started growing again in recent years:

 

image

Contrary to the UN’s report that the Hima­layan glaciers would melt within a quarter of a centu­ry, a new study by research­ers at the Universities of California and Potsdam has found out that the Himala­yan glaciers are advancing rather than retreating.

Researchers studied 286 glaciers in six areas between the Hindu Kush on the Af­ghan-Pakistan border till Bhutan.

The report published in the journal Nature Geosci­ence found that the key fac­tor affecting the advance or retreat of the Himalayan glaciers is the amount of debris— rocks and mud— strewn on their surface and not the general nature of cli­mate change.

The report states that glaciers surrounded by high mountains and covered with more than two centimetres of debris are protected from melting.

Debris-covered glaciers are common in the rugged central Himalayas, but they are almost absent in sub­dued landscapes on the Ti­betan Plateau, where retreat rates are higher.

In contrast, more than 50 percent of observed glaciers in the Karakoram range spanning the borders be­tween Pakistan, India and China region in the north-western Himalayas are ad­vancing or stable, states the report.

“Our study shows that there is no uniform re­sponse of Himalayan gla­ciers to climate change and highlights the importance of debris cover for under­standing glacier retreat, an effect that has so far been neglected in predictions of future water availability or global sea level,” the authors wrote in the journal.

Contrary to popular be­lief, researchers have also discovered that half of the ice flows in the Himalayas are actually growing rather than shrinking.

The discovery adds a new twist to the row over whether global warming is causing the world’s highest mountain range to lose its ice cover.

The new study has found that half of the glaciers in the Karakoram range in the north-western Hima­layas are in fact advancing and that global warming is not the deciding factor in whether a glacier survives or melts.

https://web.archive.org/web/20110207213137/http://www.bhutanobserver.bt/himalayan-glaciers-not-retreating-says-new-report/

 

The real picture is much more complex than the BBC misleadingly portray.

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June 30, 2019 at 12:33PM

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Climate Faith ≠ Climate Works

Protestors march to raise awareness of climate change and ecological issues on the second day of the Glastonbury Festival at Worthy Farm, Somerset, England, Thursday, June 27, 2019. (Photo by Grant Pollard/Invision/AP) GRANT POLLARD/INVISION/AP

Michael Lynch writes at Forbes Is The Climate Change Debate A Replay Of The Reformation? Excerpts in italics with my bolds.

During the Reformation, there was an intense debate over whether Christians could enter paradise by doing good works, or whether faith alone allowed such a benefit. (See Fatal Discord: Erasmus, Luther and the Fight for the Western Mind by Michael Massing) This reminds me of the current attitude many have towards climate change policy, where some appear to think that faith alone is sufficient to solve the problem.

In the early days of the global warming debate, I read an English writer praising his country’s example of recognizing climate change compared to American skepticism, although he did admit the British hadn’t actually taken steps to address the problem. Similarly, the U.S. has reduced greenhouse gas emissions more than most countries in the past few years, but incidentally, mostly due to cheap natural gas, and it remains the climate villain in the eyes of many because the president is a denier.

Additionally, a lot of energy, well, effort, goes into demonizing actors or actions that have no practical impact on climate. For example, opposing the construction of oil and gas pipelines does not reduce consumption of oil and gas, and usually increases emissions. Suing the oil or auto industries for blocking climate policies or misleading the public about climate science appeals to many, but with no measurable environmental impact. The same with demanding divestment in fossil fuel company stocks.

Some of the new proposals to address climate change put me mind of the debate between faith and works, especially when they seem more for demonstration purpose than actually reducing emissions. Numerous governments have suggested phasing out all carbon-based electricity generation or all petroleum-fueled vehicles by a point decades into the future, and these tend to be hailed by activists as representing, if not solutions, then great strides forward. New York state, for example, just proposed phasing out carbon-based electricity by 2050; France wants to ban conventional vehicles by 2040, the U.K. by 2050. But as Michael Coren notes, “So far, it’s just words.”

Which reminds me of comedian Billy West who, in the persona of a radio personality, bragged to someone about his fund-raising, adding, “…but mostly it’s just pledges.” Governments have been great at setting goals, but implementation has been seriously lacking. The setting of goals seems more an act of faith than a carrying out of works.

And we have been here before. Many other national and sub-national environmental programs were later abandoned; the 1990s saw California enact mandates for electric vehicle sales—requiring 10% of sales in 2003 be zero emission vehicles—which was adopted by a number of other states, primarily in New England. Ultimately, it was abandoned after wasting billions of dollars. Numerous locales in the U.S. signed on to requirements for oxygenated gasoline, only to back out at the last minute when the cost became apparent.

Technology mandates are a mix of demonizing the producers and demonstrations of faith: telling utilities to buy a certain portion of carbon-free electricity is calling on someone else to act, while hiding the cost of the action. Those who believe in works would do better to buy their own renewable power, either producing it directly or from an independent power producer.

Automobile efficiency standards arguably fall into this category as well, that is, making it seem as if the manufacturers are to blame for consumers’ desire to purchase large, powerful vehicles. There are very fuel-efficient vehicles for sale in the United States, and they are much cheaper than the sauropods dominating American highways, so addressing manufacturer behavior is not the issue. Mandating vehicle efficiency is rather like demanding that a portion of butchers’ sales be veggie burgers; Beyond Meat has shown that success for veggie burgers comes from satisfying consumers, not lecturing them on environmental ethics.

This is where a carbon tax comes in: it is designed to change consumer preferences, reducing carbon emissions in favor of other consumables. It would also motivate producers to meet the demand for products that require less carbon emissions, either in their production or operation. Although the impact would grow over time, it would begin immediately upon implementation, and while it could theoretically be reversed, taxes on consumption tend to be extremely persistent.

Comment:

I like the author’s comparing of the climate faithful marching in processions to the religious faithful marching on Holy Days. He is right to point out the hypocrisy of of those obsessed over CO2 demonstrating their belief, while still enjoying fossil fuel benefits. And he ridicules the symbolic but ineffectual policies proposed, noting they are merely another form of showing faith rather than taking action that works.

But he ends up accepting the warmist unproven premise: We are sinners because we burn fossil fuels. Moreover, he seems to suggest that imposing a carbon indulgence tax overcomes the moral shortcoming. In fact Reformers strongly opposed the Catholic Church practice of taking money for future benefits which they could not deliver. Now this scam returns with governments taking the opportunity to fill their coffers. Further, as Bill Gates explained, the tax has a faulty premise: There is presently no substitute for fossil fuels powering modern societies.

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June 30, 2019 at 11:29AM

A simple model: when capacity of solar and wind increase

In previous post, I described the particular dynamics in which electricity production from intermittent energy sources, when growing in capacity, will not increase much at the production valleys, but will steeply increase at the production peaks. This means that, when capacity increases, the needed backup capacity will stay high, even at multiples of the current capacity, but at the same time measures have to be taken to suppress the ever growing peaks.

I illustrated this with a (celebrated) record high of wind production on June 8, followed by a (neglected) low production (June 9). In less than 12 hours, the production fell from almost 3,000 MWh (capacity factor of 81%) to almost 20 MWh (capacity factor of 0.5%). This illustration was only for electricity production by wind energy. There is a complicating factor: solar is also an intermittent energy source and can intensify as well as dampen the effect of wind.

That made me wonder how this interaction would look like when capacity of solar and wind increases over time. In real-life, this is not witnessed yet, this is still to come. It is however possible to study the dynamics of such a system by modeling it.

As I already mentioned several times, I have no problem with mathematical models per se. These are useful tools to study a certain mechanism. Models surely have their limitations though. Their outcome is not necessarily what will happen in reality and the more (uncertain) factors are involved, the more uncertain this outcome will be.

However, the model of the dynamics of intermittent energy on a service based grid is at its core rather simple, there are not that many factors involved and there are detailed measurements of all of those.

This post will be about a very simple model that I created. Maybe I will add more functionally in the future. The program of my choice to build this model is Python and the Pandas module. This module is used for data analysis and can handle large datasets. It is very powerful and lightning fast compared to what I used before. Graphing is done using the matplotlib module.

This is how the simple model works:

  1. Loading in the data of a reference year (in this case 2018):
  2. Sum the value of solar production and wind production for every timeslot (quarter hourly)
  3. Use the multiplier. I used the sum solar + wind times two, three (as expected by 2030), four, five and six (as expected by 2050). I will show graphs for current capacity and the three and six times current capacity
  4. Compare this with the total load data.

If there is one datapoint missing from or solar or wind or total load, then this timeslot is discarded. There were only 17 discarded timeslots in 2018.

I assume a linear relationship between capacity and production. Meaning that if capacity of solar and wind doubles, then I assume that the production of electricity by solar and wind would also double in the same situation. These simulations are in fact what-if questions: what would have been the effect if in 2018 the capacity of solar and wind was two, three, four, five or six times the capacity?

The model surely has its limitations. For example, I use a reference year, but every year is different from the previous and the next. There will be slightly different numbers of minimum and maximum production and these will occur on different dates. I am however not interested in what happens at individual timeslots, I want to view an overall effect. I also ran the model with the reference years 2016 and 2017, the overall outcome of those runs is very similar.

Another limitation is that the share of solar and wind might change in the future. At the end of 2018, solar had a capacity of 3,369.05 MW, wind had a capacity of 3,757.185 MW. If wind increases relatively more than solar, then the end result might differ. Also here, I am not trying to predict anything, I just want to find some general trend and see what will happen with the dynamic range when capacities increase.

Only solar and wind data goes into the model. There is no data of other power sources (natural gas, import, pumped storage, nuclear and so on). I consider those as a black box. I currently only look at shortages or overproduction of solar and wind. What will replace the missing power or what will be powered back/exported when overproduction is not something that I am currently interested in.

While running the model for the first time, I realized that the capacity of solar and wind had increased during that year. This made it difficult to distinguish between the effect of those energy sources on the system and the effect of the increase of the capacity. Therefor I converted all production values to the capacity of the last day of 2018 (3,369.05 MW for solar and 3,157.185 MW for wind). Again, The goal of the model is to discover the pure effect of those energy sources on the system and this capacity increase in the reference year might interfere with that.

That being said, this is the production of solar and wind of the reference year:

solar wind model charts007a solar and wind production 1x reference year: 2018

(It is not a coincidence that the y-axis rises well above the maximum 13,000 MW of total load. Those who read the previous post will probably understand why I left so much space on the y-axis).

A first observation is that solar and wind power production stayed well below demand in 2018. Which is not surprising, it is still a small part of our energy supply.

It is also clear that power demand is higher in winter than in summer at our latitude. This because the days are shorter and it is colder, so we stay more indoors and need more lighting, heating and so on.

When viewing this graph at a higher magnification (click to see the larger version), then there is a difference in appearance of the solar + wind line. Peaks are higher in summer and more frequent than in winter. This has to do with the abundance of solar energy in summer and the lacking of it in winter. However, monthly averages of solar plus wind production are roughly in the same ballpark (except for the first three months of 2018). There is more solar power production in summer than in winter and there is more wind power production in winter than in summer. On average one compensates the other. I also came to that same conclusion in a previous post (however, high production does not necessarily coincide with high demand).

In the first three months there was a lot more wind than in the last month of 2018 and also more than in the first months of 2019. So higher production is possible, but it is not guaranteed.

The fact that production of solar and wind power is well below power demand doesn’t necessarily mean that no problems are expected. There were already issues with the stability of the grid with lower capacities than this. This occurred at times when there was a lot of sun and wind, a low demand and other power sources not being able to power down/off quick enough. This is happening in the black box though, so I will not go into detail here.

This is when capacity increases three times (expected by 2030):

solar wind model charts007a solar and wind production 3x reference year: 2018

Notice the difference in dynamic range between this graph and the previous one. In the reference year, production of solar and wind goes from 2 MWh to 4,338 MWh. In the 3x scenario, it goes from 6 MWh until 13,015 MWh.

The reason for the oversized y-axis becomes clear when capacity increases six times (expected in 2050):

3x solar wind production reference-year: 2018, thumb size

I used the same y-axis in all three cases so I could compare the three graphs with each other. Dynamic range now stretches along the entire y-axis.

The more the capacity increases, the more balancing mechanisms will be needed. Not only those measures to add more power when there is not enough (fast cycling dispatchable power sources, import, pumped storage, demand response,…), but also those when there is too much power (export, pumped storage, demand response,…). Our politicians are not in a particular hurry to take those measures. They are very focused on adding more solar and wind capacity, not in adding the installations that will balance the intermittent power. That is not really surprising. Adding solar and wind to the mix made energy prices soar and then it might be difficult to admit that adding solar and wind was only the first step. The next step is installing (expensive) balancing systems…

There is another way to view the same data: relative to total load. Total load is then at zero and everything below is a shortage (less energy produced than consumed), everything above overproduction (more energy produced than is consumed). This is the reference year 2018:

1x solar wind share reference-year: 2018, small size

Because there is no overproduction the blue line (surplus) is a straight line glued to zero.

The deficit curve bends upwards in the middle because in summer we need less electricity and there is more electricity from solar installations. Meaning less deficit that has to be filled in by some other energy source.

Now 3 times solar plus wind compared to the total load line:

3x solar wind share reference-year: 2018, small size

Solar and wind stay mostly below total load, but already some peaks stick out. At those peaks, all other power sources have to power off or some other balancing measures needs to be in place, whether it is fast backup, storage, export (get more difficult when other countries bet on the same horse and will have the same problem), curtailment, use the excess production for something else, …) to be able to balance this system.

This is what we get when 6 times solar plus wind is compared to total load:

6x solar wind share reference-year: 2018, small size

After increasing the 2018 capacity by six times (that is 20,214 MW installed solar capacity plus 18,942 MW installed wind capacity), deficits are still larger than surpluses.

Here are some numbers that the model spitted out for all multipliers:

Multiplier Minimum needed backup
(MW)
Lowest production
(MW/quarter-hour)
Highest production
(MW/quarter-hour)
Total overproduction
(GWh)
Total shortage
(GWh)
1 13,251 2 4,338 0 77,066
2 13,191 4 8,677 0 66,685
3 13,131 6 13,015 82 56,386
4 13,071 9 17,354 884 46,807
5 13,592 11 21,692 3,496 39,037
6 13,013 13 26,031 8,204 33,364

It is clear that the minimum needed backup (the largest deficit of the year) stays roughly the same. Previous years had also similar deficit numbers. Which is also not that surprising: multiply a tiny number by three or by six or even by ten will make not much difference. Multiplying a small number just results in another small number (albeit somewhat bigger than the previous). Subtracting this small number from the total load will result in a deficit that barely get smaller. Even multiplying by 12, the minimum needed backup stayed high (still 12,741), but at the same time maximum production skyrocketed to four times the maximum demand in 2018).

The next questions now are whether theoretically some kind of storage could be used to top off the peaks and fill in the valleys (therefor balancing the grid) and what is the theoretical multiplier to be able to balance the grid with solar and wind only. That is for a next iteration of the model.

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June 30, 2019 at 11:22AM

More Failed Predictions: May Was The Second Wettest Month In US History

Credit: weather.com

H/T Climate Change Dispatch

An obvious clue in this report is the mention of the jetstream, which is not known to be related to minor trace gases in the atmosphere, despite wishful thinking in some quarters. Why do leaders ignore these failures of climate science, yet listen avidly to misguided doomsayers demanding vast spending and taxes?

National Oceanic and Atmospheric Administration reported the month of May was the second wettest and temperatures were in the bottom-third for its 125-year US history, reports American Thinker.

The 2010 publication titled, ‘A Global Overview Of Drought and Heat-Induced Tree Mortality Reveals Emerging Climate Change Risks for Forests’, was accepted by the Obama administration as scientific evidence that climate change had made the Earth:

“…increasingly vulnerable to higher background tree mortality rates and die-off in response to future warming and drought, even in environments that are not normally considered water-limited.”

But NOAA just reported that May US precipitation totaled an average of 4.41 inches, 1.50 inches above average, and ranked second wettest in the 125-year period of record for May as well as second wettest for all months since January 1895.

The only wetter month in US history was May 2015 with 4.44 inches of precipitation.

The 37.68 inches of precipitation across the contiguous U.S. from June 2018 to May 2019 shattered the previous 1982-83 12-month period by 1.48 inches.

Near-record to record precipitation was observed from the West Coast through the central Plains and into the Great Lakes and parts of the Northeast.

As a result, severe May flooding was observed along the Arkansas, Missouri, and Mississippi rivers. Vicksburg, MS, reported ongoing flooding since mid-February.

A southward dip in the jet stream over the western contiguous U.S. during May contributed to above-average late-season snowfall, with Denver reporting its snowiest May in 44 years with 3.9 inches total for the month.

Duluth, MN, reported 10.6 inches of snow on the May 9 for the snowiest day since records were first kept in 1884.

May’s average contiguous U.S. temperature was 59.5°F, 0.7°F below the 20th-century average and ranking in the bottom third of the 125-year record.

Read more here.

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June 30, 2019 at 09:13AM