Vulnerabilities

Posted on March 27, 2018 by climatechangefork

My last five blogs (starting on February 20, 2018) have focused on some key indicators of the global energy transition as they relate to climate change and the IPAT identity. I examined the 12 most populous countries, which together represent more than half of the world’s population as well as the full spectrum of economic development. I also looked at five small, developed countries that are at the forefront of the transition into a more sustainable energy mix. I presented almost all of the indicators in this series on a per person basis so that we could compare countries with different populations.

This blog will open a new series that will present both the global picture and the specific data for the same set of countries in terms of vulnerabilities. Over the nearly six years since I started this blog, I have repeatedly mentioned that the main driving force for climate change is our disruption of the global energy cycle through our energy use. Most of the biggest impacts have occurred via our disturbance of the global water cycle. I have also said that this is not just about our future; there are early signs of impacts that are already taking place.

The vulnerabilities that we will talk about are existential but they do not take place uniformly throughout the planet. As a result, many people are trying to escape from vulnerable areas to more stable ones. Since our global governance system relies on sovereign states, the flux of environmental refugees is now awakening jurisdictional issues that never occurred to most of us (especially in light of the increasing number of political refugees). This series of blogs will mix the vulnerabilities to changes in the climate with the rise in people leaving their climate-affected countries in search of safer places for themselves and their families – often against the wishes of their target countries.

I am starting here with four important water-related indicators: employment in agriculture (% of total employment); agricultural value added; annual fresh water withdrawal (% of internal resources); and population living in areas where elevation is at or below five meters above sea level (% of total population). I am sourcing all of my statistics from the World Bank database.

Figures 1 and 2 show the global trend in the first two indicators over the last 20 years. We see a sharp decline both in terms of global employment in agriculture and in the fraction that agriculture contributes to GDP. The trend, coupled with the large increase in global population that took place over the same period strongly suggests that the agricultural industry is becoming much more efficient in feeding the growing global population.

Figure 1 – Global employment in agriculture (% of total employment)

Figure 2 – Global agricultural value added (% of GDP)

However, when we refocus our attention to individual countries, the situation changes.

Table 1 – Indicators of water-related vulnerabilities to climate change impact among the world’s most populous countries

Table 2 – Same indicators as in for five small, developed countries that are ahead in their energy transitions

Agriculture is very sensitive to climate conditions, especially when it is dependent on natural precipitation. Rich countries can produce food using many fewer workers and the activity constitutes a small part of the GDP. In poor countries the situation is markedly different. A large percentage of the employment in countries such as India (44%), Indonesia (31%), Pakistan (42%), Bangladesh (41%), Ethiopia (71%), and DR Congo (65%) is agricultural. When long-term droughts hit, people are driven from their plots. They must move to places that give them better chances of survival. In the same line, extraction of fresh water from nonrenewable sources reaches (or exceeds) dangerous levels in poor countries that lack the resources to supplement their water with sources such as desalination. I will expand on the issue of regional water stress in future blogs.

Annual fresh water withdrawal can exceed 100% when extraction from nonrenewable sources becomes significant (this correlates with water stress). The last column maps the percentage of the population that lives below 5 meters above sea level – those most susceptible to climate change-driven sea level rise. Nine percent of the population of Bangladesh amounts to 15 million people vulnerable to perpetual flooding threat. In China the 7% of the population in these circumstances amounts to a staggering 100 million people.

My next blog will focus on some of the global consequences behind these numbers.

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Energy Transition: Regional Impacts and Highlights

Posted on March 20, 2018 by climatechangefork

I started this series (February 20, 2018) by introducing energy-related indicators for the ten most populous countries (with the addition of two African countries that are projected to join those ranks by 2040). I aim to use these indicators as markers for the ongoing energy transition, revisiting them periodically to inform us on our progress. Hopefully they will also demonstrate steps that can better prepare us for this transition.

The selected twelve countries represent more than half of the world’s population, as well as the full spectrum of economic wellbeing as characterized by GDP/Capita. These tables include only one fully developed country – the US. Most of the other countries are still struggling to offer their citizens the standard of living already enjoyed by richer countries. To balance my checkup on global progress I have also included the performance of five small, wealthy countries that have the resources to mitigate the environmental impact of their energy use. In almost all cases the indicators were represented on a per-capita basis so that we could quantitatively compare different countries regardless of their populations.

Selected indicators include population (from the UN), GDP/Capita (from the World Bank) and the energy and emissions statistics from the most recent British Petroleum (BP) review.

The BP site also features a section on regional impacts that includes projections for 2040 energy indicators. Among those regions are: Africa, Brazil, China, the European Union, Indonesia, the Middle East, Russia, the UK, and the US.

I will try to summarize the series of indicators by using two methods: the first is to encapsulate the global perspective via graphic representation. Second, I will include a direct comparison between BP’s projections for the energy indicators of China and the US in 2040. In a previous blog (February 27, 2018) we saw that China and the US now account for 43% of global carbon emissions. So what these two countries do in the near future is of prime importance from the global perspective.

Figure 1 shows regional non-hydro renewable power generation as compiled by The Economist.

Figure 1

Figure 2 shows a broader picture of global power generation by energy source, as compiled by the US Energy Information Administration (EIA).

Figure 2

Figure 3 shows a detailed examination of distribution of renewable energy use by the countries within the European Union. Here again, The Economist is sourcing its figures from the BP database.

Figure 3 

The two tables below show direct comparisons between BP’s projections of the energy transitions in China and the US.

Table 1 – Summary of 2040 projections of the US and China’s energy indicators

Table 2 – Trends in 2040 projections of the US and China’s energy indicators

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Fossil Fuel Preferences and BP’s Energy Outlook

Posted on March 13, 2018 by climatechangefork

I started this series on February 20, 2018 to explore the IPAT identity. The last term within that identity that I have yet to cover includes the nature of the fossil fuels used. The popular perception is that use of coal is down while use of natural gas has risen. This blog examines this issue with the same set of countries that I have studied in the last few blogs.

As before, I am using statistical data from British Petroleum (BP). I will end this blog by citing BP’s short summary of how it views the nature of the energy transition that we are undergoing.

Next week I will conclude this series by using graphics from different sources to demonstrate the trends expressed in this series. I will also add BP’s regional summary of trends, including those within Africa, China and the US.

Table 1 illustrates trends in the use of coal and natural gas by the world’s twelve most populous countries (whose combined populations account for more than half the global total and the full spectrum of economic development). Table 2 includes the five much smaller, developed countries that we have used throughout this series as examples of sovereign states farther along in their energy transitions.

The data in both tables are expressed as percentages of the total primary energy use:

Table 1 – Indicators related directly to carbon emissions of the 12 most populous countries

 

Table 2 – Same indicators as in Table 1 for the world and five small, developed countries that are ahead in their energy transitions

BP provides a summary of what it calls Energy Outlook based on present and past performances on its website:

BP Energy Outlook

The Energy Outlook explores the forces shaping the global energy transition out to 2040 and the key uncertainties surrounding that transition. It shows how rising prosperity drives an increase in global energy demand and how that demand will be met over the coming decades through a diverse range of supplies including oil, gas, coal and renewables.

The speed of the energy transition is uncertain and the new Outlook considers a range of scenarios. Its evolving transition (ET) scenario, which assumes that government policies, technologies and societal preferences evolve in a manner and speed similar to the recent past, expects:

  • Fast growth in developing economies drives up global energy demand a third higher.
  • The global energy mix is the most diverse the world has ever seen by 2040, with oil, gas, coal and non-fossil fuels each contributing around 25%.
  • Renewables are by far the fastest-growing fuel source, increasing five-fold and providing around 14% of primary energy.
  • Demand for oil grows over much of Outlook period before plateauing in the later years.
  • Natural gas demand grows strongly and overtakes coal as the second largest source of energy.
  • Oil and gas together account for over half of the world’s energy
  • Global coal consumption flat lines with Chinese coal consumption seeming increasingly likely to have plateaued.
  • The number of electric cars grows to around 15% of the cars, but because of the much higher intensity with which they are used, account for 30% of passenger vehicle kilometers.
  • Carbon emissions continue to rise, signaling the need for a comprehensive set of actions to achieve a decisive break from the past.

Looking forward to 2040

  • Extending the Energy Outlook by five years to 2040, compared with previous editions, highlights several key trends.
  • For example, in the ET scenario, there are nearly 190 million electric cars by 2035, higher than the base case in last year’s Outlook of 100 million. The stock of electric cars is projected to increase by a further 130 million in the subsequent five years, reaching around 320 million by 2040.
  • Another trend that comes into sharper focus by moving out to 2040 is the shift from China to India as the primary driver of global energy demand. The progressively smaller increments in China’s energy demand – as its economic growth slows and energy intensity declines – contrasts with the continuing growth in India, such that between 2035 and 2040, India’s demand growth is more than 2.5 times that of China, representing more than a third of the global increase.
  • Africa’s contribution to global energy consumption also becomes more material towards the end of the Outlook, with Africa accounting for around 20% of the global increase during 2035-2040; greater than that of China.

You can compare the BP Energy Outlook with the data in the last four blogs.

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Electricity Generation

Posted on March 6, 2018 by climatechangefork

This week, I’m looking at the role of electricity in the ongoing global energy transition. Dieter Helm argued (see the February 13, 2018 blog about his book, Burn Out) that our increased usage of electricity is an indicator of our decreasing reliance on oil companies as the main stewards of our energy needs. It seems to me that Prof. Helm came to his conclusions mainly by observing trends within developed countries, emphasizing those countries’ growing focus on electric cars. His book, among other popular media, portrays electric cars as a triumph in decreasing our dependence on fossil fuels. Unfortunately, few of these sources say much about how we generate the electricity to power these cars.

All the data in this blog come from the World Bank database.

Figure 1 – Global electric power consumption in kwh/capita

Figure 1 demonstrates a steady increase in the global use of electricity that far outpaces the population growth, GDP increase, or increase in global primary energy use that I described in last week’s blog. Figure 2 shows the role that renewable energy has played in the global electricity output. It clearly reaches its lowest point around 2003, after which it grows steadily.

Figure 2 – Renewable energy in electricity output (% of total electricity output)

Tables 1 and 2 reference those shown in previous blogs and show us global trends, as represented by the twelve most populous countries (whose combined populations account for more than half the global total and the full spectrum of economic development).

Table 1 – Indicators related to electricity consumption of the 12 most populous countries

Table 2 – Same indicators for five small, developed countries that are ahead in their energy transition and three different global entities

The tables show 100% access to electricity for the high-income countries (the five in Table 2 and the US in Table 1), upper-middle income countries (China, Brazil, Russia, and Mexico in Table 1), and two of the lower-middle income countries (Indonesia and Pakistan). However, just counting within the countries in Table 1 (India, Nigeria, Bangladesh, Ethiopia, and Democratic Republic of Congo) more than 500 million people don’t have access to electricity. Globally, the number exceeds 1 billion. There is no question that for these countries, providing universal access to electricity and everything else that such access affords is the top priority – ahead of any environmental consideration.

Focusing now on the consumption of electricity in countries that have universal access and correlating it with the wealth of these countries, one can observe interesting trends. Let’s look at the high- and upper-middle income countries from both tables. The full ranges and means of the GDPs/Capita (2016) and electricity consumptions (2014) of both groups can be summarized as follows. Both reference years are the latest available on the World Bank database.

GDP/Capita of high-income countries = $52,800 ±11,000

GDP/Capita of upper-middle income countries = $8,400 ±295

Electricity consumption (in kwh/capita) of high-income countries = 13,000 ± 3,000

Electricity consumption (in kwh/capita) of upper-middle income countries = 3,800 ±200

The electricity consumption per GDP for the high-income countries is 0.25 kwh/US$ and that for the upper-middle income is 0.45 kwh/US$. As in many other circumstances, the US$ is less expensive in lower income countries compared to the rich ones (economists call the exchange rate Purchasing Power Parity).

Hydroelectric power plays a critical role in the variability of individual countries’ abilities to generate electricity using renewable energy sources. This is observable in the countries with a negative difference in the percentage of renewable energy needed to generate electricity. Ethiopia and DR Congo, which are listed as using close to 100% renewable energy, owe that accomplishment to hydroelectricity. Similarly, 67% of Norway’s energy comes from hydroelectricity. The other countries in Table 2 use hydroelectricity for approximately 30% of what they need for their electricity production. While an extremely important renewable source, hydroelectric facilities produce variable units of electric power, depending on changes in climate and weather.

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Primary Energy

Posted on February 27, 2018 by climatechangefork

As promised, this blog and the next (barring unforeseen circumstances) will focus on some key indicators of the global energy transition – specifically with regard to climate change and the IPAT identity. I am continuing my study of the same 12 most populous countries, which together represent more than half of the world’s population as well as the full spectrum of economic development. Almost all of the indicators will be presented on a per person basis that will enable us to compare countries with different populations.

As before, the population data are based on the UN accounting and the GDP data comes from the World Bank. I have sourced the primary energy and carbon data from BP’s annual statistical review of global energy, which I consider one of the most reliable and politically neutral sources for this kind of data.

Unfortunately BP does not include specific data for Nigeria, Ethiopia or the Democratic Republic of Congo. In its section on Africa, BP only lists Algeria, Egypt, South Africa, and “the rest of Africa.” This is likely because of the individual countries’ low GDPs/Capita and their comparatively low carbon emissions and energy use. That said, the omission of Nigeria in a database focused on energy baffles me. For the moment I won’t try to fill this gap with information from a different database. In the future I may revisit this decision.

My perspective of human equality informs my choice to present data in its intensive form (per person). The only extensive presentation (not dividing by population) that I am including is the share of global carbon emissions. This is important because regardless of the degree to which we contribute to it, we all share the impacts of carbon emissions.

Table 1 – Indicators directly related to carbon emissions of the 12 most populous countries

Table 2 – Same indicators for five small, developed countries that are ahead in their energy transition and three different global entities.Carbon emissions indicators 5 small energy transition leaders and 3 global entities

Close observation of Table 1 reveals that the carbon intensity of the countries is approximately constant, regardless of the country’s size or wealth. Taking the average and the standard deviations of these values we arrive at:

Average carbon intensity = 0.6 ± 0.53

Table 2 shows direct measurement of the global carbon intensity; it is 0.43 – well within the range of the carbon intensities of the countries in Table 1.

The carbon intensities of the five small, developed countries shown in Table 2 are on the low end of the range of those shown in Table 1. They can be labeled “black swans” (low probability) in the distribution and are among the strongest driving forces that account for the countries’ low emissions. Russia stands out as the black swan on the other end of this spectrum.

Table 1’s column dedicated to each country’s global share of carbon emissions shows us that together, the US and China account for about half of all global emissions. This underlines how much they need our special attention in pushing for effective mitigation policies.

Table 3 – Indicators related directly to primary energy use of the same countries as in Table 1

Table 4 – Indicators from Table 3 for countries and regions listed in Table 2

Tables 3 and 4 show that the energy intensity reflects a similar pattern to the carbon emissions– but with an even narrower spread:

Average energy intensity = 10.2 ± 3.5

The similarities in the behaviors of the energy intensity and carbon intensity include all the same aspects of uniformity of behavior. These similarities include the agreement with the global direct measurements, as shown in Table 4, and the black swans on either side of the distribution (Norway, New Zealand, Finland, Sweden, Denmark, and Russia).

There is an argument that once a country passes through some development threshold, further development is less energy intensive. Thus, it posits, the same energy distribution will show smaller energy intensity (Energy/GDP), resulting in smaller carbon intensity (CO2/GDP) because most anthropogenic carbon comes from sourcing energy from fossil fuels. I will address this issue later.

It is certainly true that at less than 15%, the United States doesn’t stand out in its use of alternative (non-fossil fuel) energy sources. However, the five small countries that I selected as examples of this transition show much larger shifts. Among these countries, only Sweden uses nuclear energy as one of its alternative sources.

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Markers for the Global Energy Transition

Posted on February 20, 2018 by climatechangefork

Last week I talked about Dieter Helm’s book, where he portrayed a future in which oil companies are going broke and fossil fuel prices are collapsing due to their practically infinite supply (via fracking and horizontal drilling). Growing awareness of climate change has led to strong public pressure to reduce our carbon footprints and we are experiencing major shifts in how we source and use energy. This includes the growing conversion to electric power. The energy industry was (and is still) structured to accommodate the now-outdated perception that energy supply is finite and shrinking and electrical distribution continues to rely on archaic structures. Helm presents a rosy future for the US because we can adapt to this post-peak-oil reality. But his prediction does not factor in our election of President Trump, who is bent on returning us to the yester-world in terms of our treatment of the physical environment.

As I mentioned in last week’s blog, I agree with some of Helm’s observations and disagree with others. I do feel, however, that it is easy to make predictions about a far future that many of us will not live to see (nor will we be held accountable for our predictions). Helm’s book suffers from that sort of glib certainty and I am aware that I have to some extent shared in the same practice throughout the five years I have been writing this blog.

Helm’s book triggered in me a strong desire to change my ways so I will be starting a series about the recent past. My jumping-off point is the IPAT identity that I have repeatedly referenced here (the first reference was from November 26, 2012). Below is a much more recent mention of this important identity:

There is a useful identity that correlates the environmental impacts (greenhouse gases, in Governor’s Romney statement) with the other indicators. The equation is known as the IPAT equation (or I=PAT), which stands for Impact Population Affluence Technology. The equation was proposed independently by two research teams; one consists of Paul R. Ehrlich and John Holdren (now President Obama’s Science Adviser), while the other is led by Barry Commoner (P.R. Ehrlich and J.P. Holdren; Bulletin of Atmospheric Science 28:16 (1972). B. Commoner; Bulletin of Atmospheric Science 28:42 (1972).)

The identity takes the following form:

Impact = Population x Affluence x Technology

Almost all of the future scenarios for climate change make separate estimates of the indicators in this equation. The difference factor of 15 in GDP/Person (measure of affluence), between the average Chinese and average American makes it clear that the Chinese and the rest of the developing world will do everything they can to try to “even the score” with the developed world. The global challenge is how to do this while at the same time minimizing the environmental impact.

The Population and Affluence terms are self-explanatory. The Impact term in this case refers to emissions of carbon dioxide. The Technology term consists of the three terms combined below:

Technology = (Energy/GDP)x(Fossil/Energy)x(CO2/Fossil)

The first term in this equation refers to Energy Intensity – how much energy we need to generate a unit of GDP (Gross Domestic Product, used here as an Affluence metric). The second term represents the fraction of the total energy that is being generated from fossil fuels. The last term specifies the kind of fossil fuel that is being used (coal, natural gas or oil).

The actual decomposition of global CO2 emissions is shown in Figure 2, which clearly demonstrates that for at least the last decade, Affluence has been the dominant contributor to emissions.

What I referred to in that blog as Figure 2 I am reposting here as Figure 1.

Figure 1 – Decomposition of the change in total global CO2 emissions from fossil fuel combustion by decade (January 24, 2017 blog) (IPCC report analyzing the IPAT identity)

In past blogs I referred to the various indicators in the identity in global terms. Here, and in the next few blogs I will instead cite individual countries. There are two main reasons for doing so. First, our global system is made up of sovereign countries, each of which can (for the most part) enforce actions only within its own borders. The second reason is that once I focus on individual countries I can emphasize those that are more/less successful at transitioning their energy away from carbon-based sources.

Table 1 includes population and GDP/Capita in current US$ for 12 countries. As Figure 1 shows, these are the two main indicators that drive carbon dioxide emissions. The population information came from the United Nations population review and the GDP/Capita came from the World Bank data case.

The sum total of the current population of the 10 most populous countries amounts to more than half of the global total so it is a fair indicator of the whole number.

Table 1 – Population and GDP/Capita of the twelve countries that I will use to analyze the global energy transition (in order of current population)

I have added Ethiopia and the Democratic Republic of Congo because the UN is projecting that given their current growth rates they will be among the 10 most populous countries by 2050. According to World Bank classification, this table includes one high-income (the US), three low-income (Bangladesh, Ethiopia, and DR Congo), four lower-middle income (India, Indonesia, Pakistan, and Nigeria) and four upper-middle income economies (China, Brazil, Russia, and Mexico). In other words, it represents the full range of global income distribution.

The table includes each of the twelve countries’ current GDP/Capita, growth rate, and population as well as their projected populations for 2030 and 2050.

Next week I will focus on trends in primary energy use, emphasizing alternative energy and the resulting change in carbon footprints that these newly diversified economies generate. I will follow it with a compilation of electricity use and the drivers being used to collect the electricity.

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Back to the Global Energy Transition

Posted on February 13, 2018 by climatechangefork

A short while back, I got an email from a Dutch friend’s brother, who had just finished reading my book on climate change. I have taken out any personal comments but am including his thoughts on renewable energy as well as his summary of his own background: 

Although I think it’s necessary to develop fusion reactors there is less and less support in doing so and I’m not so sure that the global decline in forest area can be stopped. I’m pretty sure I’s possible to enhance efficiencies on all levels but I’m not so sure it’s wise to subsidize the current renewable technologies until the time these technologies become competitive with fossil fuels. Wouldn’t such a strategy consume to much subsidy monies?

Did you happen to read the book “Burn Out” (2017) written by Dieter Helm? He is convinced that the low fossil price is a direct consequence of the lower energy intensive growth in China combined with a gas surplus communing from fracking in the states combined with renewables with nearly zero marginal cost that undermine the spot wholesale markets. He is also very optimistic that the climate change will get solved if we encourage entrepreneurs in developing new technologies. I’m very interested to hear your view on this technological development approach.

By the time that I got around to his email, my fall semester was over and I found myself with some available extra time. I promised him I would read Dieter Helm’s book, Burn Out: The End Game of Fossil Fuels (Yale University Press, 2017) and get in touch with him as soon as I finished. Now that I have, I’m sharing my thoughts.

Here are Helm’s main credentials, as taken from his website:

  • Professor Dieter Helm is an economist specializing in utilities, infrastructure, regulation and the environment, and concentrating on the energy, water, communications and transport sectors primarily in Britain and Europe. He is a Professor at the University of Oxford and Fellow of New College, Oxford.

  • In December 2015, Dieter was reappointed as Independent Chair of the Natural Capital Committee.

  • Dieter’s recent books include: Burn Out: The Endgame for Fossil Fuels, The Carbon crunch: Revised and Updated and Natural Capital: Valuing the Planet. These books were published by Yale University Press.

  • Nature in the Balance, edited with Cameron Hepburn, was published in early 2014 by Oxford University Press. Links below to other titles.

  • During 2011, Dieter assisted the European Commission in preparing the Energy Roadmap 2050, serving both as a special advisor to the European Commissioner for Energy and as Chairman of the Ad Hoc Advisory Group on the Roadmap. He also assisted the Polish government in their presidency of the European Union Council.

The Preface to Burn Out starts as follows:

When you read this, the oil price could be anywhere between$20 and $100 a barrel. It could even be outside these boundaries. Although it will matter a lot to the companies, traders and consumers, it will not tell you very much about the price in the medium-to-longer term. The fact that the price was $147 in 2008 and $27 in early 2016 just tells you that it is volatile.

He is absolutely right. I’m including recent oil and natural gas prices as taken from Bloomberg below.

Crude Oil and Natural Gas


JPY is Japanese Yen. Bbl, kl, and MMBtu are all measurement units. Bbl stands for barrel: one barrel equals 42 US gallons or 35 UK (imperial) gallons. One barrel of crude oil equals 5604 cubic-feet of natural gas. One US bbl= 6.2898 kiloliters (kl). One MMBtu is equal to 1 million BTU (British Thermal Units). One standard cubic foot of natural gas yields ≈ 1030 BTU.

I am not going to get into the book’s conclusions and analyses but I think that my friend’s brother was a bit premature in giving up on fusion and subsidies for sustainable energy sources. We just need to make sure that at the same time, we keep subsidizing the fossil fuel industries and allowing them to amortize – pay off – their oil reserves.

Here are my main takeaways from Helm’s book:

  • Traditional energy companies are going broke
    • The concept of Peak oil has been disproven, according to Shell oil, i.e. demand will peter out before supply does
    • Oil/gas reserves are basically infinite (and thus the prices are never going to reflect decreasing supply)
    • There are increasingly strong social pressures to limit the ability of oil companies to tap reserves
    • People would rather transition to electric power and the way we generate electricity is changing
  • The electric car revolution is both pushing and reflecting the alternative energy revolution
  • As supporting evidence of this shift, he cites the declining profits of current oil producers in the Middle East, Venezuela, Russia, etc. – with the noticeable exception of the United States. The year of publication of the book is listed as 2017 but he doesn’t mention President Trump or his contributions to the energy transitions that we are experiencing. I assume that this means the book was written before the 2016 election and published early last year.

My overall perspective is that the author is writing this as a Big Oil man predicting the downfall of the oil industry. But it is conspicuously lacking any mention of the industry’s intensive efforts to prevent/delay its collapse by helping to finance climate change deniers.

I am in full agreement with the author’s conclusion that with the advances in fracking and horizontal drilling, peak oil doesn’t exist anymore. I also think he is right that the global economy is shifting to other forms of producing electricity.

One of the first things that I teach my students is that electricity is not a primary energy source – it is a product. Even when we shift to electric power (e.g. electric cars), it is critical that we are aware of the primary energy sources (e.g. solar, wind, natural gas, etc.) that give us that power.

My practical conclusion from reading the book is that I want to use this blog as a platform – at least twice a year – to analyze our progress in the energy transition.

I will start this process in next week’s blog.

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Use of Computer Simulations in Climate Change Mitigation and Adaptation

Posted on February 6, 2018 by climatechangefork

Figure 2 in last week’s blog demonstrated our most important tool for determining how we identify human contributions to climate change on both a local and global scale. The figure, taken from the NASA site, describes simulated 20th century global mean temperatures (with and without human influences), as compared to observed temperatures. This important comparison determines the validity of the simulation and its conclusion regarding the driving forces behind a particular event:

Computer simulations reproduce the behavior of a system using a mathematical model. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry and biology, human systems in economics, psychology, social science, and engineering. Simulation of a system is represented as the running of the system’s model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.[1]

In other words, this tool is used everywhere in science, to such an extent that it is unimaginable that any branch of science – including social sciences – could survive today without it. These mathematical models are designed to reproduce the behavior of real systems. How good the model is depends on how well it incorporates actual measured results into the assumed driving forces within the simulated system. This works especially well for simulating a complex system with many individual components and driving forces. As such, it is perfectly suited to simulate climate change. Figure 3 in last week’s blog showed an example of the framework of the forces assumed to drive climate and weather.

Per definition, mathematical models are products of human efforts, which means that they are subjective: they can be skewed by the unscrupulous to agree with preconceived opinions and mispresented as “real science.” Climate change is obviously a fertile ground for this kind of misbehavior, especially because the consensus among the scientific community implies real consequences for all of our futures. Cases of intentional cheating in science are not confined to climate change; in most cases those creating the false information do so while seeking career advancement, promotion or fame. The general public is not usually privy to these shameful activities and usually lacks the prerequisites to understand either what is being done or why it matters. Climate change is different because it affects all of us – the public needs to understand both.

Some scientists object to other scientists’ attempts to warn us about the consequences of climate change. They claim that predicting consequences equates to predicting the future, a field outside the mandate of science (May 7, 2012 blog):

The letter was signed by 49 former NASA employees that included seven Apollo astronauts and two former directors of NASA’s Johnson Space Center calling NASA to move away from climate model predictions and to limit its stance to what can be “empirically proven.” The letter specifically targets James Hansen – Director of NASA Goddard Institute (GISS) (Hansen and the GISS have been acting as the “the canary in the coal mine,” warning, for years, about the consequences of relying on fossil fuels as our main source of energy.) The letter states that, “We believe the claims by NASA and GISS, that man-made carbon dioxide is having a catastrophic impact on global climate change are not substantiated.”  The reason for the doubt includes that.”NASA is relying too heavily on complex climate models that have proven scientifically inadequate in predicting climate only one or two decades in advance” and that “There’s a concern that if it turns out that CO2 is not a major cause of climate change, NASA will have put the reputation of NASA, NASA’s current and former employees, and even the very reputation of science itself at risk of public ridicule and distrust.” This is backwards; it’s the letter that should be held up to public ridicule.

If the senior NASA employees had had their way, Figure 2 probably wouldn’t exist and I would have had a much harder time demonstrating how we can show the degree to which we are responsible for weather events and what we can do to modify our collective behavior. NASA’s fear of looking bad in the case that some of its predictions turn out somewhat differently than expected should not take precedence over our chances to decrease the odds of local disasters.

Not long after I started this blog (December 10, 2012), I described a situation in which I gave a talk to another campus’ physics department and got embroiled in a useless argument about whether or not trying to measure human contributions to climate change counted as “real” science – as compared to investigating the physical properties of a specific semiconducting material. At the end of this chat I was asked if I had heard Freeman Dyson’s take on the topic. I have.

Freeman Dyson is one of the most distinguished and recognized physicists, with many major accomplishments in a wide range of topics:

Freeman Dyson FRS is an English-born American theoretical physicist and mathematician. He is known for his work in quantum electrodynamics, solid-state physics, astronomy and nuclear engineering. Born: December 15, 1923 (age 94), Crowthorne, United Kingdom. Awards: Templeton Prize, Wolf Prize in Physics, Hughes Medal, MORE

Education: Bates College, University of Cambridge, Cornell University, Winchester College

In 2009 David Biello published an interview with him in the Scientific American blog. Biello’s take on Dyson’s points prompted the rather unsubtle title, “Freeman Dyson and the irresistible urge to be contrary about climate change”:

At the luncheon put on by the Cato Institute, when the talk turned to climate change Dyson started out sounding as if the whole thing was overblown, noting that the prospect of global warming is a problem that should be taken seriously. But he also said that no one should be alarmed about it yet.

Then he outlined his main criticism: Too much of the science of climate change relies on computer models, he argued, and those models are crude mathematical approximations of the real world. After all, a simple cloud—small in scale, big in climate effects, the product of evaporation and condensation, all of which it is difficult to create equations for—eludes the most sophisticated climate models.

So climate modelers turn to what they call parameters or, as Dyson likes to call them: “fudge factors.” These are approximations, such as the average cloudiness of a particular spot at a particular time, that are then applied globally. With the help of about 100 of these parameters, models can now closely match the world’s present day climate, Dyson says. These models then, like the one developed at Princeton University where Dyson is a professor emeritus, are “useful for understanding climate but not for predicting climate.”

That’s too much of a temptation for scientists working on the problem, however. “If you live with models for 10 to 20 years, you start to believe in them,” Dyson said, witness the implosion of the financial markets after over-reliance on quantitative models. He characterized this over-reliance as a disease infecting everything from physics to biology: “A model is such a fascinating toy that you fall in love with your creation.”

Ultimately, “every model has to be compared to the real world and, if you can’t do that, then don’t believe the model.” And he noted that the real world has been through some significant climate changes before: witness a lush Sahara Desert thousands of years ago or the forests that once covered Greenland.

Of course, models have been tested against the real world (both today’s and eons ago’s) and many of Dyson’s other objections have been rebutted elsewhere. He also did not address the real world impacts already observed: ice melt, sea level rise, ocean acidification and more. His main concern seems to be that worrying about climate change distracts from more important problems such as poverty and infectious disease. Many might note that poverty (the inundation of Bangladesh) and infectious disease (improved conditions for transmission) are also problems exacerbated by climate change.

But Dyson’s purpose seems to be to throw out “heretical” ideas that can then spur further debate. (As even he would admit, his heresies are a little more grounded in the real world when he’s talking about nuclear weapons. Before discussing climate change, he told a roomful of people who probably want to put former President Ronald Reagan on Mount Rushmore that the Great Communicator blew a real chance to rid the world of nuclear weapons in 1986 because he was too attached to the “Star Wars” missile defense program.) As he said: “I know a lot about nuclear weapons and nothing about climate change.”

“I like to express heretical opinions,” Dyson said, with an impish gleam in his eye. “They might even happen to be true.”

I am sure that Dyson himself has used extensive computer modeling of various sorts, opening him up to the same kind of possible abuses that he describes. He is also more than familiar with the ways in which scientists work to minimize the impact of such manipulations.

I will let you draw your own conclusions.

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Local Attributions

Posted on January 30, 2018 by climatechangefork

Myles Allen, one of the pioneers of the emerging science of determining how we attribute extreme climate events to humans, featured prominently in a Scientific American piece earlier this month:

But the radio voice added that it would be “impossible to attribute this particular event [floods in southern England] to past emissions of greenhouse gases,” said Allen in a commentary published in Nature shortly thereafter.

In 2003, that was the predominant view in the scientific community: While climate change surely has a significant effect on the weather, there was no way to determine its exact influence on any individual event. There are just too many other factors affecting the weather, including all sorts of natural climate variations.

His hunch held true. Nearly 15 years later, extreme event attribution not only is possible, but is one of the most rapidly expanding subfields of climate science.

The Bulletin of the American Meteorological Society now issues a special report each year assessing the impact of climate change on the previous year’s extreme events. Interest in the field has grown so much that the National Academy of Sciences released an in-depth report last year evaluating the current state of the science and providing recommendations for its improvement.

In 2004, he and Oxford colleague Daithi Stone and Peter Stott of the Met Office co-authored a report that is widely regarded as the world’s first extreme event attribution study. The paper, which examined the contribution of climate change to a severe European heat wave in 2003—an event which may have caused tens of thousands of deaths across the continent—concluded that “it is very likely that human influence has at least doubled the risk of a heat wave exceeding this threshold magnitude.”

The breakthrough paper took the existing science a step further. Using a climate model, the researchers compared simulations accounting for climate change with scenarios in which human-caused global warming did not exist. They found that the influence of climate change roughly doubled the risk of an individual heat wave. The key to the breakthrough was framing the question in the right way—not asking whether climate change “caused” the event, but how much it might have affected the risk of it occurring at all.

It is much easier to determine how humans have contributed to global climate change. Specific local weather event attribution requires in-depth examinations of driving forces in a particular location at a particular time, as well as an attempt to determine how to describe a system that fits within a global structure.

Figure 1 – Average yearly global temperature with (empty circles) and without (full circles) El Niño Southern Oscillations

Figure 1 describes average global temperature changes from 1880 until now. It also distinguishes between years that experienced El Niño Southern Oscillations and years that didn’t. On a global scale, the trend in both categories follows the same pattern. This is obviously not necessarily true on the local scale. Indeed, many of the papers that try to describe weather on a local scale incorporate the estimated extent of the El Niño impact. The figure emphasizes that – global or local – one of the best ways to determine attribution for a particular weather event is to try to identify a multi-year pattern in which one can distinguish the presence or absence of a particular driving force.

I discussed Figure 2 in an earlier blog (October 3, 2017) in the context of global attribution and as we will see below, it is currently the main tool being used to determine local weather event attributions. Figure 2 clearly shows the measured and calculated anomaly between human-influenced temperature and that without human influence.

Such a combination of superimposing observational data with and without human influence is now the cornerstone in determining human contributions to any extreme weather event.

Global warming attributions – simulation of 20th century global mean temperatures (with and without human influences) compared to observations (NASA)Figure 2 – Global warming attributions – simulation of 20th century global mean temperatures (with and without human influences) compared to observations (NASA)

Figure 3 shows the driving forces being shown in climate simulations, as taken from the most recent National Climate Assessment Report (August 15, 2017 blog). For the first time in the history of the report, this edition includes a full chapter on climate event attribution (Chapter 3).

The human attributions are included among sections of the gray box labeled “climate forcing agents.”

Once the simulations fit the measured results as they do in Figure 2, one can choose particular sections of interest within that gray box. In Figure 2, this is done using “human influence,” a broad term that includes CO2, non CO2 greenhouse gases, aerosols and land use.

simplified conceptual modeling framework of climate systemFigure 3 – Simplified conceptual modeling framework for the climate system as implemented in many climate systems (CSSR 4th National Climate Assessment Chapter 2)

The PRECIS model is a great example of a relatively simple model that was adopted to determine specific contributions to weather and climate events:

PRECIS is a regional climate model (RCM) ported to run on a Linux PC with a simple user interface, so that experiments can easily be set up over any region of the globe. PRECIS is designed for researchers (with a focus on developing countries) to construct high-resolution climate change scenarios for their region of interest. These scenarios can be used in impact, vulnerability and adaptation studies, and to aid in the preparation of National Communications, as required under Articles 4.1 and 4.8 of the United Nations Framework Convention on Climate Change (UNFCCC).[1]

The American Meteorological Society is now publishing yearly reports covering such events. Figure 4 summarizes those that took place in 2016, as shown in its January 2018 supplementary bulletin.

Location and types of events analyzed in BAMS supplement 2018Figure 4 – Location and types of events analyzed in the special supplement to the recent Bulletin of the American Meteorological Society Vol 99, No 1, January 2018

Each event constitutes a chapter in this report. Here are two such events (in bold) and the authors’ conclusions as to their attributions.

The 2016 extreme warmth across Asia would not have been possible without climate change. The 2015/16 El Niño also contributed to regional warm extremes over Southeast Asia and the Maritime Continent.

Conclusions. All of the risk of the extremely high temperatures over Asia in 2016 can be attributed to anthropogenic warming. In addition, the ENSO condition made the extreme warmth two times more likely to occur. It is found that anthropogenic warming contributed to raising the level of event probability almost everywhere, although the 2015/16 El Niño contributed to a regional increase of warm events over the Maritime Continent, the Philippines, and Southeast Asia, but had little significant contribution elsewhere in Asia.

The record temperature of April 2016 in Thailand would not have occurred without the influence of both anthropogenic forcing and El Niño, which also increased the likelihood of low rainfall.

Conclusions. Our analysis demonstrates that anthropogenic climate change results in a clear shift of the April temperature distribution toward warmer conditions and a more moderate, albeit distinct, shift of the rainfall distribution toward drier Aprils in Thailand. The synergy between anthropogenic forcings and a strong El Niño was crucial to the breaking of the temperature record in 2016, which our results suggest would not have occurred if one of these factors were absent. Rainfall as low as in 2016 is found to be extremely rare in La Niña years. The joint probability for hot and dry events similar to April 2016 is found to be relatively small (best estimate of about 1%), which implies that in addition to the drivers examined here, other possible causes could have also played a role, like moisture availability and transport (especially in the context of the prolonged drought), atmospheric circulation patterns, and the effect of other non-ENSO modes of unforced variability.

The attributions described here are mostly determined via a mixed analysis of direct observations and computer simulations. Such techniques are common in science. But some of my colleagues believe that the use of computer simulations to address issues such as anthropogenic climate change is inappropriate. I will address an example taken from cosmology of how computers have been used successfully to describe physical phenomena.

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Fake News: Attributions

Posted on January 23, 2018 by climatechangefork

Figure 1 – Snow in the Sahara

If you do a Google image search for “Snow in the Sahara,” you will get a screen full of images similar to this one. What would bring you to search for that particular term? I read The New York Times every morning and a couple of weeks ago I came across a story about just that phenomenon. For me, the photograph raised memories from close to 50 years ago when I first came to the US to do my postdoctoral education. I grew up in Israel, a small country with a Mediterranean climate where snow was a relatively rare occurrence. On my first visit to the US, my family and I drove from the east coast to California. We took Interstate 40 through New Mexico and Arizona, passing through the Mojave Desert. When I looked out the window I saw the unbelievable sight of a snow-covered terrain with the odd-looking Joshua trees emerging throughout.

Figure 2 – The Mojave Desert in winter

The photograph in the NYT triggered in me a similar wonder on a global scale. Going through the article I got some explanations:

“The Sahara is as large as the United States, and there are very few weather stations,” he added. “So it’s ridiculous to say that this is the first, second, third time it snowed, as nobody would know how many times it has snowed in the past unless they were there.”

Rein Haarsma, a climate researcher at the Royal Netherlands Meteorological Institute, cautioned against ascribing the white-capped dunes to changing temperatures because of pollution.

“It’s rare, but it’s not that rare,” said Mr. Haarsma said in an interview. “There is exceptional weather at all places, and this did not happen because of climate change.”

The snow fell in the Sahara at altitudes of more than 3,000 feet, where temperatures are low anyway. But Mr. Haarsma said cold air blowing in from the North Atlantic was responsible.

Those icy blasts usually sweep into Scandinavia and other parts of Europe, Mr. Haarsma explained, but in this case, high-pressure systems over the Continent had diverted the weather much farther south.

Mr. Haarsma offered a mechanism to explain why we see snow in the Sahara: he claimed it is not due to climate change but instead caused by cold air blowing down from the North Atlantic. He did not, however, explain why we are now seeing so much cold air from the North Atlantic reach as far south as the Sahara. Well, snow in the Sahara is not the only so-called unique extreme weather we are seeing.

I live in New York City and we have just experienced an extreme cold event that started around Christmas and lasted until mid-January with a few short breaks. The freeze affected the entire northeastern US, prompting President Trump to tweet:

In the East, it could be the COLDEST New Year’s Eve on record. Perhaps we could use a little bit of that good old Global Warming that our Country, but not other countries, was going to pay TRILLIONS OF DOLLARS to protect against. Bundle up!

4:01 PM – 28 Dec 2017

He got more than 60,000 retweets, more than 200,000 likes, comments and replies. Such is the power of social media these days.

Erik Ortiz wrote about this event:

“Why climate change may be to blame for dangerous cold blanketing eastern U.S”:

A study published last year in the journal WIRES Climate Change, however, lays out how the warming Arctic and melting ice appear to be linked to cold weather being driven farther south.

“Very recent research does suggest that persistent winter cold spells (as well as the western drought, heatwaves, prolonged storminess) are related to rapid Arctic warming, which is, in turn, caused mainly by human-caused climate change,” Jennifer Francis, a climate scientist at Rutgers University and one of the study’s authors, said in an email.

But researchers have said the loss of sea ice and increased snow cover in northern Asia is helping to weaken the polar vortex.

In addition, “abnormally” warm ocean temperatures off the West Coast are causing the jet stream over North America — which moves from west to east and follows the boundaries between hot and cold air — to “bulge” northward, Francis said.

That scenario is what has caused a lack of storms so far this winter in California and Alaska’s unusually warm and record-breaking temperatures, she added.

Meanwhile, the wrinkled jet stream as it travels east is also being pushed farther south, according to her research.

Mr. Ortiz included a picture from NOAA (National Oceanic and Atmospheric Administration) that shows the schematic shift between a strong jet stream – which more or less confines the extreme cold air to the Arctic – and the global-warming-caused weaker jet stream, which allows the air to push further south to regions including the northeastern US and the Sahara Desert (Figure 3).

Figure 3 – The shifting jet stream (NOAA)arctic-temperature-rise-arrFigure 4 – Change of temperature in the Arctic vs globally, 1880-2010s

Figure 4 summarizes the Arctic temperature changes as compared to the average global temperature changes from 1880 to the 2010s.

Bloomberg published a series of three articles on “How a Melting Arctic Changes Everything,” in which it tried to summarize the global changes resulting from the warming Arctic:

Part I – The Bare Arctic

 Part II – The Political Arctic

Part III – The Economic Arctic

There is almost universal agreement that the accelerated temperature increase in the Arctic is the result of climate change by way of changes in the region’s ground reflectivity. In other words, while Mr. Haarsma was basically right about the mechanism of the snow falling in the Sahara Desert, he was wrong about its attribution.

It is much more difficult to determine how to attribute localized, specific events to human activities than it is to attribute the culpability of climate change (multiyear weather) on a global scale. But most people’s perception of blame derives from specific extreme events in specific locations (i.e. we understand concrete examples best). So understanding where we stand in our efforts to create/connect culpability maps is of the utmost importance. Next week I will try to show an analysis of a specific case.

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