Larsen C and solar geoengineering

cxaovzcwqaa0nllPhoto: nasa.gov

Michael Thompson, of the Forum for Climate Engineering Assessment wrote me asking me about my thinking regarding the Larsen Ice Shelf and solar geoengineering. My responses, alongside others, were published on this web page:  http://ceassessment.org/larsen-c-climate-engineering-and-polar-ice-melt .

My comments are repeated here:

Ken Caldeira: “Melting in Antarctica is strongly influenced by interactions involving the circulation of seawater, and its interaction with glacial ice, sea ice, surface winds and temperature, sunlight and so on. Many important interactions are occurring on small spatial scales that have not yet been successfully integrated into models simulating large-scale phenomena — and so the influence of various possible solar geoengineering deployments on Antarctic ice sheet dynamics remains largely unknown and unexplored.

The governing hypothesis is that if warming temperatures lead to ice melt, cooler temperatures are likely to help slow or even stop that melt.

It might turn out that it is effectively impossible to cool the water adjacent to ice shelves with solar geoengineering techniques. However, I would be surprised if that turns out to be the case. My expectation is that the primary factors limiting the amount cooling produced by solar geoengineering would be unintended consequences and sociopolitical acceptance.

We should be researching the potential effectiveness and unintended consequences of using solar geoengineering techniques to reduce the amount of damage caused by climate change. However, I would want to know a lot more about potential efficacy and unintended consequences, and understand how the solar geoengineering deployment fits into the broader spectrum of efforts undertaken to avoid climate damage, before I would want to consider using solar geoengineering approaches to protect Antarctica.”

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Red team, blue team

Justin Gillis of the New York Times asked me to comment on a proposal by EPA head Scott Pruitt regarding a red-team/blue-team approach to climate science. I wrote this set of possible quotes, and one of them appeared in this article: EPA to Give Dissenters a Voice on Climate, No Matter the Consensus, by Brad Plumer and Coral Davenport.

 

I would love to hear Scott Pruitt explain how he thinks the scientific process works. What does he think scientists are doing all day? Scientists are already spending most of their time trying to poke holes in what other scientists are saying.

The whole red team / blue team concept misunderstands what science is all about. Scott Pruitt seems to imagine that science today is like a football game with a single team on the field. In fact, science is like having thousands of people out on the field, each playing for themselves, fighting tooth and nail to show that they are right and everyone else is wrong.

We don’t want red team / blue team because science doesn’t line up monolithically for or against specific positions. Science is an ongoing process of thousands of people constantly chipping away at or refining a set of hypotheses.

Scientists in the United States have organized themselves to create the best scientific infrastructure in the world, and now we need a politician to tell scientists how to do science? If Scott Pruitt really wants to improve climate science, he should be fighting for bigger budgets for climate scientists.

Why do politicians who have never engaged in any scientific inquiry in their lives believe themselves to be the experts who should tell scientists how to conduct their business? A little more humility would be appreciated. This is yet another example of politicians engaging in unhelpful meddling in things they know nothing about.

Why is Scott Pruitt trying to ‘fix’ climate science. It is not broken. If Scott Pruitt really wanted to help climate science, he would be fighting to increase budgets for climate science research.

All of science is about one person claiming to have evidence supporting a hypothesis, and then other people trying to show that the first person was either wrong or missing something important.

Science is a constant process of scientists challenging the claims of other scientists.

Some more thoughts (written after the original email to the Times):

Isn’t science all red team? Isn’t all of science aimed at falsifying hypotheses? Popper would say that if you are trying to prove that something is true, then you aren’t doing science.

I just don’t understand which hypotheses Scott Pruitt thinks climate scientists are being insufficiently rigorous about testing. Scott Pruitt should clearly state the hypotheses that he thinks climate scientists are accepting prematurely. 

Will Pruitt commit to vigorous and effective action on emissions reduction if the main findings of climate science are shown to be sound? [Hasn’t the science already been shown to be sound?] Is the Trump Administration committed to basing policy on the best-available scientific information?

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Trump, climate, and energy

The journalist Jeff Goodell asked me for some comments about Trump and his decision to not honor climate pledges made by the United States. In lightly edited form, this is what I replied, largely as stream-of-consciousness writing:

The United Nations Framework Convention on Climate Change (UNFCCC) was adopted on 9 May 1992, just over 25 years ago. The Convention was signed by then-President George H. W. Bush. The UN Framework Convention was negotiated by a Republican Administration.

In signing the Convention, President Bush spoke of “crucial long-term international efforts to address climate change”, and said “I am confident the United States will continue to lead the world in taking economically sensible actions to reduce the threat of climate change.”

Nearly 20 years ago, Marty Hoffert and I, with other colleagues, published the first peer-reviewed study outlining how much carbon-emission-free energy we would need to support economic growth while protecting our environment. So far, the world has done about one-tenth of what we projected would need to be done by now.

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Fig. 1. Hoffert et al., (1998) estimated that by now, nearly half of our primary power would need to come from sources that do not dump carbon dioxide pollution into the atmosphere. Even with the most generous accounting, less than 20% of our energy comes from such sources today.

I am confident that history will come to look upon the Trump Administration and his congressional co-conspirators as dark stain on American history, as a failed effort by regressive forces to return to a world that never was.

Trump and his Republican co-conspirators will be swept aside by demographic trends.

I have to believe that what is good in America will reassert itself. Compassion, tolerance, respect, and caring for others will supplant greed and fear-mongering.

With this resurgence of positive spirit, expressed through our newly restored political institutions, we will reach a national consensus that nothing can really be good for America if it is not also good for the rest of the world.

The United States will lead an energy system transition, and build a new economy with new jobs, and create an energy system that can promote economic growth while protecting natural assets.

After the Dark Ages, came the Renaissance.

We are in our political Dark Ages, but there will be a political Renaissance.

The ascent of Trump and his cohort, with all their foolish actions, are but a temporary setback. They cannot for long hold back the forces of history.

The global historical trends show people lifted out poverty, with better education, better health care, becoming more tolerant, and taking better care of the environment. These trends will continue.

Yes, there are setbacks, driven by fact-denying forces both at home and abroad, but these setbacks do not undermine broader historical trends.

The political survival of the Republicans and Democrats alike will depend on embracing these trends.

It is important that thinking, caring Americans make it clear that Trump is an aberration; he is the noise and not the signal. Our system will self correct. America will be great again.

The Democrats share responsibility for Trump and his misguided policies. They have not fought for policies that help the average person whose job is threatened by automation or globalization. They too have failed to put in place policies that recognize the magnitude of the energy system transformation that lies before us.

If two good things can come out of the Trump Administration, they will be (1) undermining the Republican Party by exposing them as craven, venal and unprincipled, and (2) re-invigorating the Democratic Party to fight for policies that are as ambitious as the problems that face us are large; these problems include health care, education, employment, and the challenge of radically transforming our energy system.

The reckless and ignorant actions undertaken by the Trump Administration are but a temporary setback. It is important to keep in mind that addressing the climate problem will involve a century-scale energy system transition, and that we have already let the clock run for a quarter-century without getting very far from the starting gate.

The United States, along with the rest of the world, will be building an energy system that does not rely on using the sky as a waste dump for our CO2 pollution.

For now, the rest of the world (and the states and non-government actors) will have to make progress without support from the White House. The next Presidential Administration will have to work harder to make up the ground we are losing now.

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Learning curves and clean-energy R&D: Follow the curve or try to leap?

For most energy technologies, cost decreases with as the cumulative amount of deployment increases. Often, the cost is seen to decrease approximately the same amount with each doubling of the amount deployed. Straight line curve fits to cost data are commonly known as ‘learning curves’ and look like this:

1-s2.0-S0301421517303130-gr1Fig. 1. (from Shayegh et al., 2017) Learning curves for clean and conventional energy technologies. The horizontal axis represents cumulative quantity of electricity generation and the vertical axis represents the unit cost of electricity generation. Both scales are logarithmic. Learning rates (R) are shown in parentheses. Q0 indicates starting quantity and C0 is starting cost. With this axis scaling, straight lines represent power laws (Eq. (1)). We use data from this figure for subsequent analysis of the impact of different types of R&D (EIA, 2015, Wene, 2000 ; Rubin et al., 2015).

The idea is that as more of a technology gets deployed, people learn how to make the technology more cheaply, although economies of scale and other factors also play roles in bringing down costs.

I had the good fortune to be in a discussion with venture capitalists who were investing in companies doing research and development (R&D) on energy technologies. They mentioned that they didn’t want their R&D investment to just push them down the learning curve — they didn’t want to just learn things that they would have learned as production scaled up.

They said they want to shift the learning curve downward. They wanted to invest in innovations that would decrease cost as a result of innovations that would not come about simply by scaling up manufacture. (For example, maybe by shifting solar photovoltaic cells to a new kind of substrate.) This raises the question: If an R&D investment could reduce cost by the same amount by shifting the cost-starting-point of the learning curve downward (“curve-shifting R&D”) instead of effectively following the learning curve to that cost (“curve-following R&D”), which one would be better and how much better would it be?


To address this question, I got the help of Soheil Shayegh, a specialist in optimization and other mathematical and technical arts, and talked him into leading this project.

The “learning subsidy” is the total amount subsidy that would be needed to make a new more-expensive technology competitive with an incumbent technology. The needed subsidy to make a more expensive technology competitive in the marketplace starts out high but decreases as learning brings down the costs of the new technology (Fig 2a).

Figure 1 above shows cumulative quantity on the horizontal axis on a logarithmic scale, but such a figure can be redrawn with a linear axis, which turns the straight lines into curves. Figure 2 shows such curves for an idealized case:

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Fig. 2. (from Shayegh et al., 2017) Illustration of two stylized types of R&D for solar PV, a clean energy technology. (a) Learning-by-doing reduces the cost of the clean energy technology as the cumulative quantity of electricity generation increases. (b) Curve-following R&D reduces cost by producing the same knowledge as learning-by-doing, with an effect equivalent to increased cumulative quantity. (c) Curve-shifting R&D reduces cost by producing knowledge that would not have been gained by learning-by-doing, scaling the learning curve downward by a fixed percentage. The learning investment is the total subsidy necessary to reach cost parity with fossil fuels. For the same initial reduction in cost, curve-shifting R&D reduces the learning investment more than curve-following R&D. Note that horizontal and vertical scales are linear. The learning curves would be straight lines if both scales were logarithmic as in Fig. 1.

We found was that cost reductions brought about by breakthrough curve-shifting innovations were much more effective and bringing technologies closer to market competitiveness than were the same cost reductions brought about by incremental curve-following innovations. For example, that cost reductions in wind and bioenergy that come from curve-shifting research are more than 10 times more valuable than cost reductions that come about from curve-following research.

Further, we found in this idealized framework that the relative benefit of breakthrough innovations depended on only two things: (1) the initial cost of the new technology relative to the incumbent technology, and (2) the slope of the learning curve. The more expensive the new technology and the shallower the learning curve, the higher the value of breakthrough curve-shifting R&D.


I am principally a physical scientist, and Soheil Shayagh is principally some sort of mathematically-oriented engineer. To make a successful study, we needed to make some contact with the real world, or at least the academic literature on learning and technological innovation. To this end, we brought Dan Sanchez into the project. Dan thinks a lot about how public policy can help promote innovation and the deployment of new, cleaner energy technologies.

The result of our work was a study titled “Evaluating relative benefits of different types of R&D for clean energy technologies“, published in the journal Energy Policy. (Unfortunately, due to an oversight, our study ended up behind a paywall but you can email me at kcaldeira@carnegiescience.edu to request a copy.)

Our results indicate that, even if steady curve-following research produces results more reliably, when successful, step-wise curve-shifting research produces much greater benefits.

Our simple calculations are highly idealized and schematic and are designed only to illustrate basic principles.

I infer from our study that, other things equal (and other things are never equal), government funded R&D should focus on trying to achieve step-wise curve-shifting breakthroughs.

I suppose my group’s research should also focus on trying to achieve step-wise breakthroughs, but that’s a tough challenge.

 

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Will using a carbon tax for revenue generation create an incentive to continue CO2 emissions?

Most economists think a tax on carbon dioxide emissions is the simplest and most efficient way to get us to stop using the sky for disposal of our waste CO2. This tax could be applied when fossil fuels are extracted from the ground or imported, and credits could be given if someone could show that they permanently buried the waste CO2 underground.

This carbon tax would make fossil fuels more expensive. If the tax level continued to increase, eventually using fossil fuels (without underground CO2 disposal) would become more expensive than every other energy technology, and economics would squeeze carbon dioxide emissions our of our economy.

A tax is an anathema to most politicians. One proposal to make such a tax more palatable would be to distribute the revenue evenly on a per capita basis. Due to inequalities in income distribution, this would result in most people receiving a direct net economic benefit. This could make it politically popular. In terms of net transfer, money would be transferred from the rich and given to the poor and middle classes. Thus, a revenue-neutral carbon tax would both help eliminate carbon dioxide emissions and help reduce economic inequality. Sounds like a good thing. (Why aren’t we doing it?)

Another idea would be to institute a carbon tax to generate tax revenue that could then be used to help provide essential services such as health care, education, income subsidies, and so on. Some of the carbon tax could potentially be used to help pay down the national debt, which in the United States now stands at $165,000 per taxpayer.

Today, we have a carbon tax rate of zero and get no carbon tax revenue. When the carbon tax rate is so high that there are no longer any carbon emissions from our energy system, the carbon tax revenue will again be zero. There is some tax level in-between that would maximize revenue generation from the carbon tax. An increase in tax rate beyond this level would reduce carbon dioxide emissions so much that carbon tax revenues would start to diminish.

Whether the proceeds of the tax are distributed on a per capita basis, or used to provide essential services, people will not be happy to see tax rates rise while direct and immediate benefits from the tax decrease. These tax increases could be a tough sell for politicians. Politicians could be motivated to avoid raising the carbon tax rate, so that they can continue providing the benefits of the revenue generation to their constituents. This would result in continued CO2 emissions.


This issue had been nagging me for over a decade, and I have long tried to interest someone in taking the lead on addressing this question. (Avoiding work is one of my key objectives, so my usual strategy is to try to talk people into doing the work that I am trying to avoid doing myself.) Luckily, I was able to talk Rong Wang into addressing this problem. Because Rong is a physical scientist and not an economist, we were fortunate to be able to lure the economist Juan Moreno-Cruz into helping us.

Together, we produced a study titled “Will the use of a carbon tax for revenue generation produce an incentive to continue carbon emissions?“, and published it in Environmental Research Letters, where it is available for free download.

The key conclusion is represented in this figure:

Wang_fig 1b_170523

Figure 1b. Projected emissions under a set of standard assumptions for three scenarios: Zero carbon tax, welfare-maximizing carbon tax, and revenue-maximizing carbon tax. Under the revenue maximizing assumption, CO2 emissions continue long into the future but at a level that is lower than would occur if there were no carbon tax at all.

Our main conclusions are: For the next decades, the incentive to generate revenue would provide motivation to increase carbon tax rates and thus achieve even lower emissions than would occur at an economically optimal ‘welfare-maximizing’ tax rate. However, by the end of this century, the incentive to generate revenue could result in too low a tax rate — a tax rate that would allow CO2 emissions to persist far into the future.

Overall, I see our result as rather encouraging. Right now, the problem is that we don’t have enough disincentives on carbon emission and politicians are having trouble motivating themselves to provide this disincentive. If revenue generation provides an additional incentive (beyond the incentive of generating climate benefits) to institute a carbon tax, that is all well and good. As mentioned above, these revenues could be distributed on a per capita basis to help combat inequality, or they could be used to provide essential services.

By the end of the century, the incentive to generate revenue could become a perverse incentive to keep carbon taxes low so that CO2 emissions might continue. However, for now, the incentive to generate revenue would motivate increased carbon tax rates, which would cause carbon emissions to decrease.

Given that today’s carbon tax rate is zero, which is clearly too low, the incentive to generate revenue can help motivate politicians to do the right thing for the climate system. That is a good thing.

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Climate of Risk and Uncertainty

ClimateFeedback.org contacted me asking what I thought about Bret Stephens’ first column in the New York Times. Here, is a slightly edited version of my response.

Bret Stephens’ opinion piece, titled “Climate of Complete Certainty”, is attacking a straw man. No working scientist claims 100% certainty about anything.

Science is the process of falsification. Hypotheses that have withstood a large number of attempts at falsification, and that are consistent with a large body of established theory that has also resisted falsification, are widely regarded as true (e.g., the Earth is approximately spherical). Many hypotheses of modern climate science fall into this category.

It is also true that some ‘environmentalists’ go far beyond the science in making claims, but that is not cause to denigrate the science.

Bret Stephens writes of ‘sophisticated but fallible models’ as if ‘sophisticated but fallible’ gives one license to ignore their predictions. A wide array of models of different types and levels of complexity predict substantial warming to be a consequence of continued dependence on using the sky as a waste dump for our CO2 pollution. It doesn’t take much scientific knowledge to understand that the end consequence of this process involves approximately 200 feet of sea-level rise. We already see the coral reefs disappearinga predicted consequence of our CO2 emissions. How much more do we need to lose before recognizing that our ‘sophisticated but fallible models’ are the best basis for policy that we have?

Yes, we should take uncertainty into account when developing policy, but we should recognize that those ‘sophisticated but fallible models’ are as likely to underpredict as overpredict the potential consequences of our greenhouse gas emissions.

Stephens would have been on more solid ground if he would have confined his comments to uncertainty in the ability of human systems to adapt to the relatively more certain projections of changes in the physical climate system. Will we be able to give up low-lying countries and the major coastal cities of the world (New York, London, Tokyo) without much of a transition cost? Will people in India and the Sahel be able to migrate or air-condition their way out of the harsh conditions projected for those areas? These are open questions about which well-informed people can disagree.

It is dangerous to act as if uncertainty in climate model projects justifies inaction. Uncertainty equals risk. One way to reduce uncertainty is to increase the amount and quality of climate science being conducted. Another and more important way of reducing uncertainty is to reduce human influence on the climate system. This requires a major transformation of our energy system to one that does not rely on the atmosphere as a waste dump for our CO2 pollution.

Climate science does not offer complete certainty about the future. Instead, it points to substantial risks and ways to avoid that risk.

Straw-man attacks on climate scientists do not productively advance the discussion.

For reference: My first reaction to the announcement of the The New York Times decision to hire Bret Stephens.

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Scientific reproducibility, communication of uncertainty, and the US House of Representatives

Bobby Magill of Climate Central sent me an email asking me to comment on several bills recently passed by the US House of Representatives. Specifically, he asked me to comment on bills rescentely
– H.Res.229. … would require EPA actions to be based on science that is “transparent” and “reproducible.”
– HR 1431, …would prevent the EPA Science Advisory Board from communicating uncertainties.
My email comments to Bobby are below: His story is here:
http://www.climatecentral.org/news/congress-political-theater-threatens-science-climate-21316

All of the climate science, and related environmental science, that I read in the peer-reviewed literature is in-principle reproducible. The reason it is not in fact reproducible is that the Federal funding agencies rarely provide resources to redo work that someone else has already done.

If our Congressional and Senate representatives really want to see us do science that is reproducible in a practical sense, they should double or triple research budgets so that scientists can afford to do the same thing two or three times.

In practice, once someone has done something to the satisfaction of thee peer-review system and the broader scientific community, people move on to try to make new discoveries.

You can be sure that scientists love nothing more than showing that our colleagues are wrong. If something gets published that smells fishy, that’s when scientists are motivated to redo the study and show that they are wrong. This, for example, famously happened with the cold fusion studies that were published some years ago.

Why would a working scientist with limited funds want to allocate scarce resources to testing something the scientific community already thinks it knows rather than exploring something new? To have enough resources to want to spend it treading over old ground would require a substantial increase in science funding.

Science is all about communicating uncertainty. Science doesn’t prove things. Science works by trying to disprove things. The harder people work to disprove a statement, and the more they fail at disproving it, the more people come to believe the statement is true. Science is about expanding the range of what we know to be false. Truth lies in the range of what we don’t know to be false — that is the uncertainty range.

People who don’t want scientists to communicate the uncertainty range are people who don’t understand what science is all about.

Image from Climate Central story: Credit: C.L.Baker/flickr