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If you pay attention to the media reporting on “climate change” (note 1) you will often read/hear something like this:

Under a business as usual scenario..

And then some very worrying future outcomes. Less misleading, but still very misleading, you might read:

Under a high emissions scenario..

Every time I have checked the papers that are behind the press release faithfully reproduced by the stenographers in the media, they are referring to a model simulation using a scenario of CO2 emissions known as RCP8.5.

This scenario – explained below – is a fantastic scenario, and was not created because it was expected to happen.

So – calling it “business as usual”, charitably speaking, is from climate scientists who know nothing about history, demography, current & past trends. And uncharitably, is from climate scientists who are activists, pressing a cause, knowing that stenographers don’t do research or ask difficult questions.

I have been reading climate science for a long time – textbooks, papers, IPCC reports – and for sure when I read “under a business as usual scenario..” I always thought that climate scientists meant – “if we continue doing what we are doing, and don’t immediately reduce CO2 emissions”.

Then I read the papers on the scenarios.

Let me explain. It’s worth spending a few minutes of your time to understand this important subject..

Pre-industrial levels of CO2 were about 280ppm. Currently we are at just over 400ppm. Half a century ago climate scientists used rudimentary climate models and tried doubling CO2 to find out what the new climate equilibrium would be. Some time later people tried 3x and 4x the amount of CO2. It’s a good test. Do the simulated effects of CO2 on climate keep on increasing once we get past 2x CO2? Do the effects flat-line? Skyrocket?

Very worthwhile simulations.

Today we have lots of climate models. There are about 20 modeling centers around the world, each producing results that often vary significantly from other groups (more on that in future articles). How can we compare the results for 2100 from these different models? We need to know how much CO2 (and methane) will be emitted by human activity. We need to know land use and agricultural changes. Of course, no one knows what they will be, but for the purposes of comparison different modeling groups need to work from identical conditions (note 2).

So a bunch of scenarios were created, too many probably. It takes lots of computing power to run a simulation for 100 years. For IPCC AR5 in 2013 these were slimmed down to four Representative Concentration Pathways, or RCPs. One of these is RCP8.5.

The paper writers didn’t come up with RCP8.5 because they felt this was a likely scenario. They were told to come up with RCP8.5:

By design, the RCPs, as a set, cover the range of radiative forcing levels examined in the open literature and contain relevant information for climate model runs..

..The four RCPs together span the range of year 2100 radiative forcing values found in the open literature, i.e. from 2.6 to 8.5 W/m². The RCPs are the product of an innovative collaboration between integrated assessment modelers, climate modelers, terrestrial ecosystem modelers and emission inventory experts. The resulting product forms a comprehensive data set with high spatial and sectoral resolutions for the period extending to 2100..

..The RCPs are named according to radiative forcing target level for 2100. The radiative forcing estimates are based on the forcing of greenhouse gases and other forcing agents. The four selected RCPs were considered to be representative of the literature, and included one mitigation scenario leading to a very low forcing level (RCP2.6), two medium stabilization scenarios (RCP4.5/RCP6) and one very high baseline emission scenarios (RCP8.5).

From IPCC AR5, chapter 2, p.167 we can see that the change in CO2 per year (the bottom graph) is about 2ppm. The legend means, in English rather than maths, the increase in CO2 concentration in ppm per year:

So a naive expectation based on current increases would be around 570 ppm in 2100 (410ppm + 80 years x 2ppm)

This is pretty close to the scenario RCP6, and a very long way from RCP8.5.

To get to RCP8.5 requires almost 1000ppm of CO2 (plus large increases in methane concentrations). This requires about 7ppm per year increase in CO2 starting soon. It’s a “fantastic” scenario that is extremely unlikely to happen, and if by some strange set of circumstances it was, the world could stop it simply by ensuring that sub-Saharan Africa had access to cheap natural gas, rather than coal (see note 3).

If climate scientists and media outlets wrote “under a very unlikely emissions scenario that we can’t see happening we get a few outlier models that predict..” it wouldn’t make good headlines.

It wouldn’t make good climate advocacy.

When you see a story about possible futures, check what scenario is being used. If it’s RCP8.5 (“business as usual” or “a high emissions scenario”) then you can just ignore it – or be concerned and start petitioning your government to encourage more natural gas production.

– Update Jan 1, 2019 (Dec 31st, 2018 in some parts of the world) -just added Opinions and Perspectives – 3.5 – Follow up to “How much CO2 will there be?” due to comments

Further Reading

Impacts – II – GHG Emissions Projections: SRES and RCP

Notes

Note 1: I put “climate change” in quotes to distinguish it from climate change that happened up until 1900 or thereabouts. I’m trying to keep this series non-technical, and also assume that readers haven’t read/remembered previous articles in the series. See Opinions and Perspectives – 2 – There is More than One Proposition in Climate Science

Note 2: A significant part of climate modeling is assessing results and trying to figure out why, say, the GISS model differs substantially from the MPI model. To do that we need to be sure that the model results are based on the same conditions.

Note 3: Natural gas produces about 1/2 the CO2 of coal, per unit of energy produced. If you read the paper for RCP8.5 you will see it depends upon a very high sub-Saharan African population burning huge amounts of coal. No demographic transition. No technological progress. A Victorian technology.

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Continuing from Opinions and Perspectives – 1 – The Consensus a friend said to me a little while back, “Oh, you don’t believe in climate change do you?”

Ye gods, where to start?

At some exhibition which included a questionnaire that visitors were encouraged to take, one of the later questions was “Do you believe in climate change?”. My uncle remarked, “A question that reveals more about the questioner than about the respondents”. I wish I had his gift.

Let me outline some propositions required for basic climate literacy. That is, whether you agree or not with these propositions, you should know that they are distinct, and important:

1. Before “climate change” there was lots of climate change. That is, before humans began emitting large quantities of CO2 (and other GHGs) by burning fossil fuels the climate experienced large changes on time scales ranging from decades to centuries to millennia and longer.

2. Burning fossil fuels like coal and natural gas adds CO2 to the atmosphere.

3.  More CO2, methane and a few other inappropriately-named “greenhouse” gases in the atmosphere increase the surface temperature of the earth.

Items 2 and 3 above can be summarised with the term “anthropogenic global warming” or AGW.

4. Just because there was lots of climate change before AGW doesn’t mean that humans can’t alter the climate.

5. AGW will lead to catastrophe for our planet (perhaps we can call this CAGW).

Each one of these propositions is distinct. And proposition 5 could be broken down into a number of different propositions (which we will look at).

For example, many people “believe in climate change” while refuting even AGW. Their argument is sometimes, “The climate was changing long before we started burning fossil fuels, that’s why I don’t believe in AGW”.

I don’t share that point of view. But wrapping causes around catchy phrases can, of course, backfire.

It is possible to believe in proposition 2 and not proposition 3. It is possible to believe in AGW (2 & 3) and not proposition 5.

Most people, after at least a decade and a half of the media blaring at them (from whatever ideological position), don’t realize that these propositions are not all: “Do you believe in climate change?”

It’s almost as though the media is completely counter-productive for grasping complex issues.

Note to commenters – if you want to question the “greenhouse” effect post your comment in one of the many articles about that, e.g. The “Greenhouse” Effect Explained in Simple Terms. Comments placed here on the science basics will just be deleted.

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When I started this blog I said:

Opinions are often interesting and sometimes entertaining. But what do we learn from opinions? It’s more useful to understand the science behind the subject.

Of late I’ve been caught up with work, other intellectual interests and (luckily) some fun stuff and haven’t spent any time on climate. So I feel it’s time to put forward a few opinions on climate.


The often-cited consensus on climate is:

a) we add CO2 and other GHGs to the atmosphere by burning fossil fuels (and other human activity)

b) these increase the inappropriately-named “greenhouse effect”

c) this increases the surface temperature over some time period

This scientific consensus is rock solid, like gravity or momentum. It’s not particularly intuitive, but tough luck, physics is often like that. By itself, the consensus doesn’t tell you a lot. It just says that if we keep burning fossil fuels then the earth’s surface temperature will increase.

This scientific consensus doesn’t say that urgent action is needed on climate, or that without urgent action society is doomed, or that rapid adoption of renewable energy towards 100% of current energy consumption is a net cost benefit.

These are different propositions.

Note to commenters – if you want to question the “greenhouse” effect post your comment in one of the many articles about that, e.g. The “Greenhouse” Effect Explained in Simple Terms. Comments placed here on the science basics will just be deleted.

 

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At least 99.9% of physicists believe the theory of gravity, and the heliocentric model of the solar system. The debate is over. There is no doubt that we can send a manned (and woman-ed) mission to Mars.

Some “skeptics” say it can’t be done. They are denying basic science! Gravity is plainly true. So is the heliocentric model. Everyone agrees. There is an overwhelming consensus. So the time for discussion is over. There is no doubt about the Mars mission.

I create this analogy (note 1) for people who don’t understand the relationship between five completely different ideas:

  • the “greenhouse” effect
  • burning fossil fuels adds CO2 to the atmosphere, increasing the “greehouse” effect
  • climate models
  • crop models
  • economic models

The first two items on the list are fundamental physics and chemistry, and while advanced to prove (see The “Greenhouse” Effect Explained in Simple Terms for the first one) to people who want to work through a proof, they are indisputable. Together they create the theory of AGW (anthropogenic global warming). This says that humans are contributing to global warming by burning fossil fuels.

99.9% of people who understand atmospheric physics believe this unassailable idea (note 2).

This means that if we continue with “business as usual” (note 3) and keep using fossil fuels to generate energy, then by 2100 the world will be warmer than today.

How much warmer?

For that we need climate models.

Climate Models

These are models which break the earth’s surface, ocean and atmosphere into a big grid so that we can use physics equations (momentum, heat transfer and others) to calculate future climate (this class of model is called finite element analysis). These models include giant fudge-factors that can’t be validated (by giant fudge factors I mean “sub-grid parameterizations” and unknown parameters, but I’m writing this article for a non-technical audience).

One way to validate models is to model the temperature over the last 100 years. Another way is to produce a current climatology that matches observations. Generally temperature is the parameter with most attention (note 4).

Some climate models predict that if we double CO2 in the atmosphere (from pre-industrial periods) then surface temperature will be around 4.5ºC warmer. Others that the temperature will be 1.5ºC warmer. And everything in between.

Surely we can just look at which models reproduced the last 100 years temperature anomaly the best and work with those?

From Mauritsen et al 2012

If the model that predicts 1.5ºC in 2100 is close to the past, while the one that predicts 4.5ºC has a big overshoot, we will know that 1.5ºC is a more likely future. Conversely, if the model that predicts 4.5ºC in 2100 is close to the past but the 1.5ºC model woefully under-predicts the last 100 years of warming then we can expect more like 4.5ºC in 2100.

You would think so, but you would be wrong.

All the models get the last 100 years of temperature changes approximately correct. Jeffrey Kiehl produced a paper 10 years ago which analyzed the then current class of models and gently pointed out the reason. Models with large future warming included a high negative effect from aerosols over the last 100 years. Models with small future warming included a small negative effect from aerosols over the last 100 years. So both reproduced the past but with a completely different value of aerosol cooling. You might think we can just find out the actual cooling effect of aerosols around 1950 and then we will know which climate model to believe – but we can’t. We didn’t have satellites to measure the cooling effect of aerosols back then.

This is the challenge of models with many parameters that we don’t know. When a modeler is trying to reproduce the past, or the present, they pick the values of parameters which make the model match reality as best as they can. This is a necessary first step (note 5).

So how warm will it be in 2100 if we double CO2 in the atmosphere?

Somewhat warmer

Models also predict rainfall, drought and storms. But they aren’t as good as they are at temperature. Bray and von Storch survey climate scientists periodically on a number of topics. Here is their response to:

How would you rate the ability of regional climate models to make 50 year projections of convective rain storms/thunder storms? (1 = very poor to 7 = very good)

Similar ratings are obtained for rainfall predictions. The last 50 years has seen no apparent global worsening of storms, droughts and floods, at least according to the IPCC consensus (see Impacts – V – Climate change is already causing worsening storms, floods and droughts).

Sea level is expected to rise between around 0.3m to 0.6m (see Impacts – VI – Sea Level Rise 1 and IX – Sea Level 4 – Sinking Megacities) – this is from AR5 of the IPCC (under scenario RCP6). I mention this because the few people I’ve polled thought that sea level was expected to be 5-10m higher in 2100.

Actual reports with uneventful projections don’t generate headlines.

Crop Models

Crop models build on climate models. Once we know rainfall, drought and temperature we can work out how this impacts crops.

Will we starve to death? Or will there be plentiful food?

Past predictions of disaster haven’t been very accurate, although they are wildly popular with generating media headlines and book sales, as Paul Ehrlich found to his benefit. But that doesn’t mean future predictions of disaster are necessarily wrong.

There are a number of problems with trying to answer the question.

Even if climate models could predict the global temperature, when it comes to a region the size of, say, northern California their accuracy is much lower. Likewise for rainfall. Models which produce similar global temperature changes often have completely different regional precipitation changes. For example, from the IPCC Special Report on Extremes (SREX), p. 154:

At regional scales, there is little consensus in GCM projections regarding the sign of future change in monsoon characteristics, such as circulation and rainfall. For instance, while some models project an intense drying of the Sahel under a global warming scenario, others project an intensification of the rains, and some project more frequent extreme events..

In a warmer world with more CO2 (helps some plants) and maybe more rainfall, or maybe less what can we expect out of crop yields? It’s not clear. The IPCC AR5 wg II, ch 7, p 496:

For example, interactions among CO2 fertilization, temperature, soil nutrients, O3, pests, and weeds are not well understood (Soussana et al., 2010) and therefore most crop models do not include all of these effects.

Of course, as climate changes over the next 80 years agricultural scientists will grow different crops, and develop new ones. In 1900, almost half the US population worked in farming. Today the figure is 2-3%. Agriculture has changed unimaginably.

In the left half of this graph we can see global crop yield improvements over 50 years (the right side is projections to 2050):

From Ray et al 2013

Economic Models

What will the oil price be in 2020? Economic models give you the answer. Well, they give you an answer. And if you consult lots of models they give you lots of different answers. When the oil price changes a lot, which it does from time to time, all of the models turn out to be wrong. Predicting future prices of commodities is very hard, even when it is of paramount concern for major economies, and even when a company could make vast profits from accurate prediction.

AR5 of the IPCC report, wg 2, ch 7, p.512, had this to say about crop prices in 2050:

Changes in temperature and precipitation, without considering effects of CO2, will contribute to increased global food prices by 2050, with estimated increases ranging from 3 to 84% (medium confidence). Projections that include the effects of CO2 changes, but ignore O3 and pest and disease impacts, indicate that global price increases are about as likely as not, with a range of projected impacts from –30% to +45% by 2050..

..One lesson from recent model intercomparison experiments (Nelson et al., 2014) is that the choice of economic model matters at least as much as the climate or crop model for determining  price response to climate change, indicating the critical role of economic uncertainties for projecting the magnitude of price impacts.

In 2001, the 3rd report (often called TAR) said, ch 5, p.238, perhaps a little more clearly:

..it should be noted however that hunger estimates are based on the assumptions that food prices will rise with climate change, which is highly uncertain

Economic models are not very good at predicting anything. As Herbert Stein said, summarizing a lifetime in economics:

  • Economists do not know very much
  • Other people, including the politicians who make economic policy, know even less about economics than economists do

Conclusion

Recently a group, Cook et al 2013, reviewed over 10,000 abstracts of climate papers and concluded that 97% believed in the proposition of AGW – the proposition that humans are contributing to global warming by burning fossil fuels. I’m sure if the question were posed the right way directly to thousands of climate scientists, the number would be over 99%.

It’s not in dispute.

AGW is a necessary theory for Catastrophic Anthropogenic Global Warming (CAGW). But not sufficient by itself.

Likewise we know for sure that gravity is real and the planets orbit the sun. But it doesn’t follow that we can get humans safely to Mars and back. Maybe we can. Understanding gravity and the heliocentric theory is a necessary condition for the mission, but a lot more needs to be demonstrated.

The uncertainties in CAGW are huge.

Economic models that have no predictive skill are built on limited crop models which are built on climate models which have a wide range of possible global temperatures and no consensus on regional rainfall.

Human ingenuity somehow solved the problem of going from 2.5bn people in the middle of the 20th century to more than 7bn people today, and yet the proportion of the global population in abject poverty (note 6) has dropped from over 40% to maybe 15%. This was probably unimaginable 70 years ago.

Perhaps reasonable people can question if climate change is definitely the greatest threat facing humanity?

Perhaps questioning the predictive power of economic models is not denying science?

Perhaps it is ok to be unsure about the predictive power of climate models that contain sub-grid parameterizations (giant fudge factors) and that collectively provide a wide range of forecasts?

Perhaps people who question the predictions aren’t denying basic (or advanced) science, and haven’t lost their reason or their moral compass?

—-

[Note to commenters, added minutes after this post was written – this article is not intended to restart debate over the “greenhouse” effect, please post your comments in one of the 10s (100s?) of articles that have covered that subject, for example – The “Greenhouse” Effect Explained in Simple Terms – Comments on the reality of the “greenhouse” effect posted here will be deleted. Thanks for understanding.]

References

Twentieth century climate model response and climate sensitivity, Jeffrey Kiehl (2007)

Tuning the climate of a global model, Mauritsen et al (2012)

Yield Trends Are Insufficient to Double Global Crop Production by 2050, Deepak K. Ray et al (2013)

Quantifying the consensus on anthropogenic global warming in the scientific literature, Cook et al, Environmental Research Letters (2013)

The Great Escape, Angus Deaton, Princeton University Press (2013)

The various IPCC reports cited are all available at their website

Notes

1. An analogy doesn’t prove anything. It is for illumination.

2. How much we have contributed to the last century’s warming is not clear. The 5th IPCC report (AR5) said it was 95% certain that more than 50% of recent warming was caused by human activity. Well, another chapter in the same report suggested that this was a bogus statistic and I agree, but that doesn’t mean I think that the percentage of warming caused by human activity is lower than 50%. I have no idea. It is difficult to assess, likely impossible. See Natural Variability and Chaos – Three – Attribution & Fingerprints for more.

3. Reports on future climate often come with the statement “under a conservative business as usual scenario” but refer to a speculative and hard to believe scenario called RCP8.5 – see Impacts – II – GHG Emissions Projections: SRES and RCP. I think RCP 6 is much closer to the world of 2100 if we do little about carbon emissions and the world continues on the kind of development pathways that we have seen over the last 60 years. RCP8.5 was a scenario created to match a possible amount of CO2 in the atmosphere and how we might get there. Calling it “a conservative business as usual case” is a value-judgement with no evidence.

4. More specifically the change in temperature gets the most attention. This is called the “temperature anomaly”. Many models that do “well” on temperature anomaly actually do quite badly on the actual surface temperature. See Models, On – and Off – the Catwalk – Part Four – Tuning & the Magic Behind the Scenes – you can see that many “fit for purpose” models have current climate halfway to the last ice age even though they reproduce the last 100 years of temperature changes pretty well. That is, they model temperature changes quite well, but not temperature itself.

5. This is a reasonable approach used in modeling (not just climate modeling) – the necessary next step is to try to constrain the unknown parameters and giant fudge factors (sub-grid parameterizations). Climate scientists work very hard on this problem. Many confused people writing blogs think that climate modelers just pick the values they like, produce the model results and go have coffee. This is not the case, and can easily be seen by just reviewing lots of papers. The problem is well-understood among climate modelers. But the world is a massive place, detailed past measurements with sufficient accuracy are mostly lacking, and sub-grid parameterizations of non-linear processes are a very difficult challenge (this is one of the reasons why turbulent flow is a mostly unsolved problem).

6. This is a very imprecise term. I refer readers to the 2015 Nobel Prize winner Angus Deaton and his excellent book, The Great Escape (2013) for more.

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A long time ago I wrote The Confirmation Bias – Or Why None of Us are Really Skeptics, with a small insight from Nassim Taleb. Right now I’m rereading The Righteous Mind: Why Good People are Divided by Politics and Religion by Jonathan Haidt.

This is truly a great book if you want to understand more about how we think and how we delude ourselves. Through experiments cognitive psychologists demonstrate that once our “moral machinery” has clicked in, which happens very easily, our reasoning is just an after-the-fact rationalization of what we already believe.

Haidt gives the analogy of a rider on an elephant. The elephant starts going one way rather than another, and the rider, unaware of why, starts coming up with invented reasons for the new direction. It’s like the rider is the PR guy for the elephant. In Haidt’s analogy, the rider is our reasoning, and the elephant is our moral machinery. The elephant is in charge. The rider thinks he is.

An an intuitionist, I’d say that the worship of reason is itself an illustration of one of the most long-lived delusions in Western history: the rationalist delusion..

..The French cognitive scientists Hugo Mercier and Dan Sperber recently reviewed the vast research literature on motivated reasoning (in social psychology) and on the biases and errors of reasoning (in cognitive psychology). They concluded that most of the bizarre and depressing research findings make perfect sense once you see reasoning as having evolved not to help us find truth but to help us engage in arguments, persuasion and manipulation in the context of discussions with other people.

As they put it, “skilled arguers ..are not after the truth but after arguments supporting their views.” This explains why the confirmation bias is so powerful and so ineradicable. How hard could it be to teach students to look on the other side, to look for evidence against their favored view? Yet it’s very hard, and nobody has yet found a way to do it. It’s hard because the confirmation bias is a built-in feature (of an argumentative mind), not a bug that can be removed (from a platonic mind)..

..In the same way, each individual reasoner is really good at one thing: finding evidence to support the position he or she already holds, usually for intuitive reasons..

..I have tried to make a reasoned case that our moral capacities are best described from an intuitionist perspective. I do not claim to have examined the question from all sides, nor to have offered irrefutable proof.

Because of the insurmountable power of the confirmation bias, counterarguments will have to be produced by those who disagree with me.

Haidt also highlights some research showing that more intelligence and education makes you better at generating more arguments for your side of the argument, but not for finding reasons on the other side. “Smart people make really good lawyers and press secretaries.. people invest their IQ in buttressing their own case rather than in exploring the entire issue more fully and evenhandedly.”

The whole book is very readable and full of studies and explanations.

If you fancy a bucket of ice cold water thrown over the rationalist delusion then this is a good way to get it.

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In Parts VI and VII we looked at past and projected sea level rise. It is clear that the sea level has risen over the last hundred years, and it’s clear that with more warming sea level will rise some more. The uncertainties (given a specific global temperature increase) are more around how much more ice will melt than how much the ocean will expand (warmer water expands). Future sea level rise will clearly affect some people in the future, but very differently in different countries and regions. This article considers the US.

A month or two ago, via a link from a blog, I found a paper which revised upwards a current calculation (or average of such calculations) of damage due to sea level rise in 2100 in the US. Unfortunately I can’t find the paper, but essentially the idea was people would continue moving to the ocean in ever increasing numbers, and combined with possible 1m+ sea level rise (see Part VI & VII) the cost in the US would be around $1TR (I can’t remember the details but my memory tells me this paper concluded costs were 3x previous calculations due to this ever increasing population move to coastal areas – in any case, the exact numbers aren’t important).

Two examples that I could find (on global movement of people rather than just in the US), Nicholls 2011:

..This threatened population is growing significantly (McGranahan et al., 2007), and it will almost certainly increase in the coming decades, especially if the strong tendency for coastward migration continues..

And Anthoff et al 2010

Fifthly, building on the fourth point, FUND assumes that the pattern of coastal development persists and attracts future development. However, major disasters such as the landfall of hurricanes could trigger coastal abandonment, and hence have a profound influence on society’s future choices concerning coastal protection as the pattern of coastal occupancy might change radically.

A cycle of decline in some coastal areas is not inconceivable, especially in future worlds where capital is highly mobile and collective action is weaker. As the issue of sea-level rise is so widely known, disinvestment from coastal areas may even be triggered without disasters..

I was struck by the “trillion dollar problem” paper and the general issues highlighted in other papers. The future cost of sea level rise in the US is not just bad, it’s extremely expensive because people will keep moving to the ocean.

Why are people moving to the coast?

So here is an obvious take on the subject that doesn’t need an IAM (integrated assessment model).. Perhaps lots of people missed the IPCC TAR (third assessment report) in 2001. Perhaps anthropogenic global warming fears had not reached a lot of the population. Maybe it didn’t get a lot of media coverage. But surely no could have missed Al Gore’s movie. I mean, I missed it from choice, but how could anyone in rich countries not know about the discussion?

So anyone since 2006 (arbitrary line in the sand) who bought a house that is susceptible to sea level rise is responsible for their own loss that they incur around 2100. That is, if the worst fears about sea level rise play out, combined with more extreme storms (subject of a future article) which create larger ocean storm surges, their house won’t be worth much in 2100.

Now, barring large increases in life expectancy, anyone who bought a house in 2005 will almost certainly be dead in 2100. There will be a few unlucky centenarians.

Think of it as an estate tax. People who have expensive ocean-front houses will pass on their now worthless house to their children or grandchildren. Some people love the idea of estate taxes – in that case you have a positive. Some people hate the idea of estate taxes – in that case strike it up as a negative. And, drawing a long bow here, I suspect a positive correlation between concern about climate change and belief in the positive nature of estate taxes, so possibly it’s a win-win for many people.

Now onto infrastructure.

From time to time I’ve had to look at depreciation and official asset life for different kinds of infrastructure and I can’t remember seeing one for 100 years. 50 years maybe for civil structures. I’m definitely not an expert. That said, even if the “official depreciation” gives something a life of 50 years, much is still being used 150 years later – buildings, railways, and so on.

So some infrastructure very close to the ocean might have to be abandoned. But it will have had 100 years of useful life and that is pretty good in public accounting terms.

Why is anyone building housing, roads, power stations, public buildings, railways and airports in the US in locations that will possibly be affected by sea level rise in 2100? Maybe no one is.

So the cost of sea level rise for 2100 in the US seems to be a close to zero cost problem.

These days, if a particular area is recognized as a flood plain people are discouraged from building on it and no public infrastructure gets built there. It’s just common sense.

Some parts of New Orleans were already below sea level when Hurricane Katrina hit. Following that disaster, lots of people moved out of New Orleans to a safer suburb. Lots of people stayed. Their problems will surely get worse with a warmer climate and a higher sea level (and also if storms gets stronger – subject of a future article). But they already had a problem. Infrastructure was at or below sea level and sufficient care was not taken of their coastal defences.

A major problem that happens overnight, or over a year, is difficult to deal with. A problem 100 years from now that affects a tiny percentage of the land area of a country, even with a large percentage (relatively speaking) of population living there today, is a minor problem.

Perhaps the costs of recreating current threatened infrastructure a small distance inland are very high, and the existing infrastructure would in fact have lasted more than 100 years. In that case, people who believe Keynesian economics might find the economic stimulus to be a positive. People who don’t think Keynesian economics does anything (no multiplier effect) except increase taxes, or divert productive resources into less productive resources will find it be a negative. Once again, drawing a long bow, I see a correlation between people more concerned about climate change also being more likely to find Keynesian economics a positive. Perhaps again, there is a win-win.

In summary, given the huge length of time to prepare for it, US sea level rise seems like a minor planning inconvenience combined with an estate tax.

Articles in this Series

Impacts – I – Introduction

Impacts – II – GHG Emissions Projections: SRES and RCP

Impacts – III – Population in 2100

Impacts – IV – Temperature Projections and Probabilities

Impacts – V – Climate change is already causing worsening storms, floods and droughts

Impacts – VI – Sea Level Rise 1

Impacts – VII – Sea Level 2 – Uncertainty

References

Planning for the impacts of sea level rise, RJ Nicholls, Oceanography (2011)

The economic impact of substantial sea-level rise, David Anthoff et al, Mitig Adapt Strateg Glob Change (2010)

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A long time ago, in About this Blog I wrote:

Opinions
Opinions are often interesting and sometimes entertaining. But what do we learn from opinions? It’s more useful to understand the science behind the subject. What is this particular theory built on? How long has the theory been “established”? What lines of evidence support this theory? What evidence would falsify this theory? What do opposing theories say?

Now I would like to look at impacts of climate change. And so opinions and value judgements are inevitable.

In physics we can say something like “95% of radiation at 667 cm-1 is absorbed within 1m at the surface because of the absorption properties of CO2″ and be judged true or false. It’s a number. It’s an equation. And therefore the result is falsifiable – the essence of science. Perhaps in some cases all the data is not in, or the formula is not yet clear, but this can be noted and accepted. There is evidence in favor or against, or a mix of evidence.

As we build equations into complex climate models, judgements become unavoidable. For example, “convection is modeled as a sub-grid parameterization therefore..”. Where the conclusion following “therefore” is the judgement. We could call it an opinion. We could call it an expert opinion. We could call it science if the result is falsifiable. But it starts to get a bit more “blurry” – at some point we move from a region of settled science to a region of less-settled science.

And once we consider the impacts in 2100 it seems that certainty and falsifiability must be abandoned. “Blurry” is the best case.

 

Less than a year ago listening to America and the New Global Economy by Timothy Taylor (via audible.com) I remember he said something like “the economic cost of climate change was all lumped into a fat tail – if the temperature change was on the higher side”. Sorry for my inaccurate memory (and the downside of audible.com vs a real book). Well it sparked my interest in another part of the climate journey.

I’ve been reading IPCC Working Group II (wgII) – some of the “TAR” (= third assessment report) from 2001 for background and AR5, the latest IPCC report from 2014. Some of the impacts also show up in Working Group I which is about the physical climate science, and the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation from 2012, known as SREX (Special Report on Extremes). These are all available at the IPCC website.

The first chapter of the TAR, Working Group II says:

The world community faces many risks from climate change. Clearly it is important to understand the nature of those risks, where natural and human systems are likely to be most vulnerable, and what may be achieved by adaptive responses. To understand better the potential impacts and associated dangers of global climate change, Working Group II of the Intergovernmental Panel on Climate Change (IPCC) offers this Third Assessment Report (TAR) on the state of knowledge concerning the sensitivity, adaptability, and vulnerability of physical, ecological, and social systems to climate change.

A couple of common complaints in the blogosphere that I’ve noticed are:

  • “all the impacts are supposed to be negative but there are a lot of positives from warming”
  • “CO2 will increase plant growth so we’ll be better off”

Within the field of papers and IPCC reports it’s clear that CO2 increasing plant growth is not ignored. Likewise, there are expected to be winners and losers (often, but definitely not exclusively, geographically distributed), even though the IPCC summarizes the expected overall effect as negative.

Of course, there is a highly entertaining field of “recycled press releases about the imminent catastrophe of climate change” which I’m sure ignores any positives or tradeoffs. Even in what could charitably be called “respected media outlets” there seem to be few correspondents with basic scientific literacy. Not even the ability to add up the numbers on an electricity bill or distinguish between the press release of a company planning to get wonderful results in 2025 vs today’s reality.

Anyway, entertaining as it is to shoot fish in a barrel, we will try to stay away from discussing newsotainment and stay with the scientific literature and IPCC assessments. Inevitably, we’ll stray a little.

I haven’t tried to do a comprehensive summary of the issues believed to impact humanity, but here are some:

  • sea level rise
  • heatwaves
  • droughts
  • floods
  • more powerful cyclones and storms
  • food production
  • ocean acidification
  • extinction of animal and plant species
  • more pests (added, thanks Tom, corrected thanks DeWitt)
  • disease (added, thanks Tom)

Possibly I’ve missed some.

Covering the subject is not easy but it’s an interesting field.

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