Tag Archives: climate science

Manufacturing climate nightmares: misusing science to create horrific predictions

Summary: Scientists and journalists bombard us with news about the coming climate catastrophe, described as certain unless we drastically change our economy. This has plunged many into despair. The hidden key to these forecasts is RCP8.5, the worst case scenario of the IPCC’s Fifth Assessment Report — often erroneously described as the “business as usual” scenario. Understanding this misuse of science reveals the weak basis of the most dire warnings (which set the mood at the Paris Conference), and helps explain why the US public assigns a low priority to fighting climate change despite the intense decades-long publicity campaign.

“We’re going to become extinct. Whatever we do now is too late.”
— Frank Fenner (Prof emeritus in microbiology at the Australian National U); Wikipedia describes his great accomplishments), an interview in The Australian, 10 June 2010.

Climate nightmares

In the IPCC’s Fifth Assessment Report four scenarios describe future emissions, concentrations, and land-use. They are Representative Concentration Pathways (RCPs), the inputs to climate models that generate the IPCC’s projections. Strong mitigation policies lead to a low forcing level of 2.6 W/m2 by 2100 (RCP2.6). Two medium stabilization scenarios lead to intermediate outcomes in RCP4.5 and RCP6.0.

RCP8.5 gets the most attention, with its bold and dark assumptions. It is a useful and important scenario, a warning of what might happen if the 21st century goes badly. It should spur us to act. Unfortunately from its creation RCP8.5 has often been misrepresented as the “business as usual” scenario — and so became the basis for hundreds or thousands of predictions about our certain doom from climate change.

The result of this (part of a decade-long campaign) is widespread despair among climate scientists and more broadly, among Leftists. This misuse of RCP8.5 is a triumph of propaganda, but polls show its ineffectiveness (with climate change ranking at or near the bottom of public policy concerns). Yet each month brings more of the same.

What future does RCP8.5 describe?

“In 2002, as I edited a book about global climate change, I concluded we had set events in motion that would cause our own extinction, probably by 2030. I mourned for months …”
— “Apocalypse or extinction?” by Guy McPherson (Prof Emeritus of Natural Resources and Ecology, U AZ), Oct 2009.

The papers describing the RCP’s clearly state their assumptions, unlike most of those that follow them. RCP8.5 describes a bleak scenario, a hot and dark world in 2100 (since it’s powered by coal, perhaps literally dark) — even before considering the effects of climate change. Below are the key points, with graphs from “The representative concentration pathways: an overview” by Detlef P. van Vuuren et al in Climatic Change, Nov 2011. See this post for a more detailed look.

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Assigning blame for the flooding of Pacific atolls

Summary: Polar bears and pacific atolls (flooded by rising seas) are the poster children for climate change. Oddly, both are weak examples. Previous posts discussed polar bears. Here Judith Curry (Prof Atmospheric Science, GA Inst Tech) discusses the effect of rising sea levels on coral atolls.

Fanning Island, Kiribati

Fanning Island, Kiribati

Kiribati crisis: the blame game

Small atoll islands may grow, not sink, as sea level rises.
Judith Curry, posted at Climate Etc, 1 November 2015
Reposted under her Creative Commons License

Recent headlines highlight the plight of Kiribati:

Can we blame climate change, or more specifically sea level rise, for the problems of the atoll islands?  From a June 2 article in the New Scientist Small atoll islands may grow, not sink, as sea level rises, we find:

Rising seas are eating away at small islands and will eventually turn their inhabitants into climate refugees, right? Not so for some of the world’s most threatened islands, which have grown despite experiencing dramatic sea level rise.

After poring over more than a century’s worth of data, including old maps and aerial and satellite imagery, they conclude that 18 out of 29 islands have actually grown. As a whole, the group grew by more than 18 hectares, while many islands changed shape or shifted sideways. “There is still considerable speculation that islands will disappear as sea level rises,” says Kench. “Our data indicates that the future of islands is significantly different.”

Storms and other disturbances that churn up the sea seem to be more important than sea level in influencing stability, says Kench. Storms break up coral, which then gets deposited on the atolls. He says other coral reef islands are likely to evolve in the same way, and that the Maldives seem to be showing a similar effect.

“There will be less emphasis on external migration of ‘environmental refugees’ from atoll nations that has gained such prominence in the last few years,” he says. But he notes that the atoll-building sediment comes from productive coral reefs, which face a range of threats such as warming oceans and pollution.

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A way to break the gridlock on the climate change debate

Summary: The public policy debate about climate change consists largely of people tossing confetti at one another, with the reasonable people either pushed to the sidelines or buried. There is a simple step that both sides can agree on, with the potential to break the gridlock. Here’s a brief version of my proposal.

Trust can trump Uncertainty.”
Presentation by Leonard A Smith (Prof of Statistics, LSE), 6 February 2014.

The most important graph from the IPCC’s AR5

Figure 1.4 from the IPCC's AR5

Figure 1.4 from AR5: Estimated changes in the observed globally and annually averaged surface temperature anomaly relative to 1961–1990 (in °C) since 1950 compared with the range of projections from the previous IPCC assessments. Click to enlarge.

After 26 years of debate, US public policy on climate change remains paralyzed. Polls show that it ranks near the bottom of American’s concerns. We even remain poorly prepared for a repeat of past extreme weather, such as a major hurricane hitting an East coast city – let alone future climate. Both sides rehash their arguments, accomplishing nothing.

Climate scientists can begin to restart the debate and rebuild public confidence: re-run the climate models from the first three IPCC assessment reports (ARs) with actual emission data (from their future). See if they can show that models predict the observed global temperatures with reasonable accuracy. The cost of this project would be small compared to the overall funding of climate research and a dot compared to the potential costs of climate change.

The famous “spaghetti graphs” — one the most-cited graphics from the ARs — shows the forecast of models used in each report vs. actual global average atmospheric temperature. They tell us little, as in the above graph from the most recent AR. It packs too much information into one graph, but does not show what we most want to know: the accuracy of their forecasts. Each line represents a temperature forecast with specific assumptions of greenhouse gas (GHG) emissions.

How well do the models work if input with accurate GHG emissions? The answer can provide a fair test of the major climate models, one acceptable to both sides in the policy debate. A brief explanation of the models shows why.

{Read the full article at Climate Scepticism.}

“In an age of spreading pseudoscience and anti-rationalism, it behooves those of us who
believe in the good of science and engineering to be above reproach whenever possible.“
P. J. Roach, Computing in Science and Engineering, Sept-Oct 2004 — Gated.

Other posts in this series

  1. How we broke the climate change debates. Lessons learned for the future.
  2. A new response to climate change that can help the GOP win in 2016.
  3. The full version: climate scientists can restart the climate change debate – & win.

Climate scientists can restart the climate change debate & win: test the models!

Summary; Public policy about climate change has become politicized and gridlocked after 26 years of large-scale advocacy. We cannot even prepare for a repeat of past extreme weather. We can whine and bicker about who to blame. Or we can find ways to restart the debate. Here is the next of a series about the latter path, for anyone interested in walking it. Climate scientists can take an easy and potentially powerful step to build public confidence: re-run the climate models from the first 3 IPCC reports with actual data (from their future): how well did they predict global temperatures?

Trust can trump Uncertainty.”
Presentation by Leonard A Smith (Prof of Statistics, LSE), 6 February 2014.

The most important graph from the IPCC’s AR5

Figure 1.4 from the IPCC's AR5

Figure 1.4 from AR5: Estimated changes in the observed globally and annually averaged surface temperature anomaly relative to 1961–1990 (in °C) since 1950 compared with the range of projections from the previous IPCC assessments. Click to enlarge.

Why the most important graph doesn’t convince the public

Last week I posted What climate scientists did wrong and why the massive climate change campaign has failed. After 26 years, one of the largest longest campaigns to influence public policy has failed to gain the support of Americans, with climate change ranking near the bottom of people’s concerns. It described the obvious reason: they failed to meet the public’s expectations for behavior of scientists warning about a global threat (i.e., a basic public relations mistake).

Let’s discuss what scientists can do to restart the debate. Let’s start with the big step: show that climate models have successfully predicted future global temperatures with reasonable accuracy.

This spaghetti graph — probably the most-cited data from the IPCC’s reports — illustrates one reason for lack of sufficient public support in America. It shows the forecasts of models run in previous IPCC reports vs. actual subsequent temperatures, with the forecasts run under various scenarios of emissions and their baselines updated. First, Edward Tufte probably would laugh at this The Visual Display of Quantitative Information — too much packed into one graph, the equivalent of a Powerpoint slide with 15 bullet points.

But there’s a more important weakness. We want to know how well the models work. That is, how well each forecast if run with a correct scenario (i.e., actual future emissions, since we’re uninterested here in predicting emissions, just temperatures). Let’s prune away all those extra lines on the spagetti graph, leaving forecasts from 1990 to now that match the actual course of emissions.

The big step: prove climate models have made successful predictions

“A genuine expert can always foretell a thing that is 500 years away easier than he can a thing that’s only 500 seconds off.”
— From Mark Twain’s A Connecticut Yankee in King Arthur’s Court.

A massive body of research describes how to validate climate models (see below), most stating that they must use “hindcasts” (predicting the past) because we do not know the temperature of future decades. Few sensible people trust hindcasts, with their ability to be (even inadvertently) tuned to work (that’s why scientists use double-blind testing for drugs where possible).

But now we know the future — the future of models run in past IPCC reports — and can test their predictive ability.

Karl Popper believed that predictions were the gold standard for testing scientific theories. The public also believes this. Countless films and TV shows focus on the moment in which scientists test their theory to see if the result matches their prediction. Climate scientists can run such tests today for global surface temperatures. This could be evidence on a scale greater than anything else they’ve done.

Model of a hurricane.

A hurricane in the Weather Research & Forecasting (WRF) Model. From NCAR/UCAR.

Testing the climate models used by the IPCC

“Probably {scientists’} most deeply held values concern predictions: they should be accurate; quantitative predictions are preferable to qualitative ones; whatever the margin of permissible error, it should be consistently satisfied in a given field; and so on.”
— Thomas Kuhn in The Structure of Scientific Revolutions (1962).

The IPCC’s scientists run projections. AR5 describes these as “the simulated response of the climate system to a scenario of future emission or concentration of greenhouse gases and aerosols … distinguished from climate predictions by their dependence on the emission/concentration/radiative forcing scenario used…”. The models don’t predict CO2 emissions, which are an input to the models.

So they should run the models as they were when originally run for the IPCC in the First Assessment Report (FAR, 1990), in the Second (SAR, 1995), and the Third (TAR, 2001). Run them using actual emissions as inputs and with no changes of the algorithms, baselines, etc. How accurately will the models’ output match the actual global average surface temperatures? This was proposed by Roger Pielke Jr (Prof Environmental Studies, U CO-Boulder) in “Climate predictions and observations“, Nature Geoscience, April 2008.

Of course, the results would not be a simple pass/fail. Such a test would provide the basis for more sophisticated tests. Judith Curry (Prof Atmospheric Science, GA Inst Tech) explains here:

“Comparing the model temperature anomalies with observed temperature anomalies, particularly over relatively short periods, is complicated by the acknowledgement that climate models do not simulate the timing of ENSO and other modes of natural internal variability; further the underlying trends might be different. Hence, it is difficult to make an objective choice for matching up the observations and model simulations. Different strategies have been tried… matching the models and observations in different ways can give different spins on the comparison.”

On the other hand, we now have respectably long histories since publication of the early IPCC reports: 25, 20, and 15 years. These are not short periods, even for climate change. Models that cannot successfully predict over such periods require more trust than many people have when it comes to spending trillions of dollars — or even making drastic revisions to our economic system (as urged by Naomi Klein and Pope Francis).


Re-run the models. Post the results. More recent models presumably will do better, but firm knowledge about performance of the older models will give us useful information for the public policy debate. No matter what the results.

As the Romans might have said when faced with a problem like climate change: “Fiat scientia, ruat caelum.” (Let science be done though the heavens may fall.)

“In an age of spreading pseudoscience and anti-rationalism, it behooves those of us who
believe in the good of science and engineering to be above reproach whenever possible.“
P. J. Roach, Computing in Science and Engineering, Sept-Oct 2004 — Gated.

World Models

Other posts in this series

These posts sum up my 330 posts about climate change.

  1. How we broke the climate change debates. Lessons learned for the future.
  2. A new response to climate change that can help the GOP win in 2016.
  3. The big step climate scientists can make to restart the climate change debate – & win.

For More Information

(a)  Please like us on Facebook, follow us on Twitter, and post your comments — because we value your participation. For more information see The keys to understanding climate change and My posts about climate change. Also see these about models…

(b)  This is an obvious idea. I saw one mention of this on the web (e.g., by Carrick in this Sept 2013 thread at Climate Audit) — there are probably others — but nothing by a climate scientist — formally or informally made. We can only guess why.

The odds that no scientist thought of this are IMO zero. Why has this not been done? We can only guess.

(c)  I learned much, and got several of these quotes, from a 2014 presentations by Leonard A Smith (Prof of Statistics, LSE): the abridged version “The User Made Me Do It” and the full version “Distinguishing Uncertainty, Diversity and Insight“. Also see “Uncertainty in science and its role in climate policy“, Leonard A. Smith and Nicholas Stern, Phil Trans A, 31 October 2011.

(d)  Introductions to climate modeling

These provide an introduction to the subject, and a deeper review of this frontier in climate science.

Judith Curry (Prof Atmospheric Science, GA Inst Tech) reviews the literature about the uses and limitation of climate models…

  1. What can we learn from climate models?
  2. Philosophical reflections on climate model projections.
  3. Spinning the climate model – observation comparison — Part I.
  4. Spinning the climate model – observation comparison: Part II.

(e)  Selections from the large literature about validation of climate models

  1. Evaluating Jim Hansen’s 1988 Climate Forecast“, Roger Pielke Jr, May 2006 — Compares Hansen’s assumptions about future emissions with the actual history.
  2. How Well Do Coupled Models Simulate Today’s Climate?“, BAMS, March 2008 — Comparing models with the present, but defining “present” as the past (1979-1999).
  3. Similar proposal to mine, but more complete: “Climate predictions and observations“, Roger Pielke Jr., Nature Geoscience, April 2008.
  4. Should we believe model predictions of future climate change?”, Reto Knutti, Philosophical Transactions A, December 2008.
  5. Should we assess climate model predictions in light of severe tests?”, Joel Katzav, Eros, 7 June 2011.
  6. More hindcasting: “Skillful predictions of decadal trends in global mean surface temperature“, J. C. Fyfe et al, Geophysical Research Letters, November 2011. Gated; open draft here. Comments by Pielke Sr here.
  7. Reliability of multi-model and structurally different single-model ensembles“, Tokuta Yokohata et al, Climate Dynamics, August 2012. Uses the rank histogram approach.
  8. The Elusive Basis of Inferential Robustness“, James Justus, Philosophy of Science, December 2012. A creative look at a commonly given reason to trust GCMs.
  9. Test of a decadal climate forecast“, Myles R. Allen et al, Nature Geoscience, April 2013 — Gated. Test of one model’s forecasts over subsequent 10 years. Doesn’t state what emissions data  used for validation (scenario or actual). The forecast was significantly below consensus, and so quite accurate. Which is why we hear about it.
  10. Overestimated global warming over the past 20 years” by John C. Fyfe et al, Nature Climate Change, Sept 2013.
  11. Can we trust climate models?” J. C. Hargreaves and J. D. Annan, Wiley Interdisciplinary Reviews: Climate Change, July/August 2013.
  12. Can climate models explain the recent stagnation in global warming?“, H. Von Storch et al, 2013 — unpublished. Hindcast of models used in AR4 and AR5 vs. two scenarios.
  13. Well-estimated global surface warming in climate projections selected for ENSO phase“, James S. Risbey et al, Nature Climate Change, September 2014. Hindcasting of CMIP5. Reported as “Study vindicates climate models accused of ‘missing the pause’“.
  14. The Robustness of the Climate Modeling Paradigm“, Alexander Bakker, Ph.D. thesis, VU University (2015).
  15. Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise“, Patrick T. Brown et al, Scientific Reports, April 2015.
  16. Uncertainties, Plurality, and Robustness in Climate Research and Modeling: On the Reliability of Climate Prognoses“, Anna Leuschner, Journal for General Philosophy of Science, 21 July 2015. Typical cheerleading; proof by bold assertion.
  17. Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures“, Kevin Cowtan et al, Geophysical Research Letters, 15 August 2015. Open copy here.

What are your recommendations: how to re-start the climate change debate?

Summary:  Today’s post gives a challenge and two useful presentations about climate change. How can climate scientists restart the debate — and gain majority support for large-scale public policy measures for mitigation of and adaptation to climate change? Plus a presentation overflowing with insights about climate science (not what you might expect from a Professor of Statistics), and a presentation by Roger Pielke Sr. about the state of the art in climate science.

“The climate is what you expect; the weather is what you get.”
— From Robert Heinlein’s Time Enough for Love.

Your ideas

A challenge for you

Last week I posted What climate scientists did wrong and while the massive climate change campaign has failed. After 26 years, one of the largest longest campaigns to influence public policy has failed to gain the support of Americans, with climate change ranking near the bottom of people’s concerns. It described the obvious reason: they failed to meet the public’s expectations for behavior of scientists warning about a global threat (i.e., a basic public relations mistake).

The next few posts explain how climate scientists can re-start the public policy debate about climate change and win — gaining approval of large-scale public policy measures for mitigation and adaptation). It’s taken longer than I planned to research; the first goes up tomorrow. So for today I pass the ball to you. Post your recommendations for climate scientists in the comments!

Recommendation #1, a brilliant presentation overflowing with ideas

Here are the slide decks to two interesting presentations. The first is by Leonard A Smith (Prof of Statistics, London School of Economics): “Distinguishing Uncertainty, Diversity and Insight“.  It’s long, dense, and difficult to follow without the speech — but brilliant and over-flowing with insights. I especially recommend slides 76-86 describing the constraints limiting climate models. He does it unusually clearly using simple graphs of model complexity (run time) vs. forecast lead time (how far in the future it can see).

Presentation by Leonard Smith

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How climate change can help the GOP win in 2016

Summary: Republicans have adopted a purely negative platform for dealing with climate change, a difficult to explain policy that puts them in opposition to most scientists. This post describes an alternative platform, one that is consistent with their principles, easy to explain, appealing to undecided voters, and cuts through the chaff of factional bickering. It’s the kind of policy that helps create coalitions that win elections.

“… a genuine leader is not a searcher for consensus, but a molder of consensus.”
— Martin Luther King’s speech “Remaining Awake Through a Great Revolution“, at the Episcopal National Cathedral in Washington on 31 March 1968.

Republicans Flag


  1. GOP weakness on climate change
  2. An agenda for the 21st century.
  3. Conclusions.
  4. Other posts in this series.
  5. For more information.

(1) The Republicans’ weak stance on climate change

The Republicans have ceded the politics of climate change to the Democrats. The only mention of it in the 2012 Republican platform is trivial…

“Finally, the strategy subordinates our national security interests to environmental, energy, and international health issues, and elevates “climate change” to the level of a “severe threat” equivalent to foreign aggression.”

So far the GOP’s 2016 presidential candidates have little to say about it. I see no policy statements about climate change on the issues pages of campaign websites for Rick Santorum, Jeb Bush, Ben Carson, Mike Huckabee, Marco Rubio, Ted Cruz, and Rand Paul.

Carly Fiorina says that the solution to climate change is “innovation not regulation”, without many details (her website points to video clip here, and here). She also says — logically — that California should have prepared better for the drought — although her specific recommendations are illogical: more dams and water infrastructure (ineffective and too expensive to cope with multi-year droughts) and massive destruction of California’s ecology (e.g., damage to key species such as the delta smelt — calling it unimportant because it’s a “small fish”).

When questioned, Republican candidates tend to respond with evasions and half-understood techno-babble (even if they understood it, the public would not) — or just deny the problem (see responses at the CNN debate). There is a better way, one consistent with their commitment to a strong defense and a sound infrastructure for America.

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Alaska’s climate scientists tell us the rest of the news, what Obama forgot to mention

Summary: Obama journeys to Alaska and says things. Our journalist-stenographers reprint this as news. They do not consult local experts, and so miss an important part of the story. This post gives you the rest of the news.  {2nd of 2 posts today.}

Alaska temperatures 1949-2014

From the Alaska Climate Research Center. Click to enlarge.

The great oddity of the climate change campaign is the disinterest of journalists in reporting it well. Stories about our certain doom often omit vital context (e.g., burning off the world’s fossil fuels means the 21st century relies on coal for energy, like the 19thC), forget to mention the IPCC when it disagrees with alarmists (e.g., about the danger of a methane apocalypse), and ignore the host of research facilities studying relevant aspects of our changing world.

We see that last factor at work in journalists’ reporting about Obama’s climate campaign tour of Alaska. Google News shows no stories in the mainstream news mentioning the findings of the Alaska Climate Research Center. I have posted their work in response to previous panicky stories about Alaska melting in 2009, in 2013, and again here.

Here is their Temperature Changes in Alaska page (updated annually; red emphasis added). It’s quite clear.

“This page features the trends in mean annual and seasonal temperatures for Alaska’s first-order observing stations since 1949, the time period for which the most reliable meteorological data are available. The temperature change varies from one climatic zone to another as well as for different seasons. If a linear trend is taken through mean annual temperatures, the average change over the last 6 decades is 3.0°F.

“… Considering just a linear trend can mask some important variability characteristics in the time series. The figure at right shows clearly that this trend is non-linear: a linear trend might have been expected from the fairly steady observed increase of CO2 during this time period. The figure shows the temperature departure from the long-term mean (1949-2009) for all stations. It can be seen that there are large variations from year to year and the 5-year moving average demonstrates large increase in 1976.

“The period 1949 to 1975 was substantially colder than the period from 1977 to 2009, however since 1977 little additional warming has occurred in Alaska with the exception of Barrow and a few other locations. The stepwise shift appearing in the temperature data in 1976 corresponds to a phase shift of the Pacific Decadal Oscillation from a negative phase to a positive phase. Synoptic conditions with the positive phase tend to consist of increased southerly flow and warm air advection into Alaska during the winter, resulting in positive temperature anomalies.”

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