Washington Times: The globe cools, and Al Gore’s ‘Climate Reality’ does too

Reality intrudes on a hot dream

The globe cools, and Al Gore’s ‘Climate Reality’ does, too

The Washington Times 9/5/13

Al Gore's multimillion-dollar scheme to persuade the world that global warming is about to boil, fry or saute us all is disappearing faster than an ice cube on the sidewalk on a summer day.

The former vice president reached the peak of his popularity with the 2006 release of his Oscar-winning scare-film "An Inconvenient Truth," for which he was hailed as a genius and basked in A-list status at Hollywood soirees. He wasted no time on his return to the spotlight and founded the Climate Reality Project, a group to spread the word about an imminent planetary cataclysm that could only be averted by adopting his agenda. That's the scheme melting now.

Al collected $87.4 million in donations in 2008, and the website BuzzFeed reports that once it caught fire, he could spend nearly $30 million annually on television commercials and grass-roots operations. It even conned some conservatives, such as former House Speaker Newt Gingrich and the Rev. Pat Robertson, to appear in television commercials. Mr. Gingrich, sadder but wiser, later called the commercial the "dumbest thing I've done in the last four years."

In hindsight, perhaps the Climate Reality Project wasn't such a scorcher of an idea after all. By 2011, receipts had fallen 80 percent, to $17.6 million. Even wealthy liberals aren't terminally stupid. Wallets and purses snapped shut as reality arrived and the promised cataclysmic warming never happened.

The scorekeepers of global-warming alarmism, the U.N. Intergovernmental Panel On Climate Change, is about to release its fifth Assessment Report, which is said to admit that the planet has been cooling, not warming. A leaked draft version of the report concedes the very inconvenient truth, and casts doubt on the claim that man plays a role in triggering "extreme weather."

The U.N. forgot to send an advance copy of the findings to Mr. Gore. On Thursday, the Nobel Peace Prize winner pointed to dramatic photographs of wildfires in Yosemite National Park, as if the arid Western region had never burned prior to the Industrial Revolution. "As temps rise," Mr. Gore tweeted, "fires are becoming worse and worse across our country."

 Actually, the fires are doing no such thing.

This wildfire season has been the weakest in at least a decade, according to statistics provided by the National Fire Information Center. There were 83,919 blazes in the first nine months of the year that Mr. Gore was the toast of Tinseltown. So far this year, there have only been 35,566 fires, down dramatically from the usual. The total number of scorched acres is down 38 percent from the 10-year average of 6.2 million acres.

Facts aren't likely to deter Mr. Gore's diehard fans. They're the loyal sort who will stand up and say he did, so, invent the Internet, and he really was the inspiration for the insipid movie "Love Story." The groupies will keep mailing in the donations, and so will the crony-capitalist "green" companies, which would profit handsomely if Mr. Gore's dreams and schemes should become actual policy. The cash will keep the incandescent lights on in the Gore house of many mansions, but it won't be enough to restore his credibility. Since the planet hasn't been warming at all, it's only a matter of time before the public catches on and good ol' Al and his schemes will be put permanently on ice.

Read more: http://www.washingtontimes.com/news/2013/sep/5/editorial-reality-intrudes-on-a-hot-dream/#ixzz2e4CXLmXm Follow us: @washtimes on Twitter



H/T Junk Science

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Gore's Climate Reality Project says Gore & climate change are "one of the favorite piñatas for media and easy targets for headlines. Always have been and always will be"

According to an internal email from Gore's own Climate 'Reality' Project, "As many of you already know, AG [Al Gore] and climate change are one of the favorite piñatas for media and easy targets for headlines. Always have been and always will be."

Boo hoo hoo

Gore's false claims at left

Internal email by Dan Stiles, the Climate Reality Project’s chief operating officer and the main spokesman for the group:

Subject: Buzzfeed and the piñata

Good morning/mid-day,

I am sure many of you have read the article Buzz Feed published today about our fearless leader and our fearless non-traditional efforts. I thought I would share a few thoughts that, of course, did not make it into the article.

As many of you already know, AG [Al Gore] and climate change are one of the favorite piñatas for media and easy targets for headlines. Always have been and always will be. In that stead, this particular piece was written with a narrative that the reporter had no interest in changing despite both GPG’s and my efforts to educate them on the great work of this team. You would think Buzzfeed, of all places, would understand our approach to tackle this issue. But, alas, headlines are more important to and easier for them than writing about innovation and a community effort.

AG and Maggie have both read the article and are unwavering in their support of each of you and our strategy. As are the Climate Leaders and others who have weighed in the comment section to the article already.

...

I can’t think of anything more inspiring than the opportunity to prove a naysayer wrong. And, I hope you find that inspiring too as we head into an action packed fall.

Let’s get out there and show them how its done!

Dan

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New paper in Nature Climate Change says IPCC uses statistical techniques 'out of date by well over a decade'

A new paper published in Nature Climate Change finds, "Use of state-of-the-art statistical methods could substantially improve the quantification of uncertainty in [IPCC] assessments of climate change" and that "The forthcoming Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and the US National Climate Assessment Report will not adequately address this issue. Worse still, prevailing techniques for quantifying the uncertainties that are inherent in observed climate trends and projections of climate change are out of date by well over a decade. Modern statistical methods and models could improve this situation dramatically." The authors recommend, "Including at least one author with expertise in uncertainty analysis on all chapters of IPCC and US national assessments" and that the IPCC "Replace qualitative assessments of uncertainty with quantitative ones." A prime example of this would be the ludicrous IPCC claim that it is "very likely" man is the cause of climate change, a claim that very likely would not hold up if subject to modern statistical analysis of uncertainty.

Box 1: Recommendations to improve uncertainty quantification.

• Replace qualitative assessments of uncertainty with quantitative ones.


• Reduce uncertainties in trend estimates for climate observations and projections through use of modern statistical methods for spatio-temporal data.


• Increase the accuracy with which the climate is monitored by combining various sources of information in hierarchical statistical models.


• Reduce uncertainties in climate change projections by applying experimental design to make more efficient use of computational resources.


• Quantify changes in the likelihood of extreme weather events in a manner that is more useful to decision-makers by using methods that are based on the statistical theory of extreme values.


• Include at least one author with expertise in uncertainty analysis on all chapters of IPCC and US national assessments.

Uncertainty analysis in climate change assessments

• Richard W. Katz,


• Peter F. Craigmile,


• Peter Guttorp,


• Murali Haran,


• Bruno Sansó


• & Michael L. Stein

• Affiliations


• Corresponding author

Nature Climate Change

 

3,

 

769–771

 

(2013)

 

doi:10.1038/nclimate1980

Published online

 

28 August 2013

Article tools

Use of state-of-the-art statistical methods could substantially improve the quantification of uncertainty in assessments of climate change.

Because the climate system is so complex, involving nonlinear coupling of the atmosphere and ocean, there will always be uncertainties in assessments and projections of climate change. This makes it hard to predict how the intensity of tropical cyclones will change as the climate warms, the rate of sea-level rise over the next century or the prevalence and severity of future droughts and floods, to give just a few well-known examples. Indeed, much of the disagreement about the policy implications of climate change revolves around a lack of certainty. The forthcoming Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and the US National Climate Assessment Report will not adequately address this issue. Worse still, prevailing techniques for quantifying the uncertainties that are inherent in observed climate trends and projections of climate change are out of date by well over a decade. Modern statistical methods and models could improve this situation dramatically.

Uncertainty quantification is a critical component in the description and attribution of climate change. In some circumstances, uncertainty can increase when previously neglected sources of uncertainty are recognized and accounted for (Fig. 1 shows how uncertainty can increase for projections of sea-level rise). In other circumstances, more rigorous quantification may result in a decrease in the apparent level of uncertainty, in part because of more efficient use of the available information. For example, despite much effort over recent decades, the uncertainty in the estimated climate sensitivity (that is, the long-term response of global mean temperature to a doubling of the CO2 concentration in the atmosphere) has not noticeably decreased¹. Nevertheless, policymakers need more accurate uncertainty estimates to make better decisions².

Figure 1: Uncertainty of projected sea-level rise for 2075 in Olympia, Washington under high emission scenario RCP 8.5 (ref. 23).

Results are from 18 models included in the CMIP5 experiment²⁴. The median climate model projection with no uncertainty is indicated by the vertical grey line, and uncertainty due to different climate model projections is shown by the histogram (white bars). The coloured curves represent cumulative uncertainty taking into account errors from the prediction of global mean sea-level rise from global mean temperature (red line), from the relation between Seattle sea-level rise and global mean sea-level rise (blue line) and from that between Seattle and Olympia sea-level rise (green line). Figure courtesy of Peter Guttorp, University of Washington.

• Full size image (84 KB)

Detailed guidance provided to authors of the IPCC AR5 and the US National Climate Assessment Report emphasizes the use of consistent terminology for describing uncertainty for risk communication. This includes a formal definition of terms such as 'likely' or 'unlikely' but, oddly, little advice is given about what statistical techniques should be adopted for uncertainty analysis^3, 4. At the least, more effort could be made to encourage authors to make use of modern techniques.

Historically, several compelling examples exist in which the development and application of innovative statistical methods resulted in breakthroughs in the understanding of the climate system (for example, Sir Gilbert Walker's research in the early twentieth century related to the El Niño–Southern Oscillation phenomenon⁵). We anticipate that similar success stories can be achieved for quantification of uncertainty in climate change.

Although climate observations and climate model output have different sources of error, both exhibit substantial spatial and temporal dependence. Hierarchical statistical models can capture these features in a more realistic manner⁶. These models adopt a 'divide and conquer' approach, breaking the problem into several layers of conceptually and computationally simpler conditional statistical models. The combination of these components produces an unconditional statistical model, whose structure can be quite complex and realistic. By using these models, uncertainty in observed climate trends and in projections of climate change can be substantially decreased. This decrease is obtained through 'borrowing strength', which exploits the fact that trends or projections ought to be similar at adjacent locations or grid points. Methods that are currently applied usually involve analysing the observations for each location — or the model output for each grid point — separately. Hierarchical statistical models can also be applied to combine different sources of climate information (for example, ground and satellite measurements), explicitly taking into account that they are recorded on different spatial and temporal scales^7, 8.

Even without any increase in computational power, statistical principles of experimental design can reduce uncertainty in the climate change projections produced by climate models through more efficient use of these limited resources⁹. Rather than allocating them uniformly across all combinations of the alternatives being examined, the allocation can be made in a manner that maximizes the amount of information obtained. For example, in assessing the impact of different global and regional climate models on climate projections, we do not need to examine all combinations of these models.

The recent IPCC Special Report Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation¹⁰, on which the IPCC AR5 relies, does not take full advantage of the well-developed statistical theory of extreme values¹¹. Instead, many results are presented in terms of spatially and temporally aggregated indices and/or consider only the frequency, not the intensity of extremes. Such summaries are not particularly helpful to decision-makers. Unlike the more familiar statistical theory for averages, extreme value theory does not revolve around the bell-shaped curve of the normal distribution. Rather, approximate distributions for extremes can depart far from the normal, including tails that decay as a power law. For variables such as precipitation and stream flow that possess power-law tails, conventional statistical methods would underestimate the return levels (that is, high quantiles) used in engineering design and, even more so, their uncertainties. Recent extensions of statistical methods for extremes make provision for non-stationarities such as climate change. In particular, they now provide non-stationary probabilistic models for extreme weather events, such as floods, as called for by Milly and colleagues¹².

We mention just one concrete example for which reliance on extreme value methods could help to resolve an issue with important policy implications. It has recently been claimed that, along with an increase in the mean, the probability distribution of temperature is becoming more skewed towards higher values^13, 14. But techniques based on extreme value theory do not detect this apparent increase in skewness¹⁵. Any increase is evidently an artefact of an inappropriate method of calculation of skewness when the mean is changing¹⁶.

Besides uncertainty quantification, the communication of uncertainty to policymakers is an important concern. Several fields conduct research on this topic, in addition to statistics (including decision analysis and risk analysis). Statisticians have made innovative contributions to the graphic display of probabilities to make communication more effective¹⁷, methods that have not yet found much, if any, use in climate change assessments (see Spiegelhalter et al.¹⁷ for an example of how uncertainties about future climate change are now presented).

It should be acknowledged that statisticians alone cannot solve these challenging problems in uncertainty quantification. Rather, increased collaboration between statistical and climate scientists is needed. Examples of current activities whose primary purpose is to stimulate such collaborations include: CliMathNet¹⁸, the Geophysical Statistics Project at the National Center for Atmospheric Research¹⁹, International Meetings on Statistical Climatology²⁰, the Nordic Network on Statistical Approaches to Regional Climate Models for Adaptation²¹ and the Research Network for Statistical Methods for Atmospheric and Oceanic Sciences²².

Recommendations

• Recommendations•

 

• References•

 

• Author information

To bring uncertainty quantification into the twenty-first century, we offer a number of suggestions (Box 1). Elaborated on here, these range from how climate change research is conducted to the process by which climate change assessments are produced.

Box 1: Recommendations to improve uncertainty quantification. [see above]

Full box

Whenever feasible, qualitative uncertainty assessments should be replaced with quantitative ones. At a minimum, a standard error should be attached to any estimate, along with a description of how it was calculated. Ideally, an entire probability distribution should be provided — innovative graphical techniques can be used to communicate these uncertainties in a more effective manner. In addition, modern statistical methods for spatio-temporal data, such as hierarchical models, should be used to reduce uncertainties in trend estimates for climate observations and projections of climate change. By taking into account spatial and temporal dependence, these techniques provide a powerful tool for detection of observed and projected changes in climate. To increase the accuracy with which the climate is monitored, various sources of information need to be combined using hierarchical statistical models. Such techniques can take into account differences in the uncertainties of, for instance, in situ and remotely sensed measurements.

Statistical principles of experimental design should be applied to climate change experiments using numerical models of the climate system. Through making more efficient use of computational resources, uncertainties in climate change projections can be reduced. Methods based on the statistical theory of extreme values should be used to quantify changes in the likelihood of extreme weather events, whether based on climate observations or on projections from climate models. In this way, information more useful to decision-makers about the risk of extreme events (for example, in terms of changing return levels) can be provided. Finally, to improve the quality of the treatment of uncertainty, at least one author with expertise in uncertainty analysis should be included on all chapters of IPCC and US national assessments. These authors could come from the field of statistics, as well as from other related fields including decision analysis and risk analysis.

If these recommendations are adopted, the improvements in uncertainty quantification would thereby help policymakers to better understand the risks of climate change and adopt policies that prepare the world for the future.

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New paper finds climate model assumptions on cloud-aerosol interactions may be off by ± 100%

A paper published today in Atmospheric Chemistry and Physics demonstrates the huge uncertainties of computer modeling of aerosol–cloud interaction effects, which are one of the "major sources of uncertainty in climate models." According to the authors, the standard deviation around the mean cloud condensation nuclei varies globally between a minimum of about ± 30% over some marine regions to ± 40–100% over most land areas and high latitudes. This is only one of the factors affecting clouds in climate models, and clouds are but one of the many major uncertainties in climate models. 

Dr. Judith Curry points out why climate models might be wrong in her "uncertainty monster" paper, and has pointed out for years the need for realistic assessments of the uncertainty of climate projections. This paper takes one step in that much need direction and shows only a tiny fraction [but still huge] of the "uncertainty monster." Meanwhile, the IPCC remains blissfully ignorant of the "uncertainty monster" and increases its confidence level from 90 to 95% without any basis in statistical analysis or science. 

Atmos. Chem. Phys., 13, 8879-8914, 2013 www.atmos-chem-phys.net/13/8879/2013/ doi:10.5194/acp-13-8879-2013

The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei

L. A. Lee¹, K. J. Pringle¹, C. L. Reddington¹, G. W. Mann¹, P. Stier², D. V. Spracklen¹, J. R. Pierce³, and K. S. Carslaw<sup>1

1</sup>Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK
²Department of Physics, University of Oxford, Oxford, UK
³Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA

Abstract. Aerosol–cloud interaction effects are a major source of uncertainty in climate models so it is important to quantify the sources of uncertainty and thereby direct research efforts. However, the computational expense of global aerosol models has prevented a full statistical analysis of their outputs. Here we perform a variance-based analysis of a global 3-D aerosol microphysics model to quantify the magnitude and leading causes of parametric uncertainty in model-estimated present-day concentrations of cloud condensation nuclei (CCN). Twenty-eight model parameters covering essentially all important aerosol processes, emissions and representation of aerosol size distributions were defined based on expert elicitation. An uncertainty analysis was then performed based on a Monte Carlo-type sampling of an emulator built for each model grid cell. The standard deviation around the mean CCN varies globally between about ±30% over some marine regions to ±40–100% over most land areas and high latitudes, implying that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol–cloud effects on climate. Among the most important contributors to CCN uncertainty are the sizes of emitted primary particles, including carbonaceous combustion particles from wildfires, biomass burning and fossil fuel use, as well as sulfate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulfur emissions. Aerosol emission-related parameters dominate the uncertainty close to sources, while uncertainty in aerosol microphysical processes becomes increasingly important in remote regions, being dominated by deposition and aerosol sulfate formation during cloud-processing. The results lead to several recommendations for research that would result in improved modelling of cloud–active aerosol on a global scale.

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New paper finds another non-hockey-stick in the Mediterranean Sea

A paper published today in The Holocene reconstructs temperatures in the central Mediterranean Sea over the past 2,500 years and finds another non-hockey-stick demonstrating warmer temperatures during the Medieval Warming Period and Roman Warming Period than at the end of the record in the year 2000.

d18O is a proxy for temperature and precipitation and shows warmer temperatures during the Medieval Warming Period and Roman Warming Period than at the end of the record in 2000. Graph source is a pre-print of the paper below.

Climate of the past 2500 years in the Gulf of Taranto, central Mediterranean Sea: A high-resolution climate reconstruction based on δ¹⁸O and δ¹³C of Globigerinoides ruber (white)

1. Anna-Lena Grauel1


2. Marie-Louise S Goudeau2


3. Gert J de Lange2


4. Stefano M Bernasconi1

1. 
   ¹Geological Institute, ETH Zurich, Switzerland
   
   


2. 
   ²Institute of Earth Sciences - Geochemistry, Geosciences, Utrecht University, The Netherlands
   
   

1. Anna-Lena Grauel, Department of Earth Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EQ, UK. 

Abstract

We present a high-resolution isotope stratigraphy based on Globigerinoides ruber (white) over the past 2500 years in the Gulf of Taranto, central Mediterranean. G. ruber (white) reflects summer conditions in the Gulf of Taranto but is influenced by two major surface water masses: the Western Adriatic Current (WAC) and the Ionian Surface Water (ISW) and their variations on a decadal to multicentennial scale. Our analysis of the δ¹³C and δ¹⁸O of G. ruber (white) allows the distinction of several climatic periods: the ‘Roman Warm Period’ (RWP) (450–0 BC), with relatively wet and warm conditions and a higher influence of the WAC; the ‘Roman Classical Period’ (RCP) (AD 1–200) characterized by salinity increase resulting from circulation changes; the ‘Dark Ages Cold Period’ (DCP) (AD 500–750), where wetter conditions in the Gulf of Taranto region are coherent with an increase dominance of the WAC; the ‘Medieval Warm Period’ (MWP), with wet and warm conditions in the first, and a gradual drying in the second half; and finally, the transition from the MWP to the ‘Little Ice Age’ (LIA), which is characterized by continuing dry conditions.

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New paper claims reduced personal income from climate policies will only make you unhappy for 1 year

A paper published today in Global Environmental Change finds that a reduction in income, such as due to climate change policies, causes unhappiness for one year, but don't worry, after a year you'll adapt to it and the unhappiness will subside. According to the authors, "effects are however temporary and do not hold for a period longer than a year, probably for reasons of adaptation and a downward adjustment of reference consumption and income levels... Our results suggest that climate policy need not reduce happiness in the long run, even when it reduces income and carbon-intensive consumption." However, the paper does not mention the effect of climate policies which call for increasing reductions of income and consumption over time. 

 

Climate change, income and happiness: An empirical study for Barcelona

• Filka Sekulova^a, , , , 


• Jeroen C.J.M. van den Bergh^a, b, c, 

• ^a Institute for Environmental Science and Technology, Universitat Autònoma de Barcelona, Spain


• ^b ICREA, Barcelona, Spain


• ^c Faculty of Economics and Business Administration, Institute for Environmental Studies, VU University Amsterdam, The Netherlands

Highlights

•

Experiencing forest fires, has a permanent negative effect on life-satisfaction.

•

Climate policy which affects income and consumption may not reduce overall happiness.

•

Happiness adapts to income decreases after one year.

---

Abstract

The present article builds upon the results of an empirical study exploring key factors which determine life satisfaction in Barcelona. Based on a sample of 840 individuals we first look at the way changes in income, notably income reductions, associated with the current economic situation in Spain, affect subjective well-being. Income decreases which occur with respect to one year ago have a negative effect on happiness when specified in logarithmic terms, and a positive one when specified as a dummy variable (and percentage change). The divergence in results is discussed and various explanations are put forward. Both effects are however temporary and do not hold for a period longer than a year, probably for reasons of adaptation and a downward adjustment of reference consumption and income levels. Next, we examine the implications of experiencing forest fires and find a lasting negative effect on life satisfaction. Our results suggest that climate policy need not reduce happiness in the long run, even when it reduces income and carbon-intensive consumption. Climate policy may even raise life well-being, if accompanied by compensatory measures that decrease formal working hours and reference consumption standards, while maintaining employment security.

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Obama's increased 'social cost of carbon' raises ire of critics

Greenhouse-Gas Fight Escalates

Administration's Higher Estimate for Cost of Carbon Raises Ire of Critics

By 

• KEITH JOHNSON

WASHINGTON—WSJ.COM 9/2/13:  A quiet move by the Obama administration to put a higher price tag on greenhouse-gas emissions has sparked a big fight, prompting new legislation in Congress and sniping in academic circles.

Buried in new energy-efficiency standards the Department of Energy released in May for microwave ovens was an administration estimate that the cost to the country for each ton of carbon dioxide emitted was $36 in 2007 dollars—up from its 2010 estimate of $21 a ton.

The number is important because the more costly carbon pollution is deemed to be, the greater the apparent economic benefits of new environmental regulations. The climate plan hinges on such regulations, including restrictions on new power plants that the Environmental Protection Agency is set to release in late September.

House Republicans passed a bill in August that would bar the administration from using the new estimates.

Critics said administration officials calculated the numbers behind closed doors without transparency. "You can't just step in and change the number, especially to that level, without some kind of input," said Rep. James Lankford (R., Okla.), chairman of the House Oversight Subcommittee on Energy Policy. He said he would prefer that Congress determine the price of carbon emissions. U.S. officials and advocates of carbon pricing dismissed the criticism.

Howard Shelanski, who heads the Office of Information and Regulatory Affairs, told the House the revised estimate reflects new research on the impact of climate change.

Energy Secretary Ernest Moniz said the $36 figure is in line with or lower than estimates used by many corporations and national governments. Exxon Mobil Corp. assumes a carbon price of $80 per ton by 2040 for investment decisions, while BP PLC, another oil giant, assumes a $40 price today, according to the companies. The British government pegs the 2020 price at the equivalent of about $47 a ton.

The administration has used "the most mainstream, the most well-validated, the most broadly accepted methodology for assigning benefits," said Michael Livermore, a cost-benefit expert at the University of Virginia law school. He said "the entire process has been on the record."

Putting a price on carbon emissions assumes that increased levels of carbon dioxide in the atmosphere will lead to greater climate change, which in turn is assumed to cause more hurricanes and rising sea levels. Not everyone agrees with those assumptions, which are shared by nearly all climate scientists. Even those who agree that climate change is bad disagree about how much it is worth today to prevent an additional hurricane in, say, 2050.

The effort to put a price tag on carbon emissions has been years in the making. Under the George W. Bush administration, a federal appeals court rejected new fuel-economy rules because they didn't put a price on greenhouse-gas emissions and, according to the ruling, understated the potential benefits of regulation. "We recognized the link between greenhouse gases and climate change, but the process of putting a dollar value on the impacts of climate change was extremely uncertain," said John Graham, who headed the Office of Information and Regulatory Affairs in the Bush administration.

Early in the Obama administration, officials from nearly a dozen agencies, including the Department of Energy and the EPA, took a first stab, using several computer economic models. The administration said the figure would be continually revised.

Critics maintain the whole question is too uncertain to be entrusted to computer models. They fear the higher $36-a-ton figure will be used to justify tighter regulation on coal-fired power plants, which could raise consumers' electricity costs.

Robert Pindyck, an economics professor at the Massachusetts Institute of Technology, slams the models in a coming paper to be published by the National Bureau of Economic Research, saying they use essentially arbitrary inputs and give a misplaced illusion of scientific certainty.

Though his work has given ammunition to skeptics of global-warming science, Mr. Pindyck said his point is really about the difficulty of modeling possible catastrophic impacts of climate change. "We know there's a social cost of carbon, and we know it's above $0," he said. "If anything, the cost of carbon could be higher" than the administration's models suggest.

Creators of the models concede they aren't perfect. But Yale economics professor William Nordhaus, the creator of the best-known model, said they have improved and can provide a starting point for policy makers. Damage estimates from warming "are based on literally hundreds of studies of the impact of climate change on different sectors of the economy," he said.

Related: Why Obama's social cost of carbon models are bogus

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