Monthly Archives: October 2011

New Paper Just Accepted “The Pacific Sea Surface Temperature” By David H. Douglass 2011

David Douglass has alerted us to a new paper that was just accepted for publication.  It extends the results reported in his paper

Topology of Earths climate indices and phase-locked states David H. Douglass Physics Letters A 374 (2010) 4164–4168

The new paper is

David H. Douglass, 2011: The Pacific Sea Surface Temperature accepted by Physics Letters A.

The abstract reads

        The Pacific sea surface temperature data contains two components: NL, a signal that exhibits the familiar El Niño/La Niña phenomenon and NH, a signal of one-year period. Analysis reveals: (1) The existence of an annual solar forcing FS; (2) NH is phase locked directly to FS while NL is frequently  phase locked to the 2nd or 3rd subharmonic of FS.  At least ten distinct subharmonic time segments of NL since 1870 are found. The beginning or end dates of these segments have a near one-to-one correspondence with the abrupt climate changes previously reported. Limited predictability is possible.

When the page proofs arrive David will put them on his publication list web page.

David e-mailed that if he had been able to attend the Santa Fe meeting this week he would have “shouted” that calculating trends across a climate shift has no meaning.

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Filed under Climate Change Forcings & Feedbacks, Climate Change Metrics, Research Papers

Recommended Weblog Post By Bob Tisdale – PRELIMINARY October 2011 SST Anomaly Update

Bob Tisdale has been very effective at keeping us updated on the multi-year surface ocean temperature trends in his posts at

Bob Tisdale – Climate Observations

His latest post is very much worth reading

PRELIMINARY October 2011 SST Anomaly Update

The figures he presents clearly show the effect of ENSO events which are superimposed on a longer term trend.  One also does not need to perform a statistical analysis to see a long term increase from 1982 until 1998 following by a step-function type of change after the large El Nino in 1998 and nearly flat trend, or even a slight decline, since, as illustrated in the figure below from Bob’s excellent post.

Roy Spencer adds further perspective to this slight cooling in SSTs in recent years, this time with respect to the lower troposphere, in his post

Brrr…the Troposphere Is Ignoring Your SUV

from which I have reproduced his figure below.

These two pieces of evidence document that global warming has certainly slowed (if we accept that the heat is going into the deeper ocean (without being seen in the upper ocean), or global warming has halted, at least for this time period. 

Since the observations show a continued increase of atmospheric CO2 [which has still unknown biogeochemical effects], yet global warming is not behaving as the IPCC claimed in 2007, this suggests a new approach is needed to assess social and environmental risks from extreme weather and other climate effects.

This observational evidence, in which models are not able to adequately explain, is a major reason policymakers should adopt the bottom-up, resource-based perspective that we propose in

Pielke Sr., R.A., R. Wilby, D. Niyogi, F. Hossain, K. Dairuku, J. Adegoke, G. Kallos, T. Seastedt, and K. Suding, 2011: Dealing with complexity and extreme events using a bottom-up, resource-based vulnerability perspective. AGU Monograph on Complexity and Extreme Events in Geosciences, in press

where we wrote the goal should be

“…..the determination of the major threats to local and regional water, food, energy, human health, and ecosystem function resources from extreme  events including climate, but also from other social and environmental issues. After these threats are identified for each resource, then the relative risks can be compared with other risks in order to adopt optimal preferred mitigation/adaptation strategies.”

The posts by Bob Tisdale and Roy Spencer illustrate from real world observations why a new approach is needed, since the models are not skillfully simulating the actual behavior of the climate system even in a global average.  The continued defense of the dominance of CO2 with respect to climate change on multi-decadal time scales is placing those proponents of that view as increasingly looking as out of touch with the reality of the real-world climate system.

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Two Questions To Skeptical Science Regarding i) The Relation of Global Warming To Climate Change and ii) The Predictive Skill Of Multi-Decadal Global Climate Models

UPDATE November 12 2011: So far Skeptical Science has not responded to my questions. 

I posted a summary list of titles of the weblog posts over the last few weeks between Climate Science and Skeptical Science, and my conclusion from those interactions  in the weblog post

Response To Skeptical Science On A Series Of Weblog Posts

As part on a contining effort to reach out in this post, I request their answer to the following two questions:

1. Is global warming (and cooling) a subset of climate change or does it dominate climate change?

As documented in

National Research Council, 2005: Radiative forcing of climate change: Expanding the concept and addressing uncertainties. Committee on Radiative Forcing Effects on Climate Change, Climate Research Committee, Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, The National Academies Press, Washington, D.C., 208 pp,

climate change is much more than what is occurring from the radiative addition of CO2 and a few other greenhouse cases. The Skeptical Science viewpoint on this question would be informative.

In the Executive Summary of the NRC (2005), it is written

“Despite all these advantages, the traditional global mean TOA radiative forcing concept has some important limitations, which have come increasingly to light over the past decade. The concept is inadequate for some forcing agents, such as absorbing aerosols and land-use changes, that may have regional climate impacts much greater than would be predicted from TOA radiative forcing. Also, it diagnoses only one measure of climate change—global mean surface temperature response—while offering little information on regional climate change or precipitation. These limitations can be addressed by expanding the radiative forcing concept and through the introduction of additional forcing metrics. In particular, the concept needs to be extended to account for (1) the vertical structure of radiative forcing, (2) regional variability in radiative forcing, and (3) nonradiative forcing. “

The hypothesis is that neither the multi-decadal global-annual average surface temperature trend nor the global-annual average surface temperature offer little information on changes in the regional climate.

2.  What evidence exists that the multi-decadal global climate models can skillfully predict i) the real-world observed behaviour of large-scale atmospheric-ocean circulation features such as ENSO, the NAO, the PDO, ect. and ii) CHANGES in the statistics (patterning and in time) of these circulation features?

As I reported in the post

Insightful Interview In EOS Of Dr. De-Zheng Sun “Climate Dynamics: Why Does Climate Vary?”

De- Zheng Sun

“….the state-of- the- art three- dimensional models still do not properly simulate natural variability such as MJO and ENSO. As a result, models are not yet able to capture the anthropogenic effect that takes place in the form of climate variability. In other words, our models may be underestimating the effect from anthropogenic forcing on natural variability.It is time to look seriously at an alternative hypothesis, which is that the defining feature of global warming will be changes in the magnitude of climate variability…”

The hypothesis is that multi-decadal global climate model predictions (projections) , even when statistically or dynamically downscaled to regional and local spatial scales in a hindcast mode, provide no skillful predictions of regional and local climate change.

I look forward to the SkS  response on their weblog and what evidence they have to refute these hypotheses.

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Filed under Q & A on Climate Science

A Literature Debate On The Lack Of Skill Of Global Climate Model Multi-Decadal Predictions As Reported In The Peer Reviewed Literature By Demetris Koutsoyiannis

UPDATE: The papers are now available (h/t to Dan Hughes) ; See the blue Free Access to the right edge and under the titles.


Demetris Koutsoyiannis has provided information on a very informative debate on the quality of models to predict climate in the future. Demetris is the 2009 Henry Darcy Medal  for his outstanding contributions to the study of hydrometeorological variability and to water resources management.

A year ago I posted about a paper that his  group published in Hydrological Sciences Journal:

Very Important New Paper “A Comparison Of Local And Aggregated Climate Model Outputs With Observed Data” By Anagnostopoulos Et Al 2010

In late 2009 I had posted about the editorial processing of this paper:

A Suggested Approach To Reviews of Scientific Papers By Zbigniew W. Kundzewicz

Last week a discussion paper was published 

Huard, D. (2011) A black eye for the Hydrological Sciences Journal. Discussion of ‘A comparison of local and aggregated climate model outputs with observed data’ by G.G. Anagnostopoulos et al. (2010, Hydrol. Sci. J. 55 (7), 1094–1110). Hydrol. Sci. J. 56(7), 1330–1333.was published in Hydrological Sciences Journal

The abstract reads

“A paper published by Anagnostopoulos et al. in volume 55 of the Hydrological Sciences Journal (HSJ) concludes that climate models are poor based on temporal correlation between observations and individual simulations. This interpretation hinges on a common misconception, that climate models predict natural climate variability. This discussion underlines fundamental differences between hydrological and climatological models, and hopes to clear misunderstandings regarding the proper use of climate simulations.”

Thr Reply is entitled

Koutsoyiannis, D., Christofides, A., Efstratiadis, A., Anagnostopoulos, G.G. and Mamassis, N. (2011) Scientific dialogue on climate: is it giving black eyes or opening closed eyes? Reply to “A black eye for the Hydrological Sciences Journal” by D. Huard, Hydrol. Sci. J. 56(7), 1334–1339.Scientific dialogue on climate: is it giving black eyes or opening closed eyes? Reply to “A black eye for the Hydrological Sciences Journal ” by D. Huard

These are two very important communications. It is anticipated that the publisher will make them open access by next week so everyone can read the entire exchange and comment on the publisher’s website [which I highly recommend!]

In the interim, below are excerpts from each paper [highlight added].

From Huard 2011

The main issue with the AKCEM paper is that it is based on a false premise, namely that the selected climate simulations predict (forecast)  climate in a deterministic sense.

Global climate models (GCM) are externally driven by solar radiation and planetary orbital parameters. An additional external forcing is the manmade emission of greenhouse gases (GHG) and aerosols. None of these external forcings, however, can explain the inter-annual variability present in all climate variables. Variability at the annual and decadal scales emerges spontaneously from the dynamics of the climate system and is only weakly influenced by external forcing (massive volcanic eruptions are an exception). At the multi-decadal scale, variability is caused by a mix of natural variability and changes in external forcing conditions. Murphy et al. (2009) provide a clear and crisp discussion around these concepts.

One of other metrics used by AKCEM to evaluate model performance is the correlation between the 30-year running mean of simulations and observations. This case is much more interesting, since we expect external forcing by GHGs to play a role at that time scale, and, thus, to explain a portion of the observed variability. Note that this is very different
from saying that climate models predict climate; under constant external forcing, TAR and AR4 simulations have no predictive skill whatsoever on the chronology of events beyond the annual cycle. A climate projection is thus not a prediction of climate, it is an experiment probing the model’s response to change in GHG concentrations.

AKCEM expected individual models to show some skill in predicting multi-decadal climate variations. They do, but their skill is limited to the small fraction of climate’s variability driven by external forcing. To evaluate model performance, it is fundamental to extract the model’s response to the external forcing from the background natural variability (Randall et al. 2007). Failing to do this, AKCEM have merely shown that climate models display chaotic behaviour at small and long time scales, not that they are poor.

So why was the paper published even though its methodology is naive and the conclusion misleading? I asked Frances Watkins from the HSJ Editorial Office for a copy of the anonymous reviews, the author’s response and the editor’s decision letter. The authors, editors and reviewers all agreed to make these documents available and with those I could make some sense of what happened.

The paper received three evaluations. Reviewer A provided a solid review which identified unsubstantiated or false claims and methodological shortcomings. The evaluation included a comment on the general lack of rigour of the paper with a recommendation not to publish. Reviewer B rated the paper as “Very good to excellent” and made three superficial
suggestions for improvements. Reviewer C rated the paper as “Poor to fair” and specifically stated: “This paper is misleading as it is based on a wrong assumption related to the climate system predictability.” Reviewer C also criticized the methodology as inappropriate and recommended the paper be rejected outright.

….the editorial piece (Kundzewicz and Stakhiv 2010) indicates the editor shares the same misguided assumptions about climate simulations as AKCEM and there is little hope that an additional review would have made any difference. This is in my view a black eye for HSJ coming out as lacking the discrimination required to identify poor science.

From Koutsoyiannis et al 2011

“….we tested whether the model outputs are consistent with reality (which reflects the entire variability, due to combined natural and anthropogenic effects). Our results extend Huard’s statements further. Specifically, we show that, climate models are not only unable to predict the variability of climate, but they are also unable to reproduce even the means of temperature and rainfall in the past. For example, as we stated in our paper, “In some [models], the annual mean temperature of the USA is overestimated by about 4–5◦C and the annual precipitation by about 300–400 mm”.

Given our results, an interesting question would be: Under what premise could one, in order to derive meaningful results for the future, use models that fail to reproduce the known past, in terms of both mean level and variability? Huard does not ask this straight question. Yet he admits no predictive skill of models for the past. In his own words, “under constant external forcing, TAR and AR4 simulations have no predictive skill whatsoever on the chronology of events beyond the annual cycle”, and quotes Smith et al. (2007): “Previous climate model projections of climate change accounted for external forcing from natural and anthropogenic sources but did not attempt to predict internally generated natural variability”. Thus, he implies a skill for the future, regardless of poor behaviour in the past.

IPCC …. uses climate model outputs as predictions. Calling these by another name, such as “credible quantitative estimates of future climate change” (Randall et al., 2007, p. 591) does not change the essence. For example, in IPCC (2007, Fourth Assessment Report—AR4; Summary for policymakers, p. 15), we read (our emphasis): “It is very likely that hot extremes, heat waves and heavy precipitation events will continue to become more frequent”. This is one of a total of six occurrences of the word “will” in a similar context (in the three next pages of the section “Projections of future changes in climate”), the last one being “… anthropogenic carbon dioxide emissions will continue to contribute to warming and sea level rise for more than a millennium”— not to mention the over 20 appearances of expressions such as “it is expected”, “it would”, etc.

Huard writes: “The natural variability of the climate system is largely chaotic” and thus “unpredictable”. Not only do we endorse this statement, and not only have we presented research results on this issue (Koutsoyiannis 2003, 2006, 2010, Koutsoyiannis et al. 2009, Christofides and Koutsoyiannis 2011), but we have also pointed to this problem in the second paragraph of the conclusions of our paper, the one that begins: “However, we think that the most important question is not whether GCMs can produce credible estimates of future climate, but whether climate is at all predictable in deterministic terms.” It is climate modellers who say or imply otherwise; for example Schmidt (2007, our emphasis):

Weather is chaotic; imperceptible differences in the initial state of the atmosphere lead to radically different conditions in a week or so. Climate is instead a boundary value problem— a statistical description of the mean state and variability of a system, not an individual path through phase space. Current climate models yield stable and nonchaotic climates, which implies that questions regarding the sensitivity of climate to, say, an increase in greenhouse gases are well posed and can be justifiably asked of the models.

Therefore, again we are not the right recipients of Huard’s warning that climate is chaotic.

Near the end of his Discussion, Huard makes an appeal to the “mutual respect and trust in the professionalism of our peers”, which makes an interesting contrast with several of his statements referring to us, the editor, the reviewers and other authors, and ultimately the “… HSJ coming out as lacking the discrimination required to identify poor science”.
Whether the HSJ got “a black eye” is for the reader to judge, as is whether “reviewers A and C rejected the paper on technical and methodological grounds, not philosophy”, since the entire review file is now public [As it has already been available to Huard, it is annexed also to this Reply as a Supplementary Information on the HSJ online site.].

This Comment/Reply illustrates, in my view, the continued pressure on Editors not to publish papers that conflict with the IPCC perspective of the climate system and the ability of global climate models to provide skillful predictions decades into the future. Instead of showing in a quantifiable manner any flaws in the work by Demetris Koutsoyiannis and colleages, Huard 2011 resorts to semantics and criticisms of the review process. Whenever authors resort to such arguments, it illustrates that they cannot refute the substance of the research study.

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Filed under Climate Models, Q & A on Climate Science

Candid Comments From Climate Scientists

UPDATE:   Paul Voosen has sent the url for his excellent article so everyone can  see the story’s quotes situated in their actual context. It is available from


There is  a news release by Paul Voosen on Greenwire titled

Provoked scientists try to explain lag in global warming (Tuesday, October 25, 2011)

There are some interesting quotes from climate scientists in this article that highlight a large degree of uncertainty with respect to the climate system, and the human role in it, even among scientists closely involved with the IPCC reports.  The long article focuses on the question

 ‘Why, despite steadily accumulating greenhouse gases, did the rise of the planet’s temperature stall for the past decade?”

Interesting quotes and text {rearranged to order the persons’ quoted; I highly recommend reading the entire article  include [highlight added]:

From John Barnes [Barnes’s specialty is measuring stratospheric aerosols].

If you look at the last decade of global temperature, it’s not increasing,” Barnes said. “There’s a lot of scatter to it. But the [climate] models go up. And that has to be explained. Why didn’t we warm up?”

Barnes has kept a lonely watch for 20 years [at the Mauna Loa Observatory in Hawaii]. Driving the winding, pothole-strewn road to this government-run lab, he has spent evening after evening waiting for the big one. His specialty is measuring stratospheric aerosols, reflective particles caused by volcanoes that are known to temporarily cool the planet. Only the most violent volcanic eruptions are able to loft emissions above the clouds, scientists thought, and so Barnes, after building the laser, waited for his time.

To this day, there hasn’t been a major volcanic eruption since 1991, when Mount Pinatubo scorched the Philippines, causing the Earth to cool by about a half degree for several years. But Barnes diligently monitored this radio silence, identifying the background level of particles in the stratosphere. And then, sitting in his prefab lab four years ago, not far from where Charles Keeling first made his historic measure of rising atmospheric carbon dioxide levels, Barnes saw something odd in his aerosol records.

Barnes laments the boggling complexity of separating all the small forcings on the climate. It makes Charles Keeling’s careful work identifying rising CO2 levels seem downright simple.

“It’s really subtle,” he said. “It’s hard to track how much is going into the oceans, because the oceans are soaking up some of the heat. And in a lot of places the measurements just aren’t accurate enough. We do have satellites that can measure the energy budget, but there’s still assumptions there. There’s assumptions about the oceans, because we don’t have a whole lot of measurements in the ocean.”

From Jean-Paul  Vernier

Five years ago, a balloon released over Saharan sands changed Jean-Paul Vernier’s life.

Climbing above the baked sand of Niger, the balloon, rigged to catch aerosols, the melange of natural and man-made particles suspended in the atmosphere, soared above the clouds and into the stratosphere. There, Vernier expected to find clear skies; after all, there had been no eruption like Pinatubo for more than a decade. But he was wrong. Twelve miles up, the balloon discovered a lode of aerosols.

Vernier had found one slice of the trend identified by Barnes at Mauna Loa in Hawaii. It was astonishing. Where could these heat-reflecting aerosols be originating? Vernier was unsure, but Barnes and his team hazarded a guess when announcing their finding. It was, they suggested, a rapidly increasing activity in China that has drawn plenty of alarm.

A French scientist who moved to NASA’s Langley Research Center in Virginia to study aerosols, Vernier, like Barnes, turned toward a laser to understand these rogue sulfates. But rather than using a laser lashed to the ground, he used a laser in space.

The same year as the Niger balloon campaign, NASA had launched a laser-equipped satellite aimed at observing aerosols among the clouds. Vernier and his peers suspected, with enough algorithmic ingenuity, that they could get the laser, CALIPSO, to speak clearly about the stratosphere. The avalanche of data streaming out of the satellite was chaotic — too noisy for Barnes’ taste, when he took a look — but several years on, Vernier had gotten a hold of it. He had found an answer.

Mostly, the aerosols didn’t seem to be China’s fault.

From Kevin Trenberth

The hiatus [in warming] was not unexpected. Variability in the climate can suppress rising temperatures temporarily, though before this decade scientists were uncertain how long such pauses could last. In any case, one decade is not long enough to say anything about human effects on climate; as one forthcoming paper lays out, 17 years is required.

For some scientists, chalking the hiatus up to the planet’s natural variability was enough. Temperatures would soon rise again, driven up inexorably by the ever-thickening blanket thrown on the atmosphere by greenhouse gases. People would forget about it.

But for others, this simple answer was a failure. If scientists were going to attribute the stall to natural variability, they faced a burden to explain, in a precise way, how this variation worked. Without evidence, their statements were no better than the unsubstantiated theories circulated by climate skeptics on the Internet.

“It has always bothered me,” said Kevin Trenberth, head of the climate analysis section at the National Center for Atmospheric Research. “Natural variability is not a cause. One has to say what aspect of natural variability.”

Until 2003, scientists had a reasonable understanding where the sun’s trapped heat was going; it was reflected in rising sea levels and temperatures. Since then, however, heat in the upper ocean has barely increased and the rate of sea level rise slowed, while data from a satellite monitoring incoming and outgoing heat — the Earth’s energy budget — found that an ever increasing amount of energy should be trapped on the planet. (Some scientists question relying on this satellite data too heavily, since the observed energy must be drastically revised downward, guided by climate models.) Given this budget ostensibly included the solar cycle and aerosols, something was missing.

Where was the heat going? Trenberth repeated the question time and again.

Recently, working with Gerald Meehl and others, Trenberth proposed one answer. In a paper published last month, they put forward a climate model showing that decade-long pauses in temperature rise, and its attendant missing energy, could arise by the heat sinking into the deep, frigid ocean waters, more than 2,000 feet down. The team used a new model, one prepared for the next U.N. climate assessment; unlike past models, it handles the Pacific’s variability well, which “seems to be important,” Trenberth said.

“In La Niña, the colder sea surface temperatures in the Pacific mean there is less convective action there — fewer tropical storms, etc., and less clouds, but thus more sun,” he said. “The heat goes into the ocean but gets moved around by the ocean currents. So ironically colder conditions lead to more heat being sequestered.”

It is a compelling illustration of how natural variability, at least in this model, could overcome the influence of increasing greenhouse gases for a decade or more, several scientists said. However, according to one prominent researcher — NASA’s Hansen — it’s a search for an answer that doesn’t need to be solved.

That is because, according to Hansen, there is no missing energy.

Trenberth questions whether the Argo measurements are mature enough to tell as definite a story as Hansen lays out. He has seen many discrepancies among analyses of the data, and there are still “issues of missing and erroneous data and calibration,” he said. The Argo floats are valuable, he added, but “they’re not there yet.”

From Susan Solomon

“What’s really been exciting to me about this last 10-year period is that it has made people think about decadal variability much more carefully than they probably have before,” said Susan Solomon, an atmospheric chemist and former lead author of the United Nations’ climate change report, during a recent visit to MIT. “And that’s all good. There is no silver bullet. In this case, it’s four pieces or five pieces of silver buckshot.”

Already Solomon had shown that between 2000 and 2009, the amount of water vapor in the stratosphere declined by about 10 percent. This decline, caused either by natural variability — perhaps related to El Niño — or as a feedback to climate change, likely countered 25 percent of the warming that would have been caused by rising greenhouse gases. (Some scientists have found that estimate to be high.) Now, another dynamic seemed to be playing out above the clouds.

In a paper published this summer, Solomon, Vernier and others brought these discrete facts to their conclusion, estimating that these aerosols caused a cooling trend of 0.07 degrees Celsius over the past decade. Like the water vapor, it was not a single answer, but it was a small player. These are the type of low-grade influences that future climate models will have to incorporate, Livermore’s Santer said.

Solomon was surprised to see Vernier’s work. She remembered the Soufrière eruption, thinking “that one’s never going to make it into the stratosphere.” The received wisdom then quickly changed. “You can actually see that all these little eruptions, which we thought didn’t matter, were mattering,” she said.

From Jim Hansen

These revelations are prompting the science’s biggest names to change their views.

Indeed, the most important outcome from the energy hunt may be that researchers are chronically underestimating air pollution’s reflective effect, said NASA’s James Hansen, head of the Goddard Institute for Space Studies.

Recent data has forced him to revise his views on how much of the sun’s energy is stored in the oceans, committing the planet to warming. Instead, he says, air pollution from fossil fuel burning, directly and indirectly, has been masking greenhouse warming more than anyone knew.

It was in no “way affected by the nonsensical statements of contrarians,” Hansen said. “These are fundamental matters that the science has always been focused on. The problem has been the absence of [scientific] observations.”

NASA’s Hansen disputes that worry about skeptics drove climate scientists to ignore the sun’s climate influence. His team, he said, has “always included solar forcing based on observations and Judith’s estimates for the period prior to accurate observations.”

“That makes the sun a bit more important, because the solar variability modulates the net planetary energy imbalance,” Hansen said. “But the solar forcing is too small to make the net imbalance negative, i.e., solar variations are not going to cause global cooling.”

“Unfortunately, when we focus on volcanic aerosol forcing, solar forcing and stratospheric water vapor changes, it is a case of looking for our lost keys under the streetlight,” Hansen said. “What we need to look at is the tropospheric aerosol forcing, but it is not under the street light.”

“I suspect that there has been increased aerosols with the surge in coal use over the past half decade or so,” he said. “There is semi-quantitative evidence of that in the regions where it is expected. Unfortunately, the problem is that we are not measuring aerosols well enough to determine their forcing and how it is changing.”

More fundamentally, the Argo probe data has prompted Hansen to revise his understanding of how the climate works in a fundamental way, a change he lays out in a sure-to-be-controversial paper to be published later this year.

For decades, scientists have known that most of the heat trapped by greenhouse gases was going into the ocean, not the atmosphere; as a result, even if emissions stopped tomorrow, they said, the atmosphere would continue to warm as it sought balance with the overheated oceans. In a term Hansen coined, this extra warming would be “in the pipeline,” its effects lingering for years and years. But exactly how much warming would be in the pipeline depended on how efficiently heat mixed down into the oceans.

Hansen now believes he has an answer: All the climate models, compared to the Argo data and a tracer study soon to be released by several NASA peers, exaggerate how efficiently the ocean mixes heat into its recesses. Their unanimity in this efficient mixing could be due to some shared ancestry in their code. Whatever the case, it means that climate models have been overestimating the amount of energy in the climate, seeking to match the surface warming that would occur with efficient oceans. They were solving a problem, Hansen says, that didn’t exist.

At first glance, this could easily sound like good news, if true. But it’s not.

“Less efficient mixing, other things being equal, would mean that there is less warming ‘in the pipeline,'” Hansen said. “But it also implies that the negative aerosol forcing is probably larger than most models assumed. So the Faustian aerosol bargain is probably more of a problem than had been assumed.”

From John Daniel [a researcher at the Earth System Research Lab of the National Oceanic and Atmospheric Administration]

When the record came in 1998, though, scientists faltered. It’s a pattern often seen with high temperatures. They cut out too much nuance, said John Daniel, a researcher at the Earth System Research Lab of the National Oceanic and Atmospheric Administration.

“We make a mistake, anytime the temperature goes up, you imply this is due to global warming,” he said. “If you make a big deal about every time it goes up, it seems like you should make a big deal about every time it goes down.”

From Ben Santer

For a decade, that’s exactly what happened. Skeptics made exaggerated claims about “global cooling,” pointing to 1998. (For one representative example, two years ago columnist George Will referred to 1998 as warming’s “apogee.”) Scientists had to play defense, said Ben Santer, a climate modeler at Lawrence Livermore National Laboratory.

“This no-warming-since-1998 discussion has prompted people to think about the why and try to understand the why,” Santer said. “But it’s also prompted people to correct these incorrect claims.”

“Susan’s stuff is particularly important,” Santer said. “Even if you have the hypothetical perfect model, if you leave out the wrong forcings, you will get the wrong answer.”

From Judith Lean

The answer to the hiatus, according to Judith Lean, is all in the stars. Or rather, one star.

Only recently have climate modelers followed how that 0.1 percent can influence the world’s climate over decade-long spans. (According to best estimates, it gooses temperatures by 0.1 degrees Celsius.) Before then, the sun, to quote the late comedian Rodney Dangerfield, got no respect, according to Lean, a voluble solar scientist working out of the the space science division of the Naval Research Laboratory, a radar-bedecked facility tucked away down in the southwest tail of Washington, D.C.

Climate models failed to reflect the sun’s cyclical influence on the climate and “that has led to a sense that the sun isn’t a player,” Lean said. “And that they have to absolutely prove that it’s not a player.”

According to Lean, the combination of multiple La Niñas and the solar minimum, bottoming out for an unusually extended time in 2008 from its peak in 2001, are all that’s needed to cancel out the increased warming from rising greenhouse gases. Now that the sun has begun to gain in activity again, Lean suspects that temperatures will rise in parallel as the sun peaks around 2014.

This consistent trend has prompted Lean to take a rare step for a climate scientist: She’s made a short-term prediction. By 2014, she projects global surface temperatures to increase by 0.14 degrees Celsius, she says, driven by human warming and the sun.

From Graeme Stephens

Over the past decade, for the first time, scientists have had access to reliable measures of the ocean’s deep heat, down to 5,000 feet below sea level, through the Argo network, a collection of several thousand robotic probes that, every few days, float up and down through the water column. This led Hansen to conclude that net energy imbalance was, to be briefly technical, 0.6 watts per square meter, rather than more than 1 watt per square meter, as some had argued.

(Recently, the satellite group measuring the energy imbalance has revised its figure, which now sits at 0.6 watts, matching Hansen’s estimate, according to Graeme Stephens, the head of NASA’s Cloudsat mission. It suggests there isn’t a missing energy. Trenberth disagrees with this analysis, and it’s likely to be a question of ongoing debate.)

From Robert Kaufmann

This past summer, Robert Kaufmann, the BU geographer, made waves when he released a modeling study suggesting that the hiatus in warming could be due entirely to El Niño and increased sulfates from China’s coal burning. While the figures Kaufmann used for the study were based on the country’s coal combustion, and not actual emissions — a big point of uncertainty — many scientists saw some truth in his assertions.

From Martin Wild

During the 1980s and ’90s, the rapid decline of air pollution in the United States and Europe dominated the world’s aerosol trends. While those emissions have continued to decline in the West, returns, from a brightening standpoint, have diminished, just as coal combustion ramped up in Asia. It’s not that the world is getting dimmer again; it’s that it’s no longer getting brighter.

“It’s not an obvious overall trend anymore,” said Martin Wild, a lead author of the United Nations’ next climate assessment at the Swiss Federal Institute of Technology, Zurich. But, he added, “it fits quite well with [coal power] generation. For me, it’s quite striking that it seems to fit quite nicely. But it could still be by chance.”

From Daniel Jacobs

Kaufmann’s findings may only be relevant for so long. Since 2006, China has begun to mandate scrubbers for its coal-fired power plants, though it is uncertain how often the scrubbers, even when installed, are operated. But change is coming, said Daniel Jacob, an atmospheric chemist at Harvard University.

“The sulfate sources have been leveling off, because they’ve been starting to put serious emission controls on their power plants,” Jacob said. “It’s interesting. When you look at the future emission scenarios from the [next U.N. climate report], you see SO2 emissions dropping like a rock, even in the coming decades. Because basically China is going to have to do something about its public health problem.”

The end of the article highlights the developing debate among even these scientists.

“….many of the scientists sorting out the warming hiatus disagree with one another — in a chummy, scholarly way. Judith Lean, the solar scientist, finds Kaufmann’s work unpersuasive and unnecessarily critical of China. Kaufmann finds Solomon’s stratosphere studies lacking in evidence. Hansen and Trenberth can’t agree on a budget.

It seems staggering, then, that in a few years’ time a new consensus will form for the next U.N. climate change report. But it will, and lurking beneath it will remain, as always, the churning theories and rivalries, the questions, the grist of scientific life.

So, in the end, can anyone say explicitly what caused the warming hiatus?

“All of these things contribute to the relative muted warming,” Livermore’s Santer said. “The difficultly is figuring out the relative contribution of these things. You can’t do that without systematic modeling and experimentation. I would hope someone will do that.”

Barnes, for his part, would love to separate whether any background aerosols he found tucked away in the stratosphere came from Chinese coal burning. It is difficult to figure out, but he has some hope it could happen.

“Maybe when coal burning triples,” he said, “then we might sort it out.”

These extracts from the Greenwire article illustrate why the climate system is not yet well understood. The science is NOT solved.

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Schedule Of Presentations At The Third Santa Fe Conference On Global and Regional Climate Variability, October 31-November 4, 2011

This promises to be an interesting Conference. The Schedule is presented below. [the formating is not set clear but the titles and presentors should be clear enough]. This meeting will have a diverse set of viewpoints presented.


The Third Santa  Fe Conference on Global and Regional Climate Variability, October 31-November 4, 2011

Schedule of Presentations 

Monday Morning, October 31, 2011
Registration and continental breakfast ……..7:20-8:20
Welcome: Duncan McBranch, LANL, Deputy Principal Associate Director    ………………………………………………   8:20-8:30
Introduction: Petr Chylek ……………………..8:30-8:40
M-I: Models, Forcing, and Feedbacks  (Chairs: Jerry North and  V. Ramaswamy)
M-1: P. Huybers (Harvard) Regional Temperature Predictions from a Minimalist Model   8:50-9:10
M-2: J. Curry (Georgia Tech) A Critical Look at the IPCC AR4 Climate Change Detection and Attribution   9:10-9:30
M-3: R. Lindzen (MIT) Climate v. Climate Alarm   9:30-9:50
M-4: A. Tsonis (Wisconsin) A new dynamical mechanism for major climate shifts   9:50-10:10

Discussion   10:10-10:25
Coffee and Refreshment   10:25-10:55
M-II: Aerosols and Clouds  (Chairs: Hans von Storch and Jon Reisner)  
M-5: P. Rasch (PNNL) Exploration of aerosol, cloud and dynamical feedbacks in the climate-cryosphere system   10:55-11:15
M-6: D. Rosenfeld (Hebrew U Jerusalem) Number of activated CCN as a key property in cloud-aerosol interactions   11:15-11:35
M-7: W. Cotton (CSU) Potential impacts of aerosols on water resources in the Colorado River Basin………………….…..11:35-11:55
M-8: B. Stevens (Max Planck Institute) The Cloud Conundrum   11:55-12:15

Discussion   12:15-12:30

Monday Afternoon, October 31
M-III: The Arctic (Chairs: Peter Webster and William Lipscomb)
M-9:  I. Polyakov (U Alaska) Recent and Long-Term Changes in the Arctic Climate System   2:00-2:20
M-10: J. Sedlacek (ETH Zurich) Impact of a reduced sea ice cover on lower latitudes   2:20-2:40
M-11: S. Mernild (LANL) Accelerated melting and disappearance of glaciers and ice caps.   2:40-3:00  
M-12: D. Easterbrook (Western Washington U) Ice core isotope data: The past is the key to the future   3:00-3:20

Discussion   3:20-3:35
Coffee and Refreshment     3:35-4:05

M-IV: Models, Forcing, and Feedbacks  (Chairs: Anastasios Tsonis and Anjuli Bamzai)
M-13: J-S von Storch (Max Planck Institute) Dynamical impact of warming pattern     4:05-4:25
M-14: Q. Fu (U Washington) Warming in the tropical upper troposphere: Models versus observation   4:25-4:45
M-15: S. Schwartz (BNL) Earth’s transient and equilibrium climate sensitivities   4:45-5:05
M-16: R. Salawitch (U Maryland) Impact of aerosols, ocean circulation, and internal feedbacks on climate   5:05-5:25
M-17: N. Andronova (U Michigan) Climate sensitivity and climate feedbacks ………………………………………………..5:25-5:45
Discussion   5:45-6:00

Poster Session P-I  (with Refreshment)   6:00-8:00
Poster Session P-I
Monday, October 31
Chairs:  Graeme Stephens, Roger Davis, and Brad Flowers
Tim Garret, U Utah
Will a warmer Arctic be a cleaner Arctic?
H. von Storch, A. Bunde,
Inst. of Coastal Res., Germany
Examples of using long term memory in climate analysis
P. Chylek, C. Folland, et al
LANL, UK Met Office
Observed and model simulated 20th century Arctic temperature variability: Anthropogenic warming and natural climate variability
K. McKinnon, P. Huybers, Harvard U
The fingerprint of ocean on seasonal and inter-annual temperature change
Anthony Davis, JPL
Frontiers in Remote Sensing: Multi-Pixel and/or Time-Domain Techniques
Christopher Monckton
Is CO2 mitigation cost-effective?
H. Moosmuller, et al
Desert Res. Inst., U Nevada
A Development of a Super-continuum Photoacoustic Aerosol Absorption and Albedo Spectrometer for the Characterization of Aerosol Optics
H. Inhaber, Risk Concept
Will Wind Fulfill its Promise of CO2 Reductions?
M. Chen, J. Rowland, et al
Temporal and Spatial Patterns in Thermokarst Lake Area Change in Yukon Flats, Alaska: an Indication of Permafrost Degradation
M. Kafatos, H. El-Askary, et al
Schmid College, WMO
Multi-Model Simulations and satellite observations for Assessing Impacts of Climate Variability on the Agro-ecosystems
C. Xu, et al, LANL, NCAR
Toward a mechanistic modeling of nitrogen limitation on vegetation dynamics
H. Hayden, U Connecticut
Doing the Obvious: Linearizing
L. Hinzman, U Alaska
The Need for System Scale Studies in Polar Regions
X. Jiang, et al, LANL, NCAR
Regional-scale vegetation die-off in response to climate Change in the 21st century

Tuesday Morning, November 1
Registration and continental breakfast   7:30-8:30
T-I: Models, Forcing and Feedbacks  (Chairs: Peter Huybers and Joel Rowland)
T-1: V. Ramaswamy (NOAA GFDL) Addressing the leading scientific challenges in climate modeling,   8:30-8:50
T-2: P. Webster (Georgia Tech) Challenges in deconvoluting internal and forced climate change   8:50-9:10
T-3: H. von Storch (Institute for Coastal Research, Hamburg) Added value generated by regional climate models   9:10-9:30   
T-4: A. Solomon (U Colorado) Decadal predictability of tropical Indo-Pacific Ocean temperature trends   9:30-9:50
Discussion     9:50-10:05
Coffee and Refreshment   10:05-10:35
T-II: Observations (Judy Curry and Manvendra Dubey)
T-5: S. Wofsy (Harvard) HIAPER Pole to Pole Observations (HIPPO) of climatically important gases and aerosols   10:35-10:55
T-6: R. Muller (UC Berkeley) The Berkeley Earth Surface Temperature Land Results     10:55-11:15
T-7: R. Rohde (Berkeley Temp Project) A new estimate of the Earth land surface temperature   11:15-11:35
T-8: F. Singer (SEPP) Is the reported global surface warming of 1979 to 1997 real?   11:35-11:55
T-9: J. Xu (NOAA) Evaluation of temperature trends from multiple Radiosondes and Reanalysis products   11:55-12:15
Discussion   12:15-12:30

Tuesday Afternoon, November 1
T-III: Cosmic Rays, and the Sun  (Chairs: Don Wuebbles and Anthony Davis)
T-10: P. Brekke (Space Center, Norway) Does the Sun Contribute to climate change? An update   2:00-2:20
T-11: G. Kopp (U Colorado) Solar irradiance and climate   2:20-2:40
T-12: A. Shapiro (World Radiation Center, Davos) Present and past solar irradiance: a quest for understanding     2:40-3:00  
T-13: B. Tinsley (U Texas) The effects of cosmic rays on CCN and climate     3:00-3:20
Discussion   3:20-3:35
Coffee and Refreshment   3:35-4:05

T-IV: Aerosols and Clouds (Chairs: William Cotton and Daniel Rosenfeld)
T-14:  J. Vernier (NASA Langley) Accurate estimate of the stratospheric aerosol optical depth for climate simulations     4:05-4:25
T-15: J. Coakley (Oregon SU) Knowledge gained about marine stratocumulus and the aerosol indirect effect   4:25-4:45
T-16: G. Stephens (NASA JPL) Clouds, aerosols, radiation, rain and climate   4:45-5:05
T-17: J. Augustine (NOAA) Surface radiation budget measurements from NOAA’s SURFRAD network   5:05-5:25
T-18: G. Jennings (Ireland National U) Direct Radiative Forcing over the North East Atlantic …………………….5:25-5:40
Discussion   5:40-5:55
Banquet   6:30-8:00
B-1: Judy Curry (Georgia Tech) The uncertainty monster at the climate science-policy interface
B-2: Anjuli Bamzai (NSF) Global and regional climate change research at NSF: Current activity and future plans

Wednesday Morning, November 2
Registration and continental breakfast   7:10-8:10
W-I: Weather, Climate, and Arctic Terrestrial Processes (Chairs: Larry Hinzman and Cathy Wilson)
W-0: T. Schuur (U Florida) Vulnerability of Permafrost Carbon Research Coordination Network ………………8:10-8:30
W-1: H. Epstein (U Virginia) Recent dynamics of arctic tundra vegetation: Observations and modeling   8:30-8:50
W-2: E. Euskirchen (U Alaska) Quantifying CO2 fluxes across permafrost and soil moisture gradients in arctic Alaska   8:50-9:10
W-3: D. Lawrence (NCAR) High-latitude terrestrial climate change feedbacks in an Earth System Model   9:10-9:30   
W-4: D. Wuebbles U Illinois) Severe weather in a changing climate     9:30-9:50

Discussion   9:50-10:05
Coffee and Refreshment   10:05-10:35
W-II: The Arctic  (Chairs: Qiang Fu and Keeley Costigan)
W-5: M. Flanner (U Michigan) Arctic climate: Unique vulnerability and complex response to aerosols   10:35-10:55
W-6: R. Stone (NOAA) Characterization and direct radiative impact of Arctic aerosols: Observed and modeled   10:55-11:15
W-7: M. Zelinka (LLNL) Climate feedbacks and poleward energy flux changes in a warming climate   11:15-11:35
W-8: G. De Boer (U Colorado) The present-day Arctic atmosphere in CCSM4   11:35-11:55
W-9: R. Peltier (U Toronto) Rapid climate change in the Arctic: the case of Younger-Dryas cold reversal     11:55-12:15

Discussion   12:15-12:30
Wednesday Afternoon, November 2
W-III: Arctic and Global Climate Variability (Chairs: Igor Polyakov and Sebestian Mernild)
W-10: G. North (Texas A&M) Looking for climate signals in ice core data   2:00-2:20
W-11: T. Kobashi (National Inst Polar Research, Tokyo) High variability of Greenland temperature over the past 4000 years   2:20-2:40
W-12: M. Palus (Inst Comp Sci, Prague) Phase coherence between solar/geomagnetic activity and climate variability     2:40-3:00  
W-13: N. Scafetta (Duke U) The climate oscillations: Analysis, implication and their astronomical origin   3:00-3:20

Discussion …………………………………3:20-3:35
Coffee and Refreshment …………………3:35-4:05
W-IV: Greenhouse Gases, Aerosols, and Energy Balance (Steve Wofsy and James Coakley)
W-14: M. Dubey (LANL) Multiscale greenhouse gas measurements of fossil energy emissions and climate feedbacks   4:05-4:25
W-15: C. Loehle (Nat Council for Air Improvement) Climate change attribution using empirical decomposition     4:25-4:45
W-16: R. Davies (U Auckland) The greenhouse effect of clouds: Observation and theory   4:45-5:05
W-17: V. Grewe (Inst Atmos Physics, Oberpfaffenhofen) Attributing climate change to NOx emissions   5:05-5:25
Discussion………………………………. 5:25-5:40
Poster Session P-II……………………5:40-7:00
Poster Session P-II

Wednesday November 2, 2011
Chairs: Mark Flanner, Hans Moosmuller, and Dave Higdon
Chris Borel-Donohue,
Air Force Institute of Technology
Novel Temperature/Emissivity Separation Algorithms for Hyperspectral Imaging Data
R. Stone, J. Augustine, E. Dutton,    NOAA, Earth System Res. Lab.
Radiative Forcing Efficiency of the Fourmile Canyon Fire Smoke: A Near-Perfect Ad Hoc Experiment
Fred Singer,
Are observed and modeled patterns of temperature trends ‘Consistent’? Comparing the ‘Fingerprints’
Brian A Tinsley,
University of Texas at Dallas
Charge Modulation of Aerosol Scavenging (CMAS): Causing Changes in Cyclone Vorticity and European Winter Circulation?
A. V. Shapiro, et al, World Rad. Center, Davos, Switzerland
The stratospheric ozone response to a discrepancy of the SSI data
M. Palus, et al, Inst. of Computer Science, Prague, Czech Republic
Discerning connectivity from dynamics in climate networks
Mark Boslough, SNL
Comparison of Climate Forecasts: Expert Opinions vs. Prediction Markets
C. Gangodagamage, et al
Clustering and Intermittency of Daily Air Temperature Fluctuations
in the North-Central Temperate Region of the U.S.
Michael LuValle,
OFS Laboratories
Suggested attribution of Irene’s flooding in New Jersey (2011) via statistical postdiction derived from chaos theory
A. Winguth, et al.,
University of Texas, Arlington
Climate Response at the Paleocene-Eocene Thermal Maximum to Greenhouse Gas Forcing – An Analog for Future Climate Change
David Mascarenas, et al
The development of Autonomous Mobile Sensor Nodes for CO2 Source/Sink                 Characterization
Richard Field, Paul Constantine, and Mark Boslough, SNL
Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change
Steve Schwartz, BNL
Earth’s transient and equilibrium climate sensitivities

Thursday Morning, November
Registration and continental breakfast   7:30-8:30
Th-I: Theory, Experiment, and Observations (Chairs: Brian Tinsley and Nick Hengartner)
Th-1: J. Curtius (Frankfurt U) Atmospheric aerosol nucleation in the CLOUD experiment at CERN   8:30-8:50
Th-2: E. Dunne (U Leeds) The influence of ion-induced nucleation on atmospheric aerosols in CERN CLOUD experiment   8:50-9:10
Th-3: W. Hsieh (UBC) Machine learning methods in climate and weather research   9:10-9:30
Th-4: C. Essex (U Western Ontario) Regime algebra and climate theory   9:30-9:50
Discussion   9:50-10:05
Coffee and Refreshment   10:05-10:35
Th-II: Atlantic Ocean and Climate (Chairs: Anastasios Tsonis and Nicola Scaffeta)
Th-5: M. Hecht (LANL) A perspective on some strength and weaknesses of ocean climate models…………………10:35-10:55
Th-6: L. Frankcombe (Utrecht U) Atlantic multidecadal variability – a stochastic dynamical systems point of view ………10:55-11:15
Th-7: S. Mahajan (ORNL) Impact of the AMOC on Arctic Sea-ice variability …………………………..11:15 11:35

Th-8: P. Chylek (LANL) Ice core evidence for a high spatial and temporal variability of the AMO…………………. 11:35-11:55

Th-9: M. Vianna (Oceanica, Brazil) On the 20 year sea level fluctuation mode in Atlantic Ocean and the AMO   11:55-12:15

Discussion   12:15-12:30

Thursday Afternoon, November 3

Th-III: Climate Change and Vegetation (Chairs: Michael Cai and Thom Rahn)
Th-10: N. McDowell (LANL) Climate, carbon, and vegetation mortality   2:00-2:20
Th-11: D. Gutzler (UNM) Observed and projected hydroclimatic variability and change in the southwestern United States     2:20-2:40
Th-12: C. Allen (USGS) Tree mortality and forest die-off response to climate change stresses at regional to global scales   2:40-3:00
Th-13: J. Chambers (LBL) Carbon balance of an old-growth Central Amazon forest   3:00-3:20
Discussion   3:20-3:35
Coffee and Refreshment   3:35-4:05
Th-IV: Climate Change and Economics (Chairs: Richard Lindzen and John Augustine)
Th-14: T. Garrett (U Utah) Thermodynamic constrains on long-term anthropogenic emission scenarios   4:05-4:25
Th-15: C. Monckton   Is CO2 mitigation cost-effective?   4:25-4:45
Th-16: D. Pasqualini (LANL) Does the climate change the economy? An investigation on local economic impact   4:45-5:05
Th-17: M. Boslough (SNL) Using prediction market to evaluate various global warming hypotheses   5:05-5:25
Discussion     5:25-5:40      

Friday Morning, November 4
Registration and continental breakfast   7:30-8:30
F-I: Observations (Chairs: Steve Love and Brad  Henderson)
F-1: A. Davis (NASA JPL) Cloud and aerosol remote sensing: Thinking outside the photon state-space box   8:30-8:50
F-2: H. Moosmuller (DRI U Nevada) Aerosol optics, direct radiative forcing, and climate change   8:50-9:10
F-3: N-A Morner (Paleogeophysics, Stockholm) Sea level changes in the Indian Ocean: Observational facts   9:10-9:30   
F-4: O. Kalashnikova (NASA JPL) MISR decadal aerosol observations   9:30-9:50
Discussion     9:50-10:05
Coffee and Refreshment   10:05-10:35
F-II: Models, Forcing, and Feedbacks  (Chairs: Tim Garrett and Chris Essex)
F-5: D. Lemoine (U Arizona) Formalizing uncertainty about climate feedbacks   10:35-10:55
F-6: P. Knappenberger, Short-term climate model projected trends of global temperature and observations   10:55-11:15
F-7: C. Keller (LANL) Solar forcing of climate: A review   11:15-11:35
F-8: W. Gray (CSU) Recent multi-century climate changes as a result of variation in the global ocean’s deep MOC   11:35-11:55
F-9: C. Folland (UK Met Office) Global surface temperature trends from six forcing and internal variability factors   11:55-12:15
Discussion   12:15-12:30
Conference ends   12:30


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New Paper “Origin Of The Arctic Warming In Climate Models” By Chung And Räisänen 2011

There is a new paper [h/t to Dallas Staley]

Chung, C. E., and P. Räisänen (2011), Origin of the Arctic warming in climate models, Geophys. Res. Lett., doi:10.1029/2011GL049816, in press

which presents quite a new perspective on the inability of climate models to realistically simulate the real world climate.

The abstract reads [highlight added]

There is a debate on whether the snow/ice change feedback or poleward energy transport from lower latitudes generates the observed Arctic warming amplification. There is another possibility that remotely induced warming in the Arctic can be amplified by snow/ice feedbacks. We demonstrate that this mechanism plays an important role in two independent climate models: CAM3 and ECHAM5. We also show with these two models that the June-August temperature structure in the vertical is a good indicator of how much the climate forcing from lower latitudes contributes to Arctic warming. Compared with the June-August 3D temperature trend in ERA Interim reanalysis, the CMIP3 models simulate warming at higher levels, suggesting that the models over-simulate the role of poleward energy transport in Arctic warming. This finding has implications for climate feedback and aerosol forcing.

They have a section of their paper titled “Implications” which reads

Based on the vertical structure of JJA temperature trends, we have inferred that CMIP3 models overestimate significantly the contribution of poleward energy transport to Arctic warming compared to ERA Interim data. This has important implications for either climate feedbacks or climate forcing. Assuming that the forcing is represented correctly, the implication is that either (1) the model-simulated net feedback is too large (i.e. too positive) at low latitudes (< 60ºN), or (2) the feedback is too small (i.e. too negative) in the Arctic, or both. The first scenario would provide some support to the findings of Lindzen and Choi [2009] who argue that at low latitudes the real feedback is less positive than in climate models (or even negative), with the implication that models overestimate the global climate sensitivity. On the other hand, if the second scenario is correct so that models underestimate the high latitude feedback while representing low-latitude feedbacks more or less correctly, they would tend to underestimate the climate sensitivity. The present analysis does not allow us to distinguish between these two scenarios. Note that the ice feedback in the Arctic warms the lower atmosphere, while the heat transport and general circulation respond mainly to the distribution of the mid-tropospheric temperature [Cai, 2006]. Thus, the ice albedo feedback is largely irrelevant to the strengthening/weakening of poleward energy transport.

If the models simulate climate feedbacks correctly, the indication is that models have significantly incorrect climate forcing. Since GHG forcing is well established, the problem is likely in how the models treat aerosol effects. In this scenario, the real aerosol forcing might be significantly positive in the Arctic and significantly negative outside of the Arctic, while the models miss this feature entirely. Future studies are needed to verify that the models indeed over-simulate the energy transport into the Arctic and to understand why the models do so.

This paper is yet another study that documents shortcomings in the global climate models that are used to make multi-decadal climate predictions.

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Response To Skeptical Science On A Series Of Weblog Posts

Over the last several weeks, I have been involved with comments and replies on the weblog Skeptical Science (SkS). Despite a rocky start (e.g. see) they finally engaged in constructive interactions, although they still often fell into snarky tones. 

Here are their posts

Continued Lower Atmosphere Warming

Pielke Sr. and SkS Warming Estimates

Pielke Sr. and SkS Disagreements and Open Questions

One post of SkS that, given its context that they have spent so much time being critical of my conclusions and insisting on statistical analyses, is

SkS Highlights

which, the disclaimer to the contrary in one of the comments, clearly is intended to relate to me  [reminds me of this cartoon by Tom Peterson of NCDC ].  :-)

My posts on my interaction with SkS include

Response To A Question On Skeptical Science On The Fraction Of Positive Radiative Forcing From CO2

Final Comments On My Interaction With Skeptical Science

Skeptical Science Responses To My Questions And My Reply

My Interactions With Skeptical Science – A Failed Attempt (So Far) For Constructive Dialog

My Further Response To Skeptical Science’s Questions Of September 16 2011

My Response To The Skeptical Science Post “One-Sided ‘Skepticism”

Scientific Robustness Of The University Of Alabama At Huntsville MSU Data

Their comments focus on three issues that I have raised:

1. That the positive radiative forcing is less than 50% (and I used information from the literature to show how it could be 28%)

2. The reduction of heating of the upper oceans in recent years.

3. The lack of a continued warming in the lower troposphere over the time period since 1998 (and even more clearly since 2002).

On their comments on these three issues, their rebuttal of #1 is the only one where they have merit, in my view.  The concluded that I double counted when I obtained the 28% value, and that several of the values I used were feedbacks, not forcings.  Thus I countered that using a different approach, I could still show a value of 35%.  Even they reported that the estimate of the positive radiative forcing is less than 50% [they reported on a value of 48% from Skeie 2011 which is a reduction of 4% from IPCC AR4].  SkS accepts a lower value of the positive radiative forcing from soot (black carbon), dismisses two other aerosol effects from NRC 2005 and ignores that some of the radiative effect from the added CO2 would have been adjusted for by a warmer climate system since its introduction.

I view the disagreement as to whether the value is 50% or 25% a minor issue, as we both agree it is a first order climate forcing.

However, if we want a better answer,  they ignored  was my fundamental question:

What is the current radiative forcing from the different human climate forcings with the water vapor overlap excluded?

I proposed a way to estimate this (which they also mostly ignored) –

  •   Use a column radiative transfer model (for all wavelengths – i.e. short and long wave) on a vertical profile of temperature, humidity and clouds at a sufficient number of locations (grid points) around the world (using all global reanalysis grid points) during a year (with hourly time intervals) to determine the baseline current radiative forcing. If resources permit, do more than one year. Calculate the global average radiative forcing by integrating over the year at each grid point. While radiative feedbacks, of course, are implicit in the vertical profiles, the radiative transfer model provides the instantaneous forcing at that time.
  •   Use the column radiative transfer model with these same soundings but  [sensitivity test #1] change the CO2 level back to pre-industrial,  [sensitivity test #2] change the aerosol load back to preindustrial,  [sensitivity test #3]  change the land cover back to natural, etc and express the values in Watts per meter squared.
  •  For each of these sensitivity tests, sum up the differences in radiative forcings to obtain the global annual average in Watts per meter squared.

On the three issues, with respect to items #2 and #3, they are ignoring the obvious in my view.

First,  the figure below shows that warming on the upper ocean has been reduced in recent years. SkS spent a lot of time trying to argue that this is not significant and that one needs to perform statistical tests to show it is. The reason, as far as I can ascertain, is that see everything in the climate arena as involving the overarching study forcing of CO2 which they see as the dominate human climate forcing . Thus, such short-term excursions are “noise”.  I agree we can not say anything about the long-term trends, but to ignore that heating of the upper ocean has mostly stopped ignores the obvious.

upper ocean heat content anomaly

On their focus on a similar issue (#3) with respect to lower tropospheric temperature trends, they similarly conclude that any short-term excursion from a long-term linear warming is “noise” due to natural climate variations.  Maybe they are right. However, to ignore the obvious that the warming of the lower troposphere has halted, when averaged over the time period 1998 (or 2002) to the present, ignores the obvious signal in the data. Below is the latest RSS MSU TLT data

Channel TLT Trend Comparison

Now, what SkS ignored in my questions to them was the lack of cooling in the lower stratosphere since about 1995; see below

Channel TLS Trend Comparison

My Final Comments

I appreciated the opportunity to engage on SkS, and will ask them a couple of further questions this week, but find they still persist in an argumentative manner of debate. Instead of focusing on areas of agreement, they repeatedly argue (often with snarky tone) the same points over and over again.  Instead of accepting there is disagreement, they dogmatically insist that they are right.   I suspect many readers turn off SkS because of the tone they use in the comments. That is too bad, as a site that accepts the IPCC but is willing to constructively and courteously debate science issues, is very  much-needed. Hopefully, SkS will work to improve the tone of their weblog, and recognize that you can disagree on issues, but still respect those you are debating with. Snarky comments are not constructive.

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New Paper “Sensitivity Of Distributions Of Climate System Properties To The Surface Temperature Dataset ” By Libardoni and Forest 2011

There is a new paper [h/t to Dallas Staley]

Libardoni, A. G., and C. E. Forest (2011), Sensitivity of Distributions of Climate System Properties to the Surface Temperature Dataset, Geophys. Res. Lett., doi:10.1029/2011GL049431, in press

which presents evidence for a diversity of ranges with respect to the analysis of global surface temperature data sets that are used to examine multi-decadal global climate model performmance.

The abstract of this paper reads [highlight added]

“Surface temperature, upper-air temperature, and ocean heat content data are used to constrain the distributions of the parameters that define three climate system properties in the MIT Integrated Global Systems Model: effective climate sensitivity, the rate of ocean heat uptake into the deep ocean, and net anthropogenic aerosol forcing. Five different surface temperature data records are used to show that the resulting parameter distribution functions are sensitive to the dataset used to estimate the likelihood of model output given the observed climate records. Estimates of effective climate sensitivity mode and mean differ by as much as 1 K between the datasets, with an overall range of 1.2 to 5.3 K. Ocean effective diffusivity distributions are poorly constrained by any dataset. The overall range net aerosol forcing values, -0.19 to -0.83 Wm-2, is small compared to other uncertainties in climate forcings. Transient climate response (TCR) estimates derived from these distributions range between 0.87 and 2.41 K and the shapes of individual TCR distributions depend on the surface dataset. Understanding the differences in parameter distributions and climate system properties derived from them is critical for understanding the full range of uncertainty involved in climate model calibration and prediction results.”

They write in the introduction that

“This study explores the impact that the surface temperature dataset used to compare model output to observed values has on the parameter constraints. To date, few studies have investigated how the surface temperature dataset used to compare model output with observational data impacts the parameter and TCR distributions. In total, five surface temperature data records representing three well-known climate centers are used in this study. Estimates of TCR are also investigated from the parameter distributions derived from each dataset. The resulting distributions show that model calibration is sensitive to the specific surface temperature dataset.”

The article has a succinct summary of the different global surface temperature analyses. They write

We use surface temperature data from five climate data records. The first two data records are HadCRUT2 [Jones and Moberg, 2003] and HadCRUT3 [Brohan et al., 2006]. The third is the NCDC merged land-ocean dataset [Smith et al., 2008]. The remaining two records are GISTEMP 250 and GISTEMP 1200 [Hansen et al., 2010] from NASA, with the distinctions reflecting the 250 km and 1200 km radii of influence used in the interpolation algorithm. All data are reported as monthly surface temperature anomalies with respect to a given base period on a 5◦x5◦ grid. The data records differ from one another and potential reasons for these differences are now discussed briefly.

One difference between the records is the land surface data used in the analyses. All records obtain a majority of their land surface data from the Global Historical Climatology Network (GHCN) [Peterson and Vose, 1997], but each utilizes the available data differently. For example, the Hadley Centre requires stations to have sufficient data between 1961 and 1990, their climate normal period, to be used in the analysis [Jones and Moberg, 2003; Brohan et al., 2006]. Alternatively, NASA requires that stations have a period of overlap of at least 20 years with stations inside of a 1200 km radius to be used in the analysis [Hansen et al., 2010]. A second difference between the data records is that each uses a different sea surface temperature (SST) dataset. Because oceans cover 70% of the Earth’s surface, these choices lead to differences between the temperature data records [Smith et al., 2008]. In a test of the sensitivity to ocean data choice, [Hansen et al., 2010] showed that the global mean temperature calculated from GISTEMP data is affected by the choice of SST data. A last difference between the data records is the method for filling regions with missing data and how the 5×5 grid box anomalies are calculated. Specific details of infilling and grid box averaging methods for each data record can be found in corresponding references. At this stage, we have five surface temperature data records and choose to treat them each as equally plausible. We present the results derived from each of them and do not attempt to merge the results into a single posterior distribution.

The conclusion has the text

The results presented here show that climate model parameter constraints are sensitive to the surface dataset used to compare with model output. In general, the ranges of the effective climate sensitivity parameter distributions are comparable, but are shifted relative to each other depending on which surface dataset is used. The biggest shift in effective climate sensitivity distributions is observed when the GISTEMP datasets are used. Using the 95-percent confidence intervals and considering all datasets, climate sensitivity is found to be between 1.2 and 5.3 K. Regardless of the surface data used, effective ocean diffusivity is poorly constrained by the data. Anthropogenic aerosol forcing is found to be between -0.19 and -0.83 Wm−2 when considering all datasets. TCR estimates are also sensitive to the choice of surface data. When all surface datasets are considered, transient warming is found to lie between 0.87 and 2.31 K. However, this range masks the differences that exist between the individual distributions.The TCR distribution derived from GISTEMP data is narrower and yields only minimal warming. In contrast, distributions derived from Hadley Centre datasets are wider and yield stronger warming. Given that both the parameter and TCR distributions differ when using different datasets, additional uncertainty is present in model calibration and climate projection studies. Future studies using these datasets must account for these differences to avoid overconfidence in predictions through mistreatment of the uncertainty.”

This study is of interest since it shows a new perspective on the large uncertainty that remains in climate prediction.  It also highlights how poorly the ocean uptake of heat is simulated in the models.

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Comments And Questions On The BEST Analyses

Update #2 –  In response to the comment on the weblog The Way Things Break in the post

Initial thoughts on the Berkeley Earth Surface Temperature release where my perspective on the BEST research was misstated, I wrote


“Roger Pielke Sr. was quick to attempt to downplay the BEST results by implying they were not independent of previous analyses. Perhaps Roger should have actually bothered to read the papers he was attacking”

I have not discredited the BEST study. I have raised issues that they have not covered in their papers; please see my post

Update: In terms of my view on the global average surface temperature trend as the tool to assess climate change (and thus the importance of the BEST analysis on this subject), please see my post

So-Called “Climate-Sensitivity” – A Dance On The Head Of A Pin

The BEST analysis is of scientific interest (e.g. it addresses one of the issues in our paper Pielke et al 2007), but it does not deserve the large overstated media response to it (e.g. see


I have posted comments that I have provided on Watts Up With That, Climate Etc and Blackboard (asking questions on their study, which, in my view, should be answered before the papers are accepted for publication) regarding the BEST Analysis as an Update to

Comment On The Article in the Economist On Rich Muller’s Data Analysis

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