Dan Hughes alerted us to this new paper. It is

Gillett, N. P., V. K. Arora, G. M. Flato, J. F. Scinocca, and K. von Salzen  (2012), Improved constraints on 21st-century warming derived using 160 years of temperature observations, Geophys. Res. Lett.,39, L01704, doi:10.1029/2011GL050226.

The abstract reads [highlight added]

Projections of 21st century warming may be derived by using regression-based methods to scale a model’s projected warming  up or down according to whether it under- or over-predicts the response to anthropogenic forcings over the historical period.Here we apply such a method using near surface air temperature observations over the 1851–2010 period, historical simulations  of the response to changing greenhouse gases, aerosols and natural forcings, and simulations of future climate change under  the Representative Concentration Pathways from the second generation Canadian Earth System Model (CanESM2). Consistent with previous studies, we detect the influence of greenhouse gases, aerosols and natural forcings in the observed temperature record. Our estimate of greenhouse-gas-attributable warming is lower than that derived using only 1900–1999 observations. Our analysis  also leads to a relatively low and tightly-constrained estimate of Transient Climate Response of 1.3–1.8°C, and relatively  low projections of 21st-century warming under the Representative Concentration Pathways. Repeating our attribution analysis with a second model (CNRM-CM5) gives consistent results, albeit with somewhat larger uncertainties.

I have just two comments. First, while some will be pleased with the smaller global average temperature increase predicted for the 21st century, the assessment is based on:

i) a surface temperature record back to 1851 which not spatially representative and has unknown biases with respect to the changes in local conditions where the temperature measurements were made during this time period (e.g. see Fall, 2011),

and

ii) a model is used for the attribution study of the forcings, yet these models do not have all of the first order climate forcings and feedbacks accurately represented (e.g. see NRC, 2005).

When they write

 ”……we detect the influence of greenhouse gases, aerosols and natural forcings in the observed temperature record”

they more accurately should state

“…….we detect IN THE MODEL the influence of greenhouse gases, aerosols and natural forcings WHEN COMPARED WITH the observed temperature record.

At some point, the entire climate science community is going to realize that models are just hypotheses; e.g. see

Short Circuiting The Scientific Process – A Serious Problem In The Climate Science Community

 Scientific rigor requires that real world observations be used to test the models, not the other way around! It is inappropriate to use multi-decadal climate model predictions (even in a hindcast mode) to make conclusions on real world attributions without such an observational validation. They are only a guide as to how we should set up observational studies in order to perform scientifically robust attribution studies.  

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In response to the post

Inadequate Poll Of Views On Climate Science By Scott Learn Of The Oregonian – But At Least An Opportunity To Debate The Climate Issue

George Taylor and I exchanged the e-mails below. George was in the debate in Oregon sponsored by the state chapter of the American Meteorological Society.  I am pleased that George is leading an effort for constructive debate on the climate issue.

George’s Comment On The Debate

Thanks, Roger. Good to hear from you. All in all it went well. There were over 500 people in attendance! I stated out loud that “human activities DO affect climate, in a variety of ways. CO2, in my opinion, exerts a relatively minor influence but there are many other human factors, such as land use change and particulate emissions, that influence climate. All in all, however, it is my opinion that natural variations, notably solar radiation and tropical Pacific SST, have exerted a greater influence on GLOBAL climate than have human activities.”

You were the one who influenced my thinking, years ago, on the multiplicity of human influences!

http://www.oregonlive.com/environment/index.ssf/2012/01/presentation_by_global_warming.html

George

My Reply

Hi George

It is good to hear from you!

Can I post your e-mail below on my weblog? In terms of global, I have concluded that the global average of surface temperature, etc, is almost a worthless metric, as what really matters is the extent (and if) large scale regional circulation patterns are changed. If we [convince] the IPCC (and AMS and AGU leadership) that they are looking at the wrong metrics, we might be able to make some progress. :-)

With Best Regards

Roger

P.S. Can I post your e-mail below on my weblog?

George’s Response

Absolutely. And I concur about “global temp” and said so last night. The McKitrick-Essex book has a chapter on the meaninglessness of that statistic, and I referred to that.

Sure, use my email!

GT

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In my posts, I have urged that the focus of climate modeling research change from focusing on providing multi-decadal climate predictions to the assessment of predictability; e.g. see

The Difference Between Prediction and Predictability – Recommendations For Research Funding Related to These Distinctly Different Concepts

I was alerted by Jos de Laat of KNMI to an important new research paper that specifically addresses this issue. This paper is

Oldenborgh, G.J. van, F.J. Doblas-Reyes, B. Wouters and W. Hazeleger,  2012: Skill in the trend and internal variability in a multi-model decadal prediction ensemble.  accepted, Clim. Dyn.

The abstract [as it reads here] is [highlight added]

Decadal climate predictions have skill due to predictable components in boundary conditions (mainly greenhouse gases) and initial conditions (mainly the ocean). We investigated the skill of temperature and precipitation hindcasts from a set of four coupled ocean-atmosphere models. Regional variations in skill with and without trend due to global warming point to separate effects of the boundary forcing and the ocean initial state. In temperature most skill comes from the prescribed boundary forcing. The trend of the global mean temperature is represented well in the hindcasts, but variations around the trend show little skill. The models have non-trivial skill in hindcasts of North Atlantic SST beyond the trend. The same may hold for the decadal ENSO region, although the signal is less clear. Hence we conclude that the ocean initial state contributes significantly to skill in forecasting SST in these regions.

The conclusion contains the text

A 4-model 12-member ensemble of 10-yr hindcasts has been analysed for skill in SST, 2m temperature and precipitation. The main source of skill in temperature is the trend, which is primarily forced by greenhouse gases and aerosols. This trend contributes almost everywhere to the skill. Variation in the global mean temperature around the trend do not have any skill beyond the first year. However, regionally there appears to be skill beyond the trend in the two areas of well-known low-frequency variability: SST in parts of the North Atlantic and Pacific Oceans is predicted better than persistence. A comparison with the CMIP3 ensemble shows that the skill in the northern North Atlantic and eastern Pacific is most likely due to the initialisation, whereas the skill in the subtropical North Atlantic and western North Pacific are probably due to the forcing.

In the Atlantic, the ensemble shows clear skill in predicting an AMO index that is orthogonal to the trend in yrs 2–5, and reasonable skill in yrs 6–9. The skill in decadal ENSO is lower, not statistically significant, but in agreement with other studies. The CMIP3 ensemble shows less skill in both these indices. There is also an indication of skill in hindcasting decadal Sahel rainfall variations, which are known to be teleconnected to North Atlantic and Pacific SST. The uninitialised CMIP3 ensemble that includes volcanic aerosols reproduces these variations as well, but the models without volcanic aerosols do not. It therefore remains an open question whether initialisation improves predictions of Sahel rainfall.

The modelled trends agree well with observations in the global mean, but the agreement is not so good at the local scale.

These experiments are only a first step towards decadal forecasting using non-optimised methods from seasonal forecasting. The skill assessment does not take into account the considerable biases and drift of the models. It is based on only nine or ten data points and hence suffers from large statistical uncertainties. Larger ensembles sizes per model and more frequent and earlier starting dates will be required to characterise the skill of decadal forecasts better. The verification of decadal hindcasts can then be used to improve the climate models, their forcings and initialisation procedures to give more reliable and skilful climate forecasts.

The authors should be commended for focusing on this assessment of predictability. We need more such excellent studies! 

I learned about this interview with Michael Mann

Michael Mann Defends Climate Computer Models

from Judy Curry’s post

Week in review 1/13/12

The text is below with highlights added and my comments inserted at several places in the text.  As I discuss below, Mike is misleading in his defense of multi-decadal climate models predictions as a robust scientific tool to forecast changes in climate statistics decades from now.

The interview starts with highlight added]

Penn State climate modeler Michael Mann talks about what computer models can tell us–and what they don’t need to. David Biello reports

Fair warning: the following is more than 60 seconds, and it’s about climate change.

“Even in high school my idea of a good time was sitting in front of a computer and solving problems.” Climatologist Michael Mann. “And that has always been true. I love using computational methods to learn about the way, hopefully, the way the world actually works.”

Some critics, such as physicist Freeman Dyson, charge that climate change science relies too much on such computer models. And even worse, that the climate scientists behind them are too much in love with their computational creations. Such mathematical approximations are crude, failing to capture the real world climate impacts of a cloud, for example. That makes them useful for understanding climate but not for predicting climate change, Dyson has argued. I asked Mann in a recent phone interview how he responded to such arguments.

My Comment:   Freeman Dyson is 100% correct.  As an example of this adoration of climate modeling, below is a quote from the 2006 report CCSP 1.1. in the Executive Summary

Although the majority of observational data sets show more warming at the surface than in the troposphere, some observational data sets show the opposite behavior. Almost all model simulations show more warming in the troposphere than at the surface. This difference between models and observations may arise from errors that are common to all models, from errors in the observational data sets, or from a combination of these factors. The second explanation [i.e. "errors in the observational data sets"] is favored, but the issue is still open.

As indicated by that quote, the preference is to believe the models over real-world observations. That is backwards thinking!  At least they accept that the issue is still open.

The Scientific American interview continues

“I have to wonder if Freeman Dyson will get on an airplane or if he’ll drive a car because a lot of the modern day conveniences of life and a lot of our technological innovations of modern life are based on phenomena so complicated that we need to be able to construct models of them before we deploy that technology.

My Comment: Mike does not properly distinguish between the types of modeling. When airplanes or cars are built, the engineers are testing their models using real world airplanes and cars, as well as with wind tunnel evaluations. They can ground-truth their models.

With respect to atmospheric modeling, numerical modeling prediction of the weather for the coming days is ground-truthing, as the forecasts can be compared with real-world observations just a few days later.

With multi-decadal climate predictions, they can only realistically be tested from past climate conditions, unless we wait for the coming decades to pass. Even in the hindcast mode, however, the global climate models (whether downscaled to regions or not) have failed to predict changes in the statistics of regional climate. I invite any climate scientist to present evidence on my weblog (as an unedited guest post] that refutes this conclusion.

The interview continues

“In the case of the climate, of course, there is only one Earth, so we can’t do experiments with multiple Earths and formulate the science of climate change as if it’s an entirely observationally based, controlled experiment. We need to rely on conceptual models of the system we’re studying and it’s no different in any other field of science. In fact, the way science progresses is by conceptual models being put forward and then testing them against observations. One of the most, I think, striking examples of that was just within the last month, this announcement, the Higgs Boson.

“Its existence was predicted by the standard model of particle physics and the fact that there’s—we got a glimpse of it, it looks like it may very well be there—is a real victory for that model of science where you test, you put forward conceptual models of the way the world or the universe works and test those models against the observations and see the extent to which they can predict new observations and when they do, it gives you increased confidence in the models.

“It’s no different in the case of climate change.  The models are simply at some level a formulation of our conceptual understanding and when someone says they don’t like models then I’m wondering what alternative they have in mind.

My Comment: Mike is in error.  With the Higgs Boson, its existence (the theory) is being tested against real world data. With the prediction of climate change,  even with coarse metrics such as the magnitude of global warming as diagnosed by changes in the heat content of the climate system, these global average forecasts on the verge of failing (e.g. see)!  With respect to the prediction of multi-decadal changes in regional climate statistics, which are needed by the impact community, these models have failed so far to show any skill.

The Scientific american interview continues

“How do they formalize their conceptual understanding? Through back-of-the-envelope, poorly conceived thought experiments?  It’s somewhat bewildering when I hear something like that from a premier scientist, and I think it belies a misunderstanding of the way models are used. In climate science, for example, where we don’t need an elaborate climate model to understand the basic physics and chemistry of greenhouse gases, so at some level the fact that increased CO2 warms the planet is a consequence of very basic physics and chemistry.

My Comment:   Mike is correct - ”we don’t need an elaborate climate model to understand the basic physics and chemistry of greenhouse gases, so at some level the fact that increased CO2 warms the planet is a consequence of very basic physics and chemistry.”  However, Mike misses the point that this knowledge of physics does not then result in skillful global and regional predictions of changes in climate statistics.  The climate system is much more than just changes in the atmospheric concentration of CO2 and a few other greenhouse gases.  Mike is misunderstanding “the way models are used“.  He is confusing tested and verified model predictions with unverified model results.

The interview continues

“The details, how much warming you get, depend on things like feedbacks. And you can’t incorporate feedbacks through a back of the envelope approach.  You actually have to critically think about the interactions that take place in this very complex system. And those feedbacks ultimately determine the extent to which that initial warming will be amplified, but they don’t even change the fact that you elevate greenhouse gas concentrations in the atmosphere and you’ll get a warming of the surface. That’s basic physics and chemistry and people who claim that they don’t believe that, they don’t believe we’re warming the planet through increasing CO2 levels because of climate models, they don’t understand the fact that you don’t need a climate model to come to that conclusion. It’s basic physics and chemistry.

My Comment:   Mike is arguing about an issue that is not in disagreement!  Of course, if you add greenhouse gases, there is a radiative warming effect. However, its magnitude is relatively small unless there is a significant positive radiative feedback from added water vapor. It is this feedback, which involves the entire hydrologic cycle that is still so poorly understood; e.g. see

Stephens, G. L., T. L’Ecuyer, R. Forbes, A. Gettlemen, J.‐C. Golaz, A. Bodas‐Salcedo, K. Suzuki, P. Gabriel, and J. Haynes (2010), Dreary state of precipitation in global models, J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.

The interview continues

“The climate models come in because we wanna know how that’s modified by feedback.  What are the important feedbacks?  How will atmospheric circulation patterns change? And again, does Freeman Dyson, assuming he is willing to get on an airplane even though models have been used to test the performance of the airplane, assuming he does and he knows he’s going somewhere where they’ve predicted, where weather models have predicted rainfall for the next seven days, does he not pack his umbrella because he doesn’t believe the models? It’s just in that case the worst that will happen is somebody gets wet when they wouldn’t otherwise have. In this case, the worst that can happen is that we ruin the planet.”

—David Biello

My Comment:  Mike is misleading in his answer. As I wrote earlier, the ability of an airplane to fly and of a weather forecast days from now is tested against real data! Climate predictions over decadal time periods, in contrast, when tested in a hindcast mode, are failing to provide skillful forecasts. In fact they are misleading policymakers in their decision making.  Mike is misleading readers when he equates testable predictions which have been confirmed with real world observations with predictions which have failed to show any skill.  He implicitly recognizes this, as of yet lack of skill with the models when he writes “What are the important feedbacks?  How will atmospheric circulation patterns change?”   Indeed, it these are two major issues we still do not understand and Mike should have emphasized that.

As written in the Scientific American Interview, Freeman Dyson is 100% correct

  “that climate change science relies too much on such computer models. And even worse, that the climate scientists behind them are too much in love with their computational creations. Such mathematical approximations are crude, failing to capture the real world climate impacts of a cloud, for example. That makes them useful for understanding climate but not for predicting climate change”

It is an open question as to how long it is going to take funding agencies and policymakers to recognize this reality.

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UPDATE JANUARY 26 2012:  An update to the meeting is given at [h/t to Marc Morano]

Presentation by global warming skeptics draws big crowd in Portland

Don Bishop has alerted us to an article by Scott Learn of The Oregonian titled

*************************Original Post*****************************

Global warming skeptics to take center stage in Portland (poll)

The article refers to a meeting tomorrow evening in Portland. The article starts with the text [highlight added]

When it comes to global warming, the American Meteorological Society has strong views: “Human activities are a major contributor to climate change,” the society says, and “increases in greenhouse gases are nearly certain to produce continued increases in temperature.”

But at 7 p.m. Wednesday, the society’s Oregon chapter will give three opponents of those propositions their biggest stage, two hours before an expected audience of several hundred in a ballroom at the Portland Airport Shilo Inn.

The chapter’s invitation asks the question: “Is human caused global warming the greatest scientific myth of our generation?”

The article contains misinformation from some of those quoted; e.g.

Justin Sharp, a meteorologist for wind-power firm Iberdrola Renewables, declined to renew his membership in the local chapter. There are legitimate uncertainties to discuss about climate projections, he says.

“But devoting equal time on all subject matters just doesn’t make a lot of sense,” says Sharp, who adds that he is not speaking for Iberdrola. “If you had a panel with both sides represented in proportion to what the field believes, you’d have 900 scientists on one side and George (Taylor) on the other.”

Justin misses the critical point that there is a diversity of views on the climate issue, as illustrated by the article

Pielke Sr., R., K.  Beven, G. Brasseur, J. Calvert, M. Chahine, R. Dickerson, D.  Entekhabi, E. Foufoula-Georgiou, H. Gupta, V. Gupta, W. Krajewski, E.  Philip Krider, W. K.M. Lau, J. McDonnell,  W. Rossow,  J. Schaake, J.  Smith, S. Sorooshian,  and E. Wood, 2009: Climate change: The need to consider human forcings besides greenhouse gases.   Eos, Vol. 90, No. 45, 10 November 2009, 413. Copyright (2009) American   Geophysical Union.

which was co-authored by only Fellows of the American Geophysical Union (and only 0.1% of the members receive this honor). George Taylor, who is a very well-respected climate scientist, is far from alone in his concerns is to how the climate issue is being misrepresented.

The news article has a poll with the following questions

Is human-caused global warming the greatest scientific myth of our generation?

• Yes. Human-caused global warming is a myth.
• No. Human-caused global warming is based in fact.
•  I’ll take no formal position, like the Oregon Chapter of the American Meteorological Society.

This is a ridiculous poll as almost all climate scientists agree that the human addition of CO2 and other greenhouse gases into the atmosphere has a radiative warming effect. The substantive issues, however, that are naively ignored in these poll questions include, in terms of how weather patterns are affected, what is the effect of CO2 radiative forcing relative to other human and natural radiative forcings, as well as the role of negative and positive radiative feedbacks. Indeed, radiative forcing is just one of a range of climate forcings (e.g. the role human aerosol emissions on cloud and precipitation processes) as discussed in detail 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.

A much better set of questions to ask tomorrow evening are:

• Hypothesis 1: Human influence on climate variability and change is of minimal importance, and natural causes dominate climate variations and changes on all time scales. In coming decades, the human influence will continue to be minimal.
• Hypothesis 2a: Although the natural causes of climate variations and changes are undoubtedly important, the human influences are significant and involve a diverse range of first-order climate forcings, including, but not limited to, the human input of carbon dioxide (CO2). Most, if not all, of these human influences on regional and global climate will continue to be of concern during the coming decades.
• Hypothesis 2b: Although the natural causes of climate variations and changes are undoubtedly important, the human influences are significant and are dominated by the emissions into the atmosphere of greenhouse gases, the most important of which is CO2. The adverse impact of these gases on regional and global climate constitutes the primary climate issue for the coming decades.

The Pielke et al 2009 paper provides evidence why hypothesis 2a is the only one that has not been refuted. However, this would be a much more appropriate poll for the Oregonian to run than the poll that is in their newspaper. The paper however, should be commended for at least permitting a much needed debate on the climate issue.

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Suryachandra A. Rao, Ashish R. Dhakate, Subodh K. Saha, Somnath Mahapatra, Hemantkumar S. Chaudhari, Samir Pokhrel and Sobhan K. Sahu, 2012, Why is Indian Ocean warming consistently?
Climatic Change Volume 110, Numbers 3-4, 709-719, DOI: 10.1007/s10584-011-0121-x

The abstract reads [highlight added]

“Observations have shown that the Indian Ocean is consistently warming and its warm pool is expanding, particularly in the recent decades. This paper attempts to investigate the reason behind these observations. Under global warming scenario, it is expected that the greenhouse gas induced changes in air–sea fluxes will enhance the warming. Surprisingly, it is found that the net surface heat fluxes over Indian Ocean warm pool (IOWP) region alone cannot explain the consistent warming. The warm pool area anomaly of IOWP is strongly correlated with the sea surface height anomaly, suggesting an important role played by the ocean advection processes in warming and expansion of IOWP. The structure of lead/lag correlations further suggests that Oceanic Rossby waves might be involved in the warming. Using heat budget analysis of several Ocean data assimilation products, it is shown that the net surface heat flux (advection) alone tends to cool (warm) the Ocean. Based on above observations, we propose an ocean-atmosphere coupled positive feedback mechanism for explaining the consistent warming and expansion of IOWP. Warming over IOWP induces an enhancement of convection in central equatorial Indian ocean, which causes anomalous easterlies along the equator. Anomalous easterlies in turn excite frequent Indian ocean Dipole events and cause anti-cyclonic wind stress curl in south-east and north-east equatorial Indian ocean. The anomalous wind stress curl triggers anomalous downwelling oceanic Rossby waves, thereby deepening the thermocline and resulting in advection of warm waters towards western Indian ocean. This acts as a positive feedback and results in more warming and westward expansion of IOWP.”

As the authors succinctly conclude in the final section of their paper

“This study explains the consistent warming in last two decades.”

My only issue with their study that the Indian Ocean has not been consistently warming. At the top of this post is the latest surface temperature anomaly analysis for this region. While parts of the southern part of the ocean are warmer than average, the northern part is not. In the Rao et al 2012 paper shows no warming since the late 1990s – and perhaps even earlier in the 1990s (see their Figure 1).

In any case, warming that has occurred in this region can be explained by regional circulation changes, and while human climate forcings certainly could be playing an important role (e.g. the heterogeneous heating from black carbon – see Figure 1 bottom in Matsui and Pielke, 2006), a global average warming is not the most important effect (if it has any appreciable effect at all). Moreover, the multi-decadal global model predictions, to my knowledge,  have failed to skillfully predict the changes observed in the Indian Ocean.

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Temperature trends: MSU vs. an atmospheric model

There is a healthy debate on this post (of which we need much more of!) that includes an insightful response which was just posted from John Christy of the University of Alabama at Huntsville

Isaac:

Someone directed me to your interesting post.  I have some comments as this is a topic with which I’m all too familiar.

1. In several papers, summarized in Christy et al. 2010, we and others investigated the accuracy of the various tropical upper air temperature datasets in detail.  It was shown that RSS contained spurious tropical warming in the 1990s due to the overcorrection for the diurnal cooling that characterizes the drifting afternoon spacecraft.  RSS was clearly the outlier (see Fig. 4.)  Thus, using RSS as the comparison does not represent the real observational evidence and portrays too much apparent agreement.

2. The comparison in the posting above does not contradict the evidence in our papers that the CMIP3 models overstate the amplification ratio.  The HiRAMC180 comparison is using a model, but tightly constrained by real temperatures, i.e. an AMIP style run. The CMIP3 coupled model runs show more warming than actually occurred in this time period (globally about twice too much) with a tropical amplification factor around 1.37 (ratio of trends Tlt/Tsfc, see Fig 10 in Christy et al.)

3. In the runs of your model I see a TLT trend of +0.148 C/decade for 1979-2009.  Observational tropical trends, as published, are +0.09 +/- 0.03 C/decade, producing an amplification ratio of 0.8 +/- 0.3 (Christy et al. 2010.)  The HiRAMC180 model indicates a scaling ratio (using trend of Tsfc as +0.12 C/decade) of 1.23 – a little less than the typical GCM, but outside of the observed ratio.

4. The same comments apply to T2 (RSS has some extra warming not found in the other datasets except for STAR which was examined in detail in Christy et al. 2011 and found also to have instituted RSS’s diurnal correction, so suffers from the same problem as RSS.)  Thus the red dots in your Fig. should be accompanied by many others further to the left (see our Fig. 10).

5. With much misinformation on this issue I want to indicate that any model/observation comparisons should be normalized (i.e. such as using the amplification ratio to eliminate variations due to volcanoes and ENSOs) and use the full tropical surface temperatures (rather than say SSTs only.)

6. Perhaps the first paper that recognized the tight coupling between tropospheric layers temperatures and the surface was Christy and McNider 1994 .

Thank you for the post and the opportunity to provide information that evidently was not used in your post.

Christy et al. 2010,  What do observational datasets say about modeled tropospheric temperature trends since 1979?  Rem. Sens., 2, doi:10.3390/rs2092148.

I recommend readers visit this post on Issac Held’s weblog to follow what is a really important issue.

We have been alerted to a 2011 paper [h/t to Souleymane Fall and Dallas Staley] that provides additional evidence why the assessment of regional atmospheric circulation patterns (rather than a global average surface temperature trend) should be a primary focus of climate research. It is the multi-decadal prediction of the changes in the statistics of these large scale circulation patterns that is required to make claims of impacts from “climate change” on that time period. As has been discussed; e.g. see

Pielke Sr., R.A., R. Wilby, D. Niyogi, F. Hossain, K. Dairuku, J. Adegoke, G. Kallos, T. Seastedt, and K. Suding, 2012: 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.

there is no predictive skill on this time period.

The new paper is

Wu, Z., H. Lin, J. Li, Z. Jiang, and T. Ma (2012),  Heat wave frequency variability over North America: Two distinct leading modes,  J. Geophys. Res., 117, D02102, doi:10.1029/2011JD016908.

with the abstract [highlight added]

Seasonal prediction of heat wave variability is a scientific challenge and of practical importance. This study investigates the heat wave frequency (HWF) variability over North America (NA) during the past 53 summers (1958–2010). It is found that  the NA HWF is dominated by two distinct modes: the interdecadal (ID) mode and the interannual (IA) mode. The ID mode primarily depicts a HWF increasing pattern over most of the NA continent except some western coastal areas. The IA mode resembles a tripole HWF anomaly pattern with three centers over the northwestern, central, and southern NA. The two leading modes have different dynamic structures and predictability sources. The ID mode is closely associated with the prior spring sea surface  temperature anomaly (SSTA) in the tropical Atlantic and tropical western Pacific that can persist throughout the summer, whereas the IA mode is linked to the development of El Niño–Southern Oscillation. A simplified general circulation model is utilized to examine the possible physical mechanism. For the ID mode the tropical Atlantic SSTA can induce a Gill-type response which extends to NA, while the northwestern Pacific SSTA excites a Rossby wave train propagating eastward toward NA. These two flow patterns jointly contribute to the formation of the large-scale circulation anomalies associated with the ID mode. For the IA mode the corresponding circulation anomalies are basically similar to a Pacific-North America pattern. The subsidence associated with high-pressure anomalies warms and dries the boundary layer, inhibiting cloud formation. The resulting surface radiative  heating further warms the surface. For the low-pressure anomalies the situation is just opposite. Through such processes these SSTAs can exert profound influences on the HWF variability over NA.”

The conclusion has the summary text

“Namias [1982, 1983] found that a protracted heat wave during summer was a manifestation of an abnormal form of the general circulation. Hoskins et al. [1983] suggested a theory of positive feedback between the synoptic eddies and the seasonal mean flow. On the basis of the results in this study and those obtained by Hirschi et al. [2010] and Alexander [2010], the physical processes between the circulation anomalies and HWF may be summarized as following. The SSTAs associated with the ID and IA modes trigger the corresponding teleconnection patterns propagating toward NA and excite high- or low-pressure anomalies over the local region. The subsidence associated with high-pressure anomalies warms and dries the boundary layer, inhibiting cloud formation. The resulting surface radiative heating further warms the surface. For the low pressure anomalies, the situation is just opposite. Through such processes, these SSTAs can exert profound influences to the HWF variability over NA.”

The challenge for the IPCC community (as of yet unfulfilled) is to skillfully predict CHANGES in these circulation features.

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Update January 24 2012:  There are other insightful discussions of this paper, including

Statistics Of Loeb’s “Observed Changes In Top-Of-The-Atmosphere Radiation And Upper-Ocean Heating Consistent Within Uncertainty”

at William M. Biggs weblog and

Missing(?) heat isn’t missing after all

at Judy Curry’s weblog  Climate Etc

************************Original Post******************************
I posted earlier today on a report regarding the Earth’s heat balance; see

Missing Ocean Heat Study Reported On By Climate Wire – Response From Josh Willis

The Nature Geosciences paper referred to in that post is

Norman G. Loeb, John M. Lyman, Gregory C. Johnson, Richard P. Allan, David R. Doelling, Takmeng Wong, Brian J. Soden & Graeme L. Stephens, 2012:
Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty. Nature Geoscience (2012)doi:10.1038/ngeo1375

An interesting conclusion of that paper is that

We combine satellite data with ocean measurements to depths of 1,800 m, and show that between January 2001 and December 2010, Earth has been steadily accumulating energy at a rate of 0. 50  +/0.43  Watts per meter squared (uncertainties at the 90% confidence level). We conclude that energy storage is continuing to increase in the sub-surface ocean.

This is larger than is justified based on the upper ocean heat changes, as has been discussed by myself and others (e.g. Knox and Douglas 2010).  The heat accumulation they refer to also has hardly been “steady”. However, lets just use these values.

Jim Hansen concluded in 2005 that the decadal mean planetary energy imbalance at the end of the 1990s was

,…..0.85 Watts per meter squared is the imbalance at the end of the decade.”

This value falls within the uncertainty range of the Leob et al 2012 study.  However, we are 13 years since the end of the 20th century, so Jim Hansen’s value for the imbalance must be larger (~0.95 Watts per meter squared from GISS?).

This question about whether or not the IPCC model predictions (as represented by the GISS models) are still consistent even with the large Loeb et al estimate should have been a major part of their article.  The Loeb et al 2012 even cited the Hansen paper but did not take the next step and complete model and observational comparisons. That the IPCC models are close to being refuted with respect to the magnitude of global warming even with the large Loeb et al values is an unspoken result of their findings. They missed a major implication from their results.

source of image

source of image – NOAA’s PMEL [note the data have not yet been updated for the last couple of years]

There is a Climate Wire report on a new Nature Geosciences paper.

The Climate Wire article reads [highlight added]

 Researchers puzzle over measurements of ocean-stored heat  (Monday, January 23, 2012)

Lauren Morello, E&E reporter

Earth’s “missing heat” might not be missing after all.

That’s the conclusion of a new study that examines how accurately satellites and floating ocean instruments track the flow of energy from the sun to Earth and back again.

Those measurements are at the heart of a puzzle climate scientists have been trying hard to crack: why, as greenhouse gas emissions rose and satellite data showed an increasing amount of energy trapped in the planet’s atmosphere, the amount of heat absorbed by the world’s oceans — a major heat sink — wasn’t rising as quickly.

One answer to the puzzle came from climate scientists Kevin Trenberth and John Fasullo of the National Center for Atmospheric Research, who coined the term “missing heat” — and later suggested it may be stored in the deep ocean, where there are few measurements to track the energy’s path.

But new research, published yesterday in the journal Nature Geoscience, argues that what Trenberth and Fasullo dubbed “missing heat” isn’t missing, after all — that the amount of radiation trapped in Earth’s atmosphere, as measured by satellite sensors, is consistent with measurements of heat absorbed by the ocean.

Any discrepancy falls within the margin of error on those measurements, say the study’s authors, led by NASA climate scientist Norman Loeb.

Part of the problem, Loeb said, is that the margin of error on the ocean measurements is large, a legacy of the early 2000s switch from an instrument originally developed in the the 1960s — the expendable bathythermograph, or XBT — to the more accurate Argo float.

Today, roughly 3,200 Argos are traveling the world’s oceans, collecting data as they repeatedly sink to prescribed depths, pop back up again and transmit the information they’ve collected to waiting satellites.

Diving into uncertainty

“Given that there’s a lot of uncertainty in the ocean measurements, given that there was this transition from XBT to Argo right around the time that satellite data and ocean data deviated, it raises a lot questions in my mind about whether you can say there is missing energy,” Loeb said.

His analysis examining the amount of solar radiation entering and leaving the atmosphere estimates the heat content of the upper ocean using three different data sets.

Loeb’s conclusion? That, if you consider the margin of error on the satellite and ocean measurements, the two data sources are in agreement — and there may not be any “missing energy.”

“It’s not to say that it’s not happening,” Loeb said. “It’s just that you can’t easily make that conclusion from the data.”

Not so fast, says Trenberth. “One of the key points of our paper was, when you try to do this inventory and things didn’t add up, if you take things at face value, that is an indicator by itself that the error bars are very large,” Trenberth said. “We were very aware of that — but they shouldn’t be that large.”

Trenberth said he also believes Loeb overestimated the error bars for the satellite data, which show the potential margin of error for those measurements.

But both scientists agree that the ongoing debate over the accounting of Earth’s energy budget demonstrates the need to improve monitoring of the Earth’s climate and to better understand sources of error in older measurements, like the ocean data collected for decades by XBTs.

“There are at least 10 estimates of upper ocean heat content,” Trenberth said. “They are all over the place, in spite of the fact that we have the best ocean observing system, with Argo floats, that we’ve ever had.”

My Request To Josh Willis of JPL for a response [Josh, as most of you already know, is an internationally well-respected expert on ocean heat content analyses]

Hi Josh

Would you be willing to comment on this for my weblog?

Roger

Josh’s Response

Hi Roger,

You bet.  You can post these comments on your blog.  However, since I comment on Kevin’s quote, perhaps you could be sure to include the paragraph below in its entirety.

I think that the Loeb et al. paper is an important step forward in our understanding of the Earth’s energy balance and our ability to observe it. As I have said for some time, I think a fair accounting of the uncertainties in the observations would cast serious doubt on the “missing heat” hypothesis, and I think the Loeb et al. paper confirms that.  I also disagree with Trenberth’s comment that the estimates of ocean warming are all over the place.  All the estimates that I am aware of agree quite well over the period from 2005 to the present, which is dominated by the Argo data.  It is true, however, that there are still large uncertainties for the period before 2005 due to unresolved biases in the XBT data.  But even with these biases, it is still possible to see the human-caused signal over a long enough period of time–like 15 to 20 years.

Hope this helps.

Cheers, Josh

My Comment: 

As I have urged in my papers

Pielke Sr., R.A., 2003: Heat storage within the Earth system. Bull. Amer.  Meteor. Soc., 84, 331-335.

Pielke Sr., R.A., 2008: A broader view of the  role of humans in the climate system. Physics Today, 61, Vol. 11, 54-55.

the assessment of ocean heat content changes is the robust approach to diagnose climate system heat changes (global warming and cooling). The ocean itself does the time and space integration needed to diagnose the accumulation or loss of heat to the climate system over time. Radiative fluxes as viewed from space is a much more difficult way to diagnose this heating. We should have the most confidence in the upper ocean data, particularly since 2005, as Josh reports.

The papers [with more on the way by these internationally well-respected climate scientists]

R. S. Knox, David H. Douglass 2010: Recent energy balance of Earth  International Journal of Geosciences, 2010, vol. 1, no. 3 (November) – In press doi:10.4236/ijg2010.00000.

D.H. Douglass. The Pacific sea surface temperature.  Physics Letters A (2011). doi:10.1016/j.physleta.2011.10.042

provide quantitative examples of the value of using this ocean data in order to improve our understanding of the climate system.