Monthly Archives: August 2007

December 2007 Session ‘The “Divergence Problem’ In Northern Forests

This is a copy of the e-mail sent to a number of scientists about an important upcoming meeting. I made a suggestion to add a topic, which I have included at the end of this weblog. The topic of the ‘divergence problem” was discussed on Climate Science; see

A New Paper On The Differences Between Recent Proxy Temperature And In-Situ Near-Surface Air Temperatures

“Dear colleagues,

We would like to encourage you to submit an abstract to the following session for the fall AGU meeting to be held in San Francisco, CA from Dec 10-14, 2007. Please also pass on to any interested parties. This is for the Paleoclimatology and Paleoceanography session PP04 – The “Divergence Problem” in Northern Forests.

Go to for the most recent program listing and the abstract submission tool. The abstract deadline is September 6, 2007.

The session abstract is:

The “Divergence Problem” in Northern Forests

An anomalous reduction in forest growth indices and temperature sensitivity has been detected in tree-ring width and density records from many circumpolar northern latitude sites in recent decades. This phenomenon, also known as the “divergence problem”, is often expressed as an offset between warmer instrumental temperatures and their underestimation in reconstruction models based on tree rings. The divergence problem has potentially significant implications for large-scale patterns of forest growth, the development of paleoclimatic reconstructions based on tree-ring records from northern forests, and the global carbon cycle. The causes of this phenomenon, which appear to be several and sometimes regionally specific, are not well understood and are difficult to test due to the existence of a number of covarying environmental factors that may potentially impact recent tree growth. Although limited evidence suggests that the divergence may be anthropogenic in nature and restricted to the recent decades of the 20th century as well as higher latitudes, one current challenge is to confirm these observations. We welcome papers that address this issue using tree rings, remote sensing, vegetation models, and other methods.


Some issues to keep in mind:

1.. The “Divergence Problem” (DP) is not noted at all sites. Which regions in the Northern Hemisphere show this problem and which do not. Why are some regions affected worse than others? Are there species specific issues to consider as well?

2.. Has the DP been observed in the Tropics or Southern Hemisphere?

3.. Implications for palaeoclimate reconstruction

4.. Implications for forests as carbon sinks

5.. Is the DP restricted to only temperature sensitive tree-ring chronologies, or has anyone noted it in precipitation sensitive TR series as well.

6.. How about tree-ring isotopic data? Is the DP observed in any isotopic series? If not, could isotopic series aid the identification of the DP in the past?

7.. Can the use of forward modeling approaches aid the identification of the DP as well as explore reasons for it?

8.. In some regions the DP may be physically observed as a browning of the needles. Can remote sensing identify regions where DP is occurring?

9.. And of course, causes of the DP. Anthropogenic vs. natural reasons etc etc.

A critical mass of 20 abstracts is likely needed for this session to be oral in nature. There are, however, no guarantees though. The more the merrier!!

We look forward to your submissions and seeing you all in December.

Best regards,

Rob Wilson and Rosanne D’Arrigo

My reply is

“Dear Drs. Wilson and D’Arrigo

Thank you for your announcement and invitation for this very important
session. While I will not be able to attend the AGU Conference this
December, I did want to e-mail to encourage you to add another topic to
your list of questions. This is

How accurately does the in-situ (station data), when used to construct the
regional temperature trends, compare with the tree-ring data that are used
represent the actual temperature environment in which the trees grow?
Also, is the statistical relationship improved when the comparision with
the tree ring derived data is compared with maximum and minimum
temperatures, as well as different temperature measures of the growing
season, such as first and last date below selected threshold temperatures.

For the growing set of documentation of the USHCN sites, the siting of the
in-situ temperature measurement sites is a major problem (see and A
presentation of photographs for the surface temperature stations that are
used as part of the calculation of the temperature trends for each region
might be very insightful. Satellite derived surface temperatures (e.g. see
Comiso, 2006: Weather. pages 70-76) can be very helpful also in this
assessment, but the interpretation to the heights that the tree responds
to is also a challenge, as well as that the satellite is not sampling on
all days.

The testing of the robustness of the air temperature data trends would be
quite informative, and the availability of these photographs would be

Best Regards


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Weblog Back Up!

Our site has been down for several days as many of you have noticed and emailed us to inquire about. We had a power outage last Friday and problems following that, but we are back up this morning and back on track.

thanks! dallas

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New Paper On The Diagnosis and Significance Of Ocean Heat Content Changes

A new paper has appeared that further documents the value of using ocean heat trends to diagnose global climate heat system changes, which we have identified as being the most accurate way to diagnose global warming and cooling;

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

The paper is

Schwartz, S. E. 2007: Heat capacity, time constant, and the sensitivity of Earth’s climate system. JRG. accepted.

The abstract reads,

“The equilibrium sensitivity of Earth’s climate is determined as the quotient of the relaxation time constant of the system and the pertinent global heat capacity. The heat capacity of the global ocean, obtained from regression of ocean heat content vs. global mean surface temperature, GMST, is 14 ± 6 W yr m-2 K-1, equivalent to 110 m of ocean water; other sinks raise the effective planetary heat capacity to 17 ± 7 W yr m-2 K-1 (all uncertainties are 1-sigma estimates). The time constant pertinent to changes in GMST is determined from autocorrelation of that quantity over 1880-2004 to be 5 ± 1 yr. The resultant equilibrium climate sensitivity, 0.30 ± 0.14K/(W m-2), corresponds to an equilibrium temperature increase for doubled CO2 of 1.1 ± 0.5 K. The short time constant implies that GMST is in near equilibrium with applied forcings and hence that net climate forcing over the twentieth century can be obtained from the observed temperature increase over this period, 0.57 ± 0.08 K, as 1.9 ± 0.9 W m-2. For this forcing considered the sum of radiative forcing by incremental greenhouse gases, 2.2 ± 0.3 W m-2, and other forcings, other forcing agents, mainly incremental tropospheric aerosols, are inferred to have exerted only a slight forcing over the twentieth century of -0.3 ± 1.0 W m-2.”

This paper provides a valuable assessment that needs to be performed by others. Other questions remain, of course, such as whether the surface temperature data used in such studies is robust; i.e. see

Pielke Sr., R.A., C. Davey, D. Niyogi, S. Fall, J. Steinweg-Woods, K. Hubbard, X. Lin, M. Cai, Y.-K. Lim, H. Li, J. Nielsen-Gammon, K. Gallo, R. Hale, R. Mahmood, S. Foster, R.T. McNider, and P. Blanken, 2007: Unresolved issues with the assessment of multi-decadal global land surface temperature trends. J. Geophys. Res. in press.

which will affect the evaluation of climate sensitivity.

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General Notice On Tone Of Comments

We continue to receive comments with insults and name calling. I have reluctantly posted some despite some of this, because there was substance in part of the comments. From now on, however, there will be a no tolerance policy, and regardless of the merit of a comment, it will not be posted if there is any of this objectionable personal attack material in them. They detract very significantly from any scientific substance that is present.

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New Paper On The Assessment Of Relative Risks Of Future Damage From Tropical Cyclones

There is a new paper on the relative role of society and climate change with respect to future damages from tropical cyclones. This study fits within the concept of the need to quantify the vulnerability of social and environmental threats to important resources that is one of the major themes on Climate Science.

The paper is

Pielke, R.A. Jr., 2007; Future economic damage from tropical cyclones: sensitivities to societal and climate changes, Phil. Trans. R. Soc. A doi:10.1098/rsta.2007.2086 Published online

The abstract reads,

“This paper examines future economic damages from tropical cyclones under a range of assumptions about societal change, climate change and the relationship of climate change to damage in 2050. It finds in all cases that efforts to reduce vulnerability to losses, often called climate adaptation, have far greater potential effectiveness to reduce damage related to tropical cyclones than efforts to modulate the behaviour of storms through greenhouse gas emissions reduction policies, typically called climate mitigation and achieved through energy policies. The paper urges caution in using economic losses of tropical cyclones as justification for action on energy policies when far more potentially effective options are available.”

A very positive review in Science has also just appeared by Nathan E. Hultman entitled To Arbitrate or to Advocate? on Roger’s book

The Honest Broker Making Sense of Science in Policy and Politics

by Roger A. Pielke Jr. Cambridge University Press, Cambridge, 2007 198 pp.

The paper in the Philosophical Transactions of the Royal Society is an excellent example of applying the perspective presented in the Cambridge University Press book.

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Two Studies That Document Further Why We Need A Regional Focus to The Study of Climate Variability and Change.

There are two new studies that assess climate variability and change with a regional focus, as was recommended 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.

The first one is

Tsonis A. A., K. Swanson, S. Kravtsov (2007), A new dynamical mechanism for major climate shifts, Geophys. Res. Lett., 34, L13705, doi:10.1029/2007GL030288. [thanks to Bill Nichols for alerting us to the paper].

The abstract reads,

“We construct a network of observed climate indices in the period 1900–2000 and investigate their collective behavior. The results indicate that this network synchronized several times in this period. We find that in those cases where the synchronous state was followed by a steady increase in the coupling strength between the indices, the synchronous state was destroyed, after which a new climate state emerged. These shifts are associated with significant changes in global temperature trend and in ENSO variability. The latest such event is known as the great climate shift of the 1970s. We also find the evidence for such type of behavior in two climate simulations using a state-of-the-art model. This is the first time that this mechanism, which appears consistent with the theory of synchronized chaos, is discovered in a physical system of the size and complexity of the climate system.”

Their methodoloy of analysis is introduced as

“First we construct a network from four major climate indices. The network approach to complex systems is a rapidly developing methodology, which has proven to be useful in analyzing such systems’ behavior [Albert and Barabasi, 2002; Strogatz, 2001]. In this approach, a complex system is presented as a set of connected nodes. The collective behavior of all the nodes and links (the topology of the network) describes the dynamics of the system and offers new ways to investigate its properties. The indices represent the Pacific Decadal Oscillation (PDO), the North Atlantic Oscillation (NAO), the El Niño/Southern Oscillation (ENSO), and the North Pacific Oscillation (NPO) [Barnston and Livezey, 1987; Hurrell, 1995; Mantua et al., 1997; Trenberth and Hurrell, 1994]. These indices represent regional but dominant modes of climate variability, with time scales ranging from months to decades.”

The conclusions read,

“The above observational and modeling results suggest the following intrinsic mechanism of the climate system leading to major climate shifts. First, the major climate modes tend to synchronize at some coupling strength. When this synchronous state is followed by an increase in the coupling strength, the network’s synchronous state is destroyed and after that climate emerges in a new state. The whole event marks a significant shift in climate. It is interesting to speculate on the climate shift after the 1970s event. The standard explanation for the post 1970s warming is that the radiative effect of greenhouse gases overcame shortwave reflection effects due to aerosols [Mann and Emanuel, 2006]. However, comparison of the 2035 event in the 21st century simulation and the 1910s event in the observations with this event, suggests an alternative hypothesis, namely that the climate shifted after the 1970s event to a different state of a warmer climate, which may be superimposed on an anthropogenic warming trend.”

Their study provides quantitative substance to what Climate Science has concluded, that

“The needed focus for the study of climate change and variability is on the regional and local scales. Global and zonally-averaged climate metrics would only be important to the extent that they provide useful information on these space scales”,


“Global and zonally-averaged surface temperature trend assessments, besides having major difficulties in terms of how this metric is diagnosed and analyzed, do not provide significant information on climate change and variability on the regional and local scales”.

This has also been discussed elsewhere on Climate Science; e.g. see

What is the Importance to Climate of Heterogeneous Spatial Trends in Tropospheric Temperatures?

The second new study that further supports this view that a regional focus is needed in order to improve our understanding of natural- and human- caused climate variability and change will be presented in an August 23 2007 NCAR seminar

“Ocean Influences on Recent Continental Warming” by Prashant Sardeshmukh of CIRES University of Colorado at Boulder.

The abstract for the talk reads,

“Evidence will be presented that the worldwide warming of the continents in the last several decades has occurred largely in response to a worldwide warming of the oceans rather than as a direct response to increases of greenhouse gases (GHGs) over the continents. Atmospheric General Circulation Model simulations of the last half century with
prescribed observed oceanic temperature changes, but without prescribed GHG changes, account for most of the continental warming. The oceanic influence has occurred through hydrodynamic-radiative teleconnection mechanisms, primarily by moistening the air over the continents and increasing the downward longwave radiation at the surface. The oceans may themselves have warmed from a combination of natural and anthropogenic influences.”

Clearly, these two significant research studies should further illustrate the serious limitations of the 2007 IPCC Reports which focused on downscaling global average radiative forcings as the main way to describe climate change, rather than developing an improved understanding of the observed regional variations in climate, such as we reported in

Chase, T.N., R.A. Pielke Sr., J.A. Knaff, T.G.F. Kittel, and J.L. Eastman, 2000: A comparison of regional trends in 1979-1997 depth-averaged tropospheric temperatures. Int. J. Climatology, 20, 503-518.

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New Observational Paper On Surface Temperature Trends In the USA

We have submitted a new paper that documents the important role of vegetation on long term near surface temperature trends, as well as the actual observed trends that have been occurring. The paper is

Fall, S., D. Niyogi, R.A. Pielke Sr., A. Gluhovsky, and E. Kalnay, 2007: Impacts of land surface properties on temperature trends using North American regional reanalysis over the USA. Int. J. Climatol., submitted,

with the abstract

“Recent studies have confirmed the impacts of land surface processes on surface temperature trends. We investigate this relationship over the conterminous United States (CONUS) by using the observation minus reanalysis (OMR) approach. We derive OMR trends for the 1979-2003 period from two datasets: the US Historical Climate Network (USHCN observations), and the NCEP-NCAR North American Regional Reanalysis (NARR).

We use the mean square differences (MSDs) for the comparisons between temperature anomalies from station observations (both unadjusted and adjusted) and interpolated reanalysis data. Trends of monthly mean temperature anomalies at individual station level and over the CONUS show the agreement between USHCN and NARR and demonstrate that NARR captures the climate variability at different time scales. Temperature anomalies exhibit a spatial variability, with amplitudes increasing from south to north. As a further evaluation of spatial patterns, the RMS differences also depict a good agreement over the eastern CONUS (0.29°C to 0.6°C).

OMR trend results suggest that unlike findings from studies based on the global reanalysis, NARR often has a larger warming trend than observations, but 10-year moving window trends reveal that this situation varies considerably over time, from one station to another. Most of the warming accounts for large winter increases (adjusted USHCN: 0.46°C/decade; NARR: 0.5°C/decade), especially over the Midwest. In contrast, other seasons –in particular summer and fall- exhibit low trends. This difference in winter and other season trends is a prominent feature.

OMR trends are sensitive to land cover types. Evergreen needleleaf forests, open shrublands, bare soils and urban areas exhibit the largest warming. Moreover, the OMR method captures the trend variability of land types that are subject to seasonality (e.g. croplands, deciduous forests). Overall the results obtained with the regional reanalysis are consistent with findings from global reanalysis (R1 and ERA 40).”

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Agenda now available for August 27-29 Boulder Workshop

The agenda (draft) is now available for the upcoming Workshop to be held in Boulder from August 27-29 on Detecting the Atmospheric Response to the Changing Face of the Earth: A Focus on Human-Caused Regional Climate Forcings, Land-Cover/Land-Use Change, and Data Monitoring. We will still accept registrations, so if you are interested, please email me at All of the student travel support is gone, so only paid registrations can be accommodated.

Thanks! dallas

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Important New Paper On Cloud-Precipitation Interactions by Roy Spencer and colleagues

An important new paper has appeared that observationally documents cloud-precipitation feedbacks. It is

Spencer R. W., W. D. Braswell, J. R. Christy, J. Hnilo (2007), Cloud and radiation budget changes associated with tropical intraseasonal oscillations, Geophys. Res. Lett., 34, L15707, doi:10.1029/2007GL029698.

The abstract reads,

“We explore the daily evolution of tropical intraseasonal oscillations in satellite-observed tropospheric temperature, precipitation, radiative fluxes, and cloud properties. The warm/rainy phase of a composited average of fifteen oscillations is accompanied by a net reduction in radiative input into the ocean-atmosphere system, with longwave heating anomalies transitioning to longwave cooling during the rainy phase. The increase in longwave cooling is traced to decreasing coverage by ice clouds, potentially supporting Lindzen’s “infrared irisâ€? hypothesis of climate stabilization. These observations should be considered in the testing of cloud parameterizations in climate models, which remain sources of substantial uncertainty in global warming prediction.”

The conclusion reads,

The composite of fifteen strong intraseasonal oscillations we examined revealed that enhanced radiative cooling of the ocean-atmosphere system occurs during the tropospheric warm phase of the oscillation. Our measured sensitivity of total (SW + LW) cloud radiative forcing to tropospheric temperature is −6.1 W m−2 K−1. During the composite oscillation’s rainy, tropospheric warming phase, the longwave flux anomalies unexpectedly transitioned from warming to cooling, behavior which was traced to a decrease in ice cloud coverage. This decrease in ice cloud coverage is nominally supportive of Lindzen’s “infrared irisâ€? hypothesis. While the time scales addressed here are short and not necessarily indicative of climate time scales, it must be remembered that all moist convective adjustment occurs on short time scales. Since these intraseasonal oscillations represent a dominant mode of convective variability in the tropical troposphere, their behavior should be considered when testing the convective and cloud parameterizations in climate models that are used to predict global warming.”

This new study also provides important new insight into what modelers need to improve to accurately represent these cloud-precipitation feedbacks. During my class at the University of Colorado at Boulder last spring, one of our excellent speakers was Dr. De-Zheng Sun. In his powerpoint presentation,

Validating and Understanding Feedbacks in Climate Models,

he showed the skill of the models when sea surface temperatures (SSTs) variations over time were prescribed for input into an atmospheric global model, and when the sea surface temperatures were predicted as part of a coupled model. He and his colleagues on this study focused on the equatorial Pacific cold tongue for their analysis. They examined the response of energy fluxes to El Nino warming.

A very important result of this study is that when the SSTs were predicted, they were higher than with the observed data (see slide 17), which indicates that the models miss an important real-world negative feedback. Their conclusion is that “The models tend to overestimate the positive feedback from water vapor in El Nino warming” and “The models tend to underestimate the negative feedback from cloud albedo in El Nino warming.”

This study by Dr. Sun and colleagues is consistent with what is reported in the new paper by Dr. Spencer and colleagues. When Dr. Sun publishes this study, Climate Science intends to weblog on it again.

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Positive Feedback: Have We Been Fooling Ourselves? by Roy Spencer

There are three main points/opinions/issues I’d like to explore, which are all interrelated:

  1. The traditional way in which feedbacks have been diagnosed from observational data has very likely misled us about the existence of positive feedbacks in the climate system.
  2. Our new analyses of satellite observations of intraseasonal oscillations suggest negative cloud feedbacks, supporting Lindzen’s Infrared Iris hypothesis.
  3. I am increasingly convinced that understanding precipitation systems is the key to understanding climate sensitivity.

Unfortunately, the three of these represents too much material to present today. Since the second (Infrared Iris) results were just published by us in GRL (August 9, 2007), it would seem to be the logical one for me to discuss before the others. But the first issue is, in some sense, much more important and fundamental, and will help us put the newly published results in a more meaningful context.

So, for now, I’m going to discuss just the first issue (potential biases in feedback diagnosis) and then maybe Roger will have me back to continue with the second and third issues.

What you are about to read is, I believe, more than a little alarming. And maybe someone here will even point out the obvious error in my analysis that will render my conclusions silly and meaningless. After all, that would save me the effort of writing and submitting our next journal article, wouldn’t it? So, let’s forge ahead with the first, feedback diagnosis issue.

The Feedback Concern

Feedbacks are at the heart of most disagreements over how serious man-induced global warming and climate change will be. To the climate community, a feedback is by definition a RESULT of surface temperature change. For instance, low cloud cover decreasing with surface warming would be a positive feedback on the temperature change by letting more shortwave solar radiation in.

But what never seems to be addressed is the question: What caused the temperature change in the first place? How do we know that the low cloud cover decreased as a response to the surface warming, rather than the other way around?

For awhile, a few people had me convinced that this question doesn’t really matter. After all, cause and effect are all jumbled up in the climate system, so what’s the point of trying to separate them? Just build the climate models, and see if they behave the way we observe in nature, right?

Well, that’s true – but I think I can demonstrate that the way we have been doing that comparison is seriously misleading.

Feedbacks from observational data have traditionally been diagnosed by plotting the co-variability between top-of-atmosphere radiation budget changes and surface temperature changes, after the data have been averaged to monthly, seasonal, or annual time scales. The justification for this averaging has always remained a little muddy, but from what I can gather, researchers think that it helps to approach a quasi-equilibrium state in the climate system.

The trouble with this approach, though, is that when we average observational data to seasonal or annual time scales in our attempts to diagnose feedbacks, it turns out that there are a variety of very different physical ways to get the very same statistical relationships. (Be patient with me here, I’ll demonstrate this below).

In particular, ANY non-feedback cloud variations that cause surface temperature to change will, necessarily, look like a positive feedback — even if no feedback exists. And the time averaging that everyone employs actually destroys all evidence that could have indicated to us that we were misinterpreting the data.

I am not the first one to discuss this issue, although the way I am expressing it might be different. Graham Stephen’s 2005 J. Climate review paper on cloud feedbacks (if you read carefully) was implying the same thing. Similarly, Aires and Rossow (2003 QJRMS) presented a new method of diagnosing feedbacks, arguing that one needs to go to very short time scales in our diagnostics to have any hope of providing meaningful validation for climate models.

But the issue has not been well articulated, and I fear that many climate scientists simply haven’t understood what these few investigators were trying to get across to us. For instance, Stephens spent a lot of time discussing how clouds are very dependent upon aspects of the atmospheric circulation, not just upon surface temperature, but it took me a while before I realized the practical importance of what he was saying.

Stephens was pointing out that our diagnosis of what has caused a certain relationship in observational data depends entirely upon on how we view the climate “systemâ€?. In other words, it matters a lot what we think is causing what. Again, once you have averaged the data to seasonal or annual time scales, you have destroyed most of the information that would have allowed you to diagnose what kind of system you are looking at.

More recently, a 2006 J. Climate paper by Forster and Gregory presented equations to allow us to discuss individual terms in feedback analysis; theirs is the most thorough treatment I’m aware of in this regard. But they made a critical assumption – a claim – that sounded good at first, but upon a little reflection, I find it can not be supported. In fact, it was a single sentence that ends up totally changing the analysis of feedbacks.

Forster and Gregory included a term to represent internal variability – appropriately called an “Xâ€? term – but they claimed that, to the extent that any internal variability was uncorrelated to surface temperature change, it would not corrupt the regression slope when plotting radiation changes versus temperature changes. In other words, we’d still diagnose a good feedback number, even in the presence of internal variability.

Well, while that statement is literally true, the assumption that any internally-caused fluctuations in the radiation budget would be uncorrelated with surface temperature is not true. It is the radiation changes that CAUSE temperature change – the two cannot be uncorrelated!

So far, what I have presented is admittedly hand waving, and all of the above-mentioned investigators also addressed the problem in a hand-waving fashion. So, what to do? How do we quantitatively demonstrate something in simple terms that is also physically realistic?

I know! Let’s build a model!

A Simple Model Demonstration

So, Danny Braswell and I built a simple energy balance model based upon the global-average vertical energy flux diagram that is famously attributed to Trenberth. But our model has some enhancements. It has three time-dependent temperature equations, for (1) the ocean surface, (2) a lower atmospheric temperature that radiates downward, and (3) an upper atmospheric temperature that radiates out to space. We gave it a swamp ocean with ten times the heat capacity of the atmosphere (about 190 m deep). We found that the model equilibrates to a new energy balance state in about 5 years after an imbalance in any of the terms is imposed.

In order to demonstrate elements of the problem, we need up to three sources of temperature variability. We chose the following: (1) daily random non-cloud SST forcing (e.g. from evaporation), (2) daily random cloud forcing, and (3) cloud feedbacks on any surface temperature changes.

With these three sources of variability, we discovered we could get a wide variety of model behaviors, so I decided that we had to constrain our simulations to physically realistic ranges.

To do this, I computed from 6 years of Terra CERES tropical radiation budget data that the standard deviation of 30 day anomalies in tropical oceanic reflected shortwave (SW) was about 1.3 W m-2. So, we made model runs where the SW variability (from all cloud variations, no matter the source) produced similar 30-day statistics.

The following is a 30 year plot from one run, forced only with daily random cloud variations, and no cloud feedback. Note that yearly, and even decadal, variability in the surface temperature occurs in a random walk fashion, but one that is constrained to meander around the equilibrium SST value of 288 K (the value which is consistent with Trenberth’s energy balance numbers).


Now, when we plot this model run’s output of SW variability versus surface temperature variability (365 day averages), we get a diagnosed “feedbackâ€? parameter of -1.4 W m-2 K-1. This is very close to the average of what the IPCC AR4 models produce for their SW cloud feedback — even though we haven’t yet imposed a feedback in the model!


Furthermore, note that the explained variance is relatively low. This is just like what has been reported for “feedbacksâ€? diagnosed from observational data (Forster and Gregory, 2006 J. Climate). In contrast, when the source of the SW variability in the model is specified to be through cloud feedback, the explained variance is always very high.

In other words, it appears that low explained variance is evidence of non-feedback cloud forcing, as opposed to cloud feedback.

Finally, we also find that there is NO WAY to get anywhere near a 30 day s.d. of 1.3 W m-2 in SW variability out of the model with only cloud feedback. You must invoke non-feedback sources of cloud variability.

In other words, the large amount of variability in the CERES SW data argues for a non-feedback cloud source of SST variability.

After running many different combinations of model forcings and feedbacks, we concluded the following: To the extent that non-feedback cloud sources of SST variations occur, they ALWAYS lead to positive bias in diagnosed “feedbackâ€?. The bias is especially strong if the real cloud feedback is negative, and can easily obscure a negative cloud feedback with a diagnosed “false positiveâ€?. Note that the reason the bias is always in the direction of positive feedback is because the alternative is energetically impossible (you can’t force an SST increase by reducing SW input into the ocean).

This is indeed the general behavior I expected to find, but I needed a simple model demonstration to convince myself.

Pinatubo: A Negative Feedback “Unmaskedâ€??

Now, what we really need in the climate system is some big, non-cloud source of radiative forcing, where the cloud feedback signal is not so contaminated by the obscuring effect of cloud forcing. The only good example we have of this during the satellite era is the cooling after the 1991 eruption of Mt. Pinatubo.

And guess what? The SW cloud feedback calculation from the Pinatubo-caused variability in Forster and Gregory was – surprise, surprise! – anomalously negative, rather than positive like all of their other examples of feedback diagnosed from interannual variability!


I think it is time to provoke some serious discussion and reconsideration regarding what we think we know about feedbacks in the real climate system, and therefore about climate sensitivity. While I’ve used the example of low cloud SW feedback, the potential problem exists with any kind of feedback.

For instance, everyone believes that water vapor feedback is positive, and conceptually justifies this by saying that a warmer surface causes more water to evaporate. But evaporation is only half the story in explaining the equilibrium concentration of atmospheric water vapor; precipitation is the other half. What if a decrease in precipitation efficiency is, instead, the cause of the surface warming, by not removing as much water vapor from the atmosphere? Then, it would be the water vapor increase driving the surface temperature change, and this would push the (unknown) diagnosed water vapor feedback in the positive direction.

Of course, researchers still have no clue about what control precipitation efficiency, although our new GRL paper suggests that, at least in the case of tropical intraseasonal oscillations, it increases with tropospheric warming.

What I fear is that we have been fooling ourselves with what we thought was positive cloud feedback in observational data, when in fact what we have been seeing was mostly non-feedback cloud “forcingâ€? of surface temperature. In order to have any hope of ferreting out feedback signals, we must stop averaging observational data to long time scales, and instead examine short time-scale behavior. This is why our GRL paper addressed daily variability.

Will this guarantee that we will be able to observationally estimate feedbacks? No. It all depends upon how strong they are relative to other non-feedback forcings.

It seems like this whole issue should have been explored by someone else that I’m not aware of, and maybe someone here can point me in that direction. But I think that a simple model demonstration, like the one I’ve briefly presented, is the only way to convincingly demonstrate, in a quantitative fashion, how much of a problem this issue might be to the observational determination of climate sensitivity.

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