How many people take Global Warming totally on faith?

[jshore]

@brazil84

There is plenty of evidence in this very thread. Evidence that climate models are tested on historical (as opposed to future) data and evidence that the testers in many cases refuse to release their source code and/or data which would allow people to check, verify, and reproduce their results.

And, here we see the crux of the problem:

(1) You are setting standards for what you believe to be correct practice in science that are not the standards that actually exist. Maybe they are the standards that you think ought to exist…but then, who are you to decide?

(2) You are listening to only one side when complaints arise about people refusing to provide data or code or whatever.

As one example of the first issue above: You have arbitrarily decided that you will only accept evidence of testing on future data, which conveniently means we will have to wait a long time for verification since the future is…well…the future and even intention will tell you that because of fluctuations it takes more than a decade’s worth of data…probably more like two…to do any sort of meaningful comparison.

And, when it is pointed out that there has been enough time to see how Hansen’s predictions from 20 years ago fared, you come up with excuses not to believe the results. (E.g., you demand that the original code be re-run rather than looking at the results that were produced back then and picking the emissions scenario that came closest to the actual forcings that materialized.)

As a second example, you have determined that everyone must release their source codes even though this is simply not standard practice in most fields. Replication in the physical sciences means that you follow the procedures that the original paper has outlined and see if you get similar results. It has never to my knowledge meant that you check the other person’s code line-by-line and/or requiring that the procedure be precisely mathematically duplicated. [As it turns out, I believe that Mann has now released his code although the NSF was very clear in stating that there was absolutely no requirement on him whatsoever to do so.]

One other point I should make here since you keep making references to what I assume to be Michael Mann’s behavior: It is worth noting that Mann’s temperature reconstruction is not what we are (or at least I am) talking about when we refer to climate modeling. It is not a climate model; it is a proxy temperature reconstruction. As such, it is much more of a statistical process of fitting to data and issues such as tests of robustness to having certain data included or not included do become much more important. (However, despite what others have claimed, Mann et al. have always discussed these issues, even going back to this paper.)

[brazil84]

@jshore

And, here we see the crux of the problem:

(1) You are setting standards for what you believe to be correct practice in science that are not the standards that actually exist.

Here’s an article on what’s known as the “Scientific Method,” with a few excerpts:

Scientific method

Another basic expectation is to document, archive and share all data and methodology so it is available for careful scrutiny by other scientists, thereby allowing other researchers the opportunity to verify results by attempting to reproduce them.

Any useful hypothesis will enable predictions, by reasoning including deductive reasoning. It might predict the outcome of an experiment in a laboratory setting or the observation of a phenomenon in nature. The prediction can also be statistical and only talk about probabilities.

It is essential that the outcome be currently unknown. Only in this case does the eventuation increase the probability that the hypothesis be true. If the outcome is already known, it’s called a consequence and should have already been considered while formulating the hypothesis.

Crucially, experimental and theoretical results must be reproduced by others within the science community. Researchers have given their lives for this vision; Georg Wilhelm Richmann was killed by lightning (1753) when attempting to replicate the 1752 kite-flying experiment of Benjamin Franklin.[20]

So you see, the basic concepts of prediction, disclosure, and reproducibility are not stuff that I just made up.

[brazil84]

@jshore

(2) You are listening to only one side when complaints arise about people refusing to provide data or code or whatever.

If there is another side to the story, I am happy to listen to it.

You have arbitrarily decided that you will only accept evidence of testing on future data,

Please show me which post of mine says that.

And, when it is pointed out that there has been enough time to see how Hansen’s predictions from 20 years ago fared, you come up with excuses not to believe the results. (E.g., you demand that the original code be re-run rather than looking at the results that were produced back then and picking the emissions scenario that came closest to the actual forcings that materialized.)

It’s far from clear that Scenario “B” was the closest.

As a second example, you have determined that everyone must release their source codes even though this is simply not standard practice in most fields.

(As a side note, anyone who is asking the world to spend gigadollars had damn well better release every last shred of information, including source code.)

But please give me a cite that release of source code is not standard practice in physical science modeling outside of climate modeling.

[brazil84]

@GIGObuster

Just about all the basis of the New Scientist articles already posted that you did not bother to explain away.

Please give me a few links then.

As I pointed several times already, you do not bother to bring good examples. And you do not bring any new evidence to discuss about.

Does that mean 3 examples will suffice? 5?

[jshore]

@brazil84

Here’s an article on what’s known as the “Scientific Method,” with a few excerpts:

Scientific method - Wikipedia

So you see, the basic concepts of prediction, disclosure, and reproducibility are not stuff that I just made up.

I never claimed that you made up the basic concepts. What I think you have made up is how these concepts are translated into actual practice in the real world.

Just out of curiosity, do you accept Big Bang Theory, which talks about the past and not predicting the future…or evolution, which also doesn’t make predictions about the future which are testable on reasonable timescales? (Yes, one can see evolution of drug resistance in bacteria and such, but as creationists like to point out there is still a big jump from that kind of evolution on a small scale and the development of entirely new species and families and classes of plants and animals.)

[intention]

@jshore

I never claimed that you made up the basic concepts. What I think you have made up is how these concepts are translated into actual practice in the real world.

Just out of curiosity, do you accept Big Bang Theory, which talks about the past and not predicting the future…or evolution, which also doesn’t make predictions about the future which are testable on reasonable timescales? (Yes, one can see evolution of drug resistance in bacteria and such, but as creationists like to point out there is still a big jump from that kind of evolution on a small scale and the development of entirely new species and families and classes of plants and animals.)

Say what? Both the Big Bang theory and evolution make testable predictions. The Big Bang theory predicts the cosmic background radiation, and the theory of evolution predicts observed phenomena such as color changes in moths, divergence in DNA over millenia, different beaks in Darwin finches, and (as you mention) drug resistance in bacteria.

Einstein’s theory predicted aspects of the orbit of Mercury which had never been observed, but which had been going on since Mercury started orbiting. Was this a prediction about the future, since it had never been observed, or about the past, since it had always been there? Makes no difference. The issue is not past or future. It is testable predictions, either about phenomena which have been or will be observed.

It is worth noting, however, that hindcasting the past with a model tuned to hindcast the past is neither a test nor a testable prediction …

w.

[brazil84]

@jshore

I never claimed that you made up the basic concepts. What I think you have made up is how these concepts are translated into actual practice in the real world.

No, how the concepts have been translated into actual practice is the PROBLEM. And I wish I could claim credit for being the first to notice.

Just out of curiosity, do you accept Big Bang Theory, which talks about the past and not predicting the future…or evolution, which also doesn’t make predictions about the future which are testable on reasonable timescales? (Yes, one can see evolution of drug resistance in bacteria and such, but as creationists like to point out there is still a big jump from that kind of evolution on a small scale and the development of entirely new species and families and classes of plants and animals.)

I accept the Big Bang Theory with some skepticism. I wouldn’t be shocked if it turned out to be dead wrong.

I am more confident in Evolution. Actually, evolution is a good example of a theory that is widely accepted by reasonable people even though it has not been thoroughly tested. To me, that’s because it is simple, powerful in its ability to explain the facts, and most importantly, there is no other reasonable explanation that I am aware of.

See, contrary to the strawman you have set up, my position is not that prediction is an absolute necessity to do science.

[GIGObuster]

@intention

Say what? Both the Big Bang theory and evolution make testable predictions. The Big Bang theory predicts the cosmic background radiation, and the theory of evolution predicts observed phenomena such as color changes in moths, divergence in DNA over millenia, different beaks in Darwin finches, and (as you mention) drug resistance in bacteria.

Einstein’s theory predicted aspects of the orbit of Mercury which had never been observed, but which had been going on since Mercury started orbiting. Was this a prediction about the future, since it had never been observed, or about the past, since it had always been there? Makes no difference. The issue is not past or future. It is testable predictions, either about phenomena which have been or will be observed.

It is worth noting, however, that hindcasting the past with a model tuned to hindcast the past is neither a test nor a testable prediction …

w.

Field Tests Unite Weather And Climate Models

The newly completed field tests, known as interoperability experiments, show that this new approach in coupling models can be successful. For instance, temperature and wind outputs are similar around the globe when ingesting data into an NSF-NASA atmosphere model as they are for the atmosphere model used operationally by NOAA. Although most of the experiments would require exhaustive tuning and validation to be scientifically sound, they already show that ESMF can be used to assemble coupled applications quickly, easily and with technical accuracy.

“These interoperability experiments illustrate the role ESMF can play in integrating the national Earth science resources,” da Silva said. "Using existing data assimilation technology from NCEP, the finite-volume Community Atmosphere Model, or fvCAM, was able to ingest conventional and satellite observations, a capability that could open the door to using the fvCAM for weather as well as climate prediction."The fvCAM, which includes land surface capabilities, was developed by NCAR, with key components from GSFC.

The second experiment combines NCEP’s data assimilation technology with the Aries atmosphere model originally developed by the NASA Seasonal-to-Inter annual Prediction Project. Aries is typically coupled with an ocean model to run experimental forecasts of phenomena such as El Niño and its effects on precipitation. Among additional advantages,the two field tests enable the inter comparison of systems for satellite data assimilation.

The coupled experiments have many other potential applications. The third experiment, combining a GFDL atmosphere-land-ice model with an MIT ocean-sea ice model (known as MIT gcm), may ultimately bring new insights into ocean uptake of carbon dioxide and other important atmospheric gases and how this process affects the climate.

In an early independent adoption of ESMF technology, UCLA researchers have successfully coupled their Atmospheric General Circulation Model to the MIT gcm for the first time and updated a previous coupling to the LANL Parallel Ocean Program model. They made experimental predictions of the El Niño/Southern Oscillations with the coupled models using initial states provided by JPL’s Estimating the Circulation and Climate of the Ocean (ECCO) project. These preliminary results validate ESMF performance in terms of scientific fidelity and support the importance of ECCO products for improving short-term climate forecasts.

Demonstrations of the software and the field tests are taking place at the 4th ESMF Community Meeting at MIT in Cambridge, Mass., July2022. Reaching beyond the ESMF partnership, the research team releases the software to the scientific community via the Internet. NCAR, home of the core implementation team, is scheduled to release ESMF Version2.2.0 this month.

http://www.physicstoday.org/vol-60/iss-1/72_1.html

Testing climate models

Model assessment occurs on two distinct levels—the small scale at which one evaluates the specifics of a parameterization and the large scale at which predicted emergent features can be tested. The primary test bed is the climate of the present era, particularly since 1979, when significant satellite data started to become readily available.

The 1991 eruption of Mount Pinatubo provided a good laboratory for model testing (see the figure). Not only was the subsequent global cooling of about 0.5 °C accurately forecast soon after the eruption, but the radiative, water-vapor, and dynamical feedbacks included in the models were quantitatively verified.

More than a dozen facilities worldwide develop climate models, whose ability to simulate the current climate has improved measurably over the past 20 years. Interestingly, the average across all models almost invariably outperforms any single model, which shows that the errors in the simulations are surprisingly unbiased. Significant biases common to most models do exist, however—for instance, in patterns of tropical precipitation.

Climate modelers are particularly interested in testing the variability of their models. Some variability is intrinsic, but modelers also study variability caused by changes in external forcings, such as in Earth’s orbit or in solar activity. Those studies are complicated by incomplete observations, the nature of satellite data, uncertainties in the forcings, and other issues.

The most comprehensive comparison of models ever conducted is now under way using simulations that were performed in 2004 and 2005 for the Intergovernmental Panel on Climate Change. Those simulations for the 20th century and beyond are being examined by hundreds of independent teams who will assess the robustness of the results and help illuminate persistent problems.

Many challenging climate questions remain unanswered. Examples include how climate conditions influence El Niño; how responses can be predicted at the regional scale; and how simulations of rare, extreme events such as hurricanes and heat waves can be validated. Such issues may require better encapsulations of, for example, the turbulent behavior of the near-surface atmosphere, the effects of ocean eddies, or the microphysics of clouds and aerosols. The implementation of more sophisticated parameterizations and the ongoing increases in resolution as computer resources increase suggest that models will continue to improve. However, many results, such as the warming effect of increasing greenhouse gases that was first demonstrated in much simpler models decades ago, have proved extremely robust.

Climate models are unmatched in their ability to quantify otherwise qualitative hypotheses and generate new ideas that can be tested against observations. The models are far from perfect, but they have successfully captured fundamental aspects of air, ocean, and sea-ice circulations and their variability. They are therefore useful tools for estimating the consequences of humankind’s ongoing and audacious planetary experiment.

http://celebrating200years.noaa.gov/breakthroughs/climate_model/welcome.html#descendents

Computer models have long predicted that the climate change will affect Arctic and subarctic regions earlier and more dramatically than other parts of the world. Recent studies show marked increases in temperature and many other climate variables across much of the far north. Observed trends in surface air temperature from 1960-1990 (shown above in degrees centigrade) demonstrate "polar amplification,” with largest temperature increases (shown in red and magenta) occurring near the North Pole.

[GIGObuster]

http://gristmill.grist.org/story/2006/11/19/51921/827

Still, there are global temperature predictions that have been validated. We can start with one of the pioneers in climate science. Over 100 years ago, in 1896, Svante Arrhenius predicted that human emissions of CO2 would warm the climate. Obviously he used a much simpler model than current Ocean Atmosphere Coupled Global Climate models, which run on super computers.

Arrhenius overestimated the climate’s sensitivity to CO2 by a factor of 2. At the same time, he hugely underestimated the degree of warming, assuming CO2 would rise very slowly (who could have predicted the emissions the future held?). Still, it was a pretty impressive early success for models.

Running the clock forward: in 1988, James Hansen of NASA GISS fame predicted [PDF] that temperature would climb over the next 12 years, with a possible brief episode of cooling in the event of a large volcanic eruption. He made this prediction in a landmark paper and before a Senate hearing, which marked the official “coming out” to the general public of anthropogenic global warming. Twelve years later, he was proven remarkably correct, requiring adjustment only for the timing difference between the simulated future volcanic eruption and the actual eruption of Mount Pinatubo.

And let’s face it, every year of increasing global mean temperature is one more year of success for the climate models. The acceleration of the rise is also playing out as predicted, though to be fair, decades will need to pass before such confirmation is inarguable.

Putting global surface temperatures aside, there are some other significant model predictions made and confirmed:

* models predict that surface warming should be accompanied by cooling of the stratosphere, and this has indeed been observed;
* models have long predicted warming of the lower, mid, and upper troposphere, even while satellite readings seemed to disagree -- but it turns out the satellite analysis was full of errors and on correction, this warming has been observed;
* models predict warming of ocean surface waters, as is now observed;
* models predict an energy imbalance between incoming sunlight and outgoing infrared radiation, which has been detected;
* models predict sharp and short-lived cooling of a few tenths of a degree in the event of large volcanic eruptions, and Mount Pinatubo confirmed this;
* models predict an amplification of warming trends in the Arctic region, and this is indeed happening;
* and finally, to get back to where we started, models predict continuing and accelerating warming of the surface, and so far they are correct.

It is only long-term predictions that need the passage of time to prove or disprove them, but we don’t have that time at our disposal. Action is required in the very near term. We must take the many successes of climate models as strong validation that their long-term predictions, which forecast dire consequences, are accurate.

Hindcasting comes later, as another way to test the models, it is not the main way to test them.

[intention]

GIGObuster, your habit of quoting press releases and miscellaneous web sites leads to the following kinds of foolishness:

@GIGObuster

Although most of the experiments would require exhaustive tuning and validation to be scientifically sound, they already show …

I thought you guys were claiming that the models weren’t “exhaustively tuned” …

@GIGObuster

• models have long predicted warming of the lower, mid, and upper troposphere, even while satellite readings seemed to disagree – but it turns out the satellite analysis was full of errors and on correction, this warming has been observed;

No, it has not. This claim is wrong on two counts. First, the models predict, not that the troposphere will warm, but that it will warm faster than the surface. Second, both the HadCRUT3 and GISS datasets show the surface as warming faster than the troposphere, using either the RSS or UAH troposphere data.

@GIGObuster

• models predict an energy imbalance between incoming sunlight and outgoing infrared radiation, which has been detected;

As I have pointed out numerous times, both our measurements and our models of outgoing infrared radiation are far too inaccurate to indicate any such imbalance.

@GIGObuster

• and finally, to get back to where we started, models predict continuing and accelerating warming of the surface, and so far they are correct.

The warming of the surface has not been continuing, there is no warming in the recent years. And there is absolutely no evidence that the warming has been accelerating.

Think I’m wrong? Provide scientific citations for your unsubstantiated claims.

w.

[brazil84]

• models predict that surface warming should be accompanied by cooling of the stratosphere, and this has indeed been observed;
• models have long predicted warming of the lower, mid, and upper troposphere, even while satellite readings seemed to disagree – but it turns out the satellite analysis was full of errors and on correction, this warming has been observed;
• models predict warming of ocean surface waters, as is now observed;
• models predict an energy imbalance between incoming sunlight and outgoing infrared radiation, which has been detected;
• models predict sharp and short-lived cooling of a few tenths of a degree in the event of large volcanic eruptions, and Mount Pinatubo confirmed this;
• models predict an amplification of warming trends in the Arctic region, and this is indeed happening;
• and finally, to get back to where we started, models predict continuing and accelerating warming of the surface, and so far they are correct.

Where were all these predictions published? Why does this article mention predictions about the Arctic, but not about the Antarctic?

[intention]

GIGObuster, you say the models have been tested, so let’s see how well the models do at hindcasting. Here’s a comparison of the control runs for sixteen climate models.

Forget about trends, forget about what the temperature will be, they don’t even agree about what todays global temperature actually is. The difference between the high and low models is 9°F!!.

This is a result of the tuning problem. If your model is not correct and you have to tune it to correct it, something’s gotta give. As I showed above, if you tune for albedo and outgoing radiation, the clouds are wrong. If you tune for the trend, the absolute temperature is wrong. You can’t get all of it right.

As the graph shows, they can’t get today’s temperature right within 9°F, but you believe that they can predict a 1° temperature rise a century from now? Get real. Put your name down on the list of those that take global warming on faith.

w.

[jshore]

@intention

No, it has not. This claim is wrong on two counts. First, the models predict, not that the troposphere will warm, but that it will warm faster than the surface. Second, both the HadCRUT3 and GISS datasets show the surface as warming faster than the troposphere, using either the RSS or UAH troposphere data.

As you well know, the U.S. Climate Change Science Program’s first report concluded that the temperatures had, to within their error bars, been reconciled on a global scale. There is still a discrepancy remaining in the tropics…although they believe it is quite likely due to problems with the data:

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 is favored, but the issue is still open.

Note that the U.S. Climate Change Science Program was created under the Bush Administration.

[jshore]

@intention

the theory of evolution predicts observed phenomena such as color changes in moths, divergence in DNA over millenia, different beaks in Darwin finches, and (as you mention) drug resistance in bacteria.

I seem to recall that there have been significant problems identified with the moth research, the finch beak stuff was presumably some of what went into Darwin’s formulation of the theory and as such is not an after-the-fact test, and as I noted the drug resistance in bacteria just shows that microevolution can occur within species. It is a far cry from showing how algae can evolve into humans!

Einstein’s theory predicted aspects of the orbit of Mercury which had never been observed, but which had been going on since Mercury started orbiting. Was this a prediction about the future, since it had never been observed, or about the past, since it had always been there? Makes no difference. The issue is not past or future. It is testable predictions, either about phenomena which have been or will be observed.

So, what about all the detection and attribution work that has looked at the warming that has occurred and compared it to the fingerprint predicted by the models? Even if this some of this data existed while the models were being formulated, there is no evidence whatsoever that it was used to “tune” the models. I think you have different standards for different areas of science depending on whether or not you are inclined to believe or disbelieve the conclusions reached.

[jshore]

@brazil84

If there is another side to the story, I am happy to listen to it.

Here.

Please show me which post of mine says that.

You: “There is plenty of evidence in this very thread. Evidence that climate models are tested on historical (as opposed to future) data…”

It’s far from clear that Scenario “B” was the closest.

It is clear that the radiative forcings due to the adding greenhouse gases in fact aligned quite well with Scenario B. Since it is this radiative forcing that is relevant for a well-mixed greenhouse gas, that is the relevant thing to compare to. It is also worth noting that Hansen apparently noted that Scenario B seemed the most likely but then purposely had two other less likely scenarios bracketing it.

(As a side note, anyone who is asking the world to spend gigadollars had damn well better release every last shred of information, including source code.)

Well, the National Science Foundation seems to disagree with you on issues on intellectual property of this source code. (See the e-mail from the Dr. David Verardo that Mann quotes in his letter I linked to above.) Perhaps it is because they recognize that there is little to be gained and a lot to be lost by asking scientists to do this.

But please give me a cite that release of source code is not standard practice in physical science modeling outside of climate modeling.

Here is an article that discusses the issue in a general context. In fact, they note the obvious fact that many employers don’t allow their scientists to release their codes. I have written papers in the refereed physics literature in the past few years using a model that I could (and probably would) be fired by my corporate employer for releasing. While government labs and universities have a little less onerous sorts of policies, they are not that much less onerous, as the article I linked to makes clear.

Like the author of that article, I am personally in favor of trying to facilitate more openness when possible…but there are good reasons why we can’t go to the extreme of having scientists forfeit their intellectual property completely.

[jshore]

@intention

I don’t trust James Hansen’s figures, for the obvious reasons. Here is the HadCRUT data. It shows the Southern Hemisphere cooling more than the Northern.

I don’t see how you get from that plot that the Southern Hemisphere cooling is more than the Northern. Both of them are not very pronounced but if you forced me to say which one was more pronounced, I don’t think I’d choose the Southern. In fact, Hansen’s northern hemisphere data is the one with the most pronounced cooling of all the data sets…and since it is skeptics who have harped on this cooling for so long, I am interested that you are now doubting this data.

The myriad differences between HadCRUT and GISS, which are the premium global temperature datasets, and which are both based on the same raw data, should give you a clue about the relative maturity of climate science. We don’t even have an established baseline to work from. When you claim that the models reproduce the historical record … exactly which record are you talking about?

The “myriad differences”?!!? Actually, those data sets are look pretty damn close to me. This is one of the most significant differences between them that I can see and it is in a feature that was pretty subtle even in the data set where it was most pronounced.

[intention]

@jshore

@intention

No, it has not. This claim is wrong on two counts. First, the models predict, not that the troposphere will warm, but that it will warm faster than the surface. Second, both the HadCRUT3 and GISS datasets show the surface as warming faster than the troposphere, using either the RSS or UAH troposphere data.

As you well know, the U.S. Climate Change Science Program’s first report concluded that the temperatures had, to within their error bars, been reconciled on a global scale. There is still a discrepancy remaining in the tropics…although they believe it is quite likely due to problems with the data

Did I say they weren’t reconciled within the error bars? The trends during the overlap period, January 1979 to July 2007, are as follows:

HadCRUT3: 0.24 ±0.05°C/decade
GISS: 0.19 ±0.04°C/decade
MSU RSS: 0.18 ±0.07°C/decade
MSU UAH: 0.14 ±0.07°C/decade

As you can see, due to the shortness of the record, the uncertainties are quite large. Because of this, the temperatures are reconciled within each other’s error bars … and I didn’t say otherwise.

However, as I did say, the models predict the troposphere warming faster than the surface. Despite GIGObuster’s claim, there is no evidence that this is so. Both tropospheric trends are lower than both surface trends.

w.

[GIGObuster]

@intention

The warming of the surface has not been continuing, there is no warming in the recent years. And there is absolutely no evidence that the warming has been accelerating.

Think I’m wrong? Provide scientific citations for your unsubstantiated claims.

w.

http://www.marshall.org/article.php?id=312

What will all of this mean for the global warming debate? Probably less than the media spin will make of it. At a minimum, the new reports show that it is indeed possible to analyze different temperature datasets in such a way that they agree with current global warming theory. Nevertheless, all measurements systems have errors (especially for climate trends), and researchers differ in their views of what kinds of errors exist, and how they should be corrected. As pointed out by Santer et al., it is with great difficulty that our present weather measurement systems (thermometers, weather balloons, and satellites) are forced to measure miniscule climate trends. What isn’t generally recognized is that the satellite-thermometer difference that has sparked debate in recent years has largely originated over the tropical oceans – the trends over northern hemispheric land areas, where most people live, have been almost identical.

On the positive side, at least some portion of the disagreement between satellite and thermometer estimates of global temperature trends has now been removed. This helps to further shift the global warming debate out of the realm of “is warming happening?” to “how much has it warmed, and how much will it warm in the future?”. (Equally valid questions to debate are “how much of the warmth is man-made?”, “is warming necessarily a bad thing?”, and “what can we do about it anyway?”). And this is where the debate should be.

Environmental News Network - Global Warming Differences Resolved with...

Global Warming Differences Resolved with Corrections in Readings

WASHINGTON — A nagging difference in temperature readings that had raised questions about global warming has been resolved, a panel of scientists reported Tuesday.

Findings of the report include:

– Since the 1950s all data show the Earth’s surface and the low and middle atmosphere have warmed, while the upper stratosphere has cooled. Those changes were expected from computer models of the effects of greenhouse warming.

– Radiosonde readings for the midtroposphere – the nearest portion of the atmosphere – show it warming slightly faster than the surface, also an expected finding.

– The most recent satellite data also show tropospheric warming, though there is some disagreement among data sets. This may be caused by uncertainties in the observations, flaws in climate models or a combination. The researchers think it is a problem with the data collection.

– The observed patterns of change over the past 50 years cannot be explained by natural processes alone.

The report came a day after the government reported that the greenhouse gases widely blamed for raising the planet’s temperature are still building up in the atmosphere.

The National Oceanic and Atmospheric Administration said Monday there was a continuing increase in carbon dioxide and nitrous oxide in the air last year, though methane leveled off. Overall, NOAA said, its annual greenhouse gas index “shows a continuing, steady rise in the amount of heat-trapping gases in the atmosphere.”

[GIGObuster]

@intention

GIGObuster, you say the models have been tested, so let’s see how well the models do at hindcasting. Here’s a comparison of the control runs for sixteen climate models.

The makers of that had this to say (and it seems the report is from the year 2000):

http://www-pcmdi.llnl.gov/projects/cmip/overview_ms/ms_text.php

The Coupled Model Intercomparison Project (CMIP) collects output from global coupled ocean-atmosphere general circulation models (coupled GCMs). Among other uses, such models are employed both to detect anthropogenic effects in the climate record of the past century and to project future climatic changes due to human production of greenhouse gases and aerosols. **CMIP has archived output from both constant forcing (“control run”) and perturbed (1% per year increasing atmospheric carbon dioxide) simulations. ** This report summarizes results from 18 CMIP models. A third of the models refrain from employing ad hoc flux adjustments at the ocean-atmosphere interface. The new generation of non-flux-adjusted control runs are nearly as stable as – and agree with observations nearly as well as – the flux-adjusted models. **Both flux-adjusted and non-flux-adjusted models simulate an overall level of natural internal climate variability that is within the bounds set by observations. ** These developments represent significant progress in the state of the art of climate modeling since the Second (1995) Scientific Assessment Report of the Intergovernmental Panel on Climate Change (IPCC; see Gates et al. 1996). In the increasing-CO2 runs, differences between different models, while substantial, are not as great as one might expect from earlier assessments that relied on equilibrium climate sensitivity.

Of course, all that sounds to me like testing, but I guess you have to continue to deny this is taking place.

What I see is that they are constantly verifying the accuracy of the models, and you are confusing the testing (that damn word again!) with the discrediting of them. What I can deduce, is that this shows efforts to identify the less reliable models and the phasing off of models that had to adjust for data not available that was deduced by other means.

Incidentally, the new NASA missions that used IV&V, are going up presisely to get the data that modelers had more trouble getting, and the new models will get the data from those missions plugged in. What is clear to me is that more evidence is coming soon that will make the lives of skeptics even harder.

[intention]

@jshore

I don’t see how you get from that plot that the Southern Hemisphere cooling is more than the Northern. Both of them are not very pronounced but if you forced me to say which one was more pronounced, I don’t think I’d choose the Southern. In fact, Hansen’s northern hemisphere data is the one with the most pronounced cooling of all the data sets…and since it is skeptics who have harped on this cooling for so long, I am interested that you are now doubting this data.

My point remains. The aerosols were almost entirely in the Northern Hemisphere. Accordingly, we should see the NH cooling, but not the SH. We don’t see that.

Regarding Hansen, his data checking skills have recently been shown to be inadequate. He has refused to reveal the code used to calculate the global averages. Each time that he has revised the data set, the temperature drop from the 1940s has grown smaller, and the overall trend has increased.

It takes a real leap of faith to blindly trust the data of a man who has done all that.

@jshore

The “myriad differences”?!!? Actually, those data sets are look pretty damn close to me. This is one of the most significant differences between them that I can see and it is in a feature that was pretty subtle even in the data set where it was most pronounced.

Your comment illustrates perfectly the difference between science and faith. Faith looks at a couple of graphs, and says “looks pretty damn close to me”.

Science, on the other hand analyzes the datasets. The GISS dataset is here, , and the HadCRUT3 dataset is here. Take the anomalies about a common period (I used 1970-1999), take the differences, and analyze them. Don’t just squint at them across the room and say “looks pretty damn good”, analyze them.

When you do that, you’ll find the following regarding the differences (HadCRUT3 - GISS) in the monthly data;

STATISTICS REGARDING THE DIFFERENCE BETWEEN DATASETS

Average:  -0.04
Std. Dev.:  0.16
Max:  0.65
Min:  -0.65
Jarque-Bera Statistic:  179.82
Skew:  -0.33
Kurtosis:  0.97

STATISTICS REGARDING THE DATASETS THEMSELVES

HadCRUT3 Trend:  0.071°/decade ±0.004
GISS Trend:  0.060°/decade ±0.003
R^2 (coefficient of correlation):  0.84

Since both of these datasets are calculated from the identical raw data, they should be identical. They are far from identical. Among other differences, the months are as much as 0.65° different from each other. This is the total temperature change for the last century, and we see it month to month in the data difference.

Now, we’d expect random differences between the datasets. If these were just random differences, with 1,530 months in each dataset, the differences would follow a normal distribution, and they would average out.

But as the Jarque-Bera statistic shows, the data is wildly non-normal. It is heavily skewed, with large kurtosis. Thus, the differences will not average out.

Finally, their trends are significantly different (p=0.01), and the overall correlation between the two is not impressive (0.84). These are from the same raw data, the correlation should be in the high nineties.

That’s what a scientist means when he says “myriad differences”.

w.