One hardly knows where to begin in assessing the sanity of the recent claim that “climate change” (aka dangerous manmade global warming renamed to hide the fact that far less warming is happening than predicted) could suffocate—yes, suffocate!—sea creatures by reducing ocean oxygen levels.

The scary story comes mainly from popular reports.

Take, for example, how blogger Cat DiStasio (“a writer, storyteller, and community architect” who “holds a B.A. in Ethnic, Gender, and Labor Studies”) at Inhabitat (“a weblog devoted to the future of design, tracking the innovations in technology, practices and materials that are pushing architecture and home design towards a smarter and more sustainable future”) reported it. Her headline proclaimed, “Climate change could suffocate the Pacific Ocean in less than 20 years.”

DiStasio’s source, perennial Green alarmist Chris Mooney (who “writes about energy and the environment at The Washington Post” and “previously worked at Mother Jones“), writing in the Post, got a headline only slightly less panicky: “Global warming could deplete the oceans’ oxygen—with severe consequences.”

Mooney’s source, a study by three modelers with the National Center for Atmospheric Research/University Corporation for Atmospheric Research stopped a little—well, more than a little—short of such panic in the title of the NCAR/UCAR press release: “Widespread loss of ocean oxygen to become noticeable in 2030s.”

And the title of the academic study published in the journal Global Biogeochemical Cycles, on which the press release was based, was about as calm as could be: “Finding forced trends in oceanic oxygen.”

Like most people, my blood oxygen levels rise and fall through every 24-hour cycle, and the differences are “noticeable.” But to go from “noticeable” to “suffocate” is something of a stretch.

DiStasio leads her story: “A new study suggests that human activity is having a devastating effect on oxygen levels in the world’s oceans, and could cause parts of the Pacific Ocean to essentially suffocate in as little as 15 years.” Well, no, the underlying study doesn’t say the effect is (that’s a present-tense verb) having a “devastating” effect. It says it’s “noticeable.”

Mooney’s report is considerably less alarmist than its headline (a common occurrence on these subjects), and he makes explicit something DiStasio and others don’t: the study depends on “a high-powered climate model.”

Therein lies the catch. The study was of model output, not of the oceans themselves. That’s something the press release revealed, though one has to read carefully to recognize just how comprehensive the substitution of model for reality was. The emphases added here (by italics) are the telltale signs:

Using the [model] simulations to study dissolved oxygen gave the researchers guidance on how much concentrations may have varied naturally in the past. With this information, they could determine when ocean deoxygenation due to climate change is likely to become more severe than at any point in the modeled historic range.

The research team found that deoxygenation caused by climate change could already be detected in the southern Indian Ocean and parts of the eastern tropical Pacific and Atlantic basins. They also determined that more widespread detection of deoxygenation caused by climate change would be possible between 2030 and 2040. However, in some parts of the ocean, including areas off the east coasts of Africa, Australia, and Southeast Asia, deoxygenation caused by climate change was not evident even by 2100.

The unsuspecting reader—the person who naively thinks scientists study the real world out there—is likely to think after reading these two paragraphs that actual measurements of the chemical composition of the oceans yield the picture of “how much concentrations … varied naturally in the past,” but in reality it’s all modeling. She’s likely to think “deoxygenation caused by climate change [has] already been detected in the southern Indian Ocean and parts of the eastern tropical Pacific and Atlantic basis,” but in reality, it’s all modeling. And she’s likely to think “was not evident” has reference to some sort of real-world observation, but that, too, is all modeling—to which she should be tipped off by the words “even by 2100,” which hasn’t come around yet so is a little difficult to observe.

And the climate models that are the sole basis for all the speculations in this study—maybe it deserves scare quotes: “study”—predict, on average, 2 to 3 times observed warming; 95% predict more rather than less than observed warming, indicating that their errors are not random but driven by bias (honest mistake or dishonest lie); and none predicted the absence of warming from early 1997 to late 2015. I.e., the models are wrong, calling for much more warming than actually comes from added CO2, which implies that everything the modelers in this study assume about that is wrong, too.

As someone named “Hivemind” (alluding to the group-think common to climate alarmists) commented on the press release when the CAGW-skeptic blog WattsUpwithThat.com published the press release, “they didn’t ‘follow the evidence’. All they did was program a computer to tell them that it’s worse than we thought. There is no sign of validation, at least not in the above report. No validation, no validity, garbage science.”

Well, yes, that does seem implicit in study lead author Matthew Long’s comment in the press release, “We need comprehensive and sustained observations of what’s going on in the ocean to compare with what we’re learning from our models and to understand the full impact of a changing climate.” Yes, indeed, some empirical evidence would be a handy thing, but then that would require these scientists to leave their computers and go take and analyze some water samples from oceans all around the world—thousands and thousands (actually, millions and millions) of them, carefully distributed over time (scores to hundreds to thousands of years) and space (at many depths and spread randomly at all latitudes and longitudes), to make the sample representative. So much more comfortable to stay in the office and run computer simulations!

It’s hard to not to conclude that this study commits the fallacy of confusing models with reality—a fallacy to which all modelers are prone but climate modelers seem to be especially vulnerable. As sociologist of science Myanna Lahsen (herself a believer in dangerous manmade warming) wrote after spending years studying climate modelers at NCAR (source of the study cited above), they “tend to think their model is reality” because they spend so much time working with it that they lose objective distance (rather like teenagers who get caught up in virtual reality worlds). In her seminal article “Seductive simulations? Uncertainty distributions around climate models,” she wrote of climate modelers’ nervously laughing when she (or sometimes they themselves) caught them(selves) speaking of their model outputs as if they were the real atmosphere and oceans. For instance:

… modelers easily forget to preface each of their representations with the words ‘simulated’ or ‘modeled’ (‘the simulated ocean’, ‘the modeled ocean–atmosphere dynamic’, and so on). At other times, modelers may have been strategic when alternating between speaking of their models as heuristics and presenting them as ‘truth machines’. However, the oscillation also may reflect how some modelers think and feel about their models at particular moments when they fail to maintain sufficient critical distance. In interviews, modelers indicated that they have to be continually mindful to maintain critical distance from their own models. For example:

Interviewer: Do modelers come to think of their models as reality?

Modeler A: Yes! Yes. You have to constantly be careful about that [laughs].

He described how it happens that modelers can come to forget known and potential errors:

“You spend a lot of time working on something, and you are really trying to do the best job you can of simulating what happens in the real world. It is easy to get caught up in it; you start to believe that what happens in your model must be what happens in the real world. And often that is not true . . . The danger is that you begin to lose some objectivity on the response of the model [and] begin to believe that the model really works like the real world . . . then you begin to take too seriously how it responds to a change in forcing. Going back to trace gases, CO2 models – or an ozone change in the stratosphere: if you really believe your model is so wonderful, then the danger is that it’s very tempting to believe that the way it responds to a change in forcing must be right. [Emphasis added]

This modeler articulates that the persuasive power of the simulations can affect the very process of creating them: modelers are at times tempted to ‘get caught up in’ their own creations and to ‘start to believe’ them, to the point of losing awareness about potential inaccuracies. …

The following interview extract arguably reflects such an instance of forgetting. This modeler had sought to model the effects of the possible ‘surprise’ event of a change in the ocean’s climate-maintaining thermohaline circulation. On the basis of his simulation he concluded that the widely theorized change in the ocean’s circulation due to warmer global temperatures is not likely to be catastrophic:

Modeler C: One of the surprises that people have been worrying about is whether the thermohaline circulation of the oceans [the big pump that could change the Gulf Stream] shuts off . . . . If the models are correct, the effect even of something like that is not as catastrophic as what most people think. You have to do something really nasty to [seriously perturb the system] . . . The reality is, it really is an ocean thing, it is basically an ocean phenomenon; it really doesn’t touch land very much.

Interviewer: But wouldn’t it change the Gulf Stream and therefore . . . ?

Modeler C: Yes, look right here [shows me the model output, which looks like a map]. If the model is right. [Slight pause] I put that caveat in at the beginning [laughs]. But right there is the picture.

Modeler C struggles to not speak of his model as a ‘truth machine’, but lapses before catching himself when presented with a question. Though he starts off indicating that the models could be wrong (‘if the models are correct’), he soon treats the model as a truth machine, referring to the modeled phenomena as reliable predictions of future reality (‘The reality is, it really is an ocean thing’). Catching himself, he then refers back to the caveat, followed by a little laugh.

I began by saying, “One hardly knows where to begin in assessing the sanity of the recent claim that “climate change” … could suffocate … sea creatures by reducing ocean oxygen levels.” That word, sanity, was no exaggeration. One of the marks of insanity is an inability to distinguish fantasy from reality.

 

 

Featured image “Learning about climate modeling” courtesy of “asdfgasdf,” Flickr Creative Commons.