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Brown & Caldeira Notwithstanding, Warming Will not be Worse than we Thought

Jack Hays

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Nic Lewis is an astute climate observer and researcher. Here he gently but firmly deflates an earnest but flawed paper predicting greater than expected global warming.


Brown and Caldeira: A closer look shows global warming will not be greater than we thought

Posted on December 15, 2017 | Leave a comment
by Nic Lewis

Continue reading

Last week a paper predicting greater than expected global warming, by scientists Patrick Brown and Ken Caldeira, was published by Nature.[1] The paper (henceforth referred to as BC17) says in its abstract:
“Across-model relationships between currently observable attributes of the climate system and the simulated magnitude of future warming have the potential to inform projections. Here we show that robust across-model relationships exist between the global spatial patterns of several fundamental attributes of Earth’s top-of-atmosphere energy budget and the magnitude of projected global warming. When we constrain the model projections with observations, we obtain greater means and narrower ranges of future global warming across the major radiative forcing scenarios, in general. In particular, we find that the observationally informed warming projection for the end of the twenty-first century for the steepest radiative forcing scenario is about 15 per cent warmer (+0.5 degrees Celsius) with a reduction of about a third in the two-standard-deviation spread (−1.2 degrees Celsius) relative to the raw model projections reported by the Intergovernmental Panel on Climate Change.” . . . .

The paper is well written, the method used is clearly explained in some detail and the authors have archived both pre-processed data and their code.[3] On the face of it, this is an exemplary study, and given its potential relevance to the extent of future global warming I can see why Nature decided to publish it. I am writing an article commenting on it for two reasons. First, because I think BC17’s conclusions are wrong. And secondly, to help bring to the attention of more people the statistical methodology that BC17 employed, which is not widely used in climate science. . . .

Conclusion
To sum up, I have shown strong evidence that this study’s results and conclusions are unsound. Nevertheless, the authors are to be congratulated on bringing the partial least squares method to the attention of a wide audience of climate scientists, for the thoroughness of their methods section and for making pre-processed data and computer code readily available, hence enabling straightforward replication of their results and testing of alternative methodological choices.


 
It would be interesting to have easy access as to who funds what study.

I find this of interst too. The Editorial Summary:


Editorial Summary
Warmer future forecast

Climate models project that anthropogenic emissions of greenhouse gases will continue to warm the global climate. But the projected warming varies extensively among models, complicating efforts to mitigate and adapt to climate change. Now, Patrick Brown and Ken Caldeira assess the plethora of available climate models, and reduce the influence of those that do not achieve realistic simulations of Earth's observable top-of-atmosphere energy budget. Using the constrained models, the authors reveal that, under the worst-case emissions scenario, the estimated warming by 2100 is about 15% higher than the best estimate from the Intergovernmental Panel on Climate Change. Their models also reduce the uncertainty of these previous projections by a third.
 
Nic Lewis replies to the author's reply.

[h=2]Reply to Patrick Brown’s response to my article commenting on his Nature paper[/h]Dec 23, 2017 – 2:07 PM
Introduction
I thank Patrick Brown for his detailed response (also here) to statistical issues that I raised in my critique“Brown and Caldeira: A closer look shows global warming will not be greater than we thought” of his and Ken Caldeira’s recent paper (BC17).[1] The provision of more detailed information than was given in BC17, and in particular the results of testing using synthetic data, is welcome. I would reply as follows.
Brown comments that I suggested that rather than focusing on the simultaneous use of all predictor fields, BC17 should have focused on the results associated with the single predictor field that showed the most skill: The magnitude of the seasonal cycle in OLR. He goes on to say: “Thus, Lewis is arguing that we actually undersold the strength of the constraints that we reported, not that we oversold their strength.”
To clarify, I argued that BC17 undersold the statistical strength of the relationships involved, in the RCP8.5 2090 case focussed on in their Abstract, for which the signal-to-noise ratio is highest. But I went on to say that I did not think the stronger relationships would really provide a guide to how much global warming there would actually be late this century on the RCP8.5 scenario, or any other scenario. That is because, as I stated, I disagree with BC17’s fundamental assumption that the relationship of future warming to certain aspects of the recent climate that holds in climate models necessarily also applies in the real climate system. I will return to that point later. But first I will discuss the statistical issues. Continue reading →
 
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