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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
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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.
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.