“About thirty years ago there was much talk that geologists ought only to observe and not theorise; and I well remember some one saying that at this rate a man might as well go into a gravel-pit and count the pebbles and describe the colours. How odd it is that anyone should not see that all observation must be for or against some view if it is to be of any service!”
“If we had observations of the future, we obviously would trust them more than models, but unfortunately…"
"What is the use of having developed a science well enough to make predictions if, in the end, all we're willing to do is stand around and wait for them to come true?"
This is a post in a new series on whether we have underestimated global warming; this installment is inspired by a recent article on climate sensitivity discussed at And Then There's Physics.
The quirky Gavin Schmidt quote naturally wanted to say something similar to Sherwood Rowlands, but contrasted to Darwin I have to agree with Darwin and disagree with Schmidt. Schmidt got the quote from to Knutson & Tuleya (thank you ATTP in the comments).
The point is that you cannot look at data without a model, at least a model in your head. Some people may not be aware of their model, but models and observations always go hand in had. Either without the other is nothing. The naivete so often displayed at WUWT & Co. that you only need to look at the data is completely unscientific, especially when it is in all agony their cherry picked miniature part of the data.
Philosophers of science, please skip this paragraph. You could say that initially, in ancient Greece, philosophers only trusted logic and heavily distrusted the senses. This is natural at this time, if you put a stick in the water it looks bent, but if you feel with your hand it is still straight. In the 17th century British empiricism went to the other extreme and claimed that knowledge mainly comes from sensory experience. However, for science you need both, you cannot make sense of the senses without theory and theory helps you to ask the right questions to nature, without which you could observe whatever you'd like for eternity without making any real scientific progress. How many red Darwinian pebbles are there on Earth? Does that question help science? What do you mean with red pebbles?
In the hypothetical case of observations from the future, we would do the same. We would not prefer the observations, but use both observations and theory to understand what is going on. I am sure Gavin Schmidt would agree; I took his beautiful quote out of context.
Why I am writing this? What is left of "global warming has stopped" or "don't you know warming has paused?" is that models predicted more warming than we see in the observations. Or as a mitigation sceptic would say "the models are running hot". This difference is not big, this year we will probably get a temperature that fits to the mean of the projections, but we also have an El Nino year, thus we would expect the temperature to be on the high side this year, which it is not.
Figure from Cowtan et al. (2015). Caption by Ed Hawkins: Comparison of 84 RCP8.5 simulations against HadCRUT4 observations (black), using either air temperatures (red line and shading) or blended temperatures using the HadCRUT4 method (blue line and shading). The shaded regions represent the 90% range (i.e. from 5-95%) of the model simulations, with the corresponding lines representing the multi-model mean. The upper panel shows anomalies derived from the unmodified RCP8.5 results, the lower shows the results adjusted to include the effect of updated forcings from Schmidt et al. . Temperature anomalies are relative to 1961-1990.
If there is such a discrepancy, the naive British empiricist might say:
- "the models are running hot",
- "the observations are running cold" or
- "the comparison is not fair".
If there is any discrepancy a naive falsificationist may say that the theory is wrong. However, discrepancies always exist; most are stupid measurement errors. If a leaf does not fall to the ground, we do not immediately conclude that the theory of gravity is wrong. We start investigating. There is always the hope that a discrepancy can help to understand the problem better. It is from this better understanding that scientists conclude that the old theory was wrong.
Estimates of equilibrium climate sensitivity from the recent IPCC report. The dots indicate the mean estimates, the horizontal lines the confidence intervals. Only studies new to this IPCC report are labelled.
Looking at projections is "only" the last few decades, how does it look for the entire instrumental record? People have estimated the climate sensitivity from the global warming observed until now. The equilibrium climate sensitivity indicates how much warming is expected on the long term for a doubling of the CO2 concentration. The figure to the right shows that several lines of evidence suggest that the equilibrium climate sensitivity is about 3. This value is not only estimated from the climate models, but also from climatological constraints (such as the Earth having escaped from [[snow-ball Earth]]), from the response to volcanoes and from a diverse range of paleo reconstructions of past changes in the climate. And newly Andrew Dessler estimated the climate sensitivity to be 3 based on decadal variability.
The outliers are the "instrumental" estimates. Not only do they scatter a lot and have large confidence intervals; that is to be expected because global warming has only increased the temperature by 1°C up to now. However, these estimates are on average also below 3. This is a reason to critically assess the climate models, climatological constraints and paleo reconstructions, but the most likely resolution would be that the outlier category, the "instrumental" estimates, are not accurate.
The term "instrumental" estimate refers to highly simplified climate models that are tuned to the observed warming. They need additional information on the change in CO2 (quite reliable) and on changes in atmospheric dust particles (so-called aerosols) and their influence on clouds (highly uncertain). The large spread suggests that these methods are not (yet) robust and some of the simplifications also seem to produce biases towards too low sensitivity estimates. That these estimates are on average below 3 is likely mostly due to such problems with the method, but it could also suggest that "the observations are running cold".
In this light, the paper discussed over at And Then There's Physics is interesting. The paper reviews the scientific literature on the relationship between how well climate models simulate a change in the climate for which we have good observations and which is important for the climate sensitivity (water vapour, clouds, tropical thunderstorms and ice) and the climate sensitivity these models have. It argues that:
the collective guidance of this literature [shows] that model error has more likely resulted in ECS underestimation.Given that these "emergent constraint" studies find that the climate sensitivity from dynamic climate models may well be too low rather than too high, it makes sense to investigate whether the estimates from the "instrumental" category, the highly simplified climate models, are too low. One reason could be because we have underestimated the amount of surface warming.
The top panel (A) shows a measure for the mixing between the lower and middle troposphere (LTMI) over warm tropical oceans. The observed range is between the two vertical dashed lines. Every coloured dot is a climate model. Only the models with a high equilibrium climate sensitivity are able to reproduce the observed lower tropospheric mixing.
The lower panel(B) shows a qualitative summary of the studies in this field. The vertical line is the climate sensitivity averaged over all climate models. For the models that reproduce water vapour well this average is about the same. For the models that reproduce ice (cryosphere), clouds, tropical thunder storms (ITCZ) well the climate sensitivity is higher.
Concluding, climate models and further estimates of the climate sensitivity suggest that we may underestimate the warming of the surface temperature. This is certainly not conclusive, but there are many lines of evidence that climate change is going faster than expected as we will in further posts in this series: Arctic sea ice and snow cover, precipitation, sea level rise predictions, lake and river warming, etc. In combination the [[consilienceof evidence]] suggests at least that "the observations running cold" is something we need to investigate.
Looking at the way station measurements are made there are also several reasons why the raw observations may show too little warming. The station temperature record is rightly seen as a reliable information source, but in the end it is just one piece of evidence and we should consider all of the evidence.
There are so many lines of evidence for underestimating global warming that science historian Naomi Oreskes wondered if climate scientists had a tendency to "err on the side of least drama" (Brysse et al., 2013). Rather than such a bias, all these underestimates of the speed of climate change could also have a common cause: an underestimate of global warming.
I did my best to give a fair view of the scientific literature, but like for most posts in this series this topic goes beyond my expertise (station data). Thus a main reason to write these posts is to get qualified feedback. Please use the comments for this or write to me.
Related informationGavin Schmidt wrote the same 2 years ago from a modellers perspective: On mismatches between models and observations.
Gavin Schmidt's TED talk: The emergent patterns of climate change and corresponding article.
Climate Scientists Erring on the Side of Least Drama
Why raw temperatures show too little global warming
First post in this series wondering about a cooling bias: Lakes are warming at a surprisingly fast rate
ReferencesCowtan, Kevin, Zeke Hausfather, Ed Hawkins, Peter Jacobs, Michael E. Mann, Sonya K. Miller, Byron A. Steinman, Martin B. Stolpe, and Robert G. Way, 2015: Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures. Geophysical Research Letters, 42, 6526–6534, doi: 10.1002/2015GL064888.
Fasullo, John T., Benjamin M. Sanderson and Kevin E. Trenberth, 2015: Recent Progress in Constraining Climate Sensitivity With Model Ensembles. Current Climate Change Reports, first online: 16 August 2015, doi: 10.1007/s40641-015-0021-7.
Schmidt, Gavin A. and Steven Sherwood, 2015: A practical philosophy of complex climate modelling. European Journal for Philosophy of Science, 5, no. 2, 149-169, doi: 10.1007/s13194-014-0102-9.
Brysse, Keynyn, Naomi Oreskes, Jessica O’Reilly and Michael Oppenheimer, 2013: Climate change prediction: Erring on the side of least drama? Global Environmental Change, 23, Issue 1, February 2013, Pages 327–337, doi: 10.1016/j.gloenvcha.2012.10.008.