A Few Quick Comments on the New Paper by Gavin Schmidt and Steven Sherwood

In his post Schmidt and Sherwood on climate models, Andrew Montford of BishopHill commented on the new paper by Schmidt and Sherwood A practical philosophy of complex climate modelling (preprint).  I haven’t yet studied the Schmidt and Sherwood paper in any detail, but in scanning it, a few things stood out.  Those of you who have studied the paper will surely have additional comments.


The abstract of Schmidt and Sherwood reads (my boldface):

We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project (CMIP). We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill against more naïve predictions. The framework we use for making inferences about reality using simulations is naturally Bayesian (in an informal sense), and has many points of contact with more familiar examples of scientific epistemology. While the use of complex simulations in science is a development that changes much in how science is done in practice, we argue that the concepts being applied fit very much into traditional practices of the scientific method, albeit those more often associated with laboratory work.

The boldfaced sentence caught my attention. A straight line based on a linear trend should be considered a more naïve method of prediction. A linear trend is a statistical model and it is definitely a whole lot simpler than all of those climate models used by the IPCC.  So I thought it would be interesting to see if, when and by how much the CMIP5 climate models simulated global surface temperatures better than a simple straight line…a linear trend line based on global surface temperature data.

Do climate models simulate global surface temperatures better than a linear trend? Over the long-term, of course they do, because many of the models are tuned to reproduce global surface temperature anomalies.  But the models do not always simulate surface temperatures better than a straight line, and currently, due to the slowdown in surface warming, the models perform no better than a trend line.

Figure 1 compares the modeled and observed annual December-to-November (Meteorological Annual Mean) global surface temperature anomalies.  The data (off-green curve) are represented by the GISS Land-Ocean Temperature Index.  The models (red curve) are represented by the multi-model ensemble mean of the models stored in the CMIP5 archive.  The models are forced with historic forcings through 2005 (later for some models) and the worst-case scenario (RCP8.5) from then to 2014.  Also shown is the linear trend (blue line) as determined from the data by EXCEL.  The data and models are referenced to the full term (1881 to 2013) so not to skew the results.

Figure 1

Figure 1

Over the past decade or so, the difference between the models and the data and the difference between the trend and the data appear to be of similar magnitude but of opposite signs.  So let’s look at those differences, where the data are subtracted from both the model outputs and the values of the linear trend. See Figure 2.  I’ve smoothed the differences with 5-year running-mean filters to remove much of the volatility associated with ENSO and volcanic eruptions.

Figure 2

Figure 2

Not surprisingly, in recent years, the difference between the models and the data and the difference between the trend line and the data are in fact of similar magnitudes.  In other words, recently, a straight line (a linear trend) performs about as well at modeling global surface temperatures as the average of the multimillion dollar climate models used by the IPCC for their 5th Assessment Report.  From about 1950 to the early 1980s, the models perform better than the straight line.  Now notice the period between 1881 and 1950.  A linear trend line, once again, performs about as well at simulating global surface temperatures as the average of the dozens of multimillion dollar climate models.

Obviously, the differences between the trend line and the data are caused by the multidecadal variability in the data. On the other hand, differences between the models and the data are caused by poor modeling of global surface temperatures.

For those interested, Figure 3 presents the results shown in Figure 2 but without the smoothing.

Figure 3

Figure 3


The other thing that caught my eye was the comment by Schmidt and Sherwood about the findings of Swanson (2013) “Emerging Selection Bias in Large-scale Climate Change Simulations.”   The preprint version of the paper is here.  In the Introduction, Swanson writes (my boldface):

Here we suggest the possibility that a selection bias based upon warming rate is emerging in the enterprise of large-scale climate change simulation. Instead of involving a choice of whether to keep or discard an observation based upon a prior expectation, we hypothesize that this selection bias involves the ‘survival’ of climate models from generation to generation, based upon their warming rate. One plausible explanation suggests this bias originates in the desirable goal to more accurately capture the most spectacular observed manifestation of recent warming, namely the ongoing Arctic amplification of warming and accompanying collapse in Arctic sea ice. However, fidelity to the observed Arctic warming is not equivalent to fidelity in capturing the overall pattern of climate warming. As a result, the current generation (CMIP5) model ensemble mean performs worse at capturing the observed latitudinal structure of warming than the earlier generation (CMIP3) model ensemble. This is despite a marked reduction in the inter-ensemble spread going from CMIP3 to CMIP5, which by itself indicates higher confidence in the consensus solution. In other words, CMIP5 simulations viewed in aggregate appear to provide a more precise, but less accurate picture of actual climate warming compared to CMIP3.

In other words, the current generation of climate models (CMIP5) agrees better among themselves than the prior generation (CMIP3), i.e., there is less of a spread between climate model outputs, because they are converging on the same results.  Overall, however, the CMIP5 models perform worse than the CMIP3 models at simulating global temperatures. “[M]ore precise, but less accurate.”  Swanson blamed this on the modelers trying to better simulate the warming in the Arctic.

Back to Schmidt and Sherwood: The last paragraph under the heading of Climate model development in Schmidt and Sherwood reads (my boldface):

Arctic sea ice trends provide an instructive example. The hindcast estimates of recent trends were much improved in CMIP5 compared to CMIP3 (Stroeve et al 2012). This is very likely because the observation/model mismatch in trends in CMIP3 (Stroeve et al 2007) lead developers to re-examine the physics and code related to Arctic sea ice to identify missing processes or numerical problems (for instance, as described in Schmidt et al (2014b)). An alternate suggestion that model groups specifically tuned for trends in Arctic sea ice at the expense of global mean temperatures (Swanson 2013) is not in accord with the practice of any of the modelling groups with which we are familiar, and would be unlikely to work as discussed above.

Note that Schmidt and Sherwood did not dispute the fact that the CMIP5 models performed worse than the earlier generation CMIP3 models at simulating global surface temperatures outside of the Arctic over recent decades.  Schmidt and Sherwood simply commented on the practices of modeling groups.  Regardless of the practices, in recent decades, the CMIP5 models perform better (but still bad) in the Arctic but worse outside the Arctic than the earlier generation models.

As a result, the CMIP3 models perform better at simulating global surface temperatures over the past 3+ decades than their newer generation counterparts. Refer to Figure 4.  That fact stands out quite plainly in a satellite-era sea surface temperature model-data comparison.

Figure 4

Figure 4


Those are the things that caught my eye in the new Schmidt and Sherwood paper.  What caught yours?

About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
This entry was posted in Model-Data Comparison SST, Model-Data LOST. Bookmark the permalink.

8 Responses to A Few Quick Comments on the New Paper by Gavin Schmidt and Steven Sherwood

  1. Thanks, Bob.
    My attention was also caught by:
    “One plausible explanation suggests this bias originates in the desirable goal to more accurately capture the most spectacular observed manifestation of recent warming, namely the ongoing Arctic amplification of warming and accompanying collapse in Arctic sea ice.”
    The “ongoing Arctic amplification of warming” is theory, at best.
    The “accompanying collapse in Arctic sea ice” is science-fiction, at its worst.

  2. skeohane says:

    Going from models that are 50% off to models that are 100% off in the consensus community, now that’s progressive progress!
    This is anything but science.

  3. The climate models are built without regard to the natural 60 and more importantly 1000 year periodicities so obvious in the temperature record. The modelers approach is simply a scientific disaster and lacks even average commonsense .It is exactly like taking the temperature trend from say Feb – July and projecting it ahead linearly for 20 years or so. They back tune their models for less than 100 years when the relevant time scale is millennial. The whole exercise is a joke.
    The entire UNFCCC -IPCC circus is a total farce- based, as it is, on the CAGW scenarios of the IPCC models which do not have even heuristic value. The earth is entering a cooling trend which will possibly last for 600 years or so.
    For estimates of the timing and extent of the coming cooling based on the natural 60 and 1000 year periodicities in the temperature data and using the 10Be and neutron monitor data as the most useful proxy for solar “activity” check the series of posts at
    The post at
    is a good place to start. One of the first things impressed upon me in tutorials as an undergraduate in Geology at Oxford was the importance of considering multiple working hypotheses when dealing with scientific problems. With regard to climate this would be a proper use of the precautionary principle .-
    The worst scientific error of the alarmist climate establishment is their unshakeable faith in their meaningless model outputs and their refusal to estimate the possible impacts of a cooling rather than a warming world and then consider what strategies might best be used in adapting to the eventuality [that cooling actually develops].

  4. The end of my previous post should read …….. the eventuality that cooling actually develops.

    [Added the correction to the previous post. — Bob]

  5. catweazle666 says:

    Why anyone would suggest that the performance of a phenomenon that is very clearly the product of a number of cyclic components – of which the most visible have ~60 year and ~1000 year periods – can be best simulated by a linear trend is a complete mystery to me.

    But hey, I’m only an old engineer with dirt under my fingernails who is only concerned with what works in the real world, so what would I know?

    The ways of climate McScientists are passing strange…

  6. Raving says:

    Very painful to read this brave attempt at putting the SSNF genie back in the bottle. They should have started here 30 years ago

    *SSNF (settled science and natural fact)

  7. Gary says:

    So there’s hope for curve-fitting after all. (/sarc but only partly).

  8. Climate Researcher says:

    May I suggest that 2015 will be the year when astute climate researchers come to the realisation that surface temperatures are controlled by thermodynamic processes (sensible convective heat transfers) as distinct from radiative heat transfer. This is the 21st century new paradigm shift I first wrote about late in 2012. Momentum is gathering as other climate blogs like The Hockey Schtick, Clive Best and Tallbloke get onto the effect of gravity, even though they have not correctly explained the energy transfer mechanisms which can be explained by the Second Law of Thermodynamics. You only have to wonder a little about how the Venus surface warms when the Sun shines to start to realise that it’s not all about radiation absorbed by the surface.

    Have a happy and relaxed New Year, everyone, comforted by the now proven fact that carbon dioxide does not warm at all because valid physics shows that all it can do is cool by a minuscule amount.

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