Past in Not Always Prologue -- a rebuttal of “It’s Not the Heat, It’s The Tepidity” by Clifford Asness and Aaron Brown

Clifford Asness is a hedge fund manager and co-founder of AQR Capital Management, with degrees in English, Economics and Finance.  Together with his colleague Aaron Brown, he recently published a note that analyzes global warming data and concludes that the data do not support the projections made by climate-science based IPCC models.  In these comments, I will point out some of the problems with the analysis of Asness and Brown (hereafter referred to as AB), which make their conclusions somewhat unsubstantiated and perhaps even misleading.

AB start with the global land-ocean annual mean surface temperatures, as shown in their Figure 1.  This is their “data”, where time is the independent variable or “predictor”, and the global temperature index (i.e., deviation of the recorded temperature from the 1951-1980 average) is the dependent variable or “response”.  They proceed to fit a linear regression trend line to the data (Figure 2), and compare the projections from this simple “model” with IPCC model results (Figures 3-5).  Since the linear fit in Figure 2 does not fully match the curvilinear trend in the data, especially for the post 1950 period, AB then fit a quadratic regression model to the data and compare the corresponding updated projections to IPCC model results (Figure 6).  Their main conclusion is that the global warming trend, extrapolated from a time series of past temperature readings, is at odds with IPCC projections.  To quote AB, “You can believe the models if you like, or you can look at the data and assume the most likely future is an extrapolation of the past (my bold).  What you cannot do is both.”

Let us consider the following thought experiment.  I want to predict the future movement of the S&P 500 index.  Using AB’s analytical premise, I should be able to look at the past history of this index and project it into the future.  To illustrate this point, I will use a time series of the S&P 500 Index values from 1950 until the present.  The figure below shows a chart of this data, where the index values are plotted in a logarithmic scale per normal practice.  Next, I fit a log-linear model to this data, with an R^2 value of 0.9533, indicating an excellent fit. 

Even though this fit is as good as what AB show with their analysis, does this give me any confidence that I can predict the future trajectory of the S&P 500 based on past history?  Not really.  Would any investment manager make decisions based on these projections of the S&P 500’s values – without doing his or her due diligence about future conditions?  I think not.  A reasonable analysis would require us to consider how future estimates of corporate earnings, interest rates, Eurozone conditions, investor sentiment, etc., compare to the immediate past and then decide if inferences about the future can and should be made from past data.

Extending this thought process to AB’s analysis of the global warming data, it is clear that time is simply a proxy for some underlying causal variable.  We can assume that the future can be extrapolated from the past, if and only if the time-dependence of key causal variables affecting global warming in the past is similar to that expected for the future.  In my view, this is the fundamental flaw in AB’s analysis.  The IPCC models have various scenarios that describe how CO2 emissions and other drivers of global warming can change with time as compared to current conditions.  This is what is missing in the AB analysis.  Not only that, but AB also appear to be unaware of the fact that all physics-based IPCC models are calibrated to past temperature trends and are consistent with the data.  The divergence in their projections is caused by different assumptions about future conditions with respect to CO2 emissions.  A credible model has to explain the past before it can be applied for predicting the future.

In summary, as the simple S&P 500 index example is intended to show, past is prologue only if “before” and “after” operating conditions are similar.  By most accounts, that is not the case with our understanding of how climate is changing.  The “climate-knowledge-free-statistics” based simplistic AB analysis fails to properly take into account the influence and time-dependent behavior of the relevant causal variables, thus leading to results that are incorrect and misleading.  If only AB had applied the same analytical rigor to this problem as for an investment decision!

POSTSCRIPT:  AB’s central premise (i.e., the future can be extrapolated from the past) also brings to mind the simplest weather prediction model – which posits that the weather tomorrow will be the same as that today.  Suffice it to say, in the words of the inimitable H.A.L. Mencken, “for every complex problem there is a solution which is simple, neat and wrong”.  Caveat Emptor.