Grasping at straws – Watts, GWPF vs. Reality, Berkeley Earth

Anthony Watts is desperately trying to spin the initial findings of the BEST re-examination of land-surface temperature data, which has absolutely obliterated his claims to “skeptic” fame (that UHI, station quality, station dropout, etc. are responsible for all or much of the warming in the official instrumental records). His latest misdirection is a regurgitation of an ignorant email blast from David Whitehouse/Benny Peiser, which is little more than creationist-level quote-mining:

The quote cited by Whitehouse/Peiser/Watts comes from the fourth BEST draft paper, “Decadal Variations in the Global Atmospheric Land Temperatures” (Muller et al., 2011).

The context of the paper is that BEST finds a high correlation between land surface temperature variability and the Atlantic Multidecadal Oscillation on interannual (2-15 years). They claim that this result is surprising:

Interannual to decadal variations in Earth global temperature estimates have often been identified with El Nino Southern Oscillation (ENSO) events. However, we show that variability on timescales of 2-­‐15 years in mean annual global land surface temperature anomalies, Tavg are more closely correlated with variability in sea surface temperatures in the North Atlantic.

As an aside, I objected yesterday that I thought this was a little bit of a “bait and switch”- BEST is talking about land-only temps, whereas ENSO is the dominant source of interannual variability in global temps (due to its influence on SSTs).

In any event, the quote-mining by GWPF/WUWT comes from the following statement:

Since 1975, the AMO has shown a gradual but steady rise from -­‐0.35 C to +0.2 C (see Figure 2), a change of 0.55 C. During this same time, the land-­‐average temperature has increased about 0.8 C. Such changes may be independent responses to a common forcing (e.g. greenhouse gases); however, it is also possible that some of the land warming is a direct response to changes in the AMO region. If the long-­‐term AMO changes have been driven by greenhouse gases then the AMO region may serve as a positive feedback that amplifies the effect of greenhouse gas forcing over land. On the other hand, some of the long-­‐term change in the AMO could be driven by natural variability, e.g. fluctuations in thermohaline flow. In that case the human component of global warming may be somewhat overestimated.

Let’s make some things clear up front. The BEST paper does not actually look at the issue discussed in this paragraph. Rather, it looks at how AMO variance on 2-15 year timescales correlates to the land-only temperature record. In the BEST analysis, both are “cleaned” of any long-term signal, such as the anthropogenic warming trend.

To emphasize the decadal-­‐scale variations, the long-­‐term changes in the temperature records and oceanic indices were “pre-­‐whitened.” This is a process to remove a large signal that is not being studied in order to reduce bias in the remainder. To do this, we fit each record (yearly data sets) separately to 5th order polynomials using a linear least-­‐squares regression; we subtracted the respective fits, and normalized the results to unit mean-­‐square deviation. This procedure effectively removes slow changes such as global warming and the ~70 year cycle of the AMO, and gives each record zero mean. The 12-­‐month smoothing removes high frequency (e.g. monthly) changes. All of the remaining analysis in this paper is based on the pre-­‐whitened temperature records and oceanic indices.

So BEST’s actual analysis does not in any way support the claim that”the human component of global warming may be somewhat overestimated”- it cannot by definition.

However, that does not mean that we can’t examine the question they raise. How large is the contribution of actual natural variability in the North Atlantic compared to the contribution of anthropogenic warming?

It’s important to note that the AMO is computed in such a way as to remove linear trends only. As such, the existence of a non-linear external trend like man-made warming of the ocean (including the North Atlantic) is going to show up in the AMO index as though it were internal variability, even though it’s obviously not.

This has led to some “skeptics” attempting to blame anthropogenic ocean warming on the AMO, thinking that the AMO is driving the changes in the global ocean rather than the other way around. Both Tamino and Zeke have had a go at showing why this is exactly backwards.

So how much of the temperature change in the North Atlantic is attributable to the AMO rather than man-made global warming? Ting et al., 2009 take up the question, first reviewing several methods of attempting to isolate the AMO from the global warming signal using observations alone:

Figure 2 shows the application of two of the previously proposed approaches designed to remove the forced signal associated with both anthropogenic and other natural (volcanic and solar) forcing from the total observed NASSTI, with the purpose of uncovering the internal component of the variability. The first commonly used method is to remove the linear trend from the observed North Atlantic SST index, as shown in Fig. 2a (e.g., Enfield et al. 2001; Sutton and Hodson 2005; Knight et al. 2006). This method assumes that the forced trend is linear and uniform over time. The linear detrending method suggests that the positive anomaly in NASSTI at the end of the twentieth century (0.4°C) is equally divided between the externally forced trend and the internal AMO variability (amplitude 0.2°C) and that the latter is currently at a peak state, similar to its state in the middle of the twentieth century. A second method is to use the global mean sea surface temperature as a proxy for the externally forced signal (Trenberth and Shea 2006; Mann and Emanuel 2006). When subtracting the global mean SST anomalies from the tropical North Atlantic SST to remove the forced signal, Trenberth and Shea (2006) concluded a predominant contribution from the anthropogenically forced warming to the total North Atlantic SST anomalies. In this study, we regress the two dimensional SST field on the time series of globally averaged SST (SSTg) and obtain an estimate of the internal component as the local difference between the total field and the regression pattern. The North Atlantic average of both the regressed NASSTI and the residual is shown in Fig. 2b. The regression method used here accounts for the fact that the forced SST is not uniform spatially, which differs from that used in Trenberth and Shea (2006).

Comparing Figs. 2a and 2b, one sees that the two methods imply considerable differences in the amplitude and temporal properties of the forced and internal variability. Unlike linear detrending, regression on the global mean SST implies that the positive NASSTI anomaly at the end of the twentieth century is largely due to the forced signal (~0.34°C) and only a small portion is caused by internal AMO variability (~0.06°C), consistent with Trenberth and Shea (2006). Furthermore, although linear detrending might suggest that theAMOis at its peak amplitude and that the internal variability in the next 2 decades would stay at the same amplitude or decrease, regression on the global mean SST suggests that the internal component of the AMO could cause even warmer north Atlantic SST in the coming years. Another commonly used measure of the anthropogenically forced variability is the global mean surface temperature (Tg), as shown in Fig. 2c. This method suggests an even weaker recent warming due to internal variability than when global mean SST is used, leaving the externally forced signal to explain almost all of the observed change during the late twentieth century. In addition to the difference in relative contribution to forced and internal components of NASSTI, the overall amplitude of the AMO is about 20% weaker using the global mean SST and global mean surface temperature as a proxy for forced trend.

And then using climate models:

Observed internally generated AMO index constructed by subtracting from the observed index the model estimates of the forced NA SST... The black dashed line... is the average across all six models (Ting 2009).

To remove the model-based estimate of the forced change from the observed North Atlantic SST record, we averaged the six models’ forced changes…and subtracted it from the observed time series. The uncertainty in this estimate is represented by the spread generated when each model’s forced component is separately removed from the data (see Fig. 5b). The amplitude of the oscillation, to which we hereafter refer to as AMO, is between -0.3° and +0.2°C, which is comparable to the detrended NASSTI in Fig. 2a but larger than those in Figs. 2c and 2e. In terms of the phase of the oscillation, Fig. 5b indicates that theAMOso defined is similar to that using the global mean surface temperature or global mean sea surface temperature as the forced signal (and shown in Fig. 2).

[In other words, the black, dashed line in the second plot is the modeled AMO version of the computed AMO (blue and red) curve in the upper group of plots.]

Ting et al. demonstrate that while there is a range of methods available to examine the issue of how much AMO variability plays a role in addition to the anthropogenically forced (i.e. global warming) component of North Atlantic warming, there does appear to be a genuine AMO contribution- though of arguable size. However, Ting et al. are also clear that the AMO contribution is to variability, rather than to any long term trend:

The results presented here do not lead to dramatically different conclusions from the earlier studies dealing with the same issue. We believe, however, that our rigorous statistical analysis puts the claim that the North Atlantic displayed in the twentieth century an internal ‘‘oscillation’’ of considerable magnitude (compared to overall externally forced trend) on a more robust footing. We were also able to show that this internal variation led to sharp decadal changes in temperature, but due to its oscillatory nature these transitions led to an overall compensation on a century time scale.

In other words, the AMO is not driving long term North Atlantic ocean warming.

While the BEST analysis is interesting, it says nothing about the issue WUWT and GWPF claim it is supporting, and there is no evidence to support the hypothesis that the anthropogenic component of global warming is overstated, based on AMO data.

References:

  • Muller, R.A., et al. (2011): Decadal Variations in the Global Atmospheric Land Temperatures. Berkeley Earth Surface Temperature project, submitted.
  • Ting, M., et al. (2009): Forced and Internal Twentieth-Century SST Trends in the North Atlantic. Journal of Climate, 22, 1469-1481, doi: 10.1175/2008JCLI2561.1.

6 responses to “Grasping at straws – Watts, GWPF vs. Reality, Berkeley Earth

  1. Pingback: What I’m Reading Friday, October 21, 2011 | Rationally Thinking Out Loud

  2. Pingback: The Berkeley Earth Surface Temperature project puts PR before peer review « Wott's Up With That?

  3. I love this one-two punch from the Willis-meister at http://wattsupwiththat.com/2011/10/24/what-the-best-data-actually-says/#more-49905:

    Punch 1: “For example, we certainly cannot say that the current temperatures are greater than anything before about 1945. The uncertainty bands overlap, and so we simply don’t know if e.g. 2010 was warmer than 1910. Seems likely, to be sure … but we do not have the evidence to back that up.”
    Punch 2: “PS—I remind folks again that the hype about BEST showing skeptics are wrong is just that. Most folks knew already that the world has been generally warming for hundreds of years, and BEST’s results in that regard were no surprise.”

    Yeah. Brilliant. “Despite BEST, we simply don’t know that its warmer than a hundred years ago… but, you know, just in case BEST was really convincing, I’ll also argue that we ALREADY KNEW that it was warmer than a hundred years ago. Because, you know, consistency is the hobgoblin of small minds. So is reality. Which has been shown to have a liberal bias…

  4. There is an opinion expressed around theweb that teh 4th BEST paper will not make it past peer review in its present form. Watts and Peoser have conveniently forgotten that.

  5. Wait, I’ve figured it out — Watts isn’t right _yet_ but he _may_ be eventually. He’s just got his arrow of time backwards and instead of looking for stations in the past, he should be checking out stations in the future:

    “1.3.1 Potential intensification of the urban
    heat-island effect
    Removal of vegetation, construction of buildings, roads, pavement and other human transformations of the natural environment, together with direct heat generation from human activity, are known to cause
    the temperatures of urban areas to rise above those of surrounding rural areas. In the US this urban heat-island effect was estimated at an average of just over 1.1°C for a sample of 30 US cities and about 2.9°C for New York City (Viterito, 1989). In Moscow, USSR, the heat-island effect is projected to add about 3°-3.5°C to average annual temperatures (Izrael, 1989).
    The urban heat-island effect in Shanghai, China, is quite pronounced, with a potential intensity as high as 6.5 °C on a calm, clear December night in 1979, but no heat-island effect observed on days with strong wind and heavy rain (Zhou, 1989). The urban heat-island effect has been reduced significantly in one city by large-scale tree planting. In Nanking, China, the planting since 1949 of 34 million trees has been credited with a significant cooling of the cities’ average temperature (De La Croix, 1990)…”

    http://www.ipcc.ch/ipccreports/far/wg_II/ipcc_far_wg_II_chapter_05.pdf

  6. In case anyone wants to play with BEST-AMO relationships, I’ve gone some way to replicating their graphs in the ‘decadal oscillation’ paper on WoodForTrees. I’ve used my “isolate” function which does an N-year moving average and then subtracts it from the signal, leaving the residual; this is something like their removal of trend and > 15 year oscillation, I think!

    http://www.woodfortrees.org/plot/esrl-amo/from:1950/mean:12/isolate:180/plot/best/from:1950/mean:12/isolate:180

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