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All IPCC definitions taken from Climate Change 2007: The Physical Science Basis. Working Group I Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Annex I, Glossary, pp. 941-954. Cambridge University Press.

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Archived Rebuttal

This is the archived Advanced rebuttal to the climate myth "No warming in 16 years". Click here to view the latest rebuttal.

What the science says...

Once natural influences, in particular the impact of El Niño and La Niña, are removed from the recent termperature record, there is no evidence of a significant change in the human contribution to climate change.

Introduction.

The human contribution to the 16 year trend

The instrumental temperature record shows continuing warming over the past 16 years, although at a reduced rate. In the case of GISTEMP at least the reduction is not statistically significant. The myth commonly claims that there has been no warming for 16 years. It is sometimes coupled with an explicit conclusion concerning the existance of anthropogenic global warming (e.g. in articles discussed here and here), however the conclusions are sometimes left to the reader without providing the contextal information to enable the reader to make an informed conclusion.

There are multiple problems with the myth. The first is that testing for a statistically significant trend is the wrong approach - it can only falsify the claim that there has been no warming. A better test would be to check for a statistically significant deviation from a long term trend or projection.

However this is still a straw man argument. Climate science does not claim that all climate change is anthropogenic - there are both natural and human causes. The natural causes are particularly important over short time scales, and so any attempt to analyse short term trends must take them into account. The '16years' video demonstrates graphically how the natural and human factors can be separated, and thus what has been happening to the human contribution.

In order to address the audience of the original articles, the language in the video has necessarily been heavily simplified. Clarifications are provided in the description and in the following sections.

Method

When using short time spans of less than two decades, natural variations can easily overwhelm greenhouse warming. In order to determine whether there has been a change in the human contribution to climate change over the past 16 years, it is necessary to isolate the human contribution from the natural variations. To do this, we must estimate the size of the natural variations in the temperature record, and subtract them from the temperature signal.

To do this we use the technique of multivariate regression, in which we construct timeseries for all the expected influences on climate. We then determine a value for the size of each contribution so that the sum of all the contributions gives the best approximation to the observed temperature data.

The method employed here is related to that used by Foster and Rahmstorf (2011), but with a couple of differences. Rypdal (2012) notes that the chance synchronization of several major volcanoes with declining solar activity, coupled with the longer term temperature impact of the volcanic cooling, may lead to the volcanic and solar terms being out of balance. To address this issue the volcanic and solar terms have been placed on the same scale (i.e. forcing in W/m²) and combined. Instead of time-shifting the combined term, an exponential lag is used to capture the longer term temperature impacts. The resulting model has two fewer parameters than that of Foster and Rahmstorf but still captures the bulk of the variation. As expected the volcanic contribution is somewhat increased and the solar contribution somewhat decreased in comparison to the Foster and Rahmstorf approach.

All calculations are performed using monthly data, however a 12-month moving average has been used for presentation of the graphs. Autocorrelation has been taken into account when calculating the statistical significance of trends.

Testing for a change in trend

The calculation presented above tries to extract a best estimate of the human contribution to recent climate change in order to refute the 'no anthropogenic contribution to warming' claim. To answer the more correct question of whether there has been a change in the rate of warming since 1997, the calculation must be modified to address that specific question. Multiple approaches have been used.

In the first method a second trend term was added covering the period before 1997 only. This allows for a difference in the trend pre and post 1997. The resulting trend for the post-1997 period is again highly significant. The difference term, which small, is positive indicating a slightly higher trend prior to 1997. However the magnitude difference falls short of the 1σ, and does not therefore reach even the 66% significance threshold, i.e. the difference in trend is not distinguishable from noise.

In the second method the regression model was fitted to the pre-1997 data only. The resulting coefficients were used to remove the natural contributions from the post-1997 data, and a trend calculated using just these data. Again the difference in trends falls short of even the 1σ threshold.

In the third approach, a two-box response function model was used to fit forcings and El Nino to temperatures on the whole record from 1880 to 2010. This avoids the assumption of a linear underlying trend. This very different approach leads to an almost identical estimate of the El Niño term.

Other versions of the temperature record

GISTEMP was used for this analysis because it is the only dataset with global coverage, which is critical when determining recent temperature trends - see for example these posts.

Foster and Rahmstorf applied their methodology to both the near-global record from NASA, and the substantially less complete HadCRUT and NCDC datasets. The adjusted series are remarkably similar, however there is a risk that the natural climate influences are being used to incorrectly adjust for the known lack of coverage. It may be more informative to derive near-global versions of the HadCRUT and NCDC data, using kriging for example. This approach will be explored in coming months.

Ocean heat content: the rest of the climate system

For Dana

Updated on 2013-01-08 by Kevin C.

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