Climate Science Glossary

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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.

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.

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 the value for the strength of each contribution so that the sum of all the contributions gives the best approximation to the observed temperature data.

The method 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. To answer the specific 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. Two methods 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 is only 0.8σ, 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 this data. Again the difference in trends falls short of even the 1σ threshold.

The other temperature records

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 post.

Foster and Rahmstorf applied their methodology to both the near-global record from NASA, and the substantially less complete HadCRUT and NCDC datasets. The resulting records were remarkably similar, however there is a risk that the natural climate influences are being used to incorrectly correct 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.

Updated on 2013-01-07 by Kevin C.

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