Working Group I: The Scientific Basis

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12.2 The Elements of Detection and Attribution 12.2.1 Observed Data

Figure 12.1: Global mean surface air temperature anomalies from 1,000-year control simulations with three different climate models, HadCM2, GFDL R15 and ECHAM3/LSG (labelled HAM3L), compared to the recent instrumental record (Stouffer et al., 2000). No model control simulation shows a trend in surface air temperature as large as the observed trend. If internal variability is correct in these models, the recent warming is likely not due to variability produced within the climate system alone.

Ideally, a detection and attribution study requires long records of observed data for climate elements that have the potential to show large climate change signals relative to natural variability. It is also necessary that the observing system has sufficient coverage so that the main features of natural variability and climate change can be identified and monitored. A thorough assessment of observed climate change, climate variability and data quality was presented in Chapter 2. Most detection and attribution studies have used near-surface air temperature, sea surface temperature or upper air temperature data, as these best fit the requirement above.

The quality of observed data is a vital factor. Homogeneous data series are required with careful adjustments to account for changes in observing system technologies and observing practices. Estimates of observed data uncertainties due to instrument errors or variations in data coverage (assessed in Chapter 2) are included in some recent detection and attribution studies.

There have been five more years of observations since the SAR. Improvements in historical data coverage and processing are described in Chapter 2. Confidence limits for observational sampling error have been estimated for the global and hemispheric mean temperature record. Applications of improved pre-instrumental proxy data reconstructions are described in the next two sections.

12.2.2 Internal Climate Variability

Detection and attribution of climate change is a statistical “signal-in-noise” problem, it requires an accurate knowledge of the properties of the “noise”. Ideally, internal climate variability would be estimated from instrumental observations, but a number of problems make this difficult. The instrumental record is short relative to the 30 to 50 year time-scales that are of interest for detection and attribution of climate change, particularly for variables in the free atmosphere. The longest records that are available are those for surface air temperature and sea surface temperature. Relatively long records are also available for precipitation and surface pressure, but coverage is incomplete and varies in time (see Chapter 2). The instrumental record also contains the influences of external anthropogenic and natural forcing. A record of natural internal variability can be reconstructed by removing estimates of the response to external forcing (for example, Jones and Hegerl, 1998; Wigley et al., 1998a). However, the accuracy of this record is limited by incomplete knowledge of the forcings and by the accuracy of the climate model used to estimate the response.

Estimates using palaeoclimatic data
Palaeo-reconstructions provide an additional source of information on climate variability that strengthens our qualitative assessment of recent climate change. There has been considerable progress in the reconstruction of past temperatures. New reconstructions with annual or seasonal resolution, back to 1000 AD, and some spatial resolution have become available (Briffa et al., 1998; Jones et al., 1998; Mann et al., 1998, 2000; Briffa et al., 2000; Crowley and Lowery, 2000; see also Chapter 2, Figure 2.21). However, a number of difficulties, including limited coverage, temporal inhomogeneity, possible biases due to the palaeo-reconstruction process, uncertainty regarding the strength of the relationships between climatic and proxy indices, and the likely but unknown influence of external forcings inhibit the estimation of internal climate variability directly from palaeo-climate data. We expect, however, that the reconstructions will continue to improve and that palaeo-data will become increasingly important for assessing natural variability of the climate system. One of the most important applications of this palaeo-climate data is as a check on the estimates of internal variability from coupled climate models, to ensure that the latter are not underestimating the level of internal variability on 50 to 100 year time-scales (see below). The limitations of the instrumental and palaeo-records leave few alternatives to using long “control” simulations with coupled models (see Figure 12.1) to estimate the detailed structure of internal climate variability.

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