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  • Author or Editor: T. P. Barnett x
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T. P. Barnett
,
W. C. Patzert
,
S. C. Webb
, and
B. R. Bean

Two different satellite estimates of sea-surface temperature (SST) have been compared with observed temperature sections in the central tropical Pacific Ocean. The satellite products were found to be biased with respect to the observations by approximately 1–4°C. The bias field had a strong latitudinal and longitudinal structure. The spatial structure of this field and the large magnitude of errors in estimates of SST, if a normal situation, preclude the use of the satellite products by themselves in climatological studies of the area. However, if some means can be found to remove the bias from the satellite products then they will be marginally useful in the study of interannual variations of SST in the tropical Pacific.

The errors associated with the estimates of satellite SST are strongly linked to cloud cover and the amount of water vapor in the atmosphere, indicating present methods of correcting for these types of contamination are inadequate. The errors also depend on the number of observations that have gone into the satellite estimate of SST.

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T. P. Barnett
,
K. Hasselmann
,
M. Chelliah
,
T. Delworth
,
G. Hegerl
,
P. Jones
,
E. Rasmusson
,
E. Roeckner
,
C. Ropelewski
,
B. Santer
, and
S. Tett

This paper addresses the question of where we now stand with respect to detection and attribution of an anthropogenic climate signal. Our ability to estimate natural climate variability, against which claims of anthropogenic signal detection must be made, is reviewed. The current situation suggests control runs of global climate models may give the best estimates of natural variability on a global basis, estimates that appear to be accurate to within a factor of 2 or 3 at multidecadal timescales used in detection work.

Present uncertainties in both observations and model-simulated anthropogenic signals in near-surface air temperature are estimated. The uncertainty in model simulated signals is, in places, as large as the signal to be detected. Two different, but complementary, approaches to detection and attribution are discussed in the context of these uncertainties.

Applying one of the detection strategies, it is found that the change in near-surface, June through August air temperature field over the last 50 years is generally different at a significance level of 5% from that expected from model-based estimates of natural variability. Greenhouse gases alone cannot explain the observed change. Two of four climate models forced by greenhouse gases and direct sulfate aerosols produce results consistent with the current climate change observations, while the consistency of the other two depends on which model's anthropogenic fingerprints are used. A recent integration with additional anthropogenic forcings (the indirect effects of sulfate aerosols and tropospheric ozone) and more complete tropospheric chemistry produced results whose signal amplitude and pattern were consistent with current observations, provided the model's fingerprint is used and detection carried out over only the last 30 years of annually averaged data. This single integration currently cannot be corroborated and provides no opportunity to estimate the uncertainties inherent in the results, uncertainties that are thought to be large and poorly known. These results illustrate the current large uncertainty in the magnitude and spatial pattern of the direct and indirect sulfate forcing and climate response. They also show detection statements depend on model-specific fingerprints, time period, and seasonal character of the signal, dependencies that have not been well explored.

Most, but not all, results suggest that recent changes in global climate inferred from surface air temperature are likely not due solely to natural causes. At present it is not possible to make a very confident statement about the relative contributions of specific natural and anthropogenic forcings to observed climate change. One of the main reasons is that fully realistic simulations of climate change due to the combined effects of all anthropogenic and natural forcings mechanisms have yet to be computed. A list of recommendations for reducing some of the uncertainties that currently hamper detection and attribution studies is presented.

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