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  • Author or Editor: H. E. Brindley x
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H. E. Brindley
and
J. E. Harries

Abstract

A recent comparison between data taken by two different satellite instruments, the Interferometric Monitor of Greenhouse Gases (IMG) that flew in 1997 and the Infrared Interferometer Spectrometer (IRIS) that flew in 1970, showed evidence of a change in the clear-sky greenhouse radiative forcing due to the increase in greenhouse gas concentrations between those years. A possibly even more intriguing question is whether the data can be used to extract unambiguous information about the radiative feedback processes that accompany such a change of forcing, especially cloud feedback. This paper is an investigation of this question, with particular reference to the uncertainties introduced into the differences between IMG and IRIS spectra due to their different patterns of temporal and spatial sampling. This has been approached by modeling the sampling problem, using high-resolution proxy scenes of top-of-the-atmosphere 11-μm brightness temperature, T B11, taken from International Satellite Cloud Climatology Project (ISCCP) data, sampled according to the characteristics of IRIS and IMG, respectively. The results suggest that while the sampling pattern of the IRIS instrument is sufficiently well distributed and dense to generate monthly regional mean brightness temperatures that are within 1.5 K of the true all-sky values, the IMG sampling is too sparse and yields results that differ from the true case by up to 6.0 K. Under cloud-free conditions the agreement with the true field for both instruments improves to within a few tenths of a kelvin. Comparisons with the observed IMG–IRIS difference spectra show that these uncertainties due to sampling presently limit the conclusions that can be drawn about climatically significant feedback processes. However, further analysis using the sampling characteristics of the Advanced Infrared Sounder (AIRS) instrument suggests that as climate change progresses, spectral measurements may be able to pick out significant changes due to processes such as cloud feedback.

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H. E. Brindley
,
A. J. Geer
, and
J. E. Harries

Abstract

Several recent studies have highlighted the potential of utilizing statistical techniques to pattern match observations and model simulations in order to establish a causal relationship between anthropogenic activity and climate change. Up to now these have tended to concentrate upon the spatial or vertical patterns of temperature change. Given the availability of contiguous, global-scale satellite observations over the past two decades, in this paper the authors seek to employ an analogous technique to spatially match model predictions to directly measured radiances. As part of the initial investigations, the technique to channel 1 of the Stratospheric Sounding Unit, sensitive to stratospheric temperature and carbon dioxide concentrations, is applied. Over the majority of the globe the observations show a negative trend in brightness temperature, with significant decreases occurring throughout the Tropics. The influence of the volcanic eruptions of El Chichón and Mount Pinatubo can also be clearly identified. Simulated brightness temperature fields, against which the satellite data are compared, are calculated using atmospheric temperature profiles from a transient climate change run of the Hadley Centre GCM. The modeled change pattern also indicates a global reduction in brightness temperature but with an altered spatial distribution relative to the observations. This tendency is reflected in the trends seen in the correlation statistics. One, dominated by the spatial mean change, shows a significant positive trend; while the other, influenced by patterns around this mean, exhibits a reducing correlation with time. Possible reasons for this behavior are discussed, and the importance of both improving model parameterizations and performing additional“unforced” simulations to assess the role of natural variability is stressed.

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A. J. Geer
,
J. E. Harries
, and
H. E. Brindley

Abstract

The use of multivariate fingerprints and spatial pattern correlation in the detection and attribution of climate change has concentrated on radiosonde temperature fields. However, the large body of radiance data from satellite-borne instruments includes contiguous datasets of up to 17 yr in length and in future years will present the most well-calibrated and large-scale data archive available for climate change studies. Here the authors give an example of the spatial correlation technique used to analyze satellite radiance data. They examine yearly mean brightness temperatures from High Resolution Infrared Spectrometer (HIRS) channel 12, sensitive to upper-tropospheric water vapor and temperature. Atmospheric profiles from a climate change run of the Hadley Centre GCM (HADCM2) are used to simulate the pattern of brightness temperature change for comparison to the satellite data. Investigation shows that strong regional brightness temperature changes are predicted in the Tropics and are dominated by changes in relative humidity in the upper troposphere. At midlatitudes only small changes are predicted, partly due to a compensation between the effects of temperature and relative humidity. The observational data showed some significant regional changes, especially at 60°S, where there was a trend toward lower brightness temperatures. The pattern similarity statistics revealed a small trend between 1979 and 1995 toward the predicted climate change patterns but this was not significant. The detection of any trend is complicated by the high natural variability of HIRS-12 radiances, which is partly associated with the El Niño–Southern Oscillation.

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R. J. Bantges
,
H. E. Brindley
,
X. H. Chen
,
X. L. Huang
,
J. E. Harries
, and
J. E. Murray

Abstract

Differences between Earth’s global mean all-sky outgoing longwave radiation spectrum as observed in 1970 [Interferometric Infrared Spectrometer (IRIS)], 1997 [Interferometric Monitor for Greenhouse Gases (IMG)], and 2012 [Infrared Atmospheric Sounding Instrument (IASI)] are presented. These differences are evaluated to determine whether these are robust signals of multidecadal radiative forcing and hence whether there is the potential for evaluating feedback-type responses. IASI–IRIS differences range from +2 K in the atmospheric window (800–1000 cm−1) to −5.5 K in the 1304 cm−1 CH4 band center. Corresponding IASI–IMG differences are much smaller, at 0.2 and −0.8 K, respectively. More noticeably, IASI–IRIS differences show a distinct step change across the 1042 cm−1 O3 band that is not seen in IASI–IMG comparisons. This step change is a consequence of a difference in behavior when moving from colder to warmer scenes in the IRIS data compared to IASI and IMG. Matched simulations for the relevant periods using ERA reanalyses mimic the spectral behavior shown by IASI and IMG rather than by IRIS. These findings suggest that uncertainties in the spectral response of IRIS preclude the use of these data for quantitative assessments of forcing and feedback processes.

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