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.

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.

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.

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.

Abstract

The method used to estimate the unfiltered shortwave broadband radiance from the filtered radiances measured by the Geostationary Earth Radiation Budget (GERB) instrument is presented. This unfiltering method is used to generate the first released edition of the GERB-2 dataset. The method involves a set of regressions between the unfiltering factor (i.e., the ratio of the unfiltered and filtered broadband radiances) and the narrowband observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument. The regressions are theoretically derived from a large database of simulated spectral radiance curves obtained by radiative transfer computations. The generation of the database is fully described.

Different sources of error that may affect the GERB unfiltering have been identified and the associated error magnitudes are assessed on this database. For most of the earth–atmosphere conditions, the error introduced during the unfiltering process is below 1%. In some conditions (e.g., low sun elevation above the horizon) the error can present a higher relative value, but the absolute error value remains well under the accuracy goal of 1% of the full instrument scale (2.4 W m⁻² sr⁻¹).

To increase the confidence level, the edition 1 unfiltered radiances of GERB-2 are validated by cross comparison with collocated and coangular Clouds and the Earth’s Radiant Energy System (CERES) observations for different scene types. In addition to an overall offset between the two instruments, the intercomparisons indicate a scene-type dependency up to 4% in unfiltered radiance. Further studies are required to confirm the cause, but an insufficiently accurate characterization of the shortwave spectral response of the GERB instrument in the visible part of the spectrum is one area under further investigation.

This paper reports on a new satellite sensor, the Geostationary Earth Radiation Budget (GERB) experiment. GERB is designed to make the first measurements of the Earth's radiation budget from geostationary orbit. Measurements at high absolute accuracy of the reflected sunlight from the Earth, and the thermal radiation emitted by the Earth are made every 15 min, with a spatial resolution at the subsatellite point of 44.6 km (north–south) by 39.3 km (east–west). With knowledge of the incoming solar constant, this gives the primary forcing and response components of the top-of-atmosphere radiation. The first GERB instrument is an instrument of opportunity on Meteosat-8, a new spin-stabilized spacecraft platform also carrying the Spinning Enhanced Visible and Infrared (SEVIRI) sensor, which is currently positioned over the equator at 3.5°W. This overview of the project includes a description of the instrument design and its preflight and in-flight calibration. An evaluation of the instrument performance after its first year in orbit, including comparisons with data from the Clouds and the Earth's Radiant Energy System (CERES) satellite sensors and with output from numerical models, are also presented. After a brief summary of the data processing system and data products, some of the scientific studies that are being undertaken using these early data are described. This marks the beginning of a decade or more of observations from GERB, as subsequent models will fly on each of the four Meteosat Second Generation satellites.

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a “NIST [National Institute of Standards and Technology] in orbit.” CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.