Since early 2000, significant progress has been made in our ability to monitor changes in spectral OLR and use this information to test climate models. The launch of AIRS aboard the Aqua satellite in 2002, and the subsequent longevity of the instrument, has meant that researchers have finally had the opportunity to test the evolution of the OLR spectrum as simulated by climate models against sustained hyperspectral measurements. In concert, rigorous approaches have been developed to identify the spectral fingerprints of change and evaluate the timescales on which these will emerge given confounding factors such as natural variability and instrument calibration uncertainty [55]. These efforts have led to initiatives to develop a new category of ‘climate change’ satellite missions. Principal amongst these is the Climate Absolute Radiance and Refractory Observatory (CLARREO). Selected as a Tier-1 NASA Decadal Survey mission in 2007 [56], CLARREO objectives include the provision of in-orbit absolutely calibrated spectral radiances, spanning the longwave and shortwave domains, in concert with GPS radio occultation measurements.

Insights from AIRS

Huang and Yung were perhaps the first to use AIRS observations to directly evaluate the variability of the OLR spectrum [57]. They used Empirical Orthogonal Function (EOF) analysis of the AIRS observations to investigate the modes of spectral variability in different climate zones. Although employing only 1 month of data, the authors showed that on this timescale whilst the contrast between cloud top and surface temperature was the dominant factor in driving variability, the patterns and ordering of the modes varied with zone.

The publication was the first in a series using AIRS to evaluate the performance of variants of the Geophysical Fluid Dynamics Laboratory general circulation model (GFDL). Huang et al. performed a detailed comparison of a year of AIRS observations with simulated global mean radiance spectra over the global oceans [58]. The comparison was able to diagnose biases in the model temperature and moisture fields and, more critically, demonstrate that the effects of these biases could compensate such that they would not be apparent in a comparison with broadband flux observations. Comparisons by Huang et al. between GFDL simulations over the tropical oceans and clear-sky spectrally resolved flux spectra derived from AIRS and the Clouds and the Earth’s Radiant Energy System (CERES) broadband radiometer yielded similar findings [59] (Fig. 4). The studies also showed that incorporating extra dimensions beyond the spectral to the observation-model comparison added significant discriminatory power. For example, temporal-spatial behaviour was exploited to identify day-night model-observation biases consistent with an incorrect phasing of model convection over the Indian Ocean and Indonesian warm pool [58]. Similarly, Huang et al. were able to suggest a dynamical cause for the discrepancies seen in the water vapour absorption bands by spatially and seasonally decomposing their results [59]. Comparisons of model-observed spectral cloud-radiative forcing by the same authors illustrated marked discrepancies within specific cloud regimes that were not seen when results were averaged over larger spatial domains [60].

Fig. 4 a Annual mean broadband clear-sky OLR (in W m−2) over the tropical oceans simulated by AM2 (Atmospheric GCM, GFDL) for 2004. b Difference between the AM2-simulated and AIRS inferred broadband clear-sky OLR. c, d As (a) and (b) but for the spectral flux over the combined bands of 0–560 and 1400–2200 cm−1. e–h As (c) and (d) but for the spectral ranges of 560–800 and 990–1070 cm−1, respectively. Note the changing scales associated with each panel. Both the sign and spatial distribution of the simulated versus observed differences in the spectrally integrated OLR can change when compared with equivalent differences in selected spectral bands. (Adapted from Huang et al. [59]) Full size image

Focusing more on factors related to climate sensitivity, Huang and Ramaswamy investigated the spectral variation in the super greenhouse effect (SGE) as manifested in AIRS and GFDL [61]. The SGE phenomenon, essentially a strong anti-correlation between sea surface temperature (SST) and OLR within the tropics, has a strong regional pattern, tending to occur as zones transition between ascent and descent due to the seasonal shift of the Hadley circulation [62]. Analysis of observed and modelled OLR spectra in SGE regions demonstrated the ability to identify compensating errors not only in the mean model spectrum but also in the responses of cloud and the water vapour vertical distribution to a changing SST.

Feedbacks

The AIRS studies indicated the power of using spectral observations to directly test climate model performance, but techniques to formally discriminate between different feedbacks and unambiguously detect change using radiance spectra needed development. Recognising this, Leroy et al. derived feedback signals due to temperature and water vapour changes realised by an ensemble of the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3 (CMIP3) climate models under a particular emissions scenario [63]. They showed an optimal detection technique, incorporating uncertainty in the spectral shape of the feedback signals, could be used to distinguish different signals. They also noted the dominant role of inter-annual variability in determining the accuracy with which a particular feedback could be identified given a 20-year record length.

Although this study provided a framework, the calculations were limited to clear-sky conditions. Using a doubled CO 2 climate model simulation, Huang et al. extended the work to separate feedbacks due to temperature, water vapour and, crucially, cloud, in different vertical layers [16]. Key findings related to the similarity between certain feedback signals (Fig. 5) and the impact of uncertainties in the spectral shape of a given fingerprint. Whilst tropospheric temperature and water vapour feedbacks were unambiguously detected for the particular model and forcing scenario considered, distinguishing between cloud and surface temperature feedback signals was difficult. The authors used their results to make the case for auxiliary observations that could help resolve the ambiguity. They highlighted the potential of shortwave spectral reflectance to separate low cloud from surface temperature response and the use of GPS radio occultation measurements to disentangle atmospheric temperature and humidity responses, innovations promised by the CLARREO mission.

Fig. 5 Examples of the spectral signatures associated with different climate parameter perturbations as realised in a double CO 2 experiment using the Canadian Centre for Climate Model Analysis model. Blue curves show the all-sky global mean normalised spectral radiance change (δRN) due to the change seen in CO 2 , surface temperature, tropospheric temperature, stratospheric temperature, tropospheric humidity, stratospheric humidity and lower, middle and upper tropospheric cloud. Red curves represent the spatial standard deviation in the δRN values across the globe and can be interpreted as a measure of the ‘noise’ in the change signal. Strong similarities in the spectral shape of δRN can clearly be seen in the temperature and low cloud panels (blue curves) indicating the difficulty associated with separating these responses from each other. (Taken from Huang et al. [16]) Full size image

Unlocking Information from the RSW Spectrum

The previous sections highlight the relatively large body of work concerned with discerning climate forcing and feedback processes directly from OLR spectral radiances. Analogous efforts utilising the shortwave spectrum are less well-developed. However, the realisation that the two regions contain complementary information regarding key feedback processes, particularly those involving cloud, coupled with the availability of hyperspectral observations of RSW, has motivated a number of recent studies. Roberts et al. used reflected spectral radiances from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) to quantify and attribute the variability in the hyperspectral observations covering the ultraviolet (0.3 μm) through to the near infrared (1.75 μm) [64]. Employing principal component analysis (PCA) techniques similar to those employed in OLR studies, they showed that for all of the cases studied, six components or fewer explain over 95 % of the variance in the SCIAMACHY spectra. Perhaps more interestingly, they were able to relate specific PCs to variations in cloud, water vapour, vegetation and sea ice. An alternative approach to characterise variability was proposed by Jin et al. who derived spectral radiative kernels to explore the sensitivity of the RSW spectrum to perturbations of individual parameters such as water vapour, aerosol optical depth and cloud properties. They found, at low-mid latitudes, interannual variability in the RSW spectrum was generally dominated by variability in cloud amount and optical depth [65]. At higher latitudes, snow and sea ice played a more important role. Whilst these effects dominate at wavelengths below ∼1.3 μm, the impact of water vapour and cloud height variability is manifested within the strong water vapour absorption bands across the RSW spectrum.

Focusing more on longer-term signals of change that might be expected to emerge in the RSW spectrum as the Earth’s climate evolves, Feldman et al. pioneered the idea of applying techniques usually employed in the planning of missions or Observing System Simulation Experiments (OSSEs), to climate model output [17]. Coupling CMIP3 simulation results from Community Climate System Model 3 (CCSM3) with radiative transfer modelling across the shortwave domain and applying statistical detection techniques, Feldman et al. were able to determine, as a function of wavelength and zonal band, when the reflectance trend associated with a particular climate model simulation would emerge from the underlying ‘natural’ variability, or the ‘time-to-change-detection’ [66]. Focusing on simulations employing the Special Report on Emissions Scenarios (SRES) A2 emission scenario [67], they found that across much of the spectrum, for both clear and all-sky conditions, trends tended to emerge earlier in spectrally resolved reflectances than in broadband albedo (Fig. 6). Typically, signals were faster to emerge at low-mid latitudes compared to higher latitudes, with underlying variability due to cloud accounting for an increase in detection time between the clear and all-sky cases.

Fig. 6 a Time when the reflectance trend emerges from the underlying ‘natural’ variability (‘time-to-change-detection’) as a function of wavelength and zonal band as realised in MODTRAN5 simulations of twenty-first century nadir clear-sky reflectance using output from the CCSM3 model under the SRES A2 scenario. b As (a) but white regions show where detection times are the same or faster using spectrally integrated broadband albedo. c As (a) for all-sky conditions. d As (b) for all-sky conditions. The approach used to derive each panel follows that described in Feldman et al. [66] Full size image

Bringing It Together: a Pan-Spectral Approach and the Question of Natural Variability

Following up on their initial OSSE work, Feldman et al. exploited CMIP3 runs of CCSM3 at a variety of spatial resolutions to investigate whether pan-spectral information, incorporating both the RSW and longwave regimes, could discriminate between models exhibiting different climate sensitivities [68]. In the context of the study goal, to distinguish between models showing different low cloud and sea ice feedback strengths, the RSW domain was shown to have greater sensitivity although the longwave window region between 8 and 12 μm also showed promising discriminatory ability. More recent work using output from the HadGEM2-ES and MIROC5 models to simulate multi-decadal spectral trends for a given climate change scenario reiterates the potential of pan-spectral information [27•]. Differences between the two models are apparent in both the RSW and longwave spectral domains (Fig. 7). To fully exploit the spectral dimension requires an understanding of the processes which affect the response at different wavelengths (by, for example, constructing pan-spectral radiative kernels). However, using information contained in both the solar and outgoing longwave regimes simultaneously has the potential to enable feedback processes that appear degenerate when considering either regime in isolation [16, 65] to be distinguished from one another.

Fig. 7 Pan-spectral all-sky decadal trends in shortwave reflectance and longwave radiance for the HadGEM2-ES and MIROC5 models under the IPCC AR5 RCP8.5 scenario over the period 2005–2035 averaged over the Arctic (70–90° N, 0–100° E) (top) and the tropical western Pacific (10° S–10° N, 100–150° E) (bottom). (Taken from Feldman et al. [27•]) Full size image

Clearly, a model needs to accurately capture natural variability to reliably inform on when a climate change signal might be detectable. In the RSW domain, attempts have been made to compare the variability diagnosed from SCIAMACHY with that captured by the CCSM3 OSSE simulations. Roberts et al. used a PCA approach to show that, for selected observation rich months, the two data sets share a large fraction of their spectral variability. This suggests that the OSSE is adequately capturing the spectral variability of the early twenty-first century as observed by SCIAMACHY [69]. Jin et al., focusing on interannual variability over relatively large spatial domains, showed the normalised OSSE and SCIAMACHY reflectance variabilities are both typically less than 1 % across the RSW spectrum for all regions considered. Their work also highlighted a reduction in inter-annual variability as spatial and temporal averaging scale increases, with a reduction of ∼50 % when moving from monthly to annual averages [70•].

Similar work assessing the variability manifested in the OSSE simulations in the longwave regime has not yet been formally reported. However, there are studies providing insight into short-term variability using observations from AIRS and more recently from the Infrared Atmospheric Sounding Interferometer (IASI). Using IASI data, Brindley et al. found interannual variability reduces across the longwave spectrum as spatial scale increases but the rate of reduction varies with spectral region [71•]. As scale increases, variability across the atmospheric window, most sensitive to surface temperature and cloud, reduces relatively rapidly, but the reduction in variability is smaller for spectral regions sensitive to mid-upper troposphere temperature and water vapour (Fig. 8). At the global scale, interannual variability across the entire spectrum is less than 0.17 mW m−2 cm sr−1, reducing to 0.05 mW m−2 cm sr−1 in the window. A similar magnitude of spectral interannual variability had previously been reported from an analysis of AIRS data, but the spectral shape of the variability was markedly different [72]. The quality of recent radiance inter-comparisons made between AIRS, IASI and the Cross-Track Infrared Sounder (CrIS) suggests that this difference is most likely due to the different periods considered by the two analyses [73]. However, it would be interesting to carry out a similar study over a common period to confirm that this is indeed the case.