Radiation plays a central role in the global energy budget. It is closely intertwined with atmospheric dynamics and cloud microphysics that lie at the heart of global climate change studies. On the other hand, radiation is not merely a type of energy flux. It is spectrally dependent, and such spectrally resolved radiation contains detailed information about geophysical variables. Recently more and more high-quality measurements of top-of-atmosphere (TOA) longwave radiation at very high spectral resolution (~1cm-1 or higher; a.k.a. hyperspectral measurements) have become available. Motivated by such measurements, in particular by their perspectives for climate studies, this thesis explores which new insights into the climate change and variability we could draw from the spectral dimension of such measurements and their counterparts based on model simulation and reanalysis data. First the spectrally resolved radiances in stratospheric channels observed by AIRS (Atmospheric infrared Sounder) over the last decade have been examined. Their secular trends are estimated and compared with counterparts of two sets of synthetic AIRS radiances. One set was generated using atmospheric profiles from the free-running GFDL AM3 forced by the observed sea surface temperature and the other using ECMWF ERA-interim reanalysis. AIRS lower-stratospheric channels exhibit a cooling trend of brightness temperature no more than 0.23 K decade-1 while its middle- and upper-stratospheric channels consistently show a statistically significant cooling trend of brightness temperature as large as 0.58 K decade-1. Neither of the synthetic radiances is capable of capturing these trends. Optimal fingerprinting technique is further applied to the trends of radiances in AIRS stratospheric channels and in AMSU stratospheric channels to derive global-mean temporal changes of stratospheric temperature and CO2 due to anthropogenic activities (so-called average-then-retrieve approach). The retrievals are not only consistent with trend estimates using other data sets such as layer-mean stratospheric temperature observations by SSU but also improve the vertical resolution of such temperature trend estimates. Furthermore, synergistic use of microwave radiances effectively helps to disentangle covariance of the temperature and CO2 changes.Traditionally, radiative feedbacks have been considered regarding the perturbation to broadband flux. Because of the compensating biases among spectral bands, it is possible that global climate models (GCMs) produce similar broadband feedback but the spectral decomposition of such broadband feedback can be considerably different, implying various changes of geophysical variables leading to such seemingly agreement in the broadband feedback. Spectral relative humidity (RH) longwave feedbacks of CMIP5 GCMs are calculated and then are analyzed utilizing the spectral RH radiative kernels. The spectral and spatial compensations lead to a consistent and nearly zero RH broadband feedback among models, usually referred to as ;;constant RH” concerning global warming. Further analysis reveals that spectral details in RH feedbacks can provide more information about the changes of geophysical variables than the broadband RH feedback does. Similar to the trend-detections studies for the stratospheric temperatures and CO2, the hyperspectral measurements also have the potential for providing constraints on the changes of temperature, humidity, and cloud properties in the troposphere using the average-then-retrieve approach. Meanwhile, more than a decade of hyperspectral data also provides a new opportunity to test climate models more rigorously by comparing the spectrally resolved radiances. Discrepancies in such comparison can be more attributable to the causes than those in broadband comparison, thus bridging the model assessments in the radiation field and in the geophysical field.
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Climate Change Analysis from the TOA Spectrally Resolved IR Radiation
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