I: Variability of the outgoing thermal IR spectra and its application in GCM validation. II: The detection of cloud/aerosol in the outgoing thermal IR spectra

cirrus detection;GCM validation;Infrared spectra;tri-spectral algorithm;variability

Huang, Xianglei ; Yung, Yuk L.

University:California Institute of Technology

Department:Geological and Planetary Sciences

关键词: cirrus detection;    GCM validation;    Infrared spectra;    tri-spectral algorithm;    variability;   

Others  :  https://thesis.library.caltech.edu/1590/1/alltogether_final_version.pdf

美国|英语

来源: Caltech THESIS

【 摘 要 】

The theme of this thesis is studying the outgoing thermal IR spectra of Earth and Mars. It is divided into two parts: the first part (Chapters 1-4) is focused on the variability seen in the outgoing thermal IR spectra and its application in validating model simulation, and the second part (Chapters 5-6) concentrates on the detection of cirrus (cirrus/dust aerosol) from terrestrial (Martian) outgoing thermal IR spectra.In Chapter 1, an example of climate change seen from two spectrometers seperated by 26 years is used to illustrate the singular importance of the outgoing thermal IR spectra in climate observations. The importance of testing the variability of models and the feasibility of using the outgoing thermal IR spectra in such tests are discussed.In Chapter 2, a study of the temporal variability at the tropical and midlatitude Pacific Oceans seen from IRIS (Infrared Interferometer Spectrometer) spectra and corresponding synthetic spectra based on simulations from two GCMs (UCLA GCM and NCAR CAM2) is presented. The discrepancies between modeled and observed temporal variability are substantial. The differences between two GCMs are also significant. Further examination shows that these discrepancies are insensitive to the parameterization of cloud optical properties and most likely due to deficiencies in simulating the seasonal and intraseasonal variations of the Walker Circulation in the tropical Pacific and the seasonal variations of boundary-layer temperature, low cloud, and stratospheric temperature in the midlatitude Pacific.In Chapter 3, a survey of the spatial variability seen from AIRS (Atmospheric Infrared Sounder) spectra and corresponding synthetic spectra based on NCAR CAM2 simulation is presented. To a large extent, the simulated spatial variability agrees well with the observed counterpart. The major discrepancies between model and observation can be attributed to the incorrect location of ITCZ in the western Pacific, the underrepresented dust aerosol at the Arabian Sea and off the Atlantic Coast of North Africa, and the overestimated spatial variation of stratospheric temperature in the model.Chapter 4 presents a comparative study of the temporal and spatial variability seen in the Martian outgoing thermal IR spectra collected by MGS-TES (Thermal Emission Spectrometer). Surface temperature variation is the dominant contributor to the temporal and spatial variability seen here. The variations of CO2 column abundance, dust aerosol and water ice cloud associated with topography, as well as the imprint of dust storms, can be also seen from such analysis. The negative correlation between dust and water ice spectral features seen from this analysis suggests that, to some extent, dust and water ice cloud are mutually exclusive of each other in the Martian atmosphere.Chapter 5 presents a sensitivity study of identifying optically thin cirrus from high-resolution (each individual absorption line is almost resolved) thermal IR spectra based on the line shapes of the residual spectra. This cirrus-detection approach is different from all previous cirrus-detection algorithms in the sense of making use of information content contained in the high-resolution measurements.Chapter 6 presents a tri-spectral algorithm to detect water ice cloud, dust, and surface anisothermality from low-resolution Martian outgoing thermal IR spectra, such as MGS-TES spectra. This algorithm is complementary to any more sophisticated retrieval scheme and can be used to screen large amounts of data to get a quick overview.