The Application of Infrared Spectral Radiances and Fluxes for Arctic Climate Monitoring and Cloud Phase Determination from Space

Abstract

The Arctic climate is strongly influenced by infrared (IR) radiation emitted and absorbed by greenhouse gases, clouds, and the surface. As the Arctic continues to rapidly change, it is crucial to further understand how changes in such geophysical variables influence changes in IR flux at the Arctic surface and the top-of-atmosphere. Cloud phase (i.e., ice, liquid, and mixed) can affect the clouds’ overall contributions to the IR fluxes. However, the spatial and temporal occurrences of Arctic cloud phase are not well characterized. Satellite observations of spectrally resolved IR fluxes can be used to connect changes in the atmosphere and surface to broadband IR flux changes, however, such studies have not been performed in the Arctic. Spectral IR radiances can be used for satellite-based cloud phase retrievals, but conventional methods using the mid-IR window region (~800-1250 cm-1) have limitations in polar regions, especially for mixed phase clouds. It may be possible to improve Arctic mid-IR cloud phase retrievals with far-IR (<~600 cm-1) measurements. However, few studies have investigated far-IR cloud phase retrievals from space. Overall, this dissertation studies the potential and limitations of spectral mid-IR and far-IR radiances and fluxes for monitoring Arctic IR radiation and identifying cloud phase from space. It contains four studies. The first study examines the trends of zonal mean spectral outgoing longwave radiation (OLR) and greenhouse efficiencies (GHE) in the Arctic from 2003 to 2016 using spectral flux derived from collocated Atmospheric IR Sounder (AIRS) and the Clouds and the Earth's Radiant Energy System observations in conjunction with AIRS retrievals. Positive and negative trends in Arctic OLR and GHE are observed across the far-IR and mid-IR spectral regions, depending on the season, and the largest positive OLR and GHE trends occur in spring. Sensitivity studies reveal that surface temperature increases contribute most to the OLR and GHE trends, but the effects of atmospheric humidity and temperature are discernable. In the second study, AIRS cloud phase retrievals, which were never evaluated over the Arctic, are evaluated against four years of combined CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation measurements over the Arctic Ocean. AIRS classification skill for single-layer ice- and liquid-phase clouds ranges from 85%–95% and 22%–32%, respectively. Most unknown and liquid AIRS phase classifications correspond to mixed-phase clouds. The third study explores the synergy between the far- and mid-IR for polar ice cloud detection. A far-IR brightness temperature difference (BTD) test is developed and applied to simulated IR radiances and the results are compared to those from a mid-IR BTD test. Scattering leads to the far-IR being most sensitive to small ice particles, and the increase of cloud optical depth contributing to stronger far-IR BTD signals. Synergy between the mid-IR and far-IR is most useful for identifying cloud ice particles with an effective diameter around 40 µm. The final study examines the sensitivity of simulated 11 µm brightness temperature (BT11) to cloud ice changes within Arctic liquid-topped mixed phase clouds (LTMs). It was determined that BT11 can be sensitive to cloud ice for a range of commonly observed Arctic LTMs. By utilizing channels in the mid- and far-IR, it may be possible to use BTD tests together with a machine learning approach to detect Arctic LTMs from space.