image)Climate Change Detection and Attribution Using Long-Term DLR Record
Long-term climate change detection requires high-quality observational records. In this study, we analyzed a 23-year record of downwelling longwave radiation (DLR) observed by two Atmospheric Emitted Radiance Interferometers (AERIs) at the Southern Great Plains (SGP) site. The record was homogenized to account for an overlapping 10-year period, ensuring consistency in the dataset.
A neural network was employed to classify the DLR spectra into three distinct sky conditions: clear-sky, thin-cloud, and thick-cloud. This classification enabled a more detailed analysis of trends across different atmospheric conditions. Using a weighted linear regression model with a first-order autoregressive process, we identified significant positive trends in temperature-sensitive DLR channels, indicating atmospheric warming near the surface.Furthermore, trends in the window band differed between all-sky and clear-sky conditions, revealing the significant role of clouds in modulating the surface energy balance. These findings highlight the importance of detailed spectral analysis for understanding climate change signals.
We further employ a novel optimal spectral fingerprinting method to disentangle surface forcings and feedbacks from the long-term DLR record at SGP. Under clear-sky conditions, we found that the increase in downwelling longwave flux (DLF) is primarily attributable to rising air temperatures and water vapor concentrations. Additionally, increasing CO2 concentrations contribute to the increased DLF. Under all-sky conditions, besides the DLF increase caused by rising air temperatures and greenhouse gas concentrations, we observed that changes in clouds mitigate the increase in DLF. This is evidenced by site measurements at the SGP site, ERA5 reanalysis, and CERES datasets over land. The determined negative surface longwave cloud feedback is mainly due to the decreasing low cloud cover, which reduces the emitting temperature in the DLR in the window band.
Collocated Hyperspectral Instruments:Radiative Closure and Atmospheric Retrievals
We conducted a detailed comparison of the radiative accuracy and atmospheric retrieval performance of AERI and the High Spectral Resolution Atmospheric Measurement System (HiSRAMS) during three field campaigns in Ottawa, Canada. Radiative closure tests were used to evaluate HiSRAMS’s nadir-pointing measurements against AERI, which served as a benchmark. HiSRAMS demonstrated comparable radiative accuracy to corrected AERI observations, which accounted for the warm bias in the window band. However, HiSRAMS’s zenith-pointing measurements exhibited lower accuracy.
To further assess performance, we employed an optimal estimation method to retrieve vertical profiles of temperature and water vapor under clear-sky conditions. AERI showed superior retrieval capabilities, particularly in resolving fine vertical features, thanks to its higher degree of freedom of signal (DFS) and lower retrieval uncertainty.
Combining HiSRAMS’s high-altitude nadir-pointing measurements with AERI’s ground-based zenith-pointing observations significantly improved the retrieval accuracy and reduced uncertainties. This synergy between instruments highlights their complementary roles in advancing atmospheric profiling for climate research.
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