Sensitivity of spectral climate signals to the emissions of atmospheric dust

Abstract:

Mineral dust particles profoundly influence the Earth climate due to their varied affects on the radiation and cloud physics. The knowledge of dust emissions from daily to seasonal scales is thus important for interpreting the past and predicting the future climate changes. Satellite measured radiances in the shortwave and thermal infrared are sensitive to the amount and properties of mineral dust present in the atmosphere. Therefore, the climate (i.e., monthly averages) of these reflectance spectra could contain valuable information on the change of dust emissions. In this work, we investigate the feasibility of using the climate of spectral radiances for recovering dust emissions. An observation simulation system (OSS) that incorporates the Unified Linearized Vector Radiative Transfer Model (UNL-VRTM) with forward and adjoint global chemistry transport models (GEOS-Chem and FIM-Chem) has been applied to generate synthetic hyperspectral climate data in the shortwave and thermal infrared (TIR) for summer 2008. Along with the calculation of radiances at the top of the atmosphere (TOA), the OSS also computes their Jacobians of these synthetic data to dust optical depth, plume height, and effective radius, as well as the adjoint gradients of spectral radiances to dust emissions. We found that the brightness temperature (BT) in the TIR spectra at TOA is sensitive to both of the dust plume height and particle size. For the same relative changes of these parameters, BT shows largest change with respect to particle size at the wavenumber of 890-1200 cm^-1. This demonstrates the potential for retrieving three-dimensional dust information along with the particle size from hyperspectral TIR measurements. We also assess the information content of monthly versus instantaneous radiances for constraining dust emissionsthe from the calculated adjoint gradients. Our analysis may guide new applications of long-term spectral radiance measurements (such as those from GOME, AIRS, IASI, and CrIS instruments) to constrain dust sources, and thus reduce uncertainty in our broader understanding of the impacts of mineral dust on climate.