Radiation-Based Analytic Approaches to Investigate the Earth’s Atmosphere

Radiation, propagating through Earth’s atmosphere, plays an important role in the Earth system. Solar radiation is the major source of energy, followed by thermal infrared radiation emitted by the Earth. The total radiative energy budget affects dynamic, thermodynamics, photochemical and biological processes. In addition, by measuring the reflected and emitted radiation at a distance (e.g., satellite or aircraft), we can detect and monitor the physical characteristics of a region which can help researchers get a better understanding of Earth’s atmosphere. Therefore, radiation-based analytic approaches are powerful tools in Earth Science. This thesis focuses on using radiation-based analytic tools to study the Earth’s atmosphere and to understand human impacts on the Earth system. First, we develop novel machine learning methods for hyperspectral radiative transfer simulations. Hyperspectral technique is one of the most popular and powerful methods for atmospheric remote sensing and is widely used for temperature, gas, aerosol, and cloud retrievals. However, accurate forward radiative transfer simulations are computationally expensive since they require a larger number of monochromatic radiative transfer calculations. We, therefore explore the feasibility of machine learning techniques for fast hyperspectral radiative transfer simulations that perform calculations at a small fraction of hyperspectral wavelengths and extend them across the entire spectral range. The machine learning-based approach achieves better performance than the traditional principal component analysis (PCA) method. Second, we evaluate modeled hyperspectral infrared spectra against satellite all-sky observations. The national weather centers obtain data from hyperspectral infrared sounders on a global scale. The cloudless scenario of this data is used to initialize weather forecasts, including temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these satellites are sensitive to the vertical distribution of ice and liquid water in the clouds, this information is not fully utilized. In this study, we evaluate how well the modeled spectra compare to AIRS observations using different cloud overlap models. We hope that this information can be used to verify clouds in the National Meteorological Center model and to initialize forecasts in the future. In the last chapter, we use radiation-based analytic approaches to study human impacts on the Earth system. In the first study case, we show that the radiative forcing due to geospatially redistributed anthropogenic aerosols mainly determined the spatial variations of winter extreme weather in the Northern Hemisphere during 1970-2005, which is a unique transition period for global aerosol forcing. In the second case, we review satellite and ground-based observations and conduct state-of-art atmospheric model simulations during the COVID-19 lockdown period. The halted human activities during the COVID-19 pandemic in China provided a unique experiment to assess the efficiency of air-pollution mitigation.</p

A High-performance Atmospheric Radiation Package: with applications to the radiative energy budgets of giant planets

A High-performance Atmospheric Radiation Package (HARP) is developed for studying multiple-scattering planetary atmospheres. HARP is an open-source program written in C++ that utilizes high-level data structure and parallel-computing algorithms. It is generic in three aspects. First, the construction of the model atmospheric profile is generic. The program can either take in an atmospheric profile or construct an adiabatic thermal and compositional profile, taking into account the clouds and latent heat release due to condensation. Second, the calculation of opacity is generic, based on line-by-line molecular transitions and tabulated continuum data, along with a table of correlated-k opacity provided as an option to speed up the calculation of energy fluxes. Third, the selection of the solver for the radiative transfer equation is generic. The solver is not hardwired in the program. Instead, based on the purpose, a variety of radiative transfer solvers can be chosen to couple with the atmosphere model and the opacity model. We use the program to investigate the radiative heating and cooling rates of all four giant planets in the Solar System. Our Jupiter's result is consistent with previous publications. Saturn has a nearly perfect balance between the heating rate and cooling rate. Uranus has the least radiative fluxes because of the lack of CH4 and its photochemical products. Both Uranus and Neptune suffer from a severe energy deficit in their stratospheres. Possible ways to resolve this issue are discussed. Finally, we recalculate the radiative time constants of all four giant planet atmospheres and find that the traditional values from (Conrath BJ, Gierasch PJ, Leroy SS. Temperature and Circulation in the Stratosphere of the Outer Planets. Icar. 1990;83:255-81) are significantly overestimated.Comment: 28 pages, 8 figure

A Spectral Data Compression (SDCOMP) Radiative Transfer Model for High-Spectral-Resolution Radiation Simulations

With the increasing use of satellite and ground-based high-spectral-resolution (HSR) measurements for weather and climate applications, accurate and efficient radiative transfer (RT) models have become essential for accurate atmospheric retrievals, for instrument calibration, and to provide benchmark RT solutions. This study develops a spectral data compression (SDCOMP) RT model to simulate HSR radiances in both solar and infrared spectral regions. The SDCOMP approach “compresses” the spectral data in the optical property and radiance domains, utilizing principal component analysis (PCA) twice to alleviate the computational burden. First, an optical-property-based PCA is performed for a given atmospheric scenario (atmospheric, trace gas, and aerosol profiles) to simulate relatively low-spectral-resolution radiances at a small number of representative wavelengths. Second, by using precalculated principal components from an accurate radiance dataset computed for a large number of atmospheric scenarios, a radiance-based PCA is carried out to extend the low-spectral-resolution results to desired HSR results at all wavelengths. This procedure ensures both that individual monochromatic RT calculations are efficiently performed and that the number of such computations is optimized. SDCOMP is approximately three orders of magnitude faster than numerically exact RT calculations. The resulting monochromatic radiance has relative errors less than 0.2% in the solar region and brightness temperature differences less than 0.1 K for over 95% of the cases in the infrared region. The efficiency and accuracy of SDCOMP not only make it useful for analysis of HSR measurements, but also hint at the potential for utilizing this model to perform RT simulations in mesoscale numerical weather and general circulation models

Reduced European aerosol emissions suppress winter extremes over northern Eurasia

Winter extreme weather events receive major public attention due to their serious impacts, but the dominant factors regulating their interdecadal trends have not been clearly established. Here, we show that the radiative forcing due to geospatially redistributed anthropogenic aerosols mainly determined the spatial variations of winter extreme weather in the Northern Hemisphere during 1970–2005, a unique transition period for global aerosol forcing. Over this period, the local Rossby wave activity and extreme events (top 10% in wave amplitude) exhibited marked declining trends at high latitudes, mainly in northern Eurasia. The combination of long-term observational data and a state-of-the-art climate model revealed the unambiguous signature of anthropogenic aerosols on the wintertime jet stream, planetary wave activity and surface temperature variability on interdecadal timescales. In particular, warming due to aerosol reductions in Europe enhanced the meridional temperature gradient on the jet’s poleward flank and strengthened the zonal wind, resulting in significant suppression in extreme events over northern Eurasia. These results exemplify how aerosol forcing can impact large-scale extratropical atmospheric dynamics, and illustrate the importance of anthropogenic aerosols and their spatiotemporal variability in assessing the drivers of extreme weather in historical and future climate

Reduced European aerosol emissions suppress winter extremes over northern Eurasia

Winter extreme weather events receive major public attention due to their serious impacts, but the dominant factors regulating their interdecadal trends have not been clearly established. Here, we show that the radiative forcing due to geospatially redistributed anthropogenic aerosols mainly determined the spatial variations of winter extreme weather in the Northern Hemisphere during 1970–2005, a unique transition period for global aerosol forcing. Over this period, the local Rossby wave activity and extreme events (top 10% in wave amplitude) exhibited marked declining trends at high latitudes, mainly in northern Eurasia. The combination of long-term observational data and a state-of-the-art climate model revealed the unambiguous signature of anthropogenic aerosols on the wintertime jet stream, planetary wave activity and surface temperature variability on interdecadal timescales. In particular, warming due to aerosol reductions in Europe enhanced the meridional temperature gradient on the jet’s poleward flank and strengthened the zonal wind, resulting in significant suppression in extreme events over northern Eurasia. These results exemplify how aerosol forcing can impact large-scale extratropical atmospheric dynamics, and illustrate the importance of anthropogenic aerosols and their spatiotemporal variability in assessing the drivers of extreme weather in historical and future climate

Constraining the vertical distribution of coastal dust aerosol using OCO-2 O₂ A-band measurements

Quantifying the vertical distribution of atmospheric aerosols is crucial for estimating their impact on the Earth's energy budget and climate, improving forecast of air pollution in cities, and reducing biases in the retrieval of greenhouse gases (GHGs) from space. However, to date, passive remote sensing measurements have provided limited information about aerosol extinction profiles. In this study, we propose the use of a spectral sorting approach to constrain the aerosol vertical structure using spectra of reflected sunlight absorption within the molecular oxygen (O₂) A-band collected by the Orbiting Carbon Observatory-2 (OCO-2). The effectiveness of the approach is evaluated using spectra acquired over the western Sahara coast by comparing the aerosol profile retrievals with lidar measurements from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Using a radiative transfer model to simulate OCO-2 measurements, we found that high-resolution O₂ A-band measurements have high sensitivity to aerosol optical depth (AOD) and aerosol layer height (ALH). Retrieved estimates of AOD and ALH based on a look up table technique show good agreement with CALIPSO measurements, with correlation coefficients of 0.65 and 0.53, respectively. The strength of the proposed spectral sorting technique lies in its ability to identify spectral channels with high sensitivity to AOD and ALH and extract the associated information from the observed radiance in a straightforward manner. The proposed approach has the potential to enable future passive remote sensing missions to map the aerosol vertical distribution on a global scale