The Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) mission represents the National Aeronautics and Space Administration’s (NASA) next investment in satellite ocean color and the study of Earth’s ocean–atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth’s changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles, food-web processes, and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and air and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument is a spectrometer that spans the ultraviolet to shortwave-infrared wavelengths, with a ground sample distance of 1 km at nadir. This payload is complemented by two multiangle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022 to early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean–atmosphere–land system. Here, we present an overview of the PACE mission, including its developmental history, science objectives, instrument payload, observatory characteristics, and data products.

[1] We present a new approach to retrieve the aerosol properties over land that uses accurate polarization measurements over a broad spectral (410-2250 nm) and angular (±60°from nadir) ranges. The approach uses longer wavelength observations to accurately estimate the surface effects, and it is incorporated into an optimal estimation framework for retrieving the particle number density and a detailed aerosol microphysical model: effective radius, variance, and complex refractive index. A sensitivity analysis shows that the uncertainties in aerosol optical thickness (AOT) increase with AOT while the uncertainties in the microphysical model decrease. The uncertainty in the single scattering albedo (SSA) is notably less than 0.05 by the time the AOT is greater than 0.2. We find that calibration is the major source of uncertainty and that perfect angular and spectral correlation of calibration errors reduces the uncertainties in retrieved quantities. Finally, we observe that shorter wavelength (<500 nm) observations are crucial for determining the aerosols vertical extent and imaginary refractive index from polarization measurements. The retrieval approach is tested under pristine and polluted conditions using observations made by the Research Scanning Polarimeter during the Aerosol Lidar Validation experiment and over California Southern wild fires. In both cases we find that the retrievals are within the combined uncertainties of the retrieval and the Aerosol Robotic Network Cimel products and Total Ozone Mapping Spectrometer Aerosol Index. This demonstrates the unique capability of polarization measurements to accurately retrieve AOTs under pristine conditions and provide estimation of the SSA at higher AOTs.

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Classifying observed aerosols into types (e.g., urban-industrial, biomass burning, mineral dust, maritime) helps to understand aerosol sources, transformations, effects, and feedback mechanisms; to improve accuracy of satellite retrievals; and to quantify aerosol radiative impacts on climate. The number of aerosol parameters retrieved from spaceborne sensors has been growing, from the initial aerosol optical depth (AOD) at one or a few wavelengths to a list that now includes AOD, complex refractive index, single scattering albedo (SSA), and depolarization of backscatter, each at several wavelengths, plus several particle size and shape parameters. Making optimal use of these varied data products requires objective, multidimensional analysis methods. We describe such a method, which makes explicit use of uncertainties in input parameters. It treats an N-parameter retrieved data point and its N-dimensional uncertainty as an extended data point, E. It then uses a modified Mahalanobis distance, D EC , to assign an observation to the class (cluster) C that has minimum D EC from the point. We use parameters retrieved from the Aerosol Robotic Network (AERONET) to define seven prespecified clusters (pure dust, polluted dust, urban-industrial/developed economy, urban-industrial/developing economy, dark biomass smoke, light biomass smoke, and pure marine), and we demonstrate application of the method to a 5 year record of retrievals from the spaceborne Polarization and Directionality of the Earth's Reflectances 3 (POLDER 3) polarimeter over the island of Crete, Greece. Results show changes of aerosol type at this location in the eastern Mediterranean Sea, which is influenced by a wide variety of aerosol sources.