Modeling the airborne particle complex as a source-oriented external mixture

A Lagrangian air quality model is developed which represents the airborne particle complex as a source-oriented external mixture. In a source-oriented external mixture, particles of the same size can evolve to display different chemical compositions that depend on the chemical and hygroscopic properties of the primary seed particles initially emitted from different sources. In contrast, previous models initialize the airborne particles as an internal mixture in which all particles of the same size are assumed to have the same chemical composition. Test cases show that representation of the aerosol as an internal mixture can distort the predicted particle composition and concentration in the HNO_3/NH_3/HCl/H_2SO_4/aerosol Cl^−/SO_4=/NO_3^−/NH_4^+/Na^+ system when Na^+ and SO_4^(=) exist in separate particles, as may occur when sea spray coexists with long-distance transport of anthropogenic sulfates. Tests also indicate that the external mixture model can predict the evolution of a nearly monodisperse aerosol into a bimodally distributed aerosol as relative humidity increases, qualitatively matching observations. The source-oriented external mixture model is applied to predict the size and composition distribution of airborne particles observed at Claremont, California, on August 28, 1987. Calculations produce an aerosol mass distribution that is distinctly bimodal in the size range from 0.1 μm to 1.0 μm particle diameter, matching field observations. External mixture calculations also predict specific differences in composition between particles of the same diameter. The external mixture model is expected to have applications including exploration of the cause of the particle-to-particle differences seen by time-of-flight mass spectrometers that measure single particle size and composition in the atmosphere

The Orbiting Carbon Observatory-2: first 18 months of science data products

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO_2) with the accuracy, resolution, and coverage needed to quantify CO_2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO_2 dry air mole fraction, X_(CO)_2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of X_(CO)_2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes X_(CO)_2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south X_(CO)_2 gradient is small. Enhanced X_(CO)_2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south X_(CO)_2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in X_(CO)_2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO_2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart X_(CO)_2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset

The Orbiting Carbon Observatory-2: first 18 months of science data products

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO_2) with the accuracy, resolution, and coverage needed to quantify CO_2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO_2 dry air mole fraction, X_(CO)_2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of X_(CO)_2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes X_(CO)_2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south X_(CO)_2 gradient is small. Enhanced X_(CO)_2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south X_(CO)_2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in X_(CO)_2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO_2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart X_(CO)_2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset

Direct retrieval of isoprene from satellite-based infrared measurements.

Isoprene is the atmosphere's most important non-methane organic compound, with key impacts on atmospheric oxidation, ozone, and organic aerosols. In-situ isoprene measurements are sparse, and satellite-based constraints have employed an indirect approach using its oxidation product formaldehyde, which is affected by non-isoprene sources plus uncertainty and spatial smearing in the isoprene-formaldehyde relationship. Direct global isoprene measurements are therefore needed to better understand its sources, sinks, and atmospheric impacts. Here we show that the isoprene spectral signatures are detectable from space using the satellite-borne Cross-track Infrared Sounder (CrIS), develop a full-physics retrieval methodology for quantifying isoprene abundances from these spectral features, and apply the algorithm to CrIS measurements over Amazonia. The results are consistent with model output and in-situ data, and establish the feasibility of direct global space-based isoprene measurements. Finally, we demonstrate the potential for combining space-based measurements of isoprene and formaldehyde to constrain atmospheric oxidation over isoprene source regions

How bias correction goes wrong: measurement of X_(CO_2) affected by erroneous surface pressure estimates

All measurements of X_(CO_2) from space have systematic errors. To reduce a large fraction of these errors, a bias correction is applied to X_(CO_2) retrieved from GOSAT and OCO-2 spectra using the ACOS retrieval algorithm. The bias correction uses, among other parameters, the surface pressure difference between the retrieval and the meteorological reanalysis. Relative errors in the surface pressure estimates, however, propagate nearly 1:1 into relative errors in bias-corrected X_(CO_2). For OCO-2, small errors in the knowledge of the pointing of the observatory (up to ∼130 arcsec) introduce a bias in X_(CO_2) in regions with rough topography. Erroneous surface pressure estimates are also caused by a coding error in ACOS version 8, sampling meteorological analyses at wrong times (up to 3 h after the overpass time). Here, we derive new geolocations for OCO-2's eight footprints and show how using improved knowledge of surface pressure estimates in the bias correction reduces errors in OCO-2's v9 X_(CO_2) data

Evaluation and attribution of OCO-2 XCO_2 uncertainties

Evaluating and attributing uncertainties in total column atmospheric CO_2 measurements (XCO_2) from the OCO-2 instrument is critical for testing hypotheses related to the underlying processes controlling XCO_2 and for developing quality flags needed to choose those measurements that are usable for carbon cycle science. Here we test the reported uncertainties of version 7 OCO-2 XCO_2 measurements by examining variations of the XCO_2 measurements and their calculated uncertainties within small regions (∼  100 km  ×  10.5 km) in which natural CO_2 variability is expected to be small relative to variations imparted by noise or interferences. Over 39 000 of these small neighborhoods comprised of approximately 190 observations per neighborhood are used for this analysis. We find that a typical ocean measurement has a precision and accuracy of 0.35 and 0.24 ppm respectively for calculated precisions larger than  ∼  0.25 ppm. These values are approximately consistent with the calculated errors of 0.33 and 0.14 ppm for the noise and interference error, assuming that the accuracy is bounded by the calculated interference error. The actual precision for ocean data becomes worse as the signal-to-noise increases or the calculated precision decreases below 0.25 ppm for reasons that are not well understood. A typical land measurement, both nadir and glint, is found to have a precision and accuracy of approximately 0.75 and 0.65 ppm respectively as compared to the calculated precision and accuracy of approximately 0.36 and 0.2 ppm. The differences in accuracy between ocean and land suggests that the accuracy of XCO2 data is likely related to interferences such as aerosols or surface albedo as they vary less over ocean than land. The accuracy as derived here is also likely a lower bound as it does not account for possible systematic biases between the regions used in this analysis

Sensitivity Analysis of Cirrus Cloud Properties from High-Resolution Infrared Spectra. Part I: Methodology and Synthetic Cirrus

A set of simulated high-resolution infrared (IR) emission spectra of synthetic cirrus clouds is used to perform a sensitivity analysis of top-of-atmosphere (TOA) radiance to cloud parameters. Principal component analysis (PCA) is applied to assess the variability of radiance across the spectrum with respect to microphysical and bulk cloud quantities. These quantities include particle shape, effective radius (reff), ice water path (IWP), cloud height Zcld and thickness ΔZcld, and vertical profiles of temperature T(z) and water vapor mixing ratio w(z). It is shown that IWP variations in simulated cloud cover dominate TOA radiance variability. Cloud height and thickness, as well as T(z) variations, also contribute to considerable TOA radiance variability. The empirical orthogonal functions (EOFs) of radiance variability show both similarities and differences in spectral shape and magnitude of variability when one physical quantity or another is being modified. In certain cases, it is possible to identify the EOF that represents variability with respect to one or more physical quantities. In other instances, similar EOFs result from different sets of physical quantities, emphasizing the need for multiple, independent data sources to retrieve cloud parameters. When analyzing a set of simulated spectra that include joint variations of IWP, reff, and w(z) across a realistic range of values, the first two EOFs capture approximately 92%–97% and 2%–6% of the total variance, respectively; they reflect the combined effect of IWP and reff. The third EOF accounts for only 1%–2% of the variance and resembles the EOF from analysis of spectra where only w(z) changes. Sensitivity with respect to particle size increases significantly for reff several tens of microns or less. For small-particle reff, the sensitivity with respect to the joint variation of IWP, reff, and w(z) is well approximated by the sum of the sensitivities with respect to variations in each of three quantities separately

The Geostationary Carbon Process Mapper

The Geostationary Carbon Process Mapper (GCPM) is an earth science mission to measure key atmospheric trace gases and process tracers related to climate change and human activity. The measurement strategy delivers a process based understanding of the carbon cycle that is accurate and extensible from city to regional and continental scales. This understanding comes from contiguous maps of carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and chlorophyll fluorescence (CF) collected up to 10 times per day at high spatial resolution (~4km × 4km) from geostationary orbit (GEO). These measurements will capture the spatial and temporal variability of the carbon cycle across diurnal, synoptic, seasonal and interannual time scales. The CO2/CH4/CO/CF measurement suite has been specifically selected because their combination provides the information needed to disentangle natural and anthropogenic contributions to atmospheric carbon concentrations and to minimize key uncertainties in the flow of carbon between the atmosphere and surface since they place constraints on both biogenic uptake and release as well as on combustion emissions. Additionally, GCPM's combination of high-resolution mapping and high measurement frequency provide quasi-continuous monitoring, effectively eliminating atmospheric transport uncertainties from source/sink inversion modeling. GCPM uses a single instrument, the “Geostationary Fourier Transform Spectrometer (GeoFTS)” to make measurements in the near infrared spectral region at high spectral resolution. The GeoFTS is a half meter cube size instrument designed to be a secondary “hosted” payload on a commercial GEO satellite. NASA and other government agencies have adopted the hosted payload implementation approach because it substantially reduces the overall mission cost. This paper presents a hosted payload implementation approach for measuring the major carbon-containing gases in the atmosphere from the geostationary vantage point, to affordably advance the scientific understating of carbon cycle processes and climate change

Effect of environmental conditions on the relationship between solar induced fluorescence and gross primary productivity at an OzFlux grassland site

Recent studies have utilized coarse spatial and temporal resolution remotely sensed solar induced fluorescence (SIF) for modeling terrestrial gross primary productivity (GPP) at regional scales. Although these studies have demonstrated the potential of SIF, there have been concerns about the ecophysiological basis of the relationship between SIF and GPP in different environmental conditions. Launched in 2014, the Orbiting Carbon Observatory-2 (OCO-2) has enabled fine scale (1.3-by-2.5 km) retrievals of SIF that are comparable with measurements recorded at eddy covariance towers. In this study, we examine the effect of environmental conditions on the relationship of OCO-2 SIF with tower GPP over the course of a growing season at a well-characterized natural grassland site. Combining OCO-2 SIF and eddy covariance tower data with a canopy radiative transfer and an ecosystem model, we also assess the potential of OCO-2 SIF to constrain the estimates of V_(cmax), one of the most important parameters in ecosystem models. Based on the results, we suggest that although environmental conditions play a role in determining the nature of relationship between SIF and GPP, overall the linear relationship is more robust at ecosystem scale than the theory based on leaf-level processes might suggest. Our study also shows that the ability of SIF to constrain V_(cmax) is weak at the selected site