ALBERT

Description: In this study, we propose a novel approach to describe the scattering effects of atmospheric aerosols in a complex urban environment using water vapor (H2O) slant column measurements in the near infrared. This approach is demonstrated using measurements from the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer on the top of Mt. Wilson, California, and a two-stream-exact single scattering (2S-ESS) radiative transfer (RT) model. From the spectral measurements, we retrieve H2O slant column density (SCD) using 15 different absorption bands between 4000 and 8000 cm−1. Due to the wavelength dependence of aerosol scattering, large variations in H2O SCD retrievals are observed as a function of wavelength. Moreover, the variations are found to be correlated with aerosol optical depths (AODs) measured at the AERONET-Caltech station. Simulation results from the RT model reproduce this correlation and show that the aerosol scattering effect is the primary contributor to the variations in the wavelength dependence of the H2O SCD retrievals. A significant linear correlation is also found between variations in H2O SCD retrievals from different bands and corresponding AOD data; this correlation is associated with the asymmetry parameter, which is a first-order measure of the aerosol scattering phase function. The evidence from both measurements and simulations suggests that wavelength-dependent aerosol scattering effects can be derived using H2O retrievals from multiple bands. This understanding of aerosol scattering effects on H2O retrievals suggests a promising way to quantify the effect of aerosol scattering on greenhouse gas retrievals and could potentially contribute towards reducing biases in greenhouse gas retrievals from space.

Description: In this study, we propose a novel approach to constrain the optical properties of atmospheric aerosol in a complex urban environment using water vapor (H2O) slant column measurements in the near infrared. This approach is demonstrated using measurements from the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer (CLARS-FTS) on the top of Mt. Wilson, California, and a two-stream-exact single scattering (2S-ESS) radiative transfer (RT) model. From the spectral measurements, we retrieve H2O slant column density (SCD) using 15 different absorption bands between 4000 and 8000 cm−1. Due to the wavelength dependence of aerosol scattering, large variations in H2O SCD retrievals are observed as a function of wavelength. Moreover, the variations are found to be correlated with aerosol optical depths (AOD) measured at the AERONET-Caltech station. Simulation results from the RT model reproduce this correlation and show that the aerosol scattering is the primary contributor to the variations in the wavelength dependence of the H2O SCD retrievals. The evidence from both measurements and simulations suggest that wavelength-dependent aerosol optical properties can be constrained using H2O retrievals from multiple bands.

Description: Megacities are major sources of anthropogenic fossil fuel CO2 emissions. The spatial extents of these large urban systems cover areas of 10,000 km2 or more with complex topography and changing landscapes. We present a high-resolution land-atmosphere modelling system for urban CO2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO2 emission product, Hestia-LA, to simulate atmospheric CO2 concentrations across the LA megacity at spatial resolutions as fine as ~ 1 km. We evaluated multiple WRF configurations, selecting one that minimized errors in wind speed, wind direction, and boundary layer height as validated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate- (4-km) and high- (1.3-km) resolution simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved PBL heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3-km resolution) fossil fuel CO2 emission products to evaluate the impact of the spatial resolution of the CO2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO2 concentrations. We find that high spatial resolution in the fossil fuel CO2 emissions is more important than in the atmospheric model to capture CO2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO2 fields to qualitatively evaluate greenhouse gas measurements over the LA megacity. Spatial correlations in the atmospheric CO2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO2 emissions monitoring in the LA megacity requires FFCO2 emissions modelling with ~ 1 km resolution since coarser resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.

Description: This paper presents an analysis of methane emissions from the Los Angeles basin at monthly timescales across a four-year time period – from September 2011 to August 2015. Using observations acquired by a ground-based near-infrared remote sensing instrument on Mount Wilson, California combined with atmospheric CH4-CO2 tracer-tracer correlations, we observed −18 % to +22 % monthly variability in CH4:CO2 from the annual mean in the Los Angeles basin. Top-down estimates of methane emissions for the basin also exhibit significant monthly variability (−19 % to +31 % from annual mean and a maximum month-to-month change of 47 %). During this period, methane emissions consistently peaked in the late summer/early fall and winter. The estimated annual methane emissions did not show a statistically significant trend over the 2011 to 2015 time period.

Description: Understanding the processes controlling terrestrial carbon fluxes is one of the grand challenges of climate science. Carbon cycle process controls are readily studied at local scales, but integrating local knowledge across extremely heterogeneous biota, landforms and climate space has proven to be extraordinarily challenging. Consequently, top-down or integral flux constraints at process-relevant scales are essential to reducing process uncertainty. Future satellite-based estimates of greenhouse gas fluxes – such as CO2 and CH4 – could potentially provide the constraints needed to resolve biogeochemical process controls at the required scales. Our analysis is focused on Amazon wetland CH4 emissions, which amount to a scientifically crucial and methodologically challenging case study. We quantitatively derive the observing system requirements for testing wetland CH4 emission hypotheses at a process-relevant scale. To capture the spatial and temporal patterns of the major hydrological and carbon controls over wetland CH4 production, a satellite mission will need to resolve monthly CH4 fluxes at a 300 km resolution and with a 25 % flux precision. We simulate a range of low-earth orbit (LEO) and geostationary orbit (GEO) CH4 observing system configurations to evaluate the ability of these approaches to meet the CH4 flux requirements. Conventional LEO and GEO missions resolve monthly 300 km × 300 km Amazon wetland fluxes at a 186 % and 33 % median uncertainty level. Improving LEO CH4 measurement precision by √2 would only reduce the median CH4 flux uncertainty to 132 %. A GEO mission with targeted observing capability could resolve fluxes at a 21–27 % median precision by increasing the observation density in high cloud-cover regions at the expense of other parts of the domain. Process-driven greenhouse gas observing system simulations can enhance conventional uncertainty reduction assessments by providing the measurement needs for testing biogeochemical process hypotheses.

Description: Atmospheric absorption in the O2 A-band (12 950–13 200 cm−1) offers a unique opportunity to retrieve aerosol extinction profiles from space-borne measurements due to the large dynamic range of optical thickness in that spectral region. Absorptions in strong O2 lines are saturated; therefore, any radiance measured in these lines originates from scattering in the upper part of the atmosphere. Outside of O2 lines, or in weak lines, the atmospheric column absorption is small, and light penetrates to lower atmospheric layers, allowing for the quantification of aerosols and other scatterers near the surface.While the principle of aerosol profile retrieval using O2 A-band absorption from space is well-known, a thorough quantification of the information content, i.e., the amount of vertical profile information that can be obtained, and the dependence of the information content on the spectral resolution of the measurements, has not been thoroughly conducted. Here, we use the linearized vector radiative transfer model VLIDORT to perform spectrally resolved simulations of atmospheric radiation in the O2 A-band for four different aerosol extinction profile scenarios: urban (urban–rural areas), highly polluted (megacity areas with large aerosol extinction), elevated layer (identifying elevated plumes, for example for biomass burning) and low extinction (representative of small aerosol extinction, such as vegetated, marine and arctic areas). The high-resolution radiances emerging from the top of the atmosphere measurements are degraded to different spectral resolutions, simulating spectrometers with different resolving powers. We use optimal estimation theory to quantify the information content in the aerosol profile retrieval with respect to different aerosol parameters and instrument spectral resolutions. The simulations show that better spectral resolution generally leads to an increase in the total amount of information that can be retrieved, with the number of degrees of freedom (DoF) varying between 0.34–2.01 at low resolution (5 cm−1) to 3.43–5.38 at high resolution (0.05 cm−1) among all the different cases. A particularly strong improvement was found in the retrieval of tropospheric aerosol extinction profiles in the lowest 5 km of the atmosphere. At high spectral resolutions (0.05 cm−1), 1.18–1.48 and 1.31–1.96 DoF can be obtained in the lower (0–2 km) and middle (2–5 km) troposphere, respectively, for the different cases. Consequently, a separation of lower and mid tropospheric aerosols is possible, implying the feasibility of identification of elevated biomass burning aerosol plumes (elevated layer scenario). We find that a higher single scattering albedo (SSA) allows for the retrieval of more aerosol information. However, the dependence on SSA is weaker at higher spectral resolutions. The vegetation (surface albedo 0.3), marine (surface albedo 0.05) and arctic (surface albedo 0.9) cases show that the dependence of DoF on the surface albedo decreases with higher resolution. At low resolution (5 cm−1), the DoF are 1.19 for the marine case, 0.73 for the vegetation case and 0.34 for the arctic case, but increase considerably at 0.05 cm−1 resolution to 3.84 (marine) and 3.43 (both vegetation and arctic), showing an improvement of a factor of 10 for the arctic case. Vegetation and arctic case also show the same DoF at higher resolution, showing that an increase of albedo beyond a certain value, i.e., 0.3 in our case, does not lead to a larger information content. The simulations also reveal a moderate dependence of information content on the integration time of the measurements, i.e., the noise of the spectra. However, our results indicate that a larger increase in DoF is obtained by an increase in spectral resolution despite lower signal-to-noise ratios.

Description: Understanding the processes controlling terrestrial carbon fluxes is one of the grand challenges of climate science. Carbon cycle process controls are readily studied at local scales, but integrating local knowledge across extremely heterogeneous biota, landforms and climate space has proven to be extraordinarily challenging. Consequently, top-down or integral flux constraints at process-relevant scales are essential to reducing process uncertainty. Future satellite-based estimates of greenhouse gas fluxes – such as CO2 and CH4 – could potentially provide the constraints needed to resolve biogeochemical process controls at the required scales. Our analysis is focused on Amazon wetland CH4 emissions, which amount to a scientifically crucial and methodologically challenging case study. We quantitatively derive the observing system (OS) requirements for testing wetland CH4 emission hypotheses at a process-relevant scale. To distinguish between hypothesized hydrological and carbon controls on Amazon wetland CH4 production, a satellite mission will need to resolve monthly CH4 fluxes at a ∼ 333 km resolution and with a ≤ 10 mg CH4 m−2 day−1 flux precision. We simulate a range of low-earth orbit (LEO) and geostationary orbit (GEO) CH4 OS configurations to evaluate the ability of these approaches to meet the CH4 flux requirements. Conventional LEO and GEO missions resolve monthly ∼ 333 km Amazon wetland fluxes at a 17.0 and 2.7 mg CH4 m−2 day−1 median uncertainty level. Improving LEO CH4 measurement precision by 2 would only reduce the median CH4 flux uncertainty to 11.9 mg CH4 m−2 day−1. A GEO mission with targeted observing capability could resolve fluxes at a 2.0–2.4 mg CH4 m−2 day−1 median precision by increasing the observation density in high cloud-cover regions at the expense of other parts of the domain. We find that residual CH4 concentration biases can potentially reduce the ∼ 5-fold flux CH4 precision advantage of a GEO mission to a ∼ 2-fold advantage (relative to a LEO mission). For residual CH4 bias correlation lengths of 100 km, the GEO can nonetheless meet the  ≤  10 mg CH4 m−2 day−1 requirements for systematic biases ≤ 10 ppb. Our study demonstrates that process-driven greenhouse gas OS simulations can enhance conventional uncertainty reduction assessments by quantifying the OS characteristics required for testing biogeochemical process hypotheses.

Description: This paper presents an analysis of methane emissions from the Los Angeles Basin at monthly timescales across a 4-year time period – from September 2011 to August 2015. Using observations acquired by a ground-based near-infrared remote sensing instrument on Mount Wilson, California, combined with atmospheric CH4–CO2 tracer–tracer correlations, we observed −18 to +22 % monthly variability in CH4 : CO2 from the annual mean in the Los Angeles Basin. Top-down estimates of methane emissions for the basin also exhibit significant monthly variability (−19 to +31 % from annual mean and a maximum month-to-month change of 47 %). During this period, methane emissions consistently peaked in the late summer/early fall and winter. The estimated annual methane emissions did not show a statistically significant trend over the 2011 to 2015 time period.

Description: Megacities are major sources of anthropogenic fossil fuel CO2 (FFCO2) emissions. The spatial extents of these large urban systems cover areas of 10 000 km2 or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO2 emission product, Hestia-LA, to simulate atmospheric CO2 concentrations across the LA megacity at spatial resolutions as fine as  ∼  1 km. We evaluated multiple WRF configurations, selecting one that minimized errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO2 emission products to evaluate the impact of the spatial resolution of the CO2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO2 concentrations. We find that high spatial resolution in the fossil fuel CO2 emissions is more important than in the atmospheric model to capture CO2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO2 emissions monitoring in the LA megacity requires FFCO2 emissions modelling with  ∼  1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.

Description: Remote sensing of greenhouse gases (GHGs) in cities, where high GHG emissions are typically associated with heavy aerosol loading, is challenging due to retrieval uncertainties caused by the imperfect characterization of scattering by aerosols. We investigate this problem by developing GFIT3, a full physics algorithm to retrieve GHGs (CO2 and CH4) by accounting for aerosol scattering effects in polluted urban atmospheres. In particular, the algorithm includes coarse- (including sea salt and dust) and fine- (including organic carbon, black carbon, and sulfate) mode aerosols in the radiative transfer model. The performance of GFIT3 is assessed using high-spectral-resolution observations over the Los Angeles (LA) megacity made by the California Laboratory for Atmospheric Remote Sensing Fourier transform spectrometer (CLARS-FTS). CLARS-FTS is located on Mt. Wilson, California, at 1.67 km a.s.l. overlooking the LA Basin, and it makes observations of reflected sunlight in the near-infrared spectral range. The first set of evaluations are performed by conducting retrieval experiments using synthetic spectra. We find that errors in the retrievals of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) due to uncertainties in the aerosol optical properties and atmospheric a priori profiles are less than 1 % on average. This indicates that atmospheric scattering does not induce a large bias in the retrievals when the aerosols are properly characterized. The methodology is then further evaluated by comparing GHG retrievals using GFIT3 with those obtained from the CLARS-GFIT algorithm (used for currently operational CLARS retrievals) that does not account for aerosol scattering. We find a significant correlation between retrieval bias and aerosol optical depth (AOD). A comparison of GFIT3 AOD retrievals with collocated ground-based observations from AErosol RObotic NETwork (AERONET) shows that the developed algorithm produces very accurate results, with biases in AOD estimates of about 0.02. Finally, we assess the uncertainty in the widely used tracer–tracer ratio method to obtain CH4 emissions based on CO2 emissions and find that using the CH4/CO2 ratio effectively cancels out biases due to aerosol scattering. Overall, this study of applying GFIT3 to CLARS-FTS observations improves our understanding of the impact of aerosol scattering on the remote sensing of GHGs in polluted urban atmospheric environments. GHG retrievals from CLARS-FTS are potentially complementary to existing ground-based and spaceborne observations to monitor anthropogenic GHG fluxes in megacities.