Absorption coefficient (ABSCO) tables for the Orbiting Carbon Observatories: Version 5.1

Payne, Vivienne H.; Drouin, Brian J.; Oyafuso, Fabiano; Kuai, Le; Fisher, B.; Sung, Keeyoon; Nemchick, Deacon J.; Crawford, Timothy J.; Smyth, Mike; Crisp, David; Adkins, Erin M.; Hodges, Joseph T.; Long, David A.; Mlawer, E. J.; Merrelli, Aronne; Lunny, Elizabeth; O’Dell, C.

doi:10.1016/j.jqsrt.2020.107217

Cited by 32 publications

(33 citation statements)

References 49 publications

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“…9. SHDOM (Evans, 1998;Pincus and Evans, 2009) is applied by specifying a 3D model atmosphere with a specified 3D field of cloud optical properties. Radiation fields at satellite Figure 1.…”

Section: Radiative Transfer Sensitivity Calculationsmentioning

confidence: 99%

“…The aerosol optical depth vertical structure is the same for all x-y grid points, but the total aerosol optical depths are equal to, e.g., 0.11 and 0.165 for the base and perturbed state O 2 A-band calculations. The OCO-2 ABSCO database of molecular line cross sections (Payne et al, 2020) is used to specify the gas optical depth structure in the x, y, and z 3D grid (of size 32 km × 32 km × 30 km, with a horizontal grid cell size of 0.5 km × 0.5 km). SHDOM was applied in monochromatic calculations at 17 wavelengths, in which the total gas plus aerosol optical depth ranges from small to large values for Lambertian surface scattering over land and Cox-Munk surface wind-dependent bidirectional diffuse reflectance over the ocean.…”

Section: Seasonmentioning

confidence: 99%

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Analysis of 3D cloud effects in OCO-2 XCO2 retrievals

et al. 2021

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Abstract. The presence of 3D cloud radiative effects in OCO-2 retrievals is demonstrated from an analysis of 2014–2019 OCO-2 XCO2 raw retrievals, bias-corrected XCO2bc data, ground-based Total Carbon Column Observation Network (TCCON) XCO2, and Moderate Resolution Imaging Spectroradiometer (MODIS) cloud and radiance fields. In approximate terms, 40 % (quality flag – QF = 0, land or ocean) and 73 % (QF = 1, land or ocean) of the observations are within 4 km of clouds. 3D radiative transfer calculations indicate that 3D cloud radiative perturbations at this cloud distance, for an isolated low-altitude cloud, are larger in absolute value than those due to a 1 ppm increase in CO2. OCO-2 measurements are therefore susceptible to 3D cloud effects. Four 3D cloud metrics, based upon MODIS radiance and cloud fields as well as stand-alone OCO-2 measurements, relate XCO2bc–TCCON averages to 3D cloud effects. This analysis indicates that the operational bias correction has a nonzero residual 3D cloud bias for both QF = 0 and QF = 1 data. XCO2bc–TCCON averages at small cloud distances differ from those at large cloud distances by −0.4 and −2.2 ppm for the QF = 0 and QF = 1 data over the ocean. Mitigation of 3D cloud biases with a table lookup technique, which utilizes the nearest cloud distance (Distkm) and spatial radiance heterogeneity (CSNoiseRatio) 3D metrics, reduces QF = 1 ocean and land XCO2bc–TCCON averages from −1 ppm to near ±0.2 ppm. The ocean QF = 1 XCO2bc–TCCON averages can be reduced to the 0.5 ppm level if 60 % (70 %) of the QF = 1 data points are utilized by applying Distkm (CSNoiseRatio) metrics in a data screening process. Over land the QF = 1 XCO2bc–TCCON averages are reduced to the 0.5 (0.8) ppm level if 65 % (63 %) of the data points are utilized by applying Diastkm (CSNoiseRatio) data screening. The addition of more terms to the linear regression equations used in the current bias correction processing without data screening, however, did not introduce an appreciable improvement in the standard deviations of the XCO2bc–TCCON statistics.

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“…9. SHDOM (Evans, 1998;Pincus and Evans, 2009) is applied by specifying a 3D model atmosphere with a specified 3D field of cloud optical properties. Radiation fields at satellite Figure 1.…”

Section: Radiative Transfer Sensitivity Calculationsmentioning

confidence: 99%

Section: Seasonmentioning

confidence: 99%

Analysis of 3D cloud effects in OCO-2 XCO2 retrievals

et al. 2021

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“…Spectroscopy errors can be estimated (Thompson et al, 2020) and should shrink in future with developments, with ongoing research in water vapour absorption spectroscopy (Elsey et al, 2020;Lechevallier et al, 2018;Menang et al, 2021) and a history of targeted development of spectroscopy to improve retrievals, such as for OCO-2 (Drouin et al, 2016;O'Dell et al, 2018;Payne et al, 2020 -Ngoie, 2008;Teillet et al, 1982), and there are also approaches to dealing with nearby clouds to minimise the effect of imperfect cloud edge identification, shadowing and 3D cloud radiative effects (Massie et al, 2021). Nevertheless, these are all topics that are worth evaluating for Isofit-like TCWV retrievals.…”

Section: Discussion Of Retrieval Results and Limitationsmentioning

confidence: 99%

Boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy

Richardson¹,

Thompson²,

Kurowski³

et al. 2021

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Abstract. Daytime clear-sky total column water vapour (TCWV) is commonly retrieved from visible and shortwave infrared reflectance (VSWIR) measurements, and upcoming missions such as the Earth Surface Mineral Dust Source Investigation (EMIT) will offer unprecedented horizontal resolution of order 30–80 m. We provide evidence that for convective planetary boundary layers (PBLs), spatial variability in TCWV corresponds to variability in PBL water vapour. Using synthetic optimal estimation retrievals applied to Large Eddy Simulation (LES) output, we show that EMIT can retrieve horizontal variability in PBL water vapour, provided that the domain surface is uniformly composed of either vegetated surfaces or mineral surfaces. Random retrieval errors are easily quantified and removed, but biases from −7 % to +34 % remain in retrieved spatial standard deviation and are primarily related to the retrieval’s assumed atmospheric profiles. Future retrieval development could greatly mitigate these errors. Finally, we account for changing solar zenith angle (SZA) from 15–60° and show that the non-vertical solar path destroys the correspondence between footprint retrieved TCWV and the true TCWV directly above that footprint. Even at the 250 m horizontal resolution regularly obtained by current sensors, the derived maps correspond poorly to true TCWV at the pixel-scale, with r2 < 0.6 at SZA = 30°. However, the derived histograms of TCWV in an area are closely related to the true histograms of TCWV at the nominal footprint resolution. Upcoming VSWIR instruments, primarily targeting surface properties, can therefore offer new information on PBL water vapour spatial statistics to the atmospheric community.

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“…These measurements continue to provide valuable insights into surface flux processes and feedbacks (e.g., Tans et al, 1990;Ballantyne et al, 2012;Keeling et al, 2017;Arora et al, 2020). Observed spatial and temporal gradients in the CO 2 mole fraction (relative to dry air) can be combined with a numerical model of atmospheric transport to infer surface fluxes (i.e., exchange of CO 2 between the atmosphere and the underlying ocean or land surface), in a top-down or inverse modeling framework (e.g., Peters et al, 2007;Gurney et al, 2002). An ever-increasing global greenhouse gas measurement network and progress in modeling techniques have tremendously improved our understanding of surface processes.…”

Section: Introductionmentioning

confidence: 99%

“…Satellite retrievals require a forward model of radiative transfer that is run through an inversion system along with satellite-obtained absorption spectra of atmospheric O 2 and CO 2 to infer X CO 2 . While a great amount of progress has been made to identify and eliminate sources of uncertainty emanating from this chain of processes, e.g., in the molecular absorption model and spectroscopy (Thompson et al, 2012;Payne et al, 2020;Hobbs et al, 2020), considerable sources of uncertainty remain. These are attributed to the presence of aerosols in the column (Connor et al, 2016), clouds and cloud shadows (Massie et al, 2021), interference of jointly retrieved parameters (Kulawik et al, 2019), surface properties, and details of the instrumentation.…”

Section: Introductionmentioning

confidence: 99%

Evaluating consistency between total column CO<sub>2</sub> retrievals from OCO-2 and the in situ network over North America: implications for carbon flux estimation

et al. 2021

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Abstract. Feedbacks between the climate system and the carbon cycle represent a key source of uncertainty in model projections of Earth's climate, in part due to our inability to directly measure large-scale biosphere–atmosphere carbon fluxes. In situ measurements of the CO2 mole fraction from surface flasks, towers, and aircraft are used in inverse models to infer fluxes, but measurement networks remain sparse, with limited or no coverage over large parts of the planet. Satellite retrievals of total column CO2 (XCO2), such as those from NASA's Orbiting Carbon Observatory-2 (OCO-2), can potentially provide unprecedented global information about CO2 spatiotemporal variability. However, for use in inverse modeling, data need to be extremely stable, highly precise, and unbiased to distinguish abundance changes emanating from surface fluxes from those associated with variability in weather. Systematic errors in XCO2 have been identified and, while bias correction algorithms are applied globally, inconsistencies persist at regional and smaller scales that may complicate or confound flux estimation. To evaluate XCO2 retrievals and assess potential biases, we compare OCO-2 v10 retrievals with in situ data-constrained XCO2 simulations over North America estimated using surface fluxes and boundary conditions optimized with observations that are rigorously calibrated relative to the World Meteorological Organization X2007 CO2 scale. Systematic errors in simulated atmospheric transport are independently evaluated using unassimilated aircraft and AirCore profiles. We find that the global OCO-2 v10 bias correction shifts the distribution of retrievals closer to the simulated XCO2, as intended. Comparisons between bias-corrected and simulated XCO2 reveal differences that vary seasonally. Importantly, the difference between simulations and retrievals is of the same magnitude as the imprint of recent surface flux in the total column. This work demonstrates that systematic errors in OCO-2 v10 retrievals of XCO2 over land can be large enough to confound reliable surface flux estimation and that further improvements in retrieval and bias correction techniques are essential. Finally, we show that independent observations, especially vertical profile data, such as those from the National Oceanic and Atmospheric Administration aircraft and AirCore programs are critical for evaluating errors in both satellite retrievals and carbon cycle models.

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Absorption coefficient (ABSCO) tables for the Orbiting Carbon Observatories: Version 5.1

Cited by 32 publications

References 49 publications

Analysis of 3D cloud effects in OCO-2 XCO2 retrievals

Analysis of 3D cloud effects in OCO-2 XCO2 retrievals

Boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy

Evaluating consistency between total column CO<sub>2</sub> retrievals from OCO-2 and the in situ network over North America: implications for carbon flux estimation

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Absorption coefficient (ABSCO) tables for the Orbiting Carbon Observatories: Version 5.1

Cited by 32 publications

References 49 publications

Analysis of 3D cloud effects in OCO-2 XCO2 retrievals

Analysis of 3D cloud effects in OCO-2 XCO2 retrievals

Boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy

Evaluating consistency between total column CO&lt;sub&gt;2&lt;/sub&gt; retrievals from OCO-2 and the in situ network over North America: implications for carbon flux estimation

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Product

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Evaluating consistency between total column CO<sub>2</sub> retrievals from OCO-2 and the in situ network over North America: implications for carbon flux estimation