Search

The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO2 over small areas, as well as XCO2 biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses.

The Geostationary Carbon Cycle Observatory (GeoCarb) was selected as NASA's second Earth Venture Mission (EVM-2). The scientific objectives of GeoCarb are to advance our knowledge of the carbon cycle, in particular land-atmosphere fluxes of the greenhouse gases carbon dioxide (CO2) and methane (CH4), and the effects on these fluxes on the Earth's radiation budget. GeoCarb will retrieve column integrated amounts of CO2 (XCO2), CH4 (XCH4) and CO (XCO; important for understanding tropospheric chemistry), in addition to Solar-Induced Fluorescence (SIF), which is proportional to the photosynthetic activity of vegetation, from hyperspectral resolution measurements in the O2 A-band at 0.76 um, the weak CO2 band at 1.6 um, the strong CO2 band at 2.06 um, and a CH4/CO band at 2.32 um. Unlike it's polar orbiting predecessors (OCO-2/3, GOSAT-1/2, TROPOMI), GeoCarb will be in a Geostationary orbit with a sub-satellite point centered over the Americas. This orbital configuration combined with its high spatial resolution imaging capabilities will provide an unprecedented view of these quantities on spatial and temporal scales accurate enough to resolve sources and sinks to improve land-atmosphere CO2 and CH4 flux calculations and reduce the uncertainty of these fluxes. This paper will present a description of the GeoCarb instrument and the level-2 retrieval algorithms which will be followed by simulation experiments to determine a relatively comprehensive error budget for each target gas. Several sources of uncertainty will be explored including that from the instrument calibration parameters for radiometric gain, the instrument line shape (ILS), the polarization, and the geolocation pointing, in addition to, forward model parameters including that from meteorology and spectroscopy. The results indicate that the errors (1σ) are less than the instrument's multi-sounding precision requirements of 1.2 ppm, 10 ppb, and 12 ppb (10 %), for XCO2, XCH4, and XCO, respectively. In particular, when considering the sources of uncertainty separately and in combination (all sources included), we find overall RMS errors of 1.06 ppm for XCO2, 8.2 ppb for XCH4, and 2.5 ppb for XCO, respectively. Additionally, we find that, as expected, errors in XCO2 and XCH4 are dominated by forward model and other systematic errors, while errors in XCO, like SIF, are dominated by measurement noise.

著者人数: 41名, 形態: カラー図版あり, Number of authors: 41, Physical characteristics: Original contains color illustrations, Accepted: 2020-01-20, 資料番号: PA2010002000

This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH₄ dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH₄) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH₄ observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH₄ data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO₂ and the XCH₄∕XCO₂ ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH₄ from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).

The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. Annual mean CO2 growth rates have been determined globally and for selected latitude bands from satellite retrievals of column-average dry-air mole fractions of CO2, i.e., XCO2, for the years 2003 to 2016. The global XCO2 growth rates agree with National Oceanic and Atmospheric Administration (NOAA) growth rates from CO2 surface observations within the uncertainty of the satellite-derived growth rates (mean difference ± standard deviation: 0.0±0.24ppm/year; R: 0.87). This new and independent data set confirms record large growth rates around 3 ppm/year in 2015 and 2016, which are attributed to the 2015/2016 El Niño. Based on a comparison of the satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Niño Southern Oscillation (ENSO) indices, we estimate by how much the impact of ENSO dominates the impact of fossil fuel burning related emissions in explaining the variance of the atmospheric CO2 growth rate.

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO$_{2}$) and methane (CH$_{4}$), denoted XCO$_{2}$ and XCH$_{4}$, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO$_{2}$) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5°x5°data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO$_{2}$ or XCH$_{4}$, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO$_{2}$ Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1$^{σ}$): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1$^{σ}$): 0.66 ppm (0.70 ppm). The corresponding values for the XCH$_{4}$ products are singleobservation random error (1$^{σ}$): 17.4 ppb (monthly: 8.7 ppb); global bias: -2.0 ppb (-2.9 ppb); and spatiotemporal bias (1$^{σ}$): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO$_{2}$ and XCH$_{4}$ growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations.

Gravity anomaly anomalies for 2015 and 2016

Gravity anomaly anomalies for 2015 and 2016

Precipitation anomalies calculated from TRMM (Tropical Rainfall Measuring Mission) (Huffman et al. 2007) version 7 and monthly daily maximum and minimum temperature from NOAA Climate Prediction Center (CPC) climatology (Fan et al. 2008).