The Greenhouse Gas Climate Change Initiative (GHG-CCI): Comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets

Abstract

(2015). The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets. Remote Sensing of Environment: an interdisciplinary journal, 162 344-362. The Greenhouse Gas Climate Change Initiative (GHG-CCI): comparison and quality assessment of near-surface-sensitive satellite-derived CO2 and CH4 global data sets Abstract The GHG-CCI project is one of several projects of the European Space Agency's (ESA) Climate Change Initiative (CCI). The goal of the CCI is to generate and deliver data sets of various satellite-derived Essential Climate Variables (ECVs) in line with GCOS (Global Climate Observing System) requirements. The "ECV Greenhouse Gases" (ECV GHG) is the global distribution of important climate relevant gases-atmospheric CO2 and CH4-with a quality sufficient to obtain information on regional CO2 and CH4 sources and sinks. Two satellite instruments deliver the main input data for GHG-CCI: SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. The first order priority goal of GHG-CCI is the further development of retrieval algorithms for near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 and XCH4, to meet the demanding user requirements. GHG-CCI focuses on four core data products: XCO2 from SCIAMACHY and TANSO and XCH4 from the same two sensors. For each of the four core data products at least two candidate retrieval algorithms have been independently further developed and the corresponding data products have been quality-assessed and inter-compared. This activity is referred to as "Round Robin" (RR) activity within the CCI. The main goal of the RR was to identify for each of the four core products which algorithms should be used to generate the Climate Research Data Package (CRDP). The CRDP will essentially be the first version of the ECV GHG. This manuscript gives an overview of the GHG-CCI RR and related activities. This comprises the establishment of the user requirements, the improvement of the candidate retrieval algorithms and comparisons with ground-based observations and models. The manuscript summarizes the final RR algorithm selection decision and its justification. Comparison with ground-based Total Carbon Column Observing Network (TCCON) data indicates that the "breakthrough" single measurement precision requirement has been met for SCIAMACHY and TANSO XCO2 (< 3 ppm) and TANSO XCH4 (< 17 ppb). The achieved relative accuracy for XCH4 is 3-15 ppb for SCIAMACHY and 2-8 ppb for TANSO depending on algorithm and time period. Meeting the 0.5 ppm systematic error requirement for XCO2 remains a challenge: approximately 1 ppm has been achieved at the validation sites but also larger differences have been found in regions remote from TCCON. More research is needed to identify the causes for the observed differences. In this context GHG-CCI suggests taking advantage of the ensemble of existing data products, for example, via the EnseMble Median Algorithm (EMMA). Abstract 41 The GHG-CCI pr...

Seasonal variability of stratospheric methane: implications for constraining tropospheric methane budgets using total column observations

“…The insensitivity of model stratospheres to tropospheric change allows for a straightforward solution: prescribed stratospheric CH 4 fields based on satellite observations from ACE-FTS, MIPAS (von Clarmann et al, 2009), or a compilation of remote sensing instruments (Buchwitz et al, 2015). As the representation of tropical convection and exchange across the UTLS advances in models and reduces stratospheric isolation, chemical loss and transport mechanisms would need to be improved.…”

Constraining sector-specific CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt; emissions in the US

“…Furthermore, Gavrilov and Timofeev (2015) find large biases (4.7 ± 2.6 ppm) in GOSAT retrievals of CO 2 . Future retrieval improvements could reduce these biases (e.g., Dils et al, S. M. Miller and A. M. Michalak: Sector-specific CO 2 and CH 4 emissions 2014; Buchwitz et al, 2015). An additional challenge is that current satellites do not provide comprehensive global mapping and are therefore not well-suited for monitoring all urban areas and point sources (Fig.…”

Consistent satellite XCO&lt;sub&gt;2&lt;/sub&gt; retrievals from SCIAMACHY and GOSAT using the BESD algorithm

“…The algorithm is a core algorithm within ESA's CCI (Hollmann et al, 2013;Buchwitz et al, 2013b;Dils et al, 2014) aiming at delivering high-quality satellite retrievals. Here we use the most recent product version (02.00.08) of SCIA-MACHY BESD, which is part of the Climate Research Data Package (CRDP#2) of the CCI project.…”

“…This data product has been used in several key European projects, e.g. ESA's Climate Change Initiative (CCI, www.esa-ghg-cci.org and Buchwitz et al, 2013b;Hollmann et al, 2013) and the EU's Monitoring of Atmospheric Composition and Climate (MACC, Hollingsworth et al, 2008) project.…”