Variability of fire carbon emissions in equatorial Asia and its nonlinear sensitivity to El Niño

The large peatland carbon stocks in the land use change-affected areas of equatorial Asia are vulnerable to fire. Combining satellite observations of active fire, burned area, and atmospheric concentrations of combustion tracers with a Bayesian inversion, we estimated the amount and variability of fire carbon emissions in equatorial Asia over the period 1997-2015. Emissions in 2015 were of 0.51±0.17Pg carbon-less than half of the emissions from the previous 1997 extreme El Niño, explained by a less acute water deficit. Fire severity could be empirically hindcasted from the cumulative water deficit with a lead time of 1 to 2months. Based on CMIP5 climate projections and an exponential empirical relationship found between fire carbon emissions and water deficit, we infer a total fire carbon loss ranging from 12 to 25Pg by 2100 which is a significant positive feedback to climate warming

On the consistency between global and regional methane emissions inferred from SCIAMACHY, TANSO-FTS, IASI and surface measurements

Satellite retrievals of methane weighted atmospheric columns are assimilated within a Bayesian inversion system to infer the global and regional methane emissions and sinks for the period August 2009 to July 2010. Inversions are independently computed from three different space-borne observing systems and one surface observing system under several hypotheses for prior-flux and observation errors. Posterior methane emissions are compared and evaluated against surface mole fraction observations via a chemistry-transport model. Apart from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY), the simulations agree fairly well with the surface mole fractions. The most consistent configurations of this study using TANSO-FTS (Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer), IASI (Infrared Atmospheric Sounding Interferometer) or surface measurements induce posterior methane global emissions of, respectively, 565 ± 21 Tg yr[superscript:−1], 549 ± 36 Tg yr[superscript:−1] and 538 ± 15 Tg yr[superscript:−1] over the one-year period August 2009–July 2010. This consistency between the satellite retrievals (apart from SCIAMACHY) and independent surface measurements is promising for future improvement of CH4 emission estimates by atmospheric inversions

The GHG-CCI project of ESA's climate change initiative: Data products and application

The goal of the GHG-CCI project (http://www.esa-ghg-cci.org/) of ESA's Climate Change Initiative (CCI) is to generate global atmospheric satellite-derived carbon dioxide (CO 2 ) and methane (CH 4 ) data sets as needed to improve our understanding of the regional sources and sinks of these important greenhouse gases (GHG). Here we present an overview about the latest data set called Climate Research Data Package No. 3 (CRDP3). We focus on the GHG-CCI project core data products, which are near-surface-sensitive column-averaged dry air mole fractions of CO 2 and CH 4 , denoted XCO 2 (in ppm) and XCH 4 (in ppb) retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode radiance observations in the near-infrared/shortwave-infrared spectral region. The GHG-CCI products are primarily individual sensor Level 2 products. However, we also generate merged Level 2 products ("EMMA products"). Here we also present a first GHG-CCI Level 3 product, namely XCO 2 and XCH 4 in Obs4MIPs format (monthly, 5°×5°)

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

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 (<3ppm) and TANSO XCH4 (<17ppb). The achieved relative accuracy for XCH4 is 3-15ppb for SCIAMACHY and 2-8ppb for TANSO depending on algorithm and time period. Meeting the 0.5ppm systematic error requirement for XCO2 remains a challenge: approximately 1ppm 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)