ALBERT

Description: Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Recent work has shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) have a larger seasonal amplitude and a more negative annual net CO2 balance than those inferred from the in situ data. The causes of this enhanced European CO2 uptake have since become the focus of recent studies. We show this elevated uptake over Europe could largely be explained by mis-fitting data due to regional biases. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate surface flask data and the XCO2 data from the surface-based Total Carbon Column Observing Network (TCCON). The same EnKF system is also used to assimilate two, independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.18 ± 0.1 GtC a−1 compared to a value of 0.56 ± 0.1 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we have performed a series of numerical experiments that assimilate observations with biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that 50–80% of the elevated European uptake in 2010 inferred from GOSAT data is due to retrievals outside the immediate European region, while most of the remainder can be explained by a sub-ppm retrieval bias over Europe. We have used data assimilation techniques to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. We find a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.18 GtC a−1.

Description: The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers (FTS) that record near-infrared (NIR) spectra of the Sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fraction (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle) Lagrangian Particle Dispersion Model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future, and possible TCCON sites. We propose a footprint-based method for the colocation of satellite and TCCON XCO2 observations, and estimate the performance of the method using the NIES model and five GOSAT XCO2 product datasets. Comparison of the proposed approach with a standard geographic method shows higher number of colocation points and average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and La Réunion sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasizes that the colocation is sensitive to local meteorological conditions and flux distributions.

Description: We present 5 years of GOSAT XCH4 retrieved using the "proxy" approach. The Proxy XCH4 data are validated against ground-based TCCON observations and are found to be of high quality with a small bias of 4.8 ppb (∼ 0.27 %) and a single-sounding precision of 13.4 ppb (∼ 0.74 %). The station-to-station bias (ameasure of the relative accuracy) is found to be 4.2 ppb. For the first time the XCH4 / XCO2 ratio component of the Proxy retrieval is validated (bias of 0.014 ppb ppm−1 (∼ 0.30 %), single-sounding precision of 0.033 ppb ppm−1 (∼ 0.72 %)). The uncertainty relating to the model XCO2 component of the Proxy XCH4 is assessed through the use of an ensemble of XCO2 models. While each individual XCO2 model is found to agree well with the TCCON validation data (r = 0.94–0.97), it is not possible to select one model as the best from our comparisons. The median XCO2 value of the ensemble has a smaller scatter against TCCON (a standard deviation of 0.92 ppm) than any of the individual models whilst maintaining a small bias (0.15 ppm). This model median XCO2 is used to calculate the Proxy XCH4 with the maximum deviation of the ensemble from the median used as an estimate of the uncertainty. We compare this uncertainty to the a posteriori retrieval error (which is assumed to reduce with sqrt(N)) and find typically that the model XCO2 uncertainty becomes significant during summer months when the a posteriori error is at its lowest due to the increase in signal related to increased summertime reflected sunlight. We assess the significance of these model and retrieval uncertainties on flux inversion by comparing the GOSAT XCH4 against modelled XCH4 from TM5-4DVAR constrained by NOAA surface observations (MACC reanalysis scenario S1-NOAA). We find that for the majority of regions the differences are much larger than the estimated uncertainties. Our findings show that useful information will be provided to the inversions for the majority of regions in addition to that already provided by the assimilated surface measurements.

Description: Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Several recent studies have shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger seasonal amplitudes and a more negative annual net CO2 balance than those inferred from the in situ data. The cause of this elevated European uptake of CO2 is still unclear, but some recent studies have suggested that this is a genuine scientific phenomenon. Here, we put forward an alternative hypothesis and show that realistic levels of bias in GOSAT data can result in an erroneous estimate of elevated uptake over Europe. We use a global flux inversion system to examine the relationship between measurement biases and estimates of CO2 uptake from Europe. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate conventional surface mole fraction observations and XCO2 retrievals from the surface-based Total Carbon Column Observing Network (TCCON). We use the same EnKF system to assimilate two independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.40 ± 0.19 GtC a−1 compared to a value of 0.58 ± 0.14 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we perform a series of numerical experiments that assimilate observations with added biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that for our global flux inversions, a large portion (60–90 %) of the elevated European uptake inferred from GOSAT data in 2010 is due to retrievals outside the immediate European region, while the remainder can largely be explained by a sub-ppm retrieval bias over Europe. We use a data assimilation approach to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. The inferred biases represent an estimate of systematic differences between GOSAT XCO2 retrievals and the inversion system at regional or sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.20 GtC a−1. Our results highlight the sensitivity of CO2 flux estimates to regional observation biases, which have not been fully characterized by the current observation network. Without further dedicated measurements we cannot prove or disprove that European ecosystems are taking up a larger-than-expected amount of CO2. More robust inversion systems are also needed to infer consistent fluxes from multiple observation types.

Description: We present 5 years of GOSAT XCH4 retrieved using the "proxy" approach. The Proxy XCH4 data are validated against ground-based TCCON observations and are found to be of high-quality with a small bias of 4.8 ppb (~ 0.27%) and a single-sounding precision of 13.4 ppb (~ 0.74%). The station-to-station bias (a measure of the relative accuracy) is found to be 4.2 ppb. For the first time the XCH4 / XCO2 ratio component of the Proxy retrieval is validated (bias of 0.014 ppb ppm−1 (~ 0.3%), single-sounding precision of 0.033 ppb ppm−1 (~ 0.72%)). The uncertainty relating to the model XCO2 component of the Proxy XCH4 is assessed through the use of an ensemble of XCO2 models. While each individual XCO2 model is found to agree well with the TCCON validation data (r=0.94–0.97), it is not possible to select one model as the best from our comparisons. The median XCO2 value of the ensemble has a smaller scatter against TCCON (a standard deviation of 0.92 ppm) than any of the individual models whilst maintaining a small bias (0.15 ppm). This model median XCO2 is used to calculate the Proxy XCH4 with the maximum deviation of the ensemble from the median used as an estimate of the uncertainty. We compare this uncertainty to the a posteriori retrieval error and find typically that the model XCO2 uncertainty becomes significant during summer months when the a posteriori error is at its lowest due to the increase in signal related to increased summertime reflected sunlight. We assess the significance of these model and retrieval uncertainties on flux inversion by comparing the GOSAT XCH4 against modelled XCH4 from TM5-4DVAR constrained by NOAA surface observations (MACC reanalysis scenario S1-NOAA). We find that for the majority of regions the differences are much larger than the estimated uncertainties. Our findings show that useful information will be provided to the inversions for the majority of regions in addition to that already provided by the assimilated measurements.