Assimilation of atmospheric methane products into the MACC-II system: From SCIAMACHY to TANSO and IASI

The Monitoring Atmospheric Composition and Climate Interim Implementation (MACC-II) delayed-mode (DM) system has been producing an atmospheric methane (CH4) analysis 6 months behind real time since June 2009. This analysis used to rely on the assimilation of the CH4 product from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument onboard Envisat. Recently the Laboratoire de Météorologie Dynamique (LMD) CH4 products from the Infrared Atmospheric Sounding Interferometer (IASI) and the SRON Netherlands Institute for Space Research CH4 products from the Thermal And Near-infrared Sensor for carbon Observation (TANSO) were added to the DM system. With the loss of Envisat in April 2012, the DM system now has to rely on the assimilation of methane data from TANSO and IASI. This paper documents the impact of this change in the observing system on the methane tropospheric analysis. It is based on four experiments: one free run and three analyses from respectively the assimilation of SCIAMACHY, TANSO and a combination of TANSO and IASI CH4 products in the MACC-II system. The period between December 2010 and April 2012 is studied. The SCIAMACHY experiment globally underestimates the tropospheric methane by 35 part per billion (ppb) compared to the HIAPER Pole-to-Pole Observations (HIPPO) data and by 28 ppb compared the Total Carbon Column Observing Network (TCCON) data, while the free run presents an underestimation of 5 ppb and 1 ppb against the same HIPPO and TCCON data, respectively. The assimilated TANSO product changed in October 2011 from version v.1 to version v.2.0. The analysis of version v.1 globally underestimates the tropospheric methane by 18 ppb compared to the HIPPO data and by 15 ppb compared to the TCCON data. In contrast, the analysis of version v.2.0 globally overestimates the column by 3 ppb. When the high density IASI data are added in the tropical region between 30° N and 30° S, their impact is mainly positive but more pronounced and effective when combined with version v.2.0 of the TANSO products. The resulting analysis globally underestimates the column-averaged dry-air mole fractions of methane (xCH4) just under 1 ppb on average compared to the TCCON data, whereas in the tropics it overestimates xCH4 by about 3 ppb. The random error is estimated to be less than 7 ppb when compared to TCCON data

Accurate mobile remote sensing of XCO₂ and XCH₄ latitudinal transects from aboard a research vessel

A portable Fourier Transform Spectrometer (FTS), model EM27/SUN, is deployed onboard the research vessel Polarstern to measure the column-average dry air mole fractions of carbon dioxide (XCO2) and methane (XCH4) by means of direct sunlight absorption spectrometry. We report on technical developments as well as data calibration and reduction measures required to achieve the targeted accuracy of fractions of a percent in retrieved XCO2 and XCH4 while operating the instrument under field conditions onboard the moving platform during a six week cruise through the Atlantic from Cape Town (South Africa, 34° S, 18° E) to Bremerhaven (Germany, 54° N, 19° E). We demonstrate that our solar tracker typically achieves a tracking precision of better than 0.05° toward the center of the sun throughout the ship cruise which facilitates accurate XCO2 and XCH4 retrievals even under harsh ambient wind conditions. We define several quality filters that screen spectra e.g. when the field-of-view is partially obstructed by ship structures or when the lines-of-sight cross the ship exhaust plume. The measurements in clean oceanic air, can be used to characterize a spurious airmass dependency. After the campaign, deployment of the spectrometer side-by-side the TCCON (Total Carbon Column Observing Network) instrument at Karlsruhe, Germany, allows for determining a calibration factor that makes the entire campaign record traceable to World Meteorological Organization (WMO) standards. Comparisons to observations of the GOSAT satellite and concentration fields modeled by the European Centre for Medium-Range Weather Forecasts (ECMWF) within the project Monitoring of Atmospheric Composition and Climate – Interim Implementation (MACC-II) demonstrate that the observational setup is well suited to provide validation opportunities above the ocean and along interhemispheric transects

Accurate mobile remote sensing of XCO₂ and XCH₄ latitudinal transects from aboard a research vessel

A portable Fourier transform spectrometer (FTS), model EM27/SUN, was deployed onboard the research vessel Polarstern to measure the column-average dry air mole fractions of carbon dioxide (XCO$_{2}$) and methane (XCH$_{4}$) by means of direct sunlight absorption spectrometry. We report on technical developments as well as data calibration and reduction measures required to achieve the targeted accuracy of fractions of a percent in retrieved XCO$_{2}$ and XCH$_{4}$ while operating the instrument under field conditions onboard the moving platform during a 6-week cruise on the Atlantic from Cape Town (South Africa, 34° S, 18° E; 5 March 2014) to Bremerhaven (Germany, 54° N, 19° E; 14 April 2014). We demonstrate that our solar tracker typically achieved a tracking precision of better than 0.05° toward the center of the sun throughout the ship cruise which facilitates accurate XCO$_{2}$ and XCH$_{4}$ retrievals even under harsh ambient wind conditions. We define several quality filters that screen spectra, e.g., when the field of view was partially obstructed by ship structures or when the lines-of-sight crossed the ship exhaust plume. The measurements in clean oceanic air, can be used to characterize a spurious air-mass dependency. After the campaign, deployment of the spectrometer alongside the TCCON (Total Carbon Column Observing Network) instrument at Karlsruhe, Germany, allowed for determining a calibration factor that makes the entire campaign record traceable to World Meteorological Organization (WMO) standards. Comparisons to observations of the GOSAT satellite and concentration fields modeled by the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) demonstrate that the observational setup is well suited to provide validation opportunities above the ocean and along interhemispheric transects

Extending methane profiles from aircraft into the stratosphere for satellite total column validation: A comparative analysis of different data sources

Airborne observations of greenhouse gases are a very useful reference for validation of satellite-based column averaged dry air mole fraction data. However, since the aircraft data are available only up to about 9-13 km altitude, these profiles do not fully represent the depth of the atmosphere observed by satellites and therefore need to be extended synthetically into the stratosphere. In the near future, observations of CO₂ and CH₄ made from passenger aircraft are expected to be available through the In-Service Aircraft for a Global Observing System (IAGOS) project. In this study, we analyse three different data sources that are available for the stratospheric extension of aircraft profiles by comparing the error introduced by each of them into the total column and provide recommendations regarding the best approach. First, we analyse CH₄ fields from two different models of atmospheric composition - the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System for Composition (C-IFS) and the TOMCAT/SLIMCAT 3-D 20 chemical transport model. Secondly, we consider scenarios that simulate the effect of using CH₄ climatologies such as those based on balloons or satellite limb soundings. Thirdly, we assess the impact of using a-priori profiles used in the satellite retrievals for the stratospheric part of the total column. We find that the models considered in this study have a better estimation of the stratospheric CH₄ as compared to the climatology-based data and the satellite a-priori profiles. Both the C-IFS and TOMCAT models have a bias of about -9 ppb at the locations where tropospheric vertical profiles will be measured 25 by IAGOS. The C-IFS model, however, has a lower random error (6.5 ppb) than TOMCAT (12.8 ppb). These values are well within the minimum desired accuracy and precision of satellite total column XCH₄ retrievals (10 ppb and 34 ppb, respectively). In comparison, the a-priori profile from the University of Leicester Greenhouse Gases Observing Satellite (GOSAT) Proxy XCH₄ retrieval and climatology-based data introduce larger random errors in the total column, being limited in spatial coverage and temporal variability. Furthermore, we find that the bias in the models varies with latitude and season. Therefore, applying appropriate bias correction to the model fields before using them for profile extension is expected to further decrease the error contributed by the stratospheric part of the profile to the total column

Impact of transport model errors on the global and regional methane emissions estimated by inverse modelling

A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH[subscript 4] model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr[superscript −1] at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr[superscript −1] in North America to 7 Tg yr[superscript −1] in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes

Ability of the 4-D-Var analysis of the GOSAT BESD XCO₂ retrievals to characterize atmospheric CO₂ at large and synoptic scales

This study presents results from the European Centre for Medium-Range Weather Forecasts (ECMWF) carbon dioxide (CO₂) analysis system where the atmospheric CO₂ is controlled through the assimilation of column-averaged dry-air mole fractions of CO₂ (XCO₂) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO₂), and they are both evaluated against XCO₂ data from the Total Carbon Column Observing Network (TCCON). We show that the assimilation of the GOSAT XCO₂ product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO₂ fields with an improved mean absolute error of 0.6 parts per million (ppm) and an improved station-to-station bias deviation of 0.7  ppm compared to the free run (1.1 and 1.4  ppm, respectively) and an improved estimated precision of 1  ppm compared to the GOSAT BESD data (3.3  ppm). We also show that the analysis has skill for synoptic situations in the vicinity of frontal systems, where the GOSAT retrievals are sparse due to cloud contamination. We finally computed the 10-day forecast from each analysis at 00:00  UTC, and we demonstrate that the CO₂ forecast shows synoptic skill for the largest-scale weather patterns (of the order of 1000  km) even up to day 5 compared to its own analysis

Off-line algorithm for calculation of vertical tracer transport in the troposphere due to deep convection

A modified cumulus convection parametrisation scheme is presented. This scheme computes the mass of air transported upward in a cumulus cell using conservation of moisture and a detailed distribution of convective precipitation provided by a reanalysis dataset. The representation of vertical transport within the scheme includes entrainment and detrainment processes in convective updrafts and downdrafts. Output from the proposed parametrisation scheme is employed in the National Institute for Environmental Studies (NIES) global chemical transport model driven by JRA-25/JCDAS reanalysis. The simulated convective precipitation rate and mass fluxes are compared with observations and reanalysis data. A simulation of the short-lived tracer [superscript 222]Rn is used to further evaluate the performance of the cumulus convection scheme. Simulated distributions of [superscript 222]Rn are evaluated against observations at the surface and in the free troposphere, and compared with output from models that participated in the TransCom-CH4 Transport Model Intercomparison. From this comparison, we demonstrate that the proposed convective scheme in general is consistent with observed and modeled results

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort