Fingerprint of rice paddies in spatial-temporal dynamics of atmospheric methane concentration in monsoon Asia.

Agriculture (e.g., rice paddies) has been considered one of the main emission sources responsible for the sudden rise of atmospheric methane concentration (XCH4) since 2007, but remains debated. Here we use satellite-based rice paddy and XCH4 data to investigate the spatial-temporal relationships between rice paddy area, rice plant growth, and XCH4 in monsoon Asia, which accounts for ~87% of the global rice area. We find strong spatial consistencies between rice paddy area and XCH4 and seasonal consistencies between rice plant growth and XCH4. Our results also show a decreasing trend in rice paddy area in monsoon Asia since 2007, which suggests that the change in rice paddy area could not be one of the major drivers for the renewed XCH4 growth, thus other sources and sinks should be further investigated. Our findings highlight the importance of satellite-based paddy rice datasets in understanding the spatial-temporal dynamics of XCH4 in monsoon Asia

Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite – Part 2: Methane

Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases. SCIAMACHY on ENVISAT is the first satellite instrument whose measurements are sensitive to concentration changes of the two gases at all altitude levels down to the Earth's surface where the source/sink signals are largest. We have processed three years (2003–2005) of SCIAMACHY near-infrared nadir measurements to simultaneously retrieve vertical columns of CO2 (from the 1.58 µm absorption band), CH4 (1.66 µm) and oxygen (O2 A-band at 0.76 µm) using the scientific retrieval algorithm WFM-DOAS. We show that the latest version of WFM-DOAS, version 1.0, which is used for this study, has been significantly improved with respect to its accuracy compared to the previous versions while essentially maintaining its high processing speed (~1 min per orbit, corresponding to ~6000 single measurements, and per gas on a standard PC). The greenhouse gas columns are converted to dry air column-averaged mole fractions, denoted XCO2 (in ppm) and XCH4 (in ppb), by dividing the greenhouse gas columns by simultaneously retrieved dry air columns. For XCO2 dry air columns are obtained from the retrieved O2 columns. For XCH4 dry air columns are obtained from the retrieved CO2 columns because of better cancellation of light path related errors compared to using O2 columns retrieved from the spectrally distant O2 A-band. Here we focus on a discussion of the XCH4 data set. The XCO2 data set is discussed in a separate paper (Part 1). For 2003 we present detailed comparisons with the TM5 model which has been optimally matched to highly accurate but sparse methane surface observations. After accounting for a systematic low bias of ~2% agreement with TM5 is typically within 1–2%. We investigated to what extent the SCIAMACHY XCH4 is influenced by the variability of atmospheric CO2 using global CO2 fields from NOAA's CO2 assimilation system CarbonTracker. We show that the CO2 corrected and uncorrected XCH4 spatio-temporal pattern are very similar but that agreement with TM5 is better for the CarbonTracker CO2 corrected XCH4. In line with previous studies (e.g., Frankenberg et al., 2005b) we find higher methane over the tropics compared to the model. We show that tropical methane is also higher when normalizing the CH4 columns with retrieved O2 columns instead of CO2. In consistency with recent results of Frankenberg et al. (2008b) it is shown that the magnitude of the retrieved tropical methane is sensitive to the choice of the spectroscopic line parameters of water vapour. Concerning inter-annual variability we find similar methane spatio-temporal pattern for 2003 and 2004. For 2005 the retrieved methane shows significantly higher variability compared to the two previous years, most likely due to somewhat larger noise of the spectral measurement

Four-dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations

Recent observations from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument aboard ENVISAT have brought new insights in the global distribution of atmospheric methane. In particular, the observations showed higher methane concentrations in the tropics than previously assumed. Here, we analyze the SCIAMACHY observations and their implications for emission estimates in detail using a four-dimensional variational (4D-Var) data assimilation system. We focus on the period September to November 2003 and on the South American continent, for which the satellite observations showed the largest deviations from model simulations. In this set-up the advantages of the 4D-Var approach and the zooming capability of the underlying TM5 atmospheric transport model are fully exploited. After application of a latitude-dependent bias correction to the SCIAMACHY observations, the assimilation system is able to accurately fit those observations, while retaining consistency with a network of surface methane measurements. The main emission increments resulting from the inversion are an increase in the tropics, a decrease in South Asia, and a decrease at northern hemispheric high latitudes. The SCIAMACHY observations yield considerable additional emission uncertainty reduction, particularly in the (sub-)tropical regions, which are poorly constrained by the surface network. For tropical South America, the inversion suggests more than a doubling of emissions compared to the a priori during the 3 months considered. Extensive sensitivity experiments, in which key assumptions of the inversion set-up are varied, show that this finding is robust. Independent airborne observations in the Amazon basin support the presence of considerable local methane sources. However, these observations also indicate that emissions from eastern South America may be smaller than estimated from SCIAMACHY observations. In this respect it must be realized that the bias correction applied to the satellite observations does not take into account potential regional systematic errors, which - if identified in the future - will lead to shifts in the overall distribution of emission estimates

Greenhouse gas emissions, inventories and validation

The emission of greenhouse gases has become a very high priority research and environmental policy issue due to their effects on global climate. The knowledge of changes in global atmospheric concentrations of greenhouse gases since the industrial revolution is well documented, and the global budgets are reasonably well known. However, even at this scale there are important uncertainties in the budgets, for example, in the case of methane while the main sources and sinks have been identified, temporal changes in the global average concentrations since the early 1990s are not understood. In the absence of a quantitative explanation with appropriate experimental support, it is clear that current knowledge of the causes of changes in the global methane budget is inadequate to predict the effect of changes in specific emission sectors. In developing control strategies to reduce emissions it is necessary to validate national emissions and their spatial disaggregation. The methodology to underpin such a process is at an early stage of development and is not fully implemented in any country, even though target emission reductions have already been announced. Furthermore, the scale of the emission reductions is large (eg of 60% reductions by 2050 relative to 1990 baseline). There is therefore an urgent requirement for measurement based verification processes to support such challenging emission reductions. In this paper we provide the background in greenhouse gas emissions globally and in the UK followed by examples of approaches to validate emissions at the UK scale and within the regions

Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground‐based TCCON data and model calculations

We report new short-wave infrared (SWIR) column retrievals of atmospheric methane (X_(CH4)) from the Japanese Greenhouse Gases Observing SATellite (GOSAT) and compare observed spatial and temporal variations with correlative ground-based measurements from the Total Carbon Column Observing Network (TCCON) and with the global 3-D GEOS-Chem chemistry transport model. GOSAT X_(CH4) retrievals are compared with daily TCCON observations at six sites between April 2009 and July 2010 (Bialystok, Park Falls, Lamont, Orleans, Darwin and Wollongong). GOSAT reproduces the site-dependent seasonal cycles as observed by TCCON with correlations typically between 0.5 and 0.7 with an estimated single-sounding precision between 0.4–0.8%. We find a latitudinal-dependent difference between the X_(CH4) retrievals from GOSAT and TCCON which ranges from 17.9 ppb at the most northerly site (Bialystok) to −14.6 ppb at the site with the lowest latitude (Darwin). We estimate that the mean smoothing error difference included in the GOSAT to TCCON comparisons can account for 15.7 to 17.4 ppb for the northerly sites and for 1.1 ppb at the lowest latitude site. The GOSAT X_(CH4) retrievals agree well with the GEOS-Chem model on annual (August 2009 – July 2010) and monthly timescales, capturing over 80% of the zonal variability. Differences between model and observed X_(CH4) are found over key source regions such as Southeast Asia and central Africa which will be further investigated using a formal inverse model analysis

Can a “state of the art” chemistry transport model simulate Amazonian tropospheric chemistry?

We present an evaluation of a nested high-resolution Goddard Earth Observing System (GEOS)-Chem chemistry transport model simulation of tropospheric chemistry over tropical South America. The model has been constrained with two isoprene emission inventories: (1) the canopy-scale Model of Emissions of Gases and Aerosols from Nature (MEGAN) and (2) a leaf-scale algorithm coupled to the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model, and the model has been run using two different chemical mechanisms that contain alternative treatments of isoprene photo-oxidation. Large differences of up to 100 Tg C yr^(−1) exist between the isoprene emissions predicted by each inventory, with MEGAN emissions generally higher. Based on our simulations we estimate that tropical South America (30–85°W, 14°N–25°S) contributes about 15–35% of total global isoprene emissions. We have quantified the model sensitivity to changes in isoprene emissions, chemistry, boundary layer mixing, and soil NO_x emissions using ground-based and airborne observations. We find GEOS-Chem has difficulty reproducing several observed chemical species; typically hydroxyl concentrations are underestimated, whilst mixing ratios of isoprene and its oxidation products are overestimated. The magnitude of model formaldehyde (HCHO) columns are most sensitive to the choice of chemical mechanism and isoprene emission inventory. We find GEOS-Chem exhibits a significant positive bias (10–100%) when compared with HCHO columns from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI) for the study year 2006. Simulations that use the more detailed chemical mechanism and/or lowest isoprene emissions provide the best agreement to the satellite data, since they result in lower-HCHO columns

Satellite Chartography of Atmospheric Methane and carbon monoxide from SCIAMACHY onboard ENVISAT

The UV/Vis/near infrared spectrometer SCIAMACHY on board the European ENVISAT satellite enables total column retrieval of atmospheric methane with high sensitivity to the lower troposphere. The vertical column density of methane is converted to column averaged mixing ratio by using carbon dioxide retrievals as proxy for the probed atmospheric column. For this purpose, we apply concurrent total column measurements of CO_2 in combination with modeled column-averaged CO_2 mixing ratios. Possible systematic errors are discussed in detail while the precision error is 1.8% on average. This paper focuses on methane retrievals from January 2003 through December 2004. The measurements with global coverage over continents are compared with model results from the chemistry–transport model TM4. In the retrievals, the north-south gradient as well as regions with enhanced methane levels can be clearly identified. The highest abundances are found in the Red Basin of China, followed by northern South America, the Gangetic plains of India and central parts of Africa. Especially the abundances in northern South America and the Red Basin are generally higher than modeled. Further, we present the seasonal variations within the investigated time period. Peak values in Asia due to rice emissions are observed from August through October. We expand earlier investigations that suggest underestimated emissions in the tropics. It is shown that these underestimations show a seasonal behavior that peaks from August through December. The global measurements may be used for inverse modeling and are thus an important step towards better quantification of the methane budget

Global column‐averaged methane mixing ratios from 2003 to 2009 as derived from SCIAMACHY: Trends and variability

After a decade of stable or slightly decreasing global methane concentrations, ground-based in situ data show that CH_4 began increasing again in 2007 and that this increase continued through 2009. So far, space-based retrievals sensitive to the lower troposphere in the time period under consideration have not been available. Here we report a long-term data set of column-averaged methane mixing ratios retrieved from spectra of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument onboard Envisat. The retrieval quality after 2005 was severely affected by degrading detector pixels within the methane 2ν_3 absorption band. We identified the most crucial problems in SCIAMACHY detector degradation and overcame the problem by applying a strict pixel mask as well as a new dark current characterization. Even though retrieval precision after the end of 2005 is invariably degraded, consistent methane retrievals from 2003 through 2009 are now possible. Regional time series in the Sahara, Australia, tropical Africa, South America, and Asia show the methane increase in 2007–2009, but we cannot yet draw a firm conclusion concerning the origin of the increase. Tropical Africa even seems to exhibit a negative anomaly in 2006, but an impact from changes in SCIAMACHY detector degradation cannot be excluded yet. Over Assakrem, Algeria, we observed strong similarities between SCIAMACHY measurements and ground-based data in deseasonalized time series. We further show long-term SCIAMACHY xCH_4 averages at high spatial resolution that provide further insight into methane variations on regional scales. The Red Basin in China exhibits, on average, the highest methane abundance worldwide, while other localized features such as the Sudd wetlands in southern Sudan can also be identified in SCIAMACHY xCH_4 averages