Inverse modeling of European CH4 emissions: sensitivity to the observational network

Inverse modeling is widely employed to provide “top-down” emission estimates using atmospheric measurements. Here, we analyze the dependence of derived CH4 emissions on the sampling frequency and density of the observational surface network, using the TM5-4DVAR inverse modeling system and synthetic observations. This sensitivity study focuses on Europe. The synthetic observations are created by TM5 forward model simulations. The inversions of these synthetic observations are performed using virtually no knowledge on the a priori spatial and temporal distribution of emissions, i.e. the emissions are derived mainly from the atmospheric signal detected by the measurement network. Using the European network of stations for which continuous or weekly flask measurements are available for 2001, the synthetic experiments can retrieve the “true” annual total emissions for single countries such as France within 20%, and for all North West European countries together within ~5%. However, larger deviations are obtained for South and East European countries due to the scarcity of stations in the measurement network. Upgrading flask sites to stations with continuous measurements leads to an improvement for central Europe in emission estimates. For realistic emission estimates over the whole European domain, however, a major extension of the number of stations in the existing network is required. We demonstrate the potential of an extended network of a total of ~60 European stations to provide realistic emission estimates over the whole European domain

Atmospheric constraints on global emissions of methane from plants

We investigate whether a recently proposed large source of CH4 from vegetation can be reconciled with atmospheric measurements. Atmospheric transport model simulations with and without vegetation emissions are compared with background CH4, delta C-13-CH4 and satellite measurements. For present - day CH4 we derive an upper limit to the newly discovered source of 125 Tg CH4 yr(-1). Analysis of preindustrial CH4, however, points to 85 Tg CH4 yr(-1) as a more plausible limit. Model calculations with and without vegetation emissions show strikingly similar results at background surface monitoring sites, indicating that these measurements are rather insensitive to CH4 from plants. Simulations with 125 Tg CH4 yr(-1) vegetation emissions can explain up to 50% of the previously reported unexpectedly high CH4 column abundances over tropical forests observed by SCIAMACHY. Our results confirm the potential importance of vegetation emissions, and call for further research

Scanning Imaging Absorption Spectrometer for Atmospheric Chartography carbon monoxide total columns: Statistical evaluation and comparison with chemistry transport model results

This paper presents a detailed statistical analysis of one year (September 2003 to August 2004) of global Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) carbon monoxide (CO) total column retrievals from the Iterative Maximum Likelihood Method (IMLM) algorithm, version 6.3. SCIAMACHY provides the first solar reflectance measurements of CO and is uniquely sensitive down to the boundary layer. SCIAMACHY measurements and chemistry transport model (CTM) results are compared and jointly evaluated. Significant improvements in agreement occur, especially close to biomass burning emission regions, when the new Global Fire Emissions Database version 2 (GFEDv2) is used with the CTM. Globally, the seasonal variation of the model is very similar to that of the SCIAMACHY measurements. For certain locations, significant differences were found, which are likely related to modeling errors due to CO emission uncertainties. Statistical analysis shows that differences between single SCIAMACHY CO total column measurements and corresponding model results are primarily explained by random instrument noise errors. This strongly suggests that the random instrument noise errors are a good diagnostic for the precision of the measurements. The analysis also indicates that noise in single SCIAMACHY CO measurements is generally greater than actual variations in total columns. It is thus required to average SCIAMACHY data over larger temporal and spatial scales to obtain valuable information. Analyses of monthly averaged SCIAMACHY measurements over 3° × 2° geographical regions indicates that they are of sufficient accuracy to reveal valuable information about spatial and temporal variations in CO columns and provide an important tool for model validation. A large spatial and temporal variation in instrument noise errors exists which shows a close correspondence with the spatial distribution of surface albedo and cloud cover. This large spatial variability is important for the use of monthly and annual mean SCIAMACHY CO total column measurements. The smallest instrument noise errors of monthly mean 3° × 2° SCIAMACHY CO total columns measurements are 0.01 × 1018 molecules/cm2 for high surface albedo areas over the Sahara. Errors in SCIAMACHY CO total column retrievals due to errors other than instrument noise, like cloud cover, calibration, retrieval uncertainties and averaging kernels are estimated to be about 0.05–0.1 × 1018 molecules/cm2 in total. The bias found between model and observations is around 0.05–0.1 1018 molecules/cm2 (or about 5%) which also includes model errors. This thus provides a best estimate of the currently achievable measurement accuracy for SCIAMACHY CO monthly mean averages

Optimizing global CO emission estimates using a four-dimensional variational data assimilation system and surface network observations

We apply a four-dimensional variational (4D-VAR) data assimilation system to optimize carbon monoxide (CO) emissions for 2003 and 2004 and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. The system is designed to assimilate large (satellite) datasets, but in the current study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-VAR system. By design, the system is capable to adjust the emissions in such a way that the posterior simulation reproduces background CO mixing ratios and large-scale pollution events at background stations. Uncertainty reduction up to 60 % in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies for 2004. However, with the limited amount of data from the surface network, the system becomes data sparse resulting in a large solution space. Sensitivity studies have shown that model uncertainties (e.g., vertical distribution of biomass burning emissions and the OH field) and the prior inventories used, influence the inferred emission estimates. Also, since the observations only constrain total CO emissions, the 4D-VAR system has difficulties in separating anthropogenic and biogenic sources in particular. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from the prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument version 4 (V4) shows a slight improved agreement over the well-constrained Northern Hemisphere and in the tropics (except for the African continent). However, the model simulation with posterior emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 10 %. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes

A hybrid multi-regional input-output model of China: integrating the physical agricultural biomass and food system into the monetary supply chain

Industrial Ecolog

Evidence for long-range transport of Carbon Monoxide in the Southern Hemisphere from SCIAMACHY observations

The SCIAMACHY satellite instrument shows enhanced carbon monoxide (CO) columns in the Southern Hemisphere during the local Spring. Chemistry-transport model simulations using the new GFEDv2 biomass-burning emission database show a similar temporal and spatial CO distribution, indicating that the observed enhancements are mainly due to biomass burning (BB). Large differences between the year 2003 and 2004 are observed in both the measurements and the model for South America and Australia. This study analyzes the origin of these observed enhancements in the Southern Hemisphere. The fact that SCIAMACHY is sensitive to surface CO allows for the observation of enhanced CO columns in both emission areas and in areas that are affected by long-range transport of CO. Model results show a large contribution of South American BB CO over Australian BB regions during the 2004 BB season of up to similar to 30-35% and up to 55% further south, with smaller contributions for 2003. BB CO transported from southern Africa contributes up to similar to 40% in 2003 and similar to 30% in 2004. The results indicate that differences between SCIAMACHY CO and the model simulations over Australian BB areas are probably not only caused by uncertainties in local emissions but also in overseas emissions

Tomato: a crop species amenable to improvement by cellular and molecular methods

Tomato is a crop plant with a relatively small DNA content per haploid genome and a well developed genetics. Plant regeneration from explants and protoplasts is feasable which led to the development of efficient transformation procedures. In view of the current data, the isolation of useful mutants at the cellular level probably will be of limited value in the genetic improvement of tomato. Protoplast fusion may lead to novel combinations of organelle and nuclear DNA (cybrids), whereas this technique also provides a means of introducing genetic information from alien species into tomato. Important developments have come from molecular approaches. Following the construction of an RFLP map, these RFLP markers can be used in tomato to tag quantitative traits bred in from related species. Both RFLP's and transposons are in the process of being used to clone desired genes for which no gene products are known. Cloned genes can be introduced and potentially improve specific properties of tomato especially those controlled by single genes. Recent results suggest that, in principle, phenotypic mutants can be created for cloned and characterized genes and will prove their value in further improving the cultivated tomato.

Capping Human Water Footprints in the World's River Basins

10.1029/2019EF001363Earth's Future82e2019EF00136