Molecular tracers in secondary organic aerosols (SOAs) can provide information on origin of SOA, as well as regional scale processes involved in their formation. In this study 9 carboxylic acids, 11 ...organosulfates (OSs) and 2 nitrooxy organosulfates (NOSs) were determined in daily aerosol particle filter samples from Vavihill measurement station in southern Sweden during June and July 2012. Several of the observed compounds are photo-oxidation products from biogenic volatile organic compounds (BVOCs). Highest average mass concentrations were observed for carboxylic acids derived from fatty acids and monoterpenes (12. 3 ± 15. 6 and 13. 8 ± 11. 6 ng m−3, respectively). The FLEXPART model was used to link nine specific surface types to single measured compounds. It was found that the surface category sea and ocean was dominating the air mass exposure (56 %) but contributed to low mass concentration of observed chemical compounds. A principal component (PC) analysis identified four components, where the one with highest explanatory power (49 %) displayed clear impact of coniferous forest on measured mass concentration of a majority of the compounds. The three remaining PCs were more difficult to interpret, although azelaic, suberic, and pimelic acid were closely related to each other but not to any clear surface category. Hence, future studies should aim to deduce the biogenic sources and surface category of these compounds. This study bridges micro-level chemical speciation to air mass surface exposure at the macro level.
Year-to-year variations in the atmospheric methane (CH
) growth rate show significant correlation with climatic drivers. The second half of 2010 and the first half of 2011 experienced the strongest ...La Niña since the early 1980s, when global surface networks started monitoring atmospheric CH
mole fractions. We use these surface measurements, retrievals of column-averaged CH
mole fractions from GOSAT, new wetland inundation estimates, and atmospheric δ
C-CH
measurements to estimate the impact of this strong La Niña on the global atmospheric CH
budget. By performing atmospheric inversions, we find evidence of an increase in tropical CH
emissions of ∼6-9 TgCH
yr
during this event. Stable isotope data suggest that biogenic sources are the cause of this emission increase. We find a simultaneous expansion of wetland area, driven by the excess precipitation over the Tropical continents during the La Niña. Two process-based wetland models predict increases in wetland area consistent with observationally-constrained values, but substantially smaller per-area CH
emissions, highlighting the need for improvements in such models. Overall, tropical wetland emissions during the strong La Niña were at least by 5% larger than the long-term mean.
Atmospheric inversions are used to derive constraints on the net sources and sinks of CO.sub.2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy ...that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied.
Atmospheric inversions are used to derive constraints on the net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the ...fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied. In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency. In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO2 inversions in LUMIA.
Atmospheric inversions are used to derive constraints on the net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the ...fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied.In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency.In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself.We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO2 inversions in LUMIA.
We present an analysis of atmospheric transport impact on estimating CO.sub.2 fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm ...(LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO.sub.2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO.sub.2 was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO.sub.2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems, show a relatively large spread for the annual fluxes, ranging between -0.72 and 0.20 PgC yr.sup.-1, which is larger than the a priori uncertainty of 0.47 PgC yr.sup.-1 . The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr.sup.-1 ), in comparison with 0.23 and 0.10 PgC yr.sup.-1 that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions led to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO.sub.2 estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.
We present an analysis of atmospheric transport impact on
estimating CO2 fluxes using two atmospheric inversion systems
(CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm ...(LUMIA)) over Europe in 2018. The main focus of
this study is to quantify the dominant drivers of spread amid CO2
estimates derived from atmospheric tracer inversions. The Lagrangian
transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of
mesoscale transport. The impact of lateral boundary conditions for CO2
was assessed by using two different estimates from the global inversion
systems CarboScope (TM3) and TM5-4DVAR. CO2 estimates calculated with
an ensemble of eight inversions differing in the regional and global
transport models, as well as the inversion systems, show a relatively large
spread for the annual fluxes, ranging between −0.72 and 0.20 PgC yr−1, which is
larger than the a priori uncertainty of 0.47 PgC yr−1. The discrepancies
in annual budget are primarily caused by differences in the mesoscale
transport model (0.51 PgC yr−1), in comparison with 0.23 and 0.10 PgC yr−1 that resulted from the far-field contributions and the inversion
systems, respectively. Additionally, varying the mesoscale transport caused
large discrepancies in spatial and temporal patterns, while changing the
lateral boundary conditions led to more homogeneous spatial and temporal
impact. We further investigated the origin of the discrepancies between
transport models. The meteorological forcing parameters (forecasts versus
reanalysis obtained from ECMWF data products) used to drive the transport
models are responsible for a small part of the differences in CO2
estimates, but the largest impact seems to come from the transport model
schemes. Although a good convergence in the differences between the
inversion systems was achieved by applying a strict protocol of using
identical prior fluxes and atmospheric datasets, there was a non-negligible
impact arising from applying a different inversion system. Specifically, the
choice of prior error structure accounted for a large part of
system-to-system differences.
We present an analysis of atmospheric transport impact on estimating CO2 fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm ...(LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO2 was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems, show a relatively large spread for the annual fluxes, ranging between -0.72 and 0.20 PgC yr-1, which is larger than the a priori uncertainty of 0.47 PgC yr-1. The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr-1), in comparison with 0.23 and 0.10 PgC yr-1 that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions led to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO2 estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.
High-precision analyses of the isotopic composition of methane in ambient air can potentially be used to discriminate between different source categories. Due to the complexity of isotope ratio ...measurements, such analyses have generally been performed in the laboratory on air samples collected in the field. This poses a limitation on the temporal resolution at which the isotopic composition can be monitored with reasonable logistical effort. Here we present the performance of a dual isotope ratio mass spectrometric system (IRMS) and a quantum cascade laser absorption spectroscopy (QCLAS)-based technique for in situ analysis of the isotopic composition of methane under field conditions. Both systems were deployed at the Cabauw Experimental Site for Atmospheric Research (CESAR) in the Netherlands and performed in situ, high-frequency (approx. hourly) measurements for a period of more than 5 months. The IRMS and QCLAS instruments were in excellent agreement with a slight systematic offset of (+0.25 ± 0.04) ‰ for δ13C and (−4.3 ± 0.4) ‰ for δD. This was corrected for, yielding a combined dataset with more than 2500 measurements of both δ13C and δD. The high-precision and high-temporal-resolution dataset not only reveals the overwhelming contribution of isotopically depleted agricultural CH4 emissions from ruminants at the Cabauw site but also allows the identification of specific events with elevated contributions from more enriched sources such as natural gas and landfills. The final dataset was compared to model calculations using the global model TM5 and the mesoscale model FLEXPART-COSMO. The results of both models agree better with the measurements when the TNO-MACC emission inventory is used in the models than when the EDGAR inventory is used. This suggests that high-resolution isotope measurements have the potential to further constrain the methane budget when they are performed at multiple sites that are representative for the entire European domain.
Atmospheric inversions have been used for the past two decades to derive large-scale constraints on the sources and sinks of CO.sub.2 into the atmosphere. The development of dense in situ surface ...observation networks, such as ICOS in Europe, enables in theory inversions at a resolution close to the country scale in Europe. This has led to the development of many regional inversion systems capable of assimilating these high-resolution data, in Europe and elsewhere. The EUROCOM (European atmospheric transport inversion comparison) project is a collaboration between seven European research institutes, which aims at producing a collective assessment of the net carbon flux between the terrestrial ecosystems and the atmosphere in Europe for the period 2006-2015. It aims in particular at investigating the capacity of the inversions to deliver consistent flux estimates from the country scale up to the continental scale.
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