Atmospheric Tracer Transport Research Articles

Atmospheric constraints on changing Arctic CH4 emissions

Rapid warming in the Arctic has the potential to release vast reservoirs of carbon into the atmosphere as methane (CH4) resulting in a strong positive climate feedback. This raises the concern that, after a period of near-zero growth in atmospheric CH4 burden from 1999 to 2006, the increase since then may be in part related to increased Arctic emissions. Measurements of CH4 in background air samples provide useful, direct information to determine if Arctic CH4 emissions are increasing. One sensitive first-order indicator for large emission change is the Interpolar Difference, that is the difference in surface atmospheric annual means between polar northern and southern zones (53°–90°), which has varied interannually, but did not increase from 1992 to 2019. The Interpolar Difference has increased moderately during 2020–2022 when the global CH4 burden increased significantly, but not yet to its peak values in the late-1980s. For quantitative assessment of changing Arctic CH4 emissions, the atmospheric measurements must be combined with an atmospheric tracer transport model. Based on multiple studies including some using CH4 isotopes, it is clear that most of the increase in global atmospheric CH4 burden is driven by increased emissions from microbial sources in the tropics, and that Arctic emissions have not increased significantly since the beginning of our measurement record in 1983 through 2022.

A comprehensive evaluation of the spatiotemporal variation of CO2 and its driving forces over China

With the improved accuracy and high spatiotemporal resolution, satellite remote sensing has provided an alternative way for monitoring the variations of CO2 in remote areas where field observations are inadequately sampled but the emissions of CO2 are increasing rapidly. Based on CO2 estimates from satellite remote sensing and the atmospheric tracer transport model, this study assessed the spatiotemporal patterns of atmospheric CO2 and its driving forces across China. Results show a consistent increase in CO2 at all levels of the troposphere, with the growth rate exceeding 2.1 ppm/year. Among them, the near surface witnessed obvious spatial heterogeneity with the highest concentrations of CO2 occurring in East China and the lowest in Northwest China. This strong spatial differentiation disappeared with increase in altitude and is replaced by a distinct south–north gradient difference at the upper troposphere. With regard to vertical variations, the concentration and growth rates of CO2 at the lower troposphere are generally higher than those at the upper troposphere. The driving mechanism analysis indicates that the variation of CO2 at the near surface is primarily caused by anthropogenic and biogenic activities, while air motion dominates the distribution of CO2 at the upper troposphere. The findings of the present study could provide a valuable reference for understanding regional carbon cycles and formulating carbon emission reduction strategies on a national scale.

Intercomparison of Atmospheric Carbonyl Sulfide (TransCom‐COS; Part One): Evaluating the Impact of Transport and Emissions on Tropospheric Variability Using Ground‐Based and Aircraft Data

AbstractWe present a comparison of atmospheric transport model (ATM) simulations for carbonyl sulfide (COS), within the framework of the atmospheric tracer transport model intercomparison project “TransCom‐COS.” Seven ATMs participated in the experiment and provided simulations of COS mixing ratios over the years 2010–2018, using state‐of‐the‐art surface fluxes for various components of the COS budget: biospheric sink, oceanic source, sources from fire and industry. The main goal of TransCom‐COS is to investigate the impact of the transport uncertainty and emission distribution in simulating the spatio‐temporal variability of tropospheric COS mixing ratios. A control case with seasonal surface fluxes of COS was constructed. The results indicate that the COS mixing ratios are underestimated by at least 50 parts per trillion (ppt) in the tropics, pointing to a missing tropical source. In summer, the mixing ratios are overestimated by at least 50 ppt above 40°N, pointing to a likely missing sink in the high northern latitudes. Regarding the latitudinal profile, the model spread is greater than 60 ppt above 40°N in boreal summer. Regarding the seasonal amplitude, the model spread reaches 50 ppt at 6 out of 15 sites, compared to an observed seasonal amplitude of 100 ppt. All models simulated a too late minimum by at least 2–3 months at two high northern‐latitude sites, likely owing to errors in the seasonal cycle in the ocean emissions. This study highlighted the shortcomings in the COS global budget that need to be resolved before using COS as a photosynthesis tracer.

Evaluation of simulated CO2 power plant plumes from six high-resolution atmospheric transport models

Abstract. Power plants and large industrial facilities contribute more than half of global anthropogenic CO2 emissions. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modeling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in situ and remote sensing observations collected during the CoMet (Carbon Dioxide and Methane Mission) measurement campaign downwind of the coal-fired power plants at Bełchatów in Poland and Jänschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models. The models include three large-eddy simulation (LES) models, two mesoscale numerical weather prediction (NWP) models extended for atmospheric tracer transport, and one Lagrangian particle dispersion model (LPDM) and cover a wide range of model resolutions from 200 m to 2 km horizontal grid spacing. At the time of the aircraft measurements between late morning and early afternoon, the simulated plumes were slightly (at Jänschwalde) to highly (at Bełchatów) turbulent, consistent with the observations, and extended over the whole depth of the atmospheric boundary layer (ABL; up to 1800 m a.s.l. (above sea level) in the case of Bełchatów). The stochastic nature of turbulent plumes puts fundamental limitations on a point-by-point comparison between simulations and observations. Therefore, the evaluation focused on statistical properties such as plume amplitude and width as a function of distance from the source. LES and NWP models showed similar performance and sometimes remarkable agreement with the observations when operated at a comparable resolution. The Lagrangian model, which was the only model driven by winds observed from the aircraft, quite accurately captured the location of the plumes but generally underestimated their width. A resolution of 1 km or better appears to be necessary to realistically capture turbulent plume structures. At a coarser resolution, the plumes disperse too quickly, especially in the near-field range (0–8 km from the source), and turbulent structures are increasingly smoothed out. Total vertical columns are easier to simulate accurately than the vertical distribution of CO2, since the latter is critically affected by profiles of vertical stability, especially near the top of the ABL. Cross-sectional flux and integrated mass enhancement methods applied to synthetic CO2M data generated from the model simulations with a random noise of 0.5–1.0 ppm (parts per million) suggest that emissions from a power plant like Bełchatów can be estimated with an accuracy of about 20 % from single overpasses. Estimates of the effective wind speed are a critical input for these methods. Wind speeds in the middle of the ABL appear to be a good approximation for plumes in a well-mixed ABL, as encountered during CoMet.

Sticky issues in turbulent transport.

We investigate the role of partial stickiness among particles or with a surface for turbulent transport. For the former case, we re-derive known results for the case of the compressible Kraichnan model by using a method based on bi-orthogonality for the expansion of the propagator in terms of left and right eigenvectors. In particular, we show that enforcing the constraints of orthogonality and normalization yields results that were previously obtained by a rigorous, yet possibly less intuitive method. For the latter case, we introduce a general model of transport within the atmospheric boundary layer. As suggested by experimental observations on the transport of atmospheric tracers, both drift and diffusivity scale with the height to the ground. The strength of the drift is parameterized by a velocity V. We use the bi-orthogonality method to show that for V in the range -1 < V < 0 and 0 < V < 1 there is a one-parameter family of boundary conditions that are a priori admissible. Outside of that range, there is a single boundary condition that is admissible. In physical terms, the one-parameter family is parametrized by the degree to which particles stick to the ground.

Sensitivity of biomass burning emissions estimates to land surface information

Abstract. Emissions from biomass burning (BB) are a key source of atmospheric tracer gases that affect the atmospheric carbon cycle. We developed four sets of global BB emissions estimates (named GlcGlob, GlcGeoc, McdGlob, and McdGeoc) using a bottom-up approach and by combining the remote sensing products related to fire distribution with two aboveground biomass (AGB) and two land cover classification (LCC) distributions. The sensitivity of the estimates of BB emissions to the AGB and LCC data was evaluated using the carbon monoxide (CO) emissions associated with each BB estimate. Using the AGB and/or LCC data led to substantially different spatial estimates of CO emissions, with a large (factor of approximately 3) spread of estimates for the mean annual CO emissions: 526±53, 219±35, 624±57, and 293±44 Tg CO yr−1 for GlcGlob, GlcGeoc, McdGlob, and McdGeoc, respectively, and 415±47 Tg CO yr−1 for their ensemble average (EsmAve). We simulated atmospheric CO variability at an approximately 2.5∘ grid using an atmospheric tracer transport model and the BB emissions estimates and compared it with ground-based and satellite observations. At ground-based observation sites during fire seasons, the impact of intermittent fire events was poorly defined in our simulations due to the coarse resolution, which obscured temporal and spatial variability in the simulated atmospheric CO concentration. However, when compared at the regional and global scales, the distribution of atmospheric CO concentrations in the simulations shows substantial differences among the estimates of BB emissions. These results indicate that the estimates of BB emissions are highly sensitive to the AGB and LCC data.

Technical note: A high-resolution inverse modelling technique for estimating surface CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; fluxes based on the NIES-TM–FLEXPART coupled transport model and its adjoint

Abstract. We developed a high-resolution surface flux inversion system based on the global Eulerian–Lagrangian coupled tracer transport model composed of the National Institute for Environmental Studies (NIES) transport model (TM; collectively NIES-TM) and the FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM–FLEXPART-variational) as it applies a variational optimization to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve the best fit to atmospheric CO2 data collected by the global in situ network as a necessary step towards the capability of estimating anthropogenic CO2 emissions. We employed the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate surface flux footprints of CO2 observations at a spatial resolution of 0.1∘×0.1∘. The LPDM is coupled with a global atmospheric tracer transport model (NIES-TM). Our inversion technique uses an adjoint of the coupled transport model in an iterative optimization procedure. The flux error covariance operator was implemented via implicit diffusion. Biweekly flux corrections to prior flux fields were estimated for the years 2010–2012 from in situ CO2 data included in the Observation Package (ObsPack) data set. High-resolution prior flux fields were prepared using the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, the Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange, and the Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and a separate simulation of CO2 fluxes at a daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for the terrestrial biosphere was scaled proportionally to the monthly mean gross primary production (GPP) by the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD17 product. The inverse system calculates flux corrections to the prior fluxes in the form of a relatively smooth field multiplied by high-resolution patterns of the prior flux uncertainties for land and ocean, following the coastlines and fine-scale vegetation productivity gradients. The resulting flux estimates improved the fit to the observations taken at continuous observation sites, reproducing both the seasonal and short-term concentration variabilities including high CO2 concentration events associated with anthropogenic emissions. The use of a high-resolution atmospheric transport in global CO2 flux inversions has the advantage of better resolving the transported mixed signals from the anthropogenic and biospheric sources in densely populated continental regions. Thus, it has the potential to achieve better separation between fluxes from terrestrial ecosystems and strong localized sources, such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as our posterior fit was better than that of the National Oceanic and Atmospheric Administration (NOAA)'s CarbonTracker for only a fraction of the monitoring sites, i.e. mostly at coastal and island locations where background and local flux signals are mixed.

Dependence of Atmospheric Transport Into the Arctic on the Meridional Extent of the Hadley Cell

AbstractRecent studies have shown a large spread in the transport of atmospheric tracers into the Arctic among a suite of chemistry climate models and have suggested that this is related to the spread in the meridional extent of the Hadley Cell (HC). Here we examine the HC‐transport relationship using an idealized model, where we vary the mean circulation and isolate its impact on transport to the Arctic. It is shown that the poleward transport depends on the relative position between the northern edge of the HC and the tracer source, with maximum transport occurring when the HC edge lies near the middle of the source region. Such dependence highlights the critical role of near‐surface transport by the Eulerian mean circulation rather than eddy mixing in the free troposphere and suggests that variations in the HC edge and the tracer source region are both important for modeling Arctic composition.

Data assimilation using an ensemble of models: a hierarchical approach

Abstract. One characteristic of biogeochemical models is uncertainty about their formulation. Data assimilation should take this uncertainty into account. A common approach is to use an ensemble of models. We must assign probabilities not only to the parameters of the models but also to the models themselves. The method of hierarchical modelling allows us to calculate these probabilities. This paper describes the approach, develops the algebra for the most common case and then applies it to the Atmospheric Tracer Transport Model Intercomparison Project (TransCom). We see that the discrimination among models is unrealistically strong, due to optimistic assumptions inherent in the underlying inversion. The weighted ensemble means and variances from the hierarchical approach are quite similar to the conventional values because the best model in the ensemble is also quite close to the ensemble mean. The approach can also be used for cross-validation in which some data are held back to test estimates obtained with the rest. We demonstrate this with a test of the TransCom inversions holding back the airborne data. We see a slight decrease in the tropical sink and a notably different preferred order of models.

Leveraging the signature of heterotrophic respiration on atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; for model benchmarking

Abstract. Spatial and temporal variations in atmospheric carbon dioxide (CO2) reflect large-scale net carbon exchange between the atmosphere and terrestrial ecosystems. Soil heterotrophic respiration (HR) is one of the component fluxes that drive this net exchange, but, given observational limitations, it is difficult to quantify this flux or to evaluate global-scale model simulations thereof. Here, we show that atmospheric CO2 can provide a useful constraint on large-scale patterns of soil heterotrophic respiration. We analyze three soil model configurations (CASA-CNP, MIMICS, and CORPSE) that simulate HR fluxes within a biogeochemical test bed that provides each model with identical net primary productivity (NPP) and climate forcings. We subsequently quantify the effects of variation in simulated terrestrial carbon fluxes (NPP and HR from the three soil test-bed models) on atmospheric CO2 distributions using a three-dimensional atmospheric tracer transport model. Our results show that atmospheric CO2 observations can be used to identify deficiencies in model simulations of the seasonal cycle and interannual variability in HR relative to NPP. In particular, the two models that explicitly simulated microbial processes (MIMICS and CORPSE) were more variable than observations at interannual timescales and showed a stronger-than-observed temperature sensitivity. Our results prompt future research directions to use atmospheric CO2, in combination with additional constraints on terrestrial productivity or soil carbon stocks, for evaluating HR fluxes.

Spatiotemporal reconstructions of global CO2‐fluxes using Gaussian Markov random fields

AbstractAtmospheric inverse modeling is a method for reconstructing historical fluxes of green‐house gas between land and atmosphere, using observed atmospheric concentrations and an atmospheric tracer transport model. The small number of observed atmospheric concentrations in relation to the number of unknown flux components makes the inverse problem ill‐conditioned, and assumptions on the fluxes are needed to constrain the solution. A common practice is to model the fluxes using latent Gaussian fields with a mean structure based on estimated fluxes from combinations of process modeling (natural fluxes) and statistical bookkeeping (anthropogenic emissions). Here, we reconstruct global CO2 flux fields by modeling fluxes using Gaussian Markov random fields (GMRFs), resulting in a flexible and computational beneficial model with a Matérn‐like spatial covariance and a temporal covariance arriving from an autoregressive model in time domain.In contrast to previous inversions, the flux is defined on a spatially continuous domain, and the traditionally discrete flux representation is replaced by integrated fluxes at the resolution specified by the transport model. This formulation removes aggregation errors in the flux covariance, due to the traditional representation of area integrals by fluxes at discrete points, and provides a model closer resembling real‐life space–time continuous fluxes.

ATTILA 4.0: Lagrangian advective and convective transport of passive tracers within the ECHAM5/MESSy (2.53.0) chemistry–climate model

Abstract. We have extended ATTILA (Atmospheric Tracer Transport in a LAgrangian model), a Lagrangian tracer transport scheme, which is online coupled to the global ECHAM/MESSy Atmospheric Chemistry (EMAC) model, with a combination of newly developed and modified physical routines and new diagnostic and infrastructure submodels. The new physical routines comprise a parameterisation for Lagrangian convection, a formulation of diabatic vertical velocity, and the new grid-point submodel LGTMIX to calculate the mixing of compounds in Lagrangian representation. The new infrastructure routines simplify the transformation between grid-point (GP) and Lagrangian (LG) space in a parallel computing environment. The new submodel LGVFLUX is a useful diagnostic tool to calculate online vertical mass fluxes through horizontal surfaces. The submodel DRADON was extended to account for emissions and changes of 222Rn on Lagrangian parcels. To evaluate the new physical routines, two simulations in free-running mode with prescribed sea surface temperatures were performed with EMAC–ATTILA in T42L47MA resolution from 1950 to 2010. The results show an improvement of the tracer transport into and within the stratosphere when the diabatic vertical velocity is used for vertical advection in ATTILA instead of the standard kinematic vertical velocity. In particular, the age-of-air distribution is more in accordance with observations. The global tropospheric distribution of 222Rn, however, is simulated in agreement with available observations and with the results from EMAC in grid space for both Lagrangian systems. Additional sensitivity studies reveal an effect of inter-parcel mixing on the age of air in the tropopause region and the stratosphere, but there is no significant effect for the troposphere.

Communication-Efficient Property Preservation in Tracer Transport

Atmospheric tracer transport is a computationally demanding component of the atmospheric dynamical core of weather and climate simulations. Simulations typically have tens to hundreds of tracers. A...

Optimal and scalable methods to approximate the solutions of large‐scale Bayesian problems: theory and application to atmospheric inversion and data assimilation

This article provides a detailed theoretical analysis of methods to approximate the solutions of high‐dimensional (&gt;106) linear Bayesian problems. An optimal low‐rank projection that maximizes the information content of the Bayesian inversion is proposed and efficiently constructed using a scalable randomized SVD algorithm. Optimality results for the associated posterior error covariance matrix and posterior mean approximations obtained in previous studies are revisited and tested in a numerical experiment consisting of a large‐scale atmospheric tracer transport source‐inversion problem. This method proves to be a robust and efficient approach to dimension reduction, as well as a natural framework to analyze the information content of the inversion. Possible extensions of this approach to the nonlinear framework in the context of operational numerical weather forecast data assimilation systems based on the incremental 4D‐Var technique are also discussed, and a detailed implementation of a new Randomized Incremental Optimal Technique (RIOT) for 4D‐Var algorithms leveraging our theoretical results is proposed.

Improving the inter-hemispheric gradient of total column atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt; in simulations with the ECMWF semi-Lagrangian atmospheric global model

Abstract. It is a widely established fact that standard semi-Lagrangian advection schemes are highly efficient numerical techniques for simulating the transport of atmospheric tracers. However, as they are not formally mass conserving, it is essential to use some method for restoring mass conservation in long time range forecasts. A common approach is to use global mass fixers. This is the case of the semi-Lagrangian advection scheme in the Integrated Forecasting System (IFS) model used by the Copernicus Atmosphere Monitoring Service (CAMS) at the European Centre for Medium-Range Weather Forecasts (ECMWF).Mass fixers are algorithms with substantial differences in complexity and sophistication but in general of low computational cost. This paper shows the positive impact mass fixers have on the inter-hemispheric gradient of total atmospheric column-averaged CO2 and CH4, a crucial feature of their spatial distribution. Two algorithms are compared: the simple "proportional" and the more complex Bermejo–Conde schemes. The former is widely used by several Earth system climate models as well the CAMS global forecasts and analysis of atmospheric composition, while the latter has been recently implemented in IFS. Comparisons against total column observations demonstrate that the proportional mass fixer is shown to be suitable for the low-resolution simulations, but for the high-resolution simulations the Bermejo–Conde scheme clearly gives better results. These results have potential repercussions for climate Earth system models using proportional mass fixers as their resolution increases. It also emphasises the importance of benchmarking the tracer mass fixers with the inter-hemispheric gradient of long-lived greenhouse gases using observations.

Comparison between the Local Ensemble Transform Kalman Filter (LETKF) and 4D‐Var in atmospheric CO2 flux inversion with the Goddard Earth Observing System‐Chem model and the observation impact diagnostics from the LETKF

AbstractEnsemble Kalman filter (EnKF) and 4D‐Variational (4D‐Var) are two advanced data assimilation methods that are the basis of numerical weather prediction and have been extensively used in trace gas assimilation and inverse modeling. In this study, we compare 4D‐Var and the Local Ensemble Transform Kalman Filter (LETKF), one type of EnKF, in estimating CO2 fluxes with both simulated and real satellite data from Greenhouse gases Observing Satellite (GOSAT) and propose a method to calculate flux changes and flux error reductions from assimilating each observation within the LETKF. The results show that the mean posterior flux accuracy across 11 land regions defined by the Atmospheric Tracer Transport Model Intercomparison Project is comparable between 4D‐Var and the LETKF, as shown in the Observing System Simulation Experiment, but the differences between the LETKF and 4D‐Var are relatively larger over data sparse regions. We show that this is most likely due to the fact that the observations from a much broader region have impact on flux estimation in 4D‐Var than in the LETKF. As a result, the posterior fluxes from 4D‐Var are more consistent with the atmospheric CO2 growth rate. We find that the inversion results are less dependent on inversion methods with the increase of observations. With real GOSAT observations, we show that the posterior flux changes in 2011 relative to 2010 are more consistent between these two methods than the absolute estimates.

A multi-model intercomparison of halogenated very short-lived substances (TransCom-VSLS): linking oceanic emissions and tropospheric transport for a reconciled estimate of the stratospheric source gas injection of bromine

Abstract. The first concerted multi-model intercomparison of halogenated very short-lived substances (VSLS) has been performed, within the framework of the ongoing Atmospheric Tracer Transport Model Intercomparison Project (TransCom). Eleven global models or model variants participated (nine chemical transport models and two chemistry–climate models) by simulating the major natural bromine VSLS, bromoform (CHBr3) and dibromomethane (CH2Br2), over a 20-year period (1993–2012). Except for three model simulations, all others were driven offline by (or nudged to) reanalysed meteorology. The overarching goal of TransCom-VSLS was to provide a reconciled model estimate of the stratospheric source gas injection (SGI) of bromine from these gases, to constrain the current measurement-derived range, and to investigate inter-model differences due to emissions and transport processes. Models ran with standardised idealised chemistry, to isolate differences due to transport, and we investigated the sensitivity of results to a range of VSLS emission inventories. Models were tested in their ability to reproduce the observed seasonal and spatial distribution of VSLS at the surface, using measurements from NOAA's long-term global monitoring network, and in the tropical troposphere, using recent aircraft measurements – including high-altitude observations from the NASA Global Hawk platform. The models generally capture the observed seasonal cycle of surface CHBr3 and CH2Br2 well, with a strong model–measurement correlation (r ≥ 0.7) at most sites. In a given model, the absolute model–measurement agreement at the surface is highly sensitive to the choice of emissions. Large inter-model differences are apparent when using the same emission inventory, highlighting the challenges faced in evaluating such inventories at the global scale. Across the ensemble, most consistency is found within the tropics where most of the models (8 out of 11) achieve best agreement to surface CHBr3 observations using the lowest of the three CHBr3 emission inventories tested (similarly, 8 out of 11 models for CH2Br2). In general, the models reproduce observations of CHBr3 and CH2Br2 obtained in the tropical tropopause layer (TTL) at various locations throughout the Pacific well. Zonal variability in VSLS loading in the TTL is generally consistent among models, with CHBr3 (and to a lesser extent CH2Br2) most elevated over the tropical western Pacific during boreal winter. The models also indicate the Asian monsoon during boreal summer to be an important pathway for VSLS reaching the stratosphere, though the strength of this signal varies considerably among models. We derive an ensemble climatological mean estimate of the stratospheric bromine SGI from CHBr3 and CH2Br2 of 2.0 (1.2–2.5) ppt, ∼ 57 % larger than the best estimate from the most recent World Meteorological Organization (WMO) Ozone Assessment Report. We find no evidence for a long-term, transport-driven trend in the stratospheric SGI of bromine over the simulation period. The transport-driven interannual variability in the annual mean bromine SGI is of the order of ±5 %, with SGI exhibiting a strong positive correlation with the El Niño–Southern Oscillation (ENSO) in the eastern Pacific. Overall, our results do not show systematic differences between models specific to the choice of reanalysis meteorology, rather clear differences are seen related to differences in the implementation of transport processes in the models.

Global mapping of greenhouse gases retrieved from GOSAT Level 2 products by using a kriging method

Because a synoptic overview facilitates understanding of the temporal and spatial changes in the global distribution of greenhouse gases, we developed a statistical spatial estimation method using kriging. Level 3 (L3) data products for the Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for Carbon Observation (TANSO) Fourier Transform Spectrometer (FTS) Short Wave Infrared (SWIR) were generated from column-averaged, dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4) TANSO-FTS SWIR Level 2 (L2) products using this method. Although there have been some reports on the use of kriging for analysing GOSAT products, the kriging method used in this research was specifically adapted to the statistical characteristics of GOSAT L2 products. In the context of using data for atmospheric research, spatially interpolated data (GOSAT L3 products) cannot be more accurate than model-simulated global distributions of gas concentrations (GOSAT Level 4B (L4B) products), which are generated using an atmospheric tracer transport model. However, the L3 product takes much less time to generate than the L4B. It would take about a year to produce the L4B after generation of an L2 product. The great advantage of the L3 product is that it gives a comprehensive and reasonable monthly global distribution of gas concentrations with little delay. The L3 product using the kriging method can be generated on a monthly basis by estimating global semi-variogram curves from the L2 products for each month and interpolating spatially within a region with a radius of 1000 km from existing L2 data locations. The main purpose of this paper is to describe the methodology and characteristics of kriging used to generate the GOSAT L3 product, not for strictly scientific use of the estimated values, but for a reasonable global map of gas concentrations derived statistically from the sparsely observed L2 products within a short time frame. The characteristics of this method are compared to XCO2 products simulated with an atmospheric tracer transport model. The results show that the method proposed in this study is of practical use for generating L3 products from L2 products.

Carbon monitoring system flux estimation and attribution: impact of ACOS-GOSAT XCO2 sampling on the inference of terrestrial biospheric sources and sinks

Using an Observing System Simulation Experiment (OSSE), we investigate the impact of JAXA Greenhouse gases Observing SATellite ‘IBUKI’ (GOSAT) sampling on the estimation of terrestrial biospheric flux with the NASA Carbon Monitoring System Flux (CMS-Flux) estimation and attribution strategy. The simulated observations in the OSSE use the actual column carbon dioxide (XCO2) b2.9 retrieval sensitivity and quality control for the year 2010 processed through the Atmospheric CO2 Observations from Space algorithm. CMS-Flux is a variational inversion system that uses the GEOS-Chem forward and adjoint model forced by a suite of observationally constrained fluxes from ocean, land and anthropogenic models. We investigate the impact of GOSAT sampling on flux estimation in two aspects: 1) random error uncertainty reduction and 2) the global and regional bias in posterior flux resulted from the spatiotemporally biased GOSAT sampling. Based on Monte Carlo calculations, we find that global average flux uncertainty reduction ranges from 25% in September to 60% in July. When aggregated to the 11 land regions designated by the phase 3 of the Atmospheric Tracer Transport Model Intercomparison Project, the annual mean uncertainty reduction ranges from 10% over North American boreal to 38% over South American temperate, which is driven by observational coverage and the magnitude of prior flux uncertainty. The uncertainty reduction over the South American tropical region is 30%, even with sparse observation coverage. We show that this reduction results from the large prior flux uncertainty and the impact of non-local observations. Given the assumed prior error statistics, the degree of freedom for signal is ~1132 for 1-yr of the 74 055 GOSAT XCO2 observations, which indicates that GOSAT provides ~1132 independent pieces of information about surface fluxes. We quantify the impact of GOSAT's spatiotemporally sampling on the posterior flux, and find that a 0.7 gigatons of carbon bias in the global annual posterior flux resulted from the seasonally and diurnally biased sampling when using a diagonal prior flux error covariance.

Regional CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; flux estimates for 2009–2010 based on GOSAT and ground-based CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; observations

Abstract. We present the application of a global carbon cycle modeling system to the estimation of monthly regional CO2 fluxes from the column-averaged mole fractions of CO2 (XCO2) retrieved from spectral observations made by the Greenhouse gases Observing SATellite (GOSAT). The regional flux estimates are to be publicly disseminated as the GOSAT Level 4 data product. The forward modeling components of the system include an atmospheric tracer transport model, an anthropogenic emissions inventory, a terrestrial biosphere exchange model, and an oceanic flux model. The atmospheric tracer transport was simulated using isentropic coordinates in the stratosphere and was tuned to reproduce the age of air. We used a fossil fuel emission inventory based on large point source data and observations of nighttime lights. The terrestrial biospheric model was optimized by fitting model parameters to observed atmospheric CO2 seasonal cycle, net primary production data, and a biomass distribution map. The oceanic surface pCO2 distribution was estimated with a 4-D variational data assimilation system based on reanalyzed ocean currents. Monthly CO2 fluxes of 64 sub-continental regions, between June 2009 and May 2010, were estimated from GOSAT FTS SWIR Level 2 XCO2 retrievals (ver. 02.00) gridded to 5° × 5° cells and averaged on a monthly basis and monthly-mean GLOBALVIEW-CO2 data. Our result indicated that adding the GOSAT XCO2 retrievals to the GLOBALVIEW data in the flux estimation brings changes to fluxes of tropics and other remote regions where the surface-based data are sparse. The uncertainties of these remote fluxes were reduced by as much as 60% through such addition. Optimized fluxes estimated for many of these regions, were brought closer to the prior fluxes by the addition of the GOSAT retrievals. In most of the regions and seasons considered here, the estimated fluxes fell within the range of natural flux variabilities estimated with the component models.