Atmospheric Tracer Transport Model 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.

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<sub>2</sub> 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.

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<sub>2</sub> 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.

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<sub>2</sub> flux estimates for 2009–2010 based on GOSAT and ground-based CO<sub>2</sub> 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.

Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space: Validation of PPDF-based CO_2 retrievals from GOSAT

This report describes a validation study of Greenhouse gases Observing Satellite (GOSAT) data processing using ground-based measurements of the Total Carbon Column Observing Network (TCCON) as reference data for column-averaged dry air mole fractions of atmospheric carbon dioxide (X_(CO_2)). We applied the photon path length probability density function method to validate X_(CO_2) retrievals from GOSAT data obtained during 22 months starting from June 2009. This method permitted direct evaluation of optical path modifications due to atmospheric light scattering that would have a negligible impact on ground-based TCCON measurements but could significantly affect gas retrievals when observing reflected sunlight from space. Our results reveal effects of optical path lengthening over Northern Hemispheric stations, essentially from May–September of each year, and of optical path shortening for sun-glint observations in tropical regions. These effects are supported by seasonal trends in aerosol optical depth derived from an offline three-dimensional aerosol transport model and by cirrus optical depth derived from space-based measurements of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Removal of observations that were highly contaminated by aerosol and cloud from the GOSAT data set resulted in acceptable agreement in the seasonal variability of XCO2 over each station as compared with TCCON measurements. Statistical comparisons between GOSAT and TCCON coincident measurements of CO_2 column abundance show a correlation coefficient of 0.85, standard deviation of 1.80 ppm, and a sub-ppm negative bias of −0.43 ppm for all TCCON stations. Global distributions of monthly mean retrieved X_(CO_2) with a spatial resolution of 2.5° latitude × 2.5° longitude show agreement within ∼2.5 ppm with those predicted by the atmospheric tracer transport model.

Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space: Validation of PPDF-based CO2retrievals from GOSAT

[1] This report describes a validation study of Greenhouse gases Observing Satellite (GOSAT) data processing using ground-based measurements of the Total Carbon Column Observing Network (TCCON) as reference data for column-averaged dry air mole fractions of atmospheric carbon dioxide (XCO2). We applied the photon path length probability density function method to validate XCO2retrievals from GOSAT data obtained during 22 months starting from June 2009. This method permitted direct evaluation of optical path modifications due to atmospheric light scattering that would have a negligible impact on ground-based TCCON measurements but could significantly affect gas retrievals when observing reflected sunlight from space. Our results reveal effects of optical path lengthening over Northern Hemispheric stations, essentially from May–September of each year, and of optical path shortening for sun-glint observations in tropical regions. These effects are supported by seasonal trends in aerosol optical depth derived from an offline three-dimensional aerosol transport model and by cirrus optical depth derived from space-based measurements of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Removal of observations that were highly contaminated by aerosol and cloud from the GOSAT data set resulted in acceptable agreement in the seasonal variability of XCO2 over each station as compared with TCCON measurements. Statistical comparisons between GOSAT and TCCON coincident measurements of CO2column abundance show a correlation coefficient of 0.85, standard deviation of 1.80 ppm, and a sub-ppm negative bias of −0.43 ppm for all TCCON stations. Global distributions of monthly mean retrieved XCO2 with a spatial resolution of 2.5° latitude × 2.5° longitude show agreement within ∼2.5 ppm with those predicted by the atmospheric tracer transport model.

A global coupled Eulerian-Lagrangian model and 1 × 1 km CO<sub>2</sub> surface flux dataset for high-resolution atmospheric CO<sub>2</sub> transport simulations

Abstract. We designed a method to simulate atmospheric CO2 concentrations at several continuous observation sites around the globe using surface fluxes at a very high spatial resolution. The simulations presented in this study were performed using the Global Eulerian-Lagrangian Coupled Atmospheric model (GELCA), comprising a Lagrangian particle dispersion model coupled to a global atmospheric tracer transport model with prescribed global surface CO2 flux maps at a 1 × 1 km resolution. The surface fluxes used in the simulations were prepared by assembling the individual components of terrestrial, oceanic and fossil fuel CO2 fluxes. This experimental setup (i.e. a transport model running at a medium resolution, coupled to a high-resolution Lagrangian particle dispersion model together with global surface fluxes at a very high resolution), which was designed to represent high-frequency variations in atmospheric CO2 concentration, has not been reported at a global scale previously. Two sensitivity experiments were performed: (a) using the global transport model without coupling to the Lagrangian dispersion model, and (b) using the coupled model with a reduced resolution of surface fluxes, in order to evaluate the performance of Eulerian-Lagrangian coupling and the role of high-resolution fluxes in simulating high-frequency variations in atmospheric CO2 concentrations. A correlation analysis between observed and simulated atmospheric CO2 concentrations at selected locations revealed that the inclusion of both Eulerian-Lagrangian coupling and high-resolution fluxes improves the high-frequency simulations of the model. The results highlight the potential of a coupled Eulerian-Lagrangian model in simulating high-frequency atmospheric CO2 concentrations at many locations worldwide. The model performs well in representing observations of atmospheric CO2 concentrations at high spatial and temporal resolutions, especially for coastal sites and sites located close to sources of large anthropogenic emissions. While this study focused on simulations of CO2 concentrations, the model could be used for other atmospheric compounds with known estimated emissions.

A priori covariance estimation for CO2 and CH4 retrievals

We derive the a priori covariance matrices of CO2 and CH4 for the retrieval of their profiles and columns from satellite spectral data. The monthly a priori covariance matrices of CO2 and CH4 at each grid cell (0.5° × 0.5°) on the globe are calculated using simulated data from the atmospheric tracer transport model. The a priori covariance matrix is defined as the sum of the bias and noise components, where the bias is obtained from the difference in seasonal cycle between simulated data and observation‐based reference data, and the noise is defined as synoptic and interannual variations. The use of simulated data as well as observation‐based reference data enables realistic variance and covariance values to be obtained for each temporal component. The seasonal bias is approximately 2 ppm for CO2 and 20 ppb for CH4. A large difference in synoptic variations is obtained between simulated and reference data over the source region, especially over land. The interannual variances derived from the reference data show maximum values (4 ppm2 for CO2 and 220 ppb2 for CH4) in northern midlatitudes. Global data sets of a priori covariance matrices for CO2 and CH4 are now available for the retrieval of concentrations using satellite spectral data. Furthermore, the data set has the potential to be applied in studies in other fields, including estimates of CO2 flux error using inverse modeling and planning for ground‐based observation networks.

A last‐saturation diagnosis of subtropical water vapor response to global warming

We used boreal winter output from an IPCC AR4 GCM simulation to drive an atmospheric tracer transport model with water vapor last saturation tracers over the northern hemisphere in order to identify processes that contribute to the projected increase in specific humidity of the subtropical relative humidity minimum. Most of the increase in specific humidity at the relative humidity minimum can be attributed to last saturation within what will be a warmer free troposphere. The water vapor content of the relative humidity minimum is projected to increase in the 21st century primarily as a function of increased last saturation temperatures in the tropical lower troposphere, effectively yielding more water vapor via poleward transport. The increase in specific humidity is to a degree kept in check by a poleward and upward shift of baroclinic instability and saturation patterns that effectively yield drier air to the RH minimum via isentropic transport.

A Last Saturation Analysis of ENSO Humidity Variability in the Subtropical Pacific

Abstract Water vapor tracers of last saturation were used in an atmospheric tracer transport model to evaluate ENSO variability in the generation of the dry air that defines the subtropical middle troposphere over the North Pacific. Fifteen Northern Hemisphere winters, including El Niño, La Niña, and ENSO-neutral seasons, were evaluated using both Northern Hemisphere and global last saturation water vapor tracer configurations. During El Niño northern winter, the free troposphere over the subtropical North Pacific is both drier and warmer than during La Niña. The probability distributions of the last saturation position for the dry air in the middle troposphere were evaluated over the subtropical North Pacific and were found to be further poleward, at a higher altitude, and more westerly in their components during the warm phase compared to the cold phase. During warm phase (cold phase) northern winter, 57% (49%) of the air at 20°N and 633 hPa over the North Pacific was last saturated poleward of 20°N and above 500 hPa. Coherency was demonstrated between tropical sea surface temperatures, extratropical atmospheric saturation, and subtropical aridity of the middle troposphere. The stronger westerly component of last saturation during the warm phase ties ENSO-variable subtropical aridity to midlatitude westerlies when there is enhanced baroclinicity and an equatorward migration of the Pacific storm track. Humidity reconstructions from the water vapor tracers capture observed ENSO humidity variability and demonstrate that it can be explained in terms of changes in the location of last saturation, and not by changes in the temperature field. This study shows how teleconnections between the tropical ocean and the extratropical upper troposphere can impact the humidity of the middle troposphere of the subtropical dry regions.

Identifying global dust source areas using high‐resolution land surface form

Dust originates from specific, landscape‐scale (≈1–10 km) features; however, dust erodibility is commonly parameterized in global climate models using grid‐cell‐scale (≈1–5°) relationships. We examine surface characteristics from the high‐ resolution (90 m at equator) global Shuttle Radar Topography Mission digital elevation model and aerosol optical thickness (AOT) measurements from the Multiangle Imaging Spectroradiometer (MISR) instrument to find landscape‐scale characteristics common to dust producing regions. Regions with climatologically high aerosol optical thickness are associated with extremely low‐slope landscapes at ≈5 km scale that also have high‐elevation variance, likely due to aeolian features. We refer to these landscape characteristics as “levelness” and “residual landscape roughness,” and we extrapolate these relationships globally to identify geomorphologically based dust erodibility functions on the basis of levelness and residual landscape roughness criteria. We test these with the Dust Entrainment and Deposition model embedded in the Model for Atmospheric Transport and Chemistry global atmospheric tracer transport model, driven by reanalysis meteorology. The modeled global dust spatial distributions calculated with these erodibility parameters agree well with observations of MISR AOT and deposition and in the modeling framework used here represent an improvement over existing parameterizations in predicting the spatial patterns of dust sources in the Sahara and the relative amount of emission of sources in the Dust Belt to those in the Southern Hemisphere; however, agreement with observations is less good in high‐relief areas such as the Asian dust source regions, possibly because of the coarse resolution of the meteorological fields used to drive the model.