ベリコフ ドミトリー

Abstract. This study investigated the spatiotemporal variabilitiesin nitrogen dioxide (NO2), formaldehyde (HCHO), ozone (O3), andlight-absorbing aerosols within the Greater Tokyo Area, Japan, which is the mostpopulous metropolitan area in the world. The analysis is based on totaltropospheric column, partial tropospheric column (within the boundarylayer), and in situ observations retrieved from multiple platforms as well as additionalinformation obtained from reanalysis and box model simulations. This studymainly covers the 2013–2020 period, focusing on 2020 when air quality wasinfluenced by the coronavirus 2019 (COVID-19) pandemic. Although total andpartial tropospheric NO2 columns were reduced by an average of about10 % in 2020, reductions exceeding 40 % occurred in some areas duringthe pandemic state of emergency. Light-absorbing aerosol levels within theboundary layer were also reduced for most of 2020, while smallerfluctuations in HCHO and O3 were observed. The significantly enhanceddegree of weekly cycling of NO2, HCHO, and light-absorbing aerosolfound in urban areas during 2020 suggests that, in contrast to othercountries, mobility in Japan also dropped on weekends. We conclude that,despite the lack of strict mobility restrictions in Japan, widespreadadherence to recommendations designed to limit the COVID-19 spread resultedin unique air quality improvements.

We examined methane (CH4) variability over different regions of India and the surrounding oceans derived from thermal infrared (TIR) band observations (TIR CH4) by the Thermal and Near-infrared Sensor for carbon Observation—Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) for the period 2009–2014. This study attempts to understand the sensitivity of the vertical profile retrievals at different layers of the troposphere and lower stratosphere, on the basis of the averaging kernel (AK) functions and a priori assumptions, as applied to the simulated concentrations by the MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM). We stress that this is of particular importance when the satellite-derived products are analyzed using different ACTMs other than those used as retrieved a priori. A comparison of modeled and retrieved CH4 vertical profiles shows that the GOSAT/TANSO-FTS TIR instrument has sufficient sensitivity to provide critical information about the transport of CH4 from the top of the boundary layer to the upper troposphere. The mean mismatch between TIR CH4 and model is within 50 ppb, except for the altitude range above 150 hPa, where the sensitivity of TIR CH4 observations becomes very low. Convolved model profiles with TIR CH4 AK reduces the mismatch to less than the retrieval uncertainty. Distinct seasonal variations of CH4 have been observed near the atmospheric boundary layer (800 hPa), free troposphere (500 hPa), and upper troposphere (300 hPa) over the northern and southern regions of India, corresponding to the southwest monsoon (July–September) and post-monsoon (October–December) seasons. Analysis of the transport and emission contributions to CH4 suggests that the CH4 seasonal cycle over the Indian subcontinent is governed by both the heterogeneous distributions of surface emissions and the influence of the global monsoon divergent wind circulations. The major contrast between monsoon, and pre- and post-monsoon profiles of CH4 over Indian regions are noticed near the boundary layer heights, which is mainly caused by seasonal change in local emission strength with a peak during summer due to increased emissions from the paddy fields and wetlands. A strong difference between seasons in the middle and upper troposphere is caused by convective transport of the emission signals from the surface and redistribution in the monsoon anticyclone of upper troposphere. TIR CH4 observations provide additional information on CH4 in the region compared to what is known from in situ data and total-column (XCH4) measurements. Based on two emission sensitivity simulations compared to TIR CH4 observations, we suggest that the emissions of CH4 from the India region were 51.2 ± 4.6 Tg year−1 during the period 2009–2014. Our results suggest that improvements in the a priori profile shape in the upper troposphere and lower stratosphere (UT/LS) region would help better interpretation of CH4 cycling in the earth’s environment.

The arid and semiarid regions of Uzbekistan are sensitive and vulnerable to climate change. However, the sparse and very unevenly distributed meteorological stations within the region provide limited data for studying the region’s climate variation. The aim of this work was to evaluate the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA)-Interim and ERA5 products for the fields of near-surface temperature, humidity, and precipitation over Uzbekistan from 1981 to 2018 using observations from 74 meteorological stations. Major results suggested that the reanalysis datasets match well with most of the observed climate records, especially in the plain areas. While ERA5, with a high spatial resolution of 0.1°, is able more accurately reproduce mountain ranges and valleys. Compared to ERA-Interim, the climatological biases in temperature, humidity, and total precipitation from ERA5 are clearly reduced, and the representation of inter-annual variability is improved over most regions of Uzbekistan. Both reanalyses show a high level of agreement with observations on the standardized precipitation evaporation index (SPEI) with a correlation coefficient of 0.7–0.8.Although both of these ECMWF products can be successfully implemented for the calculation of atmospheric drought indicators for Uzbekistan and adjacent regions of Central Asia, the newer and advanced ERA5 is preferred.

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.

Abstract: We analyzed 12 years (2005–2016) of continuous measurements of atmospheric CO2 and CH4 concentrations made at nine tower observation sites in the Japan–Russia Siberian Tall Tower Inland Observation Network (JR-STATION), located in Siberia. Since the data are very noisy and have a low temporal resolution due to gaps in instrument operation, we used the recently developed Prophet model, which was designed to handle the common features of time series (multiple strong seasonalities, trend changes, outliers) and has a robust performance in the presence of missing data and trend shifts. By decomposing each sampled time-series into its major components (i.e., annual trend and seasonal, weekly, and hourly variation), we observed periodically changing patterns of tracer concentrations. Specifically, we detected multi-year variability of tracers and identified high-concentration events. The frequency of such events was found to vary throughout the year, reaching up to 20% of days for some months, while the number of such events was found to be different for CO2 and CH4. An analysis of weather conditions showed that, in most cases, high-concentration events were caused by a temperature inversion and low wind speed. Additionally, wind directions were found to be different for high- and low-concentration events. For some sites, the wind direction indicated the location of strong local sources of CO2 and CH4. As well as elucidating the seasonality of greenhouse gas concentrations, this study confirmed the potential of the Prophet model for detecting periodicity in environmental phenomena.

Abstract. A three-dimensional simulation of gravitational separation, defined as the process of atmospheric molecule separation under gravity according to their molar masses, is performed for the first time in the upper troposphere and lower stratosphere. We analyze distributions of two isotopes with a small difference in molecular mass (13C16O2 (Mi=45) and 12C16O2 (Mi=44)) simulated by the National Institute for Environmental Studies (NIES) chemical transport model (TM) with a parameterization of molecular diffusion. The NIES model employs global reanalysis and an isentropic vertical coordinate and uses optimized CO2 fluxes. The applicability of the NIES TM to the modeling of gravitational separation is demonstrated by a comparison with measurements recorded by high-precision cryogenic balloon-borne samplers in the lower stratosphere. We investigate the processes affecting the seasonality of gravitational separation and examine the age of air derived from the tracer distributions modeled by the NIES TM. We find a strong relationship between age of air and gravitational separation for the main climatic zones. The advantages and limitations of using age of air and gravitational separation as indicators of the variability in the stratosphere circulation are discussed.

Carbon dioxide (CO2) fluxes by different methods vary largely at global, regional and local scales. The net CO2 fluxes by three bottom-up methods (tower observation (TWR), biogeochemical models (GTM), and a data-driven model (SVR)), and an ensemble of atmospheric inversions (top-down method, INV) are compared in Yakutsk, Siberia for 2004-2013. The region is characterized by highly homogeneous larch forest on a flat terrain. The ecosystem around Yakutsk shows a net sink of CO2 by all the methods (means during 2004-2007 were 10.9 gCm(-2) month(-1) by TWR, 4.28 gCm(-2) month(-1) by GTM, 5.62 gCm(-2) month(-1) and 0.863 g Cm-2 month(-1) by SVR at two different scales, and 4.89 g Cm-2 month(-1) by INV). Absorption in summer (June-August) was smaller by three bottom-up methods (ranged from 88.1 to 191.8 g Cm-2 month(-1)) than the top-down method (223.6 gCm(-2) month(-1)). Thus the peak-to-trough amplitude of the seasonal cycle is greater for the inverse models than bottom-up methods. The monthly-mean seasonal cycles agree among the fourmethodswithin the range of inter-model variations. The interannual variability estimated by an ensemble of inverse models and a site-scale data-driven model (the max-min range was 35.8 g Cm-2 month(-1)and 34.2 g Cm-2 month(-1)) is more similar to that of the tower observation (42.4 gCm(-2) month(-1)) than those by the biogeochemical models and the large-scale data-driven model (9.5 gCm(-2) month(-1) and 1.45 gCm(-2) month(-1)). The inverse models and tower observations captured a reduction in CO2 uptake after 2008 due to unusual waterlogging.

We present an assimilation system for atmospheric carbon dioxide (CO2) using a Global Eulerian-Lagrangian Coupled Atmospheric model (GELCA), and demonstrate its capability to capture the observed atmospheric CO2 mixing ratios and to estimate CO2 fluxes. With the efficient data handling scheme in GELCA, our system assimilates non-smoothed CO2 data from observational data products such as the Observation Package (ObsPack) data products as constraints on surface fluxes. We conducted sensitivity tests to examine the impact of the site selections and the prior uncertainty settings of observation on the inversion results. For these sensitivity tests, we made five different site/ data selections from the ObsPack product. In all cases, the time series of the global net CO2 flux to the atmosphere stayed close to values calculated from the growth rate of the observed global mean atmospheric CO2 mixing ratio. At regional scales, estimated seasonal CO2 fluxes were altered, depending on the CO2 data selected for assimilation. Uncertainty reductions were determined at the regional scale and compared among cases. As measures of the model- data mismatch, we used the model-data bias, root-mean-square error, and the linear correlation. For most observation sites, the model-data mismatch was reasonably small. Regarding regional flux estimates, tropical Asia was one of the regions that showed a significant impact from the observation network settings. We found that the surface fluxes in tropical Asia were the most sensitive to the use of aircraft measurements over the Pacific, and the seasonal cycle agreed better with the results of bottom-up studies when the aircraft measurements were assimilated. These results confirm the importance of these aircraft observations, especially for constraining surface fluxes in the tropics.

This paper presents a novel retrieval algorithm for the rapid retrieval of the carbon dioxide total column amounts from high resolution spectra in the short wave infrared (SWIR) range observations by the Greenhouse gases Observing Satellite (GOSAT). The algorithm performs EOF (Empirical Orthogonal Function)-based decomposition of the measured spectral radiance and derives the relationship of limited number of the decomposition coefficients in terms of the principal components with target gas amount and a priori data such as airmass, surface pressure, etc. The regression formulae for retrieving target gas amounts are derived using training sets of collocated GOSAT and ground -based observations. The precision/accuracy characteristics of the algorithm are analyzed by the comparison of the retrievals with those from the Total Carbon Column Observing Network (TCCON) measurements and with the modeled data, and appear similar to those achieved by full-physics retrieval algorithms. (C) 2016 Elsevier Ltd. All rights reserved.

The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier transform spectrometers (FTSs) that record near-infrared (NIR) spectra of the sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fractions (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle dispersion model) Lagrangian particle dispersion model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future and possible TCCON sites. We propose a footprint-based method for the collocation of satellite and TCCON XCO2 observations and estimate the performance of the method using the NIES model and five GOSAT (Greenhouse Gases Observing Satellite) XCO2 product data sets. Comparison of the proposed approach with a standard geographic method shows a higher number of collocation points and an average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and Reunion Island sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasises that the collocation is sensitive to local meteorological conditions and flux distributions.

Northern Eurasia is one of the largest terrestrial carbon reservoirs on the Earth's surface. However, since the coverage of surface CO2 observations is still limited, the response to the climate variability remains uncertain. We estimated monthly CO2 fluxes for three sub-regions in Northern Eurasia (north of ∼60�N), Northeastern Europe, Western Siberia and Eastern Siberia, using CO2 retrievals from the Japanese Greenhouse Gases Observing SATellite (GOSAT). The variations of estimated CO2 fluxes were examined in terms of the regional climate variability, for the three consecutive growing seasons of 2009-2011. The CO2 fluxes estimated using GOSAT data are highly correlated with the surface temperature anomalies in July and August (r &gt 0.8) while no correlation is found in the CO2 fluxes estimated only using surface observations. The estimated fluxes from GOSAT data exhibit high negative correlations with one-month lagged positive precipitation anomalies in late summer (r &gt -0.7) through surface temperature and the Normalized Difference Vegetation Index (NDVI). The results indicate that GOSAT data reflects the changes in terrestrial biospheric processes responding to climate anomalies. In 2010, a large part of Eurasia experienced an extremely hot and dry summer, while cold and wet weather conditions were recorded in Western Siberia. The CO2 fluxes estimated from GOSAT data showed a reduction of net CO2 uptake in Northeastern Europe and Eastern Siberia, but the enhancement of net CO2 uptake in Western Siberia. These opposite sub-regional flux anomalies can be explained by the different climate anomalies on a sub-regional scale in Northern Eurasia. Thus, this study demonstrates that space-based observations by GOSAT compensate for the lack of ground-based observational coverage so as to better capture the inter-annually varying atmosphere-terrestrial biosphere CO2 exchange on a regional scale.

Extremely high levels of column-averaged dry-air mole fractions of atmospheric methane (XCH4) were detected in August and September 2013 over northeast Asia (similar to 20 ppb above the averaged summertime XCH4 over 2009-2012, after removing a long-term trend), as being retrieved from the Short-Wavelength InfraRed (SWIR) spectral data observed with the Thermal And Near-infrared Sensor for carbon Observation -Fourier Transform Spectrometer (TANSO-FTS) onboard Greenhouse Gases Observing Satellite (GOSAT). Similar enhancements of XCH4 were also observed by the ground-based measurements at two Total Carbon Column Observing Network (TCCON) sites in Japan. The analysis of surface CH4 concentrations observed at three monitoring sites around the Japan archipelago suggest that the extreme increase of XCH4 has occurred in a limited area. The model analysis was conducted to investigate this anomalously high XCH4 event, using an atmospheric transport model. The results indicate that the extreme increase of XCH4 is attributed to the anomalous atmospheric pressure pattern over East Asia during the summer of 2013, which effectively transported the CH4-rich air to Japan from the strong CH4 source areas in east China. The two Japanese TCCON sites, similar to 1000 km east-west apart each other, coincidentally located along the substantially CH4-rich air flow from east China. This analysis demonstrates the capability of GOSAT to monitor an XCH4 event on a synoptic scale. We anticipate that the synoptic information of XCH4 from GOSAT data contributes to improve our understanding of regional carbon cycle and the regional flux estimation.

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 modelmeasurement 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, similar to 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 Nino-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.

The distribution of atmospheric carbon dioxide (CO2) in the subarctic was investigated using the National Institute for Environmental Studies (NIES) three-dimensional transport model (TM) and retrievals from the Greenhouse gases Observing SATellite (GOSAT). Column-averaged dry air mole fractions of subarctic atmospheric CO2 (XCO2) from the NIES TM for four flux combinations were analyzed. Two flux datasets were optimized using only surface observations and two others were optimized using both surface and GOSAT Level 2 data. Two inverse modeling approaches using GOSAT data were compared. In the basic approach adopted in the GOSAT Level 4 product, the GOSAT observations are aggregated into monthly means over 5 degrees x 5 degrees grids. In the alternative method, the model observation misfit is estimated for each observation separately. The XCO2 values simulated with optimized fluxes were validated against Total Carbon Column Observing Network (TCCON) ground-based high-resolution Fourier Transform Spectrometer (FTS) measurements. Optimized fluxes were applied to study XCO2 seasonal variability over the period 2009-2010 in the Arctic and subarctic regions. The impact on CO2 levels of emissions from enhancement of biospheric respiration induced by the high temperature and strong wildfires occurring in the summer of 2010 was analyzed. Use of GOSAT data has a substantial impact on estimates of the level of CO2 interanual variability. (C) 2014 Elsevier B.V. and NIPR. All rights reserved.

We investigated differences in the five currently-available datasets of column-integrated CO2 concentrations (X-CO2) retrieved from spectral soundings collected by Greenhouse gases Observing SATellite (GOSAT) and assessed their impact on regional CO2 flux estimates. We did so by estimating the fluxes from each of the five X-CO2 datasets combined with surface-based CO2 data, using a single inversion system. The five X-CO2 datasets are available in raw and bias-corrected versions, and we found that the bias corrections diminish the range of the five coincident values by similar to 30% on average. The departures of the five individual inversion results (annual-mean regional fluxes based on X-CO2-surface combined data) from the surface-data-only results were close to one another in some terrestrial regions where spatial coverage by each X-CO2 dataset was similar. The mean of the five annual global land uptakes was 1.7 +/- 0.3 GtC yr(-1), and they were all smaller than the value estimated from the surface-based data alone.

To assess horizontal and vertical transports of methane (CH4) concentrations at different heights within the troposphere, we analyzed simulations by 12 chemistry transport models (CTMs) that participated in the TransCom-CH4 intercomparison experiment. Model results are compared with aircraft measurements at 13 sites in Amazon/Brazil, Mongolia, Pacific Ocean, Siberia/Russia, and United States during the period of 2001-2007. The simulations generally show good agreement with observations for seasonal cycles and vertical gradients. The correlation coefficients of the daily averaged model and observed CH4 time series for the analyzed years are generally larger than 0.5, and the observed seasonal cycle amplitudes are simulated well at most sites, considering the between-model variances. However, larger deviations show up below 2 km for the model-observation differences in vertical profiles at some locations, e.g., at Santarem, Brazil, and in the upper troposphere, e.g., at Surgut, Russia. Vertical gradients and concentrations are underestimated at Southern Great Planes, United States, and Santarem and overestimated at Surgut. Systematic overestimation and underestimation of vertical gradients are mainly attributed to inaccurate emission and only partly to the transport uncertainties. However, large differences in model simulations are found over the regions/seasons of strong convection, which is poorly represented in the models. Overall, the zonal and latitudinal variations in CH4 are controlled by surface emissions below 2.5 kmand transport patterns in the middle and upper troposphere. We show that the models with larger vertical gradients, coupled with slower horizontal transport, exhibit greater CH4 interhemispheric gradients in the lower troposphere. These findings have significant implications for the future development of more accurate CTMs with the possibility of reducing biases in estimated surface fluxes by inverse modeling© 2013. American Geophysical Union.

This report is the second in a series of companion papers describing the effects of atmospheric light scattering in observations of atmospheric carbon dioxide (CO2) by the Greenhouse gases Observing SATellite (GOSAT), in orbit since 23 January 2009. Here we summarize the retrievals from six previously published algorithms; retrieving column-averaged dry air mole fractions of CO2 (XCO2) during 22 months of operation of GOSAT from June 2009. First, we compare data products from each algorithm with ground-based remote sensing observations by Total Carbon Column Observing Network (TCCON). Our GOSAT-TCCON coincidence criteria select satellite observations within a 5° radius of 11 TCCON sites. We have compared the GOSAT-TCCON XCO2 regression slope, standard deviation, correlation and determination coefficients, and global and station-to-station biases. The best agreements with TCCON measurements were detected for NIES 02.xx and RemoTeC. Next, the impact of atmospheric light scattering on XCO2 retrievals was estimated for each data product using scan by scan retrievals of light path modification with the photon path length probability density function (PPDF) method. After a cloud pre-filtering test, approximately 25% of GOSAT soundings processed by NIES 02.xx, ACOS B2.9, and UoL-FP: 3G and 35% processed by RemoTeC were found to be contaminated by atmospheric light scattering. This study suggests that NIES 02.xx and ACOS B2.9 algorithms tend to overestimate aerosol amounts over bright surfaces, resulting in an underestimation of XCO2 for GOSAT observations. Cross-comparison between algorithms shows that ACOS B2.9 agrees best with NIES 02.xx and UoL-FP: 3G while RemoTeC XCO2 retrievals are in a best agreement with NIES PPDF-D.

This paper presents an improved photon path length probability density function method that permits simultaneous retrievals of column-average greenhouse gas mole fractions and light path modifications through the atmosphere when processing high-resolution radiance spectra acquired from space. We primarily describe the methodology and retrieval setup and then apply them to the processing of spectra measured by the Greenhouse gases Observing SATellite (GOSAT). We have demonstrated substantial improvements of the data processing with simultaneous carbon dioxide and light path retrievals and reasonable agreement of the satellite-based retrievals against ground-based Fourier transform spectrometer measurements provided by the Total Carbon Column Observing Network (TCCON). (C) 2013 Optical Society of America

We present satellite-based data of the column-averaged dry air mole fraction of atmospheric carbon dioxide (XCO2) and methane (XCH4), which were derived from the radiance spectra measured by Greenhouse gases Observing SATellite (GOSAT). We have applied new version of the Photon path-length Probability Density Function (PPDF)-based algorithm to estimate XCO2 and PPDF parameters. These parameters serve to allow for optical path modification due to atmospheric light scattering and they are retrieved simultaneously with CO2 concentration using radiance spectra from all available GOSAT short wave infrared (SWIR) bands (oxygen A-band, 1.6-mu m, and 2.0-mu m CO2 absorption bands). For the methane abundance, retrieved from 1.67-mu m absorption band, we applied optical path correction based on PPDF parameters from 1.6-mu m CO2 absorption band. Similarly to widely used CO2-proxy technique, this correction assumes identical light path modifications in 1.67-mu m and 1.6-mu m bands. This approach is believed to offer some advantages over the proxy technique since it does not use any prior assumptions on carbon dioxide concentrations. Both carbon dioxide and methane GOSAT retrievals were validated using ground-based Fourier Transform Spectrometer (FTS) measurements provided by the Total Carbon Column Observing Network (TCCON). For XCO2 retrievals we found subppm station-to-station bias (GOSAT versus TCCON); single-scan precision of mostly below 2 ppm (0.5%); and correlation coefficient for the Northern Hemisphere TCCON stations above 0.8. For XCH4 retrievals over TCCON sites we found single-scan precision below 1 % and correlation coefficient above 0.8.

Being one of the largest carbon reservoirs in the world, the Siberian carbon sink however remains poorly understood due to the limited numbers of observation. We present the first results of atmospheric CO2 inversions utilizing measurements from a Siberian tower network (Japan-Russia Siberian Tall Tower Inland Observation Network; JR-STATION) and four aircraft sites, in addition to surface background flask measurements by the National Oceanic and Atmospheric Administration (NOAA). Our inversion with only the NOAA data yielded a boreal Eurasian CO2 flux of -0.56 +/- 0.79 GtC yr(-1), whereas we obtained a weaker uptake of -0.35 +/- 0.61 GtC yr(-1) when the Siberian data were also included. This difference is mainly explained by a weakened summer uptake, especially in East Siberia. We also found the inclusion of the Siberian data had significant impacts on inversion results over northeastern Europe as well as boreal Eurasia. The inversion with the Siberian data reduced the regional uncertainty by 22% on average in boreal Eurasia, and further uncertainty reductions up to 80% were found in eastern and western Siberia. Larger interannual variability was clearly seen in the inversion which includes the Siberia data than the inversion without the Siberia data. In the inversion with NOAA plus Siberia data, east Siberia showed a larger interannual variability than that in west and central Siberia. Finally, we conducted forward simulations using estimated fluxes and confirmed that the fit to independent measurements over central Siberia, which were not included in inversions, was greatly improved.

The Greenhouse gases Observing SATellite (GOSAT) measures column-averaged dry air mole fractions of carbon dioxide and methane (XCO2 and XCH4, respectively). Since the launch of GOSAT, model-simulated three-dimensional concentrations from a National Institute for Environmental Studies offline tracer Transport Model (NIES TM) have been used as a priori concentration data for operational near real-time retrievals of XCO2 and XCH4 from GOSAT short-wavelength infrared spectra at NIES. Although the choice of a priori profile has only a minor effect on retrieved XCO2 or XCH4, a realistic simulation with minimal deviation from observed data is desirable. In this paper, we describe the newly developed version of NIES TM that has been adapted to provide global and near real-time concentrations of CO2 and CH4 using a high-resolution meteorological dataset, the Grid Point Value (GPV) prepared by the Japan Meteorological Agency. The spatial resolution of the NIES TM is set to 0.5 × 0.5 in the horizontal in order to utilise GPV data, which have a resolution of 0.5 × 0.5 , 21 pressure levels and a time interval of 3 h. GPV data are provided to the GOSAT processing system with a delay of several hours, and the near real-time model simulation produces a priori concentrations driven by diurnally varying meteorology. A priori variance-covariance matrices of CO2 and CH4 are also derived from the simulation outputs and observation-based reference data for each month of the year at a resolution of 0.5 × 0.5 and 21 pressure levels. Model performance is assessed by comparing simulation results with the GLOBALVIEW dataset and other observational data. The overall root-mean-square differences between model predictions and GLOBALVIEW analysis are estimated to be 1.45 ppm and 12.52 ppb for CO2 and CH4, respectively, and the seasonal correlation coefficients are 0.87 for CO2 and 0.53 for CH4. The model showed good performance particularly at oceanic and free tropospheric sites. The high-resolution model also performs well in reproducing both the observed synoptic variations at some sites and stratospheric profiles over Japan. These results give us confidence that the performance of our GPV-forced high-resolution NIES TM is adequate for use in satellite retrievals. © Author(s) 2013.

A modified cumulus convection parametrisation scheme is presented. This scheme computes the mass of air transported upward in a cumulus cell using conservation of moisture and a detailed distribution of convective precipitation provided by a reanalysis dataset. The representation of vertical transport within the scheme includes entrainment and detrainment processes in convective updrafts and downdrafts. Output from the proposed parametrisation scheme is employed in the National Institute for Environmental Studies (NIES) global chemical transport model driven by JRA-25/JCDAS reanalysis. The simulated convective precipitation rate and mass fluxes are compared with observations and reanalysis data. A simulation of the short-lived tracer 222Rn is used to further evaluate the performance of the cumulus convection scheme. Simulated distributions of 222Rn are evaluated against observations at the surface and in the free troposphere, and compared with output from models that participated in the TransCom-CH4 Transport Model Intercomparison. From this comparison, we demonstrate that the proposed convective scheme in general is consistent with observed and modeled results. © Author(s) 2013.

We have developed an improved version of the National Institute for Environmental Studies (NIES) three-dimensional chemical transport model (TM) designed for accurate tracer transport simulations in the stratosphere, using a hybrid sigma-isentropic (σ-θ) vertical coordinate that employs both terrain-following and isentropic parts switched smoothly around the tropopause. The air-ascending rate was derived from the effective heating rate and was used to simulate vertical motion in the isentropic part of the grid (above level 350 K), which was adjusted to fit to the observed age of the air in the stratosphere. Multi-annual simulations were conducted using the NIES TM to evaluate vertical profiles and dry-air column-averaged mole fractions of CO2and CH4. Comparisons with balloon-borne observations over Sanriku (Japan) in 2000-2007 revealed that the tracer transport simulations in the upper troposphere and lower stratosphere are performed with accuracies of ∼5% for CH4and SF6, and ∼1% for CO2compared with the observed volume-mixing ratios. The simulated column-averaged dry air mole fractions of atmospheric carbon dioxide (XCO2) and methane (XCH4) were evaluated against daily ground-based high-resolution Fourier Transform Spectrometer (FTS) observations measured at twelve sites of the Total Carbon Column Observing Network (TCCON) (Bialystok, Bremen, Darwin, Garmisch, Izaña, Lamont, Lauder, Orleans, Park Falls, Sodankylä, Tsukuba, and Wollongong) between January 2009 and January 2011. The comparison shows the model's ability to reproduce the site-dependent seasonal cycles as observed by TCCON, with correlation coefficients typically on the order 0.8-0.9 and 0.4-0.8 for XCO2and XCH4, respectively, and mean model biases of ±0.2% and ±0.5%, excluding Sodankylä, where strong biases are found. The ability of the model to capture the tracer total column mole fractions is strongly dependent on the model's ability to reproduce seasonal variations in tracer concentrations in the planetary boundary layer (PBL). We found a marked difference in the model's ability to reproduce near-surface concentrations at sites located some distance from multiple emission sources and where high emissions play a notable role in the tracer's budget. Comparisons with aircraft observations over Surgut (West Siberia), in an area with high emissions of methane from wetlands, show contrasting model performance in the PBL and in the free troposphere. Thus, the PBL is another critical region for simulating the tracer total column mole fractions. © 2013 Author(s).

We present surface CO2 flux estimates obtained by an inverse modeling analysis from column-averaged dry air mole fractions of CO2 (XCO2) observed by the Greenhouse gases Observing SATellite (GOSAT) and ground-based data. Two inversion cases were examined: 1) a decadal inversion using ground-based CO2 observations by NOAA from 1999 to 2010 to derive CO2 flux interannual variability, and 2) an inversion using NOAA plus NIES GOSAT XCO2 data from June 2009 to October 2010. We used single-shot GOSAT data and individual NOAA flask data for the inversions. Our results show differences in estimated fluxes between the NOAA data inversion and the NOAA plus GOSAT data inversion, especially in Northern Eurasia and in Equatorial Africa and America where the ground-based observational sites were sparse. Uncertainty reduction rates of 40%-70% were achieved by inclusion of GOSAT data, compared to the case using just the NOAA data. The inclusion of GOSAT data in the inversion resulted in larger summer sinks in northwest Boreal Eurasia and a smaller summer sink in southeast Boreal Eurasia, with a clear uncertainty reduction in both regions. Adding GOSAT data also led to increase in Tropical African fluxes in boreal winter beyond interannual variability from NOAA data inversions.

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. © Author(s) 2013. CC Attribution 3.0 License.

Inverse estimation of surface CO2 fluxes is performed with atmospheric transport model using ground-based and GOSAT observations. The NIES-retrieved CO2 column mixing (X-CO2) and column averaging kernel are provided by GOSAT Level 2 product v. 2.0 and PPDF-DOAS method. Monthly mean CO2 fluxes for 64 regions are estimated together with a global mean offset between GOSAT data and ground-based data. We used the fixed-lag Kalman filter to infer monthly fluxes for 42 sub-continental terrestrial regions and 22 oceanic basins. We estimate fluxes and compare results obtained by two inverse modeling approaches. In basic approach adopted in GOSAT Level 4 product v. 2.01, we use aggregation of the GOSAT observations into monthly mean over 5x5 degree grids, fluxes are estimated independently for each region, and NIES atmospheric transport model is used for forward simulation. In the alternative method, the model-observation misfit is estimated for each observation separately and fluxes are spatially correlated using EOF analysis of the simulated flux variability similar to geostatistical approach, while transport simulation is enhanced by coupling with a Lagrangian transport model Flexpart. Both methods use using the same set of prior fluxes and region maps. Daily net ecosystem exchange (NEE) is predicted by the Vegetation Integrative SImulator for Trace gases (VISIT) optimized to match seasonal cycle of the atmospheric CO2. Monthly ocean-atmosphere CO2 fluxes are produced with an ocean pCO(2) data assimilation system. Biomass burning fluxes were provided by the Global Fire Emissions Database (GFED); and monthly fossil fuel CO2 emissions are estimated with ODIAC inventory. The results of analyzing one year of the GOSAT data suggest that when both GOSAT and ground-based data are used together, fluxes in tropical and other remote regions with lower associated uncertainties are obtained than in the analysis using only ground-based data. With version 2.0 of L2 X-CO2 the fluxes appear reasonable for many regions and seasons, however there is a need for improving the L2 bias correction, data filtering and the inverse modeling method to reduce estimated flux anomalies visible in some areas. We also observe that application of spatial flux correlations with EOF-based approach reduces flux anomalies.

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.

Numerical simulation and validation of three-dimensional structure of atmospheric carbon dioxide (CO2) is necessary for quantification of transport model uncertainty and its role on surface flux estimation by inverse modeling. Simulations of atmospheric CO2 were performed using four transport models and two sets of surface fluxes compared with an aircraft measurement dataset of Comprehensive Observation Network for Trace gases by AIr-Liner (CONTRAIL), covering various latitudes, longitudes, and heights. Under this transport model intercomparison project, spatiotemporal variations of CO2 concentration for 2006-2007 were analyzed with a three-dimensional perspective. Results show that the models reasonably simulated vertical profiles and seasonal variations not only over northern latitude areas but also over the tropics and southern latitudes. From CONTRAIL measurements and model simulations, intrusion of northern CO2 in to the Southern Hemisphere, through the upper troposphere, was confirmed. Furthermore, models well simulated the vertical propagation of seasonal variation in the northern free troposphere. However, significant model-observation discrepancies were found in Asian regions, which are attributable to uncertainty of the surface CO2 flux data. In summer season, differences in latitudinal gradients by the fluxes are comparable to or greater than model-model differences even in the free troposphere. This result suggests that active summer vertical transport sufficiently ventilates flux signals up to the free troposphere and the models could use those for inferring surface CO2 fluxes.

A model-based three-dimensional (3-D) climatology of atmospheric CO2 concentrations has been constructed for the analysis of satellite observations, as a priori information in retrieval calculations, and for preliminary evaluation of remote sensing products. The locations of ground-based instruments and the coverage of aircraft in situ measurements are limited and do not represent the full atmospheric column, which is a primary requirement for the validation of satellite data. To address this problem, we have developed a method for constructing a 3-D CO2 climatology from the surface up to approximately 30 km by combining information from in situ measurements and several transport models. The model-simulated CO2 concentrations have been generated in the framework of the TransCom satellite experiment. The spatial and temporal biases of the transport-model-derived data set have been corrected using in situ CO2 measurements in the troposphere and in situ profiles of the mean age of air in the stratosphere. The constructed multimodel mean CO2 climatology represents the seasonal cycle and the inter-hemispheric gradient better than each transport model. Our approach performs well near the surface and in regions where the observational network is relatively dense. The column-mean CO2 of the constructed climatology was reduced by ∼1 ppm from that of a single transport models, consistent with model validation against measurements of the CO2 total column.

We assessed the utility of global CO2 distributions brought by the Greenhouse gases Observing SATellite (GOSAT) in the estimation of regional CO2 fluxes. We did so by estimating monthly fluxes and their uncertainty over a one-year period between June 2009 and May 2010 from 1) observational data collected in existing networks of surface CO2 measurement sites (GLOBALVIEW-CO2 2010; extrapolated to the year 2010) and 2) both the surface observations and column-averaged dry air mole fractions of CO2 (X-CO2) retrieved from GOSAT soundings. Monthly means of the surface observations and GOSAT X-CO2 retrievals gridded to 5 degrees x 5 degrees cells were used here. The estimation was performed for 64 subcontinental-scale regions. We compared these two sets of results in terms of change in uncertainty associated with the flux estimates. The rate of reduction in the flux uncertainty, which represents the degree to which the GOSAT X-CO2 retrievals contribute to constraining the fluxes, was evaluated. We found that the GOSAT X-CO2 retrievals could lower the flux uncertainty by as much as 48% (annual mean). Pronounced uncertainty reduction was found in the fluxes estimated for regions in Africa, South America, and Asia, where the sparsity of the surface monitoring sites is most evident.

A chemistry-transport model (CTM) intercomparison experiment (TransCom-CH4) has been designed to investigate the roles of surface emissions, transport and chemical loss in simulating the global methane distribution. Model simulations were conducted using twelve models and four model variants and results were archived for the period of 1990-2007. All but one model transports were driven by reanalysis products from 3 different meteorological agencies. The transport and removal of CH4 in six different emission scenarios were simulated, with net global emissions of 513 +/- 9 and 514 +/- 14 TgCH(4) yr(-1) for the 1990s and 2000s, respectively. Additionally, sulfur hexafluoride (SF6) was simulated to check the interhemispheric transport, radon (Rn-222) to check the subgrid scale transport, and methyl chloroform (CH3CCl3) to check the chemical removal by the tropospheric hydroxyl radical (OH). The results are compared to monthly or annual mean time series of CH4, SF6 and CH3CCl3 measurements from 8 selected background sites, and to satellite observations of CH4 in the upper troposphere and stratosphere. Most models adequately capture the vertical gradients in the stratosphere, the average long-term trends, seasonal cycles, interannual variations (IAVs) and interhemispheric (IH) gradients at the surface sites for SF6, CH3CCl3 and CH4. The vertical gradients of all tracers between the surface and the upper troposphere are consistent within the models, revealing vertical transport differences between models. An average IH exchange time of 1.39 +/- 0.18 yr is derived from SF6 time series. Sensitivity simulations suggest that the estimated trends in exchange time, over the period of 1996-2007, are caused by a change of SF6 emissions towards the tropics. Using six sets of emission scenarios, we show that the decadal average CH4 growth rate likely reached equilibrium in the early 2000s due to the flattening of anthropogenic emission growth since the late 1990s. Up to 60% of the IAVs in the observed CH4 concentrations can be explained by accounting for the IAVs in emissions, from biomass burning and wetlands, as well as meteorology in the forward models. The modeled CH4 budget is shown to depend strongly on the troposphere-stratosphere exchange rate and thus on the model's vertical grid structure and circulation in the lower stratosphere. The 15-model median CH4 and CH3CCl3 atmospheric lifetimes are estimated to be 9.99 +/- 0.08 and 4.61 +/- 0.13 yr, respectively, with little IAV due to transport and temperature.

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 degrees x 0.5 degrees) 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 ppm(2) for CO2 and 220 ppb(2) 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.

In this paper the numerical simulation results of mean wind velocity vector and its measurement error for VAD technique using Weather Research and Forecasting Model (WRF) and Yamada-Mellor models are presented. The numerical model takes into account the non-Gaussian and nonstationary characteristics of the Doppler lidar signal. The numerical simulation results were compared with CASES-99 experimental data from balloon sonde (GLASS) and the Doppler Lidar. It shows that results of numerical simulation by WRF and Yamada-Mellor models agree well with experimental data for potential temperature. Yamada-Mellor model describes the nocturnal low-level jet only up to 100 m and above the fit is fairly bad. But WRF model allows us to have a good comparison for all levels. In case of the strong turbulence the value of measurement error can greatly surpass the value 0.5 m/s; therefore it does not satisfy World Meteorological Organization (WMO) requirements for wind. For the high spatial resolution we cannot get the required accuracy.