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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.

We developed a high-resolution surface flux inversion system based on the global Lagrangian–Eulerian coupled tracer transport model composed of National Institute for Environmental Studies Transport Model (NIES-TM) and FLEXible PARTicle dispersion model (FLEXPART). The inversion system is named NTFVAR (NIES-TM-FLEXPART-variational) as it applies variational optimisation to estimate surface fluxes. We tested the system by estimating optimized corrections to natural surface CO2 fluxes to achieve best fit to atmospheric CO2 data collected by the global in-situ network, as a necessary step towards capability of estimating anthropogenic CO2 emissions. We employ the Lagrangian particle dispersion model (LPDM) FLEXPART to calculate the surface flux footprints of CO2 observations at a 0.1° × 0.1° spatial resolution. The LPDM is coupled to 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 is being 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) dataset. High-resolution prior flux fields were prepared using Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) for fossil fuel combustion, Global Fire Assimilation System (GFAS) for biomass burning, the Vegetation Integrative SImulator for Trace gases (VISIT) model for terrestrial biosphere exchange and Ocean Tracer Transport Model (OTTM) for oceanic exchange. The terrestrial biospheric flux field was constructed using a vegetation mosaic map and separate simulation of CO2 fluxes at daily time step by the VISIT model for each vegetation type. The prior flux uncertainty for 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 vegetation productivity gradients. The resulting flux estimates improve fit to the observations at continuous observations sites, reproducing both the seasonal variation and short-term concentration variability, including high CO2 concentration events associated with anthropogenic emissions. The use of high-resolution atmospheric transport in global CO2 flux inversion has the advantage of better resolving the transport from the mix of the anthropogenic and biospheric sources in densely populated continental regions and shows potential for better separation between fluxes from terrestrial ecosystems and strong localised sources such as anthropogenic emissions and forest fires. Further improvements in the modelling system are needed as the posterior fit is better than that by the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker only for a fraction of the monitoring sites, mostly at coastal and island locations experiencing mix of background and local flux signals.

We examine CH4 variability over different regions of India and the surrounding oceanic regions using thermal infrared (TIR) band observations by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) and simulated by the updated MIROC4.0-based Atmospheric Chemistry Tracer Model (ACTM) for the period 2009-2014. GOSAT TIR provides data coverage and density captures detailed features of CH4 distributions at height levels from the top of the boundary layer to the upper troposphere even in the cloudy conditions, which is very common for the region with a monsoon climate. Analysis of transport and emission contributions to CH4 variabilities suggests that the CH4 seasonal cycle over India is controlled by the heterogeneous distribution of surface emissions under the influence of the monsoonal divergent wind circulations. Distinct seasonal variations of CH4 are observed over northern and southern regions of India corresponding to the southwestern monsoon (July–September) and early autumn (October–December) seasons. In summer, three principal circulations pumping CH4 upward over South Asia: the lateral (the cross-equatorial circulation) and transverse (flows between the arid regions of North Africa and the Near East and South Asia) monsoons, and the Walker Circulation extends across the Pacific Ocean. GOSAT TIR derives CH4 profiles due to retrieval of signal from 22 vertical levels. In general, the mean ACTM (no averaging kernel incorporated)-GOSAT misfit is within 50 ppb, excepting the level of 150 hPa and upward, where the GOSAT TIR sensitivity becomes too low. Due to the use of MIROC4.0 AGCM performance of ACTM in the upper troposphere and lower stratosphere has been improved. The GOSAT-ACTM misfit above the level of 150 hPa is likely to arise from a priori model for TIR retrievals. Convolution of the modeled profiles with retrieval a priori and averaging kernels reduces the misfit to below uncertainty. However, the weight of the a priori profiles becomes too large with such smoothing. Overall, the ACTM simulations of CH4 in the Indian regions compare favorably with the GOSAT TIR samplings, in terms of seasonality and regional variability. However, the GOSAT-ACTM inconsistencies indicate opportunities for further flux optimization and emission uncertainty reduction by inverse modeling methods.

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 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.

Extremely high levels of column-averaged dry-air mole fractions of atmospheric methane (XCH4) were detected in August and September 2013 over northeast Asia (∼ 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, ∼ 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 need to perform long-term simulations with reasonable accuracy has led to the development of mass-conservative and efficient numerical methods for solving the transport equation in forward and inverse models. We designed and implemented a flux-form (Eulerian) tracer transport algorithm in the National Institute for Environmental Studies Transport Model (NIES TM), which is used for simulating diurnal and synoptic-scale variations of tropospheric long-lived constituents, as well as their seasonal and inter-annual variability. Implementation of the flux-form method requires the mass conservative wind fields. However, the model is off-line and is driven by datasets from a global atmospheric model or data assimilation system, in which vertically integrated mass changes are not in balance with the surface pressure tendency and mass conservation is not achieved. To rectify the mass-imbalance, a flux-correction method is employed. To avoid a singularity near the poles, caused by the small grid size arising from the meridional convergence problem, the proposed model uses a reduced latitude–longitude grid scheme, in which the grid size is doubled several times approaching the poles. This approach overcomes the Courant condition in the Polar Regions, maintains a reasonably high integration time-step, and ensures adequate model performance during simulations. To assess the model performance, we performed global transport simulations for SF6, 222Rn, and CO2. The results were compared with observations available from the World Data Centre for Greenhouse Gases, GLOBALVIEW, and the Hateruma monitoring station, Japan. Overall, the results show that the proposed flux-form version of NIES TM can produce tropospheric tracer transport more realistically than previously possible. The reasons for this improvement are discussed.

This paper presents the first results of an age of air (AoA) inter-comparison of six global transport models. Following a protocol, three global circulation models and three chemistry transport models simulated five tracers with boundary conditions that grow linearly in time. This allows for an evaluation of the AoA and transport times associated with inter-hemispheric transport, vertical mixing in the troposphere, transport to and in the stratosphere, and transport of air masses between land and ocean. Since AoA is not a directly measurable quantity in the atmosphere, simulations of 222Rn and SF6 were also requested. We focus this first analysis on averages over the period 2000–2010, taken from longer simulations covering the 1988–2014 period. We find that two models, NIES and TOMCAT, show substantially slower vertical mixing in the troposphere compared to other models (LMDZ, TM5, EMAC, and ACTM). However, while the TOMCAT model, as used here, has slow transport between the hemispheres and between land and ocean, the NIES model shows efficient horizontal mixing and a smaller latitudinal gradient in SF6 compared to the other models and observations. We find consistent differences between models concerning vertical mixing of the troposphere, expressed as AoA differences and modeled 222Rn gradients between 950 and 500 hPa. All models agree, however, on an interesting asymmetry in inter-hemispheric mixing, with faster transport from the Northern hemisphere surface to the Southern hemisphere than vice versa. This is attributed to a rectifier caused by a stronger seasonal cycle in boundary layer venting over Northern hemispheric landmasses, and possibly to a related asymmetric position of the inter-tropical convergence zone. The calculated AoA in the upper stratosphere varies considerably among the models (4–7 years). Finally, we find that the inter-model differences are generally larger than differences in AoA that result from using the same model with a different resolution or convective parameterisation. Taken together, the AoA model inter-comparison provides a useful addition to traditional approaches to evaluate transport time scales. Results highlight that inter-model differences associated with resolved transport (advection) and parameterised transport (convection, boundary layer mixing) are still large, and require further analysis. For this purpose, all model output and analysis software is available.

We investigated differences in the five currently-available datasets of column-integrated CO2 concentrations (XCO2) 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 XCO2 datasets combined with surface-based CO2 data, using a single inversion system. The five XCO2 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 ~30% on average. The departures of the five individual inversion results (annual-mean regional fluxes based on XCO2-surface combined data) from the surface-data-only results were close to one another in some terrestrial regions where spatial coverage by each XCO2 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.

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.

[1] 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 km and 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.

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.

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 CO2 and 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 CH4 and SF6, and ~1% for CO2 compared 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 XCO2 and 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.

GOSAT is a joint effort of the Japan Aerospace Exploration Agency (JAXA), the National Institute for Environmental Studies (NIES), and the Ministry of the Environment (MOE), Japan. Part of this work on ACOS B2.9 was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. GOSAT spectra were kindly provided to the California Institute of Technology through a memorandum of understanding between JAXA and NASA. U. S. funding for TCCON is provided by NASA’s Terrestrial Ecology Program (grant number NNX11AG01G), the Orbiting Carbon Observatory Program, the Atmospheric CO2 Observations from Space (ACOS) Program, and the Department of Energy/Atmospheric Radiation Measurement (DOE/ARM) Program. The Darwin TCCON site was built at Caltech with funding from the OCO project and is operated by the University of Wollongong, with travel funds for maintenance and equipment costs funded by the OCO-2 project. We acknowledge funding to support Darwin and Wollongong from the Australian Research Council, Projects LE0668470, DP0879468, DP110103118, and LP0562346. Lauder TCCON measurements are funded by New Zealand Foundation of Research Science and Technology contracts C01X0204 and CO1X0406. We acknowledge financial support of the Bia- lystok and Orleans TCCON sites from the Senate of Bremen and EU projects IMECC, GEOMON and InGOS as well as maintenance and logistical work provided by AeroMeteo Service (Bialystok) and the RAMCES team at LSCE (Gif-sur-Yvette, France) and additional operational funding from the NIES GOSAT project. The Garmisch TCCON team acknowledges funding by the EC-INGOS project. Development of RemoTeC was partly funded by ESA through the GHG-CCI project (S. Guerlet) and by Deutsche Forschungsgemeinschaft (DFG) through grant BU2599/1-1 (A. Butz). The JRA-25/JCDAS data sets used for atmospheric transport modeling were provided by the cooperative, long-term reanalysis project by the Japan Meteorological Agency (JMA) and Central Research Institute of Electric Power Industry (CRIEPI). The authors thank Dr. Sasano, Director of the Center for Global Environmental Research at the NIES, the members of the NIES GOSAT and NASA ACOS projects.

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.

[1] 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.

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.

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 AIrLiner (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 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 Tg CH4 yr−1 for the 1990s and 2000s, respectively. Additionally, sulfur hexafluoride (SF6) was simulated to check the interhemispheric transport, radon (222Rn) 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 assessed the utility of global CO{sub 2} distributions brought by the Greenhouse gases Observing SATellite (GOSAT) in the estimation of regional CO{sub 2} 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 (GLOBALVIEWCO2 2010; extrapolated to the year 2010) and (2) both the surface observations and column-averaged dry air mole fractions of CO{sub 2} (X{sub CO2}) retrieved from GOSAT soundings. Monthly means of the surface observations and GOSAT X{sub CO2} retrievals gridded to 5{sup o} x 5{sup o} 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{sub CO2} retrievals contribute to constraining the fluxes, was evaluated. We found that the GOSAT X{sub 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 sparsitymore » of the surface monitoring sites is most evident.« less

We present the development of the Adjoint of the Global Eulerian–Lagrangian Coupled Atmospheric (A-GELCA) model that consists of the National Institute for Environmental Studies (NIES) model as an Eulerian three-dimensional transport model (TM), and FLEXPART (FLEXible PARTicle dispersion model) as the Lagrangian Particle Dispersion Model (LPDM). The forward tangent linear and adjoint components of the Eulerian model were constructed directly from the original NIES TM code using an automatic differentiation tool known as TAF (Transformation of Algorithms in Fortran; http://www.FastOpt.com), with additional manual pre- and post-processing aimed at improving transparency and clarity of the code and optimizing the performance of the computing, including MPI (Message Passing Interface). The Lagrangian component did not require any code modification, as LPDMs are self-adjoint and track a significant number of particles backward in time in order to calculate the sensitivity of the observations to the neighboring emission areas. The constructed Eulerian adjoint was coupled with the Lagrangian component at a time boundary in the global domain. The simulations presented in this work were performed using the A-GELCA model in forward and adjoint modes. The forward simulation shows that the coupled model improves reproduction of the seasonal cycle and short-term variability of CO2. Mean bias and standard deviation for five of the six Siberian sites considered decrease roughly by 1 ppm when using the coupled model. The adjoint of the Eulerian model was shown, through several numerical tests, to be very accurate (within machine epsilon with mismatch around to ±6 e−14) compared to direct forward sensitivity calculations. The developed adjoint of the coupled model combines the flux conservation and stability of an Eulerian discrete adjoint formulation with the flexibility, accuracy, and high resolution of a Lagrangian backward trajectory formulation. A-GELCA will be incorporated into a variational inversion system designed to optimize surface fluxes of greenhouse gases.

第6回極域科学シンポジウム分野横断セッション：[IA] 急変する北極気候システム及びその全球的な影響の総合的解明―GRENE北極気候変動研究事業研究成果報告2015―11月19日（木） 国立極地研究所１階交流アトリウム

We present a study on validation of the National Institute for Environmental Studies Transport Model (NIES TM) by comparing to observed vertical profiles of atmospheric CO2. The model uses a hybrid sigma-isentropic (σ–θ) vertical coordinate that employs both terrain-following and isentropic parts switched smoothly in the stratosphere. The model transport is driven by reanalyzed meteorological fields and designed to simulate seasonal and diurnal cycles, synoptic variations, and spatial distributions of atmospheric chemical constituents in the troposphere. The model simulations were run for biosphere, fossil fuel, air–ocean exchange, biomass burning and inverse correction fluxes of carbon dioxide (CO2) by GOSAT Level 4 product. We compared the NIES TM simulated fluxes with data from the HIAPER Pole-to-Pole Observations (HIPPO) Merged 10 s Meteorology, Atmospheric Chemistry, and Aerosol Data, including HIPPO-1, HIPPO-2 and HIPPO-3 from 128.0° E to −84.0° W, and 87.0° N to −67.2° S. The simulation results were compared with CO2 observations made in January and November 2009, and March and April 2010. The analysis attests that the model is good enough to simulate vertical profiles with errors generally within 1–2 ppmv, except for the lower stratosphere in the Northern Hemisphere high latitudes.

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-μm, and 2.0-μm CO2 absorption bands). For the methane abundance, retrieved from 1.67-μm absorption band, we applied optical path correction based on PPDF parameters from 1.6-μm CO2 absorption band. Similarly to widely used CO2-proxy technique, this correction assumes identical light path modifications in 1.67-μm and 1.6-μm 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.

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.

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?>?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?>??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.

COMMENTARY Common but differentiated IBI Prize Essay LETTERS I BOOKS I POLICY FORUM I EDUCATION FORUM I PERSPECTIVES LETTERS edited by Jennifer Sills Iconic CO 2 Time Series at Risk THE STEADY RISE IN ATMOSPHERIC LONG- lived greenhouse gas concentrations is the main driver of contemporary climate change. The Mauna Loa CO 2 time series (1, 2), started by C. D. Keeling in 1958 and maintained today by the Scripps Institution of Oceanography and the Earth System Research Laboratory (ESRL) of NOAA, is iconic evidence of the effect of human- caused fossil fuel and land-use change emis- sions on the atmospheric increase of CO 2 . The continuity of such records depends criti- cally on having stable funding, which is chal- lenging to maintain in the context of 3- to 4-year research grant funding cycles (3), and LEAH R. GERBER Faculty of Ecology, Evolution, and Environmental Sciences, School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA. E-mail: leah.gerber@asu.edu References 1. Associated Press, The New York Times, 6 July 2012, p. A7 (www.nytimes.com/2012/07/06/ world/europe/south-korea-whaling-plan-criticized.html). 2. S. Herman, Voice of America, 24 July 2012 (www.voanews.com/content/south-korea-still- considering-resumption-of-whaling/1444081.html). 3. L. R. Gerber, L. Morissette, K. Kaschner, D. Pauly, Science 323, 880 (2009). 4. R. Black, BBC News, 5 July 2012 (www.bbc.co.uk/news/science-environment-18719512). is currently threatened by the fi nancial crisis. The ESRL Global Monitoring Division maintains a network of about 100 surface and aircraft sites worldwide at which whole air samples are collected approximately every week for analysis of CO 2 , CH 4 , CO, halocar- bons, and many other chemical species (4). This is complemented by high-frequency measurements at the Mauna Loa, Barrow, American Samoa, and South Pole obser- vatories, and about 10 North American tall towers. The success of the NOAA program has inspired similar efforts in Europe (5), China (6), India (7), and Brazil (8), with the United Nations World Meteorological Orga- nization providing guidance and precision requirements through the Global Atmosphere Watch program (9), but no funding. 31 AUGUST 2012 Downloaded from www.sciencemag.org on August 17, 2015 AT THE RECENT INTERNATIONAL WHALING COMMISSION’S ANNUAL meeting in Panama, South Korean offi cials announced their plan to initiate a “scientifi c whaling” program (1). This announcement came as a surprise given the general sentiment that the global demand for whale meat is declining. After weeks of international outcry, on 17 July, South Korea reversed their decision to hunt whales for research, but the issue is not dead (2). South Korea claimed that the goal of the scientifi c whaling pro- gram is to study the types and amounts of fi sh whales eat, given confl ict with fi sher- ies. Yet, it is well established in the scien- tifi c literature that there are many ways to study whale diet without killing them (3). Decades of fruitless negotiation between pro- and anti-whaling nations suggests a broken system, wrought with loopholes that allow unsustainable whal- ing to continue. Within this broken system, there is no incentive to reduce whaling, as the recent announcement by South Korea shows. Whaling groups are unwilling to compromise by allowing a sustainable harvest of whales, so unsustainable (scientifi c) whaling continues. To ensure a future of both whales and whalers, we must har- ness the passion and value that people place on living whales, with- out telling people what to do or force one set of values on others. We need novel, out-of-the-box approaches to effective management and conservation of whales. We must compromise to ensure reduc- tions in whales being killed, better oversight of countries that har- vest them, and limited whaling that does not threaten the persis- tence of whales. For those who believe that whaling is unethical, I challenge you to put forward alternative ideas to a global moratorium that fosters the “loophole” of scientifi c whaling. With new plans to develop scientifi c whaling programs (4), the current global moratorium is clearly bro- ken. Scientists, conservation advocates, resource managers, and the public must work together to develop new approaches to ensure the persistence of whales in our oceans. The data collected by NOAA and its worldwide partners have been used not only to demonstrate the unassailable rise of atmo- spheric greenhouse gas concentrations, but also to infer the magnitudes, locations, and times of surface-atmosphere exchange of those gases based on small concentration gradients between sites (10). Important fi nd- ings from analysis of these records include the detection of a signifi cant terrestrial car- bon sink at northern mid-latitudes (11) and subsequent research aimed at identifying the mechanisms by which that sink must operate. Long-term, high-quality, atmospheric mea- surements are crucial for quantifying trends in greenhouse gas fl uxes and attributing them to fossil fuel emissions, changes in land-use and management, or the response of natural VOL 337 SCIENCE www.sciencemag.org Published by AAAS CREDIT: LEAH GERBER/ ARIZONA STATE UNIVERSITY The Scientifi c Whaling Loophole

[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.

The need to perform long-term simulations with reasonable accuracy has led to the development of mass-conservative and efficient numerical methods for solving the transport equation in forward and inverse models. We designed and implemented a flux-form (Eulerian) tracer transport algorithm in the National Institute for Environmental Studies Transport Model (NIES TM), which is used for simulating diurnal and synoptic-scale variations of tropospheric long-lived constituents, as well as their seasonal and inter-annual variability. Implementation of the flux-form method requires the mass conservative wind fields. However, the model is off-line and is driven by datasets from a global atmospheric model or data assimilation system, in which vertically integrated mass changes are not in balance with the surface pressure tendency and mass conservation is not achieved. To rectify the mass-imbalance, a flux-correction method is employed. To avoid singularity near the poles caused by the small grid size arising from the meridional convergence problem, the proposed model uses a reduced latitude-longitude grid scheme, in which the grid size is doubled several times approaching the poles. This approach overcomes the Courant condition in the Polar Regions, maintains a reasonably high integration time-step and ensures adequate model performance during simulations. To assess the model performance, we performed global transport simulations for SF6, 222Rn and CO2. The results were compared with observations available from the World Data Centre for Greenhouse Gases, GLOBALVIEW and the Hateruma monitoring station, Japan. Overall, the results show that the proposed flux-form version of NIES TM can produce tropospheric tracer transport more realistically than previously possible. The reasons for this improvement are discussed.

[1] 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.

The paper suggests a model for predicting the behavior of the optical characteristics of a medium that determine the character of laser beam propagation in the atmosphere. These are first of all the refractive index and the refractive-index-structure parameter as well as the wind velocity, kinetic turbulence energy, dissipation energy speed and other meteorological parameters that are important in the laser propagation problem. The prediction of the refractive index and the refractive-indexstructure parameter is based on the known equations of the theory of wave propagation in a turbulent atmosphere and a mathematical model for short-range forecast of evolution of local meteorology and turbulent structure of the atmospheric boundary layer (ABL).