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The World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) coordinates high-quality atmospheric greenhouse gas observations globally and provides these observations through the WMO World Data Centre for Greenhouse Gases (WDCGG) supported by Japan Meteorological Agency. The WDCGG and the National Oceanic and Atmospheric Administration (NOAA) analyse these measurements using different methodologies and site selection to calculate global annual mean surface CO2 and its growth rate as a headline climate indicator. This study introduces a third hybrid method named GFIT, which serves as an independent validation and open-source alternative to the methods described by NOAA and WDCGG. We apply GFIT to incorporate observations from most WMO GAW stations and 3D modelled CO2 fields from CarbonTracker Europe (CTE). We find that different observational networks (i.e. NOAA, GAW, and CTE networks) and analysis methods result in differences in the calculated global surface CO2 mole fractions equivalent to the current atmospheric growth rate over a 3-month period. However, the CO2 growth rate derived from these networks and the CTE model output shows good agreement. Over the long-term period (40 years), both networks with and without continental sites exhibit the same trend in the growth rate (0.030 ± 0.002 ppm yr−1 each year). However, a clear difference emerges in the short-term (1-month) change in the growth rate. The network that includes continental sites improves the early detection of changes in biogenic emissions.

Inverse analysis was used to estimate fire carbon emissions in Equatorial Asia induced by the big El Niño event in 2015. This inverse analysis is unique because it extensively used high-precision atmospheric mole fraction data of carbon dioxide (CO2) from the commercial aircraft observation project CONTRAIL. Through comparisons with independent shipboard observations, especially carbon monoxide (CO) data, the validity of the estimated fire-induced carbon emissions was demonstrated. The best estimate, which used both aircraft and shipboard CO2 observations, indicated 273 Tg C for fire emissions from September–October 2015. This 2-month period accounts for 75 % of the annual total fire emissions and 45 % of the annual total net carbon flux within the region, indicating that fire emissions are a dominant driving force of interannual variations of carbon fluxes in Equatorial Asia. Several sensitivity experiments demonstrated that aircraft observations could measure fire signals, though they showed a certain degree of sensitivity to prior fire-emission data. The inversions coherently estimated smaller fire emissions than the prior data, partially because of the small contribution of peatland fires indicated by enhancement ratios of CO and CO2 observed by the ship. In future warmer climate conditions, Equatorial Asia may experience more severe droughts, which risks releasing a large amount of carbon into the atmosphere. Therefore, the continuation of aircraft and shipboard observations is fruitful for reliable monitoring of carbon fluxes in Equatorial Asia.

A novel, practical observation system for measuring tropospheric carbon dioxide (CO2) concentrations using a non-dispersive infrared analyzer carried by a small helium-filled balloon (CO2 sonde) has been developed for the first time. Vertical profiles of atmospheric CO2 can be measured with a 240–400 m altitude resolution through regular onboard calibrations using two different CO2 standard gases. The standard deviations (1σ) of the measured mole fractions in the laboratory experiments using a vacuum chamber at a temperature of 298 K were approximately 0.6 ppm at 1010 hPa and 1.2 ppm at 250 hPa. Two CO2 vertical profile data obtained using the CO2 sondes, which were launched on 31 January and 3 February 2011 at Moriya, were compared with the chartered aircraft data on the same days and the commercial aircraft data obtained by the Comprehensive Observation Network for TRace gases by Airliner (COTRAIL) program on the same day (31 January) and 1 d before (2 February). The difference between the CO2 sonde data and these four sets of in situ aircraft data (over the range of each balloon altitude ±100 m) up to the altitude of 7 km was 0.6±1.2 ppm (average ±1σ). In field experiments, the CO2 sonde detected an increase in CO2 concentration in an urban area and a decrease in a forested area near the surface. The CO2 sonde was shown to be a useful instrument for observing and monitoring the vertical profiles of CO2 concentration in the troposphere.

To investigate the seasonal characteristics of trace gas distributions in the extratropical upper troposphere and lower stratosphere (ExUTLS) as well as stratosphere–troposphere exchange processes, origin fractions of air masses originating in the stratosphere, tropical troposphere, midlatitude lower troposphere (LT), and high-latitude LT in the ExUTLS are estimated using 10-year backward trajectories calculated with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data as the meteorological input. Time series of trace gases obtained from ground-based and airborne observations are incorporated into the trajectories, thus reconstructing spatiotemporal distributions of trace gases in the ExUTLS. The reconstructed tracer distributions are analyzed with the origin fractions and the stratospheric age of air (AoA) estimated using the backward trajectories. The reconstructed distributions of SF6 and CO2 in the ExUTLS are linearly correlated with those of AoA because of their chemically passive behavior and quasi-stable increasing trends in the troposphere. Distributions of CH4, N2O, and CO are controlled primarily by chemical decay along the transport path from the source region via the stratosphere and subsequent mixing of such stratospheric air masses with tropospheric air masses in the ExUTLS.

Measurement of atmospheric carbon dioxide (CO2) is indispensable for top-down estimation of surface CO2 sources/sinks by an atmospheric transport model. Despite the growing importance of Asia in the global carbon budget, the region has only been sparsely monitored for atmospheric CO2 and our understanding of atmospheric CO2 variations in the region (and thereby that of the regional carbon budget) is still limited. In this study, we present climatological CO2 distributions over the Asia-Pacific region obtained from the CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner) measurements. The high-frequency in-flight CO2 measurements over 10 years reveal a clear seasonal variation in CO2 in the upper troposphere (UT), with a maximum occurring in April–May and a minimum in August–September. The CO2 mole fraction in the UT north of 40° N is low and highly variable in June–August due to the arrival of air parcels with seasonally low CO2 caused by the summertime biospheric uptake in boreal Eurasia. For August–September in particular, the UT CO2 is noticeably low within the Asian summer monsoon anticyclone associated with the convective transport of strong biospheric CO2 uptake signal over South Asia. During September as the anticyclone decays, a spreading of this low-CO2 area in the UT is observed in the vertical profiles of CO2 over the Pacific Rim of continental East Asia. Simulation results identify the influence of anthropogenic and biospheric CO2 fluxes in the seasonal evolution of the spatial CO2 distribution over the Asia-Pacific region. It is inferred that a substantial contribution to the UT CO2 over the northwestern Pacific comes from continental East Asian emissions in spring; but in the summer monsoon season, the prominent air mass origin switches to South Asia and/or Southeast Asia with a distinct imprint of the biospheric CO2 uptake. The CONTRAIL CO2 data provide useful constraints to model estimates of surface fluxes and to the evaluation of the satellite observations, in particular for the Asia-Pacific region.

Composites of F82H and Fe added with W or Cu wires were sintered by spark plasma sintering in order to improve the thermal conductivity of F82H and pure Fe. W and Cu wires were two-dimensionally distributed on the plane perpendicular to pressure direction during sintering. The composites added with 10vol%Cu and 10vol%W were successfully sintered at 40 MPa at 800 °C and 1000 °C, respectively. The thermal diffusivity of the composites were improved compared to base materials, however, many pores were observed especially in the composites with 10vol%Cu. In order to improve the relative density of the composite of F82H added with 10vol%Cu, the use of smaller powder of both F82H and Cu would be needed. On the other hand, W-based compounds were observed in the composite of F82H added with 10vol%W. This would lead to not only less thermal conductivity but also mechanical property of the composite.

CO2 observations in the free troposphere can be useful for constraining CO2 source and sink estimates at the surface since they represent CO2 concentrations away from point source emissions. The thermal infrared (TIR) band of the Thermal and Near Infrared Sensor for Carbon Observation (TANSO) Fourier transform spectrometer (FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) has been observing global CO2 concentrations in the free troposphere for about 8 years and thus could provide a dataset with which to evaluate the vertical transport of CO2 from the surface to the upper atmosphere. This study evaluated biases in the TIR version 1 (V1) CO2 product in the lower troposphere (LT) and the middle troposphere (MT) (736–287 hPa), on the basis of comparisons with CO2 profiles obtained over airports using Continuous CO2 Measuring Equipment (CME) in the Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL) project. Bias-correction values are presented for TIR CO2 data for each pressure layer in the LT and MT regions during each season and in each latitude band: 40–20° S, 20° S–20° N, 20–40° N, and 40–60° N. TIR V1 CO2 data had consistent negative biases of 1–1.5 % compared with CME CO2 data in the LT and MT regions, with the largest negative biases at 541–398 hPa, partly due to the use of 10 µm CO2 absorption band in conjunction with 15 and 9 µm absorption bands in the V1 retrieval algorithm. Global comparisons between TIR CO2 data to which the bias-correction values were applied and CO2 data simulated by a transport model based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM-TM) confirmed the validity of the bias-correction values evaluated over airports in limited areas. In low latitudes in the upper MT region (398–287 hPa), however, TIR CO2 data in northern summer were overcorrected by these bias-correction values; this is because the bias-correction values were determined using comparisons mainly over airports in Southeast Asia, where CO2 concentrations in the upper atmosphere display relatively large variations due to strong updrafts.

A four-dimensional variational method (4D-Var) is a popular technique for source/sink inversions of atmospheric constituents, but it is not without problems. Using an icosahedral grid transport model and the 4D-Var method, a new atmospheric greenhouse gas (GHG) inversion system has been developed. The system combines offline forward and adjoint models with a quasi-Newton optimization scheme. The new approach is then used to conduct identical twin experiments to investigate optimal system settings for an atmospheric CO2 inversion problem, and to demonstrate the validity of the new inversion system. In this paper, the inversion problem is simplified by assuming the prior flux errors to be reasonably well known and by designing the prior error correlations with a simple function as a first step. It is found that a system of forward and adjoint models with smaller model errors but with nonlinearity has comparable optimization performance to that of another system that conserves linearity with an exact adjoint relationship. Furthermore, the effectiveness of the prior error correlations is demonstrated, as the global error is reduced by about 15 % by adding prior error correlations that are simply designed when 65 weekly flask sampling observations at ground-based stations are used. With the optimal setting, the new inversion system successfully reproduces the spatiotemporal variations of the surface fluxes, from regional (such as biomass burning) to global scales. The optimization algorithm introduced in the new system does not require decomposition of a matrix that establishes the correlation among the prior flux errors. This enables us to design the prior error covariance matrix more freely.

A four-dimensional variational (4D-Var) method is a popular algorithm for inverting atmospheric greenhouse gas (GHG) measurements. In order to meet the computationally intense 4D-Var iterative calculation, offline forward and adjoint transport models are developed based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). By introducing flexibility into the temporal resolution of the input meteorological data, the forward model developed in this study is not only computationally efficient, it is also found to nearly match the transport performance of the online model. In a transport simulation of atmospheric carbon dioxide (CO2), the data-thinning error (error resulting from reduction in the time resolution of the meteorological data used to drive the offline transport model) is minimized by employing high temporal resolution data of the vertical diffusion coefficient; with a low 6-hourly temporal resolution, significant concentration biases near the surface are introduced. The new adjoint model can be run in discrete or continuous adjoint mode for the advection process. The discrete adjoint is characterized by perfect adjoint relationship with the forward model that switches off the flux limiter, while the continuous adjoint is characterized by an imperfect but reasonable adjoint relationship with its corresponding forward model. In the latter case, both the forward and adjoint models use the flux limiter to ensure the monotonicity of tracer concentrations and sensitivities. Trajectory analysis for high CO2 concentration events are performed to test adjoint sensitivities. We also demonstrate the potential usefulness of our adjoint model for diagnosing tracer transport. Both the offline forward and adjoint models have computational efficiency about 10 times higher than the online model. A description of our new 4D-Var system that includes an optimization method, along with its application in an atmospheric CO2 inversion and the effects of using either the discrete or continuous adjoint method, is presented in an accompanying paper Niwa et al.(2016).

We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO2 and XCH4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO2/XCH4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.

The Thermal and Near Infrared Sensor for Carbon Observation (TANSO)–Fourier Transform Spectrometer (FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) has been observing carbon dioxide (CO2) concentrations in several atmospheric layers in the thermal infrared (TIR) band since its launch. This study compared TANSO-FTS TIR version 1 (V1) CO2 data and CO2 data obtained in the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project in the upper troposphere and lower stratosphere (UTLS), where the TIR band of TANSO-FTS is most sensitive to CO2 concentrations, to validate the quality of the TIR V1 UTLS CO2 data from 287 to 162 hPa. We first evaluated the impact of considering TIR CO2 averaging kernel functions on CO2 concentrations using CO2 profile data obtained by the CONTRAIL Continuous CO2 Measuring Equipment (CME), and found that the impact at around the CME level flight altitudes (∼ 11 km) was on average less than 0.5 ppm at low latitudes and less than 1 ppm at middle and high latitudes. From a comparison made during flights between Tokyo and Sydney, the averages of the TIR upper-atmospheric CO2 data were within 0.1 % of the averages of the CONTRAIL CME CO2 data with and without TIR CO2 averaging kernels for all seasons in the Southern Hemisphere. The results of comparisons for all of the eight airline routes showed that the agreements of TIR and CME CO2 data were worse in spring and summer than in fall and winter in the Northern Hemisphere in the upper troposphere. While the differences between TIR and CME CO2 data were on average within 1 ppm in fall and winter, TIR CO2 data had a negative bias up to 2.4 ppm against CME CO2 data with TIR CO2 averaging kernels at the northern low and middle latitudes in spring and summer. The negative bias at the northern middle latitudes resulted in the maximum of TIR CO2 concentrations being lower than that of CME CO2 concentrations, which led to an underestimate of the amplitude of CO2 seasonal variation.

Current knowledge about the European terrestrial biospheric carbon sink, from the Atlantic to the Urals, relies upon bottom-up inventory and surface flux inverse model estimates (e.g. 0.27±0.16 GtC a−1 for 2000–2005 (Schulze et al., 2009), 0.17±0.44 GtC a−1 for 2001–2007 (Peters et al., 2010), 0.45±0.40 GtC a−1 for 2010 (Chevallier et al., 2014), 0.40±0.42 GtC a−1 for 2001–2004 (Peylin et al., 2013)). Inverse models assimilate in situ CO2 atmospheric concentrations measured by surface-based air sampling networks. The intrinsic sparseness of these networks is one reason for the relatively large flux uncertainties (Peters et al., 2010; Bruhwiler et al., 2011). Satellite-based CO2 measurements have the potential to reduce these uncertainties (Miller et al., 2007; Chevallier et al., 2007). Global inversion experiments using independent models and independent GOSAT satellite data products consistently derived a considerably larger European sink (1.0–1.3 GtC a−1 for 09/2009–08/2010 (Basu et al., 2013), 1.2–1.8 GtC a−1 in 2010 (Chevallier et al., 2014)). However, these results have been considered unrealistic due to potential retrieval biases and/or transport errors (Chevallier et al., 2014) or have not been discussed at all (Basu et al., 2013; Takagi et al., 2014). Our analysis comprises a regional inversion approach using STILT (Gerbig et al., 2003; Lin et al., 2003) short-range (days) particle dispersion modelling, rendering it insensitive to large-scale retrieval biases and less sensitive to long-range transport errors. We show that the satellite-derived European terrestrial carbon sink is indeed much larger (1.02±0.30 GtC a−1 in 2010) than previously expected. This is qualitatively consistent among an ensemble of five different inversion set-ups and five independent satellite retrievals (BESD (Reuter et al., 2011) 2003–2010, ACOS (O’Dell et al., 2012) 2010, UoL-FP (Cogan et al., 2012) 2010, RemoTeC (Butz et al., 2011) 2010, and NIES (Yoshida et al., 2013) 2010) using data from two different instruments (SCIAMACHY (Bovensmann et al., 1999) and GOSAT (Kuze et al., 2009)). The difference to in situ based inversions (Peylin et al., 2013), whilst large with respect to the mean reported European carbon sink (0.4 GtC a−1 for 2001–2004), is similar in magnitude to the reported uncertainty (0.42 GtC a−1). The highest gain in information is obtained during the growing season when satellite observation conditions are advantageous, a priori uncertainties are largest, and the surface sink maximises; during the dormant season, the results are dominated by the a priori. Our results provide evidence that the current understanding of the European carbon sink has to be revisited.

Column-averaged dry-air mole fractions of methane (XCH4), retrieved from Greenhouse gases Observing SATellite (GOSAT) short-wavelength infrared (SWIR) spectra, were validated by using aircraft measurement data from the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. In the calculation of XCH4 from aircraft measurements (aircraft-based XCH4), other satellite data were used for the CH4 profiles above the tropopause. We proposed a data-screening scheme for aircraft-based XCH4 for reliable validation of GOSAT XCH4. Further, we examined the impact of GOSAT SWIR column averaging kernels (CAK) on the aircraft-based XCH4 calculation and found that the difference between aircraft-based XCH4 with and without the application of the GOSAT CAK was less than ±9 ppb at maximum, with an average difference of −0.5 ppb. We compared GOSAT XCH4 Ver. 02.00 data retrieved within ±2° or ±5° latitude–longitude boxes centered at each aircraft measurement site with aircraft-based XCH4 measured on a GOSAT overpass day. In general, GOSAT XCH4 was in good agreement with aircraft-based XCH4. However, over land, the GOSAT data showed a positive bias of 1.5 ppb (2.0 ppb) with a standard deviation of 14.9 ppb (16.0 ppb) within the ±2° (±5°) boxes, and over ocean, the average bias was 4.1 ppb (6.5 ppb) with a standard deviation of 9.4 ppb (8.8 ppb) within the ±2° (±5°) boxes. In addition, we obtained similar results when we used an aircraft-based XCH4 time series obtained by curve fitting with temporal interpolation for comparison with GOSAT data.

Terrestrial carbon dioxide (CO2) flux estimates in China using atmospheric inversion method are beset with considerable uncertainties because very few atmospheric CO2 concentration measurements are available. In order to improve these estimates, nested atmospheric CO2 inversion during 2002–2008 is performed in this study using passenger aircraft-based CO2 measurements over Eurasia from the Comprehensive Observation Network for Trace gases by Airliner (CONTRAIL) project. The inversion system includes 43 regions with a focus on China, and is based on the Bayesian synthesis approach and the TM5 transport model. The terrestrial ecosystem carbon flux modeled by the Boreal Ecosystems Productivity Simulator (BEPS) model and the ocean exchange simulated by the OPA-PISCES-T model are considered as the prior fluxes. The impacts of CONTRAIL CO2 data on inverted China terrestrial carbon fluxes are quantified, the improvement of the inverted fluxes after adding CONTRAIL CO2 data are rationed against climate factors and evaluated by comparing the simulated atmospheric CO2 concentrations with three independent surface CO2 measurements in China. Results show that with the addition of CONTRAIL CO2 data, the inverted carbon sink in China increases while those in South and Southeast Asia decrease. Meanwhile, the posterior uncertainties over these regions are all reduced (2–12%). CONTRAIL CO2 data also have a large effect on the inter-annual variation of carbon sinks in China, leading to a better correlation between the carbon sink and the annual mean climate factors. Evaluations against the CO2 measurements at three sites in China also show that the CONTRAIL CO2 measurements may have improved the inversion results.

Current estimates of the terrestrial carbon fluxes in Asia show large uncertainties particularly in the boreal and mid-latitudes and in China. In this paper, we present an updated carbon flux estimate for Asia ("Asia" refers to lands as far west as the Urals and is divided into boreal Eurasia, temperate Eurasia and tropical Asia based on TransCom regions) by introducing aircraft CO2 measurements from the CONTRAIL (Comprehensive Observation Network for Trace gases by Airline) program into an inversion modeling system based on the CarbonTracker framework. We estimated the averaged annual total Asian terrestrial land CO2 sink was about −1.56 Pg C yr−1 over the period 2006–2010, which offsets about one-third of the fossil fuel emission from Asia (+4.15 Pg C yr−1). The uncertainty of the terrestrial uptake estimate was derived from a set of sensitivity tests and ranged from −1.07 to −1.80 Pg C yr−1, comparable to the formal Gaussian error of ±1.18 Pg C yr−1 (1-sigma). The largest sink was found in forests, predominantly in coniferous forests (−0.64 ± 0.70 Pg C yr−1) and mixed forests (−0.14 ± 0.27 Pg C yr−1); and the second and third large carbon sinks were found in grass/shrub lands and croplands, accounting for −0.44 ± 0.48 Pg C yr−1 and −0.20 ± 0.48 Pg C yr−1, respectively. The carbon fluxes per ecosystem type have large a priori Gaussian uncertainties, and the reduction of uncertainty based on assimilation of sparse observations over Asia is modest (8.7–25.5%) for most individual ecosystems. The ecosystem flux adjustments follow the detailed a priori spatial patterns by design, which further increases the reliance on the a priori biosphere exchange model. The peak-to-peak amplitude of inter-annual variability (IAV) was 0.57 Pg C yr−1 ranging from −1.71 Pg C yr−1 to −2.28 Pg C yr−1. The IAV analysis reveals that the Asian CO2 sink was sensitive to climate variations, with the lowest uptake in 2010 concurrent with a summer flood and autumn drought and the largest CO2 sink in 2009 owing to favorable temperature and plentiful precipitation conditions. We also found the inclusion of the CONTRAIL data in the inversion modeling system reduced the uncertainty by 11% over the whole Asian region, with a large reduction in the southeast of boreal Eurasia, southeast of temperate Eurasia and most tropical Asian areas.

Column-averaged dry air mole fractions of carbon dioxide (XCO2) retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations were validated with aircraft measurements by the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. To calculate XCO2 based on aircraft measurements (aircraft-based XCO2), tower measurements and model outputs were used for additional information near the surface and above the tropopause, respectively. Before validation, we investigated the impacts of GOSAT SWIR column averaging kernels (CAKs) and the shape of a priori profiles on the aircraft-based XCO2 calculation. The differences between aircraft-based XCO2 with and without the application of GOSAT CAK were evaluated to be less than ±0.4 ppm at most, and less than ±0.1 ppm on average. Therefore, we concluded that the GOSAT CAK produces only a minor effect on the aircraft-based XCO2 calculation in terms of the overall uncertainty of GOSAT XCO2. We compared GOSAT data retrieved within ±2 or ±5° latitude/longitude boxes centered at each aircraft measurement site to aircraft-based data measured on a GOSAT overpass day. The results indicated that GOSAT XCO2 over land regions agreed with aircraft-based XCO2, except that the former is biased by −0.68 ppm (−0.99 ppm) with a standard deviation of 2.56 ppm (2.51 ppm), whereas the averages of the differences between the GOSAT XCO2 over ocean and the aircraft-based XCO2 were −1.82 ppm (−2.27 ppm) with a standard deviation of 1.04 ppm (1.79 ppm) for ±2° (±5°) boxes.

No abstract available.

Atmospheric column-averaged mole fractions of carbon dioxide (XCO2) at 53 locations around the world were derived from aircraft measurements covering the altitude range of about 1–10 km. We used CO2 vertical profile measurements from three major carbon cycle programs, a global climatological data set of air number density profiles and tropopause height for calculating XCO2 for the period of 2007–2009. Vertical profiles of the CO2 mixing ratio are complemented by tall tower data up to 400 m from the earth's surface and by simulated profiles in the stratosphere from a chemistry-transport model. The amplitude of the seasonal cycle of calculated XCO2 values shows clear latitudinal dependence, and the amplitude decreases from about 10 ppm at high latitudes in the Northern Hemisphere to at most 2 ppm in the tropics and the Southern Hemisphere. The uncertainties of XCO2 were estimated from assumptions about CO2 profiles for each flight. Typically, uncertainties were less than 1 ppm; thus, this data set is within the level of uncertainty needed for primary validation of XCO2 measurements by the Greenhouse gases Observing SATellite (GOSAT) and by future satellite missions for monitoring greenhouse gases.

We established and evaluated a flask air sampling system on a cargo C-130H aircraft, as well as a trace gas measurement system for the flask samples, as part of a new operational monitoring program of the Japan Meteorological Agency (JMA). Air samples were collected during each flight, between Kanagawa Prefecture (near Tokyo) and Minamitorishima (an island located nearly 2000 km southeast of Tokyo), from the air-conditioning system on the aircraft. Prior to the operational employment of the sampling system, a quality assurance test of the sampled air was made by specially coordinated flights at a low altitude of 1000 ft over Minamitorishima and comparing the flask values with those obtained at the surface. Based on our storage tests, the flask samples remained nearly stable until analyses. The trace gas measurement system has, in addition to the nondispersive infrared (NDIR) and vacuum ultraviolet resonance fluorescence (VURF) analyzers, two laser-based analyzers using wavelength-scanned cavity ring-down spectroscopy (WS-CRDS) and off-axis integrated cavity output spectroscopy (ICOS). Laboratory tests of the laser-based analyzers for measuring flask samples indicated relatively high reproducibility with overall precisions of less than ±0.06 ppm for CO2, ±0.68 ppb for CH4, ±0.36 ppb for CO, and ±0.03 ppb for N2O. Flask air sample measurements, conducted concurrently on different analyzers were compared. These comparisons showed a negligible bias in the averaged measurements between the laser-based measurement techniques and the other methods currently in use. We also estimated that there are no significant isotope effects for CH4, CO and N2O using standard gases with industrial isotopic compositions to calibrate the laser-based analyzers, but CO2 was found to possess isotope effects larger than its analytical precision.

Since July 2007, monthly averages of mid-tropospheric methane have been retrieved in the tropics over land and sea, by day and night, from IASI onboard MetOp-A, yielding a complete view of the geographical distribution, seasonality and long-term tendency of methane in the mid-troposphere. Retrieved methane displays a clear seasonal cycle of ~25 ppbv in the northern tropics, with a maximum in November and a minimum in April–May, a more complex cycle of ~15 ppbv in the southern tropics, and a south-to-north latitudinal variation of ~30 ppbv – in good agreement with regular aircraft measurements of the CONTRAIL program. Comparisons with CARIBIC aircraft measurements made at ~11 km yield an averaged difference between collocated IASI estimates and CARIBIC measurements of 7.2 ppbv with a standard deviation of 13.1 ppbv. Comparisons with aircraft measurements made above 6 km during five HIPPO campaigns give an averaged difference between collocated IASI estimates and HIPPO measurements of 5.1 ppbv with a standard deviation of 16.3 ppbv. These comparisons show that IASI captures well the evolution of mid-tropospheric methane. In particular, in 2007 and 2008, IASI shows an increase of mid-tropospheric methane in the tropical region of 9.5 ± 2.8 and 6.3 ± 1.7 ppbv yr−1, respectively – in excellent agreement with the rate of increase measured at the surface after almost a decade of near-zero growth. IASI also indicates a slowing down of this increase in the following years to ~2 ppbv yr−1, with the highest increase in 2010. Assuming that the recent evolution of methane is mostly due to an increase in surface emissions, IASI might indicate a decrease in tropical wetland emissions for the period 2009–2011 compared to 2007–2008, in agreement with decreasing tropical precipitation over this period, together with an increase in biomass burning emissions in 2010 in the southern tropics.

Emissions of CO, CH4 and CO2 in East Asia were estimated using the atmospheric concentration of 222Rn measured at Minamitorishima (MNM) and Yonagunijima (YON) with a high-precision 222Rn measuring system. The 222Rn measurements showed a distinct seasonal variation, as well as enhanced 222Rn (ERN) events associated with high-frequency synoptic variations. The enhancements of CO, CH4 and CO2 coincided with the ERN events at MNM and YON. The enhancement ratios ΔCH4/ΔRn, ΔCO2/ΔRn and ΔCO/ΔRn calculated for the ERN events and corrected for the radioactive decay of 222Rn during the transport were used to estimate the emissions of CO, CH4 and CO2 by multiplying a constant 222Rn exhalation rate of 1.0 atom cm−2 s−1 and the associated catchment area estimated by a 3D transport model. By modifying the categorisation of the sources, the 222Rn-derived emissions of CO2 and CH4 were found to be in agreement with the published emission databases. As for the CO emission, results of our study were consistent with the REAS CO emission database but showed that the EDGAR database underestimates the CO emission over East Asia by about 44%. It is demonstrated that the 222Rn tracer method used in this study could provide a useful and independent tool to verify the trace gas emissions in East Asia.

We used the observed CO/222Rn ratio in the Asian outflows at Minamitorishima (MNM), Yonagunijima (YON), and Ryori (RYO) in the western North Pacific from 2007 to 2011, together with a three-dimensional chemical transport model (STAG), in order to estimate anthropogenic emissions of CO in East Asia. The measurements captured high-frequency synoptic variations of enhanced 222Rn (ERN) events associated with the long-range transport of continental air masses. 222Rn and CO showed high correlation during the ERN events observed at MNM and YON in the winter and spring, but not at RYO. The STAG transport model reproduced well the concentrations of observed 222Rn when forced with a constant and uniform flux density of 1.0 atom cm−2 s−1, but underestimated the associated enhancement of synoptically variable CO caused by the underestimated flux values in the EDGAR ver. 4.1 emission database used in the model for East Asia. Better estimates for the East Asian emission were derived using a radon tracer method based on the difference in the enhancement ratio of CO/222Rn between the observation and the model. The anthropogenic emissions of CO for China, Japan, and Korea were estimated to be 203 Tg CO yr−1, 91% of which originated in China. When compared with other estimated emissions of CO, our estimated result showed consistency with those of the inverse method, whereas the emission database of EDGAR was about 45% smaller than our anthropogenic estimation for China.

We present the mixing ratio, δ13C and δD of atmospheric CH4 using commercial aircraft in the upper troposphere (UT) over the Western Pacific for the period December 2005–September 2010. The observed results were compared with those obtained using commercial container ships in the lower troposphere (LT) over the same region. In the Northern Hemisphere (NH), the UT CH4 mixing ratio shows high values in the boreal summer–autumn, when the LT CH4 mixing ratio reaches a seasonal minimum. From tagged tracer experiments made using an atmospheric chemistry transport model, we found that such high CH4 values are due to rapid transport of air masses influenced by CH4 sources in South Asia and East Asia. The observed isotopic ratio data imply that these areas have CH4 sources with relatively low δ13C and δD signatures such as biogenic sources. Latitudinal distributions of the annual average UT and LT CH4 mixing ratio intersect each other in the tropics; the mixing ratio value is lower in the UT than in the LT in the NH and the situation is reversed in the Southern Hemisphere (SH), due mainly to the NH air intrusion into the SH through the UT. Such intersection of the latitudinal distributions is observable in δD but not in δ13C, implying an additional contribution from reaction of CH4 with active chlorine in the marine boundary layer. δ13C and δD show low values in the NH and high values in the SH both in the UT and in the LT. We also observed an increase in the CH4 mixing ratio and decreases in δ13C and δ

Aircraft measurements of carbon dioxide and methane over Tsukuba (36.05° N, 140.12° E) (February 2010) and Moshiri (44.36° N, 142.26° E) (August 2009) were made to calibrate ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs) and to compare with the Greenhouse gases Observing SATellite (GOSAT). The aircraft measurements over Tsukuba in February 2010 were successful in synchronizing with both the g-b FTS and GOSAT for the first time. Airborne in situ and flask-sampling instruments were mounted on the aircraft, and measurements were carried out between altitudes of 0.5 and 7 km to obtain vertical profiles of carbon dioxide (CO2), methane (CH4), and other gaseous species. By comparing the g-b FTS measurements with the airborne measurements, the column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) retrieved from the g-b FTS measurements at Tsukuba were biased low by 0.33 ± 0.11% for XCO2 and 0.69 ± 0.29% for XCH4. The g-b FTS values at Moshiri were biased low by 1.24% for XCO2 and 2.11% for XCH4. The GOSAT data show biases that are 3.1% ± 1.7% lower for XCO2 and 2.5% ± 0.8% lower for XCH4 than the aircraft measurements obtained over Tsukuba.

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

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.

Quantifying the fluxes of carbon dioxide (CO2) between the atmosphere and terrestrial ecosystems in all their diversity, across the continents, is important and urgent for implementing effective mitigating policies. Whereas much is known for Europe and North America for instance, in comparison, South Asia, with 1.6 billion inhabitants and considerable CO2 fluxes, remained terra incognita in this respect. We use regional measurements of atmospheric CO2 aboard a Lufthansa passenger aircraft between Frankfurt (Germany) and Chennai (India) at cruise altitude, in addition to the existing network sites for 2008, to estimate monthly fluxes for 64-regions using Bayesian inversion and transport model simulations. The applicability of the model's transport parameterization is confirmed using SF6, CH4 and N2O simulations for the CARIBIC datasets. The annual amplitude of carbon flux obtained by including the aircraft data is twice as large as the fluxes simulated by a terrestrial ecosystem model that was applied to prescribe the fluxes used in the inversions. It is shown that South Asia sequestered carbon at a rate of 0.37 ± 0.20 Pg C yr−1 (1 Pg C = 1015 g of carbon in CO2) for the years 2007 and 2008. The seasonality and the strength of the calculated monthly fluxes are successfully validated using independent measurements of vertical CO2 profiles over Delhi and spatial variations at cruising altitude over Asia aboard Japan Airlines passenger aircraft.

The Total Carbon Column Observing Network (TCCON) produces precise measurements of the column average dry-air mole fractions of CO2, CO, CH4, N2O and H2O at a variety of sites worldwide. These observations rely on spectroscopic parameters that are not known with sufficient accuracy to compute total columns that can be used in combination with in situ measurements. The TCCON must therefore be calibrated to World Meteorological Organization (WMO) in situ trace gas measurement scales. We present a calibration of TCCON data using WMO-scale instrumentation aboard aircraft that measured profiles over four TCCON stations during 2008 and 2009. These calibrations are compared with similar observations made in 2004 and 2006. The results indicate that a single, global calibration factor for each gas accurately captures the TCCON total column data within error.

Column-averaged volume mixing ratios of carbon dioxide (XCO2) during the period from January 2007 to May 2008 over Tsukuba, Japan, were derived using CO2 concentrations measured by Continuous CO2 Measuring Equipment (CME). The CMEs were installed on Japan Airlines Corporation (JAL) commercial airliners, which frequently fly to and from Narita Airport. It was assumed that CO2 profiles over Tsukuba and Narita are the same. CO2 profile data for 493 flights on clear-sky days were analyzed in order to calculate XCO2 with one of two ancillary datasets: "Tsukuba observational" data (rawinsonde and meteorological tower), or "global" forecast/reanalysis and climatological data (NCEP and CIRA-86). The amplitude of the seasonal variation of XCO2 using the ancillary data measured in Tsukuba (XCO2 (Tsukuba observational)) was determined by a least squares fit using a harmonic function to roughly evaluate the seasonal variation over Tsukuba. The highest and lowest values of the obtained fitted curve in 2007 for XCO2 (Tsukuba observational) were 386.4 ± 1.0 and 381.7 ± 1.0 ppm in May and September, respectively, where the errors represent 1 standard deviation of the fit residuals. The dependence of XCO2 on the type of ancillary dataset was evaluated. The average difference between XCO2 from global climatological data, XCO2 (global), and XCO2 (Tsukuba observational), i.e., the bias of XCO2 (global) based on XCO2 (Tsukuba observational), was found to be −0.621 ppm with a standard deviation of 0.682 ppm. The uncertainty of XCO2 (global) based on XCO2 (Tsukuba observational) was estimated to be 0.922 ppm. This small uncertainty relative to the GOSAT precision suggests that calculating XCO2 using data from airliners and global climatological data can be applied to the validation of GOSAT products for XCO2 over airports worldwide.

We present carbon dioxide (CO2) estimates from the Tropospheric Emission Spectrometer (TES) on the EOS-Aura satellite launched in 2004. For observations between 40° S and 45° N, we find about 1 degree of freedom with peak sensitivity at 511 hPa. The estimated error is ~10 ppm for a single target and 1.3–2.3 ppm for monthly averages on spatial scales of 20°×30°. Monthly spatially-averaged TES data from 2005–2008 processed with a uniform initial guess and prior are compared to CONTRAIL aircraft data over the Pacific ocean, aircraft data at the Southern Great Plains (SGP) ARM site in the southern US, and the Mauna Loa and Samoa surface stations. Comparisons to Mauna Loa data show a correlation of 0.92, a standard deviation of 1.3 ppm, a predicted error of 1.2 ppm, and a ~2% low bias, which is subsequently corrected. Comparisons to SGP aircraft data over land show a correlation of 0.67 and a standard deviation of 2.3 ppm. TES data between 40° S and 45° N for 2006–2007 are compared to surface flask data, GLOBALVIEW, the Atmospheric Infrared Sounder (AIRS), and CarbonTracker. Comparison to GLOBALVIEW-CO2 ocean surface sites shows a correlation of 0.60 which drops when TES is offset in latitude, longitude, or time. At these same locations, TES shows a 0.62 and 0.67 correlation to CarbonTracker at the surface and 5 km, respectively. We also conducted an observing system simulation experiment to assess the potential utility of the TES data for inverse modeling of CO2 fluxes. We find that if biases in the data and model are well characterized, the averaged data have the potential to provide sufficient information to significantly reduce uncertainty on annual estimates of regional CO2 sources and sinks. Averaged pseudo-data at 10°×10° reduced uncertainty in flux estimates by as much as 70% for some tropical regions.

Simultaneous ground-based measurements of ozone (O3) and carbon monoxide (CO) were conducted in March 2005 as part of the East Asian Regional Experiment (EAREX) 2005 under the umbrella of the Atmospheric Brown Clouds (ABC) project. Multiple air quality monitoring networks were integrated by performing intercomparison of individual calibration standards and measurement techniques to ensure comparability of ambient measurements, along with providing consistently high time-resolution measurements of O3 and CO at the surface sites in East Asia. Ambient data collected from eight surface stations were compared with simulation results obtained by a regional chemistry transport model to infer recent changes in CO emissions from East Asia. Our inverse estimates of the CO emissions from China up to 2005 suggested an increase of 16% since 2001, in good agreement with the recent MOPITT satellite observations and the bottom-up estimates up to 2006. The O3 enhancement relative to CO in continental pollution plumes traversed in the boundary layer were examined as a function of transport time from the Asian continent to the western Pacific Ocean. The observed ΔO3/ΔCO ratios show increasing tendency during eastward transport events due likely to en-route photochemical O3 formation, suggesting that East Asia is an important O3 source region during spring.

The Nash–Sutcliffe efficiency (NSE) is a widely used score in hydrology, but it is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for the NSE from the viewpoint of signal progressing. Thus, a simulation is viewed as a received signal containing a wanted signal (observations) contaminated by an unwanted signal (noise). This view underlines an important role of the error model between simulations and observations. By assuming an additive error model, it is easy to point out that the NSE is equivalent to an important quantity in signal processing: the signal-to-noise ratio. Moreover, the NSE and the Kling–Gupta efficiency (KGE) are shown to be equivalent, at least when there are no biases, in the sense that they measure the relative magnitude of the power of noise to the power of the variation in observations. The scientific meaning of the NSE suggests a natural way to define NSE=0 as the threshold for good or bad model distinction, and this has no relation to the benchmark simulation that is equal to the observed mean. Corresponding to NSE=0, the threshold of the KGE is given by approximately 0.5. In the general cases, when the additive error model is replaced by a mixed additive–multiplicative error model, the traditional NSE is shown to be prone to contradiction in model evaluations. Therefore, an extension of the NSE is derived, which only requires one to divide the traditional noise-to-signal ratio by the multiplicative bias. This has a practical implication: if the multiplicative bias is not considered, the traditional NSE and KGE underestimate or overestimate the generalized NSE and KGE when the multiplicative bias is greater or smaller than one, respectively. In particular, the observed mean turns out to be the worst simulation from the viewpoint of the generalized NSE.

Despite the importance of a link between hydrometeorological drought hazards and their socioeconomic impact, the link at a subnational level has yet to be evaluated due to the lack of precise subnational information on disaster locations. Using the newly developed Geocoded Disasters (GDIS) dataset, we examined whether the subnational socioeconomic drought impact information in GDIS could be represented by hydrometeorological hazards quantified from soil moisture in ERA5-Land during 1964–2018. We found that the socioeconomic drought impacts shown in GDIS were generally represented by drought hazards quantified from ERA5-Land soil moisture. Our comparison between GDIS and ERA5-Land could benefit the quantification of vulnerability to drought, and we found that sub-Saharan Africa and South Asia were vulnerable to drought, while North America and Europe were less vulnerable to drought. Both GDIS and ERA5-Land indicated that the Horn of Africa, northern China, and western India were drought-prone areas. Since it is difficult for national-level analyses to accurately identify the locations of drought-prone areas, especially in large countries such as China and India, our analysis clarifies the importance of the use of subnational disaster information.

To improve the efficiency of flood early warning systems (FEWS), it is important to understand the interactions between natural and social systems. The high level of trust in authorities and experts is necessary to improve the likeliness of individuals to take preparedness actions responding to warnings. Despite many efforts to develop the dynamic model of human and water in socio-hydrology, no socio-hydrological models explicitly simulate social collective trust in FEWS. Here, we develop the stylized model to simulate the interactions of flood, social collective memory, social collective trust in FEWS, and preparedness actions responding to warnings by extending the existing socio-hydrological model. We realistically simulate the cry wolf effect in which many false alarms undermine the credibility of the early warning systems and make it difficult to induce preparedness actions. We found that (1) considering the dynamics of social collective trust in FEWS is more important in the technological society with infrequent flood events than in the green society with frequent flood events; and (2) as the natural scientific skill to predict flood events is improved, the efficiency of FEWS gets more sensitive to the behavior of social collective trust, so that forecasters need to determine their warning threshold by considering the social aspects.